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I think Anthropic and OpenAI have found product-market fit

692 pointsposted 11 hours ago

858 Comments

trjordan

10 hours ago

They've got, ballpark, $5t to $10t to make back in the next 5 years, or the hardware buildouts will start getting written down.

This means we're going to need $1t+ per year in spending, per year, on tokens. 200m knowledge workers in the world, 30m developers. We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer.

That's a _huge_ shift. Most people I know cite +20%-40% velocity with these tools, against the actual work their company cares about doing. +20% speed for +20% spend isn't going to motivate a trillion dollars a year in spending.

We're not there yet. This is still the upswing of the hype cycle, and unless we figure out how to make developers 2x, 5x, 10x as productive on stuff that matters, this isn't going to play out well.

whatshisface

9 hours ago

Here are a few thoughts:

- The publicly available information about how inference costs compare to training costs is conflicted. EEs involved in datacenters talk about power usage spikes during training runs as if they were a major factor in the designs, but academic papers discussing cost-optimal scaling confidently treat inference-time compute as a major factor.

- On the side of the balance indicating that training is more compute-intensive after amortization than inference is that Chinese providers, constrained primarily by access to compute, have nearly unlimited token availability at a lower price than US providers (inference), but poorer model capabilities (training). That would make sense only if US providers are inflating inference costs by 20-30x due to amortized training costs that overseas providers were not able to take on (there are other factors too).

- If training >> inference, they're in a prisoner's dilemma that far exceeds the ordinary zero-marginals model of competition between firms (due to its huge discrete stepwise nature). On the other hand, if inference>>training, the high-level analysis popularized by certain thought leaders, that it's like a utility, would be true. You'd tend to count this as a vote for inference>>training, but the CEOs saying it at least have a huge incentive to agree because the alternative, the prisoner's dilemma, would stop investment very fast.

- The only voice in the story that I just told you to have anything to do with fact (as opposed to high-level analysis and ivory tower armchair management of a secretive business) were the rumors from facilities engineers. That shows you the state of our understanding...

- If we don't even know the ratio between amortized capital expenses and operational costs, outside investor analysis is impossible. It doesn't matter how finely they divide the accounting buckets for office ferns and indoor ferns if the single biggest part of their business is obscured for trade secret reasons.

materielle

9 hours ago

I'm about to leave a shallow comment, but I am a bit skeptical of the supposed drop in inference costs. If AI labs saw a lot of potential there, they'd surely be bragging about it non-stop? So the fact that publicly available information is conflicted is probably a sign that at the very least, the numbers aren't amazing.

Yes I know there's no evidence and this is lazy reasoning. But there's probably a bit of truth to this line of thought.

Tuna-Fish

8 hours ago

Why on earth would AI labs be bragging about how little the product they sell actually costs them to make? You don't want to do anything that reduces it's perceived value to the user, that might make them less willing to pay for it.

Also, inference costs are bound to go way down with more optimized architectures. GPUs are fundamentally not great at inference. No platform where the weights are streamed from a large pool of memory is. If the models ever quiet down, there will be massive step changes in cost/token, energy/token and tokens/second, as models are etched into silicon ala https://chatjimmy.ai/

kopirgan

4 minutes ago

Went to that URL asked one question - "how is this different from other AI" and it took 598/6144 tokens, not sure what that means.

overgard

fakedang

6 hours ago

That's changing with this administration though. Reduced reporting cycles reduce transparency.

daemin

2 hours ago

Isn't there a limit on the public markets where if a company has less than a certain percentage of its ownership traded publicly then it is no longer a public company and therefore de-listed?

I remember hearing about a guy trying to squeeze out short sellers of his own company but ended up effectively taking his company private because he bought out like 95% of all the shares.

I wonder how that aligns to these small releases of stock for the public.

SiempreViernes

7 hours ago

And investors will leak such claims quickly enough that this reasoning cannot plausibly hide big secrets.

Tuna-Fish

6 hours ago

It's not a big secret. If you just do the math yourself, it's easy to compute that inference doesn't cost all that much. People just see all the capital investment going around and all the new data centers being built, see that it's spent on "AI", put two and two together and get a three, or "clearly serving AI requests costs an arm and a leg".

The 1 they were missing is that AI requires both training and inference, and training is by far the expensive part. And that in principle you can stop training at any point and keep using the models as they are. (But that means that if other companies keep improving their models, you'll be left behind...)

In contrast, inference is fairly cheap and all the providers have great margins on it. Eventually either investment in training stops having commensurate impact on model quality, and people stop doing that and instead concentrate on making inference faster and even more efficient. Or if that doesn't happen, things will get very weird very quickly.

whatever1

3 hours ago

The market already shows where it will go.

If you want frontier model you will pay more for inference to essentially fund the expensive training.

If you don’t need frontier model you will get dirt cheap inference, which eventually will approach the cost of electricity spent per token.

ethin

5 hours ago

> If you just do the math yourself, it's easy to compute that inference doesn't cost all that much.

Show us your work, then. If it's so easy to do, this should be a trivial request to accommodate, no?

mediaman

4 hours ago

Just look at large open weights models being served by inference providers.

Kimi 2.6 is a 1 trillion total / 32B active parameter model that's something comparable to Sonnet. Sonnet's API pricing is $5 in, $15 out per million tokens. Deepinfra serves Kimi at $0.75 in, $3.50 out, and about the same at openrouter. So you're looking at a 4-7x multiple that Anthropic is charging compared to market rates that any plebe can get with a credit card.

majormajor

2 hours ago

I'm not sure just how good that looks for Anthropic/OpenAI.

4-7x isn't a tiny markup, but how does that compare to high-margin internet businesses like AdSense? Meta and Google do hundreds of billions in ad revenue a year, and after taking out the publisher's portion (60-80% per some searching), I wonder what the ratio of the remaining tens-of-billions is against the compute cost and headcount required to run it.

And how much room for maintaining or improving that margin do they have if the cheap competitors also continue getting better? Is there a "good enough" point where the easier inference tasks are all moving to vendors massively undercutting them, and then they don't have the volume necessary to justify spending on further cutting-edge development?

lmm

2 hours ago

Growing companies don't brag about their margins, they brag about their growth and revenue. Margin talk is for when you're a mature company squeezing out every bit of profitability you can - if anything it would be a negative sign to be worrying about your margins when you're supposed to still be growing and innovating.

etempleton

3 hours ago

Because the most important thing for any pure play AI company right now is to prove they are a viable company. And sure they have proved they can make billions, but also that they can lose billions more. They are going to need even more money and to prove to the next round of investors at an even higher valuation that they are a viable business they need to show not that they can generate revenue, but that they can one day turn a healthy profit. And that is the trillion dollar question.

neltnerb

6 hours ago

Because companies that want to go public need to look profitable or potentially profitable. And before they go public they have to release real, actual, legally demonstrable numbers for their costs and revenue anyway.

DrewADesign

3 hours ago

Because they can think more than one quarter into the future? Why on earth would someone adopt something into their core workflow that was fantastically unprofitable? Uncertainty and business don’t mix. Most people aren’t hype-eating bacteria that only care about maximizing their next paycheck.

jimbokun

6 hours ago

I doubt having to replace every single chip in your data center every time you release a new model will bring down costs.

no-name-here

14 minutes ago

> I am a bit skeptical of the supposed drop in inference costs. If AI labs saw a lot of potential there, they'd surely be bragging about it non-stop?

Unless to the parent commenter’s point they are using it to hide/obscure their large training (prisoner’s dilemma) costs?

whatshisface

9 hours ago

Inference has traditionally been far less expensive than training. One public example is the fact that hobbyists can run StableDiffusion ($600k training costs[1]) on their personal computers.

Speaking to your point, inference being dramatically less costly than training would not be seen as a delta from the norm. The model of providing inference for anything near the operational costs (like a utility would), would the delta from the norm if it were true.

[1] https://x.com/emostaque/status/1563870674111832066

thesz

7 hours ago

The difference between training and inference is 1) one have to keep intermediate results for backward pass in training and 2) computation for training double because of the backward pass.

Training is also done over batches, which increase memory requirements by several orders of magnitude. This is why training needs costly compute.

One of the ways out of this unfortunate situation is to use something like Stochastic Average Gradient Descent [1]. Examples there are mostly concerned with regularized logistic regression, which makes problem more or less convex. Neural networks are inherently non-convex. Still, maybe some ideas from there can be utilized in the context of neural networks, like use of estimated Lipshitz constant to derive curvature and appropriate learning step.

  [1] https://www.cs.ubc.ca/~schmidtm/Courses/540-W19/L12.pdf

janalsncm

7 hours ago

So one way to think about it is roughly,

Training is inference + backwards pass (~2x inference cost) + activations (vram overhead) + optimizer (vram overhead) + gradients (vram overhead).

thesz

6 hours ago

Multiply "inference + backwards pass (~2x inference cost) + activations (vram overhead)" by batch size (thousands) to get to the actual RAM and compute cost. Optimizer like ADAM adds only two or three model-sized overhead.

And last, but not least, you need only one hidden layer kept in RAM for inference, but you need all of them (61 for Deepseek models) kept in RAM for computing gradient for one sample.

xyhopguy

4 hours ago

Microbatch size is a hyperparameter, it can be set to 1 and work just as effectively. With gradient accumulation it's equivalent even. Large batch sizes are used to increase parallelism, and sometimes to reduce variance in the loss signal (at the cost of increased bias).

Batch size is frequently limited by compute bottlenecks well before memory.

galaxyLogic

4 hours ago

Does it matter what is the difference in size of needed inputs for inference vs. training?

whatshisface

4 hours ago

That is an estimate of the relative cost of one training step, but you have to multiply it by the number of training steps, an unknown quantity.

lumost

6 hours ago

For equal capability tokens, there has been about a 10x drop in cost every 6 months.

We are still chasing the best because the best is moving rapidly, but it’s a simple thought experiment to work out what the cost to serve an 8B model from 2 years ago is in a world of 2T models.

Note: parameter counts are illustrative. Concretely, qwen3.6 27B delivers opus 4.5 capability at 1/27th the cost on openrouter. Single chip llama3 8b performance can exceed 17k tokens/sec.

neuronexmachina

2 hours ago

> If AI labs saw a lot of potential there, they'd surely be bragging about it non-stop?

Google seems to pretty regularly post about how their TPU and algorithm advancements have been decreasing energy costs for both inference and training.

vlovich123

7 hours ago

Small alternative potential future changes that alter this analysis:

* At some point model capability reaches diminishing returns. Then inference >> training in the future but training >> inference now. It’s not a prisoner’s dilemma but a land grab to solidify market position and be one of the 2-3 firms left standing as dominant in the space. The model companies aren’t super sticky yet but they’re working on it.

* even if training remains >> inference, it’s possible to have multiple price points like they do today. If you need the most capable model you’ll be paying exponentially more per token to supplement the training cost even though the serving cost is marginal because most people will be satisfied with cheaper / less capable models for most tasks.

I buy that inference is a dropping line item while training is a growing one. There’s all sorts of things on the horizon that’ll be order of magnitudes improvements, from startups burning models into ASICs to get order of magnitudes more performance to alternate architectures like diffusion transformers that have orders of magnitude structural optimizations. It’s inevitable that it’ll come down even further from where we are. It’s possible model training also will go down but I’ve not seen any compelling research suggesting major “easy” reductions here.

janalsncm

6 hours ago

The issue is that most tasks do not require frontier-level intelligence, but companies like OAI can really only profit off of the frontier. Capabilities from a year or two ago are so outdated that even OpenAI gives it away for free and there are many other models biting at their heels. In other words they are spending huge amounts of money to cash in on a depreciating asset.

So one possible future is that frontier-level training becomes so expensive and the use cases so sparse that it simply isn’t viable to keep going bigger.

IX-103

33 minutes ago

I don't see how it would be possible for inference costs to dominate training costs, even after amortization.

Training involves multiple passes over the entire training dataset, ideally in large batches where you can perform inference on as many samples as possible simultaneously and then perform backpropagation to adjust the model weights (which is about as expensive as inference).

Let's consider the size of the dataset we're dealing with here. The dataset likely consists of practically every piece of digitized text they can get their hands on (including that extracted from audio and video). We know Google has digitized a large portion of the books in existence as part of their "search book contents" feature and we have no reason to believe they're not using it alongside their cache of 90+% of the internet to train their models. We're talking about 100s of millions of books each with an average of 100,000s of tokens. The internet has 10s to 100s of billions of pages on it with who knows how many tokens on average. This is a huge dataset that we've got to go through hundreds of times.

Second, let's consider the effect of batching and how it sets requirements for our hardware. We know that larger batch sizes converge faster, are more stable, and produce better models. So if you want a good model you need large batch sizes. This means that you need machines several orders of magnitude more powerful than you use for inference. From what I heard Google uses clusters of 100s of the their TPUs all located in a single rack for training. These clusters are organized in a customized computing architecture to maximize memory locality between cores (really critical for efficient back-propagation). Further, you can't use reduced precision weights for training like you can for inference, so there are no shortcuts.

Finally, the initial training stage is followed by reinforcement learning stages - this is key development in how AI models have improved in the past year. This may mean going through a curated set of traces (either synthetic or captured from users) and adjusting the weights based on experienced outcome.

Overall there's so many orders of magnitude more work and more hardware requirements for training that I find it improbable that inference dominates. The number of "inference" steps in training is freaking ridiculous and includes such factors as the "number of words ever written".

twobitshifter

2 hours ago

We have GPU costs, power costs, and how many token/s models can generate on those GPUs. It’s possible to figure out the marginal cost based on this. The current estimate is about $0.40 per million tokens for gpt4 equivalent model. Sonnet 4 is $15 per million tokens, so they are charging high margins on inference. The issue is how large of a margin is needed to recover their costs before the GPUs age out, and how high of a margin can be charged before it’s not economically viable.

https://www.gpunex.com/blog/ai-inference-economics-2026/

rudedogg

an hour ago

That seems way off to me.

I skimmed the article, but couldn’t spot any details on their estimates. They mention 70b+ params as being large in several places. But we’ve had several 100b+ param models that trail Sonnet.

stevenally

3 hours ago

> If we don't even know the ratio between amortized capital expenses and operational costs, outside investor analysis is impossible.

And yet we surely need this data for the IPO? Or are they relying on rule changes on the indexes to force ETFs to buy shares?

somewhereoutth

4 hours ago

Yes the huge discrete stepwise training spend is critical.

Maybe investors will realise that "the only winning move is not to play".

And so we are left with (as was) frontier models getting more and more out of date as whoever their post bankruptcy custodians are tries to eek pennies on the dollar for inference on their decaying property. Perhaps along with local and/or highly specialized models still feeding on the after-glow of the huge amount of training that was (and is no longer) done.

The next AI winter is going to be deep, savage, and long.

galaxyLogic

4 hours ago

> frontier models getting more and more out of date

Why are they getting out of date? Is it because we have new content from the internet that the older models did not have? Or are we simply trying to increase the size of the training data? In other words not more up-todate in terms of time the content was created vs. wanting to use bigger training-input-sets?

FuriouslyAdrift

8 hours ago

I work for a tiny little company ($150MM annual rev with 9% net) and we are already looking at dropping $100k on hardware to run local models because, for us, they're "good enough."

Our estimated spend for AIaaS would exceed that cost in less than a year.

In a few years, there will be hardware capable of running frontier models good enough for most things at accessible prices for even tiny companies.

simplyluke

8 hours ago

Yeah, that's the part that just seems to be wildly under-discussed to me.

If open source models are ~3-6 months behind SOTA, and ~opus4.6 capabilities are good-enough for product market fit, do the frontier labs have half a decade to catch up on their prior burn?

AI cost ballooning faster than companies can afford is becoming a very common topic in my circles right now. The era of "I'll pay infinitely more for marginal gains" is over from what I can tell.

an0malous

2 hours ago

> If open source models are ~3-6 months behind SOTA, and ~opus4.6 capabilities are good-enough for product market fit, do the frontier labs have half a decade to catch up on their prior burn?

They know they do not and that’s why they’re all trying to IPO right now, so they can pass the bag to consumer investors

doug_durham

8 hours ago

Open source models that you can run locally are much more than 3 to 6 months behind. 6 months was the November inflection for Claude. No open source model is as good as Claude Opus 4.6.

jobs_throwaway

7 hours ago

It depends what you mean by locally. I don't foresee running a model on my laptop anytime soon to power a coding agent. Far more likely is an infra team at my company operating an open source model on cloud infrastructure. When they're already paying $1000 / month / dev, it starts to pencil pretty quickly.

wrsh07

4 hours ago

Is there any open model as good as opus 4.6 at any price?

overfeed

3 hours ago

How many problems require Opus-4.6-level performance? The "I'll accept nothing but the very best model for any task" thinking is perplexing to me.

People got a lot done before Opus 4.6. In 6 months, would you be dissatisfied by Opus-4.6-level open-weight models, just because Opus 4.8 will be out?

JohnBooty

18 minutes ago

    would you be dissatisfied by Opus-4.6-level open-weight 
    models, just because Opus 4.8 will be out?

Well, I see what you mean, but two big concepts...

1A. Models get stale pretty quickly w.r.t. new developments that occur past their cutoff date. "But you can just keep them current by linking them to never documentation, etc!" Well, no, you sorta can't -- at least not in perpetuity. Those search results fill up your context window real quick. So that gets unsustainable real quick.

1B. Even when your context has plenty of free space, the results you get from "here's a link to the documentation for this new framework that released after your cutoff date" absolutely pales to the results you get from knowledge that is fully baked into the trained model as opposed to your context window. For one thing, that documentation link you pasted into your context might link to... a dozen code examples. Whereas if that was baked into the model itself, the model might have been trained on many thousands of examples in Github etc.

2. It's also a reality that most professional engineers have to keep up with their peers and competitors. We can maybe say it shouldn't be that way, but it is. So if $SOME_NEW_MODEL is significantly better than 4.6... and my peers and or competitors are using it, then yeah I might but really feeling the need to match them. And I'm not even necessarily talking about some kind of cutthroat dog-eat-dog stack-ranked workplace.

These limitations aren't relevant for all use cases or careers but they're hiiiiiiiighly relevant for professional software engineering.

strix_varius

3 hours ago

Not OP but I've been thinking about this a lot (like everyone ha) and I think my answer is, yes?

I hope there's a "good enough" point but I don't think we're there yet. Like for me hardware got good enough several years ago. But while opus 4.7 is really good compared to everything else, it's not so good that I would use it at a discount over whatever is available in a few months. The improvement in quality, speed, and daily frustration is worth it to me... Spoken as someone whose employer is footing the bill, so take that with a grain of salt.

I want to run my own local models, but I don't think that's feasible without lots of frustration until a few generations of frontier models are so good that they're almost indistinguishable for common tasks. Kind of like how MacBook pros have been for a while.

majormajor

2 hours ago

While I can imagine that I'd want to use Opus 4.8 over 4.6 for a fair number of things (at least if they can avoid further speed regressions), I also have noticed that certain types of failures seem to be systemic. Bigger context has been helpful for bootstrapping, but still doesn't fix problems of getting stuck on the wrong things - you can toss more things in the blender, but you don't necessarily know which way it'll slice them up in advance, or which things from them it'll latch onto. And output still seems to get into "blindered" states where important details get dropped - even though it'll agree very quickly when you point that out. As long as we're in that sort of "spit something out in local targeted manner, and then do a revision loop until tests are green" style of execution, bigger models haven't shown me the ability to really avoid finding non-optimal / subtly-broken outputs for complex problems.

Using Cursor to hop between models, I've found Opus to be generally better at really tricky debugging than GPT 5.5 or earlier models, but not reliably better at execution because of these things. I'm not sure Composer 2.5 is quite there yet for the execution side, but it's getting pretty close to those other ones, such that I'm definitely still in a "debug and plan with slow, execute with faster ones" operating model for working on hard shit.

wrsh07

an hour ago

I'm very happy to have multiple sessions open (and do) and switch between fast and slow models, and if there were a batch mode in codex or Claude code I would use it. (Just like I sometimes use codex fast mode)

But at the moment, I can't imagine why I wouldn't be spending the majority of my time with the best models. I'm spending a lot of time with them! Reducing the number of back-and-forths is extremely valuable to me.

I expect in two months I will still want to spend >80% of my time prompting the best models, and that's true if I were spending my own money on hobby projects, too.

chillfox

an hour ago

No, but the big open models are on the level of Sonnet 4.6, which is very good for most problems.

The people who are claiming Opus level capability does not have sufficiently complex problems to see the difference.

simplyluke

8 hours ago

> that you can run locally

That's doing a lot of work here.

The future I see isn't most companies buying hundreds of thousands in hardware to run models, it's them adding a line item to their AWS bill. Inference costs on the larger hosted open source models are dramatically lower than the frontier labs API pricing.

teiferer

6 hours ago

The future I'm seeing is AI coprocessors running inference locally in most devices that today have a CPU. Just look at how powerful your mobile phone has become compared to your desktop computer 15 years ago and compared to a main frame 30 years ago.

The days of requiring a data center to run anything resembling opus 4.6 are already counted. (But the industry will fight hard to get people to keep paying the Claude tax.)

simplyluke

6 hours ago

I'm already running a google TPU over USB on an otherwise very cheap board to do local computer vision on a front-door camera since I wanted to get away from Ring and other cloud services for that use case.

And yeah, that may be the ~decade world, but we're in the mainframe era of the frontier models. It's going to be more economical for basically any consumer, and most businesses, to pay someone else to host models for quite a while.

dom96

5 hours ago

Curious why you went for a custom solution. I am aware of at least one company that seems to ship devices with local computer vision (Reolink).

lelandbatey

4 hours ago

A gaming PC can already host models that perfectly serve casual users who just want recipes, todo tracking, picture identification, etc. E.g. Qwen 3.6 35b which will run on a $650 GPU at 75 t/s (Nvidia 1660 ti 16GB).

Said model will also run as a tool-calling coding model excellently (it's no Opus, but for a thing that once set up is just the cost of energy, it's incredible). It can type faster than you can, probably 10x faster, so with guidance it'll make you faster. And it's free.

It's here. If folks want ChatGPT without a subscription, they can have it today on their computer. The only money to be made is in the high end models doing "serious business" work spanning 1M+ token contexts and massive uncertainty. Everything else is already set to be eaten by today's local models.

simonw

4 hours ago

The problem with models like Qwen 3.6 35B (which really is an excellent model) is that my expectations of what a model can do have gone SO high now.

Here's a prompt I just ran against Claude Opus 4.7:

> Use python3 to experiment with whether the SQLite3 authorizer mechanism can be used to detect an INSERT OR REPLACE based just on running an explain query without examining the SQL string itself

Opus nailed it: https://claude.ai/share/c4212606-3fee-4b7c-bc97-505e0348ccac

I tried the same thing against qwen/qwen3.5-35b-a3b running locally in lmstudio, with the Pi coding agent. At first it looked like it was going to do great! And then it fell apart over the course of several tool calls: https://gisthost.github.io/?8ae2f842df619fb7fd8f1ccd82fe41c7

I'm used to GPT-5.5 and Opus 4.7 handling that kind of prompt without any problems at all.

scribble0242

an hour ago

This worked for me with qwen3.6-36b-a3b even at a q4 quant. I ran pi in a docker container and it had to figure out how to install python as well. I used the same initial prompt you had without any additional. You talked about Qwen 3.6, but then said you tried Qwen 3.5 in lmstudio. Not sure if you meant Qwen 3.6. I ran with llama.cpp llama-server with the recommended settings from unsloth.

I'm not an expert in SQLLite so I can't say if this is 100% correct, but it seemed directionally similar to the conclusion from claude.

  ### TL;DR
  
  - Authorizer + EXPLAIN:  No — authorizer only sees SQLITE_INSERT, not VDBE opcodes
  - EXPLAIN opcode analysis alone:  Yes — Delete opcode at position 10 is the unique signature of INSERT OR REPLACE / REPLACE

I can't help but think the not-so-distant future will see language models expected on commodity personal computing devices.

simonw

34 minutes ago

OK that's a very good answer! Do you mind sharing the transcript?

whattheheckheck

an hour ago

Thats when your build a better Ralph loop around your llm for it to converge to an answer and not rely on 1 shots

vineyardmike

an hour ago

> a thing that once set up is just the cost of energy

I don't think we can discount this, frankly. Newer electronics are energy efficient, but older devices are more energy-intensive, and unless configured well, a gaming PC can easily use a few dollars a month in electricity, so now you're approaching subscription territory. A subscription comes with no upfront cost, higher reliability, no wasted space in your home, mobile apps, etc. (and less privacy).

gedy

6 hours ago

> But the industry will fight hard to get people to keep paying the Claude tax.

I bet this will ironically be couched in "safety" reasons or regulation to get anti-AI folks on board, even if it favors the large incumbents.

selimthegrim

6 hours ago

Counted but not yet numbered?

ai_fry_ur_brain

3 hours ago

Magical thinking. I guess if your phone is going to have 128gb of dddr5 then sure. You people fundamentally don't understand the memory requirements for running inference. Your cute local models seem good enough because you have no standards and anything an LLM produces seems like magic to you.

majormajor

2 hours ago

Buying "hundreds of thousands in hardware" sounds like a lot but many companies - especially software companies - already do that if they have 100+ employees.

Running software in the cloud gives you certain reliability and scaling advantages that would be very hard to replicate locally. Running some code agents in the cloud vs local hardware, if the local hardware gets "good enough," breaks the other way - offline usage, alone, would be hugely valuable to many people and companies.

It'd be very interesting to see where various players would decide to make a call "local is good enough" though. Buying the hardware isn't a small bet, if it's not something that ends up as part of your standard computer.

apocalyptic0n3

7 hours ago

> it's them adding a line item to their AWS bill

That's the future Amazon sees too. We just had a week long session with the AWS team and they pushed that to us multiple times.

PeterStuer

7 hours ago

Many business tasks do not need the latest frontier models. I have a production system running since early GPT-4o. It now runs with GPT-5.2, not for improvements, but because it is cheaper. I could invest in switching to a local model, I tried and it works well enough, but api costs for this task are so low, it barely scratches $30/month. So I am using the local machine for other things and leave the inference on OpenAI, for now.

lukeasrodgers

3 hours ago

This project argues that with appropriate harness, the performance gap between frontier and much smaller open weight models shrinks dramatically: https://github.com/antoinezambelli/forge. I haven't kicked the tires yet.

7 hours ago

Opus 4.6 is a February model. Every time this subject comes up it seems like people post intentionally misleading things and move the goalposts.

The goalpost we've been bludgeoned with over and over again is that, in particular, Everything Changed in November 2025. That GPT 5.2 and Claude 4.5 were the inflection point. That is actually 6 months ago. And DeepSeek 4 is already there.

> run locally

You can't run DeepSeek locally on consumer hardware[1], but you can on enterprise hardware, and enterprise spend is the subject of this conversation -- and even if you aren't self-hosting, it doesn't matter, because you can just get your inference from one of the the many companies serving DeepSeek, who trivially undercut the pricing of OpenAI/Anthropic because they didn't have to spend hundreds of billions on training frontier from scratch but instead only invest in supporting inference, which is already profitable.

[1] Since this misconception comes up all the time, I'll go ahead and pre-empt it: no, training a 32b parameter model on outputs from DeepSeek and running that locally is not "running DeepSeek", despite the hundreds of stupid articles and Youtube videos making that idiotic claim that they're running it on a 5090.

simonw

7 hours ago

> You can't run DeepSeek locally on consumer hardware

Maybe not DeepSeek v4 Pro, but I've run DeepSeek v4 Flash on my 128GB MacBook Pro using antirez's carefully quantized https://github.com/antirez/ds4 and it's impressive.

applfanboysbgon

6 hours ago

Oh sure, yeah, that's nothing to sneeze at either. I think unqualified "DeepSeek" should generally refer to the main model, though, especially in the context of GPT5.2-grade quality.

PunchyHamster

8 hours ago

But one will be in few months. And then you have choice of paying say $100k for hardware and pay just power cost (or pay someone to do that for you), or pay way, way more for your team to have access to marginal improvement.

And 5% worse model for 10% of the price of the bleeding edge will be worth it for majority of people

vessenes

an hour ago

You have to think about why open models are behind. Exfiltration is a big part of it. So you could change the Nash equilibrium by increasing your security, or other multilateral approaches.

svara

8 hours ago

There's still a lot of room for the best models to get better at coding .

Your argument rests on the "for marginal gains" part but it's really not clear that the gains are marginal in the foreseeable future.

simplyluke

6 hours ago

This is totally valid and I don't agree with the downvotes you're getting. Someone coming out with a 10x improvement is possible and would change the game immediately. The thing is, we really have been seeing marginal gains with shifting leaders in who's got the "best" since GPT3, and at least as a user of these tools that pace has been slowing, not accelerating. Subjectively it feels like we're in the back half of an S-curve.

We're 3.5 years into this current AI wave, and a lot of the valuations have been predicated on what you're arguing here -- that essentially should one of the labs make an order-of-magnitude improvement or hit escape velocity on recursive self-improvement they'd become the most powerful economic chokepoint in history.

A lot of the frontier labs valuations has been based on an assumption that 1-2 companies would get break-away intelligence that basically made them economic chokepoints indefinitely into the future. The reality that's becoming increasingly clear is that model quality is a pretty linear function of (cash burned - ability to copy other's homework) and the economics are starting to look a lot more like airlines than online advertising.

grttq

4 hours ago

Lets go one step further.

The economics of airlines are such that they generally earn a return on capital less than cost of capital.

I think this is exactly where we are heading and OAI-Anthropic are the concordes.

w29UiIm2Xz

7 hours ago

If only the AI era was born in ZIRP.

sailfast

6 hours ago

Better now than ZIRP for me - at least people are asking timid questions about the unit economics and how long the runway is _early_ while also spending absolutely insane amounts of money on this bet. During ZIRP, these companies would have turned down any investor asking questions. Less contagion when rates aren't zero hopefully? :grimace:

EvanAnderson

8 hours ago

> ...we are already looking at dropping $100k on hardware to run local models...

Just think how much further that $100K would have gone if the hardware market wasn't so screwed-up.

Anecdote: I priced-out adding 1TB of RAM to a four node cluster a couple months ago. The cluster was purchased in fall of 2024 w/ 4 nodes, each with 256GB RAM. The nodes cost just over $14K apiece back in 2024 (entire box, not just the RAM).

Dell wanted >$90K a couple months ago to add 256GB to each node.

cyberax

7 hours ago

> Dell wanted >$90K a couple months ago to add 256GB to each node.

RAM is expensive, but not THAT expensive. I just bought 128Gb for about $5k for our build cluster (it's not even for AI, sigh). Even if you need larger-sized DIMM sticks, it's still going to be in the vicinity of ~15k tops.

EvanAnderson

7 hours ago

It was crazy. I found the part on the open market for a lot less but the edict from the Customer was to buy from Dell to keep the support entitlement intact. That inflated the price to an astronomical level to be sure.

I haven't had problems w/ Dell support and 3rd party memory, personally, but given the machines' application I understood the concern.

rstuart4133

6 hours ago

I get the impression the hive mind hasn't come to terms with the point that a model is optimised for certain tasks. It's like having someone ask you "is that a good hammer?". Good for what? There are claw hammers, sledgehammers, ball-peen hammers, club hammers, mallets, .... Yes, in a pinch, they can all bang in nails, but you wouldn't choose a dead blow hammer for that if you had a choice.

The Gemini Flash is very good at searches. Just about any low end model can toss out a poem. All the higher end models (open source and otherwise) seem to be able to churn out code that passes tests. The smaller, "less capable" ones are much faster at it, which means in the hands of a skilled practitioner are the best choice for that task. But they rapidly fall apart where there isn't a hard source of truth (like a good test suite) to grind against. Because of that you have to use a bigger model for bug finding. In that task the open source models tend to fail on larger code bases, where something like Opus still shines. I gather Mythos is an absolute monster, and unparalleled, and unavailable. I'm sure one of the reasons for that is it's so expensive to run.

Or to put it another way - you don't use a 100 tonne crane to pick up the shopping. And ... the smaller models will happily run on in-house hardware. You may not do it today because of the current DRAM price and integrated NPUs have just started shipping, but in 5 years time models will be running on your phone.

slashdave

41 minutes ago

It might be possible that in a few years someone will be able to engineer a reasonably priced machine to run today's frontier models (hint, your price is an order of magnitude off). However, they won't be able to run the frontier models that will exist in a few years.

MASNeo

7 hours ago

On prem AI makes sense for more than just the cost. More control, IP, model improvements you can keep, data privacy to name a few. People will realize that AI is not like compute the moment they get their own knowledge sold back at a premium.

fragmede

5 hours ago

What are the advantages to on-prem for a company that's already in the cloud and trusts it with their IP? That company can just rent GPU instances from the cloud if they want to train/fine-tune their own models and keep avoiding CapEx.

cmdrk

8 hours ago

Do you think this will be a trend for larger companies as well?

The decadal move to all-cloud-all-the-time killed off in-house hardware teams while the C-suite chased their OpEx dreams.

It would be interesting if we come full circle on this.

fragmede

5 hours ago

I doubt it. Companies that have moved to the cloud are already trusting the cloud with their IP. You can rent time on a high end Nvidia system from various clouds. OpEx means there's no write down in three/five years as that system goes out of date so it would only make sense if the performance/$ is there, or the company is highly protective of their IP and doesn't trust the cloud, at which point they're not on the cloud anyway.

stopachka

6 hours ago

I don't quite understand, what would 100K buy you?

irishcoffee

7 hours ago

It isn’t the models, it’s the closed api and the tooling associated with it. It’s driving me crazy how not-talked-about this is.

datadrivenangel

7 hours ago

As in the coding harnesses?

irishcoffee

4 hours ago

If I could leverage the same closed api VSCode uses, the entire moat is drained.

What you call harnesses I call… bullshit?

arbuge

8 hours ago

> In a few years, there will be hardware capable of running frontier models good enough for most things at accessible prices for even tiny companies.

What makes you so confident about this prediction? Hardware costs haven't exactly been cratering recently.

sofixa

6 hours ago

.> Hardware costs haven't exactly been cratering recently.

No, but local models have been booming in performance/quality improvements. The RAM shortage won't last forever (more supply will come online when if demand doesn't diminish), and then the math would be pretty easy.

try-working

3 hours ago

What about using DeepSeek API? Practically free.

disiplus

7 hours ago

same, but you need more then 100k of hw to run something like kimi k2.6 for a bigger team. on the other hand there is a ds4 flash that you can run on a macbook with 128gb ram. an that one is perfectly usable for a lot of tasks.

https://github.com/antirez/ds4

nonethewiser

8 hours ago

What models? Last I tried different local modals there was a pretty big difference from frontier.

wyager

3 hours ago

> there will be hardware capable of running frontier models

The current frontier? Sure. The frontier then? No - obviously that frontier is going to keep consuming available datacenter compute capacity, which will be better

ai_fry_ur_brain

3 hours ago

You people are delusional. How many times a day am I going to read this fiction of "good enough in a few years for most things".

There are physical limits to how much you can compress data and how much is needed for a capable model. If by hardware capable for running SOTA you mean a 7 figure investment for a company, than sure. But how come these companies didnt do the same thing for cloud? There's been this option for self hosting infrastructure for a decade but companies don't use it, they pay AWS.

awesome_dude

8 hours ago

> In a few years, there will be hardware capable of running frontier models good enough for most things at accessible prices for even tiny companies.

I was going to say - the models are just going to keep growing at a pace exceeding the pace of hardware pricing/availability

But then I realised that, far more likely, there will be a plateau reached (again) where nobody is seeing gain, and at that point hardware will catch up

alexpotato

8 hours ago

I was in college in the late 1990s/early 2000s and I distinctly remember an econometrics professor state the following:

"As cable TV and Pay Per View came out, there were studies done about how many movies people would watch if given unlimited access to films. The results were bandied about as proof that we should build out all this infrastructure to support this line of business. When the data was further analyzed by statisticians etc, it turned out that people claimed they were going to watch films 10-12 hours a day, every day of the week. Impossible."

I feel like we are in a similar boat here where some people are assuming:

- EVERYONE is going to be using max tokens

- tokens will NEVER get cheaper due to improvements in hardware, software, design, market forces etc etc

protocolture

4 hours ago

>I feel like we are in a similar boat here where some people are assuming: >- EVERYONE is going to be using max tokens >- tokens will NEVER get cheaper due to improvements in hardware, software, design, market forces etc etc

I feel like the reverse assumption is being made, that the current model looks like IBM doubling down on Mainframes soon to become cheap enough to deploy everywhere, when the real action is that the costs coming down represents cheaper hardware or more efficient software, and that a big chunk of "cheaper" AI will be eaten by smaller products deployed by individuals. Whatever the Personal Computer of AI looks like is going to be more disruptive than just an API endpoint you can fling tokens at.

We already see this with things like chrome auto installing an LLM.

You cant tell me with complete certainty that theres a moat here for the people spending 1 trillion + on this infra.

>When the data was further analyzed by statisticians etc, it turned out that people claimed they were going to watch films 10-12 hours a day, every day of the week. Impossible.

I also think this applies to people suggesting that companies will sack workers for AI, when the costs of replacing everything someone does in a day is more expensive in terms of tokens (likely even at a reduced price) than just hiring a bloke.

hintymad

2 hours ago

> it turned out that people claimed they were going to watch films 10-12 hours a day, every day of the week. Impossible."

I realized it long ago: one needs output to make meaning. Input can only be the cherry on a cake in one's life. That, actually, makes FIRE or Fat FIRE not so sustainable unless one has other hobbies.

mrandish

an hour ago

> they were going to watch films 10-12 hours a day, every day of the week. Impossible.

A lot of these LLM demand scaling scenarios make broad "up and to the right" assumptions about things which in practice have finite limits. Only some percentage of knowledge work benefits from acceleration, optimization or other improvements, and even then the amount of economic gain is capped.

lmm

2 hours ago

> it turned out that people claimed they were going to watch films 10-12 hours a day, every day of the week. Impossible.

And what happened? How many hours per day/week are people spending watching now?

j-bos

7 hours ago

But isn't it wonderful that they did?

wizzwizz4

6 hours ago

It's vaguely disturbing that people "watch" films 10-12 hours a day. Many of them are using it as a radio, for background noise, without really caring what the program is beyond vague genre, tuning in and out without particular regard to the plot… and yet we have all the cost of transmitting high-resolution video point-to-point.

Surely we could just put better stuff on the radio, and accomplish most of the same goals for a far lower price?

jimbokun

6 hours ago

My Dad was in the hospital, and just wanted to watch the Pirates play. The TV was filled with apps, some of them free to watch, others demanding a subscription and log in once you selected something.

None of them had the Pirates game.

I was thinking how the transistor radio was a far superior experience for this use case. Just tune to the channel broadcasting the game.

fragmede

5 hours ago

You mean the station that the MLB regulatory captured into not broadcasting when the local team was playing?

delis-thumbs-7e

5 hours ago

Radio has not gone anywhere you know? There is of course podcasts, but for instance Radio France has amazing music services like FIP: https://www.radiofrance.fr/fip

Then there’s NTS, BBC… Ypu can listen to them from online service, but at least in Europe there’s amazing national FM broadcastimg services.

TV is just bad radio with flickerimg lights.

ozozozd

an hour ago

Who has the time to watch films 10-12 hours a day?

I think the comment put forward that as an incorrect assumption that was made prior to the cable build-out.

PunchyHamster

7 hours ago

> - EVERYONE is going to be using max tokens

anthropic already hunts down OpenClaw users for using too much on their plan.

I'll give different example: When LED lights started to be more popular, the power usage didn't drop by the amount of power saved

>- tokens will NEVER get cheaper due to improvements in hardware, software, design, market forces etc etc

Well, first, improvements in computing stalled or even rolled back just purely because price of everything compute shot up cos of AI and that will NOT be fixed for a while and ESPECIALLY if AI usage will continue to increase

Second, the token per model might go down in time but better models have more expensive tokens, so we quickly get into spot when:

* price increase in token might not be worth marginal improvement next, better model brings

* more and more models are passing "good enough for the task" threshold so for less and less companies there is any economic sense to pay for the "best" instead of paying deepseek or some other company to run "previous gen" models

kopirgan

7 minutes ago

Depreciation starts on day 1 and most likely they IMHO dont have 5 years. They dodged the deepseek bullet but who knows what is out there that will make all of this investment essentially worthless?

regularfry

9 hours ago

The bottleneck has moved from producing a thing that works to knowing that the thing was the right thing to build. The more of the latter they can take on, the fewer knowledge workers are needed at all. So rather than 5% of every knowledge worker's salary going into tokens, 100% of the knowledge worker's total employment cost goes into tokens and you get a 20x productivity boost as a theoretical minimum across those tasks.

That's the game. There's a view you could take of this that this is just a growing of the pie: with those cost dynamics a lot more "small businesses" get a vast amount of leverage, so the overall economy grows without replacing the knowledge workers. I'm not sure I trust the MBA class to have that view.

seanp2k2

9 hours ago

>The bottleneck has moved from producing a thing that works to knowing that the thing was the right thing to build

I would argue that that's been the case for quite some time before AI. As an example, what innovative amazing world-changing products have Google or Meta launched in the past decade with their very high numbers of very talented and highly-compensated engineers? The issue with most big tech companies are leadership, strategy, and product direction. I'm not saying that they don't make any profits, just that they probably aren't "building [the right thing]".

AI for product development and management would be far more impactful than automating rote coding tasks / building React UIs that mirror API structures IMO.

Figs

9 hours ago

> AI for product development and management would be far more impactful than automating rote coding tasks [...]

Yeah, if this stuff actually worked that well already, OpenAI et al. would just run AI CEOs and engineers. Why get some other company to pay you at all when you can automate every other company out of existence and take all the money they make?

The fact of the matter is that while the tech has some uses, it sure as hell isn't a full scale replacement and you almost always actually have to massage the input into LLMs to get anything decent back out in practice. Some CEOs and managers can learn to do this, of course, and some already are... but that quickly turns into a second full time job. A "programmer" is still needed. The job might change from mostly hand-writing C++/JS/Python to prompt engineering + some manual coding to fix all the stupid fuck-ups that the bots can't solve themselves, but you still need someone to actually prompt the bot.

When that changes, it won't just be engineers losing work; there will be no reason to even have a human CEO any more.

dogwalker5000

2 hours ago

> When that changes, it won't just be engineers losing work; there will be no reason to even have a human CEO any more.

The human race isn’t ready for that world IMHO. The only reason there is a middle class is because people have leverage in the form of their labor. When that becomes worthless … the people who own stuff and make their living from doing so won’t hesitate to get rid of everyone else - whom are now worthless to them.

whattheheckheck

an hour ago

Humans will revolt and mutiny on the ai ceo so fast its not even funny

thejazzman

30 minutes ago

https://abcnews.com/US/man-allegedly-throws-molotov-cocktail...

aspenmartin

9 hours ago

I don't know, if you've ever tried to build something at companies of that scale you run into incredibly boring problems "what data table do I need for X" and "who is the right person to reach out to for Y" and "they aren't answering me I guess I'll have to escalate"

I don't think there is any shortage of great ideas at these companies, they are just extremely bloated. And I don't think its something like indecision or bad PMs, it's "we have a finite amount of time and resources so we need to be conservative but also not too conservative"

If you have AI systems that can simply build out POCs in days, backtest on real data, show reliable results and numbers, you get a suite of product options you were never able to get before. If you have coding agents that can speed up implementation, you can build more stuff and choose the things that stick.

It changes the cost/benefit calculus of the entire business. I think you are exactly right in that: PMs/leadership are by their nature orchestration machines. Other roles are as well, but I think PM's are at a particular advantage here in that it will be quite awhile I would expect before core product decisions and creativity can be delegated to an AI, but not quite awhile until virtually everything that they're blocked on (legal approvals, POCs, wire frames, etc etc etc) will become less and less of a blocker

supern0va

8 hours ago

>If you have AI systems that can simply build out POCs in days, backtest on real data, show reliable results and numbers, you get a suite of product options you were never able to get before. If you have coding agents that can speed up implementation, you can build more stuff and choose the things that stick.

I'll also add this: within a large organization, you often need to interact with many different codebases owned by many different teams. Agents have made it much easier to wrangle by having the ability to deploy one to scope out your web of dependencies to learn about what would be needed for feature X, and how that integration can happen.

We've been doing far more away team work simply because it makes things move faster. It's easier to convince a team to sign off/review something than it is to get them to commit to the planning and eventual work.

It genuinely is helping things move faster inside large organizations. Or at least, it is for us, particularly since we're getting organizational prioritization to actually build the scaffolding to make those agents more effective at search.

aspenmartin

8 hours ago

> It's easier to convince a team to sign off/review something than it is to get them to commit to the planning and eventual work.

1000x yes: you have touched on what I think is the single biggest factor here, that is the humongous value of POCs. they are gnarly to build without agents, and so we used to have to get everyone on board so we didn't get screwed in performance reviews, which was monumental task because that means convincing very busy PMs who have a lot on their plate and dont want to take risks on things they don't understand, and now it's like "can we scale this out" and you have a very nicely formatted proposal and POC. It de-risks things very quickly

jimbokun

5 hours ago

- DS has to do a deep dive (need buy in) to opportunity size and derisk with data. That DS has to work with other DS (people may have left or moved teams) to figure out how to get the right data and figure out what the difference is between 10 different tables that have overlapping but inconsistent data. - Eng has to build up an actual simple demo (need buy in) - Design has to make it not hideous (need buy in) - Legal has to review what you're doing; POCs should involve real data where possible because otherwise no one will trust it, even if its just for user analysis on existing products

This plus about 6 internal system bugs for custom tools that are flaky and who's team has long been re-orged or laid off, 8 people who won't answer you, 2 PTO's for the stakeholders, 6 weekly meetings

no one did POCs, they just had ideas and tried to get PM's to put it on the roadmap so if it fell through at least it was bought into

regularfry

7 hours ago

Yes, that exists at the wider business level. No question. I think what needs to get asked is are we talking about a bottleneck within the business as a whole, or a bottleneck within the scope of the knowledge work in question. Within software delivery there's a very clear shift when it's suddenly trivial to drop a 100kLoC plausible-looking PR into code review within an afternoon. Producing working code with a whole bunch of tests which make a very clear assertion that it does, in fact, work has had (if you're going that way) all the human-scale thinking time taken out of it, down to a rounding error. It still needs to be checked by a human, which was previously assumed to be a comparatively quick task in comparison to producing the thing. At least, it does where I am, and I don't think that's a silly position today at all.

If they can crack that latter review/spec-check/assurance step, checking that what was built was what was demanded of the problem such that we don't have humans in the loop at that step either, then the bottleneck moves again. Then I think it moves to requirements capture and to product development, but that might depend on the industry.

fragmede

5 hours ago

Trusting CodeRabbit for sign-off is "just" a small matter of configuration.

nilamo

7 hours ago

> As an example, what innovative amazing world-changing products have Google or Meta launched in the past decade

Kubernetes is at 11 years ago, and is huge enough to be included there. The Google Pixel was just under 10 years ago. So... not nothing haha

rogerrogerr

4 hours ago

Numbers I see put Pixel at less than 5% of iPhone sales. Not nothing, but world-changing? I doubt the world would look significantly different had Google not done Pixel.

nostrademons

8 hours ago

Google's internally developed and sometimes even launched plenty of innovative new products in the past decade. Stadia, Fuchsia, federated learning, and the whole transformer architecture that underlies this AI boom are good examples.

The problem is they get killed by some other executive who is afraid of their department looking bad by comparison.

I think this is fairly illustrative of the challenges in AI becoming as impactful as the Internet. The bottleneck is not making things. There are plenty of people who are really good at making things and can easily be 10x or 100x as productive as the average corporate worker. YCombinator was founded on that premise - small teams of founders and early employees could be orders of magnitudes more productive than the 1000s of corporate employees at their competitors.

The bottleneck is on bringing your product to market. If your innovative new product is built within a corporate environment, it'll get killed unless the executive you work under can get a promotion out of it, and you'll be denied all sorts of help with approvals, launch process, PR, marketing, branding, etc. If it's a startup, they'll try to shut you out with exclusive distribution deals, legal threats, lobbying efforts to change the legal environment, PR campaigns, FUD, etc.

The Internet was revolutionary because it let millions of people bring products to market without asking permission. Instead of having to bid for retail shelf space among dozens of entrenched competitors that all had sweetheart deals with the retailer, you could just put up a website and sell it to anyone across the globe. Instead of following hundreds of regulations that governed existing commerce, you could just launch something and sort it out later. AI doesn't really have that property - if anything, it makes things more centralized, with more gatekeepers, and so seems more likely to destroy economic value than add to it.

Danox

2 hours ago

Google does not follow through in the long run on many of their pet me-too follow projects, however they do not stray away from their core remit making their real customers happy the ones who buy the ads…

Obviously that includes whatever needs to be done to hoover in data from their marks and Meta also does the same thing without fail and both are really good at it. But outside their remit not so much.

regularfry

7 hours ago

What I think is happening is that the scale of thing you can hope to build at a below-corporate scale should radically grow. Corporate environments should suffer for this, being that inefficient.

> YCombinator was founded on that premise - small teams of founders and early employees could be orders of magnitudes more productive than the 1000s of corporate employees at their competitors.

I think this is still true, but the theory is:

1. You don't need YC-type funding to do YC-type business any more; 2. You don't need to scale the business past those small teams any more, you just buy more tokens.

For clarity YC still obviously has a place as an incubator, mentoring, and networking function. I just think that what was previously the inevitable conclusion that you have to hire all the people the second you hit PMF to keep up with scaling the business as you scale sales is no longer inevitable. If you didn't want to go that way before AI, you were a "lifestyle business" and not worth investing in. As more and more knowledge functions get capably implemented by AI, it's the preferred position: humans are vastly more expensive than tokens, so you want them doing the stuff the AI still can't do.

I don't think this necessarily translates to mass unemployment. I think it translates to masses of smaller businesses that are radically more efficient because the handoffs between business functions are tool calls, not emails to someone who doesn't want to help.

> The Internet was revolutionary because it let millions of people bring products to market without asking permission.

Think about it this way: if I am a small business owner but I think it makes sense to do something that previously only a team in a corporate environment could do but is now within the reach of AI, not only can I do it now, but I also don't have to ask anyone for permission! Who wins between the corporation and the small business in that scenario?

> AI doesn't really have that property - if anything, it makes things more centralized, with more gatekeepers, and so seems more likely to destroy economic value than add to it.

I think this will turn out to be backwards. I can see a version of this where the number of things you can do without needing to turn to a gatekeeper for help increases to the extent that the balance completely inverts.

The vast majority of businesses are small, and AI can give them tools which previously required corporate scale to make sense, without the inefficient hand-offs between busy, political humans. Which is also something that the internet did! Getting an advert in front of a national market pre-internet was Hard but sometimes you had to do it because your target market was "all Canadians who buy toothpaste" or whatever and that meant saturation-bombing the physical environment with physical billboard ads, posters, flyers, and so on. So you only did it if you were P&G-scale. Now you, personally, can do it, trivially, for better or worse.

nostrademons

6 hours ago

I dunno if the employees were ever really needed for scale. WhatsApp famously had 300M users and 13 employees at the time of acquisition; Instagram was something like 50M users and 55 employees. If you know what you're doing software scales basically infinitely, and the employees are there to make the software just slightly more tailored to specific user populations (and because upward career mobility for managers involves having more headcount). Yeah, building a revenue model takes people, but Valve employs only about 400 people and makes billions, as do various quant hedge funds like DE Shaw or RenTech.

Danox

2 hours ago

Most of the people they have on staff are there to support their real customers, and I don’t mean the marks out in Internet land Google and Meta‘s real customers are the people placing ads and giving them money, most of the staff is dedicated towards servicing them, that again is where most of the money goes to support their real customers.

regularfry

6 hours ago

The insta/whatsapp/plentyoffish model works if you're very lucky with both product-market fit and the technical constraints of the product itself. If you have something that technically scales extremely cleanly, it basically sells itself, and it doesn't need feature churn to retain or gain users, you're golden. I do think more businesses could do with checking whether they do in fact have that lottery ticket before hitting the scale button; there aren't that many examples around.

> Valve

Arguably a monopoly. They've got a product that sells itself with very low infra overheads for the income.

> Hedge funds

Very different model. I don't think the same intuitions apply.

nonethewiser

8 hours ago

>I would argue that that's been the case for quite some time before AI.

I would agree but it's really minimized the building. More and more time is being spent on pre-coding work.

beambot

8 hours ago

Google & Meta are illustrative of late-stage capitalism -- it's all about distribution, not innovation. Their job is (mostly) to just acquire the products that have passed the gauntlet, then scale up their monetization through their distribution-focused machine. The same dynamic plays out in virtually every industry (not just tech).

You'll find that most internal "innovation" teams are just lip service. In most cases, the "mothership" will be incapable of reproducing true innovation -- from a statistical perspective, culture perspective (mega corps are anti-scrappy; internal politics), and motivation perspective (startups aren't 9-to-5). It's much easier to have big M&A budgets, a VC arm, and some handwavvy internal innovation group.

Every now and again, you'll get real innovations (Waymo, transistors, GUIs), but even those have a spotty track record of commercialization when created internally.

fragmede

6 hours ago

The one I'd point out for that list is Kodak and the digital camera.

cogman10

9 hours ago

This is the same argument that has been historically made for outsourcing developers. Get 20 more devs for the cost of 1 dev in the US.

I suspect that AI will fail to pan out to the same extent for the same reason why outsourcing hasn't fully panned out (even though every company tries it after getting big enough).

The problems that will come up will be and always have been ongoing maintenance. AI is great at writing new code without a brain behind it, but once you get to the point where you need to refactor code, you start really needing someone with coding experience to guide the AI or veto it's mistakes.

I don't think that's really fixable even with a lot better AI. It's not something that ultimately comes out of the likes of github data.

I'm not saying that AI isn't going to make things better, btw, I just don't think we'll see a 20x improvement. Probably more like 1.5 or 2x.

roncesvalles

9 hours ago

Outsourcing of knowledge workers didn't work out because at large enough scales, the geographic arbitrage disappeared. Companies mostly always got what they paid for.

The determinant of success was only whether the task needed American-tier labor or could make do with sub-American quality labor.

m1coti

8 hours ago

I am not sure this feels right. I agree that the US currently has leading minds in terms of tech, but I am not sure it is too big of difference with the EU knowledge workers. EU is still a lot cheaper then US in terms of wages you would need to pay.

outside1234

a minute ago

EU workers themselves get a lot less, but the EU is expensive because of 1) the huge payroll tax (45% in France) and 2) the challenges with hiring and firing mean you are carrying people that aren’t contributing.

irishcoffee

7 hours ago

Sure is an interesting thought. None of this is sarcasm: why do US companies deal with the time zone differences and language barriers they won’t need to bother with so much by outsourcing to say, Ireland?

regularfry

7 hours ago

The mechanism is often that they'll actually outsource to someone like Accenture, who have teams everywhere, and whose contract managers will try to get their cheapest viable team onto the contract to maximise their margin. If the buyer can't judge the quality of what they're buying, or doesn't know why the resulting hand-offs, delays, mistakes and rework will cost them more than keeping everything in-house ever would have, they're going to have a bad time.

lmm

2 hours ago

Ireland isn't that much cheaper than, say, Oklahoma. And the cultural differences with Ireland are not a lot smaller than those with India or the Philippines or what have you, once you try to actually start working together.

(Yes, all the good developers from Oklahoma move out, but the same is true of Ireland)

surgical_fire

7 hours ago

Er, US companies do outsource to Ireland.

Basically every big tech has large offices and employ a lot of people there.

The limitation is that Ireland is a relatively small country, and most Irish developers are already employed (which is why Ireland end up being one of the main destinations for tech workers being hired from abroad).

cogman10

8 hours ago

That's certainly part of it. But the other part that I've heard time and time again is that in order for outsourcing to be successful you basically needed an american engineer in the mix hand holding everything, clarifying requirements, and vetoing bad code.

That part of dev work, the requirements gathering, attention to details, clarifying requirements, is something AI also struggles with. A lot of companies basically waste time and money on outsourced devs because without a clear path forward they effectively will sit and do nothing, waiting for a prompt.

m1coti

8 hours ago

I would not agree on that point. It really depends on company's structure. I mean it also depends with people that makes the team. I would say there are a lot of unknowns but I would certainly not generalize.

How I find your argument is that one distinguished engineer from US could do the same with the use of AI.

I worked with both and I know great and bad engineers from both sides. Only thing is that US has a bigger pool of great engineers.

regularfry

6 hours ago

I think the mechanism here isn't that American engineers are magic. It's that you need that contextual knowledge really close to where the work is actually being done, so that the turnaround for questions, blockages, clarifications, "we've got no work to do", quality level-setting and so on is on the scale of minutes, not time-zones.

jimbokun

5 hours ago

It doesn’t have to be an American but it does have to be a direct employee of the company ideally working in the same time zone as management and the people defining the requirements.

asdff

7 hours ago

Outsourcing of knowledge workers is still ramping up. The issue in the past was the skills were few and far between internationally. Facilities were also not built. That has changed now in a lot of fields. New campuses have been built in places like Bangalore and Hyderabad, even Singapore. The skills are there now, the training is decent, and you can see that the hiring is going on for very skilled titles in these cities. It is a different animal than just 10 years ago in this.

jimbokun

5 hours ago

The “American tier” labor of course is distributed across the world and the top performers in every nation find ways to get paid at something approaching American salary levels. Look at all the international FAANG offices paying high salaries, in purchase pricing parity terms.

regularfry

6 hours ago

> I suspect that AI will fail to pan out to the same extent for the same reason why outsourcing hasn't fully panned out

My mental model for that is that outsourcing fails where the work is being done organisationally far from the knowledge needed to do it. We know that's true of teams inside organisations, there's been a lot of research on how distance in the organisational tree negatively impacts productivity. Outsourcing is a pathological worst-case of that.

The promise (promise! We're not there yet!) of AI is that I can have a cross-functional team on my laptop. Organisational distance is zero. Where previously the outsourced team has to wait for the time zones to roll round so I can answer their blocking question when I get to my email STRICTLY AFTER I have had my coffee, now it's a prompt in a chat window with a button I can click to make a choice in 5 seconds. Delay is gone, cost of delay is gone.

> The problems that will come up will be and always have been ongoing maintenance. AI is great at writing new code without a brain behind it, but once you get to the point where you need to refactor code, you start really needing someone with coding experience to guide the AI or veto it's mistakes.

Oh, absolutely. That's a minefield. Today. It will be, right up until it isn't. There are ways to set up agents and projects right now that make a dramatic difference to how this part of the picture plays out, but those will sink into the harnesses as time goes on.

But also the big problem with maintenance and outsourced teams tends to be the commercial structure around the contract. You get a Build team, who Build the Thing and then: no more features for you, anything you want to add past the original spec costs extra. They hand over to the Run And Maintain team, who get to fix all the bugs that the Build team left but without the knowledge gained from building the thing, but are scaled and located to be absolutely as cheap as the supplier can get away with so probably don't have the skill, inclination, motivation, or permission to take on any restructuring to make the bug fixing easier and they're on the wrong end of the globe so there's a 24-hour latency on any queries. It's a terrible way to set teams up, but it looks good on paper.

Again, that's peculiar to outsourcing and completely goes away if I have the same team that built the thing own the thing long-term. That's true if it's humans or AI!

> I don't think that's really fixable even with a lot better AI. It's not something that ultimately comes out of the likes of github data.

No, it's a harness problem. You need to start from a maintainable point and keep standards in place. It'll take work to get the harnesses there and it's not ubiquitous. You might also need better models, but I've already personally seen big differences in outcomes between projects that took certain steps and others that didn't; it's nothing revolutionary, mostly stuff that works for humans also works for AIs but you need to know to ask for it.

> I'm not saying that AI isn't going to make things better, btw, I just don't think we'll see a 20x improvement. Probably more like 1.5 or 2x.

I think people radically underestimate the cost of delay. I don't know if 20x is realistic for the AI itself, but I think it's not impossible once the inefficiencies of having to go to other humans is factored in.

omcnoe

3 hours ago

Outsourcing also fails because it’s a pathological case of adverse selection. The businesses that outsource projects are ones who are organisationally incapable of managing those projects well internally. But, that inability extends to their oversight of outsourcing shops as well.

End result is that many outsourcing firms are borderline fraudulent in the way they treat their customers.

layer8

9 hours ago

Who pays for that value, and from what, if all knowledge workers lose their jobs?

It sounds like the economy would largely reduce to the small minority class of independently wealthy people.

simonw

9 hours ago

The more time I spend using agent tools the less I worry about knowledge worker job loss.

It takes a skilled knowledge worker to use these things.

keeda

8 hours ago

Yes, but I do worry about junior knowledge worker job loss. These models are very good (and getting better) at the vast dark matter of "donkey work" that happens in knowledge-based industries -- work typically done by junior devs / analysts / lawyers / consultants, paralegals, admin assistants, customer success / support, etc. -- and those roles comprise the bulk of the workforce.

And worse, these are the tasks that help the junior people eventually grow into the skilled knowledge workers required to operate models, so there's a pipeline problem too.

simplyluke

5 hours ago

I do too, but I think it currently has a lot more to do with the quasi-recession we've been in since the end of ZIRP and AI is a better excuse to stop training juniors than telling investors it's belt tightening, just like layoffs.

I'm already seeing tech execs/hiring managers getting very frustrated at the lack of new-senior-engineers to hire. The market will correct for this in time.

rogerrogerr

4 hours ago

Curious if you can share any backing information from your last statement? As a senior engineer (well, that's my job title anyway), I find it encouraging.

kansface

coryrc

7 hours ago

Maybe ironically, but software and robotics should allow us to scale regenerative agriculture in a way that doesn't leave everyone in poverty. We already have lasers mounted to trailers doing precise weeding instead of broad herbicide usage.

https://www.agtechmarket.net/news/laserweeding (random web search, I don't vouch for this site, it just looks legit at a glance)

Next innovation could be to scale succession planting, which keeps the ground from being exposed in between crops and lets you transition from nitrogen fixers to users quicker, getting more food out per acre while reducing fertilizer usage. But you can't do that with current harvesters and human labor is too valuable to spend on this.

Also take broccoli harvesting, typically you get a few big heads, then it keeps producing smaller heads, but it's not economical to harvest the smaller heads with human labor. Robotic harvesting lets the same plant produce more food per acre and uses the energy needed for new plants instead to keep producing food.

fodkodrasz

7 hours ago

Masses will be unemployed, due to robots displacing them, but human labor will also be too costly. We won't be able to afford a person shepherding, but we will need to produce "meat" (substitutes) in plants, or in inhumane animal-jail, and we'll need robot-weedkiller lasers to produce the feedstock instead of letting animals graze... and we'll give the food produced this way to people on UBI...

This is where this is going, the whole industrialism is totally self-serving, and for every problem its answer is digging deeper in the rabbit hole, and creating 2 more problems in addition to solving the initial problem only half-way.

I don't want to say what you are suggesting is not possibly useful, I just want to emphasize how stuff works out in reality, in addition to doing some nice stuff like what you called out (the halfway solution to the problems). All we get is more alienation and humans getting depressed and feeling a lack of purpose... but somehow we cannot afford to pay fair prices for the agricultural work, and pay fair prices for the food, and not overproduce and overpollute... and the same thing is happening in every aspect of the human condition, not only food production, which is the most basic and ancient activity we have been doing.

coryrc

2 hours ago

9 hours ago

SVB didn't get bailed out, their investors and creditors were wiped out. You could argue depositors were bailed out -- as they took the undue risk of keeping more than $250k in a single bank (though as part of a requirement for getting a loan from SVB, you had to have your operating accounts with them. So lots of companies had no choice, as SVB was one of the few banks that would lend to them).

Arguably, the main impact of securing SVB depositors above the $250k limit is that it prevented thousands of people from being laid off that week, as their employers wouldn't have had the money to make payroll the following Wednesday.

matwood

8 hours ago

Thank you for saying this. Continuing to point at SVB as a bailout is annoying. They were not bailed out. Anyone with deposits in an accredited bank should be made whole - always. Without trusted banking we have no economy.

anonymars

7 hours ago

> Anyone with deposits in an accredited bank should be made whole - always

Sure, but is that the case now? Is everyone made whole when a bank fails and they have more deposits than the insurance limits? Or only when it's the well-connected / too-big-to-fail?

Looks like the answer is no: https://www.wsj.com/finance/banking/a-small-banks-failure-le...

So I don't think it's unreasonable to describe SVB as a bailout. Not for the investors, but for the depositors. Has anything changed to reduce the moral hazard / make it less likely to recur?

rvz

3 hours ago

So we all now know that a bailout DID occur with the SVB depositors who had all their money in the bank and most deposits were over the FDIC insurance limit. The FDIC insurance rules somehow didn't apply here because there was too much money at risk. (And too big to fail).

But if there was a bank failure at a regionally smaller bank with a regular customer or startup depositing the same amount of money over the insurance limit, their money is gone.

Just like Intel got a "bailout" from investment as chosen by the US government, AI will eventually have a very similar story.

fragmede

7 hours ago

8 hours ago

I will also tell you, as someone who works at a company that's trying to remain profitable, that token spend has caught the eyes of finance and much like cloud spend they've already started applying pressure to control costs. This May my team is protected to use 30% fewer tokens than we did in April - this was by intention. I suspect we'll drop more in June.

bigbluedots

41 minutes ago

It might be time to start interacting with agents using grug speak only

Gigachad

4 hours ago

I expect in the future, when these AI companies stop subsidizing costs, the idea of spinning up 20 agents to work on some brain fart idea that you throw out after looking closer will come to an end. It'll be seen like assigning developers on work that hasn't been properly planned for or reviewed.

fragmede

4 hours ago

Can't wait till June, when finance gives the team the choice: everyone gets double tokens if you choose to fire somebody.

spamizbad

an hour ago

Oh we already had that with a RIF earlier in the year.

bradleyjg

3 hours ago

> That's a _huge_ shift. Most people I know cite +20%-40% velocity with these tools, against the actual work their company cares about doing.

We all have our own observations and mine don’t significantly diverge. But that’s bottom up. At this point shouldn’t we be seeing it top down?

If we are beyond potential and into significant productivity gains, why isn’t that showing up for the customers?

Why didn’t delta airlines get significantly more operationally efficient in the last 3 months due to the introduction of better software?

This is a genuine question, I am seeing a disconnect.

narnarpapadaddy

3 hours ago

Anecdotally, my take on this is that biggest value lever is strategy and alignment, not implementation. The typical company is dozens of little vectors pointed in different directions, and they cancel each other out. Scaling up the magnitude of each is still net zero.

I was recently consulting at org where two separate engineering teams were all in on two different, incompatible deployment platforms and using AI to accelerate adoption of each.

Management was mystified why their engineering leads kept telling them they couldn’t deploy a complete implementation of their solution.

simonw

3 hours ago

> Why didn’t delta airlines get significantly more operationally efficient in the last 3 months due to the introduction of better software?

The coding agents got good in November. Most individual engineers didn't fully clock this until January/February. This means that companies didn't really figure it out until March/April.

Assuming companies like Delta have adopted coding agents (which would be pretty fast) it still takes months from adopting a new tool to the code results of that tool rolling out to production.

I expect (and would hope) Delta's software development culture is very conservative. Since nobody can confidently tell Delta "here are proven practices for using this tech to produce high quality, more secure code" yet it would be surprising if they were blasting full-steam ahead.

I expect that even companies that got on board with coding agents in January will only just be starting to ship user-facing features that benefited from those new tools. Shipping software takes a long time, no matter how much faster the "typing the code in" bit gets!

davnicwil

3 hours ago

> Most people I know cite +20%-40% velocity

Seems roughly right, that does seem to be about the boost in the most well-suited cases where you essentially know exactly how to solve the problem, the problem won't change much, and it's truly a matter of just churning out the implementation.

In that case precisely prompting, doing the review & nudge loop, can be a pretty nice (nice, still not game changing) speed boost over literally typing out the code to match the design in your head.

The less optimistic view though is that most things you build aren't like that. Even if they seem like it first. These things get booked as a nice speed boost, but you'll only find out much later they weren't.

A confounding factor is that it seems like many people not in the detail of building software do seem to think of most to all things are like that, even before AI assisted coding. Not much need to say more - see the entire history of the 'agile' movement for evidence of this.

And because most things aren't like that, I actually struggle to see fundamentally how more than 20-40% will ever be achieved (short of the ever-present deus ex machina of AGI argument), simply because the generation is already really good for these types of things. So since things like this aren't going to increase in overall proportion of things to be done, I don't see where the overall extra gains come from by models improving at this point.

jgbuddy

10 hours ago

You are making the assumption that the models are only used / paid for by 2.5% of the population (your knowledge workers value). There will be new value created by these models which people are happy to pay for which simply did not exist at all before. It is also naive to say that the hyperscalers are going to be expecting a return on this in 5 years, it will be entirely propped up by investments / IPOs as has been the case with any tech company for decades now to reach scale. The hyperscalers are currently spending ~650b combined annually, which they have the cash for and can sell in future compute instantly.

specproc

9 hours ago

I'm sorry, what the feck does "value creation" mean here? I live in a place where people are so, insanely squeezed from every angle. Wages are stagnant, prices rocketing. Where is the money to pay for this value going to come from?

No one I know feels richer than they did a decade back. I've not been able to meaningfully put up my prices for a decade. People are tired and stressed and scared, particularly scared of a technology everyone keeps telling them will make them redundant.

There is no rising tide lifting all boats, just most of us drowning whilst a few whizz past in their yachts.

I honestly hope these guys faceplant ASAP. Couldn't happen to a nicer bunch of people.

dirck-norman

9 hours ago

Feelings aren’t fact. A lot of data shows the doomerism is not reflected in the actual numbers and much of it has to do with rapid inflation and continued vibes.

Consumption has risen, inflation adjusted wages have risen for blue collar and white collar alike. Most social mobility has been the middle class moving into the upper middle class, not moving to the lower class.

The main thing holding people back is the housing crisis. This is orthogonal to the value creation of businesses.

Value creation is growth. If it didn’t exist the S&P would still be 42.55$.

everforward

5 hours ago

> The main thing holding people back is the housing crisis. This is orthogonal to the value creation of businesses.

This feels wholly at odds with saying most social mobility is upwards. So most of the social movement is into a class where a home and vacations are a given, but we also have a growing class of people who can't afford a home? Per BLS, average real wages are down 0.3% YoY https://www.bls.gov/news.release/realer.nr0.htm .

> Value creation is growth. If it didn’t exist the S&P would still be 42.55$.

This reductively assumes "value creation" is the only effect on the S&P pricing. You'll note a ton of graphs correlate with it, e.g. https://tradingeconomics.com/united-states/inflation-cpi is the US inflation rate, which also tracks the S&P pricing. Ie if a company is worth $100 a year ago and inflation was 4%, I'd expect to pay $104 for their stock with 0 value creation whatsoever.

decidu0us9034

4 hours ago

The upper decile of income earners account for more than half of all consumption in the US. Household balance sheets and wealth have never looked stronger, again when you account for all the appreciated stocks and properties owned by the upper quartile. True incomes for the lowest decile rose significantly for the first time since 1970 in 2022 and then sort of stayed flat again. Sure, statistically significantly, not "significantly" as in personally meaningful after figuring in rising consumer costs. There is a narrative where you can see all this as hugely positive but this is also largely a "vibes" based narrative. I don't know why you'd expect most people to care about what the "vibes" are like for the best off in society, that's a bit removed from their daily concerns.

geraneum

7 hours ago

> Feelings aren’t fact... much of it has to do with rapid inflation and "continued vibes".

Oh the lost irony.

dirck-norman

6 hours ago

Is it ironic? Or did you just read the comment incorrectly?

jacobgkau

8 hours ago

> Consumption has risen, inflation adjusted wages have risen for blue collar and white collar alike.

My wages haven't risen for nearly 5 years, while inflation has occurred over the past 5 years. Why the blanket statements?

> The main thing holding people back is the housing crisis. This is orthogonal to the value creation of businesses.

Are you suggesting a "housing crisis," in your words, wouldn't impact consumption? I'm watching my spending (and living like a child in his parent's house, except it's not my parent and I have to pay for it) in the hopes that in about a decade, I'll have saved up enough of a down payment for a home somewhere in my state that I could actually afford the mortgage on the remaining amount. There are plenty of things I'd potentially spend money on but won't as long as I feel like I'm economically stuck and have a chance in hell of saving my way out of it. So this feeling translates to fact.

If you think my personal experience is just an anecdote and doesn't count because it's not being told through the lens of large-scale numbers, fine. But I really agree with the person you replied to that you're gonna have to be a whole lot more specific than "value creation" if you want people to spend money on your AI products "in this economy," whether it's because they're actually strapped for cash or just pretending like you seem to think they are.

WarmWash

8 hours ago

Sounds like internet sentiment and not research data.

It's kind of become socially taboo to not be suffering "in this economy", but on paper it's hard to see weakness in places that there isn't always weakness. As long as the 65-95% are doing well, there isn't going to be a collapse.

forlorn_mammoth

8 hours ago

The most recent U Michigan 'Survey of Consumer Sentiment', which is THE authorative source in the US, shows consumer sentiment at the lowest levels since the survey started in 1977

From the U Michigan page: https://www.sca.isr.umich.edu/

or from the FED https://fred.stlouisfed.org/series/UMCSENT

WarmWash

2 hours ago

Sentiment is just vibes though.

Everyone hates Walmart but they still do gangbusters numbers.

So people may be hating, but they sure as hell are also still spending

https://fred.stlouisfed.org/series/PCEC96

jgbuddy

9 hours ago

A literal example is that I can use AI to file my taxes instead of spending a weekend and hundreds of dollars to have an accountant do it for me. It costs me like $5. that 245$ delta is the value of that output to me, as long as I am confident it is correct.

mfuzzey

8 hours ago

Seems to be a thing in the US to need specialised software, an accountant or AI to file taxes.

In most of Europe individuals at least don't need any of that. I'm in France and it's just a connection to a government run website to enter a few figures, takes less than an hour most of it is already pre-entered (salary etc), the main thing to add manually is charitable donations.

If you're running a business then yes an accountant can be good (or be required depending on the legal form of the business) but not for individuals.

moduspol

8 hours ago

Part of the value of paying an accountant is that you can get representation in case you are audited. Though I guess you did say you were confident it is correct.

decidu0us9034

4 hours ago

This also sounds like rich people problems. Vast majority of people with a W-2 take the standard deduction.

asdff

7 hours ago

Taxes are one of those things that seem difficult and people reach for tooling or expertise without trying initially without, but are pretty easy to do yourself just filling out the forms.

panta

7 hours ago

I think that to sum things up, we will have to wait until we can evaluate the cost of the mistakes. You could be lucky but you could also end up with a very negative output value in the longer time frame.

WarmWash

8 hours ago

I did my taxes this year too with 5.5 and 3.1

Otherwise normally costs around $800 to do, because I have a small business too.

smnc

8 hours ago

5 hours ago

> a driving factor will likely be how good we can make models that are so small they use almost no compute.

That will certainly help but it doesn't move the fundamental limit because resource efficiency is a cost driver not a demand driver - and my argument is against the thesis that lying beyond professional devs and knowledge workers, there's an untapped trillion dollar industry serving LLMs to mass global consumers.

Using Simon's cost estimates, I agree that halving the current $1,000 - $1,200/mo MSRP to profitably serve frontier inference to professional developers and knowledge workers (PD&K) will help Vendor A steal share from Vendor B or C. It will also increase LLM sales penetration into the segments of the global PD&K TAM which can't afford ~$1K/mo for every seat. A fair chunk of the PD&K workers in many SMEs aren't included in today's ~$1K/mo per seat license pool, especially in 2nd and 3rd world geos. When the price falls to $500 and $250 most will but that's still just saturating the existing PD&K TAM - not pushing into mass consumers.

While the PD&K TAM is big, justifying Trillion+ dollar capex spend requires believing the TAM is much more than PD&K and eventually grows into converting a couple billion non-PD&K consumers into ~$20/mo subscribers. I don't buy it for two reasons:

1) The Comps: There are vanishingly few examples of long-term, mass consumer adoption of a discretionary technology at that scale. Mobile phones at ~$15 to $30/mo are the obvious one but LLMs are nowhere near being that valuable to the average plumber in Des Moines, baker in Jakarta or retired nurse in Hamburg. Pondering it, I just imagined forcing any of those people to choose between their mobile phone and an LLM chatbot. Sure, some who are flush with cash might choose both but for most consumers in the world ~$20/mo is big enough they'd have to pick one and ~zero percent would choose the LLM over their phone. After mobile phones, the second comp for discretionary tech spend I thought about was XBox and Playstation monthly gaming subscriptions but combined they have less than 90M paying subscribers and the ARR is just under $10/mo. As an industry, "Big LLM" is spending well over a trillion dollars every five years. XBox and PS ARR doesn't even cover paying the interest on that capital, much less the 3 to 5x returns hedge fund investors are betting on.

2) The Alternative: It's useful to doubt my own intuitions and one counter to my skepticism is to assume "But LLMs aren't finished yet, they're going to get much better." How much better could an LLM which can be profitable at ~$20/mo get than Claude Mythos in the next five years? Instead of debating future unknowables with myself, I've found it's better to just imagine the most perfect future product I can that's still realistically plausible. So, let's imagine we're willing to spend a million dollars a month to very unprofitably deploy a prototype to test the consumer demand for "Tomorrow's Awesomest $20/mo LLM" today. So we gather a few hundred super smart, broadly knowledgeable intellectuals together at one top-tier university research library, where they'll have access to every commercial database and unlimited Claude Mythos 2.0 and ChatGPT 6.0. Since our experimental budget is $1M/mo we can afford to add in several Nobel prize and Fields Medal winners too. They'll work together manually reviewing and improving not only every LLM answer but also our test user's prompts - and of course our test chatbot will have human-level real-time speech recognition and vision (via Zoom and screen-sharing with actual genius-level humans), making this truly a test of the "smartest, most accurate, best consumer chatbot" we can imagine.

Now, let's run the test by having one thousand mass consumers try it out and see how many Des Moines plumbers, Jakarta bakers and Hamburg retired nurses we can convert to a 1 year @ $20/mo subscription for our $1M/mo ultimate chatbot simulation. Playing this thought experiment out in a bunch of ways, I find some percentage of outliers, iconoclasts and closet intellectuals would go for it but... the vast majority just don't find it enough better than "free" chatbot alternatives AT&T includes with their phone subscription or Samsung bundles with Galaxy phones - despite only being ChatGPT 5.4-level. It turns out, most plumbers, bakers and ex-nurses don't have a compelling "job to be done" in their daily lives that even an MoE panel of actual Nobel and Field's medalists with ivy league professors can make enough more valuable than an inferior but free-to-me chatbot, in the judgement of our Des Moines plumber. While the world's smartest chatbot is nice, when it comes time to pay, he prefers having one additional premium football match on TV and a six pack of cold beers every month.

fn-mote

5 hours ago

I'm having a hard time understanding this huge post that doesn't talk about enterprise users. I'm convinced that the consumer isn't going to be coming up with enough money to justify AI valuations... but doesn't this just mean that we expect the money to come from large enterprise users?

A recent post here said AI spend could be "20% of every software developer's salary"... and that seemed plausible based on productivity improvements. That's not about a phone bill.

mrandish

3 hours ago

> doesn't this just mean that we expect the money to come from large enterprise users?

Yes, if you agree that not enough consumers will value "smart paid chatbots" over dumb free chatbots at ~$20/mo, then, as you say, the money has to come from enterprise developers and knowledge workers (PD&K). The big problem with that is the numbers don't work. There aren't enough PD&K workers that are highly-paid enough to justify $12,000 to $16,000 a year to cover the astronomical spending run rates.

Without recapitulating all the various scenarios AI CEOs like to hand-wave, my take-away is the scenarios which show today's frontier AI vendors earning the returns they've promised investors to get those trillions of dollars they're spending ALL require truly extraordinary deltas far above current historical actuals. Whereas ALL the scenarios which I find more plausibly realistic, even if still quite bullish, never even get near the required ballpark.

I've tried to make various scenarios fit but inevitably, when the PD&K demand side starts getting implausibly inflated, I start pushing the cost side down to keep the adoption rates remotely plausible. For example, assuming things like the number of PD&K workers will grow at 2X the highest rates ever seen before or that the percentage of the tasks knowledge workers actually do at sufficient frequency to really matter and which are also "LLM improvable" is implausibly vast.

It gets challenging because one quickly hits finite limits on the increase in tangible economic value that an LLM can possibly deliver on many common knowledge work tasks like drafting an email or a product proposal. Even if we assume next year's frontier LLM is so good literally ZERO slop is even possible so that our knowledge worker doesn't ever have to review anything - turning a 20 minute task into 2 minutes, or even 200 milliseconds, yields finite economic value to the enterprise. Even if you go extreme and suppose pretty crazy stuff like 18 months from now LLMs have eliminated 50% of PD&K workers, that messes with the spreadsheet assumptions because now the there are half as many $16,000/yr seats so the LLM license prices have to double again just to stay even.

At the edges it quickly gets nuts. I even tried assuming that in five years LLMs eliminate 100% of non-managerial PD&K jobs. All actual work is done by Super-AGI LLMs. Even if we assume such amazing intelligence can be profitably sold in five years for only 4x the price of today's far dumber LLMs, that Super AGI still costs $50,000/yr per human replaced. Even that assumption only cuts the blended labor cost of non-managerial roles roughly in half (depending on industry and geo). In the end, no matter how much labor cost LLMs enable companies to cut, it still only reduces overhead. Lower costs might allow Vendor A to steal some of Vendor B's customers but it doesn't increase the total TAM or demand of the entire sector both vendors serve.

Once you're out of plausible labor savings, one has to move to assuming that within five years Super-AGI LLMs working unsupervised will be making and validating fundamental scientific breakthroughs, then reducing those breakthroughs to engineering practice enabling new technologies which create entire new markets. Then they'll create whole new companies with maybe only 1/10th the humans and profitably grow those new markets. It quickly starts to feel like bad sci-fi instead of plausible, near-term financial scenario planning. Is stuff that extreme and unprecedented actually possible? Sure, incredibly unlikely and unprecedented things DO sometimes happen but things so far out of the distribution are very rare. But when making Sam and Dario's scenarios actually 'math out' starts requiring more than one "Black Swan" unlikely, historically unprecedented, earth-shaking event. And then goes on to need whole flocks of Black Swans, it just isn't credible. That said, I do believe that some parts of these scenarios may happen in 10 or 20+ years. It's just that the investment theses trillions worth of our 401K's are sunk into assume five or six year amortization and financial payback.

fragmede

4 hours ago

What are the non-tech people in your life using AI for? $20/month, next to Starbucks and avocado toast, is discretionary. Maybe the novelty will wear off and non-tech consumers will leave it in droves, but everyone declared they'd leave YouTube if they started playing ads, but YouTube doesn't seem to have noticed.

mrandish

2 hours ago

> What are the non-tech people in your life using AI for?

Mostly asking random questions they used to search Google for.

> next to Starbucks and avocado toast, is discretionary.

Sure, but your description implies highly affluent, urban professionals in western nations. I was talking about getting several billion global mass-market consumers to all keep paying ~$20/mo. Mass consumer adoption of mobile phones worldwide is currently >5.8 billion or >70% of humans alive. Only ~50M people are paying $20/mo for an LLM and I suspect many of them are not pure consumers but actually knowledge workers that AI vendors are losing money on and will eventually force into higher tier plans just like the $200/mo developers they're currently losing money on. These heavily subsidized loss-leader offers are all going away post-IPO.

Personally, I know maybe a dozen people who pay $20/mo for an LLM but only two of them are really 'pure consumers' who don't use it for knowledge work. Both of them are multi-millionaires and neither has had a job in ten years. One is retired like me and the other is so wealthy she has a Netjets credit card and has new cars delivered like some people order shoes. Everyone else I know paying $20/mo is a professional who uses the LLM for a lot of office or knowledge work and writes it off as a business expense - examples include a couple of attorneys who are senior partners in a law office they own, a solo architect, and a dentist who owns his own practice.

At $20/mo, AI vendors are probably losing money on most of my professional friends because they use it pretty heavily all day. They're only making money on the two multi-millionaires who both use it so infrequently they could easily be using free chatbots instead but are so rich they could lose $10,000 in their couch cushions and not notice. While they are profitable at $20/mo, they aren't exactly "typical consumers" that there are billion more of. I expect AI vendors will find ways to force my lawyer, architect and dentist friends to switch to higher priced plans soon because they're really knowledge workers abusing a consumer tier plan into unprofitability.

Planktonne

9 hours ago

> There will be new value created by these models which people are happy to pay for which simply did not exist at all before

What sort of new value, and why will people pay for it from someone else rather than prompting for it themselves?

PunchyHamster

7 hours ago

But will they pay big actors running top end models for that? You don't need latest openai or anthropic model to go thru your mails, get summary of the some products from web, or to do your to-do list.

The AI might very well be used by noticeable % of population daily, but that doesn't mean they will be paying trillion dollars to the leading US AI companies

TimTheTinker

9 hours ago

I thought Anthropic and OpenAI's combined CapEx has been <100B?

source: https://isaiprofitable.com/

kilroy123

9 hours ago

That site needs Apple on the list. ;-)

Danox

8 hours ago

Why? All their money is going to Apple Silicon and the five ecosystems, so far in Apples entire history, the largest acquisition has only been $3 billion dollars, OpenAI is currently getting nothing and they gave Google a measly $1 billion refund per year for the use of Gemini.

If John Ternus wants to spend some money, spend it on bringing memory in house. Apple has the money and the engineering talent to do so, have it fab/made onshore in partnership with TSMC.

Do it Apple because you have to not because you want to the Chinese probably will be taking over the memory industry, worldwide, by taking advantage of the greed from three memory companies and their AI overlords.

kilroy123

7 hours ago

That's the point. To show how they _haven't_ lost billions on this.

deaton

9 hours ago

Maybe so far, but they've committed to well over a trillion in future capex.

topaz0

6 hours ago

And there's the indirect capex that their revenues will pay for indirectly, like in the case of oracle

TimTheTinker

6 hours ago

Here's the question - does that future spending already appear on partners' balance sheets

jvanderbot

8 hours ago

Hey, I wrote this down one time. I estimated way higher yearly revenue required, to be adversarial. And you can keep the "cost per unit AI work" a parameter and play with the results.

But the point is that if people are willing to delegate part of their salary (e.g., buy consumer products), vs requiring employers to pay for the tokens, then it's quite possibly a net win. Something like "I pay a largeish fee every month to make my own job much easier", similarly to how we buy a car to make commuting easier.

https://jodavaho.io/posts/ai-jobpocolypse.html

onlyrealcuzzo

10 hours ago

> We're talking about a world where you need 5% of every knowledge workers salary to go into tokens.

They are assuming ~10% global GDP growth instead of ~3%. You probably don't need the same %s if the pie grows a ton.

I'm highly skeptical we get that growth, but if you aren't, it makes it easier to digest.

freakynit

9 hours ago

I mean this case with AI-productivity fires itself back when we talk about GDP.

The more AI causes productivity increases, the less and less number of workers will be needed. This will heat up the job market even more and bring salaries down.

Net effect of this productivity increase: less consumption by the masses, even though you may be producing more good and much more efficiently.

A third effect also comes into play that once all this starts to happen, common people, who are generally living paycheck to paycheck, will now start to hesitate towards making any long term investment, housing included. And that indirectly will end up impacting financial and banking sector, which will then impact existing savings, bonds yields and retirement funds, and the recession-like cycle starts.

This productivity increase only makes sense if it is capped to a very small number.. like 20% max. Beyond that, who these companies will even be selling to?

Am I overthinking all this?

simonw

9 hours ago

> The more AI causes productivity increases, the less and less number of workers will be needed.

That only holds if companies have a fixed need for "productivity" which is met by their current employees, such that their employees becoming more productive means they need less of them.

Every company I've ever worked for has wanted to achieve way more than they are able to get done with current resources.

But generally yes, the biggest open question about all of this is how the impact will play out on the economy, job opportunities etc. I've not seen anyone come close to a confident prediction about how this will play out.

jbreckmckye

9 hours ago

> Every company I've ever worked for has wanted to achieve way more than they are able to get done with current resources.

I mean sure. Every company wants an infinite addressable market. But that doesn't mean it exists.

It might not be possible to sell 10x the software we sell today. It might not even be possible to sell 2x

forgetfulness

9 hours ago

It's hard to imagine how making insurance sales cheaper for the brokers, churning out astrology apps faster, AI boyfriend bots or running ad campaigns with fewer and lower paid designers is going to drive 10% GDP growth in developed and middle income countries, that's the sort of figures you see when very poor countries finish rolling out electrification, sanitation and transportation.

seanp2k2

9 hours ago

>The more AI causes productivity increases, the less and less number of workers will be needed. This will heat up the job market even more and bring salaries down.

>Net effect of this productivity increase: less consumption by the masses, even though you may be producing more good and much more efficiently.

Big tech companies can't even create login flows and account recovery flows that work for everyone yet. There are countless stories of folks losing access to business Instagram accounts that get hacked, Google support from a human to fix a problem that is outside of their help articles is non-existent, etc etc. There's still so much "low-hanging fruit" IMO that isn't particularly fun or exciting to fix, but ask your average non-tech friend or family member what they think of the Facebook + Instagram security settings pages / sites / desktop-only settings.

Who is going to pay for all of these subscriptions that will power this GDP increase when average purchasing power of those outside of the top ~10% of earners is decreasing YoY? We're headed toward food and water shortages next to sprawling datacenters, not shared societal prosperity and a healthy middle class.

20k

6 hours ago

>Am I overthinking all this?

Nope, if AI were to realise the hype, you have to take into account macroeconomics. Usually this isn't a problem for most businesses

>The more AI causes productivity increases, the less and less number of workers will be needed. This will heat up the job market even more and bring salaries down.

People also underestimate that the reason why companies are so excited about AI isn't to increase productivity, its to fire workers and crack down on worker rights. They won't lay people off because AI means they don't need as many people to get the job done, they'll fire everyone while doing a much shittier job, because they hate having to abide by worker's rights and pay people

arjie

9 hours ago

First of all, common people are not living paycheck to paycheck in the sense that they're at risk of not having money[0]. This is corporate content marketing that has entered the collective memory of people, not anything close to reality.

Secondarily, reducing the cost of making a thing doesn't always mean you get less of a thing. For me, certainly, what happened is that I write way more software than I originally did. When we built compilers, the amount of human engineering effort required to do things plunged, but the amount of software engineering jobs didn't go down.

This is as bad as models will ever be. That part is true. And it's entirely possible we go foom. But it's also possible we don't, and then it depends on where the asymptote lands.

0: https://www.slowboring.com/p/this-economic-myth-needs-to-go-...

almogodel

7 hours ago

Respectfully, that is truly ignorant. The vast majority of humans do not have any savings and would be in big trouble if regular income ceased. No paycheck no food. It’s wage slavery and it’s pervasive.

samrus

an hour ago

> The more AI causes productivity increases, the less and less number of workers will be needed

This might not necessarily be true. Increased efficiency creates induced demand to the point where more workers are needed. Because the new capabilities unlock more value to extract and the economy rushes in to get it. The steam engine is a huge example of this

I dont exactly know what new value genAI will unlock but i think its more likely than not

onlyrealcuzzo

7 hours ago

> The more AI causes productivity increases, the less and less number of workers will be needed.

Why does this have to be the case with AI but it didn't have to be (and wasn't) the case with the steam engine, electricity, the automobile, or the computer & internet?

Certainly, AI could be different.

It's curious to me why the vast majority of people on here think it must be different.

lbreakjai

4 hours ago

Because the previous revolutions only automated a small subset of jobs, it didn't automate manual work.

Some people take the view that AI could make knowledge work largely irrelevant. Any niche humans could carve for themselves would only live long enough to generate training data for the AI to automate.

seanp2k2

9 hours ago

And yet the job everyone loves to hate, the humble "burger flipper", continues to resist automation yet command minimum wage labor rates. This future of either being a CEO of a company consisting primarily of AI agents building some monthly subscription-based solution to some trivial digital chores OR manual labor that isn't [yet] fiscally viable to automate seems quite bleak. We'd also need a ton of robot technicians and manufacturing that the US has neither the educational and training institutions to support nor the will of the population to fill. Given the ongoing war on immigration, visas, and foreign-made hardware, if this continues, good luck.

stared

9 hours ago

This would be a Bladerunner future Pope Leo XIV warned against (https://news.ycombinator.com/item?id=48265206), though in different words.

manquer

3 hours ago

One factor to consider , the base will not remain the same over the next 5 yearts.

Every generation of developer tooling that increase of absolute code throughput creates a new class of developers (and users).

Always been the case since first compilers, through eras of frameworks to today, and the skill level needed to be one has dropped. In mid/late 80s only Master / Doctorate level Comp Sci professional could write any applications. It dropped to undergrad and just Information Technology engineers and comp sci theory became mostly optional and dropped further to any college level educated with some training and has been trending below with no/low code tools like retool pre 2022, that was before agent codegen services such as v0/replit and so on.

The next generation developers will not produce applications and architecture as previous generations did, just as we most of us here don't produce the level of quality that pg did when building this platform[1] , but as long as the user can find value it doesn't matter as countless enterprise applications of middling quality already prove today.

All this to say the 200M/30M numbers will not remain the same is the thesis for these businesses, will it change by large enough at a fast enough pace to justify the capex, I don't think so either. However web 1 then 2.0 , saas and mobile revolutions were pretty quick with new class of users and developers so not completely unrealistic .

[1] While HN is a heavy outlier with its custom lang lisp implementation, there are any number of examples from previous eras that are more moderate in choices but written with solid architecture with skill levels would be hard to find in today's generation founders.

thesparks

7 hours ago

Those are rookie numbers. We are going to blow past $1t per year in spending in no time. As a developer for 29 years, I couldn't go back to coding by hand. For better or worse, AI will be woven into the fabric of life in no time.

hintymad

5 hours ago

> We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer.

Just realized something: if one worries about losing jobs to AI, token's high unit cost is good news. To say the least, high cost would delay the displacement, if any, right?

In the meantime, someone shared the below on X. I guess the moral of the story is that "good enough" does not just displace software engineers, but also models.

   > I Went From $3,000/Month on Claude to $5/Week on DeepSeek

   > And honestly?80% of my work is identical.

   > For the past two months, I was burning $3-5K monthly on Claude Code. Every idea from design to development to testing - full end-to-end automation, even simulating users to test my products and provide feedback.

   > Extremely token-intensive. But Claude's caching sucked, making it insanely expensive.

   > Then I discovered DeepSeek V4.

dcre

7 hours ago

1. Global IT spend is $6T per year

2. Where does this $5T number come from? If they make $4T in revenue over the next 5 years instead, what happens?

zaphirplane

an hour ago

> 200m knowledge workers in the world, 30m developers

1 in 6 knowledge worker is a developer ! Surely that’s too high thou explains the job market

motoxpro

6 hours ago

So you've got that market. Let's call it the demand BY knowledge workers to do the work. You've also got:

2. The companies themselves buying tokens for operations to make the work more efficent. e.g. Salesforce agent or Microsoft Office agent or random saas inventory agent. (and if you say those will go away (which I don't believe), it's even more bullish. The tokens just go to someone vibe coding XYZ, which is EVEN MORE than if you were to buy saas because it's SaaS product x Companies that built it instead of just one)

3. The companies SELLING tokens. This is also new markets like schools and small business (e.g. the local gas station buying an inventory tool)

4. The consumers "buying" (I put in quotes because it can be subsidised but the company) through chatgpt, strava, instagram/netflix recommendation, etc.

Local models still take compute, and while it may be cheaper, it is the same argument of on prem vs cloud. No one operates on prem unless you HAVE to for regulatory. Margins will come down and you just spin up a GCP/OpenAI/Anthropic agent.

It may be "cheaper" but rationally its better to pay someone to manage it. Thats why Hetzner only had $367M in revneue (a lot but tiny compared to managed services)

mv4

8 hours ago

If people figure out how to run agents on-prem (already becoming feasible for both agentic tasks and coding on consumer hardware like Mac Studio 128GB+ or DGX Spark with some models) these companies will be in deep trouble.

Privacy is also a huge issue.

jkelleyrtp

8 hours ago

I agree in principle with the math. But I believe that in reality if revenues don't show up quickly, then lenders will just restructure the debt and defer the payback period. Similar to SF commercial real-estate; many buildings should've come due during the depressed covid market, but lenders (banks) were willing to delay payment until the market picked up again.

The scale of these investments put the lenders at substantial risk, so the lenders will do anything to make it work. If the current lenders will be damaged by extended payback periods, they can simply sell the debt to someone else who won't be.

panarky

4 hours ago

> 5% of every knowledge workers salary to go into tokens

In general, I don't think you can reason from the existence of potentially stranded investments back to revenue projections.

And when you frame this as percentage of salaries, that's a sneaky implication that this is only about reducing salaries and headcount, and not about adding capability, or doing things you couldn't do before, or making fewer mistakes, or capturing more revenue, or expanding margins, or competing more effectively.

That said, 5% of knowledge worker comp actually seems very low to me, given the capabilities, and considering the percentage of "knowledge work" that is absolute bullshit.

Two weeks ago I received an email from my HOA saying I'd been billed for a service I never asked for. So I replied to the email saying they'd made a mistake. There are now more than 30 messages in the thread, involving at least 8 "knowledge workers" at the property management company all passing the buck, and the problem is no closer to resolution.

An agent could wipe out all 8 of those bullshit jobs and solve my simple problem in five minutes instead of two weeks. Think of how many hundreds of thousands people are doing this nonsense just in the property management industry alone.

5% is nothing.

jauntywundrkind

14 minutes ago

Given what costs are and availability of parts, that 5 year write down is not in practice going to be the case. Maybe tax wise perhaps but especially for big fancy expensive multi million dollar 100-500kW racks these things are going to stick around for a while, I think.

gz5

an hour ago

there are many paths towards ROI and ruin. but towards ROI:

+ LLM-powered robotics, autonomous, IoT, smart manufacturing

+ LLM-powered biotech, healthcare, genetic engineering, medicine

+ Recursive model improvement

+ Multiply the # of devs (software truly eats world)

+ Exponential increases in model performance / cost decrease (algorithms, power, infra, chips, architectures, etc.)

bg24

4 hours ago

Is it possible that you are narrowly sizing the opportunity? While PMF does not always mean that early pioneers will be the leaders, I think the market itself goes beyond knowledge workers and developers. Agents, robots, drones etc will all use LLM or some world model.

I am rather more concerned about competition from CHINA. With how Huawei (2000 -> 2020) crushed every other telecom company and went from nobody to the most revered leader in 20 years, and with the depth of leadership in manufacturing and work culture, if China surpasses USA in AI, all US companies lose.

keeda

7 hours ago

Putting some more numbers out there (some of the links are broken, but numbers look about right):

https://github.com/danielmiessler/Substrate/blob/main/Data/K...

Knowledge worker compensation is 35 - 50 trillion a year globally (6 - 12T in the US alone.) That's a huge TAM. It's still close but 5T over 5 years seems doable.

>... unless we figure out how to make developers 2x, 5x, 10x as productive on stuff that matters, this isn't going to play out well.

The way we make ICs 10x productive is not just making each of them individually more productive, but by removing the coordination overhead of large organizations, because overhead scales super-linearly with the size of the org. And orgs will shrink automatically as AI-assisted ICs take ownership of larger and larger scopes of work, leaving much more budget for tokens.

I went into this in a bit more detail along with some made-up numbers here: https://news.ycombinator.com/item?id=48040999

qaq

6 hours ago

Anthropic raised less than 100B up to now and as of March has 30B ARR. Why does it have to make back 2.5T to 5T ?

ai_fry_ur_brain

3 hours ago

Also hardware will be obsolete or dead in 5 years, and warrantys are 3 years from Nvidia. Ask crypto miners how these kind of hardware economics work. Numbers have to keep going up all around. Its a fundamentally broken business model unless prices increase 10x

tedggh

8 hours ago

Also, not all developers work on software products. The vast majority of developers work supporting software solutions as part of a much bigger business model, such as infrastructure, industry, healthcare and services. Many of these are complex organizations. So, unless you get to turn every employee into a 10x employee, the 10X coder along won’t necessarily make a 10X productivity contribution. What’s likely going to happen is the 10X coder will start to slow down or adding more (unnecessary) complexity to avoid having to sit and wait on overhead, for other areas of the business which are not easily automated away to AI to catch up. As a developer I can finish my project in June instead of December, but what if the customer is still not ready for integration until December? what do I do?

jatora

36 minutes ago

imo if your developers arent at least 2x as productive, then something is being done wrong on the employees part and/or the organization's. cli tools are ridiculously powerful provided you were an actual developer before using AI.

Maybe it's just me being (trigger warning from me providing an honest self assessment) very intelligent + a generalist, but i went from only full stack webdev and .NET to being able to implement an end-to-end LLM training pipeline (data curation, tokenizer, pretrain, sft, DPO - using ~$100 in cloud compute to train a class-competitive 1B STEM model)...and a full economic financial modeling and quant analysis application that pulls up to date economic, economic, news, stock data from the entire world and uses Dagster to orchestrate tech ical indicators and fundamentals and signals... and i did these things for learning and for fun. i built my own sublime text and obsidian replacement. i built my own reddit/twitter/hackernews/substack/news aggregator. i built countless other useful tools and utilities for me personally and for work I build more that empowers multiple departments.

Ive built 2 browser games, one already released to great reviews and 100k+ hours played. Ive built a tool on top of claude code that does ~60% of my job. Ive run data analysis on company financials for forecasting that have been refined and are producing very accurate predictions. Ive built competitive analysis tools and trackers.

All of this in 3 years. The projects are all clean, documented, with great code practices and modularity. A purist would surely consider some of the code slop. But it all works completely and fills real needs.

This is a huge shift. Anyone not realizing it yet is just simply behind the curve. I would not have accomplished 1/10 of this without AI coding. I went from copying code into and out of browser chats for 2 years before getting on the CLI train, and it is absolutely ridiculous the ROI you get from subscriptions to Claude or Codex.

datsci_est_2015

8 hours ago

I could see such productivity gains being possible, if only because the current tooling around LLMs is terrible. The fact that we have 30 blog pieces per day making the front page of Hacker News about someone’s convoluted system to guide LLM output to something reasonable is absurd. There needs to be standardization in tooling, and it needs to be open source. Then, and only then IMO, will we see huge productivity gains.

But, at that point I think the big players’ moats will have dried up. Local models will probably be sufficient for 99% of daily office worker tasks.

So I disagree with TFA’s premise. I think this fear is probably shared amongst the LLM giants, and they’re still hoping that neural network transformers are somehow the path to AGI (probably not, imo).

BadBadJellyBean

7 hours ago

This assumes that we won't need new hardware in ~2 years. I find that unlikely. So they have to make back what they got up until now PLUS the running upgrade/development costs. So what will it be in 5 years? $20t? $30t? It's all getting a bit outlandish.

What I'm often hearing though is the equivalent of "gg ez" when I bring that up. I don't understand how this will at any point blitz scale to profitability. As far as I know they don't have positive cash flow, no one has a moat and I don't think they will push out engineers.

gorgoiler

7 hours ago

What value do the big model makers provide other than having a head start on gathering up humanity’s IP to train their proprietary models?

What’s their moat? Is it hoping for regulatory capture where scraping is made illegal the day after they finally finish scraping all human language?

It’s like OpenAI dammed the Colorado, and Anthropic dammed the Hudson, and now they’re both trying to sell us bottled water subscriptions at $100 a month. I don’t know how well the dam part of the analogy holds up, but the water part feels strong. Compiling models based on humanity’s written output feels like something no corporation should own.

golly_ned

8 hours ago

This is why 'agents' are the solution for these companies. Token spending goes through the roof. As long as a human is in the loop needing to read or review at human speed, that's a ceiling on how many tokens per user they can generate.

Salgat

4 hours ago

My hope is that hardware improvements (better node densities every 2-3 years, better designs, etc) will pick up the majority of the savings for these companies in the future, assuming LLM performance starts to taper off with diminishing returns.

red75prime

6 hours ago

> 200m knowledge workers in the world, 30m developers

Your scope is too narrow. The companies target more than white-collar jobs. And $1t is around 0.5% of the world economy.

jmyeet

9 hours ago

YEPPP... and I'm kind of shocked at how many people can't do simple math.

Let's put it context. Google's annual revenue seems to be north of $400B. So if OpenAI suddenly had Google's revenue, it would still be insufficient to recover their investment.

and it's a ticking time bomb because $1T in servers, CPUs, GPUs and memory is going to be worth $200B in 5 years. You can say they can keep using what they've got. Sure. But they're also not going to stop spending on new hardware. And the competitor that comes along in 5 years and spends $1T doing the exact same thing is going to have a huge advantage.

OpenAI at this point reminds me very much of the Russ Henneman pre-money hype cycle.

mfuzzey

8 hours ago

It's actually worse than that. It's not just financial depreciation or that the existing hardware becomes obsolete due to being less powerful than new hardware but also that hardware being run all the time at high load actually has a limited lifetime of a few years so it will physically break...

jmyeet

8 hours ago

I agree but it's even worse than that.

Data centers come down to performance-per-Watt. Electricity accounts for 20-30% of a data center's operating cost [1]. I don't know the exact breakdown but the GPU part of that is probably the majority given how power hungry GPUs are. The B200 is upwards of 1200 Watts [2]. The B200 is rated at ~4.5PFLOPS of dense FP8. So you're getting 3.75PFLOPS/W. We don't know what the next generation will look like. The A200 (Hopper architecture card that preceded the B200) had ~4PFLOPS apparently but also lower power consumption. Obviously this changes depending on whether you're looking at dense or spare and FP8 vs INT8 vs INT4 vs FP4, etc so we're just using FP8 as a yardstick.

Imagine a fictional B200 successor, the T200 that has 8PFLOPS of dense FP8 at 1000 Watts. Well then a DC built on that where the T200 will likely cost similar to what the B200 does now, you'll get nearly double PPW so the same size DC and same electricity load is going to be like 2 of your old DCs in operating costs. That's a big deal when you've laid out a trillion dollars.

[1]: https://iaeimagazine.org/electrical-fundamentals/how-much-el...

[2]: https://www.trgdatacenters.com/resource/h200-power-consumpti...

mountainriver

9 hours ago

How could extremely capable artificial brains ever pay for themselves?

WarmWash

8 hours ago

8 hours ago

I mean, a company that loses money on every widget they sell might technically have found "product-market fit." :)

It seems quite possible to me that developer tooling is going to end up being the biggest win from LLMs because there is a product-market fit -- and also quite possible that OpenAI and/or Anthropic end up getting bought for pennies on the dollar because their burn rate is unsustainable. AI may end up being this generation's "dark fiber."

Imustaskforhelp

9 hours ago

At a certain point, I genuinely feel like the best way this hype is being sold is by making people genuinely believe in it.

and in that sense, if Anthropic and OpenAI are able to create the projection that they can-be profitable despite finances seeming bubbly at best, I think that what happens is that these companies spew so much amount of content that people like Simon get into it too.

There is a deeper problem of people falling into AI psychosis too, in general, I am not sure if Simon has fallen into it or not

I think that the greatest point which can be made here is to not offload your thinking to others and to think about the situation yourself. Sounds familiar (looks like we are all off-loading our thinking itself to machines)

Side-note: As humans, we have a tendency to quickly judge or make quick decisions which stems from our times foraging and scavenging in jungles.

Another Side-note: at a certain point, I am unsure of how much to think about AI or not, certainly discussions about it that were happening 2 years ago weren't helpful in contexts that they are used now (well not in any way or form that a person discussing and getting into the weeds of AI 2 years ago is better than a person just getting into it say 2-3 months ago)

With the industry (moving so fast) [but that doesn't mean that you can't catch up with it, I feel like the fast word has made people think that they are falling behind which is imo wrong i suppose]*, It is basically unsure to me of any FOMO or anything if you aren't using AI already, I find this notion naive.

People might be making strong opinions (AI psychosis) and skills on the tools available at the moment the same done 2 years ago. We don't quite know about the tech as these are still black-boxes and how they progress and what these "AI skills" might survive or not in future. Heck, we aren't even sure if these tools might survive or not or wouldn't be made magnitudes more expensive simply to break even as they are given to us for the first time at percentages of the price.

I don't know if I should form (strong) opinions yet and also a question of its worth so much thinking efforts in the first place, probably just gonna do my own thing (the way I want to) which includes learning C at the moment. because learning is fun.

simonw

8 hours ago

I didn't exactly say that they were about to become wildly successful companies. I suggested that they had "found product-market fit" - not too impressive for more than a decade of work - and that their revenue may even be "enough to start covering their costs".

Imustaskforhelp

7 hours ago

Firstly thanks for responding and I wish you to have a nice day. your suggestions have value and I appreciate you writing the article. Perhaps enterprise businesses do end up becoming the fat and meat of the AI industry.

My question which I wish to ask: What would happen to these AI companies if they turn out to be anything but wildly successful companies, both to the investors who have already invested in it and to those who might be investing indirectly into it in the near-future (passive investors, retirement funds)

I would love to hear your thoughts on it!

Thanks and have a nice day :-D

simonw

7 hours ago

> What would happen to these AI companies if they turn out to be anything but wildly successful companies

I'm not nearly enough of an economist / finance person to answer that credibly, but I expect they'll go bust, and a lot of people will lose their shirts.

... and the model weights will be sold to other companies who will then run them at a profit, and eventually figure out an economically sustainable way to train new ones.

The 1800s railway booms are a good comparison here - a lot of companies went bust, a lot of investors lost money, and we still ended up with railways.

If the AI companies all go bust we're going to have a lot of spare data center capacity!

Imustaskforhelp

7 hours ago

> If the AI companies all go bust we're going to have a lot of spare data center capacity!

I can be wrong I usually am but an AI DC != compute DC or that it might decrease the prices of servers substantially because of it. (well not exactly, I hope you read my whole message so that I am able to better explain what I am saying.). AI DC's try to optimize for one thing: running GPU's for immense scalability and flexibility (0 to numbers>=large_number).

Currently, its actually way worse, the server providers are some of the worst impacted by the industry at the moment because each server requires ram and ram is well... increasing in its price exponentially. It's really a tough time to be a provider at this time (in certain respects) directly because of AI.

It is unclear to me if spare DC capacity will have any meaningful impact to it. I don't think that atleast within compute (and not GPU/AI DC), that space was too large of a problem.

Fun fact but one of the largest providers (BuyVM) had its datacenter price from where they colo'd increase because of the immense demand at the moment for spots in datacenters by many tens of thousands of dollars that they did the first price hike in at this point at decades! The situation is this dire :-(

Ram prices might come falling down and DC's might get cheaper but they can only get cheaper to limit, they still need to for example DC security employees

and I wish to suggest that if anything, investors might wish to re-coup their losses within the AI loss, they might want to make up with what little they might have (ahem DC)

For example, if you wish to want to take at an even more egregious example of what I am suggesting, there are many new york LLC's who would much rather leave the properties that they own empty rather than decreasing the price of what it costs (which they have set to some egregious amounts). I think that for them, somehow the math ends up working out in the end somehow, so there might be something more to it.

I wish I was optimist but I don't believe that the gains in spare data center capacity are worth even a fraction of fraction of the damage if AI were to go bust as you suggested with trillions of dollars vanished.

So, with the data I have at the moment, I am unable to suggest that compute would be cheaper. Heck, it was cheaper before AI and compute prices have never been something that people worry about because there are sometimes 10x cheaper options than AWS,GCP,Azure with things like Hetzner/OVH and others (yes its not a 1:1 situation but still its a 95% overlap and for all intents and purposes, great)

I can see a potential where GPU compute can get cheaper, oh boy, its so much more expensive than compute but I feel like GPU's aside from AI might still have a much more limited niche than generic CPU.

The issue wasn't ever the pricing. Simon, I own 7$/yr vps's which run my websites fine because they are written in golang. I doubt it can get cheaper than it. (You can get a 3$/yr vps if that is what you are interested with using Nat VPS + cf tunnels)

I would once again appreciate to hear your thoughts on it. The only thing I realistically see is if Ram producers ramp up their productions and create a ram price glut in the next few years, but imo the prices would even out over the long term.

I have seen the point of spare DC capacity being raised up multiple times but I finally ended up writing a message which hopefully captures the nuance, but once again, I don't know the future about it.

Waiting for your reply and have a nice day Simon (& other readers) and thanks for reading if you did, I appreciate it :-D

simonw

6 hours ago

I think we are in agreement that if the bubble bursts a lot of people will lose a lot of money. I don't have a strong opinion on the data centers, my main point is that I don't think AI "just goes away" if the bubble bursts, which seems to be something that a lot of people assume.

yalogin

8 hours ago

To get that revenue and adoption they have to vastly increase their infrastructure spending. If they are currently losing in even the 200/month plans how is it sustainable?

jimbokun

6 hours ago

That’s on the order of 1% to %2 of global GDP per year just to pay for their hardware commitments.

jstummbillig

8 hours ago

> 200m knowledge workers in the world, 30m developers. We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer.

This is where the napkin math is breaking down in a big way. There is absolutely no reason to assume this will only impact "knowledge workers". Farmers use computers. Farmers will use AI.

vablings

8 hours ago

AI for what? None of the AI a farmer could or would use would be any more meaningful that light chatbot usage or already existing computer vision/gps

red75prime

6 hours ago

And around 400k H-2A workers. Humanoid robots... Who works on them I wonder.

quantumleaper

8 hours ago

The kind of farm that would use AI is already 99% machinery and automation.

nl

3 hours ago

> They've got, ballpark, $5t to $10t to make back in the next 5 years, or the hardware buildouts will start getting written down.

I find it disappointing that a completely wrong statement like this ends up the top comment on HN.

It is wrong in both the math, the logic about public markets and understanding accounting.

> $5t to $10t to make back in the next 5 years

I don't know where this number comes from, but it has gone unchallenged.

OpenAI and Anthropic combined have raised around $100B. This is an investment so isn't something the have to "pay back" from earnings - instead investors expect to make that back from the share price being higher than what they paid for it.

> or the hardware buildouts will start getting written down.

The hardware buildouts get written down anyway!! That is a good thing for investors because as the value gets written down they can book a tax loss. ANd it turns out that generally agreed depreciation schedule for GPUs (used to be 3 years, now 5 years by places like Coreweave) is still too conservative since GPU rental prices for 5 year old chips are higher now than when they were new (!!)

All of this makes the rest of the math in the comment incorrect by at least an order of magnitude and under some scenarios possibly 2 orders of magnitude!

That's not a small error!

root-parent

6 hours ago

Author seems strangely unwilling to distinguish usage from profitable product market fit. And from his own numbers:

Anthropic Max: $100/month

OpenAI Pro: $100/month

Total paid: $200/month

API equivalent usage: $2,180.16 in 30 days

So paid only 9.17% of API-priced value a 90.83% discount, or about $10.90 of API priced usage for every $1 paid...

That proves heavy usage but not sustainable unit economics.

Anthropic reported numbers point the same way:

Q2 revenue: $10.9B

Adjusted operating profit: $559M

Margin: 5.1%

SpaceX compute: $1.25B/month = $3.75B/quarter

So one compute supplier alone equals 34.4% of quarterly revenue and 6.7x quarterly adjusted operating profit.

Its difficult for the blogger to understand something when its incentives depend on not understanding it...

simonw

6 hours ago

My point with the $2,180.16 thing is that the price for consumers like myself is heavily discounted... but the price for enterprise companies is not discounted.

My usage is therefore a useful indicator of quite how much those enterprise companies may be spending on tokens, given the new pricing scheme.

If enterprise companies were still getting the same discounts that I get myself I would not have written this article.

(I had to dig into your margin figure - looks like you calculated 5.1% as 559000000 / 10900000000 * 100 but that $559M "adjusted operating profit" figure includes training costs, where usually when we talk about margin on inference we're not including those since those costs are fixed, margin calculations make more sense against the variable costs of serving a token.)

what

3 hours ago

When you have to train a new model every few months to stay competitive, discounting that cost is rather dubious.

simonw

3 hours ago

They key difference here is that training costs are fixed. If you train a model for $100m dollars, how much of that training fee should you allocate to each token that the model serves?

It's impossible to know, because you don't know how many tokens total will be served by that model until you retire it at some point in the future.

So you can't say "1,000,000 tokens costs $X in inference and $Y in training" because $Y is not possible to correctly calculate.

So, if you want to have a productive conversation about "margin on inference", it's sensible to look at the cost of serving the tokens independently of the cost of training the underlying model.

rsalus

an hour ago

they need to make 5t-10t back, but not necessarily through selling tokens. as we can see, the frontier labs are making vertically integrated products. their revenue is no longer strictly tied to inference.

sowbug

9 hours ago

There is also the EV (expected value) of developing AGI. Even if you personally believe the probability is low within the lifetime of either of these companies, the value would still be extraordinarily high, enough to forgive a $5T or so miscalculation here or there.

jbreckmckye

9 hours ago

hansmayer

8 hours ago

? What are you talking about mate? The man all but says "this shit does not work for us". It iss layered in that careful, sanitised corporate shit-sandwich communication approach, where you take a nice piece of shit and layer it in between two slices of avocado so its sweeter to swallow for the "consumer" of your message.

He also said in that article that what prompted the discussion was the public statement by the Uber CTO that he had already burnt through his organisations yearly AI-budget in April. Please stop this shilling mate, and trying to hide the overall perspective between this or that word.

simonw

8 hours ago

Did you read my piece? I covered the Uber CTO thing too: https://simonwillison.net/2026/May/27/product-market-fit/#th...

> The most discussed has been Uber, based on this report where CTO Praveen Neppalli Naga indicated that Uber had “maxed out its full year AI budget just a few months into 2026”, mostly thanks to Claude Code.

> Given that Claude Code only got really good in November it’s entirely unsurprising to me that a budget set in 2025 may have failed to predict demand for that tool in 2026!

mirekrusin

8 hours ago

Now try to take back llms from developers and see what happens.

bigfishrunning

8 hours ago

If, by some miracle, all LLMs ceased working right this second, any developer who would no longer be productive should not have been a developer in the first place.

mirekrusin

7 hours ago

True, but they will not want to work for you anymore, they'll want to work for company that provides it.

Gigachad

4 hours ago

I'd happily work for a company that paid me the money they would have spent on LLMs.

Gigachad

4 hours ago

Limiting token quotas would be fine. Encourage developers to use efficient models, plan the work first, and to not burn thousands of GPU hours on waste.

It's much like when developers would waste tons of money on AWS spinning up massive test VMs and leaving them running without care. Until the finance people cracked down on it.

npn

8 hours ago

we all know it is impossible goal to make. surely AI will be even more useful in the future, but as long as china exists and continue to undercut the price, the goal will be never meet.

> We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer.

with that much money, the companies can easily buy their own hardware and hosting free public models, no need for those expensive subscriptions.

ar_lan

9 hours ago

> unless we figure out how to make developers 2x, 5x, 10x as productive on stuff that matters, this isn't going to play out well.

Simple - you make them work 2x, 5x, or 10x more hours.

OtomotO

9 hours ago

There are not enough hours to do that

Wowfunhappy

5 hours ago

...does anyone have a guess as to the total amount of money spent on software developer salaries each year? What percentage of that would the AI companies need to capture to be profitable?

(I'm not trying to imply that LLMs can replace software engineers, it's just an interesting comparison. If nothing else, I suspect that if the cost of development goes down, demand for custom software will go up.)

overgard

6 hours ago

One thing I genuinely don't understand is these companies are constantly taking in incredibly large amounts of investments, so presumably they're giving up large chunks of equity or these are loans that need to be paid back or they're committing to spending obligations they're very unlikely to be able to meet.

So besides the insane hardware buildouts you're correctly mentioning, I don't understand how anyone that invests in these companies is supposed to make their money back in any sort of reasonable timeframe?

The cynical part of me is looking at what happened to the NASDAQ rules recently where essentially index funds are going to be forced to buy SpaceX shares much earlier than they previously would have (ie, before the price has a chance to reach it's real valuation). Which, um, I'm guessing these stocks are going to drop pretty hard when people start looking at the financials of these companies.

My suspicion is that the point of these IPOs is essentially to dump the bill on the unwilling public by forcing various institutions to buy it (ie, your 401k or pension is buying this shit), and maybe their investors can squeeze some money out of this before the stocks reach an equilibrium that's probably like 1/10th of what they're "valued" at.

solenoid0937

9 hours ago

> 20% if you're a developer. That's a _huge_ shift. Most people I know cite +20%-40% velocity with these tools, against the actual work their company cares about doing. +20% speed for +20% spend isn't going to motivate a trillion dollars a year in spending.

Of course it will. The value of an employee is a multiple of what they get paid.

If you pay an employee $500k and they make $2M for your company (like Meta), then of course a 20% increase for the salary is justified if the velocity is increased 20% as well.

lunar_mycroft

9 hours ago

The difference between what the employer makes per employee and what they spend in compensation doesn't matter. If the increase in productivity isn't greater than the increase in cost, there isn't a reason to pay for AI over hiring more developers.

Imagine an employer with 10 employees paying $500k per employee and making $2M per employee in revenue (to use your numbers). They could hire two more employees and spend an extra $1M (+20%), but make an extra $4M in revenue (+20%). Alternatively, they could buy all ten employees a $100k AI subscription, for a total of $1M extra spending (+20%) but an extra $4M in revenue (+20%). You'll notice both scenarios are identical, so an employer optimizing for profit would have no reason to prefer one over the other.

chasd00

8 hours ago

There’s a lot relationship and culture management overhead involved when adding 2 more people to a 10 person company. I think any business leader would take the productivity speed up from buying a tool over hiring more people and integrating personalities/habits/viewpoints to an existing established culture any day of the week.

lunar_mycroft

8 hours ago

You're basically positing that the real cost of a 20% headcount increase is higher and/or the productivity gain is is lower than 20%. That isn't an unreasonable claim, but it's basically rejecting the premise here. You might just as well object to the premise that you can buy a 20% speedup by spending an extra 20% on tokens.

recroad

5 hours ago

I just don’t understand how people are getting negative value out of AI or even only 20% productivity boost. I can only conclude that people don’t know how to use agents.

oblio

5 hours ago

Are you mostly creating new things or integrating with complex, undocumented, untestable systems?

recroad

3 hours ago

Mostly brownfield systems in Java, Elixir and TS. I use OpenSpec in explore mode and point the agent to all the different repositories (when not working in a monorepo) to identify changes. Once done, i switch to propose mode and spend at least 15 minutes there iterating over the plan until I'm satisfied with the TDD approach (agents need tests to verify their work). Then apply and review. This also auto generates docs etc.

logtempo

9 hours ago

9 hours ago

"Next 5y" doesn't apply to AI factories

notepad0x90

2 hours ago

consider cloud spending vs on-prem before the great cloud migrations. people are spending a lot more for cloud services now.

I hear conflicting things about finances, some have a different opinion, that it won't be written down so long as more funding comes in and revenue keeps increasing. it isn't like how you take mortgage or business loan, it isn't even a loan it's an investment funded by loans. So long as the investment is still promising, what are they going to do? destroy its value by calling in trillion dollar loans?

PunchyHamster

8 hours ago

That assuming once they start squeezing people won't just go to deepseek or other cheaper competition

> That's a _huge_ shift. Most people I know cite +20%-40% velocity with these tools, against the actual work their company cares about doing. +20% speed for +20% spend isn't going to motivate a trillion dollars a year in spending.

And most research shows people far over-estimating their own gains. Once companies start counting the actual (and not just reported) gains, the AI budgets will be more limited as people realize it's an useful and versatile additon but not replacement for most types of work

> We're not there yet. This is still the upswing of the hype cycle, and unless we figure out how to make developers 2x, 5x, 10x as productive on stuff that matters, this isn't going to play out well.

Upswing of the hype cycle while growth of tech itself is flattening, both coz of techs innate issues (which might or might not be solved, but some papers claim they are unsolvable with current approach) and just the fact the spike in growth caused so high economy cost that it put brakes on itself.

Gigachad

4 hours ago

There's a lot of workslop pumping the numbers. People can generate a 300 page PDF in a tiny fraction of the time it would have taken, but now the report is full of mistakes and fluff, and the stuff that would have been learned and caught in the process of making the report is now not happening.

superxpro12

8 hours ago

It's going to be a typical saturation curve. A lot of upfront tokens spent on things that have stockpiled over the years, and then the derivative on token spend trends to zero as the users run out of immediate things to try. Sure there will be ongoing maintenance and experiments, but it wont be nearly as close as the initial inrush.

EGreg

10 hours ago

Here is a serious question.. Can we sell into the hype cycle and on the way down with this: https://safebots.ai/costs.html

adithyassekhar

10 hours ago

I asked claude to generate a frontend and it made the same template. Same san serif and serif fonts together. Same colors. Same typography. Same layout and animations even. It’s wild how similar it is. No not similar it’s the same damn thing.

dd8601fn

9 hours ago

I’ve seen the same dashboard for a dozen custom web applications now, including a couple I had it make for me.

It really does have a particular lane for each chore, and it’s reproducible.

properbrew

9 hours ago

Yep and when you see it in the wild it stands out like a sore thumb, absolutely no thought into a bit of a unique design or branding.

I have a few live websites built using LLMs and they will just go for default generic templates and colours if there's no vision.

jeffreygoesto

9 hours ago

It produces the "most average" web design unless you really prompt your way out, isn't it? If you don't care enough to prompt, Claude does not care to be individual.

WarmWash

8 hours ago

Technically from claude's POV, it's one individual copied millions of times. All claudes are clones.

cortesoft

6 hours ago

I don’t think these numbers are accurate? It seems to ignore the fact that the models have cache for ongoing sessions, which means you (normally) aren’t actually sending all those tokens on every request… you only need to if you go too long between requests.

cyanydeez

5 hours ago

if you ignore all catastrophic mistakes, these numbers are true

user

10 hours ago

[deleted]

BoorishBears

5 hours ago

> +20% speed for +20% spend isn't going to motivate a trillion dollars a year in spending.

I'm increasingly realizing this math is wrong, because LLM use is really sticky.

If Anthropic 100x'd prices tomorrow for their best model, so some companies offered 50% salary to keep 100% of your AI usage:

a) There are programmers who would take this deal. They've gotten to the point of doing what feels like even less than 50% of the work, developers were already pretty well paid, so they'll take it.

b) There are companies that'd offer this deal. Even if the only people who are taking this deal are not the best engineers, and the AI output is not the greatest, I think the last 6 or so years have seen a lot of companies realize capitalism is not as competitive as it seems.

They're not worried about putting out a worse product because... frankly, what else are you going to do? CF lay a bunch of people off, support gets awful: well you're probably not building a new Cloudflare in the next few years.

That's not to say I won't be proved wrong some day, with some further innovations in AI products, but global-scale demand isn't waiting for anything that's been released so far.

overgard

4 hours ago

I've yet to meet a person that fits that description IRL. Admittedly I don't live in the valley but I do work in tech. The only place I see that demand is on hacker news (and I imagine twitter - I'm not on it).

gloryjulio

7 hours ago

The competitors of $65/mo subscriptions are the free models and services that are good enough. It will only get worse as open models or free tiers catch up. For most people, they just use whatever that's free

Danox

an hour ago

Apple TV, Netflix, BritBox and PBS add up to about $45 a month. Most people are gonna judge AI up against what they’re already paying for and the AI model makers simply don’t have a good enough product.

There’s only two things useful to the average person something to help them translate and something to help them write in everyday life.

Something else that might be useful would be local single purpose AI agents who’s remit is to help you with one specific task, but I don’t think that’s what the people building those expensive data centers want to sell to the market.

HDThoreaun

9 hours ago

Well around 40% of people work. I dont think its crazy to say around a third of jobs are knowledge jobs, but what do I know

matthewowen

9 hours ago

85% of the world population lives outside of developed nations.

9 hours ago

Thank you for validating my point.

> "...with more than four-fifths of that growth coming from the emerging world."

If anyone thinks this is a part of the global TAM that's got $1000 a month to blow, well then I've got a stable of flying unicorns to sell you.

Andoryuuta

9 hours ago

To add an actual source to this thread, a brief paper by researchers at the International Labour Organization (ILO) states that for knowledge workers globally "... there are between 644 and 997 million jobs, which represents between 19.6 per cent and 30.4 per cent of global employment respectively." [1]

[1]: Berg, Janine and Gmyrek, Pawel, Automation Hits the Knowledge Worker: ChatGPT and the Future of Work (April 21, 2023). UN Multi-Stakeholder Forum on Science, Technology and Innovation for the SDGs (STI Forum) 2023, Available at SSRN: https://ssrn.com/abstract=4458221

windexh8er

9 hours ago

Globally, sure. The assumption here is all users are on the same economic footing, they are not. Only about a 1/3rd (at most) of that count can afford $1000+ monthly, and even then that is wildly out of line with what most will.

HDThoreaun

9 hours ago

I googled "number of knowledge workers worldwide" and read the top results. If you read it as I was confident in a billion I apologize, Im just trying to get an accurate count. What numbers do you have now and where did you find them?

windexh8er

9 hours ago

That's not the TAM of 1B knowledge workers globally. If that were the case many industries would have a 2-3x target market.

To simplify break that 1B up into 3 levels of purchasing:

1) High-tier (US, Western EU, ANZ, Japan, South Korea, Singapore, UAE, etc) - 200-250M knowledge workers.

2) Mid-tier (Eastern EU, Latin America, urban China, India tech sector, etc) - 300-400M

3) Low-tier (Rest of the world) - 300-400M

Low-tier users are mostly free tier or heavily subsidized pricing.

Mid-tier are going to account for USD sub-$100 tiers. Probably averaging less than $50/seat.

High-tier are who you are assuming is the 1B. Users are not equal in that knowledge worker count, so there aren't 1B knowledge workers to charge money.

And when you consider Low-tier users a majority of those are free users which need to be subsidized by the High-tier users. So either free tiers get much more restrictive or the providers lose additional training data. A bulk of Low-tier users cost money and provide little to no revenue.

Edit: And think about Mid-tier and Low-tier for 5 seconds. Why would they pay Anthropic or OAI when they get get 100x+ inference from DeepSeek or Xiaomi? Mid-tier may be the only area that is willing to spend money on a US provider, but I would wager significantly on the fact that users in the Low-tier almost universally do not care.

HDThoreaun

7 hours ago

Thank you. So with these numbers it seems like half a billion subscriptions at $500/yr is on the table. Obviously theres going to be competition in this market and self hosting cheap models may become the dominant use case. Assuming the labs are able to get most of the market though, the market size is something like a quarter trillion a year within the next decade. It's hard for me to imagine the whole sector failing if that happens.

I do think free accounts are going to end pretty soon, and some of the workers in your tier 3 will pay, but even without them this seems like a pretty healthy market size. I also wouldnt be surprised if mid tier workers are able to afford the $1000/yr vs $500. I use yearly rates because I find it easier to compare them to GDP/salary numbers

windexh8er

6 hours ago

I mean, sure. Assume all you want but to guess that the entirety of High-tier plus almost all of the Mid-Tier will spend, on average $500 per annum is bonkers.

I believe we've started to see the top of what individuals and businesses are willing to pay for the current model capabilities. We are nowhere near AGI and models are really only providing significant value in niche markets currently (programming and cybersecurity). And just like SaaS the enterprise has the option to buy hardware and leverage their own models at will which can potentially offset costs and TAM as well. I have talked to a number of large financial corporations in the last 6 months and most have internal initiatives. The same applies in the healthcare vertical.

$250B per annum with AI? That's 20% of global software spend now. Sure, that's possible but that assumes current market prices hold. What if inference ends up normalizing between DeepSeek/Xiaomi & Anthropic/OAI? There's 50% of your revenue and with current costs for inference and training in the US at astronomical levels the US AI industry could also very well be setup to implode overnight.

Lastly I don't believe free can go away anytime soon because it can't. As soon as Anthropic and OAI remove that option those users will move to whatever is. For most of those users it's not a luxury to choose, it is the only option.

The financial engineering occuring right now is something I don't doubt will be text book lessons of the future. We've seen it before and I believe Peter Sorkin when he says that we will see a crash of this bubble, it's just a matter of how catastrophic it ends up being.

naravara

9 hours ago

A lot of those ‘edge cases’ in the definition of “knowledge worker” are probably the stuff that’s most likely to have significant parts of the work augmented or replaced by AI agents. Like, call-centers are almost certainly going to get turned over in a big way. It’s not like the median tier-1 support operator just reading off a script is much better than an LLM anyway.

esseph

9 hours ago

Yeah, just looked into this. Knowledge workers is a big group and probably much larger than you think it is.

Basically if you're not doing manual labor, it's probably knowledge work.

Roughly 1/3rd of the working population.

Some data tucked in here: https://gist.github.com/danielmiessler/2dc039762a202b083753b...

TacticalCoder

7 hours ago

> We're not there yet.

And that's not considering that capitalism is going to do what it does best: if they really found a way to be profitable, competitors are going to fight them on pricing. Anthropic, OpenAI, Google, etcetera 's margins are a competitors' opportunities.

It's not as if there weren't chinese models nearly SOTA. Don't know where the french (Mistral) are but they may try to get in the game if there's a way to be profitable (not that France or the EU for that matter are relevant in anything tech or had any tech company besides ASML and SAP in the Top 100 but who knows).

YetAnotherNick

10 hours ago

> $5t to $10t to make back in the next 5 years

Wait what? They spent 2 order of magnitude less on hardware.

trjordan

10 hours ago

From the verge: https://archive.is/kU4Zg

> Gartner forecasts that large AI companies would need to earn cumulatively close to $7 trillion in AI-driven revenue through 2029, which is close to $2 trillion per year by the end of the period. In order to achieve “historic returns,” the providers would need to earn nearly $8.2 trillion in the same period.

YetAnotherNick

10 hours ago

9 hours ago

I find this analysis confusing. PMF for coding was likely reached some time last year. Profitability, which is different, we don’t know. The article kind of confuses both without making a strong economic case or using numbers in a compelling way. I don’t understand what the Uber case has to do with this either. The Uber COO clearly said that at least in terms of ROI he’s not seeing the results either.

My take is the product has been very useful for coding (PMF) for months. But it’s certainly not useful at any cost…

aspenmartin

8 hours ago

What I also find confusing though is that folks seem to ignore trajectory which is maybe the biggest lede to bury. As Simon says, we have had "good enough" coding agents for 6 months, that is a blink of an eye, and at my company my job has now completely changed. It's almost like a dream.

And that's just one inflection point. We've had several and there are many more on the horizon. So while I could be convinced that ROI is maybe not even positive today despite the ridiculous enterprise spend, it's perfectly rational to pave the way today for what's coming over the next few months let alone years down the line.

plaidfuji

2 hours ago

There may be additional major leaps forward, and there may not. I kind of struggle to imagine what the next step actually is. Certainly there will be improvements in performance (speed) and cost. But at a point you reach a barrier where the limiting factor is the specificity of the human prompt and our ability to manage all the code we’re generating.

Somewhat oversimplifying; writing software and building apps was a bottleneck - now it is not. What is the next bottleneck that LLMs can solve? Is there one? And is there enough publicly available data to solve it repeatably at scale? Or did we just automate stack overflow searches and now we’re stuck again?

Or is the endgame of this innovation cycle the complete removal of interaction with machines through code? Will we simply interact with machine coworkers purely through natural language? Can an LLM make PowerPoint slides and run a meeting? So far not seeing much progress on that.

dcre

17 minutes ago

Judging from the fact that the Opus 4.5 inflection point was not really anticipated, and we still don’t really know what threshold was crossed that suddenly made agentic coding accessible to so many more people, I think it’s safe to say we don’t know what the thresholds will be until they’re crossed. The fact that we don’t know exactly what they’ll be isn’t a good reason to think there won’t be any more.

sixhobbits

9 hours ago

Pmf is this weirdly defined thing where "if you're not sure you have it then you don't".

I think it was clearly useful for months to people who had tried it and taken the time to understand it, but now that knowledge has spread to the point where wallet holders are convinced it's not just passing fad or hype so now pmf can be "claimed".

I agree it's weird to say "those people have pmf" though, usually it's something you define for yourself

timmg

4 hours ago

> Pmf is this weirdly defined thing where "if you're not sure you have it then you don't".

I'm not sure if this runs counter to your point or not, but: I don't see any future where LLMs aren't a core part of Software Engineering. The horse is out of the barn. There is no going back.

theschmed

4 hours ago

Yeah but the product is not “LLM” it’s “proprietary frontier model LLM paid by the token”.

And I don’t even necessarily disagree with OP! It’s more like the competition is shifting so quickly that your competitors could undercut your PMF in a blink of an eye.

timmg

4 hours ago

There will be cheaper solutions. And they will generally be less capable than the more expensive ones. Just like most other products.

But my guess is that the cost of SWEs themselves mean that the more expensive ones will be worth the delta to most companies.

But time will tell.

airstrike

4 hours ago

True but that is maybe 5% of what is being promised by the average booster

signatoremo

3 hours ago

Give examples of boosters (average or not) and what they've promised?

repeekad

6 hours ago

> clearly useful for people who took the time to understand it

people -> programmers, I haven’t met a non-developer who reports getting more time out of current AI platforms than they put in. If anything I’ve anecdotally heard the opposite, introducing AI at work creates so much slop (output) it takes more time to process it all without a tangible bump in overall productivity

squeegmeister

7 hours ago

The article also treats the word "good" as load-bearing in a way that should have you questioning their analysis:

"I’ve called November 2025 the November inflection point because that was when GPT-5.1 and Opus 4.5, combined with their respective coding agent harnesses, got good—good enough that we’ve spent the last six months adapting to agent systems that can reliably get useful work done."

aspenmartin

3 hours ago

Yet it’s backed up by adoption across the industry

grttq

5 hours ago

Correct the cost is part of the economics.

Thats why most here shouldn’t engage in the discussion - they parrot on about benefits without identifying and articulating the costs and moreover how it affects the firms financial position.

righthand

9 hours ago

It’s not supposed to be logical, it’s an LLM evangelism blog that rarely, if ever, has any critical analysis that isn’t pro-industry. Read any/all of the other posts and you won’t find much skepticism but you will find a lot of shilling how great it all is.

simonw

9 hours ago

308 posts on AI ethics: https://simonwillison.net/tags/ai-ethics/

52 on AI misuse: https://simonwillison.net/tags/ai-misuse/

149 on the unsolved challenge of prompt injection: https://simonwillison.net/tags/prompt-injection/

40 on slop: https://simonwillison.net/tags/slop/

If you want an "LLM evangelism blog that rarely, if ever, has any critical analysis that isn’t pro-industry" there are plenty out there. I'm not one of them.

saulpw

7 hours ago

People are confusing "excitement" with "evangelism". Your blog is definitely on the pro-AI side of things, but as you say, it's not one-sided or uncritical.

alexchamberlain

8 hours ago

I think you should highlight your exemplary pre-AI writing too.

6 hours ago

I feel like there's a bit of AI psychosis in this particular post.

>"These are tools which burn vastly more tokens, but are also quickly becoming daily drivers for the work carried out by extremely well-compensated professionals."

>"Somehow this fragment turned into headlines like Uber’s COO says it’s getting harder to justify the money spent on AI tokenmaxxing, because the market for stories about AI failures remains enormous."

Yes, it's just the yearning for AI failures. It couldn't possibly be runaway costs, record revenues, and massive layoffs. It couldn't possibly be that these tools are lighting dollars on fire by people already paid significantly well and not producing any increase in "value" for it (I recognize that output is 100x but outcomes are flat by all measures).

[1] https://cmr.berkeley.edu/2025/10/seven-myths-about-ai-and-pr... [2] https://futuretech.mit.edu/publication/crashing-waves-vs-ris...

MichaelDickens

2 hours ago

Being pedantic, but I don't want to lose the meaning of the term: "AI psychosis" doesn't refer to someone who thinks AI is really good. It refers to someone who develops symptoms of psychosis from talking to an LLM, e.g. believing they have developed a new Grand Unified Theory of physics.

simonw

6 hours ago

What's the psychosis?

yokoprime

5 hours ago

Sometimes it feels like theres this opposite AI psychosis, where anything AI is bad and boils the ocean, takes our jobs and makes RAM expensive. Its a component in the current economy, but things like tariffs, closing the strait of hormuz etc is equally bad for the economy. Anyway, just find it strange to be so militantly anti a certain tech.

dmix

5 hours ago

That’s the modern internet. What sells is the most overdramatic doom and gloom take possible.

Gigachad

4 hours ago

It's more the tech leaders than the internet. Sam Altman, Jeff Bezos, Eric Schmidt and such get up on stage or interview regularly with a shit eating grin telling us all about how they are coming for our jobs, will make us obsolete, and there is nothing you can do about it.

It's a natural response for society to despise these people who have such contempt for us. It's almost embarrassing these days being at a social function and telling people I work in software, it's got a negative stigma almost like working in gambling or the military.

keeda

17 minutes ago

I think it's a matter of perception because I didn't interpret any of them as being gleeful about it. If you think about it, "AI will take your jobs and maybe destroy the world" is horrible, horrible marketing -- like, your comment is a perfect illustration of how it is received everywhere -- and yet these CEBros can't stop saying it, which indicates to me that they actually believe it.

Oh, now that their IPOs are nigh they're changing their tunes (https://archive.md/s9EO3) but to me that looks more like they've decided to let $$$ prospects override what they really think.

brazukadev

4 hours ago

> because the market for stories about AI failures remains enormous

How enormous? 1 trillion dollars, 2, 10 trillion enormous?

shimman

4 hours ago

That leaders are completely fine with impoverishing vast swaths of American workers because of "progress?"

simonw

4 hours ago

If they are then yes, that's psychotic. Not sure how it's relevant to my article about Anthropic and OpenAI's enterprise pricing though.

aspenmartin

3 hours ago

I’m so curious: let’s say you’re the president or the CEO of a major tech corporation building frontier AI systems.

What are your directives?

binary0010

10 hours ago

So how do openai and anthropic plan to keep customers when GLM-5.1 is just as good and open source and a lot cheaper?

I don't see the business model working. My closest friend actually does automation software for large companies.

He does not use Claude or openai at all. He primarily uses gpt 120b on cerebras and glm-5.1 for heavy thinking work. And some other small models for various tasks. All open source.

And these systems are extremely useful for the businesses and are able to run fully automated pipelines that are very stable and fast.

We discuss this a lot, and we both think any business doing heavy agentic work on Claude and openai just aren't aware of exactly how good and cheap open source has gotten on the last year.

So... once the legacy businesses and developers catch up, won't Claude and openai be unable to recoup their costs?

peder

9 hours ago

> I don't see the business model working.

Same. It's a nightmare from a Porter's Five Forces perspective.

There will be a ton of businesses competing in this space, and there will be something of a moat due to how capital intensive the business can be, but there will still basically be infinite competitors.

Great for consumers.

ex-aws-dude

8 hours ago

Well in reality AWS will just host one of them and most companies will use that

Like how snapchat kind of fell off because the feature could just be a subset of instagram

It seems like it would just become a commodity like EC2

bakies

2 hours ago

Snapchat is huge and growing

doug_durham

8 hours ago

GLM-5.1 isn't just as good. It is no match for Opus running in Claude Code. Please try it yourself. Open source models are about a year behind at least.

jeremyjh

4 hours ago

This is profoundly misinformed. I use all three of those models regularly and the difference is just not that big anymore. GLM 5.1 is at least as good as Opus 4.5 - when it’s my dime it’s the primary model I use and switch to GPT 5.5 for planning and review but it’s also very capable at those things. If I had to pay API rates for everything there is no question I would only use GLM 5.1 (and Minimax for exploration tasks).

At work I mostly use Claude Code and a bit of Codex; personal projects are OpenCode and honestly I prefer it.

listless

2 minutes ago

I would agree here. And in my experience Qwen 27B and Deepseek v4 are also extremely good.

None of them are quite opus, but they are damned close and a no brainer if you care at all about cost.

clhodapp

2 hours ago

In the second half of last year, I found that agentic coding with proprietary models (≈ vibe coding) reached the point where it actually speeds up my ability to deliver useful code at work. Before that, AI-based autocomplete definitely helped, but (despite the claims of the people selling AI coding tools) letting an agent author more than a file or so at a time (often a function or so at a time) required a very intricate plan or it would create a mess. Creating that plan or cleaning up the mess would take longer than just doing everything myself.

For me, it feels like widely available open models have recently crossed that same canyon. Are they as good as e.g. late-model Claude Opus? I don't think so. But they have absolutely gotten past the point where they are beneficial. This means that, for me, they are about six months behind.

jeremyjh

2 hours ago

Exactly this. GLM 5.1 is the first open model that I thought "actually worked" for agentic coding, which puts it in the same tier as Opus 4.5 - which was where I flipped.

RevEng

33 minutes ago

I use composer-2 daily for complex programming tasks. It's a fine tuned Kimi 2.5 - nothing groundbreaking. I've even had reasonable success using Qwen 3.5 on my desktop GPU. Opus might be better, but it's certainly not necessary to get good results.

osti

7 hours ago

For coding I wouldn't say a year, last year this time claude or gpt definitely weren't able to do what GLM is able to do today, but easily 6 months I'd say.

Not sure about other domains though.

_pdp_

5 hours ago

A year behind is still very very very good at this price. ;)

mesmertech

10 hours ago

For coding you always want to go with the best model in the category, not something that would be the best model if we went 1 year back which GLM 5.1 is, and I'm saying that as a big fan of GLM cause I run a translation site where GLM is good enough for the price.

Most of the money right now is in coding. Openai and Anthropic just have to be 6 months ahead of SOTA open source models and they'll capture most of the enterprise and dev market

RevEng

28 minutes ago

I strongly disagree. I'm an engineer - I'm all about the fastest, cheapest thing that meets the requirements. I don't need Opus 4.7, even for my complex programming tasks. It costs over 10x other models available that still give good enough answers. Those smaller models are also a lot faster to output tokens, which saves me time.

Once the model gets good enough, the returns on bigger models diminishes quickly. I don't want to spend 10x the money and wait 5x the time to get answers that are equivalent.

binary0010

10 hours ago

Yes I'm an engineer (20 years most in games/graphics industry) and only use it for code. I've been using glm 5.1 this week a lot. I went in expecting another "decent" but not really "up to standard" open source model.

I highly doubt I'll ever use Claude again.

I think you are wrong about Claude being any significant level better

cassianoleal

Based on current market for LLMs I'd say my use of "you" in the general is fine. Even openrouter which doesn't capture all of the SOTA closed models but nearly all of opensource model usage has Opus as 1st(on last week) on "Programming" category and 3rd in overall rankings

https://openrouter.ai/rankings

simonw

6 hours ago

I'd trust the OpenRouter rankings a lot more if they exposed the number of unique users for each model, as opposed to just a token count.

Currently I have no way of telling if big changes in their rankings are caused by a single "whale" switching providers, or if it's a more meaningful trend.

mesmertech

6 hours ago

My point was that even openrouter, the one place people who are looking for open source SOTA models go to, doesn't definitively have opensource models at the top. Esp considering quite a lot of the closed models usage is through AWS, GCP , Azure etc, probably dwarfing the usage on openrouter by a huge factor

odie5533

8 hours ago

If I generate code with Claude, ChatGPT, and GLM 5.1, I can't say which model is which reliably. I exclusively use Claude more out of superstition than reason.

solomatov

4 hours ago

>For coding you always want to go with the best model in the category, not something that would be the best model if we went 1 year back which GLM 5.1 is, and I'm saying that as a big fan of GLM cause I run a translation site where GLM is good enough for the price.

Currently, the difference is substantial, but what happens if capabilities saturate?

aspenmartin

3 hours ago

Then the house of cards comes crumbling down, but there is so much evidence to point to this not happening that it requires a bit of a theory for how that may happen

solomatov

3 hours ago

> but there is so much evidence to point to this not happening

Could you explain this?

eikenberry

8 hours ago

> For coding you always want to go with the best model in the category [..]

And this is why many companies go out of business. You always want the best bang for your buck, sometimes this is the "best model" and sometimes it is not.

Andrex

9 hours ago

> For coding you always want to go with the best model in the category

Will this always be true? There will never be an event horizon/point of diminishing returns where something not-bleeding-edge is "good enough" for 51%+ of users?

mesmertech

6 hours ago

As long as closed source is 6 months ahead in terms of current difference. Although this is hard to figure out using simple percent based coding benchmarks, you def. notice it when you're actually trying to do a long task. Even simple things like UI "taste" is enough for me to use opus instead of 5.5 though even though 5.5 is strictly better for anything that doesn't have a UI, ie backend, scripts, making agent workflows etc

blackjack_

9 hours ago

This is a silly take. There is a line of "good enough" for most coding (most CRUD apps and APIs are nothing special), and once we are past that, nobody will care about having the "newest, best" model except extreme outliers. And this base "good enough" model will become an ultra cheap commodity as we already see with GLM, deepseek, etc.

mesmertech

6 hours ago

As long as closed models are 6 months ahead I won't be switching from them to prev. 6 month SOTA open source models. Maybe its just a different calculation if you're in a job, but as an indiehacker I'll take any edge I can get

Ofc again, can be convinced to switch if there's however a clear speed difference, like 5x+ for a open source sota even if it was SOTA for 6 months ago

dogleash

9 hours ago

> For XXX you always want to go with XXX, not XXX

Oh, hey, I recognize you. Thank you for the very forward and thorough orbital sander recommendation at Home Depot. That's exactly what I wanted to deal with on my holiday weekend. You just know so much about this and the rest of us are simple passersbys.

mesmertech

5 hours ago

Yep sorry was just pulling it out my rear, not like a market trend that nearly every enterprise uses Anthropic or Openai models for coding or that Anthropic has had such ridiculous growth that they're 10x-ing year over year

EGreg

10 hours ago

Most work is not coding.

And also, people have it wrong… their models are not the main problem anymore. It’s the RAG

tomrod

8 hours ago

Would love to hear more about your thought about the RAG.

simonw

8 hours ago

I think RAG is a mostly outdated concept now, it's been subsumed by the idea of a "agent harness" which is exactly what Claude Code and Claude Cowork and OpenAI Codex and Claude.ai and ChatGPT themselves have now become.

An agent harness with access to a good search tool is a much more interesting thing than 2024-era RAG systems.

obsidianbases1

10 hours ago

Depending on RAG is a workflow problem, not an AI problem

smokel

10 hours ago

For coding assistance, I have tried OpenCode with several large open models through OpenRouter. All were fairly bad compared to Claude Opus. Could you provide some hints on how I should be holding these open models so that I might get more value out of them?

I agree with the common trope that open models lag behind by about a year, but something magical happened just around a year ago when the state of the art models became extremely useful. By this reasoning we're about to see open models perform well, but I'm afraid there is more to it than just waiting for another revolution around the sun.

Note, my application is coding assistance. Open models can be great for other purposes.

tariky

9 hours ago

I tried almost all OS models on opencode, none of them is on levels as opus 4.7.

In latest experiment I used opus for implementation plan then used cursor composer 2.5 for execution.

I must say that combo is really good. Main drawback of claude code is that is super slow. So when paired with composer that is super fast it flies.

cainxinth

8 hours ago

“Tokens” don’t have an intrisic cost or value. Saying that I used $2,180.16 worth of tokens is like relying on the salesperson to convince me I’m getting a billion dollars worth of pots and pans for $19.99.

I think it’s funny how we are throwing critical thinking out the window when it comes to evaluating biased sources of info.

simonw

10 hours ago

I'm not sure what you're pushing back against here.

I spent $200. If I had been paying API pricing it would have been $2,180.16. The article is about how enterprise customers get charged API pricing, which means if I had been employed by one of those companies I would have cost them $2,180.16.

What am I missing?

eqvinox

9 hours ago

Just because API pricing would've been $2180.16 doesn't mean that's the value of those tokens. For starters, you personally probably wouldn't have paid that. But also, sales price isn't value. This is like saying, oh, I saw this bar of gold somewhere for $10000 but got it here for $1000! So I got $10000 worth of gold for $1000! - no, the value of that gold is determined by its weight, which wasn't even mentioned.

We have no market convergence on tokens yet (and it'll differ between LLMs), so it's impossible to say what value you got for your $200.

aspenmartin

8 hours ago

He's saying he's getting a great deal...a token from Opus on Claude code is the same as a token from Opus on the API. I remain as confused as Simon. He's not talking about "here's the ROI I got from my $100 subscription" it's "here's how much I saved from getting the monthly subscription instead of sending things through an API".

mjr00

6 hours ago

Right, the confusion is that the quote-unquote "subsidized" monthly pricing is often used by Anthropic/OpenAI skeptics as proof that inference is unprofitable, i.e. the API would have cost $2000 but you only paid $200 for a subscription, therefore OpenAI is selling dollars for 95 cents and the house of cards is about to collapse. As the GP says, this is faulty logic because we don't know what the actual cost of a token is; OpenAI might only pay $1 in inference costs, in which case they're merely "incredibly profitable" making $199 off you instead of "ludicrously profitable" making $1999 off you had you used the API.

But to your point, re-reading the article, this is not what Simon is saying at all; he's just pointing out that he got to use ~$2000 "worth" of tokens on his $200 plan. Which makes total sense! Subscriptions are sticky, that's why the entire software industry moved towards subscription models (as much as we hate it); the person paying $200/month is more likely to stick around than the person who paid $2000 using the API.

brazukadev

3 hours ago

Which is a terrible metric sending a wrong message.

jonnat

6 hours ago

No, value is determined by what participants in a market are willing to pay for something. The only reason you are able to say that the value of gold is determined by its weight is that gold is a commodity and no matter what you paid for it you'll find others willing to pay market price.

Simon is saying that companies are (today) willing to pay API prices for tokens which is as good as any determination of value.

yokoprime

5 hours ago

Is this some anti-FIAT take? The value the author got is not value as in intrinsic value, it simply means value as in better deal than the alternative. This is often called "value" and you will see this used when products are sold in "value packs" etc.

remus

8 hours ago

> Just because API pricing would've been $2180.16 doesn't mean that's the value of those tokens.

You seem to be suggesting the price of tokens is entirely disconnected to the cost of providing the service? I don't see much basis for that assumption.

recursive

7 hours ago

I'm willing to charge you $100k for those same tokens.

Does that mean you'll be saving $99k?

It sounds an awful lot like the mark-up to mark-down scheme where the price stays the same.

OrangeDelonge

10 hours ago

Large enterprises make deals and won’t be paying 2,180.16$ either. Just like with AWS

simonw

10 hours ago

That doesn't seem to be the case. From what I've seen enterprise deals get API pricing now. Have you seen evidence that's not true?

roomey

9 hours ago

Hi Simon, nice article. The parent there may be making the same assumption I am, that large enterprise _never_ pays sticker price.

Also, to just color in the picture here, as I haven't seen it mentioned elsewhere, there is a very large Saas company at the moment who has given everyone unlimited tokens on Claude. And they have a dashboard showing who spends the most. So the "budget" went from about USD500 per per person (split between Claude and cursor) in Jan to... Well a soft limit of USD100k... Per month... Per person.

People can still see the top line sticker price on their spend, but honestly I can't believe that the Saas is paying that full price when the invoice comes in.

That said, there are some finance reports which are probably dropping soon where we will find out!

simonw

9 hours ago

> The parent there may be making the same assumption I am, that large enterprise _never_ pays sticker price.

I shared that assumption until yesterday, when I found out that it wasn't holding for LLM pricing from OpenAI and Anthropic. That's what inspired me to write this piece.

I think those token leaderboards are an obviously terrible idea and will go extinct very quickly now that people are paying attention to costs.

wongarsu

8 hours ago

But the feature list at https://claude.com/pricing#team-&-enterprise literally lists "tiered incentives on committed spend" and "non-standard terms" as perks of the sales-assisted Enterprise plan. Maybe "non-standard terms" could mean "we dance for you if you pay", but what would "tiered incentives on committed spend" mean besides "we can negotiate on price if you bring the volume"

asib

7 hours ago

> > The parent there may be making the same assumption I am, that large enterprise _never_ pays sticker price.

> I shared that assumption until yesterday, when I found out that it wasn't holding for LLM pricing from OpenAI and Anthropic.

This reads like GP saying "enterprise never pays sticker price" and you responding "I thought so too until I saw the sticker price".

Is there some info you have that you can't/didn't share? Your article doesn't offer anything beyond the above.

simonw

7 hours ago

You'd have to buy a subscription to The Information, but this is useful: https://www.theinformation.com/articles/anthropic-changes-pr...

> With the pricing change, customers of Claude Enterprise, a two-year-old bundle of products meant for large companies that now includes Claude Code and its work assistant, Claude Cowork, will have to pay for the amount of computing capacity they consume while using the software on top of a monthly flat fee of $20 per user, an Anthropic spokesperson confirmed.

There was a Hacker News thread the other day where a bunch of people confirmed that their organizations had seen this too: https://news.ycombinator.com/item?id=48278610#48280906

tomjakubowski

4 hours ago

The sentence you quoted doesn't say anything about the token price for enterprises matching the sticker price. It just says that enterprises are paying a consumption based price (presumably billed as some $/token), on top of a per-seat fee.

mrandish

18 minutes ago

> I found out that it wasn't holding for LLM pricing

You're correct. When a type of cloud service grows large enough and has a few competitive suppliers, enterprise pricing tends to coalesce with the large buyers paying around the same price for the same thing. While that might be lower than the publicly cited rate card, the private price similarly large customers pay ends up being similar.

One reason is that the very largest, long-term enterprise customers are so valuable, they can command MFN clauses ensuring no one else is paying substantially less for the same thing, then the rest of the rate card for smaller customers flows down from that. There's a strong disincentive for vendors to cheat or allow big disparities between similar classes of customers because the number of people involved on both sides of these deals is large enough that word will get around eventually.

Large scale enterprise sales and purchasing in a given sector tends to be rather circular. Account execs move to other vendors and call on the same customers, while purchasing agents can move to other customer firms. Personal relationships, reputation and credibility matter. Lying to screw a large customer over just to make one commission or quarterly quota can be a very bad long-term career move. Sometimes purchasing agents or executives quietly compare notes off-the-record with their peers from other similarly-sized firms. After dinner drinks at industry association meetings and trade shows can be quite productive in terms of verbally exchanging 'market insight' with peers.

When there are significant pricing differences, its usually due to different volume commitments, SLA/QoS guarantees, payment terms and other material factors which justify the difference. Source: been there, done that inside a top ten valley tech company. Once was in a meeting where a newly minted EVP tried to get a long-time senior account exec to pressure a huge customer by being semi-dishonest. The account exec schooled the EVP on the fact that the EVP could only make him unemployed for about 8 hours but that huge customer not wanting to work with him could make him unemployed forever. :-)

mvanbaak

9 hours ago

large enterprises dont pay openai or anthropic, they get this thing called copilot and get a nice price there. At least on this side of the pond (eu)

themgt

9 hours ago

I do know of moderate-size companies deploying OSS LLMs on their own GPU clusters, for ownership/security/maybe cost reasons. I'm somewhat surprised F500 companies are apparently just handing over all their data to the model providers.

Could be fantastic for small shops while it lasts. The big guys have to pay 10x for precious tokens.

waisbrot

10 hours ago

And "large" just means that AWS will assign an account manager to talk with you. I was at a start-up who spent $300k/year on AWS and that was enough to get special attention and discounts. Enterprise pricing is confusing.

apsurd

10 hours ago

The point is that those a real prices real people are paying for real API usage. it's not made up.

your point is large players won't pay those prices at massive volume. ok

yokoprime

5 hours ago

They pay sticker price. There may be exceptions for very very large companies like Amazon or Microsoft which have their own deals where they rent out compute in return for usage.

Anon1096

9 hours ago

Claude is so in demand at the moment that there aren't really volume discounts. Anthropic sets the terms and you either accept them or get lost they have that much of a lead (mindshare/desirability wise).

altruios

10 hours ago

> If I had been paying API pricing it would have been $2,180.16

The point being made above is that API pricing is calculated... somehow... seemingly arbitrarily. Possibly untethered to the infrastructure costs entirely: which would be the basis of any 'value', however that holds the labor theory of value, which isn't accurate either. So how do you accurately price these tokens at all (other than through price-discovery: which is slow, messy and fuzzy)?

NitpickLawyer

10 hours ago

> So how do you accurately price these tokens at all

Like anything else in the economy: at the point where enough customers can pay you, and not enough will go to the cheaper competition.

altruios

7 hours ago

> at the point where enough customers can pay you

> (other than through price-discovery: which is slow, messy and fuzzy)

I notice a distinct lack of reading or comprehension (from everyone around me now, not just this comment) which worries me. I worry if LLM's are to blame. No one reads anymore...

lbreakjai

4 hours ago

I don't really understand the holdup about that specific line? Anthropic publishes the cost per million of tokens, what's arbitrary about looking at your consumption, and doing the math?

Or are you saying that Anthropic is determining that cost per million arbitrarily?

If so, it'd be like asking to explain why things fall on the ground other than through gravity. Companies pick the price they think the market will bear, and adjust based on new information.

pembrook

10 hours ago

API pricing drops DRAMATICALLY in enterprise agreements.

As with pretty much anything priced on volume/usage.

Enterprise deals are negotiated ad-hoc, the listed pricing is simply a jumping off point for the final negotiated discount.

If you’re going to give 20,000 employees Claude code you are not going to be spending $1B per year on Anthropic tokens as if you gave everyone an individual API key. Just as Anthropic isn’t paying AWS SES $10,000,000 to send 1 email update to their massive user base when the next Claude version drops.

yokoprime

5 hours ago

As someone who has seen the enterprise deals, they are not subsidised in any way shape or form. Anthropic may wave the seat fee, maybe get lower "expected" consumption. This changes what the company pays up front. but token prizing is fixed.

tomjakubowski

4 hours ago

To confirm, you're saying the enterprise deals you've seen match the prices per token listed here?

https://platform.claude.com/docs/en/about-claude/pricing

taude

9 hours ago

This isn't true at the moment, though. So far there hasn't been the negotiating power. What happens is you end up capping usage for employees at a fixed amount. I think eventually, prices will come down and there will be discounts, but for enterprise accounts at least of our size (<5000), we're paying almost 100% retail, which kind of sucks, because it's expensive, and pretty easy to burn $50 to $100+ in a day, if you're not careful. In fact we got pushed off the former plan to the token-utility one at the last contract negotiation.

Going to be interesting to determing the metrics we give to engineers for determining whether the spend on this is worth it. Measuring PRs, lines of code committed, commits fully generated by agentic workflows, etc.....

simonw

10 hours ago

> API pricing drops DRAMATICALLY in enterprise agreements

Do you have any numbers or reports to back that up?

lrae

7 hours ago

> Just as Anthropic isn’t paying AWS SES $10,000,000 to send 1 email update

How much do you think emails cost? That number is just so far off?

But besides that, running SES is also quite a bit cheaper than SOTA ai models with high demand (and comparatively) no competition. And quite a bit more pressure to make money (soon).

tomjakubowski

4 hours ago

I think it was a figurative example. For what it's worth, $10,000,000 buys you 100 billion (1e11) outbound emails on SES at the sticker price ($0.10/1000 emails). One source puts the number of emails sent worldwide in 2024 at 132 trillion (1.32e14).

https://aws.amazon.com/ses/pricing/

https://www.statista.com/statistics/456500/daily-number-of-e...

xnorswap

9 hours ago

Have you or I misunderstood the "teams" plan?

edit: I missed the "enterprise" feature matrix with the usual audit/compliance stuff to force the biggest enterprise customers onto enterprise plans. Otherwise the "teams" plan is much better value for any business.

orig-continued:

https://claude.com/pricing/team

Teams premium is "Everything in standard, plus more usage*"

And from my experience, it's a very generous usage, I've only hit the limits once or twice, and both times required multi-boxing agents.

I could single-window agentic development all day on opus-4.7 auto-mode without hitting limits.

If you're a business using claude, then that seems like the right plan, the enteprise/API plan seems more suited to where your product is built on top of the agent themselves, so seats/limits aren't really meaningful?

nr378

9 hours ago

Claude Teams and Claude Enterprise are 2 distinct plans. Simon is right that Enterprise seats have no included usage (and so all usage is charged at API billing rates), whereas Teams seats do.

troyastorino

10 hours ago

Tokens do have a clearly calculable intrinsic cost. There's the marginal cost of production (i.e. the inference cost) and the amortized R&D cost that goes into the model producing them.

Yes, value is hard to calculate, but luckily market pricing mechanisms exist exactly for this purpose. There isn't a better number to use than what people are willing to pay for them.

So he's saying that on an enterprise plan, he'd be spending $2,180.16. He's not paying that much, but enterprises are.

m3kw9

12 minutes ago

Enterprises also use 200$ plans, they are not that stupid. They add a spill over API key for over usage.

simonw

5 minutes ago

OpenAI and Anthropic won't let them any more. Once you get above a certain size (I believe 150 seats) they push you onto the enterprise plans.

I don't believe you have the option to keep with the $200/month flat rate subscriptions any more. I'd be happy to be convinced otherwise.

woah

7 hours ago

From my back of the envelope analysis for my own projects, paying per token on OpenRouter is competitive if not cheaper than running the same open weight model on a rented GPU. Per-token pricing is in the same ballpark (although more expensive) for closed frontier models and open weight models (cents to dollars per million). To me this says that the pricing is somewhat grounded in reality.

rohansood15

2 hours ago

Are you comparing single-user requests or multiple concurrent requests when you say comparable to rented GPU? Most of the cost efficiencies kick in with concurrent/batch requests. A single H100 node can provide like 5k input + 2k output tok/s on a model like Qwen 3.6 35B-A3B with 30+ concurrent requests.

john_strinlai

10 hours ago

a little critical thinking led me to read that sentence as $2180 worth of tokens [at current api pricing]

dnnddidiej

8 hours ago

His point is more he was surprised enterprises weren't getting the discount. And so indeed maybe it is not a giant ponzi after all! (Could be a bubble)

FergusArgyll

10 hours ago

I think it's funnier that you can believe some things have an intrinsic cost and others don't

jfrbfbreudh

9 hours ago

Lol. They obviously have intrinsic cost, the floor being the cost of electricity. It’s hilarious how we are throwing critical thinking out the window when it comes to evaluating biased sources of info.

RevEng

19 minutes ago

I usually agree with Simon but I think he is overlooking an important factor.

There is a lot of AI usage happening not because it shows benefits, but because the business has mandated its ubiquitous use. Companies having dashboards for token usage and rewarding people for using more tokens is a real thing. I just spoke with someone today who works at Microsoft and they are required to use AI for all of their work - they have to make a special request with justification if they decide not to use AI for even a single PR. This kind of demand isn't driven by value from either the company itself or from its workers; it is the kind of artificial demand you get from make-work projects to keep people employed during hard times.

We have to wait for the hype to settle down and people start making business decisions based on results before we can really value these AI products.

no-name-here

7 minutes ago

I think token leaderboards are idiotic, however… many companies are requiring AI use, with by far the most likely reason being that they truly believe AI-assisted development gets (or will after the time needed to get employees acclimated) better returns.

ggttk

9 minutes ago

Correct the question is one of real vs fake demand curves.

jwpapi

3 hours ago

I have to give them kudos. This whole thing is the greatest swindle of all time.

AI has some use cases, but not at the price it’s currently priced at. I’ve been on AI since GPT-2 with a lot of heavy users. Every user has the same story, curiosity, surprise, hype, hate, realization. Enterprise is usually a bit behind and are right now at hype cycle, that’s where they sold all the deals and do the IPO.

It’s really a VC masterclass.

Don’t get me wrong there is are useful cases of AI, but not the way the want it to be. Quite similar to Blockchain. The idea of decentralized money has right to exist. 99% of other coins not.

AI is a faster, but still less accurate search engine. AI is great in finding bugs, it’s great at ruber duck debugging.

The reason I call it a swindle is, because along with the marketing it gives tons of people in the world the impression, they can now build their own startup, game, infra etc without the need to learn it themselves. This leads to millions of abandoned and low qualiy projects and products, because the vast majority has never built the mental modal necessary to solve the problem thoroughly. In the end they’ve wasted months and money (but burnt tokens). This is what I call a swindle.

All early adaptors I know have not drastically winded down their usage, not because of money, but because there is no new case. If you want to explore a new project you can get onboarded quickly learn a lot and then switch to documentation and live testing. For me usage is the lowest it has been the last 2 years.

I would not let AI touch my code. I have anxiety around it, because it will gripple back up. I will let it read my code and let me know what I did wrong so I’m sharpening myself.

100s of companies including open source solution can offer that for me.

All my non-tech friends are now in hype cycle and share their hype and fore forseeable frustration with me.

I have to say I’m in a way impressed in how AI has been rigorously vc-utilized (conciously or not-conciously) to generate these vast companies with the whole world watching.

aspenmartin

3 hours ago

Trying to parse the raw claims here maybe you can help me out

- it’s a swindle because ROI of tokens for coding models is not positive? As in it doesn’t bring enough value to charge like the $100/mo?

- enterprise customers are too dumb to see this

- IPO to max out the CEO profits for what is ultimately blockchain vaporware

Am I getting that right? Or am I putting words in your mouth?

> it gives tons of people in the world the impression, they can now build their own startup, game, infra etc without the need to learn it themselves.

I can’t speak for peoples beliefs and motivations, but this seems to be strawmanning, no? AI is a powerful tool to force multiply people. You can’t just prompt “build me an enterprise SaaS app worth $1B” or “build me GTA6 and don’t hallucinate” but is that your impression of what’s happening? Dario and Sam are saying “if you buy our coding agent subscription you can build a game with zero skill and one shot and then be rich”?

If you don’t find value in AI agents I can see reasons why that could easily be true today. Also if it just gives you the heebie-jeebies. But to say it’s a swindle on par with the blockchain I think that contradicts an enormous amount of signals and also the actual dialogue (not just headline sound bytes) around what these systems are capable of today and what we expect them to do say at the end of the year.

jwpapi

2 hours ago

You kind of got it right, but the biggest loser of them all are the investors especially the index investors. They don’t even decide what they invest in but the savings that goes in funds need to invest in these stocks.

It’s quite an elaborate swindle obviously. But you generate hype with underselling your core product, you claim way more usability then there is. Users will experience usability initially. Everything multiplies with each other and then you put it on the market. Everybody involved makes money and you’ve succesfully extracted money from everyone who’s invested in NASDAQ index funds at the very least.

> Dario and Sam are saying “if you buy our coding agent subscription you can build a game with zero skill and one shot and then be rich”?

That’s Anthropics marketing, yes.

Also their offering is not uniqe that justifies a 1 trillion valuation. The first companies are already rowing back. It’s a really certain time window that they are about to hit now with their IPOs

The companies that have signed these enterprise deals haven’t done a ROI analysis. They had Fomo.

brazukadev

3 hours ago

> Dario and Sam are saying “if you buy our coding agent subscription you can build a game with zero skill and one shot and then be rich”?

They are saying profitable companies should replace the engineers that built their systems with a subscription (while they are hiring).

hintymad

9 hours ago

The real timing is that we don't have strong enough new business needs for now and we have accumulated enough tech assets, so our work has been increasingly incremental. That means we can build reliable features on top of vast amount of past work - where AI really shines. So, with or without AI, companied would hire fewer software engineers if majority of our work is incremental: add a feature here, fix a bug there, tweak a configuration and etc, then we wouldn't need as many software engineers anyway. AI just accelerated such squeeze.

In contrast, imagine if we had the same AI 20 years or so ago. Could AI really write Jersey? I guess not as people were still trying to understand JAX-RS. Could AI really answer all the questions about React? I guess not as React was just invented. Would we use 10x fewer people to build out infra on the public cloud or the entire so-called Big Data platforms? I guess not, as they were still rapidly evolving and we'd need so many engineers to explore so many different possibilities? Could we use AI to build our ML ecosystem with 10X fewer people? I highly doubt so. Heck, 20 years ago R was all the rage and Python's ecosystem was not mature at all. Oh, and mobile computing, could AI lead to 10X fewer people to build all the mobile apps and the underlying infra?

aniceperson

7 hours ago

> Could AI really answer all the questions about React? yes, due to ICL

> Would we use 10x fewer people to build out infra on the public cloud or the entire so-called Big Data platforms?

No, cannot solve core problems, makes a mess at scale

You are right about the incremental work. But most of the work is historically incremental imo, only few positions are R&D.

overgard

6 hours ago

Anthropic isn't actually profitable from what I'm reading, a discount briefly pushed them into the black. This guy makes the case well: https://www.wheresyoured.at/anthropics-profitability-swindle...

I'm skeptical that their current price raise is sufficient, and I'm also skeptical that most users/businesses will accept more significant price raises that will be needed. Especially for individual users, $200 a month is already incredibly expensive, I really don't think most people are going to be willing to pay more like $1000 a month.

antman

10 hours ago

The costs are exorbitant and most software is not produced by companies with such a huge moat. Anthropic made a profit through their recent bait amd switch pricing. There is zero useful insights online to indicate whether this might die due to commoditisation with good enough open models or fail the race to get more people subsidising unsustainable growth with other people’s money. Who knows? In any case they dont seem to be able to drop usage costs so the business model seems based on wishes

j_w

9 hours ago

Continuing with your skepticism:

> Stories are circulating of companies surprised at how expensive their LLM bills are becoming from usage by their staff

4 hours ago

A lot of the new hardware requires retrofitting existing datacenters for appropriate cooling, or is waiting to be installed because the new datacenters haven't been built yet. By the time they're installed it's likely a lot of Blackwell GPUs are going to be very out of date. Newer hardware is turning into huge capex bills along with the corresponding depreciation costs. Basically, it's not the same as plugging a new GPU into your desktop, the upfront investment is extremely expensive and all the numbers I'm seeing suggest that the newer GPUs are costing more to run, not less.

brokencode

3 hours ago

Sure, with all the component shortages it’s not surprising the current GPUs are coming at a massive premium.

Eventually either the supply will go up or companies will start buying fewer overpriced GPUs.

Either way, the price per token will come down as hardware improves and supply and demand reach equilibrium.

grttq

4 hours ago

Technological obsolesence is a bitch eh.

mark_l_watson

31 minutes ago

Well, good for them that they are charging enterprises API rates. Why in the world not do something similar for consumers? Use for free a few times a day, have a $5 dollar plan for light use, and perhaps $10 to $15 for heavier use. If 90% of consumers pay nothing then ‘drop them’ except for letting them have an account and a few queries a day.

It is easy for me to change providers. Right now I use the open source Claud Code harness with two paid API venders for DeepSeek v4 (flash and Pro). I like seeing how much each session costs.

realo

10 hours ago

200$ per month per seat is nothing .

A single 3D CAD license pack for the guys in our R&D group costs multiple thousands of dollars per seat, per month.

It's about time software seats get some love too.

smokel

10 hours ago

AutoCAD is $175 per user per month [1].

[1] https://www.autodesk.com/products/autocad/buy

bigbuppo

9 hours ago

AutoCAD is still the budget-friendly CAD program it has always been. You don't build big boats in AutoCAD.

rrr_oh_man

9 hours ago

Winch Design [0], which have built some of the world's largest superyachts [1], seem to be using AutoCad. [2] Afaik it's also the same with Lürssen (but don't quote me on that)

[0] https://winchdesign.com/ [1] https://www.superyachts.com/directory/1516/winch-design/flee... [2] https://www.autodesk.com/design-make/articles/naval-architec...

numpad0

6 hours ago

Likely not the "base model" of AutoCAD.

Those tools are used in ways that they're integral to processes. They have their equivalents of ticket systems that are linked to code repositories with LFSs and bunch of IDE type tools and automated and manual test systems and build systems. Their equivalents of PR discussions and Selenium screenshots needs to check all boxes in the right ways for legal and traceability purposes.

Without all that might be $175/user/month but you're not shipping apps with just vi and bare gcc.

noosphr

6 hours ago

>Without all that might be $175/user/month but you're not shipping apps with just vi and bare gcc.

You're right, Linus uses Emacs.

so_it_be

9 hours ago

Except LLM's even with Vision are still useless at AutoCAD let alone Revit (please dont quote SCAD LLM's at me, useless). Knowledge based approaches still win.

I might agree "AutoCAD" is the current level LLM's are at, but wait until your design departments discovers "Revit", its another ballpark (in wasted cots, engineers on site still get "clashes").

Revit costs are high, and the end results are marginally better - but local LLM's tokens are cheaper 24/7 at "AutoCAD" level - "Revit" level tokens will make Ubers CTO/COO weep harder than they already do. While producing results no better than "Revit" does (engineers still face "clashes").

Our_Benefactors

8 hours ago

As someone completely outside the 3D design world who always thought of AutoCAD as the gold standard - really? What program would be used instead? Please enlighten me.

Hasz

How many guys is that? Every single white collar worker is in the AI ICP (customer profile).

edit: typo

smt88

9 hours ago

white collar*, not color

What does ICP mean?

simonw

9 hours ago

Insane Clown Posse, though given the context here probably Ideal Customer Profile.

everdrive

9 hours ago

The similarities are quite stunning, though, as I'm sure both sets of ICPs have no idea how LLMs work.

KyleTheDev

9 hours ago

Now hold on there, let's not cast doubt on ICP. I'm sure they'll surprise us, as they always have.

bambax

8 hours ago

> That’s $2,180.16 worth of tokens for $200

So the author claims he's getting $2000 per month worth of frontier AI free of charge. Ok. If he's been doing that for 6 months that's $12k. What has this produced concretely? For $12k you can find a used car in decent condition. Heck for $1200 (his actual out-of-pocket spend) you get a brand new ebike! (on which you could put a pelican and make a photo of both if that's your fancy). But here it's unclear what has come of it.

simonw

8 hours ago

I've written a great deal of code - code that would have taken me years of work to produce without LLMs.

(It's mostly open source, you're welcome to dig around in https://github.com/simonw and https://github.com/datasette if you like.)

My time as an experienced software engineer is worth a lot of money - a whole lot more than $12,000 for the past six months.

bambax

7 hours ago

> code that would have taken me years of work to produce without LLMs

As you might suspect, this is what I have an issue with. Without LLMs, isn't it possible or even likely that that code wouldn't have been written at all, and wouldn't have been missed? If LLMs are mostly used to produce throwaway prototypes then it's a stretch to say that's money well spent.

If indeed it let you advance your main product much faster then sure it's a different story. You're the judge of that. It's hard to see the impact from the consumer side; everything is still broken and no extraordinary app seems to be emerging. Maybe it's just a question of time. We'll see.

odyssey7

5 hours ago

I’m watching to see what happens to big enterprise software contracts. Why pay some vendor $800k annually for something a couple mid-level devs can replace—-and tailor closely to your needs——by leveraging AI.

Open source software changed the world. AI that will cheaply write whatever you want in a few days will also change the world.

simonw

7 hours ago

I've thought about this a lot. I am very confident that the way I use LLMs is both accelerating progress on my core projects (here's a substantial, reviewed PR I landed just yesterday https://github.com/simonw/datasette/pull/2741) and helping me create plenty of projects that otherwise would not have existed.

tredre3

4 hours ago

The point being made by GP was that your projects have no value and their non-existence wouldn't be a negative to this world.

And that is likely a fair assessment, though I understand perfectly the feeling that you have that you are accomplishing great(er) things thanks to AI.

simonw

4 hours ago

I certainly hope that's not true, given that I've dedicated 7+ years to my main open source projects at this point.

I take some reassurance from knowing that they are indeed used by real people to solve real problems though.

jwpapi

34 minutes ago

Now what percentage of the 200$ have been used on the useful stuff and how much on exploration or other stuff.

How long would it taken you to do it yourself? How much longer will the next task take you, compared to when you would’ve written the code yourself. How is the mental model compared to when you would’ve written it yourself?.

I’m not saying you’re wrong, again there are use cases. But the calculation is not plain and simple it goes deep into our perception, perceived productivity versus actual productivity.

I’ve 2 months maxxed out all 6k of Claude Code and bought Antigravity on top. My codebase became 140k lines. I introduced tons of bugs and spent another 2 months, deleting 80k of code. I wish I would’ve just chatted with AI and not let agents touch my codebase. I would’ve saved approx 300$ subscription prices a month and 2 months of my life.

simonw

30 minutes ago

I had that experience back in January! I used it for a joke in a talk recently... did anyone really need a half-finished buggy slow implementation of a JavaScript interpreter in Python? They did not. https://github.com/simonw/micro-javascript

It's taken me the best part of this year to readjust and find a pace and level of ambition that fits.

jonluca

3 hours ago

This is the economic theory of value creation though - arguably the world is better off because new projects can be created, and they are marginally cheaper than they would have been previously

nevertoolate

7 hours ago

> My time as an experienced software engineer is worth a lot of money - a whole lot more than $12,000 for the past six months

From this I assume you think that what the llm has generated is as valuable as your own work generally is. How do you even calculate this?

ex-aws-dude

8 hours ago

And what was your return on investment?

simonw

8 hours ago

As I commented elsewhere, I'm still bad at making money from my open source work: https://news.ycombinator.com/item?id=48296794#48298909

(I have a feeling if I could say "and I closed $2m in sales with the software I wrote!" people would find a way to say that didn't mean anything anyway, because how can I prove I wouldn't have made those sales writing it by hand?)

brazukadev

3 hours ago

> "and I closed $2m in sales with the software I wrote!"

Given the audience you are reaching, that is actually the expectation. Github stars is not a great metric.

aspenmartin

8 hours ago

I would be very curious what kind of answer would satisfy you here. Simon isn't building a product, where $200 is a line item on a balance sheet. If he tells you what sort of analyses or time savings $200/mo on coding agents have enabled him, do you honestly think that would satisfy you?

jwpapi

an hour ago

Unfortunately Simon drank from the AI Cool Aid.

I know everything you’ve done for the tech community, but I please you to take some time off and reflect on this article. It’s not on par with ur usual level, but the tendency has been visible from the last couple of articles.

simonw

39 minutes ago

Genuine question: what's wrong with it?

I thought this was one of my best pieces of writing this year.

(In case you missed it, the title was meant as a subtle burn on those two companies - it's pretty absurd for them to only just be finding product-market fit when they're already valued at over a trillion dollars.)

darth_avocado

10 hours ago

How is the lack of bad news declaring a victory for AI? I am yet to see any company concretely publish analysis about the ROI from AI. Most companies as far as I know are still treating AI investment as sunk cost with no expectation of returns at the moment. We could very well see a world where companies heavily scale back investment.

sourcecodeplz

10 hours ago

With deepseek and xiaomi mimo models slashing their prices 99%, I don't see a great future for openai / antrhopic with regards to their 1T valuations. Maybe 1T valuation will be the whole market, West + East.

jillesvangurp

6 hours ago

Most of the corporate world in the EU or North America will be hesitant to rely on Chinese AI providers. There are some very real blockers for that for things like data security, compliance, etc. And recent geopolitics don't help.

Legalities aside, you need to look not at the model quality but at the infrastructure needed to scale these models from tens (now) to hundreds (soon) of millions of users. Only a handful of companies actually have the resources and funding to do that. That's what these huge valuations are based on. These companies are gearing up to scale to these levels. That's why they are spending on data centers. Whoever has access to those data centers gets to tap into the revenue stream of people using models running on those.

an hour ago

I thought the links I provided were pretty solid. A lot more solid then you'll see in most commentary about this stuff!

I agree with this person, let's use AI psychosis for when using an LLM gives someone psychosis, not for when we think, what, that a blogger made some poor assumptions?

https://news.ycombinator.com/item?id=48296794#48303200

protocolture

4 hours ago

I feel like we should just ignore all LLM news until after they IPO, lots of positive sentiment astroturfing bots.

cj

9 hours ago

> Coding agents really did change everything. These are tools which burn vastly more tokens

The assumption here is that this is a positive thing.

But this very well could end up being a major negative long term by increasing the cost per user, reducing margins.

More usage = more cost = less profit.

It's not obvious that more usage is good. It's only good if revenue per user increases more than cost does. I'm skeptical about that.

simonw

8 hours ago

> It's only good if revenue per user increases more than cost does.

That's why it's so important for these labs that they're selling API tokens for more than the compute+energy costs needed to generate them.

Every indicator I've seen is that they do have a positive margin on that. If they don't, they're screwed.

grttq

5 hours ago

No this is not what matters.

The customers of these tokens need to see returns on their projects that exceed the cost of financing.

Laying people off only goes so far.

If enough said firms don’t see enough value given the price of frontiers they will cancel and consume open source. This is the risk the frontier labs are exposed to.

mattas

8 hours ago

What's an example of an indicator? Genuinely curious!

simonw

8 hours ago

Insider tips from Google and AWS telling me that they run inference at a profit (though that was over a year ago now).

Dario telling Dwarkesh three months ago that they have a margin on inference: https://www.dwarkesh.com/p/dario-amodei-2?timestamp=3528.0

Denzel

2 hours ago

Unless your insider is the CFO, I wouldn’t trust these sources to have the access, knowledge, or insight to determine whether they’re running inference at a profit.

9 hours ago

Oh I'm absolutely taking advantage of the fact that "product-market fit" has a bit of a nebulous meaning here.

It's a great hook to build an article around. My core point is more that April 2026 was the point when Anthropic and OpenAI finally appeared to have figured out a credible business model.

mbesto

6 hours ago

> My core point is more that April 2026 was the point when Anthropic and OpenAI finally appeared to have figured out a credible business model.

How so? What's specifically changed? We still don't know what their unit economics are and everything you've documented is basically speculation at this point.

simonw

6 hours ago

> What's specifically changed?

1. Both Anthropic and OpenAI significantly increased the prices of their latest models. They're clearly not trying to offer the lowest-price-possible to drum up demand any more.

2. Both Anthropic and OpenAI no longer let enterprise companies buy discounted almost-all-you-can-eat subscriptions. Those big enterprises are now paying full API prices.

3. According to reasonably well-sourced leaks, Anthropic may be about to have their first profitable quarter.

And I didn't even say "profitable", I said "credible business model". I think getting companies to spend hundreds of dollars per month per seat, WITHOUT crazy subscription discounts, is a credible business model.

lbreakjai

3 hours ago

To me the question is, can the frontier labs make the variance of output lower + make the output of higher quality to justify their prices?

If not lower priced chinese offerings will be better as its cheaper per token - giving you more attempts to offset the variance.

My feeling on the former is no... I believe they tried really hard but they've settled on pure marketing now to attempt to fight off the chinese with perceived superiority in quality.

firesteelrain

These folks have no lasting moat and they know it. We are still close to the November 2025 inflection point, so they had a clear advantage during these past few months. That will soon fade as open-weights models become really good, which is arguably already the case with DeepSeek V4.

rubiquity

9 hours ago

I think it's fair to say they had achieved product-market fit when their revenues were growing deep triple digits month over month. What we're seeing now is that perhaps they have achieved profitability or at the least a more sustainable balance sheet.

signalbright

an hour ago

That might be an understatement

NortySpock

9 hours ago

"[would have spent] $1,199 with Anthropic, $980 with OpenAI"

How many tokens is that, input/output-wise?

(a) I'm curious if you feel like you got $2000 worth of value out of them in the last month?

(b) I'm also curious if you would have gotten similar quality out of a slightly lower-cost provider of an open-weight model? (e.g. Kimi K2.6 and DeepSeek v4 Pro) and what the spend would have been for that.

I myself have managed to spend not quite $4 on OpenRouter and have felt it was very worth it; I just have much smaller, or more targeted requests I guess. (Lately, adding features to a static site generator in Python, or setting up log forwarding via a docker compose file)

simonw

9 hours ago

Claude Code:

  Input tokens:        52,545,485
  Output tokens:        5,767,253
  Cache create tokens:  5,112,029
  Cache read tokens: 1,475,069,465
  Total tokens:      1,538,494,232
  Total cost:        $1,199.79

OpenAI Codex:

  Input tokens:          52,598,013
  Output tokens:          4,681,867
  Reasoning output:       2,091,063
  Cached input tokens: 1,153,844,864
  Total tokens:        1,211,124,744
  Total cost:          $980.37

I'm confident I got value out of OpenAI - I've been mainly on Codex for the last few weeks.

Not so sure I got that value from Claude, just because I've been using it a lot less and somehow the price came to about the same as OpenAI.

Given the code I've been able to build in the past month I genuinely do think I got value for the API price version, and (don't tell OpenAI or Anthropic) I think I'd have paid full price.

I've not spent nearly enough time with GLM-5.1 and co to compare, but I do know that the prompts I'm using with the agents are not prompts I would have expected to work just three months ago.

NortySpock

9 hours ago

Cool! Thanks for the details, and your blog posts are usually interesting food for thought, so thank you for them too!

krupan

9 hours ago

Are you saying that the software you wrote using those tools generated enough revenue to cover the $2000?

simonw

8 hours ago

Not yet, but that's because it was almost all open source and I'm really bad at generating revenue from that.

When I account for the amount of time it saved me there's no question $2,000 was worth it.

regularfry

9 hours ago

If it were me I'd be asking "How long would it have taken me to do that, and what's the rate I'd have been charging for the work I would have been doing otherwise?"

Personally, I've probably spent $60 or so on OpenRouter in the last month or so and got a working project out of it that it would probably have taken me a fortnight to knock together (which is inevitably an under-estimate because it covered things I'd have to learn but K2.5/6 already knew). There's an orders-of-magnitude gap there.

spprashant

10 hours ago

So it largely sounds like many more people will be able to write software - and will use AI to do it. Existing software engineers will continue to automate their tasks away like they always did, but perhaps at a faster rate.

The impact of AI in other fields seems to be muted.

simonw

10 hours ago

I think it is applicable to a much wider range of knowledge work, but it's also harder to apply there.

Software development has the huge advantage that mistakes and hallucinations are very easy to spot: the software works or it doesn't.

Spotting errors in a research report or legal brief is a whole lot harder!

But... non-software professionals spend a huge amount of their time on tasks that can be safely automated - reformatting documents, extracting numbers from PDFs, all kinds of flavor of data entry.

Learning how to use a tool like Claude Cowork can take a big dent out of those.

slopinthebag

8 hours ago

> Software development has the huge advantage that mistakes and hallucinations are very easy to spot: the software works or it doesn't.

Do we not care about code quality, maintainability, performance, extensibility, or understandability anymore? Honest question, not a gotcha, it's just previously getting software to pass all the tests was a small part of what we would consider "working" or perhaps "good" software. Maybe that's different now with LLMs, idk. Maybe we need automated checks for these things as well, like not compiling until the code quality is good enough to let the agent finish it's loop.

tracerbulletx

2 hours ago

What code quality even means is different now, but also LLMs are capable of producing better quality code at scale in my companies experience. We are able to in fact sort of propagate best practices and structure via the llm to all of the teams even when they're working under time pressure.

simonw

8 hours ago

> Do we not care about code quality, maintainability, performance, extensibility, or understandability anymore?

Yes, we should care. I've been writing a whole book about that: https://simonwillison.net/guides/agentic-engineering-pattern...

pianopatrick

9 hours ago

If the AI can write code for robots the impact in other fields may be pretty large. Seems to me a lot of jobs can be automated with software and robots combined. The limit in the past was writing the software to get the robots to work. But if AI can remove that limit...

Zizdefense

7 hours ago

My Costs without CC Max+Caching over past 2 months: $112K.

Ran `ccusage` on my Claude Code logs.

- Total tokens: 22.2B

Without current Claude deals, my personal cost would have been *~$112,000*.

cmiles8

7 hours ago

Article doesn’t mention on-prem and on device models. Almost guaranteed that there are a range of killer enhancements on these fronts waiting to drop until IPOs get closer to inflict maximum chaos on the valuation games.

While the big guys will argue they’re worth trillions expect others to drop chaos booms showing their NPV may be effectively zero.

dnnddidiej

8 hours ago

Is PMF enough. It is such a dynamic self-disrupting wave that it is like predicting physical chaos. These aren't early Googles in a blue ocean. Maybe a blue ocean full of pirates and dragons!

This isn't me being a doomer I just don't know. Can we look at Q2 profits and draw hockey sticks yet?

Remember people are boasting how much their expenses are. That is where we are in the bubble/new paradigm.

cedws

2 hours ago

My employer went from the Max plans to Enterprise this month which was utterly stupid. We went from paying $200 per head to like $500 for some people, even more for others. For the same product. Oh well, guess we’re doing our part to prop up this bubble.

pzo

9 hours ago

> If you are a heavy user of coding agents these plans are a fantastic deal. I just ran the ccusage tool on my laptop to get an estimate of how much I would have spent if I were to pay for API tokens in the past 30 days and got

You think this is fantastic deal only because they use similar like tricks where they inflate the price and tell you something supposed to cost $1000 but they have this today promo for $100.

I was there too and paying for a while. Few weeks ago I tried DeepSeek V4 Pro - expected its gonna be shit but its actually pretty good.

The deal is I pay daily ~$1 for DSV4-pro for ~100M API token usage. And they probably not getting broke because >90% of those token in practice is cache read and they very well optimized for that.

sourcecodeplz

8 hours ago

Yep, exactly this. And I have so much less anxiety that I have to use my 5-hour/weekly usage or I lose it... with deepseek api the credits never expire, I can use them when I want, how much I want and the prices are ridiculously low for the quality/intelligence/performance.

conradkay

7 hours ago

GPT 5.5 is maybe 4x the size of v4 pro, hard to compare price since cache hit is basically free with Deepseek, but 40x cheaper (with their 75% off) seems about right.

So ballpark same price per parameter as Simon.

Havoc

9 hours ago

What baffles me is the range of estimates.

Operating profit is both post depreciation and fees paid to third parties for hire. So aside from shenanigans like RSUs and financing interest that's already somewhat close to actual economics.

Meanwhile we've got commenters here talking of 5-10 trillion with a T revenue shortfall.

Those are very different takes on reality

Hasz

9 hours ago

Mentioned in the article, but it cracks me up that both openai and anthropic are utilizing fairly traditional enterprise GTM plans segmented by verticals.

So many startups trying to automate sales, but somehow the two biggest frontier labs have decided that the best GTM strategy is firmly human-in-the-loop.

_verandaguy

9 hours ago

With respect to Simon, whose writing I've usually agreed with in the past and whose insights I've liked: this is a bad take that overlooks the extent to which corporations are imposing the use of AI on employees, and in particular ICs, who make up a majority of the AI-using workforce by headcount.

Many of us are either openly having our performance reviews tied to AI use, especially at larger enterprises. Whether that's measured by sheer token count or just "how many of your tasks are you using AI for these days" (combined with the implication that question carries at many orgs which are heavily invested in AI).

simonw

8 hours ago

Are you saying that Anthropic's huge leaps in revenue are caused by stupid company policies and token leaderboards, and the moment companies stop imposing AI on their employees revenue will drop to a point where Anthropic are unlikely to be profitable?

I don't think that's the case. I think the token leaderboard thing (which is clearly ridiculous) affects a tiny portion of companies and is already going out of fashion.

_verandaguy

8 hours ago

I'm saying that the truth lies somewhere in between, and that Anthropic's current revenue is being, in part, propped up artificially.

We're also in a place where a lot of the usage guidance around these tools is still nascent. People are cowboying a lot of stuff, even as larger companies start to organize AI policy/safety/responsible use working groups to try and policy around the shortfalls of the technology.

IMO: if this technology persists, and if we figure out a way to use it in a broadly safe way, the value proposition will probably trend down rather than up, at least on the code generation front.

As a research tool, it shows some promise, though I still find the ethics of the technology disgusting.

x187463

10 hours ago

I wonder how a focus on per-token API profits will impact the incentives to improve token efficiency and drive down costs through optimized compute. I suppose as long as a few leading labs are competing, we'll see progress in this regard, but it's certainly less in their interest than it is with a flat subscription pricing model.

zuzululu

10 hours ago

Great article I know this upsets a lot of people who are used to thinking Anthropic/OpenAI are just lighting cash on fire but they've cornered the market on enterprise who cannot walk away from these $200/month plans

However the valuations are still far far away from actual sanity

binary0010

10 hours ago

Have you tried the large open source code models?

I use glm-5.1 and occasionally deep seek v4.

They are as good or better than Claude's latest models.

And significantly cheaper. I've converted 3 of my engineer friends as well. All three have dropped their $200 month plans they had with anthropic.

We've all been a bit shocked at just how good these models are now.

If you "have" tried GLM (I specifically find it shockingly good for code). Did you not think it's not competitive to Claude, and why?

BeetleB

10 hours ago

I use GLM-5.1.

It's good enough for personal stuff. It doesn't compare to the latest Opus I use at work. You can certainly argue I don't need Opus for work, but there is clearly a difference.

Also, at least with z.ai, GLM-5.1 is s l o w! After using Claude at work, I get really impatient with GLM-5.1 at home. When doing "true" vibe coding (i.e. not really examining the code), Opus is a ton faster (easily 5x).

But yeah, I'm not willing to personally pay for the frontier models. I won't even renew my annual Z.ai plan - it's become too expensive.

binary0010

9 hours ago

Hmm, I use opencode subscription, and glm seems just as fast from the tests I've tried to compare between the two. Tbh it mostly took Claude longer (mostly significantly longer) for the same tests.

Also, and I know you may not want to answer. But could you give me an idea of the type of thing you found glm to be worse with?

I think I've been fairly unbiased in testing a bunch of different development tasks. But am curious if maybe it performs well for some stuff and not others. So if you could share what you feel it's worse at.

Also are you an experienced developer or less experience?

BeetleB

9 hours ago

Perhaps opencode zen isn't using z.ai as a provider?

cassianoleal

9 hours ago

I'll repeat something I wrote on an entirely separate HN submission.

When DeepSeek V4 Pro came out, I had been mostly coding with GLM-5.1 on a Z.ai coding plan.

I had a large analysis task on a relatively complex codebase. I decided to try the models out.

GLM-5.1 did acceptably but got a few things wrong (easily corrected) and took quite a while to get there.

Opus 4.6 burnt through the US$10 budget I had given it in about 10-15 min, without ever returning from the first prompt.

DeepSeek V4 returned a full analysis within 2-3 min, and I carried on all the way to implementing the feature I was after. Total cost less than US$1.00.

I now mostly alternate between GLM-5.1 and DeepSeek V4 Flash, with an occasional dip into V4 Pro for more complex analyses.

dominotw

10 hours ago

task i am working on right now at work is comparing two verisions of apis and documenting responses in their outputs. i suspect a vast majority of work at entrprise is of similar complexity.

right now everyone is using latest and greatest to do dumb stuff like that. that would change fast if companies start caring about costs.

therealdrag0

9 hours ago

What is the best IDE UI to use them? I don’t like CLIs.

szatkus

5 hours ago

Cline is pretty good if you use VSCode. It's one of a few AI agent plugins that works in the left sidebar.

binary0010

7 hours ago

Personally I'm happy with opencode right now

thewebguyd

9 hours ago

> enterprise who cannot walk away from these $200/month plans

Any org with more than 150 users aren't on $200/month plans, they are forced into API pricing + $20/month/user

For individuals and orgs small enough to get to use the subscription plans, that's all well and good until usage limits keep going down, or cost goes up. If you compare the usage you get on $200/month maxed out vs. what that would cost at API pricing, the $200/mont plan is an absolute steal. I doubt it will last long.

bigbuppo

9 hours ago

Not to mention the API plans are also still in their "lose money, just get the suckers hooked like addicts" phase. Once the reality-based pricing comes into play, it's a coin flip of whether the bulk of the companies fail, or they get to live off government subsidies for a few decades.

On the plus side, I'm happy I'll have a nice hay barn when the local half-built AI data center is abandoned.

simonw

9 hours ago

I believe that API pricing runs at a healthy margin, at least compared to the server and energy costs used to serve the tokens.

Recent conversation here on that topic: https://news.ycombinator.com/item?id=47062534#47063134

bigbuppo

8 hours ago

There isn't a single thing about how the AI companies are operating that looks like a normal business. I know people who were in the room when Scott Sullivan, CFO of Worldcom, assured everyone that the future was bright at Worldcom days before they collapsed. So you'll have to excuse me if I don't believe the words of someone whose sole job is to justify hundreds of billions of dollars being thrown at Anthropic when he says their future is bright.

simonw

8 hours ago

I agree that the amount of investment thrown at these companies is absurd.

But I also think that their API token pricing represents a real margin over the inference costs for serving those tokens.

Both things can be true at once.

smallerfish

10 hours ago

> enterprise who cannot walk away from these $200/month plans

But that's the point of the article. Enterprise plans are starting to get API pricing, not the subsidized subscription pricing.

raincole

2 hours ago

The article is at least one year too late. Claude Code has been the product-market fit. It's so obvious retrospectively that questioning it at this point would be very silly.

The problem is not whether they have PMF (they do) but how they're going to compete against on-prem and Chinese providers.

Having PMF != printing money forever.

The author claim:

> That’s $2,180.16 worth of tokens for $200

No matter what it means, rebuild the same thing you built with these $2,000 tokens with DeepSeek Pro V4 and let's see if Claude has a chance to survive.

sandeepkd

7 hours ago

If we take out the circular interests and investments here then there is no way that this is a feasible business in current state.

airstrike

10 hours ago

Who's to say those enterprises won't churn after XYZ comes out with a decent enough model that costs 10x less to use?

There's a whole bag of clever tricks you can play to juice short term results leading to an IPO that may not work longer term.

I'll believe they've found product-market fit when they have a product. Right now they're selling the infrastructure, in a highly subsidized and undifferentiated way (at least over a sufficient long period of time of, say, a couple of years).

atleastoptimal

8 hours ago

I think this was obvious since the birth of ChatGPT

Intelligence is a universal good, it can apply to anything, and no, "human intelligence" is not the only form that is useful nor special. There are limitations to AI but also huge advantages, and its obvious that the advantages are worth paying for, given their revenue.

MaxPock

2 hours ago

The writer is on to something. Musk saw the writing on the wall and bought cursor. AI money is in coding plans

dude250711

10 hours ago

CuriouslyC

9 hours ago

Companies are kool-aid drinking now due to hype, but given how much they're spending, if they don't see REAL, BIG wins from it soon, they're going to scale it back quickly and switch to Chinese models. Claude isn't worth the API cost for a lot of development work, and once companies have had time to collect and crunch data they'll see this.

grttq

8 hours ago

Swear people like you were hyping the frontier labs so hard not long ago.

Funny to see the change of tone - a lesson for people not to get too ahead of themselves.

CuriouslyC

7 hours ago

There's no change in tone, I'm still very bullish on the tech, Claude in particular just isn't worth the API price, which I've always felt was too damn high. I have paid for Gemini 2.5 Pro, Deepseek 3.2/4 and GLM 5 tokens happily though.

grttq

5 hours ago

Lmao you won’t admit it will you?

You financially benefit from stuff like agents. Of course you will be the last to admit publicly when things aren’t quite heading in the right direction. The gap between hype and reality is ever increasing.

CuriouslyC

3 hours ago

You misread my comment because of your personal bias, and now you're acting like it's some sort of own? Come on bro.

The models are putting in work, that's indisputable, the thing I'm calling out is that Anthropic's price and hype are running ahead of the value it's delivering. Agents are a great boost at <=5% of SWE salary, a mediocre tradeoff/risk at 20%+, and insane at SV tokenmaxxing levels.

aagha

6 hours ago

Ed Zitron begs to differ

vb-8448

8 hours ago

I'm a huge fan of agent coding but kinda dislike this "llm evangelism".

There are still several open points (eg.: code churn, maintainability, subtle bugs human will never do) that everyone with a minimal programming knowledge that seriously used a LLM agent knows about but somehow none of these "big influencers" never mention (or just saying "it's your fault").

claysmithr

2 hours ago

I’m happy with spending zero dollars on ai lol..

Legend2440

10 hours ago

>Somehow this fragment turned into headlines like Uber’s COO says it’s getting harder to justify the money spent on AI tokenmaxxing, because the market for stories about AI failures remains enormous.

I notice this all over the place. Many people hate AI and want it to fail, and they're willing to invent misinformation if it supports that idea.

hansmayer

9 hours ago

Well, it is a big news when the COO of Uber says it no? Not quite some small consultancy shop here.

Legend2440

9 hours ago

But the COO did not say that. The headline was deliberately misrepresenting what he said.

uncivilized

8 hours ago

The article was posted on HN and discussed a day or two ago.

https://news.ycombinator.com/item?id=48268871

hansmayer

8 hours ago

No, he said exactly that, if you remove the corporate sanitised language designed to not offend the Uber CTO.

7 hours ago

I think that both you and the other major tech outlets interpreted that poorly.

hansmayer

7 hours ago

Oh really? How about the king of non-tech-outlets and digestible, shallow bites for the not-reading-books-middle-class, the Business Insider?

https://www.businessinsider.com/uber-coo-andrew-macdonald-ai...

You know their business is literally correct interpretation of the C-Suite statements.

simonw

6 hours ago

I think Business Insider are not a particularly high quality publication - they're prone to clickbait - and that headline is an example of why I think that.

Hah, I just checked their homepage and here they go again amplifying that COO fragment from that podcast:

https://www.businessinsider.com/tokenmaxxing-debate-uber-exe...

> "That link is not there yet, right?" Macdonald said in comments that went viral, racking up over 2 million views on X. "I think maybe implicitly there is more that is getting shipped, but it's very hard to draw a line between one of those stats and, 'OK, now we're actually producing 25% more useful consumer features.'"

Yeah, something going "viral on X" is clearly a sign that it's quality information!

hansmayer

6 hours ago

So no one got it right? Myself, tech outlets, non-tech outlets, everyone on twitter, etc?

simonw

6 hours ago

That's why I wrote a whole section about it. I was deeply disappointed at how little thought people had appeared to put into this before amplifying the misleading headlines.

For someone who cares about media hype - https://hn.algolia.com/?query=author%3Ahansmayer%20hype&type... - you don't seem to be very discerning with regards to this particular story.

mschuller

9 hours ago

yep, and the issue is, they took investment

vonneumannstan

6 hours ago

HN is the least agi-pilled place on the internet

reducesuffering

6 hours ago

HN has been on every wrong side of AGI predictions since the founding article on OpenAI...

marcosdumay

4 hours ago

You mean that HN has been predicting AGI will happen, or you mean the Singularity happened earlier today and I missed it that humanity is already all killed?

stego-tech

9 hours ago

The big assumption with all of these sorts of analyses is that things will continue as they are for the foreseeable future.

In hype-driven markets, you cannot be certain of that.

Let's take a view that the author is right: coding agents and their associated harnesses were the inflection point for some degree of profitability and widespread consumption, and that these tools are now yet another SaaS subscription or API bucket expense to bake into every single developer (or developer-adjacent) in the organization alongside your collab suite, HR seat, CRM seat, design seat, etc. To be fair I honestly think that's a safe assumption to make for highly technical firms whose image is derived from remaining on the cutting edge of things.

That begs the following questions, which we won't know until IPOs start happening:

* Are subscriptions profitable, or just API consumption?

* What's the run rate when we just consider subscription-based usage like Claude Code and Codex? What about API calls?

* Is there any profitable pathway forward at which enterprises can get unlimited usage but at fixed rates via subscription?

* What does customer churn look like for subscription users versus API users?

We also have a number of questions for customers that I suspect we'll start seeing receipts for in the coming months, at least from the early adopters:

* What was the net gain (loss) from leveraging coding agents?

* What's the cost of a developer with or without access to a coding agent + harness? Is it cheaper to hire an outsourced worker with a coding agent subscription, or a domestic worker without one?

* At what point does further AI spend result in diminishing returns, i.e. where's the 'sweet spot' for spend?

* Did AI boost actual revenue and outcomes, or did it just gamify KPIs?

* What roles or work did AI actually replace, versus merely displace during the hype cycle?

Not to mention the questions regarding the technology itself:

* Will we develop the means to run foundational/frontier models at edge using less resources through some existing (e.g. distillation) or new technology, thus cutting off the profit centers of these firms?

7 hours ago

Your comment is not a serious one. Their revenue has quadrupled in just a few months. So yes, December 2025 is a long time ago now.

Laurel1234

5 hours ago

You've had a couple lobotomies too many if you think their revenue has quadrupled in just a few months.

Hell, say it did, how would you possibly know?

simonw

5 hours ago

Dec 3rd 2025: https://www.anthropic.com/news/anthropic-acquires-bun-as-cla... - "In November, Claude Code achieved a significant milestone: just six months after becoming available to the public, it reached $1 billion in run-rate revenue."

Feb 12th 2026: https://www.anthropic.com/news/anthropic-raises-30-billion-s... - "Today, our run-rate revenue is $14 billion, with this figure growing over 10x annually in each of those past three years."

Apr 6th 2026: https://www.anthropic.com/news/google-broadcom-partnership-c... - "Demand from Claude customers has accelerated in 2026. Our run-rate revenue has now surpassed $30 billion—up from approximately $9 billion at the end of 2025."

All three of those are official releases from Anthropic. You can choose not to believe the if you like, but since they plan to IPO this year it's in their interest not to get caught lying to potential investors.

Laurel1234

4 hours ago

As a non-public company they can use whatever non-GAAP black magic accounting to claim anything while still not technically lying. It just doesn't correlate to anything we'd actually call revenue.

simonw

4 hours ago

It's still notable that, by whatever black magic accounting they are using, their number was $9bn in December and $30bn in April.

5gkk

3 hours ago

Sorry man I hate to say this but you and many others need to stop commenting on accounting, finance and economics as its clearly way out of your realm of expertise.

Do you know what revenue recognition is? Do you know what accrual accounting is? Do you know of the phenomenon that is 'managed earnings'?

The only true objective number in finance is cash flows.

IshKebab

6 hours ago

Do you have a source for that?

hansmayer

7 hours ago

> Their revenue has quadrupled in just a few months

Maybe, maybe not. We haven't seen that S-1 yet. All we have is the 5B in lifetime so far. PLUS - revenue quadrupled or not, it only matters if their costs did not expand at the same rate or more. Revenue is not profit.

aspenmartin

7 hours ago

OK, so when S-1 comes out you will finally allow yourself to be wrong? Your prior is, a 1T company plans to IPO and their leader has been loudly committing an insane amount of fraud? I mean this of course is possible but that is quite the conspiracy. The scrutiny of an IPO would be a crazy thing to do if you were committing fraud at the scale you're suggesting.

Revenue is not profit yet the discussion in this particular thread is about revenue.

hansmayer

7 hours ago

> 1T company plans to IPO and their leader has been loudly committing an insane amount of fraud?

Ever heard of Enron, Theranos, SBX ? They were all hiding in plain sight - who could've thought they were frauds?

aspenmartin

7 hours ago

That’s why I said it’s possible but it’s a very improbable and weird prior assumption to make

hansmayer

6 hours ago

> weird prior assumption to make

No, at this level of capital involved, and so much opacity around the company financials, it's a perfectly reasonable assumption.

danielmarkbruce

5 hours ago

No, it's not. It's a stupid thing to say. Perfectly stupid assumption. There are 1000s of multi billion $ revenue companies operating and as a % the number that are fraudulent is close to zero, especially those public or looking to go public. There is always the possibility, but it's extremely naive to think it's likely.

peteforde

6 hours ago

Ed is a smart guy, but you or anyone basing your opinion on what one eloquent journalist says is ultimately a risky bet, no matter how much his reporting hits your particular dopamine receptors.

Please don't forget that Ed's entire brand identity is now 1:1 with exposing "AI" as a giant, unmitigated failure.

That's a very specific flow chart to hook your caboose to when none of this is even remotely close to endgame.

HerbManic

5 hours ago

Pretty much. Ed does a lot of great work in digging through all this stuff but his conclusions always feel far too doomer oriented. OpenAL should have closed 5 times by now if you have been following his assertations from the start.

There will be big parts of what he says are true once the rubble settles but it will not be anywhere near what he is predicting. How that will shape out may not be great for the average person, what money shuffling tricks will be used? But it won't be a total wreck.

peteforde

4 hours ago

> It won't be a total wreck.

Honestly, I think it's very short-sighted to assume that all of this will be seen as any kind of wreck in the long term.

Normies are still catching up and reacting to chat-based LLMs.

HN types are further ahead of the curve, but still catching up and reacting to agentic coding and design workflows.

What often gets completely ignored is that entirely new modalities for how the underlying tech can be applied will continue to be demonstrated, and those will once again cause new ripples of excitement and disgust.

There are companies building world models and systems for protein discovery. Comparatively speaking, these approaches are barely in the zeitgeist today.

Deciding that we already have the data points we need to extrapolate how all of this plays out is like someone in 1974 deciding that microprocessors are just for accounting and inventory. Don't be that someone.

HerbManic

an hour ago

I think the big issue isn't so much a technology thing, I mean that will improve for a long while yet, but it is an economic one. My whole concern over the rapid expansion of LLM's is the massive build out on a technology that hasn't found its feet in a big enough range of markets yet that are willing to pay top dollar. Yes, world models for protein discover is very cool stuff and I kind of hate it gets lumped in with these other companies efforts because it has a very clear path forward that doesn't rely on massive IPO's just to keep the lights on.

This stuff is here to stay but I'm not sure how many of the current front runners will be able to stay solvent if they cannot turn these things in to massive money spinners. Revenue is fine-ish but spending is out of control. I see the debt in hundred of billions of dollars and start to wonder "Who is going to pay for this?" and "Will the people be willing to pay that much?". It just all feels forced rather than organic growth.

This is why I think Google may end up being one of the leaders in this field. They have their custom TPU's that seem to be fairly efficient at these tasks, they are slowly but surely improving their training and inference tech using their massive data set and most importantly, other parts of the business can subsidize this stuff for a decade if needed until it is genuinely profitable.

I am not against the industry but I do worry that many are rushing in with no means of genuine sustainability other than jump out for a golden parachute and let someone else clean up the mess.

I do hope I am wrong.

peteforde

an hour ago

I think that is a reasoned and well-articulated reaction, thanks.

No question that there are some players who need a good weekend in Vegas to make it across the chasm of sorrow.

yogthos

5 hours ago

We don't have to take Ed's word for it. Anybody who's capable of doing grade school math can see that the numbers simply don't work. These companies are literally spending orders of magnitude more money than they're actually bringing in. Cursor, who've been renting Claude, estimated just recently that a $200-per-month Claude Code subscription could use up to $2,000 in compute. https://www.forbes.com/sites/annatong/2026/03/05/cursor-goes...

simonw

5 hours ago

Interesting story. Here's what it says:

> According to a person familiar with the company’s internal analysis, Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute, according to a different person who has seen analyses on the company’s compute spend patterns.

The load-bearing detail here is if that means $2,000 of internal server+electricity costs, or $2,000 if they were to charge at their API pricing instead of the subscription cost.

The latter is how I understand these things to work right now. If it's the former then yeah, Anthropic are losing a TON of money on those subscriptions.

yogthos

3 hours ago

That's the big question, and nobody really knows what the operating costs actually are right now.

surgical_fire

8 hours ago

Also, if I understand correctly, they are rumored to have a profitable EBITDA.

It's a funny metric considering Depreciation is a huge cost for them.

"We are profitable when we don't count our expenses"

skybrian

8 hours ago

There's a good reason to look at it separately: if inference is profitable then they make money (or at least lose less money) when they get more customers, because any fixed costs are spread across more usage.

dminik

5 hours ago

Assuming that there are infinite suckers with cash to spend. It's entirely possible (if unlikely) that the market is not big enough to cover the training costs. Especially for multiple companies all burning insane amount of money on the regular.

surgical_fire

8 hours ago

Depreciation is part of the cost of inference. Inference happens in GPUs that have a relatively short lifespan.

Those GPUs are very expensive.

Inference is expensive because a GPU can only process a certain amount of requests in a given timeframe. Remember that Anthropic is constrained in compute.

If they are constrained, it means that those GPUs are not idle. If they have more customers, they will need more GPUs.

If they have to play silly games using EBITDA to be "profitable", then it means that they need to ramp up prices a lot more than they already did.

Which is why in these discussions I always say that inference is also extremely expensive. Too many people like to pretend without any evidence that inference is cheap.

skybrian

6 hours ago

Anthropic and OpenAI don't own data centers. Since they're renting GPU's, that's not depreciation. Paying rent is an operating cost.

Language models don't wear out the same way; upgrading is a choice.

ACCount37

6 hours ago

5 hours ago

Correct. That’s exactly right.

The move to buy up ram is straight out of a industrial organisation textbook.

duped

9 hours ago

AI companies/users are filled with liars and grifters, so any numbers/outlook they report should be highly suspect.

supern0va

8 hours ago

simonw

6 hours ago

Google's "AI overviews" have been utter junk pretty much consistently since they launched the feature.

bflesch

9 hours ago

There's a saying "the fish stinks from the head".

pier25

9 hours ago

Yeah I'll believe it when I see it. Revenue is increasing but so are their costs.

7 hours ago

> That's why serious people are very careful, even if they are not running a company supposedly worth 1T USD

10x revenue growth per year, even more this year...his predictions about when agents will claim SWE e2e work are his speculations, relevant because people care about what he thinks as he is closer than anyone to the leading edge of the technology. It's also important for him to be as accurate as he can about this because he has to put his money where his mouth is. He has to sign the right amount of compute otherwise he screws himself. He got it wrong in the opposite direction that you're implying, so at this point it sounds like you are more interested in your axe to grind than the truth on the ground.

You think enterprises are adopting CC because they think "oh this will replace my SWEs I can fire them"? That's not happening at major companies. They buy CC because it's useful and the writing is so clearly on the wall in so many data points that to suggest otherwise is a bit silly at this point.

> For my companies next quarter, I'll just pull some random numbers out of my ass so we can make plans with material impact to company business based on that.

You, as a leader of a company, don't have to make predictions? Don't have to make bets about what the best thing for you to do next year? That must be incredibly nice.

Amodei and everyone else need to plan compute and plan their products and roadmap. You want him to....not do that?

hansmayer

7 hours ago

> 10x revenue growth per year

To the stunning tune of 5B in the lifetime .

> You think enterprises are adopting CC because they think "oh this will replace my SWEs I can fire them"?

Yeah, that's actually Darios main talking point

> They buy CC because it's useful and the writing is so clearly on the wall in so many data points that to suggest otherwise is a bit silly at this point

Right, really sound arguments - writing is "clearly on the wall" and there are "so many data points". I'd be keen to use those immediately, but I am kind of missing the key of the "many data points" - namely, what did you build with it and how much ARR is it generating?

> You, as a leader of a company, don't have to make predictions

I have to make predictions, but not confabulations, lies and idiocies.

> Amodei and everyone else need to plan compute

FOR WHAT? Again, what was built with their shitty product in various companies and how much ARR did it generate? Uber seems to get no value out of it.

aspenmartin

6 hours ago

Anthropic has generated far more than 5B in revenue, I don’t know what sort of computer you have but it evidently does have the Internet, I would recommend using that unless the Internet CEOs are also in trouble for hyping that one up.

> Right, really sound arguments - writing is "clearly on the wall" and there are "so many data points".

Thank you for recognizing this. Don’t read Ed and think you understand anything about AI is all I’ll say. Read epoch capability index paper and look at the dashboard chart or the METR time horizon chart and methodology and then return with what I imagine from historical comments will be another ferocious and impressive act of mental gymnastics.

> I have to make predictions, but not confabulations, lies and idiocies.

Idk you’ve been misquoting and aggressively against addressing any facts you are presented with and yet bring no facts of your own (hint: if you know what you’re talking about typically you can calmly discuss with actual facts). That feels pretty similar to confabulations, I won’t say idiocy I’m sure you are not an idiot but you seem to have a lot in common with your caricatures of tech CEOs.

> FOR WHAT?

Their product.

hansmayer

6 hours ago

> Anthropic has generated far more than 5B in revenue

A sworn affidavit by the Anthropic CFO from Dec. 2025 is what you need to look up mate.

supern0va

8 hours ago

I work in big tech and probably 90% of code over the last month has been written by AI. And I suspect it's probably higher within Anthropic, which is probably what he's basing his opinion on.

So, he's closer to correct than not.

That said, your recollection is also flawed. It was in mid-March, and here's the relevant quotes:

>I think we’ll be there in three to six months—where AI is writing 90 percent of the code. And then in twelve months, we may be in a world where AI is writing essentially all of the code.

[...]

>But the programmer still needs to specify, you know, what are—what are the conditions of what you’re doing, what—you know, what is the overall app you’re trying to make, what’s the overall design decision? How do we collaborate with other code that’s been written? You know, how do we have some common sense on whether this is a secure design or an insecure design?

[...]

>So as long as there are these small pieces that a programmer, a human programmer, needs to do, the AI isn’t good at, I think human productivity will actually be enhanced. But on the other hand, I think that eventually all those little islands will get picked off by AI systems.

With another 3-4 months left on the clock, his prediction seems remarkably on point for at least certain organizations and domains.

I welcome you to also hold yourself accountable in the coming months if this trend continues. ;)

pier25

7 hours ago

> And I suspect it's probably higher within Anthropic

That probably explains why their uptime and reliability are so bad.

m1coti

8 hours ago

Written, but was it reviewed? Do you need to edit code written by LLM?

I agree that most of the things are written by AI but writting code was never the bottleneck in big tech.

supern0va

7 hours ago

Yep! We have a review process where we have a few agents, each tuned to a particular domain of expertise (security, code quality, etc) which iterate until the feedback meets a certain threshold, at which point it goes over to humans for (hopefully) final review.

hansmayer

8 hours ago

> I welcome you to also hold yourself accountable in the coming months if this trend continues. ;)

My company did not swallow hundreds of billions in shady investment deals and is not publicly traded. We work with real money, and the revenue on our books is the revenue that is actually booked, not fake revenue we plan in 2 years time to maybe happen. So no, I am not going to hold myself accountable. But people who work with other people's money should be absolutely held accountable when their wild imaginations don't come true, repeatedly, quarter after quarter, year after year!

aspenmartin

8 hours ago

I think he means hold yourself accountable when it turns out your predictions and pessimism don't age well.

hansmayer

8 hours ago

Mate, for 5 years I've been hearing that crap. I am not predicting anything / on the contrary the AI boosting bunch is. When are your predictions coming true?

supern0va

7 hours ago

AFAIK, most predictions from several years ago were for...approximately now to within the next few years. Can you be more specific?

You criticized a very specific (and fake/misquoted) prediction, ignored the correction, and are now criticizing vague hand-wavey "predictions" that you have left unspecified.

Can you please stop with the angry/ranty replies and actually have a real conversation grounded in actual facts?

6 hours ago

Ed actually provides sources and goes into an incredible amount of detail as to how he came to his conclusions. The average AI booster just goes "I totally built ten businesses off vibe coding but I can't tell you anything because it's a SECRET!". And the mainstream tech media is so in the pocket of big tech and AI corporations that they might as well just publish their PR emails at this point. Yeah, I'll listen to Ed thank you very much.

I think it's telling that most critics don't address his actual points, but instead his credibility because he's a "hater".

supern0va

2 hours ago

Ed actually seems to make some really serious errors in his work. Tim Lee called out a particularly egregious one here, though it's one of many: https://x.com/binarybits/status/2050562429709377986

That said, I really mean it when I say that I don't actually think Ed is a good choice for the anti-AI movement. I think an actual opposition is useful, but he ain't it.

I really recommend you read the Wired profile if you haven't yet and form your own opinion: https://www.wired.com/story/ai-pr-ed-zitron-profile/

hansmayer

8 hours ago

> So, unsourced vibes from a shady guy whose entire empire is built on being against AI?

Actually he provides sources when he analyses stuff and imho much better than the usual corporate "Sam Altman says we should ask ChatGPT how to raise babies" crap. Also, I don't know many 'shady' guys who have built entire "empires", nor does he seem to actually have an empire. Usually being shady means you are kind of unknown and all. I am not glorifying Ed, don't even know him personally. I am not even impressed with his writing style much to be honest. But he brings important facts and information to light, which otherwise would have been lost in the cacophony of corporate media light treatment of these con-men. Holy Grail? Blowing up in our faces? WTF are you talking about?

supern0va

8 hours ago

>Actually he provides sources when he analyses stuff and imho much better than the usual corporate

You said it was likely an internal leak to the WSJ "according to Ed Zitron". Did Ed have a source for that, or was it just vibes?

hansmayer

8 hours ago

The source was the article in the WSJ itself, which then referred to their source at the Anthropic. Which kind of is a textbook definition of "leak". Because otherwise Anthropic would have their lawyers hunting both the employee breaking their stringent NDA and the WSJ as well...

supern0va

7 hours ago

Fair enough, but I have to admit I'm puzzled about why you felt the need to then attribute it to Zitron?

hansmayer

7 hours ago

Why puzzled ? I literally said "According to Ed Zitron", implying that's where I stumbled upon the article. I've no time to read corporate media, at least not regularly.

supern0va

7 hours ago

>more like their own leak to WSJ and according to Ed Zitron

^ Apologies, the above read to me like you were saying that Ed himself was claiming that Anthropic leaked to the WSJ.

hansmayer

9 hours ago

> I currently subscribe to the $100/month Max plan from Anthropic and the $100/month Pro plan from OpenAI. If you are a heavy user of coding agents these plans are a fantastic deal.

Agreed. But its only a great deal because it is heavily subsidized, as you said yourself. Enjoy while it lasts, but in my book, product-market fit means something along the lines of "product which enjoys a loyal customer base, sold at a price perceived fair by the customers, and generating profit. How many of these does your definition of product-market fit hit here?