Something nobody's talking about: OpenAI's losses might actually be attractive to certain investors from a tax perspective. Microsoft and other corporate investors can potentially use their share of OpenAI's operating losses to offset their own taxable income through partnership tax treatment. It's basically a tax-advantaged way to fund R&D - you get the loss deductions now while retaining upside optionality later. This is why the "cash burn = value destruction" framing misses the mark. For the right investor base, $10B in annual losses at OpenAI could be worth $2-3B in tax shields (depending on their bracket and how the structure works). That completely changes the return calculation. The real question isn't "can OpenAI justify its valuation" but rather "what's the blended tax rate of its investor base?" If you're sitting on a pile of profitable cloud revenue like Microsoft, suddenly OpenAI's burn rate starts looking like a pretty efficient way to minimize your tax bill while getting a free option on the AI leader. This also explains why big tech is so eager to invest at nosebleed valuations. They're not just betting on AI upside, they're getting immediate tax benefits that de-risk the whole thing.

There is a pretty big moat for Google: extreme amounts of video data on their existing services and absolutely no dependence on Nvidia and it's 90% margin.

> AI is a world-changing technology, just like the railroads were

This comparison keeps popping up, and I think it's misleading. The pace of technology uptake is completely different from that of railroads: the user base of ChatGPT alone went from 0 to 200 million in nine months, and it's now- after just three years- around 900 million users on a weekly basis. Even if you think that railroads and AI are equally impactful (I don't, I think AI will be far more impactful) the rapidity with which investments can turn into revenue and profit makes the situation entirely different from an investor's point of view.

Anthropic is building moat around theirs models with claude code, Agent SDK, containers, programmatic tool use, tool search, skills and more. Once you fully integrate you will not switch. Also being capital intensive is a form of moat.

I think we will end up with market similar to cloud computing. Few big players with great margins creating cartel.

Google’s moat:

Try “@gmail” in Gemini

Google’s surface area to apply AI is larger than any other company’s. And they have arguably the best multimodal model and indisputably the best flash model?

> The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result.

I think this conflates together a lot of different types of AI investment - the application layer vs the model layer vs the cloud layer vs the chip layer.

It's entirely possible that it's hard to generate an economic profit at the model layer, but that doesn't mean that there can't be great returns from the other layers (and a lot of VC money is focused on the application layer).

> AI is going to be a highly-competitive, extremely capital-intensive commodity market

It already is. In terms of competition, I don't think we've seen any groundbreaking new research or architecture since the introduction of inference time compute ("thinking") in late 2024/early 2025 circa GPT-o4.

The majority of the cost/innovation now is training this 1-2 year old technology on increasingly large amounts of content, and developing more hardware capable of running these larger models at more scale. I think it's fair to say the majority of capital is now being dumped into hardware, whether that's HBM and research related to that, or increasingly powerful GPUs and TPUs.

But these components are applicable to a lot of other places other than AI, and I think we'll probably stumble across some manufacturing techniques or physics discoveries that will have a positive impact on other industries.

> that ends up in a race to the bottom competing on cost and efficiency of delivering

One could say that the introduction of the personal computer became a "race to the bottom." But it was only the start of the dot-com bubble era, a bubble that brought about a lot of beneficial market expansion.

> models that have all reached the same asymptotic performance in the sense of intelligence, reasoning, etc.

I definitely agree with the asymptotic performance. But I think the more exciting fact is that we can probably expect LLMs to get a LOT cheaper in the next few years as the current investments in hardware begin to pay off, and I think it's safe to assume that in 5-10 years, most entry-level laptops will be able to manage a local 30B sized model while still being capable of multitasking. As it gets cheaper, more applications for it become more practical.

---

Regarding OpenAI, I think it definitely stands in a somewhat precarious spot, since basically the majority of its valuation is justified by nothing less than expectations of future profit. Unlike Google, which was profitable before the introduction of Gemini, AI startups need to establish profitability still. I think although initial expectations were for B2C models for these AI companies, most of the ones that survive will do so by pivoting to a B2B structure. I think it's fair to say that most businesses are more inclined to spend money chasing AI than individuals, and that'll lead to an increase in AI consulting type firms.

I, personally, use chatGPT for search more than I do Google these days. It, more often than not, gives me more exact results based on what I'm looking for and it produces links I can visit to get more information. I think this is where their competitive advantage lies if they can figure out how to monetize that.

Eh, I wouldn't be so sure, chips with brain matter and or light are on its way and or quantum chips, one of those or even a combination will give AI a gigantic boost in performance. Finally replacing a lot more humans and whoever implements it first will rule the world.

This will remain the case until we have another transformer-level leap in ML technology. I don’t expect such an advancement to be openly published when it is discovered.

This is different because now the cats out of the bag: AI is big money!

I don't expect AGI or Super intelligence to take that long but I do think it'll happen in private labs now. There's an AI business model (pay per token) that folks can use also.

I think we'll find that that asymptote only holds for cases where the end user is not really an active participant in creating the next model:

- take your data

- make a model

- sell it back to you

Eventually all of the available data will have been squeezed for all it's worth the only way to differentiate oneself as an AI company will be to propel your users to new heights so that there's new stuff to learn. That growth will be slower, but I think it'll bear more meaningful fruit.

I'm not sure if today's investors are patient enough to see us through to that phase in any kind of a controlled manner, so I expect a bumpy ride in the interim.

Or the airlines. Airlines have created a huge amount of economic value that has mostly been captured by other entities.

>That doesn't mean AI is going to go away, or that it won't change the world - railroads are still here and they did change the world - but from a venture investment perspective, get ready for a massive downturn.

I don't know why people always imply that "the bubble will burst" means that "literally all Ai will die out and nothing will remain that is of use". The Dotcom bubble didn't kill the internet. But it was a bubble and it burst nonetheless, with ramifications that spanned decades.

All it really means when you believe a bubble will pop is "this asset is over-valued and it will soon, rapidly deflate in value to something more sustainable" . And that's a good thing long term, despite the rampant destruction such a crash will cause for the next few years.

The "Railway Bubble" analogy is spot on.

As a loan officer in Japan who remembers the 1989 bubble, I see the same pattern. In the traditional "Shinise" world I work with, Cash is Oxygen. You hoard it to survive the inevitable crash. For OpenAI, Cash is Rocket Fuel. They are burning it all to reach "escape velocity" (AGI) before gravity kicks in.

In 1989, we also bet that land prices would outrun gravity forever. But usually, Physics (and Debt) wins in the end. When the railway bubble bursts, only those with "Oxygen" will survive.

The railroads provided something of enduring value. They did something materially better than previous competitors (horsecarts and canals) could. Even today, nothing beats freight rail for efficient, cheap modest-speed movement of goods.

If we consider "AI" to be the current LLM and ImageGen bubble, I'm not sure we can say that.

We were all wowed that we could write a brief prompt and get 5,000 lines of React code or an anatomically questionable deepfake of Legally Distinct Chris Hemsworth dancing in a tutu. But once we got past the initial wow, we had to look at the finished product and it's usually not that great. AI as a research tool will spit back complete garbage with a straight face. AI images/video require a lot of manual cleanup to hold up to anything but the most transient scrutiny. AI text has such distinct tones that it's become a joke. AI code isn't better than good human-developed code and is prone to its own unique fault patterns.

It can deliver a lot of mediocrity in a hurry, but how much of that do we really need? I'd hope some of the post-bubble reckoning comes in the form of "if we don't have AI to do it (vendor failures or pricing-to-actual-cost makes it unaffordable), did we really need it in the first place?" I don't need 25 chatbots summarizing things I already read or pleading to "help with my writing" when I know what I want to say.

for me its clear OpenAI and Anthropic have a lead. I dont buy Gemini 3 being good. it isnt. whatever the benchmark said. same for meta and deepseek.

Your premise is wrong in a very important way.

The cost of entry is far beyond extraordinary. You're acting like anybody can gain entry, when the exact opposite is the case. The door is closing right now. Just try to compete with OpenAI, let's see you calculate the price of attempting it. Scale it to 300, 500, 800 million users.

Why aren't there a dozen more Anthropics, given the valuation in question (and potential IPO)? Because it'll cost you tens of billions of dollars just to try to keep up. Nobody will give you that money. You can't get the GPUs, you can't get the engineers, you can't get the dollars, you can't build the datacenters. Hell, you can't even get the RAM these days, nor can you afford it.

Google & Co are capturing the market and will monetize it with advertising. They will generate trillions of dollars in revenue over the coming 10-15 years by doing so.

The barrier to entry is the same one that exists in search: it'll cost you well over one hundred billion dollars to try to be in the game at the level that Gemini will be at circa 2026-2027, for just five years.

Please, inform me of where you plan to get that one hundred billion dollars just to try to keep up. Even Anthropic is going to struggle to stay in the competition when the music (funding bubble) stops.

There are maybe a dozen or so companies in existence that can realistically try to compete with the likes of Gemini or GPT.

Did railroads change the world though?

They only lasted a couple of decades as the main transportation method. I'd say the internal combustion engine was a lot more transformative.

Massive upfront costs and second place is just first loser. It’s like building fabs but your product is infinitely copyable. Seems pretty rough.

Um meta didn't achieve the same results yet. And does it matter if they can all achieve the same results if they all manage high enough payoffs? I think subscription based income is only the beginning. Next stage is AI-based subcompanies encroaching on other industries (e.g. deepmind's drug company)

I’m waiting to get an RTX 5090 on the cheap.

Also that open source models are just months behind

Just in time for a Government guaranteed backstop.

People seem to have the assumption that OpenAI and Anthropic dying would be synonymous with AI dying, and that's not the case. OpenAI and Anthropic spent a lot of capital on important research, and if the shareholders and equity markets cannot learn to value and respect that and instead let these companies die, new companies will be formed with the same tech, possibly by the same general group of people, thrive, and conveniently leave out the said shareholders.

Google was built on the shoulders of a lot of infrastructure tech developed by former search engine giants. Unfortunately the equity markets decided to devalue those giants instead of applaud them for their contributions to society.

If performance indeed asymptotes, and if we are not at the end of silicon scaling or decreasing cost of compute, then it will eventually be possible to run the very best models at home on reasonably priced hardware.

Eventually the curves cross. Eventually the computer you can get for, say, $2000, becomes able to run the best models in existence.

The only way this doesn’t happen is if models do not asymptote or if computers stop getting cheaper per unit compute and storage.

This wouldn’t mean everyone would actually do this. Only sophisticated or privacy conscious people would. But what it would mean is that AI is cheap and commodity and there is no moat in just making or running models or in owning the best infrastructure for them.

AI is capital intensive because autodiff kinda sucks.

I still don't understand how it's world-changing apart from considerably degrading the internet. It's laughable to compare it to railroads.

"AI is going to be a highly-competitive" - In what way?

It is not a railroad and the railroads did not explode in a bubble (OK a few early engines did explode but that is engineering). I think LLM driven investments in massive DCs is ill advised.

This is why I think China will win the AI race. As once it becomes a commodity no other country is capable of bringing down manufacturing and energy costs the way China is today. I am also rooting for them to get on parity with node size for chips for the same reason as they can crash the prices PC hardware.

  > There's no evidence of a technological moat or a competitive advantage in any of these companies.

I don't use my Google Pro subscription much. Gemini 3.0 Pro spends 1/10th of the time thinking compared to GPT 5.2 Thinking and outputs a worse answer or ignores my prompt. Similar story with Deepseek.

The public benchmarks tell a different story which is where I believe the sentiment online comes from, but I am going to trust my experience, because my experience can't be benchmaxxed.

There is clearly a very strong moat. OpenAI is close to 1 billion active users on ChatGPT while Claude barely have any non-business users. Even though Anthropic had better models at different times this year, I never stopped using ChatGPT and paying for Plus.

We just don't know who will win in which area yet. It doesn't mean there is no moat.

If you think of it like cloud, where it's a commodity that reaches competitive prices, then you can use it to build products and applications, instead of competing for infrastructure (see also: railroads, optical fiber)

There is tons of money to be made at the application layer, and VCs will start looking at that once the infrastructure layer collapses.

Here's a blog post I wrote about that: https://parsnip.substack.com/p/models-arent-moats

Because almost everyone involved in AI race grew up in "winner takes it all" environments, typical for software, and they try really hard to make it reality. This means your model should do everything to just take 90% of market share, or at least 90% of specific niche.

The problem is, they can't find the moat, despite searching very hard, whatever you bake into your AI, your competitors will be able to replicate in few months. This is why OpenAI is striking deal with Disney, because copyright provides such moat.

OpenAI is (was?) extremely good at making things that go viral. The successful ones for sure boost subscriber count meaningfully

Studio Ghibli, Sora app. Go viral, juice numbers then turn the knobs down on copyrighted material. Atlas I believe was a less successful than they would've hoped for.

And because of too frequent version bumps that are sometimes released as an answer to Google's launch, rather than a meaningful improvement - I believe they're also having harder time going viral that way

Overall OpenAI throws stuff at the wall and see what sticks. Most of it doesn't and gets (semi) abandoned. But some of it does and it makes for better consumer product than Gemini

It seems to have worked well so far, though I'm sceptical it will be enough for long

Because as with the internet 99% of the usage won’t be for education, work, personal development, what have you. It will be for effing kitten videos and memes.

Because there is only so much programmers and companies will pay for AI coders. The big prizes is AI-generated TikTok.

The entertainment industry is by far the easiest way to tap into global discretionary income.

Because OpenAI stands for AI leader.

If Gemini can create or edit an image, chatgpt needs to be able to do this too. Who wants to copy&paste prompts between ai agents?

Also if you want to have more semantics, you add image, video and audio to your model. It gets smarter because of it.

OpenAI is also relevant bigger than antropic and is known as a generic 'helper'. Antropic probably saw the benefits of being more focused on developer which allows it to succeed longer in the game for the amount of money they have.

When they released their first good image model is when they got a new 100 million users in a week.

Because for all the incessant whining about "slop," multimodal AI i/o is incredibly useful. Being able to take a photo of a home repair issue, have it diagnosed, and return a diagram showing you what to do with it is great, and it's the same algos that power the slop. "Sorry, you'll have to go to Gemini for that use case, people got mad about memes on the internet" is not really a good way for them to be a mass consumer company.

Because those and world models are the endgame, way way more than text is.

The fact that they do this isn't very bullish for them achieving whatever they define as AGI.

Because their main use is for advertising/propaganda, which is largely videoSlop & imageSlop even without AI.

because these are mostly the same players of the 2010's. So when they can't get more investor money and the hard problems are still being cracked, the easiest fallback is the same social media slop they used to become successful 10-15 years prior. Falling back on old ways to maximize engagement and grind out (eventually) ad revenue.

But how much more profitable are they? We see revenue but not profits / spending. Anthropic seems to be growing faster than OpenAI did but that could be the benefit of post-GPT hype.

What Google AI products do people not want to use? Gemini is catching up to chatpt from a MAU perspective, ai overviews in search are super popular and staggeringly more used than any other ai-based product out there, a Google ai mode has decent usage, and Google Lens has surprisingly high usage. These products together dwarf everyone else out there by like 10x.

Where does google struggle to make products people don’t want to use? Is it a personal opinion?

What "we" know already is hard to add to, as a forum that has a dozen AI articles a day on every little morsel of news.

>whereas OpenAI has a clear opportunity with advertising.

Personally, having "a clear opportunity with advertising" feels like a last ditch effort for a company that promised the moon in solving all the hard problems in the world.

There are other avenues of income. You can invade other industries which are slow on AI uptake and build an AI-from-ground competitor with large advantages over peers. There are hints of this (not AI-from-ground but with more AI) with deepmind's drug research labs. But this can be a huge source of income. You can kill entire industries which inevitably cannot incorporate AI as fast as AI companies can internally.

I don't. Google has at least a few advantages:

1. Google books, which they legally scanned. No dubious training sets for them. They also regularly scrape the entire internet. And they have YouTube. Easy access to the best training data, all legally.

2. Direct access to the biggest search index. When you ask ChatGPT to search for something it is basically just doing what we do but a bit faster. Google can be much smarter, and because it has direct access it's also faster. Search is a huge use case of these services.

3. They have existing services like Android, Gmail, Google Maps, Photos, Assistant/Home etc. that they can integrate into their AI.

The difference in model capability seems to be marginal at best, or even in Google's favour.

OpenAI has "it's not Google" going for it, and also AI brand recognition (everyone knows what ChatGPT is). Tbh I doubt that will be enough.

Probably more people use googles AI than anything else. Every search result has an LLM generated summary at the top.

Google already puts AI summaries at the top of search. It would be trivial for them to incorporate shopping. And they have infinitely more traffic than OpenAI does. I just don’t see how OpenAI could possibly compete with that. What are you seeing that I’m not?

The GPT-5 series is a new model, based on the o1/o3 series. It's very much inaccurate to say that it's a routing system and prompt chain built on top of 4o. 4o was not a reasoning model and reasoning prompts are very weak compared to actual RLVR training.

No one knows whether the base model has changed, but 4o was not a base model, and neither is 5.x. Although I would be kind of surprised if the base model hadn't also changed, FWIW: they've significantly advanced their synthetic data generation pipeline (as made obvious via their gpt-oss-120b release, which allegedly was entirely generated from their synthetic data pipelines), which is a little silly if they're not using it to augment pretraining/midtraining for the models they actually make money from. But either way, 5.x isn't just a prompt chain and routing on top of 4o.

I think you are messing up things here, and I think your comment is based on the article from semi analysis. [1]

It said: OpenAI’s leading researchers have not completed a successful full-scale pre-training run that was broadly deployed for a new frontier model since GPT-4o in May 2024, highlighting the significant technical hurdle that Google’s TPU fleet has managed to overcome.

However, pre-training run is the initial, from-scratch training of the base model. You say they only added routing and prompts, but that's not what the original article says. They most likely still have done a lot of fine tuning, RLHF, alignment and tool calling improvements. All that stuff is training too. And it is totally fine, just look at the great results they got with Codex-high.

If you got actually got what you said from a different source, please link it. I would like to read it. If you just messed things up, that's fine too.

[1] https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-s...

Didn't they create Sora and other models and literally burned so much money with their AI video app which they wanted to make a social media but what ended up happening was that they burned billions of dollars.

Why do you think they have not trained a new model since 4o? You think the GPT-5 release is /just/ routing to differently sized 4o models?

> It's also worth noting that OpenAI has not trained a new model since gpt4o (all subsequent models are routing systems and prompt chains built on top of 4), so the idea of OpenAI being stuck in some kind of runaway training expense is not real.

This isn't really accurate.

Firstly, GPT4.5 was a new training run, and it is unclear how many other failed training runs they did.

Secondly "all subsequent models are routing systems and prompt chains built on top of 4" is completely wrong. The models after gpt4o were all post-trained differently using reinforcement learning. That is a substantial expense.

Finally, it seems like GPT5.2 is a new training run - or at least the training cut off date is different. Even if they didn't do a full run it must have been a very large run.

>It's also worth noting that OpenAI has not trained a new model since gpt4o (all subsequent models are routing systems and prompt chains built on top of 4)

At the very least they made GPT 4.5, which was pretty clearly trained from scratch. It was possibly what they wanted GPT-5 to be but they made a wrong scaling prediction, people simply weren't ready to pay that much money.

They have not successfully trained a new model since 4o. That doesn’t mean they haven’t burned a pile of cash trying.

I know sama says they aren’t trying to train new models, but he’s also a known liar and would definitely try to spin systemic failure.

> The fact is nobody has any idea what OpenAI's cash burn is.

Their investors surely do (absent outrageous fraud).

> For all we know, they could be accumulating capital to weather an AI winter.

If they were, their investors would be freaking out (or complicit in the resulting fraud). This seems unlikely. In point of fact it seems like they're playing commodities market-cornering games[1] with their excess cash, which implies strongly that they know how to spend it even if they don't have anything useful to spend it on.

[1] Again c.f. fraud

Well we do know their consumption of energy is not insignificant and comes at great cost.

How are they updating the data then? Wouldn’t the cutoff date be getting further away from today?

they're paying million dollar salaries to engineers and building data centers, it's not a huge mystery where their expenses are

> they could be accumulating capital to weather an AI winter

Doubtful. This would be the very antithesis of the Silicon Valley way.

wasn't 4.5 new

thank you!

It's mathematically impossible what OpenAI is promising. They know it. The goal is to be too big to fail and get bailed out by US taxpayers who have been groomed into viewing AI as a cold war style arms race that America cannot lose.

Apparently we all have enough money to put it into OpenAI.

Some players have to play, like google, some players want to play like USA vs. China.

Besides that, chatting with an LLM is very very convincing. Normal non technical people can see what 'this thing' can already do and as long as the progress is continuing as fast as it currently is, its still a very easy to sell future.

Correction: OpenAI investors do take that risk. Some of the investors (e.g. Microsoft, Nvidia) dampen that risk by making such investment conditioned on boosting the investor's own revenue, a stock buyback of sorts.

And thats brave.

I believe in OpenAI but they were running turn your cat into studio ghibili ads on Reddit up until a week or 2 ago.

They need a better marketing strategy.

  There is a hardware cost bubble though. I'm betting OpenAI will get a lot more bang for its buck in terms of hardware by 2030. It won't be NVidia taking most of that revenue.

With more compute, you can train better models, serve them to more users, serve them faster. The more users, the more compute you can buy. It's a run away cycle. We're seeing only 3 (4 if you count Meta) frontier LLM providers left in the US market.

Nvidia's margins might come down by 2030. It won't stay in the 70s. But the overall market can expand quicker than Nvidia's profits shrink so that they can be more profitable in 2030 despite lower market share.

I’d rather stay far away from this parallel universe.

Why would you want my money to be used to build datacenter that won’t benefit me ? I might use a LLM once a month, many people never use it.

Let the one who use it pay for it.

If that did happen, how would the government then issue those resources?

OpenAI ask for 1m GPUs for a month, Anthropic ask for 2m, the government data center only has 500,000, and a new startup wants 750,000 as well.

Do you hand them out to the most convincing pitch? Hopefully not to the biggest donor to your campaign.

Now the most successful AI lab is the one that's best at pitching the government for additional resources.

UPDATE: See comment below for the answer to this question: https://news.ycombinator.com/item?id=46438390#46439067

In a completely alternate dimension, a quarter of the capital being invested in AI literally just goes towards making sure everyone has quality food and water.

Datacenters are not a natural monopoly, you can always build more. Beyond what the public sector itself might need for its own use, there's not much of a case for governments to invest in them.

That could make sense in some steady state regime where there were stable requirements and mature tech (I wouldn’t vote for it but I can see an argument).

I see no argument why the government would jump into a hype cycle and start building infra that speculative startups are interested in. Why would they take on that risk compared to private investors, and how would they decide to back that over mammoth cloning infra or whatever other startups are doing?

In a better parallel universe, we found a different innovation without using brute-force computation to train systems that unreliably and inefficiently compute things and still leaves us able to understand what we're building.

> governments invest in the compute/datacenters (read: infra), and let model makers compete on the same playing field

Hmm, what about member-owned coöperatives? Like what we have for stock exchanges.

Socialized losses, private profits

why would they do that? not to mention governments are already doing that indirectly by taking equity stakes in some of the companies.

That sounds like a nightmare.

Do you like this idea?

Prediction: on this thread you'll get a lot of talk about how government would slow things down. But when the AI bubble starts to look shaky, see how fast all the tech bros line up for a "public private partnership."

That seems like a terrible idea. Data centers aren’t a natural monopoly. Regulate the externalities and let it flourish.

That's malinvestment. Too much overhead, disconnected from long term demand. The government doesn't have expertise, isn't lean and nimble. What if it all just blows over? (It won't? But who knows?)

Everything is happening exactly as it should. If the "bubble" "pops", that's just the economic laws doing what they naturally do.

The government has better things to do. Geopolitics, trade, transportation, resources, public health, consumer safety, jobs, economy, defense, regulatory activities, etc.

Because that's normal language idioms in financial analysis reporting?

Because typically one expect a return on investment with that level of spending. Not only have they run at a loss for years, their spending is expected to increase, with no path to profitability in sight.

I suspect most of it is going to utilities for power, water and racking.

That being said, if I was Sam Altman I'd also be stocking up on yachts, mansions and gold plated toilets while the books are still private. If there's $10bn a year in outgoings no one's going to notice a million here and there.

Your burn is the money you spend that exceeds the money you earn, see also "burn rate".

“Burn rate” is a standard financial term for how much money a startup is losing. If you have $1 cash on hand and a burn rate of $2 a year, then you have six months before you either need to get profitable, raise more money, or shut down.

> They’re spending it

That's what the words mean in this context.

Only in HN and some reddit subs I even see the name claude. In many countries AI=ChatGPT.

What do you use them for?

GPT5.2 Codex is the best coding model right now in benchmarks. I use it exclusively now.

ChatGPT dominates the consumer market (though Nano Banana is singlehandedly breathing some life into consumer Gemini).

A small anecdote: when ChatGPT went down a few months ago, a lot of young people (especially students) just waited for it to come back up. They didn't even think about using an alternative.

codex cli with gpt-5.2-codex is so reliably good, it earns the default position in my book. I had cancelled my subscription in early 2024 but started back up recently and have been blown away at how terse, smart, and effective it is. Their CLI harness is top-notch and it manages to be extremely efficient with token usage, so the little plan can go for much of the day. I don’t miss Claude’s rambling or Gemini’s random refactorings.

Interestingly Claude is so far down in traffic it's below things like CharacterAI, it's the best model but it's something like 70% ChatGPT, 10% Gemini and Claude is only 1% or so

Even if there is a bailout. Will it happen in time? Once the confidence is lost it is lost and valuations have dropped. Bailout would just mean that who ever gave money would end up as bag holder of something now worth lot less.

Banks needed bailout to keep lending money. Auto industry needed one to keep employing lot of people. AI doesn't employ that many.

I just don't believe bailout can happen before it is too late for it to be effective in saving the market.

In 2008 the US government ended up making more money then they spent though (at least with the tarp), because they invested a ton of money after everything collapsed, and thus was extremely cheap. Once the markets recovered, they made a hefty sum selling all the derivatives they got at the lowest point. Seems like the epitome of buy when low and sell when high tbh.

I'll take that bet.

Banks get bailed out because if confidence in the banking system disappears and everyone tries to withdraw their money at once, the whole economy seizes up. And whoever is Treasury Secretary (usually an ex Wall Street person) is happy to do it.

I don't see OpenAI having the same argument about systemic risk or the same deep ties into government.

Not really. It was not about stocks. It was the collapse of insurance companies at the core of 2008 crisis.

The same can happen now on the side of private credit that gradually offloads its junk to insurance companies (again):

As a result, private credit is on the rise as an investment option to compensate for this slowdown in traditional LBO (Figure 2, panel 2), and PE companies are actively growing the private credit side of their business by influencing the companies they control to help finance these operations. Life insurers are among these companies. For instance, KKR’s acquisition of 60 percent of Global Atlantic (a US life insurer) in 2020 cost KKR approximately $3billion.

https://www.imf.org/en/Publications/global-financial-stabili...

I don't think so.

Elon owns a competitor and bought the White House.

It's easy to get product-market fit when you give away dollars for the price of pennies.

Does that cost to serve multiple stay the same when conventional sites are forced to shovel ai into each request? e.g. the new google search

it's a simple problem really. what is actually scarce?

a spot on the iOS home screen? yes.

infrastructure to serve LLM requests? no.

good LLM answers? no.

the economist can't tell the difference between scarcity and real scarcity.

it is extremely rare to buy a spot on the iOS home screen, and the price for that is only going up - think of the trend of values of tiktok, whatsapp and instagram. that's actually scarce.

that is what openai "owns." you're right, #5 app. you look at someone's home screen, and the things on it are owned by 8 companies, 7 of which are the 7 biggest public companies in the world, and the 8th is openai.

whereas infrastructure does in fact get cheaper. so does energy. they make numerous mistakes - you can't forecast retail prices Azure is "charging" openai for inference. but also, NVIDIA participates in a cartel. GPUs aren't actually scarce, you don't actually need the highest process nodes at TSMC, etc. etc. the law can break up cartels, and people can steal semiconductor process knowledge.

but nobody can just go and "create" more spots on the iOS home screen. do you see?

I don't think that's diagnosis (as a clinical term); closer to defamation.

Is it necessary to a point you want to make?

You can just point to behavior of a given entity, such as to conclude it's untrustworthy, without the problematic area of armchair psychoanalysis.

Ignoring that those numbers aren't directly comparable, it did make me wonder, if I had to give up either "AI" or chocolate tomorrow, which would I pick?

Even as an enormous chocolate lover (in all three senses) who eats chocolate several times a week, I'd probably choose AI instead.

OpenAI has alternatives, but also I do spend more money on OpenAI than I do on chocolate currently.

Wait, aren't you comparing revenue and market cap?

People take old things for granted often. Explains the Coolidge effect, and why plenty of people cheat.

I love AI, and ChatGPT has been transformative for me. But would I give it up for chocolate? I honestly don't think I could.

I spend a lot more time using AI for work than I do eating chocolate

I think it mostly just means a few hundred billion dollars of value wiped from the stock market - all the models that have been trained will still exist, as well as all the datacentres, even if the OpenAI entity itself and some of the other startups shut down and other companies else get their assets for pennies on the dollar.

But it might mean that LLMs don't really improve much from where they are today, since there won't be the billions of dollars to throw at training for small incremental improvements that consumers mostly don't care to pay anything for.

It probably means a price correction on cloud based AI, rather than it disappearing entirely.

It's a good question. Sad that you're downvoted.

Someone posted already the non-paywall version: https://news.ycombinator.com/item?id=46438679

Railroad peaked at 6% of GDP in the US.

AI is at 1% of total US GDP right now.

We have 6x more to go.