There is a complete lack of courage in the leadership of tech companies today, and top-down AI mandates are just another manifestation.

True visionaries think outside the box, but most tech executives are forcing their employees into black boxes, out of fear of not doing exactly what their competitors are doing.

We have lemmings for leaders, and that means that—much like the LLMs that are being shoehorned into everything—there isn’t room for original thinking. Everyone’s strategy looks exactly the same.

You're now in the last frame of the comic, getting thrown out the window.

If there are any tech CEOs out there reading, I can offer my services. I will pointlessly burn unfathomable amounts of tokens, in parallel, 24 hours a day, 7 days a week, all for you. Think big big big numbers of tokens, you know whats cooler than a trillion tokens, a quadrillion tokens.

Lets talk my bonus, I will open the bidding at $1 per token.

> and stop using token usage as a metric of productivity

I participate in some management-focused online communities. It’s crazy how many threads there are from frustrated managers trying to get their teams to stop thinking that their token use will be used as a proxy for their performance.

I think a few dumb companies did this and then it spread across social media, triggering a mass panic from engineers afraid their companies will be doing the same thing.

It’s getting so bad that the conversation is shifting to how to identify and coach the token-maxxers to stop wasting the team’s budget every week.

Not very Billion Dollar Valuation of you.

> ... stop using token usage as a metric of productivity?

and tokenmaxxing is even worse due to https://en.wikipedia.org/wiki/Goodhart%27s_law because whatever you measure with tokens, once you start "tokenmaxxing" you have no measure to look at

I feel like individually, if you sat down with literally any reasonable person on the planet they would arrive at and/or agree with the tenor here.

I'd be curious to hear from people well versed in group psychology/dynamics and/or just a lot of leadership/people experience: what leads people to this type of thinking once they get in a group setting? It just... seems endemic at this point.

Obviously nobody here is going to know what I do or don't know, but I'm just increasingly curious what I am not understanding about this type of thing. It seems so obvious, yet that makes me ever more suspect that I'm oversimplifying it, or just totally ignorant about the problem in general.

You mean… increasing AI budget has no direct relationship with productivity and therefore revenue? It’s not that simple? But… my TEDx talk and LinkedIn ramblings…

Sounds to me like you are advocating the decimation of the technology sector and a global recession that could last the better part of a decade, buddy!

That was a fun thought experiment while I waited for my ralph wiggum to finish running. Now thinking is over and back to the vibe

The people who have ascended to leadership positions are deeply divorced from reality.

"It is difficult to get a man to understand something, when his salary depends on his not understanding it." -Upton Sinclair

Come on, don’t be crazy

That is interesting but tokenmaxxing is not maximizing token usage _efficiency_. It is maximizing its usage.

Tokens are the new "lines of code per engineer". Easy to graph, easy to "manage".

You'd be surprised, I know a few devs in very big tech companies, not faang but you definitely know them, and they all have some kind of token leaderboards, a few told their dev "we don't want you to write a single line of code manually anymore", etc.

I assume the execs perspective is something like: if the top 20% of worker produce 80% of the code with LLMs and the company still works then we can get rid of the bottom 80% of devs and save money

I worked at Uber from 2022-2025. The engineering culture was pretty abysmal, so it checks out.

Meta does this. Guess what one of the criteria for their recent layoffs was.

I worked at a YC company that was doing this and left last month. I wonder where this all started from, VCs and tech execs are such a monoculture

"Using a lot of tokens to debug an easy frontend bug that could have been fixed by hand, and still took you 4 hours to complete? Waste of tokens, should be discouraged."

Hahahah good luck with that!

For many of us, what is happening now was super obvious. Telling a new formed crack addict (who you wanted to become addicted) to be more thoughtful about their consumption of crack... yeah not gonna work is it.

Most of the codebase is custom integrations for local markets. You can systematize some of it but most of the complexity comes from there.

> When will Uber (or your favourite company) be 'done'? They've been writing software for 16 years

I suppose it becomes easier once the browsers, Android and iOS have been frozen for a little longer than 16 years. Nevermind the changing regulatory field and new products (when was Uber Eats launched?).

In that 16-year period, Covid-19 emerged, as did viable self-driving and partnership with Waymo. A networked, people-facing app can't ever be "done", unless you have perfect prescience. Internal tech-stacks are a living thing: keeping a service that on the outside appears to be unchanging is a lot of work! Scaling is a lot of work! Scaling services and maintenance feed off each other.

I think you’re missing how complex international operations and optimization are.

Each country has their own laws around what uber is and isn’t allowed to do. This needs to be formalized in code. For example you actually call a taxi, though the uber app, and the amount you pay is per mile, not a fixed fare decided ahead of time. To add to this complexity, some cities will have their own laws. What happens if you take an uber from town a to b, where each one has different laws ? A lawyer probably has an answer but the app needs to adhere to that. On top of that laws change all the time.

Optimization, well you can always optimize something. speed, costs, paths etc. In a way this never ends.

I think the part we interact with as consumers is a tiny sliver of the complexity those services have to build and operate.

Weren’t they trying to do their own self-driving thing?

I think this is partly a problem with companies that have had heavy investment. Uber’s value isn’t based on what they are doing, it is based on the idea that they are going to render ideas like owning your own car or taking public transit obsolete (I mean that’s an exaggeration but less of one than it ought to be).

There are always newer technologies and techniques to be implemented. Better algorithms. Larger deployments. Better reliability. There are also almost always bugs to fix. So, so many bugs.

Well there is a lot of ongoing maintenance cost. There is probably still some marginal gains possible on the matching side. There are new products to launch. So while one specific software can mostly be finished, the total software of a company is always changing.

shiny new tools but people only want to use them on the same old problems. how can we innovate the development of crud apps even more?! that was what plagued the web dev landscape for some time. Constantly seeking newer lazier means of producing the same old product. I admit it has an allure but if companies are no longer constrained by dev effort / labour then they can only ponder their own reflection as the source of their failures.

Uber is at a large enough scale that this analysis doesn't work. You and I do not care even a tiny bit about "Eats for the Way", one of their planned features this year (https://www.uber.com/us/en/newsroom/go-get-2026/) that lets Uber Black passengers specify that their car should arrive with their Starbucks coffee order. But if 0.01% of users order 1 additional ride a month because of this, that's about 200k rides a year, which may well be sufficient to justify the development costs.

It’s death for a tech company to be “done,” since that means no more growth. So they will all bloat indefinitely until they implode or get absorbed. It’s simply the fate of all VC-fueled startups.

There is always a rewriting around the corner

It's toddler-level logic. "You can achieve positive outcomes by using X. Therefore, we need to use as much X as possible to maximize positive outcomes."

It's like trying to win a race by setting a gas station on fire.

Tokenmaxxing exists because executives think employees are resistant to change. Thats it, a way to incentivize/force every employee to experiment with a new technology. Obviously once they think everyone is utilizing AI the tokkenmaxxing stuff will end.

The argument in favor of "tokenmaxxing" has always been that it's creating space for employees to freely explore the broad and novel space of AI-enabled workflows. I've seen a number of use cases where I'm skeptical any value is being produced, but a number of others where some team or another has finally solved a long-standing problem of theirs with an agentic workflow that would have been hard to justify to a cost review committee.

> They should also promote projects that aim at saving tokens, increasing cache hits, codifying the information in ways such they use as less context as possible (graphs of knowledge are pretty good for this!)

My understanding is that most big "tokenmaxxing" companies do have teams who are working on this in the background.

I think companies are reluctantly realizing that AI is not a magic genie in a bottle, and is instead a tool.

Still very valuable. They just need to have strategies that match what the tools are capable of - not strategies that involve "rub the magic lamp and increase profits 80%".

If the market is rewarding companies going after the "rub the lamp" strategy, they're going to say they're doing that to juice stock prices.

Maybe the market is finally realizing blindly spending billions on LLMs with almost no strategy is not a good strategy.

Who knows.

But the executives need the fanciest models to evaluate how well they can replace the expensive labor costs.

Sounds like you want to be in the next round of layoffs?

The point of this was always to explore what is possible with AI as quickly as possible. Obviously, there is going to be a lot of waste, but the 5-10% of employees who are truly thinking about it and discovering novel applications are what you are truly after. Because right now, you effectively have a giant, as of yet poorly explored space of potential uses.

Anyone who can find the actually valuable portions of the space early has a potentially huge competitive advantage. Even if the result of the experiment is the negative that AI is actually mostly not that useful, that is still extremely useful information in a time of great uncertainty regarding outcomes.

The bottom line is that this approach may be expensive, but if you have the money to burn, it's far from the worst strategy if you are trying to position yourself correctly for the future.

I think unfortunately it's not about what seems obvious, or even what seems more likely, but about what seems retrospectively justifiable regardless of outcome.

The incentive structure of this type of decision is 'absolutely under no circumstances existentially mess up'. Ostensibly with respect to the organisation, but in actual reality much more so with respect to the individual(s) involved in the decision.

If everyone else is doing something that kind of obviously makes no sense, and you decide to break from the crowd by instead doing what does make sense, then there's a pretty solid chance of gaining a temporary edge while reality resolves the truth. But those gains probably won't matter all that much for the organisation, or indeed your position within it. It's a solid chance of an unimportant gain.

However on the other hand, there's a tail risk that something very unexpected happens and the thing everyone's doing that makes no sense actually turns out to make sense - sometimes even for entirely unpredictable incidental reasons - and then, well, you're in trouble. Not necessarily 'you' the organisation.. they'll likely be able to catch up and it won't matter that much. But for 'you' personally, the decision maker, it's very much not good.

As a bonus, in the much more likely scenario that the thing that makes no sense turns out to indeed make no sense, you're in the same boat as everyone else, there's no relative loss, and most importantly you don't stick out as someone who did something as risky as to go against the prevailing, albeit pretty clearly nonsensical, sentiment.

So basically, game theory tells you pretty quickly to just go with the thing that makes no sense if you're optimising for some (weighted) cross of what's best for the organisation and yourself as the decision maker.

Someday maybe Goodhart's Law will be intuitive to people making decisions like this, but not any time soon I guess

> It's amazing that it took months to figure this out

We aren’t there yet, so far it is just a COO questioning the investment

The inability of leaders to understand Goodhart’s Law is always a sight to behold. They see a number go up and pat themselves on the back for how well their employees are making it go up without ever wondering if the thing they care about is happening.

You say "amazing that it took months to figure this out" as if the answer to the question is obvious.

But it's not. Some FAANGs are doing amazing things with unlimited tokens. Other companies have no clue what to do with tokens, they've just told their engineers to max them.

It really depends on how you're using the tokens. If you're just using them for Codex and Claude Code - yeah, tokenmaxxing is incredibly dumb.

This is also Uber we’re talking about. The company that famously developed a massively engineered ledger to track every event across the entire company, globally consistently, forever, in a single database. This definitely adds enormous value to the bottom line!

>obvious thing I've heard this month

not only this month, but it is the basic statement of the single most well known 50 year old book in software project management lol. At this point we need to wipe the slate clean and start over, the industry is run by illiterates.

> Burns through tokens like water.

Pretty sure I know what you're saying, but the visual on this one doesn't match the point you're making.

2^30 tokens costs something like 2^10 dollars, order of magnitude, if that helps ballpark.

IMO, it's undoubtedly both.

The number of product teams who have shipped expensive-to-operate AI features is wayyyy up there, and for many of the scenarios I've seen, customers simply don't care or are unwilling to pay significant premium for access to it.

At the same time I'm starting to see some direction from people in leadership that I should "use the right model for the job" and things along those lines, which is a very, very different line from what I was hearing 12 months ago.

My continued prediction is that we are going to see a tweak on the SaaS model where the sweet spot moves to metered usage pricing of really fine-grained API-based access for apps which traditionally have been operated solely via the UI. Long term the trend is going to be "we'll house the data, enrich it, maintain it, provide fine-grained API access over it tailored to model usage, and you bring the model" with some services opting to give you the model interaction layer/harness. IOW I don't think SaaS is dead. Far from it. However, I do think that a lot of people are going to be looking to interact with SaaS apps via their own models with APIs that support those use cases better than a lot of those APIs do today.

^ Philip K. Dick's unreleased book title

Is this when you type the prompt into the text window, but don't hit enter? Make the GPU see the message "x is typing"? Lol.

Lol, no, no one’s becoming a 1000x engineer.

Surprisingly, Uber hasn’t had a mass layoff since 2020. The company currently has ~34,000 FTEs, which I personally think is insanely bloated for what amounts to a taxi + food delivery app.

By 2025 we will have AGI and software developers don't be necessary. Also next year we will have self driving.

Man, it sure isn’t hard for me to max it out.

Who’s it important for?

hah came here to say exactly this

You can measure productivity by hours spent at a desk?

I hereby suggest you take the fragmentary excerpts of the infamous erotic stage play The Lusty Argonian Maid shown in The Elder Scrolls series of games and extrapolate them to 100,000 additional full-length acts.

DeepSeek is an open weights model. It's possible the hosted versions are subsidized, but we know what it costs to run locally. And it's expensive, but it's also pretty clearly cheaper than an employee.

Of course, the latest DeepSeek models are not as good as Claude, but they're not super far off either.

You're assuming the price won't come down as the tech matures. That seems like a big assumption, considering how quickly open weights models are catching up to frontier models, and how little effort has been invested so far in optimizing inference costs.

It's especially a crazy assumption to make relative to the costs of employing a human. The costs of paying an entry level employee are unlikely to go down at all, and even if those costs do decline, there's a floor they can't drop below (minimum wage at the extreme end), whereas companies are free to optimize agentic costs as close to zero as possible.

So you are assuming that a cost which is extremely susceptible to optimization but which no one has yet seriously attempted to minimize will remain perpetually above a cost which is much less susceptible to optimization, is already subject to enormous efforts to minimize, and has a legally mandated floor. That seems like a bad bet.

Maybe this just counts as “light use” since I’m a hobbyist programmer and I only run one coding agent session at a time, but I get about as much done as I did back when I was working while spending a lot of time browsing the Internet, etc.

I’ve spent $10-$20 a day using Claude to write code and closer to $5 a day now that I mostly use Deepseek and GLM, using API pricing (no subscriptions) since I don’t use Claude Code.

This is a rounding error for a company. So I think there’s plenty of room to use AI extensively while being more cost-conscious.

A significant caveat is that there is a pricing mismatch that makes it so first party's can subsidize quite heavily.

Agents are expensive in large part because tool calls require round trips. It's because these APIs are stateless and not streaming so you have to resend the whole context each time. This means you have roughly #tool calls x 1/2 context size cached input tokens over any given session. Most API providers overcharge you by a huge amount for cached tokens. A exception being Deepseek. Paying OpenAI $0.05 for 100k cached GPT5.5 tokens during a possibly 2 second round trip agent tool call is like paying $100/hr for what is likely to be ~10 to 20 GB of VRAM residence (holding the KV cache).

Or it got offloaded to NVME and you are paying $0.05 for that much PCIe bandwidth.

More straightforward to talk about the hardware directly. Full Kimi K2.6 needs an 8x H200 node to run and serve around 20 heavy users. You can rent an 8x H200 node for around $30/hr.

I'd imagine GPT-5.5 and Claude Opus 4.7 could run just fine on a 16x H200 node and serve at least 10 heavy users without the token output getting choppy.

This is what I’m betting on.

The financials don’t make sense now. Based on the expenditure the finances won’t ever make sense.

What's funny is that this apparently wasn't something that the Uber COO seemed to think about when their company is arguably one of the most successful ever at the "subsidize to drive down costs until you capture nearly the entire market" strategy.

I think if local models catch up with current SOTA then that might not happen. Either way, I'm don't think the long-term for OAI, Anthropic etc. really holds up.

I have been saying the same for while. Someone always says "but Anthropic is making money on their API" or "But it's inference will get cheaper". But I don't believe it. first all the investments have to payed off at some point and second of all there are other things that cost money. I don't believe that any of them have a positive balance sheet.

I also don't think that blitz scaling will work like with Uber. The engineers are still there. We can work without the LLM tools.

Well there are already offerings like githits (https://news.ycombinator.com/item?id=46105112) that sort of promise optimize bang-per-buck of inference

If you're not tokenmaxxing, you're getting tokenmogged on the AI leaderboard, and your next review ain't gonna be pretty.

A good 80% by volume of the modern vernacular is 4chan language that got sanded down.

I like this too. I have been intentionally -maxxingmaxxing to get the meme out there. It's a good canary to sort out who gets the spicy takes from the pedestrians who probably still copy-paste into the ChatGPT web app like a psychopath.

Would you decide its usefulness based on how high the bill is, or how many things you get done while using it?

The former is the issue, and how many companies have been operating. It's like a trucking company ranking driver effectiveness by fuel used instead of by cargo moved.

Maybe that's the plan :)

But on a more serious note, do we know how much Uber spent per technical employee/month? I assume it is far more than even any of those $200 "max ai" plans.

And the other question is how much the public would be willing to spend, in my estimation this is as "cheap" as it will ever get (main-stream at least).

Probably long term each dev gets their own GPU and runs a model locally I expect. Seems like a more sustainable approach, even if a local model is not absolute SOTA.

Except you won’t because they will threaten to fire you and force you to route all of your AI through data protection proxy to stop exfiltration by filtering and tracking prompts/response tokens.

Slightly ot, but I really dislike this reddit WSBization of HN.

Adds nothing insightful to these discussions.