It's also funny because a bust require a 2 quarters time. AI cycle can be faster than that? My workflow is certainly not recognizable compared to past for every 6 months for the past 24 months.

Maybe we are closed to singularity, or maybe we'll just plateau somehow. But either case there are so much work to support the breakneck change that are not done because the change take priority every single time, there should be a lot of things to work on.

cars? airplanes?

fails are beneficial to an economy. If there are no fails, you end up with Soviet Union.

As long as there are more amount of success, then it should be net positive.

It's ridiculous to call this tulips, in the sense of a speculative asset whose price depends on resale. A more similar recent example is the dotcom boom and bust based on building internet infrastructure, or the 2008 crash which was based on cyclical infrastructure overinvestment. These crashes were characterized by demand growth not keeping up with investment because the target markets were tapped out. Not clear when we'll get there with AI. The consumer market seems saturated on chatbots but we're not even close to saturated for b2b or self driving for example. And this discounts other new technological offerings which may unlock larger consumer markets (products where people are willing to pay $100 a month instead of 10 or 20)

All that said the dotcom boom is extremely analogous and that crash was quite bad.

> Seriously, what value are tokens providing other than justifying layoffs. Concretely. Today.

Claude helped me implement a ridiculous amount of features in my programming language. It's helped me migrate the heap to an easily moveable index-based object space. It's helped me implement generators. It's helped me implement a new memory allocator. It's helped me fix a ridiculous amounts of bugs and make a huge number of small improvements everywhere. Its ability to provide me repository wide code review was a game changer for a solo developer like me. And it's doing so much more than that. I got more things done in the past few weeks than previous months even though I'm evaluating, learning, understanding and rewriting the AI output.

It's actually addictive to build things with Claude. The usage limits are starting to make me anxious, just like withdrawal syndrome. I applied for their open source max subscription program even though I'm too small for it because who knows, I might get in anyway and it costs nothing.

AI is quite literally a world changing technology. I hope the open models keep steadily progressing and that hardware remains available to all so we can run our own models on our own computers one day.

Tulip futures skyrocketed, it was economic speculation on a useless asset, not supply and demand. Crypto is the analogy, not AI. Given that the major AI labs other than GDM are private, this is even more true.

Agentic coding absolutely blew up from demand, users are not being tricked into paying $200 a month, and they’re not complaining about hitting rate limits because it’s useless.

>Seriously, what value are tokens providing other than justifying layoffs. Concretely. Today.

It's adding tests for me and doing medium complexity refactors that I'd otherwise have to spend hours on

I know there is a large force on HN that want to deny the value of tokens and I know it’s anecdotal but the writing is on the wall. If it’s not valuable to your workflow today it will be soon. I already have tests being written, automated hooks into bugs where an initial PR gets generated with a potential fix. It’s far from perfect but junior engineers are far less productive.

are you seriously comparing AI to tulips? I don't even know what to say. Even if you are very bearish about the technology certainly you can't be this detached from base reality. Yet here we are

> Seriously, what value are tokens providing other than justifying layoffs.

Coding, writing, summarizing, translating, data analysis, customer support, test generation.

In the last eight months, my solo SaaS has gone from $0 to $325k ARR, and growth is accelerating. I run tens of billions of tokens per month through automated pipelines for the product itself (which replaces an ultra niche human-driven process in a very non-technical industry), plus probably low billions more per month for coding, systems operation and management, data analysis, etc. And I feel like I'm just barely scratching the surface of what today's models are capable of.

> Seriously, what value are tokens providing other than justifying layoffs

Like the OP said, it's incredible how polarizing this debate is. When I read comments like yours, I feel like a significant part of the global workforce in IT must be living on another planet? Or they never really used Claude Code, Codex, OpenCode, ... intensively before because of company policies?

I legitimately am at least 10x more productive than a year ago, and I can prove it in number of commits and finished monetizable features developed per day. Obviously my workflows still very much require an active, constantly context-switching human-in-the-loop, but to me there's absolutely no question both output volume & quality have skyrocketed.

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Tulips had literally no economic value. LLM's do.

> We can assume they are doing so at a profit

This is false. We may assume it's the most efficient way of generating revenue given their GPUs, but their overall profitability will just be a guess. They would still have incentives to run hardware at maximum, even when it's uncertain to eventually recoup costs.

> a world where those API prices aren't profitable

A lab with employees and models in training has other costs than the operating expenses of a GPU farm.

This is like saying that innovative medical drugs could be sold at a profit if only there was no patent protection and the innovative companies would still invest in R&D. Yes, on a token level pure inference costs might be profitable, but the frontier Ai labs will surely have to recoup their R&D investments at some point.

Companies doing foundational models need to cover the cost of training which is much more expensive than training something like kimi.

> they have no incentive to do this at a loss

Are you sure? Surely there is a lot of interesting data in those LLM interactions.

The problem I have with this analysis is it's missing the multi-dimensional aspect of "is this profitable".

It's fair to say that if all these operators are competing for tokens, that the OpenRouter token operator (not sure the exact phrase but the people running the models) are accounting for some level of margin.

However, how many of these are running their own data centers and GPUs?

If they are running their own infrastructure, then it's not a simple equation of if each specific token set is profitable, since it needs to account for the cost of running the data center. It could be that they believe that it is profitable in the long term by utilizing the long tail of asset depreciation, but that isn't guaranteed.

IF they aren't running their own infrastructure, then it's much easier to claim that it's profitable and has a margin (outside of running their servers to manage the rented infrastructure).

HOWEVER, a lot of data centers have some pretty crazy low prices for GPUs that may be vying for user base and revenue over profitability. In these cases, if data center growth starts slowing due to slower buildout then it's very likely GPU prices go up and inference stops becoming profitable for the open router owners.

So long term it's not clear how profitable even these open models are.

OpenAI and Anthropic definitely fall into the latter category too. Their infrastructure requirements are much higher than the open models, and they are being given huge discounts so Microsoft/Amazon/Google can all claim revenue (since they have profitability coming from other parts). It's not clear if OpenAI and Anthropic models would be profitable at inference if they were paying rates that cloud hosts would make a profit from.

There's just way too many dimensions to this scenario to flat out state that open router proves inference is profitable at scale.

I don't think it's as easy as looking at open weight API prices. We don't know whether the operators are making a profit on all the hardware they bought. Maybe the prices we pay just cover electricity. And it's not even certain that running costs are covered by API prices: The operators may be siphoning content and subsidize from selling that.

In the current volatile environment, the API prices are more of a baseline where we can assume it can't be much cheaper to operate these models.

Anthropic also recently tweaked their usage limits to discourage use during peak hours. Why would they do that if inference was profitable?

Can you give a few penciled numbers?

I do wonder how much of the apparent demand is driven by companies automatically running these things when users didn't actually ask for it. For example every web search I make now has an AI response that I scroll right past. I'm sure that counts for someone's token usage data, but I got zero value from it. This is happening in almost every software product now.

It's not exactly stuff of "wet dreams of the ownership class" to say that of the possible white collar careers, software engineering is pretty hard to beat in terms of salary vs work you need to put in.

Isn't salary a proxy of how hard to replace one person or a group of persons is or how valuable they are?

There was a surge in demand for SWEs and scarcity brought salaries up. Are them too high? Hell no. On average, my colleagues and me generated ~2M$ each in 2025 for our company, while we get payed a fraction of that (grants and bonuses included). If you look at net income per employee we are at around 700k each in 2025.

Additionally, employers try their hardest to drive costs down (eg. offshoring as much as possible, everyone doing layoffs at the same time, ...) and average/median salaries remained high. If the salaries were overinflated those numbers should have came down I believe. The fact that they didn't makes me think that it still is a scarcity problem not an overinflation one.

> Engineering salaries are significantly higher than nearly every other industry on average and on median

now compare the profit per employee at tech (software engineering) companies and those industries..

Thank you for saying it better than I could have. It’s probably an unnecessary jab but I know how well I benefited financially in an industry where not much was expected in terms of output, lavish perks and huge base salary and stock compensation. Absolutely some companies are extremely profitable per headcount but I look at the sea of failures and how well engineers have generally done. It sets the tone for this massive negativity I see around AI when so many of us have benefited from VC money that failed.

This is a claim in the wind until you provide evidence.

Don’t confuse plan changes with profitability of inference. When people talk about the cost to serve a token and it being profitable we are referring to API cost not the plans which absolutely subsidize some level of use. Hard to know what is breakeven on plan math.

That's not a necessarily profitability thing as much as a demand thing. The only way to improve the supply for those willing to pay more is to take it away from those paying less. Once supply catches up to demand things will change

Thats like saying a restaurant is profitable because they're making money selling meals if you ignore the costs of ingredients.

Of course they are profitable if you ignore their cost to bring a product to market.

My friend has been looking for a job for the past few months and the other day he was given an HR LLM Agent to talk to.

He contacted the company saying he's not going to do that, to which they replied something among the lines of "sorry, that's our process".

This is the kind of bullshit I'm worried about the most.

Nvidia has a de facto monopoly in the datacenter-tier GPU market. He says this sort of stuff because he knows he can keep jacking up prices, because the cost will be transferred onto consumers - mainly software engineers.

Complete nonsense.

In that world there’s no reason for a business enterprise to exist.

I don't think there are enough well-paid tech workers to affect things like the (national) market for food. Local rent markets are at least partly explained by this; I agree that the $3M houses near Palo Alto, CA are because of Big Tech salaries, but not the price of ground beef.

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It was kind of a flash in the pan moment where you could leave your retail floor manager job, crash course this thing called "javascript" in a 3 month class, and then get hired for a six figure remote job if you could choke out a mildly competent github repo.

> I suspect many will find themselves locked out the middle class for generations.

On the other hand, once software as a high paying career dies there will be nothing to prop up the status quo (high cost of housing, for example) so the middle class will return to being much more accessible to modestly paid jobs.

> As long as they are paid well compared to other workers, it’s fine.

Well I’m sure they’ll be thrilled to know they can collect $100 a week more in unemployment benefits than their neighbor.

That prices change from one point in time to another is a trivial fact.

“Inflated due to a speculative environment” is not an accurate way to frame labor prices that held for many years. At that point, the prices were simply high due to high demand relative to supply (compared to other types of labor).

I think calling it inflated is to play to a narrative that labor was overvalued broadly in tech.

Salaries across industries in the US have remained flat since the 1970s. Calling the one sector that can provide access a middle class lifestyle inflated s to play into a narrative capital is eager to tell, even if OP didn't intend that.

Openrouter is a startup, what's the indication it serves token at a profit? It could be serving them at a loss to show growth.

Ty for sharing and agree. I think there is confusion with some folks in the comments for this post confusing inference profitability and plan profitability. Most plans as we can tell are probably teetering the line of profitability and that’s why we have seen some like Cursor really tighten how many tokens you get.