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I was browsing through the news today and I found an interesting article about the current state of AI for corporate productivity.
MIT report: 95% of generative AI pilots at companies are failing
There seems to have been a feeling over the last few years that generative AI was going to gut white collar jobs the same way that offshoring gutted blue collar jobs in the 1980s and 90s, and that it was going to happen any day now.
If this study is trustworthy, the promise of AI appears to be less concrete and less imminent than many would hope or fear.
I've been thinking about why that might be, and I've reached three non-exclusive but somewhat unrelated thoughts.
The first is that Gartner hype cycle is real. With almost every new technology, investors tend to think that every sigmoid curve is an exponential curve that will asymptotically approach infinity. Few actually are. Are we reaching the point where the practical gains available in each iteration our current models are beginning to bottom out? I'm not deeply plugged in to the industry, nor the research, nor the subculture, but it seems like the substantive value increase per watt is rapidly diminishing. If that's true, and there aren't any efficiency improvements hiding around the next corner, it seems like we may be entering the through of disillusionment soon.
The other thought that occurs to me is that people seem to be absolutely astounded by the capabilities of LLMs and similar technology.
Caveat: My own experience with LLMs is that it's like talking to a personable schizophrenic from a parallel earth, so take my ramblings with a grain of salt.
It almost seems like LLMs exist in an area similar to very early claims of humanoid automata, like the mechanical Turk. It can do things that seem human, and as a result, we naturally and unconsciously ascribe other human capabilities to them while downplaying their limits. Eventually, the discrepancy grows to great - usually when somebody notices the cost.
On the third hand, maybe it is a good technology and 95% of companies just don't know how to use it?
Does anyone have any evidence that might lend weight to any of these thoughts, or discredit them?
There are two companion articles of late that I'd add to comment on this.
This one is pretty short and to the point. LLMs, without any companion data management component, are prediction machines. They predict the next n-number of tokens based on the preceding (input) tokens. The context window functions like a very rough analog to a "memory" but it's really better to compare it to priors or biases in the bayesian sense. (This is why you can gradually prompt an LLM into and out of rabbit holes). Crucially, LLMs don't have nor hold an idea of state. They don't have a mental model of anything because they don't have a mental anything (re-read that twice, slowly).
In terms of corporate adoption, companies are seeing that once you get into complex, multi-stage tasks, especially those that might involve multiple teams working together, LLMs break down in hilarious ways. Software devs have been seeing this for months (years?). An LLM can make nice little toy python class or method pretty easily, but when you're getting into complex full stack development, all sorts of failure modes pop up (the best is when it nukes its own tests to make everything pass.)
"Complexity is the enemy" may be a cliche but it remains true. For any company above a certain size, any investment has to answer the question "will this reduce or increase complexity?" The answer may not need to be "reduce." There could be a tradeoff there that actually results in more revenue / reduced cost. But still, the question will come up. With LLMs, the answer, right now, is 100% "increase." Again, that's not a show stopper, but it makes the bar for actually going through with the investment higher. And the returns just aren't there at scale. From friends at large corporations in the middle of this, their anec-data is all the same "we realized pretty early that we'd have to build a whole new team of 'LLM watchers' for at least the first version of the rollout. We didn't want to hire and manage all of that."
TLDR for this one: for LLM providers to actually break even, it might cost $2k/month per user.
There's room to disagree with that figure, but even the pro version of the big models that cost $200+ per month are probably being heavily subsidized through burning VC cash. A hackernews comment framed it well - "$24k / yr is 20% of a $120k / yr salary. Do we think that every engineer using LLMs for coding is seeing a 20% overall productivity boost?"
Survey says no (Note: there are more than a few "AI makes devs worse" research papers floating around right now. I haven't fully developed my own evaluation of them - I think a few conflate things - but the early data, such as it is, paints a grim picture)
I'm a believer in LLMs to be a transformational technology, but I think our first attempt with them - as a society - is going to be kind of a wet fart. Neither "spacing faring giga-civilizaiton" nor "paperclips ate my robot girlfriend." Two topical predictions are 1) One of the Big AI companies is going to go to zero. 2) A Fortune 100 company is going to go nearly bankrupt because of negligent use of AI, but not in a spectacular "it sent all of our money to china" way ... it'll be about 1 - 2 years slow creep of fucked up internal reporting and management before, all of a sudden, "we've entered a death spiral of declining revenue and rising costs."
I'm using it for full stack development on a $20 plan and it works. I guess it depends on what you mean by complex full stack development, how complex is complex? I wouldn't try to make an MMO or code global air traffic controls with AI but it can definitely handle frontend (if supervised by a human with eyes), backend, database, API calls, logging, cybersecurity...
And sure it does fail sometimes with complex requests, once you go above 10K lines in one context window the quality lowers. But you can use it to fix errors it makes and iterate, have it help with troubleshooting, refactor, focus the context length on what's critical... Seems like there are many programmers who expect it to one-shot everything and if it doesn't one-shot a task they just give up on it entirely.
The metr paper is somewhat specialized. It tests only experienced devs working on repositories they're already familiar with as they mention within, the most favourable conditions for human workers over AI: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
Secondly, Claude 3.7 is now obsolete. I recall someone on twitter saying they were one of the devs in that study. He said that modern reasoning models are much more helpful than what they had then + people are getting better at using them.
Given that the general trend in AI is that inference costs are declining while capability increases, since the production frontier is moving outwards, then investment will probably pay off. Usage of Openrouter in terms of tokens has increased 30x within a year. The top 3 users of tokens there are coding tools. People clearly want AI and they're prepared to pay for it, I see no reason why their revealed preference should be disbelieved.
https://openrouter.ai/rankings
Two small notes. First, you are almost certainly being heavily subsidized on that $20 plan. All the evidence points in that direction. You may be paying 1-2 orders of magnitude under cost. Second, the most interesting part of the METR paper was that the devs thought they were being sped up, but the opposite was true. Provably so. Intuitions on AI efficacy cannot be trusted prima facia. Many people find them enjoyable and interesting to use, which of course is their right, but we should not trust their estimates on the actual utility of the tool. Both of these facts seriously undermine the boosters’ case.
Does anyone seriously think that these tech companies are selling $200+ worth of compute for $20? The natural assumption should be that they're making good margins on inference and all the losses are due to research/training, fixed costs, wages, capital investment. Why would a venture capitalist, who's whole livelihood and fortune depends on prudent investment, hand money to Anthropic or OpenAI so they can just hand that money to NVIDIA and me, the customer?
Anthropic is providing its services for free to the US govt but that's a special case to buy influence/cultivate dependence. If you, a normal person, mega minmax the subscription you might use more than you pay for but not by that much and the average subscriber will use less. Plus you might praise it online and encourage other people to use the product so it's a good investment.
What evidence points in this direction of ultra-benign, pro-consumer capitalism with 10x subsidies? It seems like a pure myth to me. Extraordinary claims require extraordinary evidence.
Take OpenAI. Sam Altman said he was losing money on the $200 subscription. But Sam Altman says a lot of things and he didn't say 'losing 10x more than we gain'.
If the company is losing 150% of revenue (and Anthropic is similar), not 1000% or higher, then clearly it's what I'm saying, not what you're saying. Inference/API is profitable. User subscriptions are profitable. Investment is not profitable in the short term, that's why it's called investment. And they have their fixed costs... That's why AI companies are losing money, they're investing heavily and competing for users.
Furthermore, one study of a selected group of coders doing a subset of software tasks with old models does not disprove the general utility of AI generally, it's not a major, significant fact. I could find studies that show that AI produces productivity gains quite easily. That wouldn't mean that it produces productivity gains in all settings, for all people.
Here's one such study for instance, it finds what you'd expect. Juniors gain more than seniors.
https://mitsloan.mit.edu/ideas-made-to-matter/how-generative-ai-affects-highly-skilled-workers
Or here he lists some more and finds productivity gains with some downsides: https://addyo.substack.com/p/the-reality-of-ai-assisted-software
The metr paper just tells (some) people what they want to hear, it is not conclusive any more than the other papers are conclusive. And a lot of people don't read the metr paper closely. For instance:
A couple things;
This is a fun way to say "If you don't count up all my costs, my company is totally making money." Secondarily, I don't know why you would call this a "natural" assumption. Why would I naturally assume that they are making money on inference? More to the point, however, it's not that they need a decent or even good margin on inference, it's that they need wildly good margins on inference if they believe they'll never be able to cut the other fixed and variable costs. You say "they aren't selling $200 worth of inference for $20" I say "Are they selling $2 of inference for $20"?
Because this is literally post 2000s venture capital strategy. You find product-market fit, and then rush to semi-monopolize (totally legal, of course) a nice market using VC dollars to speed that growth. Not only do VCs not care if you burn cash, they want you to because it means there's still more market out there. This only stops once you hit real scale and the market is more or less saturated. Then, real unit economics and things like total customer value and cost of acquisition come into play. This is often when the MBAs come in and you start to see cost reductions - no more team happy hours at that trendy rooftop bar.
This dynamic has been dialed up to 1,000 in the AI wars; everyone thinks this could be a winner-take-all game or, at the very least, a power low distribution. If the forecast total market is well over $1 trillion, then VCs who give you literally 10s of billions of dollars are still making a positive EV bet. This is how these people think. Burning money in the present is, again, not only okay - but the preferred strategy.
No, they are not. They are getting paid to do it because it is illegal to provide professional services to the government without compensation. Their federal margins are probably worse than commercial - this is always the case because of federal procurement law - but their costs are also almost certainly being fully covered. Look into "cost plus" contracting for more insight.
See my second point above. This is the VC playbook. Uber didn't turn a profit for ever. Amazon's retail business didn't for over 20 years and now still operates with thin margins.
I don't fully buy into the "VCs are lizard people who eat babies" reddit style rhetoric. Mostly, I think they're essentially trust fund kinds who like to gamble but want to dress it up as "inNovATIon!" But one thing is for sure - VCs aren't interested in building long term sustainable businesses. It's a game of passing the bag and praying for exits (that's literally the handle of a twitter parody account). Your goal is to make sure the startup you invested in has a higher valuation in the next round. If that happens, you can mark your book up. The actual returns come when they get acquired, you sell secondaries, or they go public ... but it all follows the train of "price go up" from funding round to funding round.
What makes a price? A buyer. That's it. All you need is for another investment firm (really, a group of them) to buy into a story that your Uber For Cats play is actually worth more now then when you invested. You don't care beyond that. Margins fucked? Whatever. Even if you literally invested in a cult, or turned your blind eye to a magic box fake product, as long as there is a buyer, it's all fine.
Why don't we try and look into this? People have tried to estimate OpenAI margins on inference and they come away with strong margins of 30, 55, 75%. We don't live in a total vacuum of information. When trying to work out their margins on inference, I base my opinion on the general established consensus of their margins.
The demand for inference is rising, Openrouter records that demand for tokens rose about 30x in the last year as AI improves. Grow big enough and the margin on inference will outweigh the costs.
It's effectively free, they're 'selling' it for $1 per agency for a whole year. OpenAI is doing the same thing. Why are you trying to correct me on something you won't even check?
There is a significant difference between making a loss as you expand your business rapidly and try to secure a strong position in an emerging market and 'subsidized by 1-2 orders of magnitude'. No evidence has been supplied for the latter case and it's unbelievable.
Amazon wasn't making a profit because they were continuously expanding and investing in their retail business, not because the actual business was unprofitable. Investors were happy to tolerate them not making profits because they were growing. Uber wasn't making a profit but there were no 10x subsidies. We can see this immediately in how taxis weren't costing $20 while Uber was costing $2 for the same trip.
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