I think you could say the same thing of tech and the economy prior to AI.
No Signs of Tech Payoff Outside the Tech Sector. Manufacturing, basic science, education, etc languished while Big Tech reaped record profits and achieved stock heights for the last 15 years.
Hell, on top of that (i.e. the confounders you mention are very very apparent everywhere,) the challenge of folks understanding how to use AI to be more productive.
Even last week I had a case where, I was reviewing code that I know the dev was vibe coding, and honestly was good up to that review.
Buuuuuut, for the stuff I reviewed that day, there was some base-class inheritance use that was so ugly that my initial teams message to the dev was along the lines of 'What crack was Copilot smoking on that one'.
Turns out, they had asked some other random-ish questions in the chat they were doing their actual work in, and those questions more or less polluted the context.
Sure, I was able to guide them for how to not do that in future (i.e. keep unrelated questions around the work in question to a separate chat, etc.) but this was also a dev that is normally both very good, but also overworked semi-related to the general 'AI will save us on dev costs thing'.
I can't help but ask whether, for the even less-technical people that are told 'yeah you lost two colleagues but here is Copilot/Claude/etc' and they don't even get enough advice to have it actually increase productivity versus chasing down gremlins in the actual work.
Just talking about salaries, revenue, and stock IN AMERICA.
Doubtlessly Chinese manufacturing benefited tremendously from those advancements. But my point is the headline whinging about AI in America would also apply to Tech in America.
I know of a brake parts factory outside dayton ohio who had the Microsoft folks come through with promises well over a year ago now. They've already overspent and had to pull back their AI budgets when it made more work than it solved.
> before most companies even realize it's an option
Couldn't be further from the truth
Have they? AI agents only really got usable around last fall. LLMs were pretty useful starting basically at the beginning of 2025, but not in the fully autonomous way that they are now.
I disagree. I was totally on the "vibe coding doesn't work and it's stupid" side a few months ago, when that was definitely true... So technically I was right, but I would say the "coding is solved!" people were more usefully right even if they were 6 months early. That's really not very early and it gave them a head start in preparing for today's reality.
Six months before the start of WW2 would your rather be "we're not at war, don't panic" or "peace is over, stockpile food"?
Vibe coding as in "Make me a postgres" is poor but if you think it hasn't affected CRUD feature work you would be wrong.
Most developers don't work building low level high performance scalable systems for a large tech firms, they are making CRUD back-ends for phone apps.
This section of the industry is definitely contracting. Even more obvious is the dev tooling part of it. Dev tooling companies are disappearing or being "bought" all over the place because what might have been a product before is now an hour or two with someone with CC.
As someone in what is relatively the equivalent of Raven's Call of Duty mines compared to past work...
Yeah, 100%. Even for relatively complex scenarios (i.e. over-engineered for purpose) as long as the engineer can prompt coherently and tweak it's a big increase in productivity, at least for those people.
The gap is in things like contract houses where you might see better code on code review from the people submitting but if you actually use the tools, you know that the hours billed are even more inflated than they might have been before.
>but if you think it hasn't affected CRUD feature work you would be wrong.
the mythical man month lesson holds for CRUD apps as well. I'd be careful to equate churning out lines of code with the health of a piece of software down the road, even a relatively straight forward one.
Keep in mind in any real world software what matters is maintenance of a system, not just how many features you can add, regardless of human or machine generated code, some badly glued together codebase that's rapidly growing is going to haunt you.
Which is also why replacing sound dev tooling and with a magical prompt genie is incredibly irresponsible and short-sighted. You could cheaply produce code by outsourcing it years ago.
Seven years ago (*!!SEVEN!!*) GPT was too dangerous to release and yet everything that AI has touched since then is WORSE now than it was then. "Three months" is entirely nonsense. Seven years. Seven. S. E. V. E. N. Y. E. A. R. S.
If you moved the goal posts any farther, they'd have traveled around the world and would be sitting at your back right now.
It did though. I've vibe coded three programs now. Two are replacements for existing programs that didn't work as well as I wanted and were missing features. The vibe coded ones work much better.
The third is definitely not as good as the commercial version, but it also doesn't cost $100k/seat/year.
Unfortunately work paid for the tokens so I can't share them, but I've seen it with my own eyes. It really works now. The code is not slop (and I am a perfectionist; it's not just that I have low standards).
It doesn't work with pre-Astra models for sure though.
That would be speculative, it should only be added in the hypothetical time the productivity actually goes up. Even then, it should be some years in and with third party sources like all of Wikipedia.
It wasn't, generally, a question. People kept buying computers, on faith.
It's just that in 1987. the computers were generally and for the most part, irrelevant to productivity, just like steam and electricity were a promising gimmick for a long time before.. they weren't.
at the level of personal employee maybe, even though 87 had lotus 123 version 2. I was barely born but I'd think people would be able to think faster through electronic spreadsheet.
Good reference. It is going to slowly change things in 20 years we'll look back and a lot of things will be wildly different because of AI systems. But its rate limited by institutions and momentum and human psychology and the way things change will be somewhat unpredictable with unintended negative and positive consequences.
Maybe it's also rate-limited by the fact that it's not actually a silver bullet and actually needs time to find the proper place for its application as well.
And likewise; computers took a few decades to actually become a widespread economic force. Arguably the impact was quite small if we separate it from the internet (which of-course depends on computers).
Good insight. For a long time, the utility of a computer was tied to its ability to print something onto a sheet of paper. User interfaces came along, too, though. Especially for industrial applications. Then internet was probably the third wave. Now AI is the fourth?
Hard to describe the impact of computing as quite small compared to anything, even the internet when you go see those old room sized early IBM computers that were the first commercially viable ones for businesses. They used to have warehouses of payroll documents and armies of people to shuffle around paperwork and crunch numbers and physical people connecting phone lines. Just absolutely fantastically massive amounts of busy work.
Yeah, it is not going to be slow. Progress compounds, everything is being built on top of fast computers and fast widespread internet connection. You are not at the beginning of the curve like you were in the 20th century when people didn't even know how to use a keyboard and we are already seeing widespread disruption in security.
Literally the entire premise of the article is that it's going slow everywhere but in software. To think otherwise is to be really myopic about how the rest of the world outside of tech operates. And in any case 20 years isn't that slow. The only things that happen faster that that will be things like adobe goes out of business. And let's say AI helps us develop medicine and robotics and better factories and all the other things that would have a big impact a lot faster.. just human trials could take a decade before you really see that much change. Will it have a wider impact faster than computing its self, or the internet? Maybe? Maybe not.
If a lot of jobs were still manufacturing jobs I would agree, but we are in a service economy that uses computers as the main tool. So I don't see how it won't come into effect fairly soon into other areas of work. You can't measure impact by profit alone so I don't find the article particularly interesting anyway.
It is like saying there is no impact on math because solving a millennium prize problem didn’t net them a profit. It is just a naive way of looking at things.
You are a demonstration that their marketing campaign is working. The reason OpenAI is focusing on math is because it is one of a very, very few domains where RLVR can be done at massive scale because large sets of problems with automatic verification can be produced and evaluated relatively cheaply. It has no value to OpenAI beyond marketing. It has negative value for mathematicians.
So far the only economically valuable activity that AI has really proven itself very capable in is coding - and its for the same reason of economical RLVR. It can do significant tasks completely end-to-end in that domain. It is very helpful in other types of tasks such as data analysis and legal research, but those things are not going to fulfill your expectations for broad economic impact.
There is simply no evidence at all for the broad claims of disruption in 1-2 years.
Seems to me like you're undervaluing the impact of software in the modern world, as if software hasn't driven massive productivity gains in every industry in the last 50 years.
I expect to be surprised by specific headlines, but I don't expect "software engineer" to be any more common a job opening than "steam locomotive engineer" in 10 years time.
Conversely, I would be very surprised if the robotics startups turn out to have solved general-purpose AI models that work in the real world, both fast enough and cheap enough (currecy or joules) to be significantly labour-displacing in 10 years.
Where I'm about 50-50 on which of "AI does it" or "AI does't do it" is more surprising, is specific physics research from AI. LLMs can do a lot of hard maths, there's lots of open problems in physics due to hard maths questions. So:
Consider the Alcubierre metric. The original is impossible. Can an LLM either design something in the same broad category which is physically instantiable but still useful (e.g. "yes it's sub-light but it manages 0.01c and you weren't getting that otherwise"), or prove this is impossible when limited to matter made from normal electrons, protons, and neutrons?
10 years? I'd be equally surprised if this is or isn't done by AI. Mainly depends on how long the growth continues looking exponential.
Progress is future knowledge. You don't know what future knowledge is, yet. When you know, it will have become present knowledge. You don't know that any particular ongoing change is progress. LOL predictions.
It IS going slow though. The rest of the economy will be slowly adapting it, just like they have slowly adapting other computer systems before. There simply is no use for frontier models in the real economy as of now. For the economy it's just another cost factor, a substantial one if they try to actually build something with it, that has no real benefits that results in increased productivity and revenue making. So why should anyone except AI companies adapt frontier models? They have no reason to. And it will be that way for many years to come.
Yep, and if it's anything like previous transformative technologies: the internet, and 100 years earlier the railways, a lot of people are going to lose a lot of money overbuilding the wrong things along the way.
The distinction is adjusted vs non-adjusted basis. Serving inference is profitable, but serving inference for the last released model doesn't cover the cost of the model in training.
Dario explained it very clearly in an older interview. If every model is bigger and more expensive to train, each model can individually be profitable, while the company as a whole still shows a loss at any given point in time. This is why he likes the idea of regulation so much, it'll let them take a breather and recoup.
> Dario explained it very clearly in an older interview. If every model is bigger and more expensive to train, each model can individually be profitable, while the company as a whole still shows a loss at any given point in time.
“We’re profitable as long as you ignore our ever increasing cost of goods sold, we promise it’s not a pyramid scheme” - ~~Charles Ponzi~~ Dario
But the reason the cost is ever increasing is the steeply ascending quality. There's no (non-doom) scenario where this game does not end with profitable cheap tokens. Wherever it'll stop scaling, it'll be profitable to serve tokens at that scale. And of course if it doesn't stop scaling, it's the singularity and all this thought of profits is quite secondary.
An industry cannot fail because it's too successful.
edit: Other explanation. The industry has found a moneyprinter. It prints money, and several companies are building them and printing money. However, each one of those companies is cashflow negative, because they're all trying their desperate best to build an even larger moneyprinter. Why? If they don't build it and the next company over does build it, and it works, then company 1 is differentially outcompeted. what's more, the more expensive each moneyprinter is, once a company gets strongly ahead the second company won't get the financing to keep up with that one. ... But it's still moneyprinters. This is not an economic race that ends up with "and thus everyone stopped printing money."
> But the reason the cost is ever increasing is the steeply ascending quality.
Sure, if you say so.
> There's no (non-doom) scenario where this game does not end with profitable cheap tokens.
I doubt it.
> Wherever it'll stop scaling, it'll be profitable to serve tokens at that scale.
It’s much more likely, imo, that local AI models will completely destroy the entire business model of inference. For any entity that would be spending enough to replace multiple employees (allegedly), the cost of the hardware to do so is completely affordable. This has the major benefit of eliminating per token costs, and practically guaranteeing that some “AI company” can’t steal your company’s secrets.
At an individual person level, this hardware will take much longer to obtain, but it will happen eventually.
> And of course if it doesn't stop scaling, it's the singularity and all this thought of profits is quite secondary.
I still believe, strongly, that LLMs are the wrong path to AGI. There’s trillions of dollars being put into turning LLMs into AGI; however, there has no zero results showing that LLMs are capable of “learning” anything. “Skills” and expanded context windows don’t count. The “model” needs to be able to modify itself, in real time, for that to be possible.
We’re in a local maxima that strongly refuses to accept that it’s a local maxima. (Diminishing returns for the amount of money poured into “training” is beyond obvious to anyone paying attention).
> An industry cannot fail because it's too successful.
Sure it can. The fossil fuel industry is wildly successful.
It’s also boiling the planet alive, but that’s okay, it’s generating shareholder value so everything’s fine.
Edit for your edit:
> edit: Other explanation. The industry has found a moneyprinter. It prints money, and several companies are building them and printing money. However, each one of those companies is cashflow negative, because they're all trying their desperate best to build an even larger moneyprinter. Why? If they don't build it and the next company over does build it, and it works, then company 1 is differentially outcompeted. what's more, the more expensive each moneyprinter is, once a company gets strongly ahead the second company won't get the financing to keep up with that one. ... But it's still moneyprinters. This is not an economic race that ends up with "and thus everyone stopped printing money."
By your own definition it is impossible to achieve profitability without an ever increasing amount of money flowing into it.
There was a business about moving stamps around that needed similar levels of cash infusion to sustain itself. Eventually its bubble popped too.
> It’s much more likely, imo, that local AI models will completely destroy the entire business model of inference.
You're massively underestimating how much cheaper it is to serve models in a datacenter than at home.
> By your own definition it is impossible to achieve profitability without an ever increasing amount of money flowing into it.
No, what? No. This is just a total misreading of what I'm saying. It's extremely easy to achieve profitability. It just requires your business to stop chasing more profitability. The problem is just that then the other businesses who are still chasing more profitability will eat its lunch. It's a market shape that easily supports n-1 bankruptcies but never n bankruptcies.
it doesn't seem that complicated to me. what do you think the cost is for AWS to serve inference it sells to Anthropic for "$1.00"? I think it's about $0.11. Then, ask yourself, did Amazon sell it to Anthropic at all? No, they traded $0.11 real dollars for $1.00 of Anthropic shares (the CLN that created the real loss Anthropic is talking about), which are now worth $10.00 (which is why people talk about this as a paper loss). So... IS it profitable? It's profitable, but Amazon didn't JUST sell inference when they turned $0.11 into $10.00...
Only a few curmudgeons at this point are claiming there is nothing, nothing whatsoever, to AI.
The question is really about the fate of the current build out. For those of us who have extensive experience with AI, which is to say, those of us here, because we are probably the only people not only using it day in and day out but potentially using multiple sessions of it all the time, I think it's not so surprising that it isn't necessarily immediately radically transforming every industry everywhere. Even as it has rewritten the minute-by-minute process of doing my job entirely, it hasn't actually changed my job all that much at the higher level and it's hard to assign much more than a 2x or a 2.5x overall improvement to it once I account for all the Amdahl's Law issues that come up from the parts that have not been so amenable to acceleration.
And without unit testing, continuous integration testing, pull request review, the fact that the AI is building code which is itself a highly reviewable artifact rather than just doing whatever directly, and all the many many guard rails we've built up in our industry that happen to be extraordinarily amenable to use by automated systems, I still wouldn't trust AI all that much for a real task. They still bang into those guard rails a lot, and still make what I consider to be deeply suboptimal decisions far too often to take my hands off the wheel. With all the stuff we have in the programming space they are a game changer, but in a field that doesn't have anywhere near that much padding and safety nets I'd be way, way more hesitant.
I want to say it's weird to talk about such things when they are also capable of such amazing math proofs, but at the same time when you consider the numbers tossed around for how much those things have cost, I think you're seeing another space that has guard rails comparable to software engineering. By contrast to the way these AIs are working, when a human solves a comparably hard problem the human tries a few things but from the outside would seem to proceed almost directly to the solution, compared to the staggering amount of blundering about the AIs do before arriving at their solutions. Now, in terms of the final results, yeah, they got there and I won't deny that. I'm just saying that in fields where the acceptance criteria are nowhere near as sharp as math or software, they're operating at a significant disadvantage still.
I think there's also something to be said about how the largest private AI enterprises are themselves mostly just software companies. There is enough widespread dogfooding of the product that the company staff are not just spending a lot of time and energy in improving how their product is specifically adapted and trained for better code but they are also intimately aware of those limitations, driving how they think about and implement their own product.
I don't think that translates well for other fields at all. You can throw an engineer or an MBA-brained exec at a business problem all you want but until they start using the products, they're not going to be effective at guiding its development.
Math is just proof of concept for the big players. The same resources could go to prompts designed to optimize the internal combustion engine or anything else nonsensical.
The biggest paradigm shift has been representing models as humans, and I don’t think that’ll fade. I highly expect the AI Model T Ford will be a generalized assistant that actually works. Likely developed by OpenAI or Anthropic. And likely 5 years from now or more.
"optimize the internal combustion engine" that requires good quality combustion simulations and iterative optimization algorithms; and we've had that for longer than the internet; that kind of work was the earliest uses of military comptuers. Why would a text model be better at it?
Maths i a cool benchmark, but has the similar benefit to software; very easy to verify.
> I think it's not so surprising that it isn't necessarily immediately radically transforming every industry everywhere.
Computers and the internet didn’t radically change every industry everywhere. But every industry everywhere uses computers and the internet, and more pertinently, they pay good money for it.
AI doesn’t need to transform the world, it just needs to pay off the sizeable investments in data centers.
Does AI transform the world more than;
- CAD software
- Office software
- Spreadsheet software
- Finance software
- Digital banking software
- Computer graphics software
- The entire US national fiber grid
Cause at the current rate and scope of data center investments; they have to transform the world more than... every other software system we've come up with until this points combined; to justify their cost.
They have to transform the world more than whatever the bond rate is right now, (which admittedly is increasing). I'm not sure that that's greater than office software, digital banking etc. But I don't think it's as high a bar as some people think.
From a paying back data centre debt point of view, yeah. The equity valuations on the other hand are priced for total transformation in the short term.
And is there enough business or computer demand for all the data centers we're building? I don't think anyone has an answer to that.
In a way it’s sad that AI came in the age where a lot of us are jaded because the internet which we built has become a place for gambling and scamming. I remember when a new language or framework was met with excitement. Today I hear (and am a source of) mostly criticism.
> I still wouldn't trust AI all that much for a real task
I wonder if you might be conflating LLM/Agent with AI
Waymo is AI, I personally would happily ride one, or a Zoox. Those are quite real tasks the AIs are performing, people put their lives on the hands of these AIs
A lot of AI models every day work to provide search results, driving directions, image recognition, automatic routing and classification (eg. Fruit packaging companies use vision models to accept/reject fruit for packaging in real time - that’s the fruit we buy at the supermarket and then eat at home), and so many other things
So inadvertently, and without much choice either, you probably already rely and depend on AI on your everyday life
A* path finding and SLAP/SLAM (used in video games and robotic systems), BFS/DFS, Alpha-Beta pruning, Simulated annealing, Genetic algorithms/programming....
All of those are considered AI, none of those are LLMs or agentic harnesses (which of course are also AI). Also, most real AI tasks systems currently deployed in the world, don't use LLMs or agentic harnesses, or diffusion models, or image/music/video generation
It's honestly distressing that the merits of AI are being discussed as a practical automation problem, and not a philosophical issue. It really seems like the entire mindspace of tech is consumed by economics and material producibles. I don't think I want to live in a world where humans are irrelevant.
Fully agree with that. The problem is the AI boosters really believe in The Singularity that will transform all of human experience. If you try to tell them AI is a high-value enterprise technology -- more valuable than docker but less than the steam engine -- they get mad at you.
And at that point, there was no much productivity gained ... and not nearly as much debt as now. The industry was also not such a huge part of stock market, GDP, etc.
This seems like a poor comparison, for any given company, benefitting from personal computing in 1987 was a significantly harder task than using AI tools in 2026 is.
The first requires a fundamental shift in not only how you work but expensive and complex infrastructure changes from the company itself just to make it possible (the first Mac for example would cost about $7000 per person in today’s money and would be outdated in maybe a year or two), an office building of The latter requires asking a subscription service to do a task for you on basically any computer connected to the internet.
I don’t know about you, but for me materially useful AI costs around $10,000/mo in tokens. But it is very useful.
Claude subscriptions help, but only if I come in with the expertise to build the entire system around the model and I don’t mind the whole thing grinding to a halt when my session limit is saturated. Anyone selling the system off the shelf is going to be charging API rates. Computers were cheap in comparison.
I can’t speak for everyone, but I would intuitively venture to guess there would be some greater than marginal productivity benefits from say, a $200/m Claude sub for each employee in many organizations outside of tech.
Perhaps they’re all on the $20/m plan which is useless?
“Marginal” improvement isn’t really going to be enough. Products need to be closer to 10x better than the alternative to get traction (which can be divided across several dimensions). AI, I think, is just starting to get there.
See sibling comment. 10x measured how? In pure coding tasks under certain conditions the speedup is huge, but seems really unclear how this would transfer to any significant speedup across the whole undertaking (project, delivery etc).
I get what you’re saying but I’m just not a fan of this type of argument. It always paints AI as this “in the future” thing which absolves any current criticisms or observations.
LLMs are great and definitely make you more productive, for some reason 493/500 firms haven’t been able to figure that out even though it’s trivially easy to provide employees with frontier models and it’s also trivially easy to use frontier models.
Which is why I say comparing it to personal computers in 1987 is not an apt comparison. That took redefining physical workspaces, new infrastructure, and new personnel which wasn’t easy to come by. Becoming a company that uses AI is just as easy (if not easier) as using any other SaaS.
Maybe, even likely. But how do you observe this? We are in this situation at work, it feels like "sure AI is helpful" but do we actually deliver any faster?
It’s a business-scale cost. When I say materially useful, I mean replacing multiple salaries and improving quality at the same time. Not some variant of “make an existing employee 20% faster.”
The personal hobby version is to get the Claude max plan.
Yeah, the person who said that obviously never checked on book and magazine and newspaper typesetting --- cue old news stories about the last compositor at a given paper finally retiring after decades of the sinecures which the composition unions bargained for rather than participating, causing a loss of typographic skill and knowledge which resulted in the ghastly appearance of the "DTP revolution".
I once participated in a project where we worked up a database-driven typesetting system for a company whose annual publication had a staff of a dozen (3 managers (one of whom mostly did ad sales), 3 full-time compositors, 6 part-time) and when we put in the system they quit paying their expensive annual license for maintenance on the proprietary composition system, and fired two managers and 1 compositor and half the part-timers.
The staff was commensurately larger when they switched to the first electronic composition system they were using from the typesetter and paste-up they initially used.
> the person who said that obviously never checked on book and magazine and newspaper typesetting
That's one of the "everywheres" the author of the statement was talking about, which is the point - you could see every part of the economy seemingly improved, but it did not show up at the macroeconomic measurement level.
It was until the 2000s that computers caused an uptick in measured productivity at the GDP level, though.
The number of books being published has been steadily increasing for decades since computers became prevalent in typesetting --- is that not measurable?
Admittedly part of that is caused by the change in tax laws making it untenable for publishers to maintain a backlist.
It was not on a level that nudges GDP growth for a long time. The irony is that maybe the productivity didn't increase until book publishing plummeted, since enabling a lack of physical books was a huge productivity win.
This isn't a debate about whether industries improved. When you look at GDP, do you see an uptick in GDP or not?
The original quote was often seen as a paradox. At least one major interpretation of the original quote is that "the computer revolution is so obviously helping the economy, that if it doesn't show up in GDP, is it even a meaningful measurement, and how can we fix that?"
But it is very fitting to discuss that quote - the original article discusses multifactor productivity, the same relevant measurement, and earnings of the S&P 493. There are two ways to interpret it - is AI not increasing productivity, or are we not measuring productivity, and is there "dark matter" somewhere in the economy that is not being properly measured?
I think we're looking at two different sides of the same coin.
The thing is, increased efficiency doesn't always yield a larger market in the short term.
There are only so many copies of a book listing television stations and demographics for marketing purposes which the market will bear as in this specific instance.
It takes a while for the displaced workers to get retrained and find other jobs which allows them to produce/consume more.
On the flip side, Nintendo hiring a couple hundred folks to make a game such as _Legend of Zelda: Skyward Sword_ suddenly created $50 x 600,000 in value on the day it was released.
Only 13% of companies see real returns from AI, survey finds - https://news.ycombinator.com/item?id=50207881 - October 2026
is this 13% of leprechauns become elves?
No Signs of Tech Payoff Outside the Tech Sector. Manufacturing, basic science, education, etc languished while Big Tech reaped record profits and achieved stock heights for the last 15 years.
There are too many confounders.
Even last week I had a case where, I was reviewing code that I know the dev was vibe coding, and honestly was good up to that review.
Buuuuuut, for the stuff I reviewed that day, there was some base-class inheritance use that was so ugly that my initial teams message to the dev was along the lines of 'What crack was Copilot smoking on that one'.
Turns out, they had asked some other random-ish questions in the chat they were doing their actual work in, and those questions more or less polluted the context.
Sure, I was able to guide them for how to not do that in future (i.e. keep unrelated questions around the work in question to a separate chat, etc.) but this was also a dev that is normally both very good, but also overworked semi-related to the general 'AI will save us on dev costs thing'.
I can't help but ask whether, for the even less-technical people that are told 'yeah you lost two colleagues but here is Copilot/Claude/etc' and they don't even get enough advice to have it actually increase productivity versus chasing down gremlins in the actual work.
I don't follow.
Doubtlessly Chinese manufacturing benefited tremendously from those advancements. But my point is the headline whinging about AI in America would also apply to Tech in America.
It'll probably be a couple of years before most companies even realise it's an option.
> before most companies even realize it's an option Couldn't be further from the truth
How long have people been warning about global warming? Do you still think it isn't real?
It does not. But it does mean they are bad at predictions and you shouldn't give their latest prediction much credence.
Six months before the start of WW2 would your rather be "we're not at war, don't panic" or "peace is over, stockpile food"?
Most developers don't work building low level high performance scalable systems for a large tech firms, they are making CRUD back-ends for phone apps.
This section of the industry is definitely contracting. Even more obvious is the dev tooling part of it. Dev tooling companies are disappearing or being "bought" all over the place because what might have been a product before is now an hour or two with someone with CC.
Yeah, 100%. Even for relatively complex scenarios (i.e. over-engineered for purpose) as long as the engineer can prompt coherently and tweak it's a big increase in productivity, at least for those people.
The gap is in things like contract houses where you might see better code on code review from the people submitting but if you actually use the tools, you know that the hours billed are even more inflated than they might have been before.
the mythical man month lesson holds for CRUD apps as well. I'd be careful to equate churning out lines of code with the health of a piece of software down the road, even a relatively straight forward one.
Keep in mind in any real world software what matters is maintenance of a system, not just how many features you can add, regardless of human or machine generated code, some badly glued together codebase that's rapidly growing is going to haunt you.
Which is also why replacing sound dev tooling and with a magical prompt genie is incredibly irresponsible and short-sighted. You could cheaply produce code by outsourcing it years ago.
If you moved the goal posts any farther, they'd have traveled around the world and would be sitting at your back right now.
The third is definitely not as good as the commercial version, but it also doesn't cost $100k/seat/year.
Unfortunately work paid for the tokens so I can't share them, but I've seen it with my own eyes. It really works now. The code is not slop (and I am a perfectionist; it's not just that I have low standards).
It doesn't work with pre-Astra models for sure though.
It took decades for effects of personal computing to show up in aggregate statistics.
See:
https://en.wikipedia.org/wiki/Productivity_paradox and https://dl.acm.org/doi/10.1145/163298.163309
edit: My comment is not exactly right, its more about general computing rather than personal computing
The decade ahead is going to be a very interesting one
It's just that in 1987. the computers were generally and for the most part, irrelevant to productivity, just like steam and electricity were a promising gimmick for a long time before.. they weren't.
Looking backwards, it is easy to miss this.
> As with previous technologies, an extremely large number of initial cutting-edge investments in IT were counterproductive and over-optimistic.
I think we are definitely seeing this today!
Yeah, it is not going to be slow. Progress compounds, everything is being built on top of fast computers and fast widespread internet connection. You are not at the beginning of the curve like you were in the 20th century when people didn't even know how to use a keyboard and we are already seeing widespread disruption in security.
So far the only economically valuable activity that AI has really proven itself very capable in is coding - and its for the same reason of economical RLVR. It can do significant tasks completely end-to-end in that domain. It is very helpful in other types of tasks such as data analysis and legal research, but those things are not going to fulfill your expectations for broad economic impact.
There is simply no evidence at all for the broad claims of disruption in 1-2 years.
Note: on an exponential, you can always say this. People will look back on today with the same "aww cute" eyes.
I expect to be surprised by specific headlines, but I don't expect "software engineer" to be any more common a job opening than "steam locomotive engineer" in 10 years time.
Conversely, I would be very surprised if the robotics startups turn out to have solved general-purpose AI models that work in the real world, both fast enough and cheap enough (currecy or joules) to be significantly labour-displacing in 10 years.
Where I'm about 50-50 on which of "AI does it" or "AI does't do it" is more surprising, is specific physics research from AI. LLMs can do a lot of hard maths, there's lots of open problems in physics due to hard maths questions. So:
10 years? I'd be equally surprised if this is or isn't done by AI. Mainly depends on how long the growth continues looking exponential.but have you looked up who invented the computer? and who was funding all the first versions of the software we use? it was the US military.
Dario explained it very clearly in an older interview. If every model is bigger and more expensive to train, each model can individually be profitable, while the company as a whole still shows a loss at any given point in time. This is why he likes the idea of regulation so much, it'll let them take a breather and recoup.
“We’re profitable as long as you ignore our ever increasing cost of goods sold, we promise it’s not a pyramid scheme” - ~~Charles Ponzi~~ Dario
An industry cannot fail because it's too successful.
edit: Other explanation. The industry has found a moneyprinter. It prints money, and several companies are building them and printing money. However, each one of those companies is cashflow negative, because they're all trying their desperate best to build an even larger moneyprinter. Why? If they don't build it and the next company over does build it, and it works, then company 1 is differentially outcompeted. what's more, the more expensive each moneyprinter is, once a company gets strongly ahead the second company won't get the financing to keep up with that one. ... But it's still moneyprinters. This is not an economic race that ends up with "and thus everyone stopped printing money."
Sure, if you say so.
> There's no (non-doom) scenario where this game does not end with profitable cheap tokens.
I doubt it.
> Wherever it'll stop scaling, it'll be profitable to serve tokens at that scale.
It’s much more likely, imo, that local AI models will completely destroy the entire business model of inference. For any entity that would be spending enough to replace multiple employees (allegedly), the cost of the hardware to do so is completely affordable. This has the major benefit of eliminating per token costs, and practically guaranteeing that some “AI company” can’t steal your company’s secrets.
At an individual person level, this hardware will take much longer to obtain, but it will happen eventually.
> And of course if it doesn't stop scaling, it's the singularity and all this thought of profits is quite secondary.
I still believe, strongly, that LLMs are the wrong path to AGI. There’s trillions of dollars being put into turning LLMs into AGI; however, there has no zero results showing that LLMs are capable of “learning” anything. “Skills” and expanded context windows don’t count. The “model” needs to be able to modify itself, in real time, for that to be possible.
We’re in a local maxima that strongly refuses to accept that it’s a local maxima. (Diminishing returns for the amount of money poured into “training” is beyond obvious to anyone paying attention).
> An industry cannot fail because it's too successful.
Sure it can. The fossil fuel industry is wildly successful.
It’s also boiling the planet alive, but that’s okay, it’s generating shareholder value so everything’s fine.
Edit for your edit:
> edit: Other explanation. The industry has found a moneyprinter. It prints money, and several companies are building them and printing money. However, each one of those companies is cashflow negative, because they're all trying their desperate best to build an even larger moneyprinter. Why? If they don't build it and the next company over does build it, and it works, then company 1 is differentially outcompeted. what's more, the more expensive each moneyprinter is, once a company gets strongly ahead the second company won't get the financing to keep up with that one. ... But it's still moneyprinters. This is not an economic race that ends up with "and thus everyone stopped printing money."
By your own definition it is impossible to achieve profitability without an ever increasing amount of money flowing into it.
There was a business about moving stamps around that needed similar levels of cash infusion to sustain itself. Eventually its bubble popped too.
Sure, if you say so.
You're massively underestimating how much cheaper it is to serve models in a datacenter than at home.
> By your own definition it is impossible to achieve profitability without an ever increasing amount of money flowing into it.
No, what? No. This is just a total misreading of what I'm saying. It's extremely easy to achieve profitability. It just requires your business to stop chasing more profitability. The problem is just that then the other businesses who are still chasing more profitability will eat its lunch. It's a market shape that easily supports n-1 bankruptcies but never n bankruptcies.
Dario also believes that verifiability is trivial, which I'm much more sceptical of.
The question is really about the fate of the current build out. For those of us who have extensive experience with AI, which is to say, those of us here, because we are probably the only people not only using it day in and day out but potentially using multiple sessions of it all the time, I think it's not so surprising that it isn't necessarily immediately radically transforming every industry everywhere. Even as it has rewritten the minute-by-minute process of doing my job entirely, it hasn't actually changed my job all that much at the higher level and it's hard to assign much more than a 2x or a 2.5x overall improvement to it once I account for all the Amdahl's Law issues that come up from the parts that have not been so amenable to acceleration.
And without unit testing, continuous integration testing, pull request review, the fact that the AI is building code which is itself a highly reviewable artifact rather than just doing whatever directly, and all the many many guard rails we've built up in our industry that happen to be extraordinarily amenable to use by automated systems, I still wouldn't trust AI all that much for a real task. They still bang into those guard rails a lot, and still make what I consider to be deeply suboptimal decisions far too often to take my hands off the wheel. With all the stuff we have in the programming space they are a game changer, but in a field that doesn't have anywhere near that much padding and safety nets I'd be way, way more hesitant.
I want to say it's weird to talk about such things when they are also capable of such amazing math proofs, but at the same time when you consider the numbers tossed around for how much those things have cost, I think you're seeing another space that has guard rails comparable to software engineering. By contrast to the way these AIs are working, when a human solves a comparably hard problem the human tries a few things but from the outside would seem to proceed almost directly to the solution, compared to the staggering amount of blundering about the AIs do before arriving at their solutions. Now, in terms of the final results, yeah, they got there and I won't deny that. I'm just saying that in fields where the acceptance criteria are nowhere near as sharp as math or software, they're operating at a significant disadvantage still.
I don't think that translates well for other fields at all. You can throw an engineer or an MBA-brained exec at a business problem all you want but until they start using the products, they're not going to be effective at guiding its development.
The biggest paradigm shift has been representing models as humans, and I don’t think that’ll fade. I highly expect the AI Model T Ford will be a generalized assistant that actually works. Likely developed by OpenAI or Anthropic. And likely 5 years from now or more.
Maths i a cool benchmark, but has the similar benefit to software; very easy to verify.
Computers and the internet didn’t radically change every industry everywhere. But every industry everywhere uses computers and the internet, and more pertinently, they pay good money for it.
AI doesn’t need to transform the world, it just needs to pay off the sizeable investments in data centers.
Cause at the current rate and scope of data center investments; they have to transform the world more than... every other software system we've come up with until this points combined; to justify their cost.
And is there enough business or computer demand for all the data centers we're building? I don't think anyone has an answer to that.
Do they? Compute and internet access are orders of magnitude cheaper than the software that runs on it.
I wonder if you might be conflating LLM/Agent with AI
Waymo is AI, I personally would happily ride one, or a Zoox. Those are quite real tasks the AIs are performing, people put their lives on the hands of these AIs
A lot of AI models every day work to provide search results, driving directions, image recognition, automatic routing and classification (eg. Fruit packaging companies use vision models to accept/reject fruit for packaging in real time - that’s the fruit we buy at the supermarket and then eat at home), and so many other things
So inadvertently, and without much choice either, you probably already rely and depend on AI on your everyday life
We’ve been arguing about this for decades. I think the less controversial take is that very specifically LLMs and their agentic harnesses are AI.
Not sure what you mean. Are you being ignorant or sarcastic?
https://en.wikipedia.org/wiki/List_of_artificial_intelligenc...
A* path finding and SLAP/SLAM (used in video games and robotic systems), BFS/DFS, Alpha-Beta pruning, Simulated annealing, Genetic algorithms/programming....
All of those are considered AI, none of those are LLMs or agentic harnesses (which of course are also AI). Also, most real AI tasks systems currently deployed in the world, don't use LLMs or agentic harnesses, or diffusion models, or image/music/video generation
The first requires a fundamental shift in not only how you work but expensive and complex infrastructure changes from the company itself just to make it possible (the first Mac for example would cost about $7000 per person in today’s money and would be outdated in maybe a year or two), an office building of The latter requires asking a subscription service to do a task for you on basically any computer connected to the internet.
Claude subscriptions help, but only if I come in with the expertise to build the entire system around the model and I don’t mind the whole thing grinding to a halt when my session limit is saturated. Anyone selling the system off the shelf is going to be charging API rates. Computers were cheap in comparison.
Perhaps they’re all on the $20/m plan which is useless?
LLMs are great and definitely make you more productive, for some reason 493/500 firms haven’t been able to figure that out even though it’s trivially easy to provide employees with frontier models and it’s also trivially easy to use frontier models.
Which is why I say comparing it to personal computers in 1987 is not an apt comparison. That took redefining physical workspaces, new infrastructure, and new personnel which wasn’t easy to come by. Becoming a company that uses AI is just as easy (if not easier) as using any other SaaS.
If it costs 2x-3x the median household income to be an AI maximalist that definitely puts some comments on this forum into context.
The personal hobby version is to get the Claude max plan.
I once participated in a project where we worked up a database-driven typesetting system for a company whose annual publication had a staff of a dozen (3 managers (one of whom mostly did ad sales), 3 full-time compositors, 6 part-time) and when we put in the system they quit paying their expensive annual license for maintenance on the proprietary composition system, and fired two managers and 1 compositor and half the part-timers.
The staff was commensurately larger when they switched to the first electronic composition system they were using from the typesetter and paste-up they initially used.
That's one of the "everywheres" the author of the statement was talking about, which is the point - you could see every part of the economy seemingly improved, but it did not show up at the macroeconomic measurement level.
It was until the 2000s that computers caused an uptick in measured productivity at the GDP level, though.
Admittedly part of that is caused by the change in tax laws making it untenable for publishers to maintain a backlist.
It was not on a level that nudges GDP growth for a long time. The irony is that maybe the productivity didn't increase until book publishing plummeted, since enabling a lack of physical books was a huge productivity win.
This isn't a debate about whether industries improved. When you look at GDP, do you see an uptick in GDP or not?
The original quote was often seen as a paradox. At least one major interpretation of the original quote is that "the computer revolution is so obviously helping the economy, that if it doesn't show up in GDP, is it even a meaningful measurement, and how can we fix that?"
But it is very fitting to discuss that quote - the original article discusses multifactor productivity, the same relevant measurement, and earnings of the S&P 493. There are two ways to interpret it - is AI not increasing productivity, or are we not measuring productivity, and is there "dark matter" somewhere in the economy that is not being properly measured?
The thing is, increased efficiency doesn't always yield a larger market in the short term.
There are only so many copies of a book listing television stations and demographics for marketing purposes which the market will bear as in this specific instance.
It takes a while for the displaced workers to get retrained and find other jobs which allows them to produce/consume more.
On the flip side, Nintendo hiring a couple hundred folks to make a game such as _Legend of Zelda: Skyward Sword_ suddenly created $50 x 600,000 in value on the day it was released.
And that's a critique of the original article... that the broader "S&P 493" isn't showing gains might be for the reasons you describe.
I guess this bubble has some room to run!
TFP data is murky at best IMO
People are, on average, too stupid to make good use of AI to the point where you can actually gain massive productivity.
The pointy haired bosses are going to do their usual approach: force people to work more, claim success, move on.