"You can see the computer age everywhere but in the productivity statistics." -- 1987
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.
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?
Note: on an exponential, you can always say this. People will look back on today with the same "aww cute" eyes.
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.
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
Dario also believes that verifiability is trivial, which I'm much more sceptical of.
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.
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.
Sure, if you say so.
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.
TFP data is murky at best IMO
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.
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 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.
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
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
> 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!
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.
I guess this bubble has some room to run!
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.