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▲ jstanley 7 hours ago

"You can see the computer age everywhere but in the productivity statistics." -- 1987

▲tracerbulletx 7 hours ago | parent | next [-]

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.

▲password54321 7 hours ago | parent | next [-]

"slowly change things in 20 years"

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.

▲tracerbulletx 7 hours ago | parent | next [-]

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.

▲password54321 6 hours ago | parent | next [-]

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.

▲ForHackernews 5 hours ago | parent [-]

I tried telling my boss "you can't measure impact by profit alone" and he said, "you're so right, we also have to think about expenses! You're fired."

▲jeremyjh 3 hours ago | parent [-]

I would also fire someone who doesn’t know the difference between revenue and profit.

▲benatkin 6 hours ago | parent | prev [-]

Do you think those offering professional translation service were obsessed with AI? Or chess players? The rest of the world doesn't get to decide...

▲ben_w 6 hours ago | parent | prev | next [-]

Both things can be true.

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.
▲williamtrask 7 hours ago | parent | prev | next [-]

"You are not at the beginning of the curve like you were in the"

Note: on an exponential, you can always say this. People will look back on today with the same "aww cute" eyes.

▲card_zero 6 hours ago | parent | prev | next [-]

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.

▲aDyslecticCrow 6 hours ago | parent | prev [-]

Disruption in security is still software field though. The topic is "the rest of the economy".

▲nicoburns 6 hours ago | parent | prev | next [-]

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.

▲ambicapter 7 hours ago | parent | prev [-]

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.

▲loeg 7 hours ago | parent [-]

Computers, of course, can be criticized in the same way.

▲aDyslecticCrow 6 hours ago | parent [-]

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).

▲loeg 24 minutes ago | parent | next [-]

I think it's pretty silly to try to separate the internet from computers.

▲tracerbulletx 5 hours ago | parent | prev | next [-]

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.

▲datsci_est_2015 5 hours ago | parent | prev [-]

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?

▲WillAdams 6 hours ago | parent | prev | next [-]

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.

▲jmalicki 6 hours ago | parent [-]

> 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.

▲WillAdams 5 hours ago | parent [-]

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.

▲jmalicki 4 hours ago | parent [-]

> is that not measurable?

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?

▲ijustlovemath 7 hours ago | parent | prev | next [-]

To be fair, in 1987 the unit economics weren't being subsidized by nation-state levels of debt and mandatory usage contracts.

▲olalonde 7 hours ago | parent | next [-]

The unit economics aren't what's subsidized. Inference is profitable.

▲emp17344 6 hours ago | parent [-]

Didn’t we just get a bunch of data from Anthropic’s IPO showing that inference is not, in fact, profitable? People are still spouting this?

▲CuriouslyC 6 hours ago | parent | next [-]

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.

▲clownstrikelol 5 hours ago | parent [-]

> 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

▲FeepingCreature 5 hours ago | parent [-]

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."

▲clownstrikelol 4 hours ago | parent [-]

> 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.

▲phoghed 4 hours ago | parent [-]

> Diminishing returns for the amount of money poured into “training” is beyond obvious to anyone paying attention

Sure, if you say so.

▲doctorpangloss 5 hours ago | parent | prev | next [-]

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...

▲SpaceNoodled 4 hours ago | parent [-]

That's a Ponzi scheme.

▲olalonde 6 hours ago | parent | prev [-]

Nope, the leak didn't show that.

▲paulddraper 7 hours ago | parent | prev | next [-]

Where do you think the internet came from?

▲za_creature 6 hours ago | parent [-]

Neither of those things. Arpanet was military and the web came out of CERN, but they were both low budget side projects.

▲doctorpangloss 7 hours ago | parent | prev | next [-]

in some kind of boring sense, sure.

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.

▲KellyCriterion 7 hours ago | parent | prev [-]

...and global usage/reach was practically zero...

▲floatingtorch an hour ago | parent | prev | next [-]

Could a Fortune 500 company survive competition from competitors if it did not use computers?

TFP data is murky at best IMO

▲ForHackernews 7 hours ago | parent | prev | next [-]

The difference, of course, is that in this analogy Commodore has to become a trillion dollar business by 1989 or the entire stock market implodes.

▲__m 17 minutes ago | parent [-]

Like the internet bubble, that’s fine, openai and anthropic don’t have to survive for ai to be successful

▲oa335 7 hours ago | parent | prev | next [-]

Came here to say this as well.

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

▲marcelo-earth 7 hours ago | parent | next [-]

Someone should add a 2025–2045 section in the Wikipedia page of Productivity Paradox

The decade ahead is going to be a very interesting one

▲thereader12 7 hours ago | parent [-]

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.

▲agumonkey 6 hours ago | parent | prev [-]

So this was about personal computing ? not computing per se ? I'd assume that by 87, the idea of digital computers benefits would be a non-question.

▲msabalau 6 hours ago | parent | next [-]

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.

Looking backwards, it is easy to miss this.

▲agumonkey 5 hours ago | parent [-]

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.

▲oa335 5 hours ago | parent | prev [-]

I was too specific, it was more about computing generally as opposed to "personal computing".

▲jerf 7 hours ago | parent | prev | next [-]

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.

▲mr_toad 6 hours ago | parent | next [-]

> 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.

▲aDyslecticCrow 5 hours ago | parent | next [-]

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.

▲mr_toad 3 hours ago | parent [-]

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.

▲datsci_est_2015 5 hours ago | parent | prev [-]

> But every industry everywhere uses computers and the internet, and more pertinently, they pay good money for it.

Do they? Compute and internet access are orders of magnitude cheaper than the software that runs on it.

▲nico 5 hours ago | parent | prev | next [-]

> 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

▲datsci_est_2015 5 hours ago | parent [-]

Is every closed loop computation system AI now? Or does it need to have a neural network?

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.

▲paimapi 6 hours ago | parent | prev | next [-]

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.

▲yoz-y 5 hours ago | parent | prev | next [-]

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.

▲throw765392 6 hours ago | parent | prev [-]

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.

▲kaffekaka 6 hours ago | parent | next [-]

Math was not "just" proof of concept, it was THE proof of concept, since iterating on combustion engines cannot be done nearly as efficiently.

▲aDyslecticCrow 5 hours ago | parent | prev [-]

"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.

▲watwut 7 hours ago | parent | prev | next [-]

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.

▲bagacrap 3 hours ago | parent | prev | next [-]

Dotcom bust - 2000

I guess this bubble has some room to run!

▲gyre007 7 hours ago | parent | prev | next [-]

I came here to say same thing :-)

▲sumeno 7 hours ago | parent | prev | next [-]

5 years ago it was the crypto bros quoting this

▲christlab 2 hours ago | parent | prev | next [-]

[dead]

▲besterman23 7 hours ago | parent | prev [-]

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.

▲computomatic 7 hours ago | parent [-]

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.

▲besterman23 6 hours ago | parent | next [-]

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?

▲computomatic 6 hours ago | parent | next [-]

“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.

▲besterman23 6 hours ago | parent | next [-]

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.

▲kaffekaka 6 hours ago | parent | prev [-]

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).

▲kaffekaka 6 hours ago | parent | prev | next [-]

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?

▲throw765392 6 hours ago | parent | prev [-]

Or just in enterprises where they can't have plans.

▲datsci_est_2015 5 hours ago | parent | prev [-]

Holy shit. I’m spending 2 orders of magnitude less per month and still getting good value. But, I’m not an AI maximalist.

If it costs 2x-3x the median household income to be an AI maximalist that definitely puts some comments on this forum into context.