No signs of AI payoff outside the tech sector (apollo.com)

AIs don't just make code they make words, images, and sounds. Hasn't worked in those markets either.

AI could probably write decently if every scraped book had all the revisions to create it embedded within or art had every brush stroke documented. As it stands programming is the first to be trained. The rest will take some more time.

Software creation tools have been worked on for decades to keep fallible human coders out of trouble as much as possible. Starting with compilers that enforce or at least encourage coding style humans can understand. Compilers embody formal definitions of language syntax. Lint and other static analysis technologies contribute by identifying problematic patterns in code. Leak detectors identify memory management problems. Profilers identify performance problems.

Few other endeavors have this much tooling that serendipitously happens to help help LLM-based agents be more useful and less error prone.

A lot of discussion around AI is very high-level and abstract. It helps to dig in and identify the characteristics that actually make a domain more or less amenable to being boosted by AI.

those who did are rather convinced it's over, for juniors at least.

hey are amazing tools, but every time I see a task that LLMs helped me complete in 30% less time, I see 5 more tasks that took me 30-50% longer to complete because of errors, lies, mistakes, hallucinations, context limits.

Do not tell me I'm holding it wrong.

Artists were already starving. The more physical trades are…physical not language so a plumber still plums. Maybe now they ask gemini instead of google tho.

Areas related most to…language are the most impact by large language models? The one that i think is just as impacted is education because it is also almost all language driven.

I see zero sign of any of the new mills coming around. Steel, crops, farming, none of it has changed and what little has is from…the software side or…language.

The SV world really over indexes on how much software can do.

It's very hard to improve something that you don't understand yourself. All the tiny decisions you make as you create a product are informed by the experience you have.

So it's seems obvious to me that a product created by software engineers would be better at software engineering. A programmer knows what a programmer needs.

To improve another industry successfully, you would really need people from that industry to be involved in a deep level in the design of the product.

Your chosen example is rather more fitting for the opposite case. Dropping an electric motor is pretty easy, you just attach it to the other side of the axle. But there is really no point in doing that. The sole benefit for that mill, will be that there is the cheap power from the water. It's also the sole reason for the current position of the mill, for the electric motor, you hence need to add long cables first. The size and operation mode of the mill is also not fit at all for the electric age. There is really no point, you are better of just building a new mill in a completely different location.

But you can do that independently of the old mill, since there is hardly any capital to be taken from the old to the new mill, short of maybe the office supply. So you just build a new mill, completely independently. But there is also no reason to decommission the old at the same time, the only reason is to shut it down when is no longer economically viable. But that also might be at a different point in time than building the new mill.

And it was in 1987 that Nobel laureate economist Robert Solow famously said “You can see the computer age everywhere but in the productivity statistics”

I mean, maybe AI will be faster, because it's especially easy to apply compared to generally applicable technologies. There was the Bank of Korea study showing this spring that did show productivity gains with single proprietors--folks who don't have to reorg employees and processes to do something with more abilty or saved time.

But, generally, I don't know that there was an reason to expect that corporate America generally was going to see an AI payoff so soon.

It's not true elsewhere. The definition of "is this a good outcome" is pretty nebulous in other areas.

"Is this what the law says?"

Yes, but that's not how the judge will interpret it, etc.

Big Tech wants to make humans the go-between, however, the thinking and problem solving wasn't the problem to begin with. Checking for mistakes, correcting them, and presenting the corrected versions is the hard part. If a human does not go through the steps of solving the problem, they can't effectively analyze or process the output, which will always lead to mistakes. The only way for LLMs to take over any jobs would be if humans were perfectionists and could check every single bit of work without fault (since LLMs will always make some mistakes: a "perfect" LLM isn't possible anymore than a perfect human being is). Humans (especially myself are disaster and mistake prone.

That's why you should not invest now unless you are short term trading, and you should get that bag of popcorn ready. Trillion dollar valuations aren't real. It's a fake number propped up by a bubble. The US is in record debt, and it will be unable to backstop the financial loss...without taxing heavily the very folks that want to be backstopped. Those folks are dimwits and are only focused on making a buck. They are assuming everything will work out, either via investors/revenue/profits or a government bailout.

Bear Stearns had the same attitude.

In 2008 the government had leeway. This time, they do not. Regardless of political party.

Disclaimer: Please note I do not have an investment in any company, not even in a 401k. I am a disabled software engineer (and manager, in some cases) who worked in/with startups with various sectors. I have knowledge about all the financial and investor nonsense that goes into startups and small businesses. As an autistic millennial, who has definitely been around the block with bubbles, I am also interested in tech and finance. I also have a decent amount of knowledge about the damn stock market. I don't and will never claim to be an expert in any of those things, however, I know enough to say that a whole bunch of folks are going to lose a TON of money at some point within the next few months/years (likely before 2030, possibly by 2028), so if you are overexposed to the stock market (pretend it drops to zero, won't happen, however, it will drop, and no stock is safe, this goes for you FIRE folks as well), you should back off your investments to an acceptable level (the most amount of money you'd be fine with losing ALL of).

Sorry for the rant. Have a good night all.

It took a massive amount of new infrastructure for electricity to benefit people. Lines had to be run, houses and businesses had to be wired, new light fixtures and appliances had to be purchased and installed.

LLMs can currently be accessed and used everywhere instantly by everybody right now and at any moment day or night. Rich and poor alike can take advantage of it. There's nothing holding back anybody. If everybody without electricity could have pulled a small rectangle out of their pocket and pressed a button to instantly have electric lights and power, the positive impact of electrification would have been immediate. This is not even close to a "same thing now" situation.

That's the core issue apollo is talking about. Investors have been told, "Buckle up, we're getting rich in 2027." Not "Hey, after 20 years, this will probably be pretty nice, maybe." This is how bubbles pop. Everyone starts to realize they were oversold and they rush to get their money out at the top. LLMs won't go away, but the financial collapse will be devastating.

Like %40 of the companies in the index are tech or IT. SO how does the supposed flat line across all those tech companies fit your thesis?

1) If a firm spends $99k on compute to replace a $100k engineer and produces 5x the output, how is that not a 5x increase in aggregate labor productivity?

2) In your aspirin example, why wouldn't a competitor with an identical 1¢ machine sell it for 50¢ or 10¢ to take 100% of your business? You are either making the assumption that 1 company is owning the market and no models are useable beyond that one, or that all of the models that are in the front of the market have a cartel and fix the price, which is not true either looking at API costs.

1. If AI would bring great productivity but all that extra value is captured by closed-source model providers, then everyone would switch to open-weights models + bare metal servers, which would non-tech companies to capture most of the value. Your argument that there is great value in the use of AI, but it's all captured by OpenAI and Antrhopic falls flat on it's face here. The market would adapt quickly.

2. OpenAI and Anthropic are still losing money. Your argument is that 100$ of extra value is produced, but the closed-source model providers capture 99.9$ of that because they are "rent extractors". If you're right, and they capture 99.9 of the value created, that's a huge issue because they spend 150$ to create that value. If even after eating the whole (value creation) pie they're still unprofitable, that's the worst news for the AI industry.

Of course every business wants to capture 99.9% of the value they offer, but competitive forces drive this down to an equilibrium.

Also, as an aside, I've noticed that there's a subset of people on hackernews who use 'weird' to describe any disagreement with their position. It's not 'weird' to have an opposing or different view and removing such language will make conversations on here more pleasant.

What do you mean by output? LoC?

Meanwhile, at least half of the engineers have been pulled off other projects to work on, well, anything that has the veneer of AI attached to it. So we've lost half of the workforce to AI. But sure, the 25% of engineer hours still working on core product have gotten 40% more productive! (Actual coding, 2-3x, but that was never the majority of the work.)

What is the long term steady state here? Not sure if there even is one. But I've always spit balled that my job would be 90% less effort if all humans were good faith actors (no need to worry about privacy and security). Just ditching those two considerations is a 10x multiplier. So conversely, if the bad actors are empowered 10x by these tools, instead of 10% of my job being actual functionality, maybe 1% can be. I need the (promised) 10x boost of ai just to get back to the historical base case.

Perhaps the people seeing massive productivity gains are ones who never bothered with security to begin with (ahem Sony).

The people talking about "increased output" ­— do you have any proofs for these words? I'm working in IT — I don't see any radical productivity growth. Yes, you can doo hello world 4 times faster now — but even then when you need to polish it the speed of development stabilizes near baseline real quick.

The early 2026 research shows only modest improvement in productivity: https://pubsonline.informs.org/doi/10.1287/mnsc.2025.00535

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 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.

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 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.

I guess this bubble has some room to run!

TFP data is murky at best IMO

AI certainly seems to be making students much less competent.

The idea of an AI tutor that could step-by-step customize an education plan to each student and patiently apply every approach to getting the students to understand each lesson, is such an attractive vision.

Both of my kids chafed under lock step pacing of lessons, even in a school where disruptive or unmotivated students were pretty much nonexistent.

It’s terribly ironic that things like calculus, linear algebra and matrix manipulation were being weeded out of the curriculum, right before machine learning took off.

There's many breadcrumbs like these, of a change in culture globally. 50s social expectations about expression and characters were way deeper. And it gradually fades away as life becomes comfy and entertainment based (80s+) I'd say.

The internet age opened a million of possibilities to improve education: improved visual explanations, scalable access to the world's best educators, lowered friction to access students to work with, replayable lessons...

We mostly just kept the old model and placed an addictive device in front of children.

Not everyone on hn is American or part of the Anglosphere.

That hasn’t stopped Tesla. Their self-driving cars work by connecting the car’s controls directly to a neural network.

and if they did for your system, then it's so simple that whether or not you used AI doesn't even really matter

Back testing, hold-out sets, cross validation.

This has all happened before.

    - fixing regular computers issues (confusing browser or os behavior)
    - finding more possibilities with your current programs (around 2020 someone once asked me if I knew a way to capitalize a word on a computer, when she realized there was a dedicated shortcut she became ecstatic)
    - having a rubberduck to discuss your daily work dead-ends
    - thus decoupling from team friction as you need to ask less questions to people that might hurt you.

I don't have much experience in robotics but I've heard that machine vision models have absolutely been changing the industry. And I imagine they are also using AI to write a lot of software too. But not my area, just what I've heard / been reading.

There's probably a good reason for this. Demos usually just show the happy case, whereas in practice you have to deal with all the nasty edge cases of reality.

No one is actually given the agency in any company to set about and engineer and implement an automated solution that replaces existing business units. They also have to be given slack to fail and iterate. Easiest to just run mcdonalds mostly as you ran it the last quarter lest you rock the boat too much. Rinse and repeat and people still bag fries today, and the deep fryer remains probably the most dangerous part of the kitchen.

It did save me a lot time to use AI, especially should next time since I now know which AI app can handle it. So, there was a bit of overhead before I found the AI-app and prompt that would do it.

I have another friend who works for SAP as a project manager. She said she hates the fact that management wants them to use AI for everything, giving them a bonus if they do. But one reason I think their employer insists on that is that when you try to use it for real tasks you discover what works and what doesn't. Gradually we should be getting better at it.

Currently there is still a penalty caused by

1. AI not being able to do a specific task

2. Users not knowing which AI-app can do the job and

3. How to prompt the AI to get it done.

Writing the prompt is what users must do, and writing a prompt that works is a skill they must learn for every task you want AI do for you. Therefore progres is is happening but incrementally.

"A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it."

Admirers of this idea shortened it to, "Science progresses one funeral at a time."

Simple organizations too, and sometimes especially so. The amount of unrealized productivity many small business stand to gain just by adopting simple automations that computers have been able to perform for decades may be as large as (or larger than) the productivity gains ML model and agent adoption enables. And that adoption may just be enabling someone who never wanted to learn Excel macros or regular expressions to command an agent to go do those things which have been possible yet unrealized in a particular small business for decades.

The problem has largely been (and remains) humans set in their ways, of the sort who stopped reading books or seeking any new knowledge whatsoever the moment their mandated or socially expected schooling was complete. Incurious midwits are definitionally the bulk of any workforce, and getting them to change any process is like pulling teeth whether they're working in a small business or a megacorporation. Change often doesn't come until the midwit on some particular throne retires, quits, or is laid off.