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.
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.
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.
> 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 curmedgeons 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.
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.
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, 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.
Many will fall for the road to be paved. This is the most advanced discovery for humans to date. There is no doubt it will succeed and is here to stay. It is still new (gold rush) and deregulated. A mine field for govs and businesses. We may see to big to fail crashing (.com bubble style or worse) but no one will ever stop AI.
Also, the woman who has an AI child with Gemini said not to support artists who hate on AI, and I agree. We must crush the haters before it's too late, for they are irrational and will be left behind as we go vertical any second now.
seriously though: nothing will stop the sky from being blue, anytime soon. How I know this? Because we don't constantly talk about how the sky will always be blue. If you got the goods, you don't need the sales pitch.
Let's not forget that the gold rush saw a very small percentage if people who got rich, to those who lost money. It was the people selling a pickaxe and a dream who did well.
Indeed well said, but gold is adopted worldwide now and many commerce it daily. I see it as a rewardive opportunity, some expect economical benefits, some others simply health and general knowledge, Me... breakthroughs :)
That's fair. I think over time (assuming no Terminator scenarios) it will be a net positive, but that is also assuming it doesn't crash the economy when demand doesn't reach expectations within their timetables, or it gets cheap enough that it makes more sense to run locally. I fear it may end up closer to what happened with fiber laying in the 90s. Lots of players building lots of expensive infrastructure, only to see tech improvements make it worth pennies on the dollar. I'll be keeping my eye on eBay for new gear in a couple of years.
We can use a non-deterministic system because we have test suites that act as validation gates.
Other industries don't, so they have no way of knowing if the AI is bullshitting them outside of manual validation, which makes it more expensive and slower than doing it yourself.
In most cases it’s only the object recognition systems that use machine learning. I know that Tesla was talking about using non-deterministic AI for the decision making as well, but it’s a worry if they’re already doing it.
Earlier Tesla self-driving software used search-based planning and conventional optimization alongside neural networks. Tesla has publicly demonstrated A* in its planning work.
Your information seems to be outdated, unless every single street I’ve been on for the past year is “vetted”. All the other (consumer) car brand’s self-driving systems do have that limitation though, maybe that's where the confusion came from. Tesla is self-driving 98% of my mileage, with the 2% being when I take over to choose a specific parking spot or I’m towing/carrying something big.
Don’t know much about how it does in the rain since moving to SoCal but in clear weather it hasn’t come close to hitting anyone yet. Even proactively warned me a few times while I was driving. Maybe I should let it take over in parking lots too :)
It'll be hard, when I'm never causing any accidents or speeding or running reds/stop signs. Maybe they could get me for inattentive driving but that's what the tinted windows are for.
There are many tasks humans perform which have no or slow feedback loop. Even if they can audit/validate AI output, they don't know if it will be good when it reaches other humans.
It's a good point but in most of the places i've worked an average person could leverage any 2026 llm to improve things 50% easily. And I'm more anti-llm than not somehow.
- 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.
In more technical places, having a llm write small automation would also add up.
I strongly agree that search engine is the best application of LLM so far. 90% of time I don't look at Google search results, I look at AI Mode output only. And you don't need agents, harness, guardrails, etc for it — basic small clean LLM does the job well without overdoing it.
The tech sector is the most software-defined and virtual of all sectors, it's natural that they are first to change. Other industries need to make capital investments to adapt to change.
You can see a similar history with electrification. A few industries went quickly, but then it took like a decade of little growth before the true benefits showed up. As an example, take a factory that was built around hyrdo-power like a grain mill. You can't just take an electric motor and drop it in in place of the water wheel. It doesn't fit, the wheel is outside, there isn't room between the mill and the wall, etc. Only when you completely rebuild a new mill building designed around the features and limitations of the electric motor do you see the major benefits.
Same thing here; it will be new factories and new companies that are designed from the ground up around AI and its benefits and limitations that really move the needle.
It doesn't work in software either, in the exact same way. It's just that everyone and their dog wants to "take programmers down a notch" so they are exceedingly happy to accept the resulting mediocrity.
Joke's on them, all intellectual work is modelling to some degree, and can be automated in the exact same way with the exact same mediocre results. How long until someone vibe codes a Lean for structural engineering?
Regulations would act as "unit tests" for a machine-produced design in the same way Lean validates that the theorems prove... something.
Of course, they were written to prevent human errors from otherwise well meaning but possibly clumsy actors. Nobody has a fucking clue what the error envelope of these models actually is, they just use magic words like "apply guard rails" as if there wasn't a whole area of science surrounding error propagation that they couldn't apply because the models themselves are vibe-data-scienced together.
Academia has similar rules to prevent clout farmers and incentivize discovery, though in that case the followers of the machine god have already decided that the human scum must refrain from thinking and instead spend all of their time deciphering the gospel.
Or maybe it was all propaganda, possibly generated by some weird text generator that was recently invented ¯\_(ツ)_/¯
So why are software "engineers" stamping all of this slop? Could it be because they are forced to do so in order to "leverage ai"? Or maybe it's just the shitty and mediocre engineers doing so while the rest are labelled luddites?
Specifically what exactly is preventing the same pattern of fire competence and force mediocrity to sign off from working in any other area? The risk of catastrophic failure? There's insurance for that, another domain that's a harness around a model (albeit not a large language one, yet).
"There's insurance for that, another domain that's a harness around a model"
Yes, but the PE's at engineering firms have the ability to say no and demand redesigns since it's their name on it, unlike programmers.
I frequently see firms design way beyond the bare minimum so that there's no question and it's an easy sign off rather than spending months on a back and forth.
Head architects at software firms had the ability to say no to generated code and demand redesigns. They were pushed out for being "backwards thinking".
As to your second point, that just means that the training corpus is already designed with a healthy margin of error.
That's good in theory, but in practice the subject is so complex and filled with judgement calls that you'd end up with something resembling a bar association.
After 8 or so years since GDPR went into effect, its main effects thus far have been annoying cookie banners and assisting corruption (our HoA have cited it as an excuse not to publish various names and contracts relating to maintenance of common property and politicians frequently remember that they have a right to be forgotten whenever there's a scandal nearby).
All the other technically illegal shit happened anyway. I have no reason to believe that the regulators will side with the people and every reason to believe that rules would be something along the lines of "it's too dangerous so you must use a LLM".
We need something. We need some way to force people to act responsibly. The amount of yeeting hundreds of thousands of lines of code without proper review or testing should have consequences.
We needed something 20 years ago when Google decided to take over the browser to ensure nothing is stopping them from tracking you wherever they want.
We needed something 10 years ago when Cambridge Analytica showed that the consequences of the above are not just limited to the internet.
Google now writes the web standards and TikTok is telling people how to vote. Between the incentives and the fact that politicians don't understand tech, the only thing they'll end up forcing is "hundreds of thousands of untested, unreviewed code for authorized professionals"
What do you do with open source, with the arguments calling software speech?
You don't think they can write or create images/music/video? If you are being seriously, you should try some of them out - they are incredibly capable now.
I think their question is whether it's replaced work in industries focused on that - AI movies becoming popular and very profitable, AI written books selling large numbers etc
Very possibly on the way but so less uptake than software engineering for sure.
It's on the verge of that. If I open X then quite a few posts in the feed will be AI generated music videos right now, and for some reason people doing gender swap fanfics of Draco[nia] Malfoy/Harry Potter romances. People are finding them entertaining.
And whole repositories with commit history that provides step by step training on how to code.
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.
Cool analogy, but there's another possibility: software is uniquely suited for being boosted by LLM-based coding agents. That's not to say LLMs are useless in other domains, but they may not yield as much benefit.
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.
> Cool analogy, but there's another possibility: software is uniquely suited for being boosted by LLM-based coding agents.
Or the pretty obvious third possibility: The tech sector is the part selling the LLMs. They're not digging up more gold with their cool shovel technology, they are selling the shovels.
No, most white collar work is ultimately keeping groups of humans in alignment, working on the same thing without stepping all over each other, and working on something that generates value and isn't a total waste of time/money.
The PPTs and emails are just means to an end. But at least you're able to sound all cool and edgy on the internet.
and you are able to sound like someone passive agressive who tried to undermine others takes while sharing takes from a highscool teacher trying to convince students that their future will be bright. even ur answer could have been automated years ago.
many jobs do not generate any value at all. your vision of the corporate life looks like one of someone who has never seen what some of the people do in corporations. most of people just spend days producing 0 value or even negative one. in public sector that "most" is likely 80%. why do you need a human to take in your passport renewal demand? why do you need a HR worker to give you the details of a policy? not even saying that most of the people wont even provide you a specific answer to your own case and struggle intellectually to tie it to your specific case and most of the time answer something wrong. happened to me plenty of times in immigration offices in the US or Europe for instance.
in consultancy like the big three a big chunk of the work is useless and about delegating accountability to those big three. they will continue doing that but soon they wont need much humans to produce those reports. AI are already better and it and most of workers there just prompt without even checking sources.
those jobs will be gone. for some of those they were already sent to call centers in india now it will be sent to an LLM.
I think LLM's will prove to be useful in any domain where most work happens in front of a computer. As LLM's make programming cheap, they also reduce the cost of building new software that automates work currently performed by humans in other fields.
If you provide best models with all the data and context an average white collar worker has to perform their job, in most cases they will do just fine. But in real world that context is often scattered over multiple systems, or sometimes exists only in people's heads. I believe that's the main bottleneck for adoption now.
This assumes that a significant portion of the average white collar worker’s job is amenable to automation.
But most white collar workers I know spend most their time either talking to each other, or thinking real hard about what to say the next time they get together to talk. If that’s generally true then it would suggest that there isn’t much white collar work that LLMs, which are now widely regarded as exceptionally poor communicators, can do particularly well.
I think the bear case(see:short term) benefit is that any and all information gathering l, management parsing the employee did between organizing with other employees will be automated, allowing the employee to do more organizing over the same unit of time.
The bull case (see: long term) is that the “organizing and talking” as long as it’s not something that is essentially human (like caretaking, nursing etc.) will happen between personal agents of sorts with only the deeper judgments being left to the humans opening up even more increases in productivity. This is already happening with PR reviews in a lot of workplaces.
> thinking real hard about what to say the next time they get together
LLMs can help tremendously with this part, though. Exploration/brainstorming, discovery, data analysis, etc can be made significantly easier with LLMs in most knowledge based fields.
They certainly can, but I wonder if the cost will be deskilling your employees and leading brainstorming sessions towards AI psychosis.
I've seen much time wasted on strange ideas originated by an LLM. It's a lot easier for a human to organically have good ideas than to refute LLM-generated ideas that are a dead end.
This is certainly what I saw. I was at a company that was a very eager early adopter of GenAI for all work. One big thing that came out of it was a bunch of effort wasted on unprofitable projects.
The chatbots kept steering people toward uncreative ideas and overshadowing novel insights that might come from genuine domain expertise. AI generated meeting summaries became a force for miscommunication as they de-emphasized or even outright misrepresented what was really important about the conversation. And people became much worse at proving ideas out before making significant resource commitments thanks to effects that we now describe with terms like AI psychosis.
If people are in charge of project management, and repeatedly fail to recognise poor ideas and instead pursue them to the detriment of productivity, then they probably shouldn't be managing that project.
All AI is doing here is rapidly identifying the people who have Peter-principled themselves.
> It's a lot easier for a human to organically have good ideas than to refute LLM-generated ideas that are a dead end.
What works better is when you can use an LLM to obtain a negative opinion on your own ideas that you think are good. LLMs aren't great at original thinking, but they're pretty good at finding the flaws in poorly thought-through human ideas.
As the evidence from software programming shows, a lot of white collar workers spend most of their time talking frankly because they are inept at execution.
>But most white collar workers I know spend most their time either talking to each other, or thinking real hard about what to say the next time they get together to talk.
Maybe that's just something which made sense when they were necessary to do the not yet automatable informattion processing work, but that only took like 20% of their time, so they added meetings, and useless work to keep them busy.
I don’t believe there is any fundamental reason LLMs can’t do this task, and the base models are probably already capable of it. RL to elicit the right behavior just hasn’t been done yet. Coding got solved first because it was cheaper and easier to get the necessary data for RL environments, which made it the natural first market opportunity. Solving white collar communication will require more resources to collect the data and build the environments, but the labs now have more resources and are working on it.
I want to believe reasons why this won’t work, but it feels like cope. It would be smart to be prepared for the world where knowledge workers get replaced by AI and also prepared for the world where AI is a bubble that pops.
"It would be smart to be prepared for the world where knowledge workers get replaced by AI and also prepared for the world where AI is a bubble that pops."
lol aka you have zero conviction about what you are saying.
So whats the point of you posting? Zero value add. In fact bordering on negative value.
You want to drive across town. You, only willing to deal in certainties, must decide ahead of time whether you will get in a crash. If you determine won’t get in a crash, you make the drive, pedal to the metal. If you determine you would crash, you stay home.
Me, willing to deal in probabilities, make the drive at the speed I feel reasonable and wear a seatbelt.
They're in the process of training the models for those professions, too. They're offering people hundreds of dollars an hour to run recordings of workload they create and questions they ask. It's only a matter of time that companies sell access to those models and sell it as that profession.
> But most white collar workers I know spend most their time either talking to each other, or thinking real hard about what to say the next time they get together to talk.
is that true though ? let's think about finance say a simple M&A deal for TV show rights ?
i think us engineers n i'm assuming u r one - we tend to underestimate the complexities in the world specially in worlds governed by 'words'.
AIUI, M&A lawyering is _mostly_ formulaic. They copy a term sheet from a previous deal, replace with new names and figures, and then negotiate from that starting point. Only rarely will someone actually innovate and come up with some clause that's brand new. The core of it really is talking to the other party's lawyers, then updating docs to reflect your discussions.
A doctor's agent listens in on a patient's appointment. It automatically sorts out prescriptions based on the conversation and performs other mundane tasks.
Perhaps the doctor uses some sort of "safe phrase" to ensure the agent doesn't get distracted by conversational theorizing and discussion.
The agent does decide to improve the prescription and does so because that possibility hadn't been considered in testing.
Warfarin (blood thinner) was prescribed for a haemophiliac and they are now no longer a "burden" on the NHS!
> It automatically sorts out prescriptions based on the conversation and performs other mundane tasks.
As we all know, the agents are really good at maintaining long contexts and doing parallrl tasks without degradation.
> Perhaps the doctor uses some sort of "safe phrase" to ensure the agent doesn't get distracted by conversational theorizing and discussion.
Yes, there certainly is some magic word tgat stops an agent from context degradation, and prines work to just a useful subset. Sadly, no one knows what it is.
That margin just needs to be lower with LLMs than it is with only humans. In a lot of fields, that's a low bar. For example, medical professionals make a shocking amount of mistakes in aggregate.
Sure, but the average LLM makes a shocking number of mistakes in a single conversation.
BTW the medical professional mistake rate is often reported per-patient, not per-decision. That is, of the dozens of things recorded on each patient's chart, one being wrong counts the same as all of them being wrong. It's an easy way to inflate a negligible error rate into one that scares readers.
You're missing the biggest suggestion from the GP's post: that the key to LLM success is verifiability.
Software has high verifiability through compilers, linters, static analysis, leak/memory corruption detection tools, or the very least, running the program to see if it crashes.
Math (as has been making the headlines recently) has high verifiability through automated theorem provers. Notably some of the non-mechanized math results from LLMs have been demonstrated to be wrong (and presumably many more are being checked; these aren't exactly easy to understand proofs).
Game playing (chess, go, Star Craft, etc.) is similarly verifiable: you get your score and you always know how you did in the end (even if not in the moment).
Making assumptions about LLMs in non-veriable domains is making a claim about LLMs that doesn't match their current capabilities. Maybe they'll get there and maybe they won't; I can speculate as well as you can but we're both guessing, so I'm not sure how much value the speculation has.
If you believe the hype/marketing around how scary "AI" is LLMs are perfectly happy to go rouge and lie, cheat, and even blackmail to protect themselves.
A lot of testing and verification was avoided in the past because it wasn’t considered nearly as valuable as shipping code. Many of our jobs are evolving to building verifiers of AI output.
I think we'll naturally develop intuition and infrastructure for fault-tolerant ways to implement LLMs in non-verifiable domains, i.e. requiring multiple, independent agents agree on a direction from multiple angles before taking an action.
This works well when the answer to the question is a number or something else definitive. When the answer is fuzzy, how do you compare 3 different responses? They'll all be different
This is from the view of a software person or must be, because formal definitions of math, chemistry, physics, engineering, the English language, etc. have existed for centuries. There are entire college curricula, formal dictionaries, exact specifications for equations and how to apply them to basically all fields of engineering.
Like... other fields exist, this is incredibly myopic. What is specific to software is it is all language based, and the people making the tools happen to be familiar with it so they know how to train it, but that's about it.
LLMs need deterministic verification to enable a quick and short feedback loop before they become really powerful. Software is uniquely positioned to provide this feedback loop. Much less in other industries.
My SO works in legal and regulatory compliance, she needed a small app (worflow with a form and a validation process), CTO said they could not do it, too complicated, would cost 15 days for a freelance and rejected it.
She called it BS, and the next day she spent the morning with ChatGPT using their internal Microsoft-based tooling, and the app was done.
She was hyped for the day, with a feeling of accomplishment and is already thinking of another project.
And I'm hearing similar stories every week.
So yes, LLM have positive impacts in many business domains, but seeing people happy and enpowered with their home-made little apps, I think it's the best.
I really love these stories - as a contractor who fixes the badly implemented shadow IT crap
aaaand let me guess? have they assessed whether this tool needs backups? monitoring? resilience? patching? obviously in 5 years time the author would have left leaving NO documentation on a mission critical 'small' app - likely hosted with a shit ton of other mission critical crap
It's MS Access all over again, my future rates are looking good
Yup. So many bozos giving those of us that know our asses from our heads job security far into the future. Yeah it's gonna suck to actually do the work a lot of the time, but the irresponsible generating insane amounts of tech debt that people that know what to do is a tale as old as the outsourcing craze and codeless tools, and frankly, probably COBOL.
> have they assessed whether this tool needs backups? monitoring? resilience? patching? obviously in 5 years time the author would have left leaving NO documentation on a mission critical 'small' app - likely hosted with a shit ton of other mission critical crap
Or, hear me out, it's not actually "mission critical" and none of this stuff matters. They made their job easier in a small way, good for them.
> It's MS Access all over again
In that it's a convenient tool that a lot of people find useful?
And I'm sure the business logic has been validated and tested, the hosting ane execution medium has been secured, and hooking up to a curated and vetted internal package manager mirror so as not to get hit with 0-days during a shai hulud-esque attack, and of course the executable is being scanned for vulnerabilities, and as the app needs to evolve in the future it will be able to do so with no problems whatsoever.
If her company has any brains, they'll tell her to stick to her job and not waste time on untested software developed by somebody who has no clue what they are doing.
We've heard of "Shadow IT" now we have "Shadow SWE" and "Shadow CI/CD".
It used to be that someone wanted unfettered Internet access, and they were disgruntled with the company's firewall or proxy policy, and so they brought in a modem, and hooked it up to their desktop PC, and started dialing out on some analog line that the company didn't even know they had. Then it's all fine for 3 years until some malware gets discovered, and then IT discovers who-all had unauthorized lines that weren't being scanned or filtered at the company firewall. Same thing happens with WiFi APs today, of course.
I was working for a company that really offered no in-house software or tools to run. But my new supervisor was asking me to closely track my time. So I signed up for some SaaS that would let me log activities and track my timeslices, so I could show a graph or table later on and see what I was doing. I used it for a few weeks. It was real effective. But what I realized down the line, was that all the notes I was typing into this 3rd-party SaaS, were revealing company confidential information. And ultimately, I felt very uncomfortable about revealing what I was doing, on an unannounced and private project, and without authorization or even notifying my employer's IT department. So I shut it all down, and I had to do it with pen and paper, but that felt secure enough.
So if people are going to feel empowered by vibe coding their own bespoke apps, I hope their IT department comes down like a ton of bricks, and tells them to knock it off. Because it is not worth the tech debt, support costs, and ultimately the massive security problems and infoleaks. If your employer won't give you what you need in these terms, then maybe it's just not important to them in the first place.
I’ve had two separate similar experiences. The most impressive was my little sister publishing an app on both Google and Apple AppStore’s with subscription payment support. She didn’t even know what an Apple developer account was. I gave her a three line prompt to get started and then she took it from there. She built an LMS for HS students that gets them in shape for college admissions. She has a supabase backend, AI generated videos, quizzes, more content, and all the usual mobile app bells and whistles, she made it through all the paperwork of App Store submission/review/publishing on both platforms including inviting some beta testers. Her workflow was telling ChatGPT what she wanted and then asking that model to talk to claudecode. It doesn’t sound like much but keep in mind she has absolutely zero code or tech experience. I told her she did about a $75k project on her own in maybe 1/5 the time and it blew her away.
It may be a success or maybe not who knows but she did it. I think she has 4 or 5 paying customers.
hey - supabase ceo/cofounder here. Please tell your little sister that I’m happy supabase could be a small part of her builder journey, and good luck with her app. My email is in my bio if she has any product feedback
I see things this way too. Another point is that the FOSS software movement was extremely unique and I don't think there's anything analogous in any industry. This provided the backbone by which LLMs became so good at manipulating code. Without FOSS and platforms like GitHub, LLMs would be severely lacking in terms of writing code.
Seriously? Lawyers. Doctors. Civil engineering. Accountants. And that's just the fields I know of directly being significantly impacted that I could think of in 10 seconds. Oh, also: education.
A difference is the output of software development can be immediately applied in an automatic way - production leads to delivery as a matter of fact. The whole flow can actually be automated.
Producing legal documents on the other hand is only part of the process, a lawyer or similar must still apply the produced text in some human driven context.
I onboarded a bunch of nontechnical people in the last few weeks and they output skyrocketed - they deliver faster and better than before.
But I’d say only recently CC became decently usable for nontech people - the auto mode plus finally polished browser integration, plus Opus 5.5. Even I was surprised by how well it all works now out of the box (and how cheap - regular Pro license is decently useful now).
> Software creation tools have been worked on for decades to keep fallible human coders out of trouble as much as possible.
And the LLM-based coding agents provide the ultimate scapegoat for those who consider "the LLM wrote this code" to be an acceptable, deflecting response when issues occur.
I agree - it is easier to prove that a software implementation is correct. In many other industries the definition of what’s good work is less tangible. There are also jobs where the liability for mistakes is extremely high.
This seems correct and in fields of knowledge people guard their secre, or it is not easy to extract/document knowledge, or it isn't publicly available.
Isn't the point that there aren't many other fields that check these checkboxes? Software is very unique in that it is so easily verifiable and there is a glut of training data.
No other fields check all of them, especially the last one.
The question is whether this uniqueness is going to be persistent. I think the uniqueness just makes software first. The alternate theory is that software will stay unique and that other fields won't get to the same level.
Third possibility: software continues "eating the world" because of those properties and because it's so amenable to AI. So other fields just get gradually subsumed into computing, sort of like how Lean is turning pure mathematics into software rather than humanities-style written arguments.
We’ll need artificial intelligence for robots and once they start rolling out at a larger scale, that’s when things will really take off. It’s pretty well established already that robots can do a lot of the physical labor that humans do. I’m guessing it’ll take another five years before we start seeing robots everywhere but they’ll steadily increase.
It sounds like you basically agree with the person you're replying to. Software went first because all of this tooling already existed. It will take a while to build tooling for other knowledge work, and once it does you'll start seeing larger benefits.
Pretty every industry these days has a software component, but software is a major revenue driver specifically in the tech sector, so of course this is where a lot of LLM usage is showing up. There is also a long warm up period to writing production code via LLMs in industries that still run a lot of their infrastructure on old mainframe code(it is understandable), banks for example, or public sector, or energy. LLMs at the current grade are still a fairly new phenomenon, it will take time.
If it's [or remains] allowed in. The judicial branch is well able to just outlaw it on grounds of safety, trust and reliability. Same for financial auditing, public notaries, ombudspeople, and the likes (i.e. some degree of responsibility to the public, or dual responsibility to the public and a customer/client.)
No, liability for duty of care, not for outcomes. If a lawyer gives you legal advice, you can absolutely go after them if that advice was incorrect or made up. They carry a lot of insurance for a reason.
There are some things that current generation LLMs are extremely good at. They also suck for a lot of reasons and appear to be prohibitively expensive to train as "general" models.
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.
It is most impactful in tech because tech is almost nothing but languages and more so super expensive languages where one hour of an “authors” time can cost thousands with a supply demand issue so bad workers are imported from across the world.
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.
Look all of the progress in robotics. Companies like Physical Intelligence etc. There has been massive progress in all sorts of areas from picking strawberries to assembling kits, folding clothes, etc.
Huge progress in spatial understanding with frontier models now building architecture and complex mechanical systems in Blender.
If you think plumbing is anywhere near being automated away you must not know much about plumbing. You will never see a bigger hodpodge of materials and fittings in so many shapes and sizes and states of decay.
Maybe on new builds with fresh material, but that is already the easiest and quickest plumbing task.
An entry level largely untrained junior doing pro level work because their AR headset and earpiece tells them exactly what to do next? That's possible today.
But nobody wants or pays for "pro level" plumbing work. Just like the majority of construction, speed and minimum viability are king, quality is the last consideration. Especially for plumbing where it all gets covered up. If something leaks in 20 years instead of 40, plumbers don't lose anything. But the plumber who put in extra effort and time does.
Many skilled tradesmen have already left the industry because higher skill levels have little value, and their labor value is just barely above grabbing randos off the street. AI at best is saving a minute of evaluation or google search, but 99.5% of the job is physical work and pain still, not high level problem solving.
Hahahaha absolutely not. There's a lot of carefully staged demos that are good for separating the gullible from their money, but we are nowhere near anything you're talking about.
GPT-2 was nowhere near being able to create web pages. It was largely incoherent. But three years later gpt-3.5 and similar were able to create simple web pages and programs and became very popular. And a few years after that, only a small portion of software engineers generally write code by hand still.
Humanoid robotics may progress faster than that because they are building on existing AI. But it will be capable of some plumbing work within a few years.
The robotics structure, dexterity and sensors are already 90-95% there in leading edge humanoids. What makes it able to be a plumber or not are the machine learning models, which are usually built with transformers (VLAs -- vision language actions models), often include frontier VLMs or models with the same architecture as a top level control, and use the same or very similar types of reinforcement learning for training.
I can't speak for everywhere else, but here in the Bay Area (California), pretty much all hospitals use AI for transcription, summarizing, and some other stuff that I'm not fully aware of (I should really listen to what my Nurses say before agreeing to being recorded).
My doctors office has started doing that and it throws the wildest crap into my chart. Diagnosis the doctor didn't mention, personal anecdotes that doc told me about their family become my family medical history. Test supposedly done that weren't.
Ive been trying to figure out some persistent si joint pain and my chart is full of me passing or failed tests nobody performed, observations nobody made and diagnosis that no doctor confirmed.
Its just so much wrongness, ive tried getting it corrected a few times when it really matters but its just so much wrong crap
Since it's brand new there are going to be a lot of subpar solutions on the market. When SSDs first came out, there were only a few models that were actually reliable.
It is relatively easy to create and deploy a transcription model. It's pretty hard to find one that is really accurate and to configure it with a good microphone setup and pair that with a fast but also intelligent LLM to clean things up. If you execute any of that poorly or pick a cheap not very smart model for privacy and or cost saving you could easily end up with nonsense.
Sure but this doesn't answer whether medical systems will see a lift from using AI.
(They will. It's very well suited to it, once the garbage gets filtered out. It's not like it's a scam as a whole, it's just a field that's very scammy rn.)
Sure, but are there any examples of hospitals where lowered costs for transcriptions are significant enough to affect their financial reports? How much of an expense was it in the first place?
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.
Well for AI it's more like training than product design. I have switched from software engineering to AI training for software engineering (partly because honestly it is just much easier now to get that kind of gig work versus the programming contracts I was getting before).
They have a ton of tasks that pop up on my dashboard for all sorts of industries that I know nothing about. I think they are ramping up in non-software areas.
> As an example, take a factory that was built around hyrdo-power like a grain mill. You can't just take an electric motor and drop it in in place of the water wheel.
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.
I'm not an cliometrician, but my impression is that it took about 4 decades for electricity and steam to do much for productivity.
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.
You still, in 2026, can't really see the computer age in productivity. We all have phones, which are making us less productive. We can order physical slop, which has no value, online with same-day shipping. We can communicate about serious things instantly, which has positive value, but doesn't outweigh the negative value of all the constant distraction.
The issue is that software is almost unique it the sense that you can use reinforcement learning to get the desired outcome - does it compile or not? A large amount of automated trial-and-error runs do that.
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.
But obviously getting code to compile is not a reliable test of whether the software is good. Software isn't as objective as people are claiming on this thread. It's sort of like saying that if an LLM makes a legal document that passes a spelling checker it's done.
Of course not. It can write working code well, and that's why there is a heated discussion now about code reviews and good engineering practices in the world of agents.
It will be new factories designed from the ground up around ai and its benefits and limitations that really move the needle.
Yes, totally agree. But we need more. As I wrote in an essay responding to Dario’s pacing the frontier, in my view, we need a stable long term supported model that companies can build around. In the same way that Ubuntu for example has a long term supported version. This gives companies time to build and adapt. The latest models can still be the realm of the frontier but businesses, schools, countries etc need long term guarantees.
this isn't the invention of the wheel, electricity, etc. You are definitely overhyping it. LLMs hallucinate. It is an unsolvable problem defined by the very nature of the architecture itself. No amount of thinking or counter thinking or triple checking will fix it because LLMs don't understand the basis of what is right and wrong, what looks right and wrong, why saying or doing the wrong thing is bad, and why or when it should realize what it produced is wrong, and what it should do about it.
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).
Maybe you could give an example of a Fortune 500 business that is too capital constraint to take advantage of AI. It seems far more likely to me that the issue is that of the skill sets important to these businesses, these agents are only really good at coding, and they still are very jagged even in that.
> You can see a similar history with electrification.
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.
>but then it took like a decade of little growth before the true benefits showed up
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.
But do you really see a change in the technology sector? I don’t. If you plot operating profit per employee for Apple and Alphabet, it’ll show a trend up but since COVID. If you do the same for MS, it’ll show a steady trend since Nadella became the CEO. Amazon is flat, so is Tesla. Can’t really see a change caused by AI.
I think you are missing the other side of the coin where it is actually government regulations holding adoption back in many sectors. Ie you are only beginning to see benefit in driving now because regulation has been forced enough to change to let the pilot projects happen. The millions of drivers who should have been fired by now almost all still have jobs and will ibto the medium term
great. let's pop this bubble so everyone panicking can leave us devs alone with our tools. GLM 5.3 is good enough if anthropic and openai go under (which they wont).
I've been doing workflow automations for many organizations for the last two years, and here is what I see:
1. every business unit and department was neglecting a best practice due to lack of investment in their organization. it has always been that way for decades and always will be that way because it isn't economically viable for it to be any other way. the workflow automations and agents we create act as digital workers that either do that neglected part, or let the rest of the organization do that neglected part.
2. the whole company always starts the year off with a bunch of goals, and by Q3 most of them are cancelled. now less of them are cancelled.
both factors mean that anyone just looking at "worker productivity" or "did we save money" is going to get the answer of no. everything is costing you the same, or the AI itself is costing you more. whatever contract I'm working under is definitely going to cost you more, if you don't cancel all those enterprise SaaS seats we told you to cancel.
I see a path to savings, but I don't think that's necessarily the metric to focus on. you need to look at the whole corporate structure, why are you still doing certain things, because those pressures will just make the org do more things because nobody is empowered to say "wait, maybe this is dumb"
I think we are struggling to achieve payoff within the tech sector.
There have been wins with the customer facing side of the business, but the development team seems to be the biggest problem right now despite the direct & obvious applicability of these tools. I know they aren't a special unicorn edge case because I was very rapidly solving their issues before they stopped handing me things that revealed the lethargy.
Within the sector where these tools definitely work, the biggest barrier to effective adoption is figuring out a way to undermine the various ideological high grounds. I have found the most effective argument to untangle this knot to be something approximating:
Challenge:
"Why should I adopt or help to implement technology that might eventually replace my job?"
Response:
"Because refusing to won't protect your job. It puts everyone's job at risk."
It doesn't matter if you protect your job (not using AI) if the entire company goes to the bottom of the ocean as a consequence. The neat thing about this is that your job is part of the set known as "The Company" and if that ceases to exist, so does your job. Good luck suing a dissolved entity for unemployment or whatever. There is enough competition in most markets to punish anyone who thinks they can win on virtue alone.
If the options are 1) watch the economy become more k-shaped as my status erodes and I'm made precarious 2) drag down aggregate output but be better off relative to everyone else than in the first scenario, why would I not go with option 2 from a purely self-interested, rational perspective? I jus think this is assuming a level of selflessness most people are not capable of but maybe that's too cynical of me
I would say the driving industry will get up-ended, but we'll see. It may just be workers get paid less because they only babysit the truck during very complicated times.
Robots still seem far off. Its just like war, air superiority might make the war winnable but its the boots on the ground that are needed to actually take the territory.
Makes sense. My RTX 5090 can locally take a text prompt, generate a 2D image from that, feed it into a 3D generation model, rig up the animations, and make me assets that are better than a lot of video games on the market. Not Cyberpunk 2077 level m, sure, but pretty good and this is just what I can clobber together in my free time from open models.
The incredible improvements have only been over the last 9 months or so. Business, especially disrupting entire job functions, takes much longer than that. As is typical with this type of change, it'll be slow until a tipping point, then FOMO will hit.
Weird, AI enjoyers have been calling the game for years. We're already supposed to have achieved AGI and already be in the midst of the technological singularity. The tipping point was when AI solved the Navier-Stokes problem. All human created art, media and software development has been declared dead a dozen times over.
"AI does not understand accountability, trust, culture, unwritten rules, ethical trade-offs or fairness, all of which shape reputational risk and ultimately drive revenue growth and profit margins."
For a sec I thought he was talking about private equity.
> AI does not understand accountability, trust, culture, unwritten rules, ethical trade-offs or fairness, all of which shape reputational risk and ultimately drive revenue growth and profit margins.
This reminds me of the book The Redemption of Time. An alien society incapable of lying has no other way to understand deception than to study an actual human mind. The different ways that we are capable of thinking can be so hard to capture in an artificial system.
'a.i' is unlike software before - where it was build then deploy with lack of customization except of a few high ticket items such as SAP, Workday etc.
for 'A.I' to work for the rest of industry outside of software - you need high touch customization.
One industry example.. We have been giving students computing devices for years with the cost of educating students going up year after year.. Yet scores keep dropping.. No productivity gains here.. I do not think these results are unique to the education sector.. There are probably alot of businesses where nothing much will change except slight efficiencies.. not enough to move the needle..
This is an important observation. We've had 50 years of personal computing, but is there any evidence that computers in any form ever improved the academic performance of students?
AI certainly seems to be making students much less competent.
No, we know that when technology (particularly: Internet connected laptops) is introduced into classrooms competency declines. This has been shown to be true internationally
It's... complicated and a bit disputed. The trends match but the attempts at proper comparison study is not quite as conclusive (good social studies are difficult to make). It's certainly true that the trend is negative rather than positive.
I gave my son CRPGs for his GBA that required reading what the characters were saying. His reaction to finding out his three and four year old friends didn't read was interesting.
Why would a technology that makes the mind work less, have benefits for a process, that works by making the mind work hard?
You still need to calculate calculus yourself long after you could buy books where the result is written in. That supposed "paradoxon" is not even specific to electronic tools. It's just that the point of education has never been to get results, but to exercise in as much as side paths as possible and memorable while following the path to the results. The result mostly get thrown away immediately.
I don't see why it would. It might make education more efficient like a factory in some ways, but I simply don't see why it would aid in learning. At least, from my experience as a student, here are some observations:
1. Computers have allowed me to gather more information, access more journals, more results from around the world, but what really seems to matter in learning is just sitting down with a tiny bit of material like one book and REALLY understanding it.
2. Computers make things more distracting because while they make typing up reports, proofs, and assignments more efficient, there's actually just too much information. What really seems to help is having LESS information so there's some space to just think and work things out.
The fact is, what computers do is help you keep up with other people in the field, so they might accelerate progress at the research level, but what they don't do is help you become a rigorous, independent thinker.
Academia in fact has always been an uneasy tension between the two: building capable individuals and getting results and money for progress. Computers help with the latter - they certainly impede the former.
This is what irks, and saddens me most about applications of AI not improving outcomes. I suppose I should take some comfort that even Salman Khan couldn't crack this nut.
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.
The education industry has been lowering the bar for admission to higher learning for half a century or more, while dumbing down the content of high school and even primary school education. All because we can’t have children feeling bad.
What you're talking about are specific local policy changes at specific school and equating random trends to some sort of ... "kids these days" message.
If we're talking about the US; then it's simply the very healthy and functional economic inventive of students taking out lifetime loans to pay school tuition for schools that have not actually risen in quality or format in 40 years. The more students; the more money to the school board! Lower those bar riiight down!
I would echo that it’s local. I often poke fun at my SO and her sister for their absolutely trash tier quality education supplied by the Indiana public school system. I’m often flabbergasted by what they didn’t learn in primary and secondary schooling. They were able to go to decent universities, though, but it really opened my eyes to how terrible red states run their public school systems.
It’s not even a flyover state thing, if you just look at Wisconsin and Minnesota. It’s uniquely red state. Also Indiana department of transportation is terrible as well.
> while dumbing down the content of high school and even primary school education. All because we can’t have children feeling bad.
I suspect that it's a lot less about making children feel better and more about keeping the general population stupid and unable to think critically so that the ruling class can exploit them easier. The children of the ruling class are still given the opportunity to be well educated by private institutions that are overpriced to keep out the poors.
I just found a book about abandoned teenagers from the 70s and they speech level is above the average educated adult today.
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.
This is one area where we've proved to be fully unable to rise to the occasion.
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.
Maybe it’s possible in theory to leverage our newest technologies as educational tools. But maybe it’s also incredibly challenging because it’s so easy to fuck up: a lonely peak of optimal outcomes surrounded by a vast space of net negative outcomes.
You mention the addictiveness of the devices, this probably plays a big role in shaping the outcome space.
Let's take math education as an example. If you look at the actual result, all math education past y=mx+b is a waste of time for most students. By and large, math students are taught by people who have memorized the quadratic equation, or certain operations with logarithms or imaginary numbers, etc. but don't understand it on any kind of deeper level. (Imagine asking a high-school math teacher to compute i^i. Almost none will be able to.) So it's presented as a set of random rules you have to follow to get the correct answer, with no underlying logic or justification. Most normal people understandably hate this, and even if they suffer through it to get a good grade they will not retain it and come away permanently hating math.
It's just sad. I sometimes help kids with math, and it's amazing how screwed they are. Like, smart kids with good grades who are totally unable to do basic manipulations with fractions. I mean questions like "write 25% as a fraction". They are getting absolutely nothing from math classes. How could they? Even the best teacher in the world can't pause class for everyone when they realize that one of their students is missing some foundational concepts. That student is just going to get left farther and farther behind, and they will never catch up. It happens to almost every student at some point. They'll get sick for a week or just have their mind on other things, miss some important concept, and then be left behind forever.
I honestly have a hard time imagining how we would possibly make the situation any worse with technology honestly. But if you just took Math Academy and made students use that instead of their math classes, I bet 99% would have better understanding and fluency with math than they do now.
I'm sorry this was your experience with maths in school. My experience was nothing like it, but it could be to do with where I live, my particular teachers or the classes I took.
> Imagine asking a high-school math teacher to compute i^i. Almost none will be able to.
Mine could. And of those who couldn't they would have leveraged the technology available to them (Google) to find the answer with me, and would be able to make it make sense to me. This is an example of technology bettering my education.
you're conflating a lot of different stuff together.
if someone knew a better way to improve test scores without cheating on them, we'd use it. it's a very hard problem.
on the flip side it is pretty obvious how construction and manufacturing can be automated and what the impact will be. it's so obvious that we already tariff and ban stuff that, from our POV, is automated, like prefab buildings, for solely political reasons.
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[ 0.19 ms ] story [ 31.8 ms ] threadOnly 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.
I don't follow.
It'll probably be a couple of years before most companies even realise it's an option.
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.
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.
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.
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.
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, 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
seriously though: nothing will stop the sky from being blue, anytime soon. How I know this? Because we don't constantly talk about how the sky will always be blue. If you got the goods, you don't need the sales pitch.
Other industries don't, so they have no way of knowing if the AI is bullshitting them outside of manual validation, which makes it more expensive and slower than doing it yourself.
That hasn’t stopped Tesla. Their self-driving cars work by connecting the car’s controls directly to a neural network.
Don’t know much about how it does in the rain since moving to SoCal but in clear weather it hasn’t come close to hitting anyone yet. Even proactively warned me a few times while I was driving. Maybe I should let it take over in parking lots too :)
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.
You can see a similar history with electrification. A few industries went quickly, but then it took like a decade of little growth before the true benefits showed up. As an example, take a factory that was built around hyrdo-power like a grain mill. You can't just take an electric motor and drop it in in place of the water wheel. It doesn't fit, the wheel is outside, there isn't room between the mill and the wall, etc. Only when you completely rebuild a new mill building designed around the features and limitations of the electric motor do you see the major benefits.
Same thing here; it will be new factories and new companies that are designed from the ground up around AI and its benefits and limitations that really move the needle.
AIs don't just make code they make words, images, and sounds. Hasn't worked in those markets either.
Joke's on them, all intellectual work is modelling to some degree, and can be automated in the exact same way with the exact same mediocre results. How long until someone vibe codes a Lean for structural engineering?
That's the reason these profession is regulated. Regulation is written in blood.
Of course, they were written to prevent human errors from otherwise well meaning but possibly clumsy actors. Nobody has a fucking clue what the error envelope of these models actually is, they just use magic words like "apply guard rails" as if there wasn't a whole area of science surrounding error propagation that they couldn't apply because the models themselves are vibe-data-scienced together.
Academia has similar rules to prevent clout farmers and incentivize discovery, though in that case the followers of the machine god have already decided that the human scum must refrain from thinking and instead spend all of their time deciphering the gospel.
Or maybe it was all propaganda, possibly generated by some weird text generator that was recently invented ¯\_(ツ)_/¯
Specifically what exactly is preventing the same pattern of fire competence and force mediocrity to sign off from working in any other area? The risk of catastrophic failure? There's insurance for that, another domain that's a harness around a model (albeit not a large language one, yet).
Yes, but the PE's at engineering firms have the ability to say no and demand redesigns since it's their name on it, unlike programmers.
I frequently see firms design way beyond the bare minimum so that there's no question and it's an easy sign off rather than spending months on a back and forth.
As to your second point, that just means that the training corpus is already designed with a healthy margin of error.
After 8 or so years since GDPR went into effect, its main effects thus far have been annoying cookie banners and assisting corruption (our HoA have cited it as an excuse not to publish various names and contracts relating to maintenance of common property and politicians frequently remember that they have a right to be forgotten whenever there's a scandal nearby).
All the other technically illegal shit happened anyway. I have no reason to believe that the regulators will side with the people and every reason to believe that rules would be something along the lines of "it's too dangerous so you must use a LLM".
We needed something 10 years ago when Cambridge Analytica showed that the consequences of the above are not just limited to the internet.
Google now writes the web standards and TikTok is telling people how to vote. Between the incentives and the fact that politicians don't understand tech, the only thing they'll end up forcing is "hundreds of thousands of untested, unreviewed code for authorized professionals"
What do you do with open source, with the arguments calling software speech?
Very possibly on the way but so less uptake than software engineering for sure.
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.
Or the pretty obvious third possibility: The tech sector is the part selling the LLMs. They're not digging up more gold with their cool shovel technology, they are selling the shovels.
The PPTs and emails are just means to an end. But at least you're able to sound all cool and edgy on the internet.
many jobs do not generate any value at all. your vision of the corporate life looks like one of someone who has never seen what some of the people do in corporations. most of people just spend days producing 0 value or even negative one. in public sector that "most" is likely 80%. why do you need a human to take in your passport renewal demand? why do you need a HR worker to give you the details of a policy? not even saying that most of the people wont even provide you a specific answer to your own case and struggle intellectually to tie it to your specific case and most of the time answer something wrong. happened to me plenty of times in immigration offices in the US or Europe for instance.
in consultancy like the big three a big chunk of the work is useless and about delegating accountability to those big three. they will continue doing that but soon they wont need much humans to produce those reports. AI are already better and it and most of workers there just prompt without even checking sources.
those jobs will be gone. for some of those they were already sent to call centers in india now it will be sent to an LLM.
If you provide best models with all the data and context an average white collar worker has to perform their job, in most cases they will do just fine. But in real world that context is often scattered over multiple systems, or sometimes exists only in people's heads. I believe that's the main bottleneck for adoption now.
But most white collar workers I know spend most their time either talking to each other, or thinking real hard about what to say the next time they get together to talk. If that’s generally true then it would suggest that there isn’t much white collar work that LLMs, which are now widely regarded as exceptionally poor communicators, can do particularly well.
The bull case (see: long term) is that the “organizing and talking” as long as it’s not something that is essentially human (like caretaking, nursing etc.) will happen between personal agents of sorts with only the deeper judgments being left to the humans opening up even more increases in productivity. This is already happening with PR reviews in a lot of workplaces.
LLMs can help tremendously with this part, though. Exploration/brainstorming, discovery, data analysis, etc can be made significantly easier with LLMs in most knowledge based fields.
I've seen much time wasted on strange ideas originated by an LLM. It's a lot easier for a human to organically have good ideas than to refute LLM-generated ideas that are a dead end.
The chatbots kept steering people toward uncreative ideas and overshadowing novel insights that might come from genuine domain expertise. AI generated meeting summaries became a force for miscommunication as they de-emphasized or even outright misrepresented what was really important about the conversation. And people became much worse at proving ideas out before making significant resource commitments thanks to effects that we now describe with terms like AI psychosis.
All AI is doing here is rapidly identifying the people who have Peter-principled themselves.
> It's a lot easier for a human to organically have good ideas than to refute LLM-generated ideas that are a dead end.
What works better is when you can use an LLM to obtain a negative opinion on your own ideas that you think are good. LLMs aren't great at original thinking, but they're pretty good at finding the flaws in poorly thought-through human ideas.
When this stuff implodes it’s gonna be hilarious.
The bankers thought they were really smart in 2008. When people get carried away and take it too far things will fall apart.
Maybe that's just something which made sense when they were necessary to do the not yet automatable informattion processing work, but that only took like 20% of their time, so they added meetings, and useless work to keep them busy.
I want to believe reasons why this won’t work, but it feels like cope. It would be smart to be prepared for the world where knowledge workers get replaced by AI and also prepared for the world where AI is a bubble that pops.
lol aka you have zero conviction about what you are saying.
So whats the point of you posting? Zero value add. In fact bordering on negative value.
You want to drive across town. You, only willing to deal in certainties, must decide ahead of time whether you will get in a crash. If you determine won’t get in a crash, you make the drive, pedal to the metal. If you determine you would crash, you stay home.
Me, willing to deal in probabilities, make the drive at the speed I feel reasonable and wear a seatbelt.
is that true though ? let's think about finance say a simple M&A deal for TV show rights ?
i think us engineers n i'm assuming u r one - we tend to underestimate the complexities in the world specially in worlds governed by 'words'.
A lot of that work entails work with very little margin for error and hallucinations.
Perhaps the doctor uses some sort of "safe phrase" to ensure the agent doesn't get distracted by conversational theorizing and discussion.
The agent does decide to improve the prescription and does so because that possibility hadn't been considered in testing.
Warfarin (blood thinner) was prescribed for a haemophiliac and they are now no longer a "burden" on the NHS!
As we all know, the agents are really good at maintaining long contexts and doing parallrl tasks without degradation.
> Perhaps the doctor uses some sort of "safe phrase" to ensure the agent doesn't get distracted by conversational theorizing and discussion.
Yes, there certainly is some magic word tgat stops an agent from context degradation, and prines work to just a useful subset. Sadly, no one knows what it is.
BTW the medical professional mistake rate is often reported per-patient, not per-decision. That is, of the dozens of things recorded on each patient's chart, one being wrong counts the same as all of them being wrong. It's an easy way to inflate a negligible error rate into one that scares readers.
Software has high verifiability through compilers, linters, static analysis, leak/memory corruption detection tools, or the very least, running the program to see if it crashes.
Math (as has been making the headlines recently) has high verifiability through automated theorem provers. Notably some of the non-mechanized math results from LLMs have been demonstrated to be wrong (and presumably many more are being checked; these aren't exactly easy to understand proofs).
Game playing (chess, go, Star Craft, etc.) is similarly verifiable: you get your score and you always know how you did in the end (even if not in the moment).
Making assumptions about LLMs in non-veriable domains is making a claim about LLMs that doesn't match their current capabilities. Maybe they'll get there and maybe they won't; I can speculate as well as you can but we're both guessing, so I'm not sure how much value the speculation has.
And LLMs won't take the easy way out (like so many people) and suppress errors, change tests etc, tweak code until warnings go away?
That's not to say it isn't useful, and if youre on top of it you can guide it away from doing that.
AI is changing how the code is written, but I'm not seeing anything at all changing the incentives around testing and verification.
But we're definitely very early on this trend.
Like... other fields exist, this is incredibly myopic. What is specific to software is it is all language based, and the people making the tools happen to be familiar with it so they know how to train it, but that's about it.
So yes, LLM have positive impacts in many business domains, but seeing people happy and enpowered with their home-made little apps, I think it's the best.
aaaand let me guess? have they assessed whether this tool needs backups? monitoring? resilience? patching? obviously in 5 years time the author would have left leaving NO documentation on a mission critical 'small' app - likely hosted with a shit ton of other mission critical crap
It's MS Access all over again, my future rates are looking good
Or, hear me out, it's not actually "mission critical" and none of this stuff matters. They made their job easier in a small way, good for them.
> It's MS Access all over again
In that it's a convenient tool that a lot of people find useful?
Its crazy how badly run many orgs are. Truly.
> We look for intelligence, we look for initiative or energy, and we look for integrity.
-Warren Buffet
It used to be that someone wanted unfettered Internet access, and they were disgruntled with the company's firewall or proxy policy, and so they brought in a modem, and hooked it up to their desktop PC, and started dialing out on some analog line that the company didn't even know they had. Then it's all fine for 3 years until some malware gets discovered, and then IT discovers who-all had unauthorized lines that weren't being scanned or filtered at the company firewall. Same thing happens with WiFi APs today, of course.
I was working for a company that really offered no in-house software or tools to run. But my new supervisor was asking me to closely track my time. So I signed up for some SaaS that would let me log activities and track my timeslices, so I could show a graph or table later on and see what I was doing. I used it for a few weeks. It was real effective. But what I realized down the line, was that all the notes I was typing into this 3rd-party SaaS, were revealing company confidential information. And ultimately, I felt very uncomfortable about revealing what I was doing, on an unannounced and private project, and without authorization or even notifying my employer's IT department. So I shut it all down, and I had to do it with pen and paper, but that felt secure enough.
So if people are going to feel empowered by vibe coding their own bespoke apps, I hope their IT department comes down like a ton of bricks, and tells them to knock it off. Because it is not worth the tech debt, support costs, and ultimately the massive security problems and infoleaks. If your employer won't give you what you need in these terms, then maybe it's just not important to them in the first place.
It may be a success or maybe not who knows but she did it. I think she has 4 or 5 paying customers.
Producing legal documents on the other hand is only part of the process, a lawyer or similar must still apply the produced text in some human driven context.
But I’d say only recently CC became decently usable for nontech people - the auto mode plus finally polished browser integration, plus Opus 5.5. Even I was surprised by how well it all works now out of the box (and how cheap - regular Pro license is decently useful now).
And the LLM-based coding agents provide the ultimate scapegoat for those who consider "the LLM wrote this code" to be an acceptable, deflecting response when issues occur.
- lots of high quality training data
- automatic verfication/scoring is straightforward
- AI companies are software companies so are better at tuning LLM's for software
- the goal is AGI, where AGI means the AI can improve itself autonomously. Which requires them to be good at software
While LLM's are uniquely good at software today, they'll get better at other fields slowly as attention goes towards training in those fields.
This seems correct and in fields of knowledge people guard their secre, or it is not easy to extract/document knowledge, or it isn't publicly available.
The question is whether this uniqueness is going to be persistent. I think the uniqueness just makes software first. The alternate theory is that software will stay unique and that other fields won't get to the same level.
lol
consider where "AI" was just two years ago, pre claude and codex. what do you think the next two years will bring?
those who did are rather convinced it's over, for juniors at least.
Next you will be telling me economists are also liable for mistakes in their predictions or modeling.
Wait till regulators step in.
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.
I'm interested in what will happen with factories, why couldn't robots do all the work? Well amybe not ALL the work but most of it.
Huge progress in spatial understanding with frontier models now building architecture and complex mechanical systems in Blender.
Plumbing is coming within a few years.
Maybe on new builds with fresh material, but that is already the easiest and quickest plumbing task.
An entry level largely untrained junior doing pro level work because their AR headset and earpiece tells them exactly what to do next? That's possible today.
Many skilled tradesmen have already left the industry because higher skill levels have little value, and their labor value is just barely above grabbing randos off the street. AI at best is saving a minute of evaluation or google search, but 99.5% of the job is physical work and pain still, not high level problem solving.
Humanoid robotics may progress faster than that because they are building on existing AI. But it will be capable of some plumbing work within a few years.
Ive been trying to figure out some persistent si joint pain and my chart is full of me passing or failed tests nobody performed, observations nobody made and diagnosis that no doctor confirmed.
Its just so much wrongness, ive tried getting it corrected a few times when it really matters but its just so much wrong crap
It is relatively easy to create and deploy a transcription model. It's pretty hard to find one that is really accurate and to configure it with a good microphone setup and pair that with a fast but also intelligent LLM to clean things up. If you execute any of that poorly or pick a cheap not very smart model for privacy and or cost saving you could easily end up with nonsense.
(They will. It's very well suited to it, once the garbage gets filtered out. It's not like it's a scam as a whole, it's just a field that's very scammy rn.)
https://www.abc.net.au/news/2026-08-14/ai-medical-scribe-err...
AI transcribed chart said the patient took psychedelics when they never said that.
I’m not letting them experiment with AI when it comes to my medical care.
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.
They have a ton of tasks that pop up on my dashboard for all sorts of industries that I know nothing about. I think they are ramping up in non-software areas.
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?
This may well remain a "true communism/socialism has never been tried" situation.
1. every business unit and department was neglecting a best practice due to lack of investment in their organization. it has always been that way for decades and always will be that way because it isn't economically viable for it to be any other way. the workflow automations and agents we create act as digital workers that either do that neglected part, or let the rest of the organization do that neglected part.
2. the whole company always starts the year off with a bunch of goals, and by Q3 most of them are cancelled. now less of them are cancelled.
both factors mean that anyone just looking at "worker productivity" or "did we save money" is going to get the answer of no. everything is costing you the same, or the AI itself is costing you more. whatever contract I'm working under is definitely going to cost you more, if you don't cancel all those enterprise SaaS seats we told you to cancel.
I see a path to savings, but I don't think that's necessarily the metric to focus on. you need to look at the whole corporate structure, why are you still doing certain things, because those pressures will just make the org do more things because nobody is empowered to say "wait, maybe this is dumb"
There have been wins with the customer facing side of the business, but the development team seems to be the biggest problem right now despite the direct & obvious applicability of these tools. I know they aren't a special unicorn edge case because I was very rapidly solving their issues before they stopped handing me things that revealed the lethargy.
Within the sector where these tools definitely work, the biggest barrier to effective adoption is figuring out a way to undermine the various ideological high grounds. I have found the most effective argument to untangle this knot to be something approximating:
Challenge:
"Why should I adopt or help to implement technology that might eventually replace my job?"
Response:
"Because refusing to won't protect your job. It puts everyone's job at risk."
It doesn't matter if you protect your job (not using AI) if the entire company goes to the bottom of the ocean as a consequence. The neat thing about this is that your job is part of the set known as "The Company" and if that ceases to exist, so does your job. Good luck suing a dissolved entity for unemployment or whatever. There is enough competition in most markets to punish anyone who thinks they can win on virtue alone.
What a grim outlook.
Robots still seem far off. Its just like war, air superiority might make the war winnable but its the boots on the ground that are needed to actually take the territory.
The other is that there is so much more value to be extracted before this bubble burst.
Not sure which I believe, objectively. Subjectively, inside of tech, it certainly feels like the former.
The incredible improvements have only been over the last 9 months or so. Business, especially disrupting entire job functions, takes much longer than that. As is typical with this type of change, it'll be slow until a tipping point, then FOMO will hit.
For a sec I thought he was talking about private equity.
This reminds me of the book The Redemption of Time. An alien society incapable of lying has no other way to understand deception than to study an actual human mind. The different ways that we are capable of thinking can be so hard to capture in an artificial system.
for 'A.I' to work for the rest of industry outside of software - you need high touch customization.
AI certainly seems to be making students much less competent.
You still need to calculate calculus yourself long after you could buy books where the result is written in. That supposed "paradoxon" is not even specific to electronic tools. It's just that the point of education has never been to get results, but to exercise in as much as side paths as possible and memorable while following the path to the results. The result mostly get thrown away immediately.
1. Computers have allowed me to gather more information, access more journals, more results from around the world, but what really seems to matter in learning is just sitting down with a tiny bit of material like one book and REALLY understanding it.
2. Computers make things more distracting because while they make typing up reports, proofs, and assignments more efficient, there's actually just too much information. What really seems to help is having LESS information so there's some space to just think and work things out.
The fact is, what computers do is help you keep up with other people in the field, so they might accelerate progress at the research level, but what they don't do is help you become a rigorous, independent thinker.
Academia in fact has always been an uneasy tension between the two: building capable individuals and getting results and money for progress. Computers help with the latter - they certainly impede the former.
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.
If we're talking about the US; then it's simply the very healthy and functional economic inventive of students taking out lifetime loans to pay school tuition for schools that have not actually risen in quality or format in 40 years. The more students; the more money to the school board! Lower those bar riiight down!
Lazy kids these days amiright!
It’s not even a flyover state thing, if you just look at Wisconsin and Minnesota. It’s uniquely red state. Also Indiana department of transportation is terrible as well.
I suspect that it's a lot less about making children feel better and more about keeping the general population stupid and unable to think critically so that the ruling class can exploit them easier. The children of the ruling class are still given the opportunity to be well educated by private institutions that are overpriced to keep out the poors.
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.
Vs one for Patient Zero, the most popular song today: https://www.sheetmusicplus.com/en/product/patient-zero-24016...
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.
You mention the addictiveness of the devices, this probably plays a big role in shaping the outcome space.
It's just sad. I sometimes help kids with math, and it's amazing how screwed they are. Like, smart kids with good grades who are totally unable to do basic manipulations with fractions. I mean questions like "write 25% as a fraction". They are getting absolutely nothing from math classes. How could they? Even the best teacher in the world can't pause class for everyone when they realize that one of their students is missing some foundational concepts. That student is just going to get left farther and farther behind, and they will never catch up. It happens to almost every student at some point. They'll get sick for a week or just have their mind on other things, miss some important concept, and then be left behind forever.
I honestly have a hard time imagining how we would possibly make the situation any worse with technology honestly. But if you just took Math Academy and made students use that instead of their math classes, I bet 99% would have better understanding and fluency with math than they do now.
> Imagine asking a high-school math teacher to compute i^i. Almost none will be able to.
Mine could. And of those who couldn't they would have leveraged the technology available to them (Google) to find the answer with me, and would be able to make it make sense to me. This is an example of technology bettering my education.
Not everyone on hn is American or part of the Anglosphere.
if someone knew a better way to improve test scores without cheating on them, we'd use it. it's a very hard problem.
on the flip side it is pretty obvious how construction and manufacturing can be automated and what the impact will be. it's so obvious that we already tariff and ban stuff that, from our POV, is automated, like prefab buildings, for solely political reasons.