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.
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.
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, management and 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.
Though I’m guessing most workplaces outside of tech will require total reorganization around this reality, but more likely this way of running will mean newer upstarts challenging incumbents refusing to change as it’s always been.
> 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.
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.
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.
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!
I’ve always had doubts about the validity of this “actionable BI,” even before it was slop, serving, mostly to serve leadership’s biases and justify their preferred ideas.
If accuracy was never that valuable to begin with then this should not be a problem.
Yeah, I think it's at the level of self-driving Excel.
Now, Excel also makes mistakes while trying to get clever - remember the biologists who had to rename several genes to prevent Excel treating them as dates - but it's also a ubiquitous business tool. And you can combine them. But equally Excel hasn't replaced every white collar job.
In 90s the show Friends, Chandler Bing’s job is “statistical analysis and data reconfiguration” which is likely unfamiliar to you because it is gone!
This was a job to manually collate financial reports into aggregated sums to sent up the hierarchy to the next level up.
This is now a trivial BI query and doesn’t require manual processing by humans.
A bigger shift is that now I only see CEOs or at most CxOs with secretaries. Mobile phones and Outlook calendars have largely wiped out that profession.
Is it? I don’t really hire for analysts anymore because you can mostly just expose chat with your data mcp tools that end users can use and that gets you 80% of the way there. The person who puts together dashboards and writes sql against the star schema is pretty much a thing of the past.
Data engineers, on the other hand… way more relevant than ever before
The issue is that data complexity is also exploding in the age of LLMs, so it still takes a lot of archaeology to get reliable (as opposed to plausible) numbers.
I think this dynamic also requires more data engineers, but I don't personally see the split in demand between those roles that you describe. Data analysts often have business or domain context that data engineers lack.
Perhaps we'll start rolling data analysis work into "operations".
> This is now a trivial BI query and doesn’t require manual processing by humans.
It also doesn't require AI. Management wants to see how the same measurements change over time. They don't necessarily want the latest AI model's hot take on this month's data. Or maybe they do, but I doubt it's very helpful.
It's like Software, its value is to be a complement and/or an abstraction of existing businesses. All of that sits on the very basics of the essence of civilization, which is how much things we can get off the soil and what how quickly and effective we can transform it.
Not to be anti-tech, but every new advancement, no matter how next-level, has to sell more of the same goods and services people have been using since the dawn of civilization, and those goods and services need to solve the mundane problems people have been getting since millenia.
BI is a good example of something where the benefits take a long time to show up. It has the most use at a senior management level where they can't use their every day experience to tell what is going on. But they don't make a lot of decisions over the course of a year and it often takes a few years before the impact is felt.
I worry about this. Dumping data and getting a report on which actions are based prolly isn’t giving real data.
I say that because I have Claude review code and then review the review and it will point out all the things it got wrong the first time. And I end up having to review it myself. Don’t get me wrong, I’m burning tokens all day and love the legit productivity gains but I don’t see how this replaces everybody in tech for a while.
>He loves being able to just dump in a bucket of seemingly unrelated data, and getting actionable BI from it.
Sorry, just don't trust accurate LLM data analysis even from the latest claude model. Have had some embarrassing bloopers over the last year. It is just better to code a deterministic tool to do the data analysis.
Even for the code. It will sometimes shit a very complex code for something with a very easy solution, and if you ask why it is so complex, it will invent some reason why. Always on 3 points, the first is true but have nothing to do with the subject, the second is plainly false, and the third is weird bullshit.
Don't get me wrong, I still use it, but the more you do, the more I see the obvious limitations.
I don't doubt it. Earlier today I got an estimate for the revenue, profit margin, and headcount for a potential supplier of a hardware component to a product I'm working on. It would've taken a couple hours of really tedious work for me to do that with Google translate, a search engine, and various market research sites. The result is I know more what I can expect from that vendor in terms of customization and support.
But those are the kinds of estimates that I can eyeball and be more than 80% certain they are plausible. That won't cut it in a lot of domains.
If companies can better understand their customers so they can give me products that better fit my needs, or inform me of products I couldn't have known about otherwise, that's a win for everyone.
I hope he validates the output, the number of times colleagues have done exactly that and gotten the wrong output is.. well, not an insignificant amount.
Not saying it can't be done, but my recommendation is still "ask your LLM to make a tool that process the data, do not process it via the LLM itself".
Yeah but theres also no null test here. He might have done anything and still been successful. He might have done something else more optimal and done even better. We can't really tell from a single anecdote.
It's really quite interesting how many HNers hate AI; which is odd, considering the zeitgeist of this community. It may even be more intense than the Apple-hatred.
Hate marketing BS and wealth extraction? Absolutely.
I see OpenAI, Anthropic, etc (the whole LLM-hype/boom) leaning far more towards being the latter than the former, in which context the dislike makes perfect sense.
I don’t hate it. I just hate the cult-like enthusiasm for it and the associated false claims. Again, your isolated anecdote does not trump the carefully compiled data presented in this post.
We get way too hung up on data; especially when it reinforces our personal bias.
Some time ago, I had a discussion here, where I mentioned that the average commute time from the suburban community in which I live, to downtown Manhattan, was generally 90 minutes to two and a half hours.
This was based on purely anecdotal data. That data includes the daily news on the radio and TV, which considers commutes from suburbs, important enough to dedicate significant airtime.
It was attacked, because some organization published some statistics, that said the average suburban commute time was 27 minutes. I was told that I was exaggerating, and being overly negative.
Here's a math word problem:
Harry lives 25 miles, as the crow flies, from his destination. However, the roads make it 40 miles.
Of those roads, 10 miles are suburban side roads that Harry needs to take, to get to the highway. The average speed is 25 MpH.
Once Harry hits the highway, though, the real fun begins. It's stop-and-go traffic (20MpH) for 28 miles. Once he gets to the city, he needs to navigate another 2 miles, through the most God-awful traffic on Earth, to where he needs to drive around for up to a half hour, looking for a parking space.
How does Harry make the drive in 27 minutes?
A fairly significant number of folks just took that ridiculous statistic, and were quite comfortable, calling me a liar, based on it. It was kind of jaw-dropping; considering the types of folks that we have, here.
That sounds a bit more "cultish" than we might be comfortable acknowledging.
> We get way too hung up on data; especially when it reinforces our personal bias.
While I don't disagree that we generally get too hung up on data, "It's a problem when the data matches the hypothesis" is an odd blanket interpretation of the scientific method.
Kidding of course. It could be that he's an expert in his domain and can quickly ascertain whether the results are quality. It may also save a lot of time and be the best modern heuristic process.
I certainly will never need to write code anymore, and I can judge the output of these agentic processes. Same thing.
Too true. Even on the software side i see people blindly “claude make it faster with no mistakes” and gushing about it when the output is genuinely not good not because claude isnt good but because the user of it isnt.
We got a new a CNC machine and everyone is carving away marveling at how easily it chips away at metal when the result is a jagged mess.
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.
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.
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.
For most businesses, it’s going to be business as usual. By the time businesses see the benefits of AI, their competitors that actually leveraged AI will have pulled too far ahead.
If the original company has distribution, they can likely catch up, but the gap we see between AI-first companies and the companies not utilizing AI will broaden exponentially in the coming years.
Yes maybe the long term impact will be there won’t be a technology sector (at least not so dominant as it is now). Instead companies in other industries will have larger technology functions with teams of agents.
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
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.
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.
> 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 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).
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.
> 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.
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.
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 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.
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.
Medical transcription isnt speech to text. The doctors I know at a major midwest provider had indian doctors live reading the speech to text and then filling out the chart which the doctor then signed off on. Now theyre using AI instead
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.
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.
> 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.
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.)
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.
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.
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.
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?
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.
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.
Well, of course not. If you borrow a trillion dollars to build a 1¢ aspirin machine to compete with $1 generic aspirin, you are strongly incentivized to sell your aspirin for 99¢. We don’t see productivity gains, because approximately all of the gain is captured as revenue for AI companies.
It’s weird that this has to be spelled out for so many engineers and economists, but to be very clear: OpenAI et al wants your token spend to go to one-minus-epsilon of what you previously spent on payroll, and wants your productivity to go to one-plus-epsilon of what it previously was. If you want 10x productivity, spend 9.9x more on tokens.
They are in the business of enclosure and rent extraction, and any innovation that comes out of their research and development - regardless of how real an innovation it is - will be completely contained and bent totally towards that business. Any leakage of the innovation that you experience as a productivity boost is literally felt as lost profits by them.
Not sure how productivity and rent capture are connected here.
1) If a firm spends $99k on compute to replace a $100k engineer and produces 5x the output, how is that not a 5x increase in aggregate labor productivity?
2) In your aspirin example, why wouldn't a competitor with an identical 1¢ machine sell it for 50¢ or 10¢ to take 100% of your business?
You are either making the assumption that 1 company is owning the market and no models are useable beyond that one, or that all of the models that are in the front of the market have a cartel and fix the price, which is not true either looking at API costs.
> 1) If a firm spends $99k on compute to replace a $100k engineer and produces 5x the output, how is that not a 5x increase in aggregate labor productivity?
I think they are saying that with 99k pure compute you are able replace 100k engineer with equal output. Or that is the goal of the AI companies. It is worse for them if you get more productivity.
Once most of this debt is gone, whether through companies going bust or the debt being paid off or inflated away, AI will become cheap enough for frontier models that any productivity improvements will be visible in the user's bottom lines.
Once all of the trillions of dollars of debt is paid off…
…plus whatever healthy premium the corporation decides it is owed for so bravely taking on those trillions of dollars of debt, once that too is paid off…
…plus whatever the expectation of return on investment the lenders were persuaded by, once that has also been met…
…only then will we get to see what proportion of the gains these corporations think it fitting to let trickle down. What do you reckon they’ll let us have? 50/50? One in ten?
I don't see why a payoff would have appeared yet. Many (but certainly not all) software companies have increased their output without hiring more people. But having software with more features doesn't necessarily drive immediate productivity gains in the rest of the economy. It will take time for those benefits to percolate. We've only had access to models good enough to majorly accelerate code writing since early-2026. I feel like we're in the very beginnings of the first part of this wave.
For sake of comparison, consider e-commerce. As a proxy for "impact on the economy", we can use Amazon's revenue. They went public in 1997 and that year had revenue of $148M (just 0.14% of Walmart's revenue that year). By 2000, revenue was $2.76B (1.65% of Walmart). Bricks and mortar didn't start feeling anything until probably 2011, when Amazon crossed 11% of Walmart's revenue. And it wasn't until 2025 that Amazon finally surpassed Walmart.
But, if you worked in tech in the e-commerce business, things were already feeling pretty hot by 2007. And at that point in time, the e-commerce sector was still miniscule compared to traditional retail (~4%). Software is very exciting these days and the most fantastic things are happening. They will eventually percolate out to the rest of the economy. But it will take many years before everyone notices the difference.
Yeah, the person who said that obviously never checked on book and magazine and newspaper typesetting --- cue old news stories about the last compositor at a given paper finally retiring after decades of the sinecures which the composition unions bargained for rather than participating, causing a loss of typographic skill and knowledge which resulted in the ghastly appearance of the "DTP revolution".
I once participated in a project where we worked up a database-driven typesetting system for a company whose annual publication had a staff of a dozen (3 managers (one of whom mostly did ad sales), 3 full-time compositors, 6 part-time) and when we put in the system they quit paying their expensive annual license for maintenance on the proprietary composition system, and fired two managers and 1 compositor and half the part-timers.
The staff was commensurately larger when they switched to the first electronic composition system they were using from the typesetter and paste-up they initially used.
> the person who said that obviously never checked on book and magazine and newspaper typesetting
That's one of the "everywheres" the author of the statement was talking about, which is the point - you could see every part of the economy seemingly improved, but it did not show up at the macroeconomic measurement level.
It was until the 2000s that computers caused an uptick in measured productivity at the GDP level, though.
The number of books being published has been steadily increasing for decades since computers became prevalent in typesetting --- is that not measurable?
Admittedly part of that is caused by the change in tax laws making it untenable for publishers to maintain a backlist.
Good reference. It is going to slowly change things in 20 years we'll look back and a lot of things will be wildly different because of AI systems. But its rate limited by institutions and momentum and human psychology and the way things change will be somewhat unpredictable with unintended negative and positive consequences.
Yeah, it is not going to be slow. Progress compounds, everything is being built on top of fast computers and fast widespread internet connection. You are not at the beginning of the curve like you were in the 20th century when people didn't even know how to use a keyboard and we are already seeing widespread disruption in security.
I expect to be surprised by specific headlines, but I don't expect "software engineer" to be any more common a job opening than "steam locomotive engineer" in 10 years time.
Conversely, I would be very surprised if the robotics startups turn out to have solved general-purpose AI models that work in the real world, both fast enough and cheap enough (currecy or joules) to be significantly labour-displacing in 10 years.
Where I'm about 50-50 on which of "AI does it" or "AI does't do it" is more surprising, is specific physics research from AI. LLMs can do a lot of hard maths, there's lots of open problems in physics due to hard maths questions. So:
Consider the Alcubierre metric. The original is impossible. Can an LLM either design something in the same broad category which is physically instantiable but still useful (e.g. "yes it's sub-light but it manages 0.01c and you weren't getting that otherwise"), or prove this is impossible when limited to matter made from normal electrons, protons, and neutrons?
10 years? I'd be equally surprised if this is or isn't done by AI. Mainly depends on how long the growth continues looking exponential.
Literally the entire premise of the article is that it's going slow everywhere but in software. To think otherwise is to be really myopic about how the rest of the world outside of tech operates. And in any case 20 years isn't that slow. The only things that happen faster that that will be things like adobe goes out of business. And let's say AI helps us develop medicine and robotics and better factories and all the other things that would have a big impact a lot faster.. just human trials could take a decade before you really see that much change. Will it have a wider impact faster than computing its self, or the internet? Maybe? Maybe not.
If a lot of jobs were still manufacturing jobs I would agree, but we are in a service economy that uses computers as the main tool. So I don't see how it won't come into effect fairly soon into other areas of work. You can't measure impact by profit alone so I don't find the article particularly interesting anyway.
Progress is future knowledge. You don't know what future knowledge is, yet. When you know, it will have become present knowledge. You don't know that any particular ongoing change is progress. LOL predictions.
Yep, and if it's anything like previous transformative technologies: the internet, and 100 years earlier the railways, a lot of people are going to lose a lot of money overbuilding the wrong things along the way.
Maybe it's also rate-limited by the fact that it's not actually a silver bullet and actually needs time to find the proper place for its application as well.
And likewise; computers took a few decades to actually become a widespread economic force. Arguably the impact was quite small if we separate it from the internet (which of-course depends on computers).
Good insight. For a long time, the utility of a computer was tied to its ability to print something onto a sheet of paper. User interfaces came along, too, though. Especially for industrial applications. Then internet was probably the third wave. Now AI is the fourth?
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.
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...
It wasn't, generally, a question. People kept buying computers, on faith.
It's just that in 1987. the computers were generally and for the most part, irrelevant to productivity, just like steam and electricity were a promising gimmick for a long time before.. they weren't.
at the level of personal employee maybe, even though 87 had lotus 123 version 2. I was barely born but I'd think people would be able to think faster through electronic spreadsheet.
That would be speculative, it should only be added in the hypothetical time the productivity actually goes up. Even then, it should be some years in and with third party sources like all of Wikipedia.
Only a few curmudgeons at this point are claiming there is nothing, nothing whatsoever, to AI.
The question is really about the fate of the current build out. For those of us who have extensive experience with AI, which is to say, those of us here, because we are probably the only people not only using it day in and day out but potentially using multiple sessions of it all the time, I think it's not so surprising that it isn't necessarily immediately radically transforming every industry everywhere. Even as it has rewritten the minute-by-minute process of doing my job entirely, it hasn't actually changed my job all that much at the higher level and it's hard to assign much more than a 2x or a 2.5x overall improvement to it once I account for all the Amdahl's Law issues that come up from the parts that have not been so amenable to acceleration.
And without unit testing, continuous integration testing, pull request review, the fact that the AI is building code which is itself a highly reviewable artifact rather than just doing whatever directly, and all the many many guard rails we've built up in our industry that happen to be extraordinarily amenable to use by automated systems, I still wouldn't trust AI all that much for a real task. They still bang into those guard rails a lot, and still make what I consider to be deeply suboptimal decisions far too often to take my hands off the wheel. With all the stuff we have in the programming space they are a game changer, but in a field that doesn't have anywhere near that much padding and safety nets I'd be way, way more hesitant.
I want to say it's weird to talk about such things when they are also capable of such amazing math proofs, but at the same time when you consider the numbers tossed around for how much those things have cost, I think you're seeing another space that has guard rails comparable to software engineering. By contrast to the way these AIs are working, when a human solves a comparably hard problem the human tries a few things but from the outside would seem to proceed almost directly to the solution, compared to the staggering amount of blundering about the AIs do before arriving at their solutions. Now, in terms of the final results, yeah, they got there and I won't deny that. I'm just saying that in fields where the acceptance criteria are nowhere near as sharp as math or software, they're operating at a significant disadvantage still.
> I 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
> 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.
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 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.
And at that point, there was no much productivity gained ... and not nearly as much debt as now. The industry was also not such a huge part of stock market, GDP, etc.
This seems like a poor comparison, for any given company, benefitting from personal computing in 1987 was a significantly harder task than using AI tools in 2026 is.
The first requires a fundamental shift in not only how you work but expensive and complex infrastructure changes from the company itself just to make it possible (the first Mac for example would cost about $7000 per person in today’s money and would be outdated in maybe a year or two), an office building of The latter requires asking a subscription service to do a task for you on basically any computer connected to the internet.
I don’t know about you, but for me materially useful AI costs around $10,000/mo in tokens. But it is very useful.
Claude subscriptions help, but only if I come in with the expertise to build the entire system around the model and I don’t mind the whole thing grinding to a halt when my session limit is saturated. Anyone selling the system off the shelf is going to be charging API rates. Computers were cheap in comparison.
I can’t speak for everyone, but I would intuitively venture to guess there would be some greater than marginal productivity benefits from say, a $200/m Claude sub for each employee in many organizations outside of tech.
Perhaps they’re all on the $20/m plan which is useless?
“Marginal” improvement isn’t really going to be enough. Products need to be closer to 10x better than the alternative to get traction (which can be divided across several dimensions). AI, I think, is just starting to get there.
I get what you’re saying but I’m just not a fan of this type of argument. It always paints AI as this “in the future” thing which absolves any current criticisms or observations.
LLMs are great and definitely make you more productive, for some reason 493/500 firms haven’t been able to figure that out even though it’s trivially easy to provide employees with frontier models and it’s also trivially easy to use frontier models.
Which is why I say comparing it to personal computers in 1987 is not an apt comparison. That took redefining physical workspaces, new infrastructure, and new personnel which wasn’t easy to come by. Becoming a company that uses AI is just as easy (if not easier) as using any other SaaS.
See sibling comment. 10x measured how? In pure coding tasks under certain conditions the speedup is huge, but seems really unclear how this would transfer to any significant speedup across the whole undertaking (project, delivery etc).
Maybe, even likely. But how do you observe this? We are in this situation at work, it feels like "sure AI is helpful" but do we actually deliver any faster?
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 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.
I just found a book about abandoned teenagers from the 70s (think juvenile held in post-offense rehabilitation centers) 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.
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.
It’s terribly ironic that things like calculus, linear algebra and matrix manipulation were being weeded out of the curriculum, right before machine learning took off.
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!
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.
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.
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 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.
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.
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.
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 path planning work.
For those who won't act ethically or at least reasonably, regulation is the answer. We're not too far away from flipping the congress into something far more willing to do just that, especially with anything connected to Elon Musk.
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.
"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.
It’d be nice to stop calling it The Magnificent 7. Perhaps The Circular 7, or The Shady 7, something more realistic about how they are treating the stock market. Yeah number go up, but the means to those ends are shady as all hell.
Even in the M7, I doubt AI is having much impact other than benefiting from AI spending. Its hard to point to any non-AI products in the M7 that have meaningfully improved since 2023.
The problem isn't that "AI doesn't understand..." this or that, as concluded by the article though. Its more a lack of creativity and incorrect reasoning about what AI can and cannot do. For one, whole role replacement is pure fiction and likely will never happen with LLMs. I expect most businesses will look very different in the next 10 years due to AI and it will not be smooth as the needed creativity will mostly happen in startups (and perhaps companies that can truly operate like startups) and diffuse out from there. I'd expect a lot of churn in the S&P493 and even the M7 most likely.
> the AI story will be decided in the S&P 493, not the Mag 7, and so far, there is no evidence that revenue growth or margins are improving outside the tech sector.
Lots of theories seeking to explain this reality, but that's all they are: theories. Maybe some will be proven right. Maybe all will be proven wrong.
As of right now, the economic reality lags far behind the hype. But down the road... I wouldn't place a bet either way.
Paradigm shifts happen when old minds retire or die. Something like that, but for companies. If you’re starting a company now, you are _definitely_ hiring less people much later, and different profiles.
For sure. The original quote by Max Planck in his "Scientific Autobiography":
"A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it."
Admirers of this idea shortened it to, "Science progresses one funeral at a time."
When I tried to use CoPilot at my day job, I set up a prompt to process scans of checks and invoices (with the MICR account bar code line covered w/ a pen so that there was no PII), it would sort of work for 20 files at a time to write a .bat file command to rename the source image based on the content of the check and invoice --- this removed the need to open up scans in batches of 50, read the Invoice ID, save using that, then run a .bat file to rename them based on the balance of information in the database.
The thing is, the failure rate was quite high --- often only 15 (sometimes fewer) lines were returned, and after a while, Microsoft imposed a limit on uploading more files, even though I was deleting the conversations immediately and quitting CoPilot and re-starting (long sessions seemed to have a progressively worse failure rate).
Since then, access to CoPilot has been removed and we're waiting on a test program to finish. Kind of curious how many tokens this was using up (and if there's a model which could be run locally which would work well and quickly enough).
I recently had a somewhat similar task: My 80-year old friend who is on Medicare got a $30,000 hospital bill which the insurance company declined to cover. So he wrote by hand a letter to the insurance company to request a reversal of their decision. I scanned his hand-written letter and asked AI to turn it into text (to create a professional looking letter). Could not get that to work on Gemini nor Perplexity, but it worked on Co-Pilot.
It did save me a lot time to use AI, especially should next time since I now know which AI app can handle it. So, there was a bit of overhead before I found the AI-app and prompt that would do it.
I have another friend who works for SAP as a project manager. She said she hates the fact that management wants them to use AI for everything, giving them a bonus if they do. But one reason I think their employer insists on that is that when you try to use it for real tasks you discover what works and what doesn't. Gradually we should be getting better at it.
Currently there is still a penalty caused by
1. AI not being able to do a specific task
2. Users not knowing which AI-app can do the
job and
3. How to prompt the AI to get it done.
Writing the prompt is what users must do, and writing a prompt that works is a skill they must learn for every task you want AI do for you. Therefore progres is is happening but incrementally.
Enterprise sales somehow convinced a lot of orgs that all AI is the same or operates on the same level. Even frontier labs have differences, and you have to use each for your own advantage.
LLMs are not the type of model to use for this task. There are more specialized models that have much higher accuracy and full results. You can probably find the latest by looking around the OCR ecosystem.
A co-worker in a different program whose invoicing was much simpler and more straight-forward tried to automate this using OCR and Python and had a higher error rate and still had to set hand-written checks aside to handle manually.
I couldn't find an OCR system which was:
- free/affordable
- able to cope with arbitrary check sizes/hand-written checks and my occasional need to arrange multiple invoices in a stack with the check rotate 90 degrees so that all the numbers are visible
- which would capture the data from a scan in such a way that it could be easily used to rename the file
If you know of a system which is flexible enough to do this which is essentially free, let me know.
When IBM PC compatibles first became widely available, most economic reports I saw said that there was no economic impact from computers becoming widely available in offices. Word processors, spreadsheets, and email didnt have any productivity improvements... and I think we know better.
It's too early and we are probably not measuring the right things.
I am in a tech role that straddles traditional business. Yes, LLMs are very adept at software development and of course we are early adopters, but the amount of other things that can be automated or at least significantly augmented hasn't scratched the surface.
At the start of this all, execs were clamoring for "AI" and at that time the models were superficially impressive but fell short. Now I think they are all underestimating the amount of improvement in the last year. There's loads of work that can be automated
No signs of AI payoff outside the tech sector...yet. I think it's partially because our harnesses are generally much, much better. And engineers seem to be more deft at figuring out where AI provides real benefit and where it needs a watchful eye.
I'm seeing non-engineers use AI to send more emails, have more meetings, create more documents, and create more dashboards. So much of the busy work that happens in corporate environments was never that valuable in the first place. A firehose of busy work doesn't move any needles that matter.
There is nothing I dislike more than people using AI to generate tens of pages in text and then expect people to read them, when they did not read them themselves.
Well this is an actual effect. It is so easy now to generate presentations etc so as a consequence there are ten times as many that people seem to expect you to read. Productivity is higher but that which is produced sucks and is pointless. Actual value creation is harder than nice looking artifacts.
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.
I'm a dev not a video or 3D guy, but I'm looking at people I used to know and also some youtube doom-scrolling, the blender MCP, cinema 4D MCP, and seeing that it's a big impact
> AI may be a great calculator, but in business, intelligence is mostly judgment. Judgment depends on context, and the answer varies with which human or which model you ask. Perhaps most importantly, 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.
All of this is true, but the reality is that LLM agents free up time for people to make judgement by taking care of the grunt work, which means each person can do more. That increased efficiency can mean higher revenues in growth industries and higher profits (through reduced labor force) in stable or declining industries.
There's a lot more to business in America than the corporate S&P. If you think AI isn't paying off for businesses, then you probably aren't a consultant right now. I have small/mid-size clients who, in pre-AI times, would have never been able to afford the work they're currently scoping out for me. Now I can quote them cheaper rates to build more custom solutions that they didn't even imagine was possible, and they are loving it.
'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.
We're not seeing full automation for things that ought to be automatable now.
Customer service chatbots still aren't very good. Salesforce ought to be taking over entire marketing operations, not just supporting sales but replacing it. The medical office side of insurance ought to be automatic now. The pick and place side of warehouse operations should be robotic. Shelf restocking in stores ought to be robotic. All of those things have been demoed, but didn't go to production.
Yes, AI is still not particularly prevalent in the customer-facing / consumer-facing things you described. But internally, I think these companies are up to their eyeballs in AI automations. At the company I work for, the customer-facing side of our business has not really been changed by AI (except for faster iteration of website design, and a somewhat limited customer service chatbot - not particularly good like your experience). But internally we are using it for so many things, for example we have brought 3rd party analytics tools in-house (cost reduction and ownership over our data) by writing our own internal tools, we have setup custom systems to give ourselves better visibility over the business to try catch issues earlier, we use it to produce internal reports, etc.
I don't have much experience in robotics but I've heard that machine vision models have absolutely been changing the industry. And I imagine they are also using AI to write a lot of software too. But not my area, just what I've heard / been reading.
> But internally we are using it for so many things, for example we have brought 3rd party analytics tools in-house (cost reduction and ownership over our data) by writing our own internal tools, we have setup custom systems to give ourselves better visibility over the business to try catch issues earlier, we use it to produce internal reports, etc.
How much of that is automation of "bullshit jobs"?
Fair enough. It's a mix. But for the most part it's a net positive.
We have replaced external tools with in-house tools written by largely AI. These are not one-prompt tools made in a day. We have built our own analytics stack which non-technical coworkers use everyday - they are not interested in "bullshit", they need real, accurate data and visualisations. And we had a cross-over period where our internal tooling was compared to our (now legacy) 3rd party analytics tools. It was done properly.
At least where I work, I don't think there is much automation of "bullshit jobs" - people don't typically just waste time chasing fairy tales (for the most part). But for things like report generation or tracing issues or generating presentations or infographics, there is certainly a much higher volume of these things getting pushed around which may or may not actually achieve anything, or be at all correct. So I reckon a large chunk of these would fall under into the "bullshit" bucket.
Because everyone can produce so much more "stuff" with AI assistance / automations, a good portion does end up spewed straight out of the slop factory. How much / what proportion kind of differs person-to-person tbh.
I think you could say the same thing of tech and the economy prior to AI.
No Signs of Tech Payoff Outside the Tech Sector. Manufacturing, basic science, education, etc languished while Big Tech reaped record profits and achieved stock heights for the last 15 years.
Hell, on top of that (i.e. the confounders you mention are very very apparent everywhere,) the challenge of folks understanding how to use AI to be more productive.
Even last week I had a case where, I was reviewing code that I know the dev was vibe coding, and honestly was good up to that review.
Buuuuuut, for the stuff I reviewed that day, there was some base-class inheritance use that was so ugly that my initial teams message to the dev was along the lines of 'What crack was Copilot smoking on that one'.
Turns out, they had asked some other random-ish questions in the chat they were doing their actual work in, and those questions more or less polluted the context.
Sure, I was able to guide them for how to not do that in future (i.e. keep unrelated questions around the work in question to a separate chat, etc.) but this was also a dev that is normally both very good, but also overworked semi-related to the general 'AI will save us on dev costs thing'.
I can't help but ask whether, for the even less-technical people that are told 'yeah you lost two colleagues but here is Copilot/Claude/etc' and they don't even get enough advice to have it actually increase productivity versus chasing down gremlins in the actual work.
Just talking about salaries, revenue, and stock IN AMERICA.
Doubtlessly Chinese manufacturing benefited tremendously from those advancements. But my point is the headline whinging about AI in America would also apply to Tech in America.
We’re heading into corporate budget season. Company after company is slashing their AI budget and the head cut wagons are circling around all the various “AI lab” teams that every company invested in two years ago with zero ROI to show for it. All while massive debt piles up to build data centers despite little evidence any of this is coming close to returning the ROI needed to justify any of it.
This is not surprising. AI in its current form is more obviously useful at generating software than it is at assisting with other kinds of knowledge work. Software can be automatically validated. Validation loops are what let these systems iterate toward acceptable solutions. If you want AI to produce a document for humans, you need to carefully review and revise the output. In that case there's little to be gained, as reviewing plausible but subtly incorrect information sentence by sentence is in some ways harder than authoring it yourself (a quote from someone who worked in like city planning). Depending on the company/industry, some of the non-tech sector workloads still involve phone calls, parcels, faxes, etc -- things AI can't really do.
Yes there are technical solutions to such problems; I'm sure there's probably a startup to make phone calls that's hooked into your company's knowledgebase, multi-LLM workflows for document review, etc. But non-tech companies aren't adopting those. Tech companies came for the software generation and stayed for the general knowledge worker productivity. Since 2023 they made the investments to procure services, change culture, and generally re-tool. Software companies are also uniquely positioned to deal with the limitations of AI in-house, like by building custom glue components to link systems, building controls to mitigate risk, etc.
So basically the tech sector leverages AI more than other sectors. At a company level, non-tech has less investment, less training, less integration of AI with business processes. At an individual level this looks like workers having fewer tools, not knowing how to use the tools they do have, not knowing even what the tools' capabilities are, and incorporating them into their work only superficially if at all.
Someone asked me for help using AI at their job (which is tech adjacent). They were trying to create a process document using ChatGPT. Their selected model was GPT Luna Light (!), and they were copying and pasting data between ChatGPT and SharePoint. There _was_ a SharePoint integration but it lacked write access. The model was not aligning the doc images. Their prompts were not specific. It was a bad experience. Their conclusion was that AI was stupid and not worth the trouble.
So even though the non-tech sector can't take advantage of AI to the same degree that the tech sector can, they're utilizing it even less than they could be.
I think most people in the tech sector and especially driving the AI hype have absolutely no clue how difficult it is to implement change in complex organisations. There are a bunch of technical reasons but half the reasons are not even technical - social, legal, etc. You have to shift the practices of humans and they have incredible inertia. Like decade level change response period. And during the change period they are less productive. Expect a decade type period before "productivity" impact manifests and even then it won't really be measurable directly because people will be doing different things.
Right now, the most effective use case for AI is coding agents. Software development is a profit center for the tech sector, so it's not surprising that they're seeing big gains. In Corporate America, software development is part of IT which is a cost center, and the software landscape is 30+ years of bandaid-ed legacy systems, maintained by offshore contractors who submitted the lowest cost bid, so attempts to gain productivity via AI have not been as successful.
Where Corporate America has seen gains from AI in my experience is with customer support chatbots, but the customer service function overall in Corporate America is an unmitigated disaster, so the gains are hard to measure.
Beyond these two use cases, most people in Corporate America are just using ChatGPT to create slop and copy/pasting it wherever, and then goofing off in their newly freed spare time.
We need to see new AI use cases in Operations, Finance, Marketing, etc. that can handle business processes end to end before the productivity gains start showing up in the financials.
„In SNP500 outside of tech”. This says little about general economy - and small companies are way faster in benefitting from CC.
Also, it’s been just 9 months of modern claude code, and for many companies less than 6 months since first employees outside of IT got access to Claude Code. What do they expect?
At a fundamental level LLMs reshape how we gather, shape, process and interact with information. Information is that which allows you to make a predictions with a probability better than chance. This will slowly change any industry where decisions are being made. So, it will touch all industries. And if we let it, all of life eventually. Better decisions, better survival.
This is a pretty pathetic analysis especially for an analysis from a firm like Apollo. A few notes:
1. Profit margins from the mag 7 are on an upward trend starting in 2020. This is pretty consistent with the insane topline growth had by covid and the most recent layoffs occurring over the last 6 years. To unilaterally attribute this to AI implementation is lazy.
2. Most non-tech firms are only implementing AI just now because we finally have better enterprise tooling required. We won't see the effects for another 12-24 months at least.
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.
The only issue is that the benefits would be more enmeshed with other gains in other industries.,
Heck, even in Software Development, the real benefits are not with mass labor elimination resulting in cost savings but with increasing available throughput. Technology if used to the fullest potential should be able to expand the proverbial pie., which is where the gains are!
That is not to say people won't try to save cost by eliminating labor due to AI., which is super shortsighted and misses the forest for the trees, but the overarching goal should be to increase the quality and quantity of deliverables, as demanded by the market.
I dont even think theres a payoff in the software dev.... seems to be great to spin something up quick as a POC, then maintaining it requires people immediately who struggle with reading the amount of code it produces.
Let's be clear: there are no signs of payoff within the tech sector, either. The supposed productivity gains are minimal to non-existent, unless you completely give up on quality. And that's the best case, the one they've put the most effort into! LLMs do not deliver what they promise, it is very clear based on real world experience.
“Tech sector is the first to adopt new tech” isn’t a groundbreaking revelation. I’m sure you could make similar charts for the computer, the internet and all other similar innovations.
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.
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.
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.
Love it when some write nonsense just to be heard or had a bad day, maybe simply ignorance!
Fascism comes from the latin word Fascia, which has nothing to with this topic. It was implemented later on as political philosophy, which still has nothing to do with this topic.
The author of the article is trying to make a point which is correct in micro but incorrect in macro/long run. My claim is that AI is working and is here to stay (but always as a tool in our hands).
Do you understand now how ignorant you sound? You got this champ, accept this critic.
That you don't know. Nobody knows what AI is going to be capable of in 2 years. It could continue improving, or not. There's no strong evidence in any direction. You're just ignoring what doesn't fit your narrative.
here's an idea: take your stupid hand-wringing over definitions and shove it directly up your ass.
when you say "Many will fall for the road to be paved", you are saying your idea is more important than human fucking lives. this is a recurring theme in every fascist regime throughout human history.
Fascism is an phenomenon that arose in early twentieth century Europe. Historians can tell you exactly how. By trying to describe regimes earlier than that, "throughout human history", as fascist, you are only engaging in that very semantic bleaching of the term that makes it useless.
Of course there have been regimes throughout human history that were unfree, even brutal, but no need to apply a term so linked to specific early twentieth century developments.
Which is to be expected. Anyone who makes a tool without targeting specific use cases is really just making it for themselves.
What's embarrassing is how many people are clueless about where AI is going. The tech sector is one giant echo chamber, so it thinks whatever it makes will be a miracle cure. Everyone outside the echo chamber has no idea what it is/how it works, so they're just hoping it's the miracle cure the echo chamber keeps saying it is.
In reality, AI is just a minor improvement in productivity that doesn't solve hard problems. It does what we could already do before, a little faster & cheaper, without determinism/trust. It's a bigger, faster truck, but somebody still has to drive it, find a place to park it, spend more on gas, etc. It may be able to "think", but it won't replace humans until it can accept liability.
The first giant web market collapse was because no-one could work out how to make money from it or do anything more useful than put the company brochure online.
We are functionally in a recession. The only way this isn't true is because of AI investment [1]. Additionally, GDP itself is flawed because it is propped up by housing, specifically imputed rent [2]. Another ticking time bomb is the $4T private equity backlog [3].
The real product of AI of labor displacement and wage suppression.
So while this article sees profits flat outside of the Mag 7, it should really be in decline to being flat is actually an improvement (IMHO).
We're not producing anything anymore. All we're doing is driving up revenues on inelastic demand on things like food, housing, electricity and health care. That's all PE is. And that's all our economy does now.
I really think people are underestimating just how close to breaking point our society is.
You won't get magical productivity gains from a workforce heavily conditioned to following orders and a pre-packaged process (the Henry Ford model and its various bastardizations). In order to extract value from an LLM, you have to be able to think and ideate so that the inputs are of higher quality.
It should be of zero surprise that the rush into AI has yielded limited results (if we're honest, both in and outside of tech)—you won't get a bunch of artists or creative thinkers out of a pliant, ironically agentic labor force. That's why most people say "it doesn't work": it's because they lack the skills and knowledge to use it properly (but corporations want to delude themselves into thinking they have a genie in a lamp).
It's too early. There was a time where economist could not find productivity gains from PC products. I think I remember reading about it in the early 2000s over a decade after computers were common in business. Part of it is that companies aren't quick to throw what's work for tech that hasn't fully proven itself.
AI productivity gains are coming make no mistake about it. My guess is that we'll see them within the next 5 years.
> 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.
> the technology has delivered zero financial payoff to corporate America outside of tech.
I wonder if the argument cannot be made that it has delivered little payoff in tech as well - in terms of actual stable revenue vis-a-vis the immense capital investments, the deep loans, the circular commitments etc.
You are missing the point: GenAI is here to stay. When and if it will bring a positive RoI for anyone other than those in related businesses, remains to be seen.
Internet has allowed a new type of businesses to appear and prosper. Some old-style businesses got the hang of it, some did not (look at all the newspaper businesses trying to figure out how to stay funded without falling under oligarch's rule).
lol, you are, sooooooo, blind, and iditic. Enjoy each day, then we, you are dead, vaporised, or some life shit. What a load of bollocks. Full of shit. POinltess drivel.
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"
Funny to see some people compare the AI rollout to the industrial revolution.
The industrial revolution produced physical goods at scale, which eventually gave regular people access to things they could not have before.
AI produces intellectual labor at scale. Regular people do not need mass-produced content in their lives: there's already too much music, too much TV, too much software that only seems to get worse year after year.
Who needs mass-produced intellectual labor? The owning class. This is the only population segment that stands to benefit from this revolution. It's the reason every tech CEO is trying to shove AI down everyone's throats. It's the reason they're resting their feet on the table while everyone below them is starting to panic about the future of their careers.
I don't believe we will see compounding acceleration of anything other than spam and enshittification in this AI-laden world. This is not technology for building futures we can barely dream of: it's a gold-plated gift to the oligarchy and a fuck-you to everyone else.
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).
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.
I know of a brake parts factory outside dayton ohio who had the Microsoft folks come through with promises well over a year ago now. They've already overspent and had to pull back their AI budgets when it made more work than it solved.
> before most companies even realize it's an option
Couldn't be further from the truth
Have they? AI agents only really got usable around last fall. LLMs were pretty useful starting basically at the beginning of 2025, but not in the fully autonomous way that they are now.
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.
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 personally have no problem per se with AI rendering certain tasks or even jobs economically redundant (although I am sceptical about many of the grander claims). What I do find tough to take is that the benefits will accrue to companies who have effectively stolen (and continue to steal) the intellectual output of everyone based on some bullshit fair use ruling for AI training. Hopefully the latter will get walked back and mechanisms and systems will be out in place to more fairly reward human intelligence.
> 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 vast majority of entire companies are going to the bottom of the ocean regardless of what individual contributors do.
Once you can replace nearly every job at a company, the company doesn't need to exist. Companies are just collections of experts, if AI replaces all the experts then it replaces the whole company.
So I've adopted the accelerationist mindset of: yes, this is absolutely going to replace my job, but it is going to replace everyone else's job too so fuck it I'll use it and we'll all go down to the bottom of the ocean together.
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.
If I were to count the number of times you all have said "incredible improvements" here on this site over the last 7 years, what number do you think I'd come up with, and would that change your tune? I'll do it, if I must, to show you all how un-serious is this claim.
If anything the expectation of incredible improvements just over the horizon makes an argument for not adopting AI or at least doing so very judiciously.
Why would you spend dollars on tokens today if you expect the same tokens will get you something vastly superior in a few months, or that they'll do the same job for nickels on the dollar? Do you just hate having money or what is the problem?
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.
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.
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.
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.
Though I’m guessing most workplaces outside of tech will require total reorganization around this reality, but more likely this way of running will mean newer upstarts challenging incumbents refusing to change as it’s always been.
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.
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.
Sigh. Where are the actual smart people here who think deeply? This surface level stuff doesn’t cut it
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!
This morning, I was talking to a friend of mine, who is a marketing (not tech) guy, and he was gushing about LLMs.
He loves being able to just dump in a bucket of seemingly unrelated data, and getting actionable BI from it.
I think we're in for a wild ride. Fasten your seat belt, and keep your hands inside the vehicle...
I’ve always had doubts about the validity of this “actionable BI,” even before it was slop, serving, mostly to serve leadership’s biases and justify their preferred ideas.
If accuracy was never that valuable to begin with then this should not be a problem.
Now, Excel also makes mistakes while trying to get clever - remember the biologists who had to rename several genes to prevent Excel treating them as dates - but it's also a ubiquitous business tool. And you can combine them. But equally Excel hasn't replaced every white collar job.
This was a job to manually collate financial reports into aggregated sums to sent up the hierarchy to the next level up.
This is now a trivial BI query and doesn’t require manual processing by humans.
A bigger shift is that now I only see CEOs or at most CxOs with secretaries. Mobile phones and Outlook calendars have largely wiped out that profession.
Data engineers, on the other hand… way more relevant than ever before
I think this dynamic also requires more data engineers, but I don't personally see the split in demand between those roles that you describe. Data analysts often have business or domain context that data engineers lack.
Perhaps we'll start rolling data analysis work into "operations".
It also doesn't require AI. Management wants to see how the same measurements change over time. They don't necessarily want the latest AI model's hot take on this month's data. Or maybe they do, but I doubt it's very helpful.
It's like Software, its value is to be a complement and/or an abstraction of existing businesses. All of that sits on the very basics of the essence of civilization, which is how much things we can get off the soil and what how quickly and effective we can transform it.
Not to be anti-tech, but every new advancement, no matter how next-level, has to sell more of the same goods and services people have been using since the dawn of civilization, and those goods and services need to solve the mundane problems people have been getting since millenia.
I say that because I have Claude review code and then review the review and it will point out all the things it got wrong the first time. And I end up having to review it myself. Don’t get me wrong, I’m burning tokens all day and love the legit productivity gains but I don’t see how this replaces everybody in tech for a while.
Sorry, just don't trust accurate LLM data analysis even from the latest claude model. Have had some embarrassing bloopers over the last year. It is just better to code a deterministic tool to do the data analysis.
Don't get me wrong, I still use it, but the more you do, the more I see the obvious limitations.
But those are the kinds of estimates that I can eyeball and be more than 80% certain they are plausible. That won't cut it in a lot of domains.
If companies can better understand their customers so they can give me products that better fit my needs, or inform me of products I couldn't have known about otherwise, that's a win for everyone.
And you can introduce people to products they didn’t know existed and now want, as opposed to showing them things they don’t need.
Not saying it can't be done, but my recommendation is still "ask your LLM to make a tool that process the data, do not process it via the LLM itself".
* what he perceives to be actionable BI.
Hate marketing BS and wealth extraction? Absolutely.
I see OpenAI, Anthropic, etc (the whole LLM-hype/boom) leaning far more towards being the latter than the former, in which context the dislike makes perfect sense.
Some time ago, I had a discussion here, where I mentioned that the average commute time from the suburban community in which I live, to downtown Manhattan, was generally 90 minutes to two and a half hours.
This was based on purely anecdotal data. That data includes the daily news on the radio and TV, which considers commutes from suburbs, important enough to dedicate significant airtime.
It was attacked, because some organization published some statistics, that said the average suburban commute time was 27 minutes. I was told that I was exaggerating, and being overly negative.
Here's a math word problem:
Harry lives 25 miles, as the crow flies, from his destination. However, the roads make it 40 miles.
Of those roads, 10 miles are suburban side roads that Harry needs to take, to get to the highway. The average speed is 25 MpH.
Once Harry hits the highway, though, the real fun begins. It's stop-and-go traffic (20MpH) for 28 miles. Once he gets to the city, he needs to navigate another 2 miles, through the most God-awful traffic on Earth, to where he needs to drive around for up to a half hour, looking for a parking space.
How does Harry make the drive in 27 minutes?
A fairly significant number of folks just took that ridiculous statistic, and were quite comfortable, calling me a liar, based on it. It was kind of jaw-dropping; considering the types of folks that we have, here.
That sounds a bit more "cultish" than we might be comfortable acknowledging.
> "Lies, damn lies, and statistics."
-Mark Twain
While I don't disagree that we generally get too hung up on data, "It's a problem when the data matches the hypothesis" is an odd blanket interpretation of the scientific method.
Kidding of course. It could be that he's an expert in his domain and can quickly ascertain whether the results are quality. It may also save a lot of time and be the best modern heuristic process.
I certainly will never need to write code anymore, and I can judge the output of these agentic processes. Same thing.
We got a new a CNC machine and everyone is carving away marveling at how easily it chips away at metal when the result is a jagged mess.
- 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.
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.
If the original company has distribution, they can likely catch up, but the gap we see between AI-first companies and the companies not utilizing AI will broaden exponentially in the coming years.
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
seriously it's like going back to the 90's
Does that software directly utilize ai?
why wouldn’t that work for other high trust industries (law, medicine, government, etc.)
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.
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).
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.
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.
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.
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.
as is translating text, deepl/google where okaisch, llm's are on another level
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.
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.
those who did are rather convinced it's over, for juniors at least.
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 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.
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.
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.
I'm interested in what will happen with factories, why couldn't robots do all the work? Well maybe not ALL the work but most of it.
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.
Very possibly on the way but so less uptake than software engineering for sure.
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.
It’s weird that this has to be spelled out for so many engineers and economists, but to be very clear: OpenAI et al wants your token spend to go to one-minus-epsilon of what you previously spent on payroll, and wants your productivity to go to one-plus-epsilon of what it previously was. If you want 10x productivity, spend 9.9x more on tokens.
They are in the business of enclosure and rent extraction, and any innovation that comes out of their research and development - regardless of how real an innovation it is - will be completely contained and bent totally towards that business. Any leakage of the innovation that you experience as a productivity boost is literally felt as lost profits by them.
1) If a firm spends $99k on compute to replace a $100k engineer and produces 5x the output, how is that not a 5x increase in aggregate labor productivity?
2) In your aspirin example, why wouldn't a competitor with an identical 1¢ machine sell it for 50¢ or 10¢ to take 100% of your business? You are either making the assumption that 1 company is owning the market and no models are useable beyond that one, or that all of the models that are in the front of the market have a cartel and fix the price, which is not true either looking at API costs.
I think they are saying that with 99k pure compute you are able replace 100k engineer with equal output. Or that is the goal of the AI companies. It is worse for them if you get more productivity.
…plus whatever healthy premium the corporation decides it is owed for so bravely taking on those trillions of dollars of debt, once that too is paid off…
…plus whatever the expectation of return on investment the lenders were persuaded by, once that has also been met…
…only then will we get to see what proportion of the gains these corporations think it fitting to let trickle down. What do you reckon they’ll let us have? 50/50? One in ten?
For sake of comparison, consider e-commerce. As a proxy for "impact on the economy", we can use Amazon's revenue. They went public in 1997 and that year had revenue of $148M (just 0.14% of Walmart's revenue that year). By 2000, revenue was $2.76B (1.65% of Walmart). Bricks and mortar didn't start feeling anything until probably 2011, when Amazon crossed 11% of Walmart's revenue. And it wasn't until 2025 that Amazon finally surpassed Walmart.
But, if you worked in tech in the e-commerce business, things were already feeling pretty hot by 2007. And at that point in time, the e-commerce sector was still miniscule compared to traditional retail (~4%). Software is very exciting these days and the most fantastic things are happening. They will eventually percolate out to the rest of the economy. But it will take many years before everyone notices the difference.
What do you mean by output? LoC?
I once participated in a project where we worked up a database-driven typesetting system for a company whose annual publication had a staff of a dozen (3 managers (one of whom mostly did ad sales), 3 full-time compositors, 6 part-time) and when we put in the system they quit paying their expensive annual license for maintenance on the proprietary composition system, and fired two managers and 1 compositor and half the part-timers.
The staff was commensurately larger when they switched to the first electronic composition system they were using from the typesetter and paste-up they initially used.
That's one of the "everywheres" the author of the statement was talking about, which is the point - you could see every part of the economy seemingly improved, but it did not show up at the macroeconomic measurement level.
It was until the 2000s that computers caused an uptick in measured productivity at the GDP level, though.
Admittedly part of that is caused by the change in tax laws making it untenable for publishers to maintain a backlist.
Yeah, it is not going to be slow. Progress compounds, everything is being built on top of fast computers and fast widespread internet connection. You are not at the beginning of the curve like you were in the 20th century when people didn't even know how to use a keyboard and we are already seeing widespread disruption in security.
I expect to be surprised by specific headlines, but I don't expect "software engineer" to be any more common a job opening than "steam locomotive engineer" in 10 years time.
Conversely, I would be very surprised if the robotics startups turn out to have solved general-purpose AI models that work in the real world, both fast enough and cheap enough (currecy or joules) to be significantly labour-displacing in 10 years.
Where I'm about 50-50 on which of "AI does it" or "AI does't do it" is more surprising, is specific physics research from AI. LLMs can do a lot of hard maths, there's lots of open problems in physics due to hard maths questions. So:
10 years? I'd be equally surprised if this is or isn't done by AI. Mainly depends on how long the growth continues looking exponential.Note: on an exponential, you can always say this. People will look back on today with the same "aww cute" eyes.
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.
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.
It took decades for effects of personal computing to show up in aggregate statistics.
See:
https://en.wikipedia.org/wiki/Productivity_paradox and https://dl.acm.org/doi/10.1145/163298.163309
edit: My comment is not exactly right, its more about general computing rather than personal computing
It's just that in 1987. the computers were generally and for the most part, irrelevant to productivity, just like steam and electricity were a promising gimmick for a long time before.. they weren't.
Looking backwards, it is easy to miss this.
The decade ahead is going to be a very interesting one
The question is really about the fate of the current build out. For those of us who have extensive experience with AI, which is to say, those of us here, because we are probably the only people not only using it day in and day out but potentially using multiple sessions of it all the time, I think it's not so surprising that it isn't necessarily immediately radically transforming every industry everywhere. Even as it has rewritten the minute-by-minute process of doing my job entirely, it hasn't actually changed my job all that much at the higher level and it's hard to assign much more than a 2x or a 2.5x overall improvement to it once I account for all the Amdahl's Law issues that come up from the parts that have not been so amenable to acceleration.
And without unit testing, continuous integration testing, pull request review, the fact that the AI is building code which is itself a highly reviewable artifact rather than just doing whatever directly, and all the many many guard rails we've built up in our industry that happen to be extraordinarily amenable to use by automated systems, I still wouldn't trust AI all that much for a real task. They still bang into those guard rails a lot, and still make what I consider to be deeply suboptimal decisions far too often to take my hands off the wheel. With all the stuff we have in the programming space they are a game changer, but in a field that doesn't have anywhere near that much padding and safety nets I'd be way, way more hesitant.
I want to say it's weird to talk about such things when they are also capable of such amazing math proofs, but at the same time when you consider the numbers tossed around for how much those things have cost, I think you're seeing another space that has guard rails comparable to software engineering. By contrast to the way these AIs are working, when a human solves a comparably hard problem the human tries a few things but from the outside would seem to proceed almost directly to the solution, compared to the staggering amount of blundering about the AIs do before arriving at their solutions. Now, in terms of the final results, yeah, they got there and I won't deny that. I'm just saying that in fields where the acceptance criteria are nowhere near as sharp as math or software, they're operating at a significant disadvantage still.
I 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.
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.
Do they? Compute and internet access are orders of magnitude cheaper than the software that runs on it.
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.
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.
The first requires a fundamental shift in not only how you work but expensive and complex infrastructure changes from the company itself just to make it possible (the first Mac for example would cost about $7000 per person in today’s money and would be outdated in maybe a year or two), an office building of The latter requires asking a subscription service to do a task for you on basically any computer connected to the internet.
Claude subscriptions help, but only if I come in with the expertise to build the entire system around the model and I don’t mind the whole thing grinding to a halt when my session limit is saturated. Anyone selling the system off the shelf is going to be charging API rates. Computers were cheap in comparison.
Perhaps they’re all on the $20/m plan which is useless?
LLMs are great and definitely make you more productive, for some reason 493/500 firms haven’t been able to figure that out even though it’s trivially easy to provide employees with frontier models and it’s also trivially easy to use frontier models.
Which is why I say comparing it to personal computers in 1987 is not an apt comparison. That took redefining physical workspaces, new infrastructure, and new personnel which wasn’t easy to come by. Becoming a company that uses AI is just as easy (if not easier) as using any other SaaS.
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.
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.
It’s terribly ironic that things like calculus, linear algebra and matrix manipulation were being weeded out of the curriculum, right before machine learning took off.
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!
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.
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.
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.
Back testing, hold-out sets, cross validation.
This has all happened before.
That hasn’t stopped Tesla. Their self-driving cars work by connecting the car’s controls directly to a neural network.
and if they did for your system, then it's so simple that whether or not you used AI doesn't even really matter
For a sec I thought he was talking about private equity.
The problem isn't that "AI doesn't understand..." this or that, as concluded by the article though. Its more a lack of creativity and incorrect reasoning about what AI can and cannot do. For one, whole role replacement is pure fiction and likely will never happen with LLMs. I expect most businesses will look very different in the next 10 years due to AI and it will not be smooth as the needed creativity will mostly happen in startups (and perhaps companies that can truly operate like startups) and diffuse out from there. I'd expect a lot of churn in the S&P493 and even the M7 most likely.
Lots of theories seeking to explain this reality, but that's all they are: theories. Maybe some will be proven right. Maybe all will be proven wrong.
As of right now, the economic reality lags far behind the hype. But down the road... I wouldn't place a bet either way.
>You can see the computer age everywhere but in the productivity statistics. Robert Solow (1987)
"A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it."
Admirers of this idea shortened it to, "Science progresses one funeral at a time."
The thing is, the failure rate was quite high --- often only 15 (sometimes fewer) lines were returned, and after a while, Microsoft imposed a limit on uploading more files, even though I was deleting the conversations immediately and quitting CoPilot and re-starting (long sessions seemed to have a progressively worse failure rate).
Since then, access to CoPilot has been removed and we're waiting on a test program to finish. Kind of curious how many tokens this was using up (and if there's a model which could be run locally which would work well and quickly enough).
It did save me a lot time to use AI, especially should next time since I now know which AI app can handle it. So, there was a bit of overhead before I found the AI-app and prompt that would do it.
I have another friend who works for SAP as a project manager. She said she hates the fact that management wants them to use AI for everything, giving them a bonus if they do. But one reason I think their employer insists on that is that when you try to use it for real tasks you discover what works and what doesn't. Gradually we should be getting better at it.
Currently there is still a penalty caused by
1. AI not being able to do a specific task
2. Users not knowing which AI-app can do the job and
3. How to prompt the AI to get it done.
Writing the prompt is what users must do, and writing a prompt that works is a skill they must learn for every task you want AI do for you. Therefore progres is is happening but incrementally.
I couldn't find an OCR system which was:
- free/affordable
- able to cope with arbitrary check sizes/hand-written checks and my occasional need to arrange multiple invoices in a stack with the check rotate 90 degrees so that all the numbers are visible
- which would capture the data from a scan in such a way that it could be easily used to rename the file
If you know of a system which is flexible enough to do this which is essentially free, let me know.
I did see a talk recently of fine-tuning a small LLM specifically for receipt transcription: https://www.youtube.com/watch?v=vDyxCHl-p8s
https://github.com/fw-ai/cookbook/tree/main/training/case-st...
HF has OCR LLM based models too, eg: https://huggingface.co/baidu/Unlimited-OCR
It's too early and we are probably not measuring the right things.
At the start of this all, execs were clamoring for "AI" and at that time the models were superficially impressive but fell short. Now I think they are all underestimating the amount of improvement in the last year. There's loads of work that can be automated
I'm seeing non-engineers use AI to send more emails, have more meetings, create more documents, and create more dashboards. So much of the busy work that happens in corporate environments was never that valuable in the first place. A firehose of busy work doesn't move any needles that matter.
All of this is true, but the reality is that LLM agents free up time for people to make judgement by taking care of the grunt work, which means each person can do more. That increased efficiency can mean higher revenues in growth industries and higher profits (through reduced labor force) in stable or declining industries.
So getting an LLM to write code is kind of self defeating.
for 'A.I' to work for the rest of industry outside of software - you need high touch customization.
I don't have much experience in robotics but I've heard that machine vision models have absolutely been changing the industry. And I imagine they are also using AI to write a lot of software too. But not my area, just what I've heard / been reading.
How much of that is automation of "bullshit jobs"?
We have replaced external tools with in-house tools written by largely AI. These are not one-prompt tools made in a day. We have built our own analytics stack which non-technical coworkers use everyday - they are not interested in "bullshit", they need real, accurate data and visualisations. And we had a cross-over period where our internal tooling was compared to our (now legacy) 3rd party analytics tools. It was done properly.
At least where I work, I don't think there is much automation of "bullshit jobs" - people don't typically just waste time chasing fairy tales (for the most part). But for things like report generation or tracing issues or generating presentations or infographics, there is certainly a much higher volume of these things getting pushed around which may or may not actually achieve anything, or be at all correct. So I reckon a large chunk of these would fall under into the "bullshit" bucket.
Because everyone can produce so much more "stuff" with AI assistance / automations, a good portion does end up spewed straight out of the slop factory. How much / what proportion kind of differs person-to-person tbh.
No Signs of Tech Payoff Outside the Tech Sector. Manufacturing, basic science, education, etc languished while Big Tech reaped record profits and achieved stock heights for the last 15 years.
There are too many confounders.
Even last week I had a case where, I was reviewing code that I know the dev was vibe coding, and honestly was good up to that review.
Buuuuuut, for the stuff I reviewed that day, there was some base-class inheritance use that was so ugly that my initial teams message to the dev was along the lines of 'What crack was Copilot smoking on that one'.
Turns out, they had asked some other random-ish questions in the chat they were doing their actual work in, and those questions more or less polluted the context.
Sure, I was able to guide them for how to not do that in future (i.e. keep unrelated questions around the work in question to a separate chat, etc.) but this was also a dev that is normally both very good, but also overworked semi-related to the general 'AI will save us on dev costs thing'.
I can't help but ask whether, for the even less-technical people that are told 'yeah you lost two colleagues but here is Copilot/Claude/etc' and they don't even get enough advice to have it actually increase productivity versus chasing down gremlins in the actual work.
I don't follow.
Doubtlessly Chinese manufacturing benefited tremendously from those advancements. But my point is the headline whinging about AI in America would also apply to Tech in America.
2027 is going to be very interesting. Buckle up.
"Tech sector" is supposed to be much bigger than just 7 companies, no matter how big those 7 are.
This looks to me more like just a huge centralization of wealth.
Yes there are technical solutions to such problems; I'm sure there's probably a startup to make phone calls that's hooked into your company's knowledgebase, multi-LLM workflows for document review, etc. But non-tech companies aren't adopting those. Tech companies came for the software generation and stayed for the general knowledge worker productivity. Since 2023 they made the investments to procure services, change culture, and generally re-tool. Software companies are also uniquely positioned to deal with the limitations of AI in-house, like by building custom glue components to link systems, building controls to mitigate risk, etc.
So basically the tech sector leverages AI more than other sectors. At a company level, non-tech has less investment, less training, less integration of AI with business processes. At an individual level this looks like workers having fewer tools, not knowing how to use the tools they do have, not knowing even what the tools' capabilities are, and incorporating them into their work only superficially if at all.
Someone asked me for help using AI at their job (which is tech adjacent). They were trying to create a process document using ChatGPT. Their selected model was GPT Luna Light (!), and they were copying and pasting data between ChatGPT and SharePoint. There _was_ a SharePoint integration but it lacked write access. The model was not aligning the doc images. Their prompts were not specific. It was a bad experience. Their conclusion was that AI was stupid and not worth the trouble.
So even though the non-tech sector can't take advantage of AI to the same degree that the tech sector can, they're utilizing it even less than they could be.
Right now, the most effective use case for AI is coding agents. Software development is a profit center for the tech sector, so it's not surprising that they're seeing big gains. In Corporate America, software development is part of IT which is a cost center, and the software landscape is 30+ years of bandaid-ed legacy systems, maintained by offshore contractors who submitted the lowest cost bid, so attempts to gain productivity via AI have not been as successful.
Where Corporate America has seen gains from AI in my experience is with customer support chatbots, but the customer service function overall in Corporate America is an unmitigated disaster, so the gains are hard to measure.
Beyond these two use cases, most people in Corporate America are just using ChatGPT to create slop and copy/pasting it wherever, and then goofing off in their newly freed spare time.
We need to see new AI use cases in Operations, Finance, Marketing, etc. that can handle business processes end to end before the productivity gains start showing up in the financials.
Also, it’s been just 9 months of modern claude code, and for many companies less than 6 months since first employees outside of IT got access to Claude Code. What do they expect?
this is literally the textbook historical process across every industry ever.
1. Profit margins from the mag 7 are on an upward trend starting in 2020. This is pretty consistent with the insane topline growth had by covid and the most recent layoffs occurring over the last 6 years. To unilaterally attribute this to AI implementation is lazy.
2. Most non-tech firms are only implementing AI just now because we finally have better enterprise tooling required. We won't see the effects for another 12-24 months at least.
3. As a former management consultant, this might be the worst use of a stacked bar chart I've ever seen: https://www.apollo.com/content/dam/apolloaem/images/daily-sp...
4. I don't know how you actually isolate AI productivity gains on a macro level. It seems pretty much impossible.
This whole thing is laughable.
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.
People are, on average, too stupid to make good use of AI to the point where you can actually gain massive productivity.
The pointy haired bosses are going to do their usual approach: force people to work more, claim success, move on.
The only issue is that the benefits would be more enmeshed with other gains in other industries.,
Heck, even in Software Development, the real benefits are not with mass labor elimination resulting in cost savings but with increasing available throughput. Technology if used to the fullest potential should be able to expand the proverbial pie., which is where the gains are!
That is not to say people won't try to save cost by eliminating labor due to AI., which is super shortsighted and misses the forest for the trees, but the overarching goal should be to increase the quality and quantity of deliverables, as demanded by the market.
Not so much in accounting, which is a much easier problem.
Software devs eagerly trained their replacement.
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.
That you don't know. Nobody knows what AI is going to be capable of in 2 years. It could continue improving, or not. There's no strong evidence in any direction. You're just ignoring what doesn't fit your narrative.
when you say "Many will fall for the road to be paved", you are saying your idea is more important than human fucking lives. this is a recurring theme in every fascist regime throughout human history.
Of course there have been regimes throughout human history that were unfree, even brutal, but no need to apply a term so linked to specific early twentieth century developments.
What's embarrassing is how many people are clueless about where AI is going. The tech sector is one giant echo chamber, so it thinks whatever it makes will be a miracle cure. Everyone outside the echo chamber has no idea what it is/how it works, so they're just hoping it's the miracle cure the echo chamber keeps saying it is.
In reality, AI is just a minor improvement in productivity that doesn't solve hard problems. It does what we could already do before, a little faster & cheaper, without determinism/trust. It's a bigger, faster truck, but somebody still has to drive it, find a place to park it, spend more on gas, etc. It may be able to "think", but it won't replace humans until it can accept liability.
We are functionally in a recession. The only way this isn't true is because of AI investment [1]. Additionally, GDP itself is flawed because it is propped up by housing, specifically imputed rent [2]. Another ticking time bomb is the $4T private equity backlog [3].
The real product of AI of labor displacement and wage suppression.
So while this article sees profits flat outside of the Mag 7, it should really be in decline to being flat is actually an improvement (IMHO).
We're not producing anything anymore. All we're doing is driving up revenues on inelastic demand on things like food, housing, electricity and health care. That's all PE is. And that's all our economy does now.
I really think people are underestimating just how close to breaking point our society is.
[1]: https://uhero.hawaii.edu/would-we-be-in-a-recession-without-...
[2]: https://www.taxresearch.org.uk/Blog/2023/02/14/10-or-gdp-is-...
[3]: https://www.notveryprivateequity.com/eight-year-hold/
It should be of zero surprise that the rush into AI has yielded limited results (if we're honest, both in and outside of tech)—you won't get a bunch of artists or creative thinkers out of a pliant, ironically agentic labor force. That's why most people say "it doesn't work": it's because they lack the skills and knowledge to use it properly (but corporations want to delude themselves into thinking they have a genie in a lamp).
I would expect new disruptors to eat the lunch of the old, but that's slow.
AI productivity gains are coming make no mistake about it. My guess is that we'll see them within the next 5 years.
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.
Only 13% of companies see real returns from AI, survey finds - https://news.ycombinator.com/item?id=50207881 - October 2026
is this 13% of leprechauns become elves?
I wonder if the argument cannot be made that it has delivered little payoff in tech as well - in terms of actual stable revenue vis-a-vis the immense capital investments, the deep loans, the circular commitments etc.
This may well remain a "true communism/socialism has never been tried" situation.
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.
Internet has allowed a new type of businesses to appear and prosper. Some old-style businesses got the hang of it, some did not (look at all the newspaper businesses trying to figure out how to stay funded without falling under oligarch's rule).
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"
The industrial revolution produced physical goods at scale, which eventually gave regular people access to things they could not have before.
AI produces intellectual labor at scale. Regular people do not need mass-produced content in their lives: there's already too much music, too much TV, too much software that only seems to get worse year after year.
Who needs mass-produced intellectual labor? The owning class. This is the only population segment that stands to benefit from this revolution. It's the reason every tech CEO is trying to shove AI down everyone's throats. It's the reason they're resting their feet on the table while everyone below them is starting to panic about the future of their careers.
I don't believe we will see compounding acceleration of anything other than spam and enshittification in this AI-laden world. This is not technology for building futures we can barely dream of: it's a gold-plated gift to the oligarchy and a fuck-you to everyone else.
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.
> before most companies even realize it's an option Couldn't be further from the truth
How long have people been warning about global warming? Do you still think it isn't real?
It does not. But it does mean they are bad at predictions and you shouldn't give their latest prediction much credence.
If you moved the goal posts any farther, they'd have traveled around the world and would be sitting at your back right now.
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.
Is there even any actual basis to walk this back at all anymore? It kinda seems like its a done deal and this is the outcome.
The vast majority of entire companies are going to the bottom of the ocean regardless of what individual contributors do.
Once you can replace nearly every job at a company, the company doesn't need to exist. Companies are just collections of experts, if AI replaces all the experts then it replaces the whole company.
So I've adopted the accelerationist mindset of: yes, this is absolutely going to replace my job, but it is going to replace everyone else's job too so fuck it I'll use it and we'll all go down to the bottom of the ocean together.
What a grim outlook.
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.
Why would you spend dollars on tokens today if you expect the same tokens will get you something vastly superior in a few months, or that they'll do the same job for nickels on the dollar? Do you just hate having money or what is the problem?