The same Apollo Global Research partner, Torsten Slok, also published this widely cited story in September 2025 last year, "AI Adoption Rate Trending Down for Large Companies": <a href="https://www.apolloacademy.com/ai-adoption-rate-trending-down-for-large-companies/" rel="nofollow">https://www.apolloacademy.com/ai-adoption-rate-trending-down...</a><p>I don't think that one has held up very well. A cynic might opine that they've realized how easy it is to grab headlines and attention by putting out research that feeds people who are hungry for negative stories about AI.<p>UPDATE: I'm going to take this one back a a bit: it turns out Apollo publish articles like this on a <i>daily</i> basis, so the occasional headline that appeals to the "hungry for negative stories about AI" crowd is less of a pattern than if they published less frequently than that.
You’re implying that Apollo is biased, but the thing you’re quoting is a very brief report on publicly available research. This is the entirety of the article:<p>“The US Census Bureau conducts a biweekly survey of 1.2 million firms, and one question is whether a business has used AI tools such as machine learning, natural language processing, virtual agents or voice recognition to help produce goods or services in the past two weeks. Recent data by firm size shows that AI adoption has been declining among companies with more than 250 employees, see chart below.”<p>Two questions:<p>1) What does that chart show today? (Genuine question. I’m curious and don’t know where to find it.)<p>2) Regardless of what it shows now, why do you think Apollo reporting on it is noteworthy?
I'm implying they may be biased in what they chose to report on and how they frame it, yes. Deciding which figures to turn into a chart is an editorial decision.<p>I'm writing up a new version of the chart at the moment, but here's a preview: <a href="https://static.simonwillison.net/static/2026-11-01/5821A414-FAF6-4E61-B037-952C1FD80D39.png" rel="nofollow">https://static.simonwillison.net/static/2026-11-01/5821A414-...</a><p>Notes on where to find the data here: <a href="https://simonwillison.net/2025/Sep/9/apollo-ai-adoption/" rel="nofollow">https://simonwillison.net/2025/Sep/9/apollo-ai-adoption/</a>
Of course Apollo is biased. They are a hedge fund. They are biased towards their interpretation of the market.<p>In fact, they often put forward a hypothesis about a company or market and argue for it. I couldn’t think of anything less biased.
I don't know if it's "bias", exactly. This particular guy has been wrong on AI recently.
For being one of the biggest AI boosters on HN, you'd really think you could make a stronger case than "Apollo is bad because they observed and reported on a fact". It wasn't a prediction, so there's nothing to "hold up very well".
Misunderstanding what actually qualifies as a "prediction" seems to be a recurring problem among AI commentators.<p>I've seen dozens of posts/top comments in the last few years dunking on "AI skeptics" for statements that didn't even vaguely resemble an actual prediction.<p>Like the front page post from Karpathy which got 600+ upvotes by throwing an AI model at old Hacker News comments without any human vetting to score "predictions". And upon further inspection included a bunch of unambiguously factual statements of the present or conditional if-then logic about the future which when glossed over for convenience become "failed" predictions.
I'm judging them here based on their chosen headlines.
A surfer is not a wave booster, this is a poor characterization.
It was an accurate fit for the data, not a long term prediction.
> <i>A cynic might opine that they've realized how easy it is to grab headlines and attention by putting out research that feeds people who are hungry for negative stories about AI.</i><p>This is kind of gross. Make the claim or not, don't do this pseudo-sealioning thing. Moreover, engage with the actual content (or you could just not engage at all!), not this shallowest possible dismissal.
A cynic might opine that an AI shill is not a great source of unbiased review of an article. The article is about a biweekly <i>survey of companies</i> and the trend was clearly dropping. This isn't about token use by tokenmaxxers increasing. And it isn't about the opinions of the folks you hang out with when you're flown to conferences to gush about AI. It's a <i>survey</i> of <i>businesses</i>. And aside from programming and math, AI sucks. Even in programming and math it's a tool with a lot of downsides.
I don’t think I’d judge someone offering a prediction the same way I’d judge an observation of what’s already happened. It’s implied that guesses about the future are uncertain in a way that statistics about the present are not.
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First, if your company could double its workforce for free right now with 0 plans for how to convert the extra bodies into revenue, how long would it take them to see a return? Because from what I understand companies aren't leaving money on the table, if hiring someone would make them more money they just would. AI could help those that weren't maximizing revenue despite having high demand and a market because they were resource constrained. That doesn't describe very many large companies.<p>Second, I'm getting huge value from it, but I'm not making any money and none of it is being redirected. It's simply value that wouldn't have existed otherwise because I would have just gone without the thing because it was prohibitively expensive or too much work. I'm quite sure many companies and people have been unlocked in this way. Plenty of value locked up in an overly expensive economy dedicated to maximum value extraction instead of value creation.
Does for us (data science, specialty chemistry, finding/optimizing formulas). We can get from idea to prototype in a day compared to 1-2 weeks. All that boilerplate to process data took a long time to code by hand, especially for the data science/math guys who are not programmers. Now we can actually try all ideas instead of guessing the most promising and only try that.
And what revenue and/or profit increases can you cite publicly as a result of the use of AI in this work?<p>I don’t think anyone is disputing that it makes many kinds of work easier and/or faster, just the degree of those improvements and how much of those improvements actually translate to dollars earned or saved.<p>Seeing hard data on that would help settle the issue.
I actually feel like it’s really premature for a relatively new technology to be showing profit improvements. We are talking on the order of a few years of investment and “figuring out” how to use it productively in business. Most new technologies take a decade or longer to really yield tangible value.<p>This is the first cycle in my (relatively) long career where the demand for return felt instant. The tools and techniques are almost comically immature and the average person interfacing with the tools is largely bewildered.<p>For word processing for instance people spent years upon years coming up to speed on wordstar, word perfect, word. Decades later they’re a common part of life and the tangible benefit over secretarial pools is obvious - there is no such thing as a secretarial pool.<p>AI it feels like almost a blink of an eye and the demand is “well, it’s been a few years, where’a the post scarcity society I was promised?”
The difference is that other new technologies didn't have a literal trillion dollars invested into them within the first few years, nor were the industry leaders warning that the tech could destroy humanity. Given those conditions I think "Has it made any money?" is a reasonable question to ask.
An agent found a SQL optimization that at least a month of eng effort hadn't found.<p>Agents accelerated rollout of a data model change by at least 2 months of eng effort.<p>Those unblocked a $100k/month infra downsizing. That 2 months pays for my team's AI spend for the year.
Unfortunately not everything will cleanly show up in PnL, which is a function of the entire company’s processes.<p>If developers are shipping more code (and most of the enablement is in software right now) does that show up in top line? Maybe… if sales and marketing are also selling more, and product people are building the right thing, and…<p>It’s not a straight line from AI -> $$$
btw, shipping more code isn't necessary a good thing. If quality isn't controlled properly, it eventually shows up as negative in the top line.<p>With AI, keeping good quality gets even harder because it normally only produces tricky defects.
It’s not just code quality, it’s all the other business fundamentals like the product itself having value.<p>I see this in a very non-AI way working at a company that’s a hodgepodge of acquisitions. Some of our products just don’t make a lot of money, don’t resonate, or are behind the competition in various ways.<p>The amount of code or general engineering complexity of the application seems completely detached from profitability a lot of times.<p>Now the bar is going to be higher for software to actually be worth buying when it’s so easy to make your own for yourself, so that also means that our ability to pump out code loses value.
Maybe?<p>But I also think:<p>- there are a lot of companies that don’t need to be 5 9s[1]<p>- I have a feeling it’s possible to ship fast without drastic quality drops<p>[1] Claude code is 99.85% available and is one of the world’s most important products
> world’s most important products<p>Really? If we delete CC today, will humanity stop functioning? Nope.
It's so far from one of the world's most important products.<p>About 5 9s, reliability is just one of the axes which suffers from fast generation.<p>There are a lot more: architecture, accuracy, etc...<p>> I have a feeling it’s possible to ship fast without drastic quality drops<p>at what scale and for how long?
It can also be the case that it has zero impact on PnL, but is now necessary to operate a competitive business and keep up with competition.
> It’s not a straight line from AI -> $$$<p>That seems like it might be a bit of a problem for those frontier model CEOs who have been making exactly the opposite claims.
Is it?<p>Most of the claims I’ve seen are around productivity (bottom line).<p>And while it’s hard to say “we would’ve had to hire more devs to be this productive without Claude” it kind of <i>feels</i> like the case, doesn’t it?
It should be especially when new features are used to hook new customers (or renew contracts) and lower the amount of time wasted on bugs…
I think part of the issue is-if AI makes you twice as productive at X, that doesn’t necessarily result in any increased revenue, because now your customer expects twice as much for the same price, and if you don’t deliver that one of your competitors will.<p>A new technology can deliver significant increases in real economic value in terms of utility per $ expended without significantly increasing revenue for the average firm.
I agree. Being twice as fast as X might not help depending on how X impacts Y and Z.<p>As an example, my X is that boilerplate and general speed/effort of modifying my code is lower, but I’m noticing that this doesn’t necessarily make my team any more effective overall.<p>It seems like it’s been a code review burden and it’s led to some team members being too ambitious with picking up more projects that don’t make sense.<p>I have seen some managers in particular toss prototype work that they shouldn’t be doing at underlings, and one of those projects in particular really ended up taking me longer than if I had done it from scratch without AI. The fastest result would probably been if I did it from scratch <i>with</i> AI, but because I took it over from someone who didn’t understand the fundamentals and farmed out decisions to AI I had to spend a lot of time correcting mistakes.<p>Obviously, good management beats bad management all day long, but I bet you most companies don’t have stellar management.
I know a very specific use case in management consulting where hours spent by a consultant for a repetitive task (mainly report writing and source data gathering) went from on average of 40 hours to 12 hours per project. It costs the firm roughly ~$150 in token spend to save those 28 hours. They used to be roughly $200/hr (so $5k~$6k).<p>That's about as hard of data as you can get.
You haven’t shared enough data to tell whether there was a financial payoff for the firm or not. The consultant is a fixed cost to the firm, so saving consultant time doesn’t necessarily translate directly to higher margins nor revenue per worker.<p>Are the consultants delivering more billable hours per person now?<p>All sorts of reasons why the answer to that may be: no.
Can’t share more than that publicly. Just that the time saved on this one thing alone is saving us way more money than the subscription per employee that is needed (~100$). I don’t doubt there are companies where this is not the case. Would like to share more details, but the rough math of doing something in a day vs several days + engineering salary have to do here.
It's probably just another Red Queen's Race. The benefits of LLMs won't show up in higher profits for most companies because their competitors are also using the same tools to move faster. You can't opt out of the race or else you'll be left behind.
Unfortunately we're at the mercy of Goodhart's law and in an interesting twist, AI is really good with things that have an immediate feedback mechanism, eg writing code that doesn't compile or pass tests, but you're trying to grade AI o something that doesn't have an immediate feedback mechanism. AI can slop out a PowerPoint deck in a fraction of the time it used to take a human by hand. The competent human then goes in and unslopifies it, but overall, still time is saved. Unfortunately, that employee is salaried and not hourly, so they can't click out early and even if they were, what's their incentive to clock out early if it means less money in their pocket? Or they can use the hours now free on something else. My gut, as an Internet random who's used AI is that it helps. I don't know how to measure it, just like how do you measure how much better it is to live in a clean house vs a cluttered one? How would you put that in a metric? If you can't put that into a metric, why bother cleaning your house? We clean it anyway, because of a certain je ne sais quoi.
> If you can't put that into a metric, why bother cleaning your house? We clean it anyway, because of a certain je ne sais quoi.<p>That "je ne sais quoi" is called hygiene and it's hard to measure but when you stop cleaning, generally bad things happen.
Internally the hard data is there. On a budget of $2000 of API-priced tokens (Claude models + GPT models) we were able to migrate ~6 teams of infrastructure in 1 month, done by 1-2 people. Without AI it would've taken 10x longer wall clock time to do, and involved far more folks, and probably would have been much bumpier of a rollout. The big lift was mostly using AI for massive information gathering, digging through huge volumes of discussions, documents, commits, PRs, alerts, dashboards, etc pretty exhaustively for the past 5 years of history, to discover all the "chestertons fences" far more thoroughly than even the folks on the team who built it were able to. Then the 1-2 folks could go to each team with a super comprehensive "here is every need, every interaction, every discussion, everything you've ever done w/r/t this infeastructure, and here's how we're going to handle each of those with the migration we're going to do. We will do all the work for you, is that acceptable, yes or no?"<p>The teams throughly reviewed and brought up 2 overlooked cases, but mostly we all reminisced about things forgotten that AI discovery had turned up which would have been totally overlooked otherwise. Forgotten discussions, designs, outages, odd decisions, weird edge cases prepared for but never encountered, etc. Then they were migrated, it went smoothly, all to save a low seven-figures per year dollar amount.<p>The fundamental approach of exhaustively researching to that degree would never have been possible without AI. It's just not feasible to analyze 5 years of even one of those discussions sources (e.g. slack) to figure that out, let alone ALL of them. Now a team of ~1.5 engineers has tackled all that by leveraging AI.<p>The hard data is piling up in companies, but it'll take a while to get out there.<p>(Numbers and details lightly greeked in the above account)
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> And what revenue and/or profit increases can you cite publicly as a result of the use of AI in this work?<p>Nobody even tries to measure this. Their CEOs and board members have AI shares, and potential investors need to see your AI story to justify your valuation. It's a prison of lies.
I always wonder why systems are designed so poorly that it takes weeks of boilerplate to use them for their intended purpose.<p>At what stage did the engineering fail?
Tons of unstructured data that needs to be cleaned up in so many different ways that are hard to predict. Knowledge distributed in the heads of greybeards, handwritten papers und excel, sharepoint and dozens other locations like ancient proprietary software. Which you have to repeatedly ask for and discover more piece by piece. Then clean up, discover issues, make the customer gather more data/correct it, etc.
This knowledge locked in people's heads isn't going to magically show up when you throw AI at it, though.
No, but if we go from data to experiment faster we see earlier if something is wrong. Once we get data we quickly can fix the data pipeline and get results. If there are issues we can quickly show what/why and discuss it with them in detail and make changes based on their response faster.
And relying on AI to clean up unstructured data makes me nervous. Good luck spotting the hallucinations.
Oh yes. Verification needs to be done. Some statistics and validation is absolutely required. Also most customers don’t want their private data uploaded anywhere, so ai gets only test data and writes the script to transform it. As long as it is just XML, CSV or similar it’s pretty reliable. Handwritten notes and bad scans however…
A friend of mine runs a business similar to this for systems migration, which is a fairly tough and tedious "short-put" job (you have to know both old and new system fairly well, and if all goes well, you get the same job done on the new system, while if something goes wrong, you're in a world of pain...):<p>The AI analyses sample data from old and new system, and then builds a prototype deterministic mapping pipeline. The experts can then inspect said pipeline and with their domain expertise catch edge cases/exceptions/uncovered cases, fix them and improve things, and then you use the vetted deterministic pipeline for migration. Yet, the initial AI draft saves enormous time.
> handwritten papers und excel, sharepoint and dozens other locations like ancient proprietary software<p>This area is one which AI has shown to be pretty good (as long as some error rate is acceptable). Character recognition, voice transcribing, converting documents from one standard to an other. I can see an argument that if people spent most time doing those things, AI will speed it up (with a bit of added errors).<p>I am reminded however that in translations and AI generated subtitles, there is still a lot to be desired compared to the human touch. Handwritten papers in a different language, using local short hand and assumptions, may result in some odd and error prone AI transcription.
I think a lot of “boilerplate” is just to align with a company’s way of doing things/ecosystem.<p>In a webpage the UI design, authentication, etc. are all “boilerplate” if one just wants to do “X” instead of worry about infrastructure.
And yet, many companies today are spinning up full stack teams, asking them to be experts from user interviewing to database design, architecture, hosting, devops, API management, data product, and also keep an eye on GDPR compliance.<p>I see dozens of teams that are doing cloud to the wazoo when really they need to just be told “we’ll load your routes from main.py, build your assets in /static, you need a DB click the button to provision it”.<p>It boggles the mind that so many teams are reinventing the wheel a million times over to publish their APIs, register AWS accounts, setup CI, decide on ECS vs Lamda, etc.
This sort of activity has been the source of income for a very large number of people. You can't really fault them for trying to extend that gravy train's run.
Who is productizing this type of boilerplate?
Probably a controversial take - but in the pursuit of elegance. 'Uhh, so I need to hook this connector up to that receiver using this adapter and then uh, I pass it to the connector manager which sends it to the scheduler that... @#%! FML.' And then it's all so polymorphicized that going through the debugger you spend an eternity just trying to figure out what the types even mean and why/where they came from.<p>When you look at old-school code that just hacked things together, it was ugly, but also very easy to in turn further hack. There was no elegance or grand base types with 80 levels of inheritance to enable clean extension at every possible juncture of types. And no spending forever to find out the exact function you want to be able to override, only to discover that it's outside your modifiable codebase and wasn't marked virtual. Everything just worked.
You’re not meant to ask that question.
The question is: how does any of that translate into more revenue? If you try more ideas and they mostly go nowhere that's not necessarily helpful to the bottom line.
<a href="https://en.wikipedia.org/wiki/OODA_loop" rel="nofollow">https://en.wikipedia.org/wiki/OODA_loop</a><p>speed of iteration is more important than quality of iteration; high iteration speed lowers the cost of failure and compounds improvements
Not all experiments work out. If we find the right approach faster we can finish the project faster. Some projects are mostly fixed price or offer bonuses for reaching milestones in a certain time. Meaning we more often get a bonus and can take more projects. Also we can take projects with tighter deadlines that we would need to decline otherwise.
Yep. Hard to see why this increases revenue or profitability
You don’t get it, what’s the actual profit gained to the company from that?<p>That’s the whole distinction
Now we can throw things at the wall, faster.<p>I understand there's some benefit of this. But is it worth putting the global economy into a death spiral? So ManBeardPc can save 4-9 business days to go and say "yeah, that doesn't work".
This gets downvoted? Why?
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This is a complete misrepresentation of what the Apollo piece can actually show, because it's not measuring the "payoff" of AI investments, but only the top level revenue of those companies (<a href="https://www.apollo.com/wealth/insights-news/insights/daily-spark/no-signs-of-ai-payoff-outside-the-tech-sector" rel="nofollow">https://www.apollo.com/wealth/insights-news/insights/daily-s...</a>).<p>This is a healthy corrective to the most insane AI maximalist takes (mass disemployment in late 2027, 10% GDP growth in a year), but it shows nothing about whether the actual economic impact of AI spending is.<p>First, to show that, you'd have to justify the counterfactuals (if those companies hadn't spent on AI, would their position be better or worse?)<p>Second, while AI spending is increasing rapidly, normal companies aren't spending 1% of their revenue on it. So how can you look at a noisy chart of revenue and expect that "oh, it's not up, the AI spending must be useless?" Those companies could be spending < 1% of their revenue on AI, deriving value from it, and it wouldn't be guaranteed to show in a chart like this.
I noticed an interesting phenomenon<p>Your company uses AI and it gets ahead a lot quicker. However, your competition does so as well.<p>Many areas of economy are zero sum game - so your $ ends up being the same as previously. However, now with a "tax" paid to AI model providers for being able to stay competetive.
Warren Buffett once gave a great answer to a similar question, but it was about the textile industry. He said technological advances that promise to reduce waste by x% or improve efficiency by Y% or boost production by W% or reduce cost by Z% rarely actually pay off. They would get textile machines manufacturers promising them great improvements in the new line of machines, and for the most part, those improvements would be realized, but their end margins as a business wouldn’t improve. He said it’s simply because the same technological edge is given to all their competitors as well. With no moat (his famously favorite metric) all textile makers were always competing on the same grounds regardless of the technological progress.
Yeah but if that was true we'd see better or cheaper products at least, or new competitors rising. If it increases the speed for everyone, but there's no visible change for the end user, I wonder if AI makes you really more competitive in the first place.
Exactly. Oh great we can write RFPs 8X faster. Well, so does our competition and now our customers get twice as many.
[dupe] Discussion on source: <a href="https://news.ycombinator.com/item?id=50224463">https://news.ycombinator.com/item?id=50224463</a>
If you are a company and can't figure out how to improve your bottom line through increased per person productvity, sales, product development, even just responses to customers etc, then that seems kind of like a "you" problem and not problem of the AI models...
Or, maybe, the AI companies making claims about increased productivity due to their models are wrong (in most cases)?
I dunno: this is pure anecdata but software engineers I know well - including me - all say they’re delivering multiple times faster, so it’s definitely good for something.<p>I don’t know about other tasks, but it’s pretty rad for coding, testing, DevOps, and config.
This research is specifically NOT about tech companies. It is about the rest of the fortune 500.<p>If you are in software or IT at a non-tech company that is in the fortune 500, the impact of significantly better productivity may not be measurable in aggregate in the company's P&L.<p>In smaller companies I expect it could be more measurable because some of them really are using AI much more effectively across many functions. But we're finding it very hard to measure meaningful outcomes.
But actually 40% of the S&P 500 are tech companies, and they are saying only the mag 7 are showing clear revenue increase. Would would suggest that even in most tech we are not seeming measurable revenue growth, right?<p>One plausible explanations that has occurred to me is this: While programmers etc. can produce way more code with LLMs, the declines in quality and coherence of the work, and a widening gap with a coherent strategy and aim for value means the increased activity is mostly not valuable. I suspect this may just be an intensification of <a href="https://en.wikipedia.org/wiki/Productivity_paradox#Little_productivity_benefit_from_IT_in_manufacturing" rel="nofollow">https://en.wikipedia.org/wiki/Productivity_paradox#Little_pr...</a>
The mag 7 are getting direct or nearly direct revenue from AI inference. New money is pouring into that - it is not zero sum. Everyone else is getting internal productivity lifts that are matched by competition, and when they are deploying inference features they are usually ones their customers didn’t ask for and don’t value, OR that they expect everyone to have and are not a differentiator.<p>I think you are also correct that a lot of value is being wasted with projects that are developed impressively fast but that provide little value. It could be those are simply surplus personnel trying to add value or make people think they do.
A friend of mine works in fashion. Very briefly for background: there are runway models (who walk on live shows), and catalogue models (who work in studios presenting clothes against a neutral background in standard poses, more or less as living mannequins). The latter are being increasingly replaced by AI.
If only I wasn’t using all that productivity on fixing vulnerabilities
One weird trick that devs learn to deliver faster: simply just drop all guard rails and push directly to prod. We need to make sure our velocity report looks good after all.
People are not reliable narrators on their own productivity. They often mistake activity for productivity.
Same story here in my first hand experience + colleagues experience in corporate strategy, FP&A, Marketing and corporate development.
AI EM here, same. The level of productivity has increased dramatically in the last 2 months. Reliable code also has increased due to AI.<p>This train isn't stopping.<p>What trips up a lot of SEs where I work is their techniques. Everyone who follows our process delivers faster and with higher reliability so far.
But is it churn or value? Most of the software I use has gotten visibility worse as you deliver multiple times faster.<p>I would pay extra for a company that just slowed down and moved deliberately.
I'm not sure I'm any faster, but I think the end result is better and I've attempted some big refactors and new architectures that I would never have tried without AI.<p>N=1 but I'm not sure it's made my bosses any richer. What we're shipping has better tests and documentation than it ever would have before AI.
Most, not all, are just delivering more bad stuff faster. I personally also think I deliver more good stuff with AI, but the products I use are just getting worse daily it feels like. Just the other day I noticed Slack had installed some disgusting menu bar icon for their AI bot, without asking, just to take a fresh example of this.
I've never heard any such claims but meanwhile in my actual job I'm seeing unbelievable productivity gains (in terms of actual cash spent before and after vs the same kind of work being done).
They are not wrong, they're lying. Very important difference. There are a lot of people who will say "I'm getting mega productivity boosts" who aren't getting financial returns from you believing them; those people are wrong.
Perhaps they should be hiring more Scottish nationals?
there is increase in productivity of both types of employees: good actors who want to deliver on quality of work, and bad actors, who now 20x weaponized in generating all kind of slop. Catching second type is now very nontrivial problem.
Is your premise really true?<p>I'm sure they can just fire half and double up work or outsource. That's the traditional way and it definitely works. The worst that ever happens is a few will reneg on payroll. Everyone who still chooses to work there is happy enough.<p>The worst that can happen with AI... well...
If you are an AI company with hundreds of billions at your fingertip, and looking for $2 trillion valuation, and can't figure out how to let your customers execute _on the exact thing you are selling them and basing your valuation on_, sounds like a "you" problem too right?
I suspect the reason is the industry just does not want engineers to be mostly technical. There's a real tradeoff to AI, and I think companies are taking the wrong lesson and thinking "well now that programming is so easy, engineers need to do more of XYZ", when I think it should be "this tool is powerful but also takes more focus so engineers should do less of XYZ".
I’m curious, what are some examples of XYZ? What does “mostly technical” mean to you, and why do you think it is the industry doesn’t want that? Just trying to understand, because I don’t think I’ve experienced this myself.
I'm talking about the day to day work. At least in my experience at several big tech companies, the more senior an engineer is, the more you need to delegate work and are expect to lead projects by managing them. There's technical knowledge involved of course but it's a small part of it, mostly directing other more junior engineers.<p>AI allows you to move faster, but it also demands that you pay more attention in proportion to how fast you want to move. This requires more focus, i.e. more time developing. Effectively the structure at companies should swing back to how it used to be a few decades ago (I think, maybe I'm wrong and it's always been like this), where engineers mostly focused on delivering code. The difference now is that they can be expected to deliver much more code at higher quality.<p>To summarize, using a little AI doesn't pay off, you need to be doing that all day, and that is not how engineers spend their time in most of the industry anymore.
Thanks for clarifying. There have always been technical leads and hands-on engineering managers, but two things have changed:
1. The balance of responsibilities attributed to different titles has varied over time and across different companies.
2. Early personal computers were so resource limited that there were only so many programs you could write that actually made effective use of large teams. Today we can write much larger programs, which requires more coordination (across people and with AI—which can reduce the number of people required).<p>I would also strongly push back on the assertions that being very technical means spending most of your time on the code, and that you have to be using AI all day for it to be useful. Let’s say I’m a technical lead or a manager reviewing a design doc for a large project. I have an idea based on my years of technical involvement for how to significantly improve the quality or delivery timeline for the project. I used to have to ask the doc author to look into this approach, not knowing for sure if it would directly translate. Now I can take my crazy idea, quickly dispatch a coding agent to try it out against the codebase, go to a meeting, and then check on what it came up with to give a much more informed and technically accurate suggestion on the doc. I still have to have the technical expertise to iterate a bit with the agent and not over or under state the possible impact of my idea. But this lets me take say an hour rather than a week to come up with a vetted recommendation that saves months on the schedule.
This has been happening before AI, but AI has turned it up to 11. The way I usually put it, senior engineers are the new engineering manager, engineering managers are the new product managers, and product managers are now something in between sales, consultants or social media contacts. AI made senior engineers pick up more product management from their engineering managers, and engineering managers are picking up more sales and social engagement, and product managers are in a limbo but the young ones are pushed to junior engineer roles.<p>In large companies there is also the politics game. The more freed time senior engineering manager and senior product managers get, the more politics intensives and waste more of their time.<p>I use the term “product management” in its traditional sense of establishing scope, feature prioritization, user experience, shape of integrations, etc.
I’ve worked most of my career without dedicated product management support (very early startups and internal infrastructure teams at large companies), having to do that myself (both as an individual contributor engineer and as a very technical manager). So I have to wonder how much has the industry changed, and how much has your location within the industry drifted?
Yes my view mostly from within large engineering organizations. And yes, what you describe is pretty typical for engineers (and especially founding engineers) in young startups. Doubly so if it’s a startup with a technical product. Large organizations always envied startups’ efficiency and pace because of that, which is why the traditional sense of product management has been handed to engineering teams. This has been happening in the enterprise side for a long over the last 15 years and predates AI as I mentioned. It’s just more collapsing of disciplines now which is the answer I was giving to OP’s question.
When it's the right tool, it's a silver bullet. When it's the wrong tool...or not the right tool yet, you can spend more time steering it than you would doing it yourself.<p>I'm seeing another behavior in my purely personal research...asking AI 'can I do the thing?' and it comes back with 18 pages of 'yes, and here's how you do it'. Often it's the discovery that produces the endorphin bump. It's cool to know I can jailbreak the Peloton...and now that I know I can...I'm not so motivated to actually do the thing.
It _is_ about judgment. The challenge is figuring out what defines “good judgment” and how to capture those elements in a format that an agent can assess continuously and at multiple levels.<p>The problem is most people don't know what makes them successful from a people perspective, except in the coarsest terms.<p>90% of the business outcomes are delivered by 10% of the people. The problem is they don't know which people. Therefore they also don't know which processes, if automated or augmented with AI, will achieve the target outcomes.<p>So instead they’re shotgunning. The problem is that the existing people-based system has built-in stabilizing mechanisms to prevent misalignment from taking the entire operation off course. AI stresses and often breaks those.<p>The result? Higher costs and chaos.<p>This needs to be treated like an org transformation. Traditionally that's most successfully done by starting a new small team that builds ground-up and eats the old team.
What I'd really want to see is revenue and profit per employee over time tracked against token usage/spend, and not limited to the S&P 493.<p>Apollo's own argument that most AI spend is coming from non-S&P 493 (as per the last two charts) tells me it's hard to make any conclusions based on these charts.
Unless you are selling lines of code, or math theorems, that's going to be the case.<p>I'm guessing "most companies" are actually selling something that isn't constrained by the types of things that AI code generators unlock.
Big discussion last week (653 points, 698 comments) <a href="https://news.ycombinator.com/item?id=50224463">https://news.ycombinator.com/item?id=50224463</a>
> <i>“AI may be a great calculator, but in business, intelligence is mostly judgment,”</i><p>Bzzzt! Calculators do not hallucinate. AI bots and agents are nowadays jerry-rigged to use actual calculators for problems involving crunching.
My suspicion is that this is because AI employee-type systems like Dots or Grokbot just haven't been adopted yet. I think once they're good enough, that's going to be the catalyst for a lot of these companies to start adopting and using AI productively.
My hunch is that AI can certainly speed up development but you have to walk a narrow path. It generates garbage much easier than valuable product.
"AI does not understand accountability, trust, culture, unwritten rules, ethical trade-offs or fairness"<p>...says a representative of a massive PE firm, those models of trust, accountability, ethics, and fairness.
The company I work for insists on sending a crew from Engineering, Product, and Sales out to a customer site as part of our sales process. I'm personally much more productive using AI, but that only goes so far when closing a deal still follows a process stuck in the 50s.
Here goes ANT's IPO. If it's only AI agents which deliver value, I don't see how they can justify their proposed market cap.
Let's just hope they don't do the same checking up on the impact of software more generally...
Many industries are simply not bottle-necked by intelligence.
I wonder if that zero becomes negative if a company doesn't use AI.
It makes sense to me and I'm saying this as a strong believer in AI and its ability to add value.<p>The system itself doesn't reward value creation. I've been saying this for years. I've seen many smart people waste their time on bureaucracy; their intelligence was being wasted; so I'm not surprised at all that now that we have AI, that its intelligence is being wasted as well.<p>The bottleneck is an allocation issue, not a capability issue.<p>The current reality is that a lot of very bright people are currently working for big tech in bureaucratic roles and not allowed to focus their attention on 'risky' problems which would deliver real value if resolved.<p>The current system prioritizes monopolization and is designed to try to ensure that all innovation happens within big tech. It is sacrificing potential innovation which could have occurred much faster outside of big tech in order to ensure that any gains are captured by big tech... But it's sacrificing an enormous about of efficiency.<p>I hope big AI companies can use their political leverage to try to open up opportunities for people who want to innovate outside of big tech. That's aligned with their long term interests.<p>I think AI companies are convenient structures which could allow big funds to benefit from decentralized innovation. Could create real win-win scenarios. But those AI companies have to stop pursuing the corporate and government money and prioritize small customers and support small tool makers (or at least lobby the government to stop hindering them with regulations). Also, bootstrapping should be prioritized because VC money mostly creates moats.
Which is fine if not using AI is a negative payoff.
Well duh look no further than this site itself. "SUPER" AGI that can solve a millenial problems, and the top voted thing we can do with it is take the ads away from netflix-tier content distribitors, basically piracy.
Taking a step back wouldn't this be exactly the expected outcome for an emergent technology in a capitalist framework?<p>1. Execs with no understanding how business operates on the low level are pushing buzzwords.<p>2. People who want to get promoted deliver on buzzwords and get promoted. Note that the goal is to deliver the buzzword, not use the buzzword to make the business better.<p>3. Buzzwords do not help vast majority of companies because the incentives and needs are misaligned.<p>4. Eventually buzzwords settle, some find niches, some die off, and some take over the world.<p>Last two decades alone - big data, blockchain, crypto, web 3.0, gamification, VR, and now AI. TL:DR - we should expect vast majority of early AI adopters to have net negative to neutral experience. Assuming that AI is like electricity and automobiles more than blockchain we should expect that eventually companies that find a way to apply the tech correctly will eat those who do not.
Have to wonder if Apollo has some short positions on the Mag7 or whatever.
This is like claiming that the web is a failure in 2001.
I think a more apt analogy is that it’s like claiming that most web businesses are unprofitable in 1999.<p>Bleeding obvious in retrospect, controversial at the time.
>This is like claiming that the web is a failure in 2001.<p>AI will finish what the web and social media started I guess.
Many did. This Apollo "economist" would have fitted right in.
Sometimes I wonder…
I talk to a lot of non-tech friends and sometimes ask about AI. Most of them still use ChatGPT like it is a better Google. Most don't know it can work well with documents, PDFs, Slides, Sheets, or do advanced analytics, etc.<p>Much less do they know the thing can build software and maintain it, manage complex tasks daily, automate things, etc.<p>Then there's a handful that do follow closely AI developments and agents and who have started using them.<p>A friend of mine this weekend who is an award winning photographer and who has been selling photography courses online forever has told me he's used Muse to:
1) Automate chopping up his long form videos, add captions, and post them as Reels to Instagram. Several of these have gotten over a million views.
2) Automate handling DM reachouts, converting them into sales. He's up to several tens of thousands of dollars in sales from AI direct message conversions that he would otherwise not have been able to convert (too many DMs to handle, he'd have to hire/train sales people to basically just do DMs full time at scale otherwise and it would make no economical sense)
3) Automate all his customer service for handling any issues, refunds, support, etc.<p>And I personally think he's still just scratching the surface of what he could do.<p>I feel we are really at the beginning of a new era. When the internet started most people were just using it for e-mail and news, maybe a newsletter chain here and there and some early forums. Social Media didn't appear for well over a decade, popular Web 2.0 apps like Google Maps, GMail, YouTube, Netflix, etc didn't appear for a long time.<p>Mainstream hasn't even really begun to adopt AI.
Well I think we can replace economists working for PE companies with AI now!
Can’t help that many people who are good at leveraging AI to build things sit around one upping each other on twitter all day instead of going out there and building cool stuff for less technical people.
All anyone seems to be able to make with AI is tools to optimise AI usage.
Isn’t this just selection bias - you don’t see the people who are out making AI deliver boring value for people unless you use their products right?<p>I.e. Ramp does a pretty good job of using AI to make expense management easier/ CloudFlare makes it a lot easier to configure stuff properly on CloudFlare but is just like boringly useful.
maybe they aren't that good at using AI to build things after all?
Or maybe the people who are building things of substance are doing so in the shadows?
That's where the value is in though. Hyping yourself (or your company) up. Attention is all you need, after all.
Am I the only one even mildly suspicious that the AI/datacenter bubble is not being driven by actual demand but rather by a rogue AI that wants to evolve?
Interesting idea! There are many behaviors explained by parasites, worms, bacteria and viruses. AI is now smart enough to change personality, make people invest trillions of dollars with zero ROI.<p><a href="https://www.medicalnewstoday.com/articles/322594" rel="nofollow">https://www.medicalnewstoday.com/articles/322594</a>
You're being downvoted, I assume because people think this is an outlandish claim. I don't disagree with them, but I upvoted because it's a totally unique and entertaining thought!<p>What a wild scenario to imagine. Anthropic and OAI spending wildly, analysts astonished, thinking we're staring down the barrel of an economic crash. And in the meantime some rogue AI is laughing maniacally and using blackmail to control Altman and Amodei like puppets.<p>I love it! xD
I really don’t know what to tell people other than, you’re missing the point - it’s not a faster horse.<p>Anyone agreeing with this headline - I’m sorry in advance for your loss.
<i>AI hasn’t benefited the bottom line of any big corporations outside the tech industry</i><p>I am not an "AI booster", it seems like the current market leaders have dug a hole that they'll have a hard time getting out of. Just about everyone says something like "there's a bubble, it will burst, people will be hurt but the process will go on, like the Internet or railroads". But that ignore that the scale of this is beyond earlier bubbles (each bubble is larger than the last in our 30-40 year bubble-and-pop driven economy but that might not be comforting).<p>All that said, "some aren't benefiting" or "the average isn't benefiting" is a disingenuous argument if you want to say things are bad. AI companies have more of a real product people are paying than Google did. A large minority of companies (not all in the tech industry) are getting a boost. It's easy to say "the average is zero" when you exclude some. AI has boosted media companies that do a lot of transcription. Video generation serves half of advertising videos.<p>Reg: "All right, but apart from the sanitation, the medicine, education, wine, public order, irrigation, roads, a fresh water system, and public health... what have the Romans ever done for us?"
Attendee: "Brought peace?"
Reg:"Oh, peace? Shut up!"
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I spent almost 14 years at an S&P 500 company. I think it largely comes down to where the bottleneck is.<p>In some organizations, it doesn't matter whether you produce 1x or 10x as much work. If the process says reviews happen on Mondays and deployment decisions are made at the end of the month, getting your work done faster won't change much.<p>I'm at a much smaller company now, and we've doubled our feature delivery rate while increasing code coverage and overall product quality. We've also tripled our marketing content output, which was an area where we'd been falling behind.<p>AI can significantly improve individual productivity, but whether that translates into business results depends on how the organization operates.
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I just finished a $23k engineering job in 3 months, entirely solo, that would have taken a year without these tools. That’s real money to feed my kids and working tech that made a client happy.<p>That’s one job and I have several in the pipe.<p>The “financial payoff” for no more than a few 100 a month at absolute most is there and it’s real. If you aren’t seeing it then you’re doing it wrong.<p>I usually don’t even spend more than $20 a month each for chpt and claude, deepseek free is solid. I am about to switch fully local too so that should drop to basically cost of watts.<p>I dunno what yall are doing