Observers suggest measurement gaps may explain productivity gap
1 Nov 1 · 10d ago · 1 article · 6 posts · 4 sources · development 1 of 1
Commentary on the findings proposes that the lack of measured payoff may reflect companies automating work that was never quantified in the first place, framing the issue partly as a measurement problem rather than pure lack of impact.
“Zero payoff outside tech is what you get when you automate work nobody measured in the first place. The productivity gap is partly a measurement gap.”
skiml.ioTorsten Slok Chief economist, Apollo Global ManagementApollo Global Management Private equity firm, former TechCrunch owner
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first by HN Best, 10d ago · also HN Frontpage
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Plot twist: AI has delivered 'zero financial payoff' to most big companies outside tech, says Apollo's chief economist. Flat margins, flat productivity. Hype 1, ROI 0.
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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 (https://www.apollo.com/wealth/insights-news/insights/daily-s...).This is a healthy corrective to the most insane AI maximalist takes (mass disemployment in…
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Zero payoff outside tech is what you get when you automate work nobody measured in the first place. The productivity gap is partly a measurement gap.
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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.The problem is most people don't know what makes them successful from a people perspective, except in the coarsest terms.90% of the business outcomes are…
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“AI may be a great calculator, but in business, intelligence is mostly judgment”
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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…
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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:“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…
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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": https://www.apolloacademy.com/ai-adoption-rate-trending-down...I don't think that one has held up very well. A cynic might opine that they've realized how easy it is to…
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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.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…
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That’s an interesting one. On a personal level, there are clear benefits. On a company level, there’s very little visible. Draw the ratio of operating profit to employee count over the past 10 years and there’s no change there you could clearly attribute to AI. It would seem the engineers are an important part of a machine, but not _the_ machine…
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> 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.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…
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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…
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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…
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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.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.
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I noticed an interesting phenomenonYour company uses AI and it gets ahead a lot quicker. However, your competition does so as well.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.
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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...
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>AI hasn’t benefited the bottom line of any big corporations outside the tech industryIf you get two companies that reduce their costs 50% but due to competition have to pass on their savings by reducing prices, the profits may remain the same even if AI works.
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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".
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Unless you are selling lines of code, or math theorems, that's going to be the case.I'm guessing "most companies" are actually selling something that isn't constrained by the types of things that AI code generators unlock.
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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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Give it time. The Great Reconfiguration will take a great time. As it did with printing press, electricity, computers, etc.
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