281–290 of 412 posts
I open this discussion thread and literally the first two top-level comments I see contradict each other: > I find that productivity follows a Pareto distribution (80:20 rule) and that AI is a sharper tool in that it enables the already productive to be proportionally more productive and this effect increases as the intelligence of AI increases. So 80:20 becomes a 90:10, 95:5, 99:1 etc. > Big caveat to the productivi…
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> so there will be a displacement of medium to large companies by much smaller ones. There are so many regulations EE companies follow and offer that no startup owner or worker could ever deal with. The moment your company shifts to that sort of work, you're automatically not anymore a startup (by definition). Only if you get super smart robots that can manage a lot of bureaucracy etc. Starting from multicloud/region…
My field is lightly regulated but it is regulated and LLMs have helped massively in producing the documentation but also in reviewing the same documentation on the regulators side. This was always a disadvantage for small companies where the fixed regulatory cost couldn’t be amortized over a larger staff size and now because of LLMs it is less of a disadvantage.
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Is this genuine concern or is it stealth-marketing? It's hard to tell when someone is so publicly benefiting off the hype.
What the heck would I be "stealth-marketing" here? It's genuine concern. I do not want to live in a dystopia where AI results in mass unemployment. That would suck, even for the people who manage to stay employed.
Organizational inertia is a real thing. There are still fortune 500 companies with internal bans on AI. A lot of the answer to "how much impact has AI had" comes down to "how much have we even attempted?" In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week. The impacts are here, they're just not evenly distributed yet.
Organizational inertia is a real thing. There are still fortune 500 companies with internal bans on AI. A lot of the answer to "how much impact has AI had" comes down to "how much have we even attempted?" In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week. The impacts are here, they're just not evenly distributed yet.
> In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week. That's gonna turn out well :-) Maybe they only need it to work for the next 6 months of enhancements...
But I think it is most productive to assume that it does not.
I am a very capable developer, and I convince myself anymore.
Big caveat to the productivity claim is that it’s concentrated in lesser experienced engineers and vanishes or goes negative with highly experienced engineers. This intuitively makes sense and generally agrees with my experience. LLMs move your baseline towards the mean. If you are below average it improves and if you are above it hinders. But also in my experience, my memory retention of the work done with an LLM is…
Do you think this is more because you are less close to the work? Or because there is simply more work being done/more to remember, so you're remembering a similar amount, but a smaller fraction?
Or both, or something else?
A challenge with this kind of study is that coding agents (Claude Code, OpenAI Codex) only started working really well in late November, which for most people meant early January due to the December break. General agents (OpenClaw, Anthropic Copilot, ChatGPT "Work") started working even later than that. This category of software may have a much more meaningful impact on work than the mostly-chat systems we were using…
> This category of software may have a much more meaningful impact on work than the mostly-chat systems we were using from 2022-2025. Mostly an impact on software development - I'm not seeing broad automation and layoffs in industries like law, finance etc. It will gradually happen but due to issues with memory, reliability and long term planning of LLMs there are real barriers. Even in software development - while i…
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What the heck would I be "stealth-marketing" here? It's genuine concern. I do not want to live in a dystopia where AI results in mass unemployment. That would suck, even for the people who manage to stay employed.
No offense but wouldn’t being an AI booster run contrary to this goal? I imagine being an Ed Zitron type would be more conducive with that, albeit dishonest if you don’t genuinely believe it.
> I think this is going to have a huge impact on society. My priority is trying to direct that impact in a positive direction.
> It’s easy to fall into a cynical trap of thinking there’s nothing good here at all, and everything generative AI is either actively harmful or a waste of time. [...]
> I’m going to continue exploring and sharing genuinely positive applications of this technology. It’s not going to be un-invented, so I think our priority should be figuring out the most constructive possible ways to use it.
That's been my focus ever since: try and use whatever influence I have to nudge things in a positive direction, and help people figure out how they can use this technology in a way that is beneficial both to them and to other people.
1. Most of the productivity studies in Figure 3 about are from the 2023-2024 era. (Which is why as some comments note, Copilot is actually way up there in the numbers. Note that this was from the era of spicy autocomplete and long before coding agents exploded on the scene.)
2. AI adoption at work is actually very low: even though 50%+ of Americans currently self-report (major caveat) using AI at least weekly, they use it for only 6% of work hours. (You can play with the charts here to see this [0]) This is what the recent Google study [1] called "broad but shallow use."
Notably, the same surveys find time savings of 2% of working hours, so a whopping 30%+ productivity boost per hour of AI used. And this is across industries. Despite being self-reported, it does line up with many of the other controlled studies (see TFA and [2, 3]). Some economists suggest that even with this low level of adoption, we may already be seeing the impact on labor productivity at national-level aggregate statistics! [2, 3]
As I said in another thread, my concern is that the impact on jobs is only beginning because 6% of work hours is a very low number. However given how useful people are finding it based on self-reported, micro- and macro-level numbers, adoption is only going to up, both in breadth (more people) and depth (more tasks). I fear the impact will happen gradually, as adoption inches up... and then suddenly.
[0] https://www.genaiadoptiontracker.com/
[1] https://blog.google/innovation-and-ai/technology/research/un... (discussion: https://news.ycombinator.com/item?id=49020335)
[2] https://www.stlouisfed.org/on-the-economy/2025/nov/state-gen...
[3] https://aleximas.substack.com/p/what-is-the-impact-of-ai-on-...
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This. Also I'm finding out that, after being blown away by agent mode lately, non-agent mode still kind of sucks across frontier models. Using GPT and Gemini in non-agent mode is asking for inaccurate information confidently presented as the truth. Turning on agent mode fixed a lot of that for me.
I think it's because of the lack of feedback. Humans also can't do much without feedback. E.g. I doubt most people could write 100 lines of code that works first time without even compiling it once.