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Joined 9 months ago
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Cake day: December 16th, 2025

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  • I guess the difference is that it I have actually seen it do legitimate work now. We have implemented outbound SDR and inbound tech support agents that do the right things. Our SDRs generate new leads for our (currently all-human) sales team to go after. They are boosting the numbers of closed deals. Our inbound tech support agents are closing a significant fraction of tickets. My last metrics call was yesterday, it was 12% of inbound tech support chats that never got escalated to a human. These are both first-draft tools, they’ll improve. They cost pennies per session to run. And client NPS scores show that they’re happy with the results. I don’t think we’ll see either of these tasks become 100% automated, but at even 25% or 50% that’s a lot of formerly-human work not being done by humans anymore. And we’re deploying more agents to handle more of the sales pipeline, development pipeline, HR and accounting. I don’t work for a huge company, there are a few hundred people total. This isn’t something that’s coming, it’s here already.


  • I’m not pro-AI by any stretch for a huge number of reasons, but in my day job as a technical product manager I have to use a number of AI-powered tools and workflows. Whereas 18 months ago they were a novelty that the bosses were enamored of, today they get real work done and speed up a number of key areas, mainly like fancy automation workflows (which have been around forever). The rate of improvement and capability for certain use cases has been very, very rapid. We do seem to be reaching a plateau though. The tools and models from 6 months ago are just about as good as the ones from today. And China’s models from today are better than the US’s models from 6 months ago, but at a fraction of the cost.

    So when I hear people talking about the “end of the world”, I think a realistic case for that is that the AI tools that come out in the next few years will be good enough to do a fair amount of white-collar work, and we won’t have a plan in place to deal with all of the now out-of-work people that leads to. Whole classes of jobs in areas like data entry, telemarketing, and customer support will be almost entirely automated out of existence. New classes of jobs will be created, of course, but it’s hard to imagine them existing in sufficient numbers to make up for the losses. And this is just for LLM-workloads.

    In other areas, other types of AI are already enabling robotic automation of factories and in the near-ish future might make self-driving truck fleets practical. This would be devastating because factory workers and truck drivers make up like 30% of the US workforce – and again, there are no signs of new classes of jobs that could absorb the out-of-work workers, and no plans for a social safety net or wealth distribution.

    So I’m not worried about a rogue AI unleashing some bioweapon or launching a nuclear attack. I’m worried about a bunch of CEOs deploying barely-acceptable AI to displace millions of workers as soon as it is safe(ish) to do so.











  • After a lengthy argument with my boss, I was allowed to “only” force the AI-first model onto half of my small dev team of 8 engineers and 4 QAs. After about a year, the results are obvious: the engineers who went AI-first are now AI-fluent: they know how to prompt the model, develop agents, AI-automate workflows, and push a TON of code. Like almost an order of magnitude more code into prod. However, to say their skills have stagnated would be generous. In some cases an engineer I could count on to debug a tricky issue is completely not able to anymore. Meanwhile, the non-AI team is now essentially my code quality and bugfix team. Their PRs are small and concise, their changes are understandable, and most importantly, their skills have remained the same (and in the case of the 2 juniors, have improved).

    If we hadn’t kept a few people in reserve, we’d be fucked, because there have been multiple times where an AI-first engineer has tried debugging an issue (and with big models - Astra, Fable, Opus) that either totally failed and gave up, or made things worse by introducing “fixes” that made problems elsewhere.

    So this is a small sample, and YMMV, but for junior engineers in particular (who usually have the least amount of say in pushing back against bad ideas) this is – not “is going to be” – a major problem.







  • In the early 2000s I was at the beach on Long Island. It was midday, a cloudless sky, and hot. Suddenly there was a lot of shouting and pointing, and off in the distance you could barely see a water spout, like a tornado over the water and made of water. It went up into this tiny, dense little cluster of angry looking clouds that were just completely alone in the sky. As it got closer it started to hail golfball-size hailstones they were kind of hollow, like the good, crunchy ice from a fast food soda fountain. We got pelted for maybe 30 seconds and then the spout changed directions and headed out to sea, and the weather went back to normal as if nothing had happened. I occasionally ask my sister (who was there with me at the time) if she remembers, because it seems so odd I think I’m gaslighting myself.