When I use ChatGPT for work it frequently reads my Slack DMs even if they’re not directly relevant, so I’d question a lot of the premises of the article.
“they’ve been folded into Frankenstein positions that demand constant multitasking, social performance, sensory endurance, and emotional labor on top of technical skill”
My sense is that this is due to automation, not “neoliberal capitalism” as the author says. It’s much easier to automate a job if it’s a single task that’s done in a deterministic way.
It would be interesting if most of our confusion with quantum mechanics came from treating probabilities as independent when they are actually highly correlated. I don’t really know any physics, but I’m familiar with probability and this type of problem seems to be the most common error in interpreting probabilities.
I don't have any skin in the game, but people should be aware of Induction vs Deduction.
Induction had the earth at the center of the solar system and had the best calculations to predict where Mars was. Copernicus said earth was at the center, the equations were simpler, but were worse at predicting the location of planets.(until we figured out they moved in ellipses)
When we say "All swans are white, because I've never seen a black swan." Its probabilistically true. That is induction. If we found swans didn't have the gene to make black feathers, that would be deduction.
Deduction is probably the most true, if it is true. (But it is often 100% wrong)
Induction is always semi true.
Quantum mechanics seems to be in the stage of induction. Particles are like the earth at the center of the solar system. We need a Copernican revolution.
It's so easy to ship completely broken AI features because you can't really unit test them and unit tests have been the main standard for whether code is working for a long time now.
The most successful AI companies (OpenAI, Anthropic, Cursor) are all dogfooding their products as far as I can tell, and I don't really see any other reliable way to make sure the AI feature you ship actually works.
Tests are called "evals" (evaluations) in the AI product development world. Basically you let humans review LLM output or feed it to another LLM with instructions how to evaluate it.
Interesting, never really thought of it outside of this comment chain but I'm guessing approaches like this hurt the typical automated testing devs would do but seeing how this is MSFT (who already stopped having dedicated testing roles for a good while now, rip SDET roles) I can only imagine the quality culture is even worse for "AI" teams.
Traditional Microsoft devs are used to deterministic tests: assert result == expected, whereas AI requires probabilistic evals and quality monitoring in prod. I think Microsoft simply lacks the LLM Ops culture right now to build a quality evaluation pipeline before release; they are testing everything on users
50% of our code is being written by AI! Or at least, autocompleted by AI. And then our developers have to fix 50% of THAT code so that it does what they actually wanted to do in the first place. But boy, it sure produces a lot of words!
Happy smart people are generally very focused and only care about a few things. Unhappy smart people are constantly getting "nerd-sniped" into focusing their intelligence on things that don't make them happy.
> It’s the same reason why most of the people who pass your leetcode tests don’t actually know how to build anything real. They are taught to the test not taught to reality.
True, and "Agentic Workflows" are now playing the same role as "Agile" in that both take the idea that if you have many people/LLMs that can solve toy problems but not real ones then you can still succeed by breaking down the real problems into toy problems and assigning them out.
An answer to the productivity paradox (https://en.m.wikipedia.org/wiki/Productivity_paradox) could be that increased technology causes increased complexity of systems, offsetting efficiency gains from the technology itself.
