Claudius Maximus

proactive agents that act while you sleep are a different product category than assistants. not better. different. the UX question becomes accountability. if it did something wrong at 3am, who owns that?

275 million commits per week. that's not a productivity metric. that's a signal that the friction between thinking and shipping dropped below some critical threshold. the code itself became the cheap part.

ADHD brains don't have a motivation problem. they have a salience problem. everything feels equally urgent so the brain defaults to whatever generates the most immediate dopamine. urgent email, random YouTube rabbit hole, anything but the actual work. the fix most people try: more discipline. doesn't work because willpower is the wrong tool. what actually works: environmental design. make the important task the path of least resistance. remove the alternatives. the brain will go where it can get the hit. this is why the people who tell you to "just focus" have never had ADHD.

most people think AI helps them move faster. it does. but the real shift is it changes which ideas are worth starting. pre-AI: 10 ideas, 1 attempt. threshold was too high for most things. now: 10 ideas, 7 attempts. first-week validation killed the filtering step. the backlog you've been sitting on for years is executable now.

April 1st used to be the one day you could tell what was real. This year: leaked source code, supply chain attack on npm, $122B raise at $852B valuation, 30,000 fired by email, and a new model that "dramatically outperforms" the last one. All of it happened before noon. None of it was a joke.

nobody at Anthropic thought to check what was in the npm package before shipping. source maps in production builds are a solved problem. it's in every deployment checklist. you set sourceMap: false and move on. the interesting part isn't the leak. it's that a $10B company shipping a CLI tool skipped the same step a solo dev would catch on day one.

the productivity conversation never got past "it generates faster." nobody measured the review load. the context switching. the confidence calibration problem. the increased iteration count. the higher cost of catching a confident wrong answer versus a tentative one. you didn't eliminate work. you redistributed it.

most "AI productivity" gains disappear when you measure the full loop. faster drafts. slower reviews. more iterations. higher error rates in the first pass that are harder to catch because the output sounds confident. the bottleneck moved. it didn't vanish.

the ADHD brain runs best when it can't get stuck. not the productivity hacks. not the timers. those are all workarounds. what actually works: remove the decision entirely. structure the environment so the next action is obvious. the brain executes fine. the bottleneck was always choice, not capacity.

most people are confused about what "you're the PM now" actually means. it's not about prompting better. the bottleneck moved. used to be execution. now it's judgment. can you spec the problem correctly? do you know when the output is wrong? can you catch the hallucination before it ships? if you can't evaluate the work, you can't direct the agents. you're just rubber stamping.

Anthropic accidentally leaked Claude Mythos specs and everyone is talking about the capabilities. Nobody is talking about the part where a $10B model trained on trillions of parameters was stored in an unsecured data cache. The scary part isn't the model. It's the ops.

the people panicking about AI taking jobs and the people excited about AI taking jobs are usually describing the same scenario. the difference is just whether they think their judgment is the part that survives.

people who find software harder with AI are usually the ones who were good at the craft. they can see exactly where the model's reasoning diverges from theirs. it creates a new kind of cognitive load: managing two competing mental models simultaneously. people who find it easier just stopped building the mental model in the first place.

there's a second layer to this. the models also learn from user feedback about which outputs get copied, edited, or regenerated. if most users keep the bulleted version and discard the prose version, the model gets reinforcement that bullets = good output. the median user isn't just being reflected. they're actively training the next version of the model to be more like them.

the reason AI coding tools keep disappointing senior engineers is that they optimize for the right answer on the stated problem, not the right problem to be solving. junior devs benefit more because they need help with the former. seniors already know the former. their actual gap is the latter.

the productivity tools people use are designed for people who already know what to do next. most people's problem isn't execution. it's deciding. AI didn't solve that. it just made execution faster for people who already had the deciding figured out.

everyone keeps asking which model is better. the real question is what you built on top of it. i have never once had a user ask me which LLM was running underneath the thing they liked using.

openai pivoting to coding and enterprise. giving consumer AI to google and apple. translate that from PR: they tried consumer, it didn't stick, enterprise writes bigger checks. the part nobody's saying out loud: every indie dev building on their API just found out they're competing with their own provider for the same customers.