I'm fascinated by this idea of <i>not reviewing</i> AI generated code. On the surface it sounds absurd - we know these machines make mistakes all the time, so how could we ever responsibly move ahead with code they have written without closely reviewing every detail?<p>Then I remembered the times I've worked at large companies and depended on code written by other teams. I didn't review every line of code they had written - I'd trust that they had done a competent job, integrate with that code myself, and only dig into the details of their code if I run into bugs or performance issues or other smells that something was wrong.<p>Trusting humans is obviously different from trusting AI - humans have reputations, and social contracts, and actual intelligence as opposed to multiplying matrices and rolling a dice. But... I do think an AI model can still earn trust over time. I've spent enough time with Opus 4.5 and 4.6 that I trust them not to make dumb mistakes with the common categories of code that I use them for. Of course now I need to rebuild that trust with 4.7!<p>I think the most interesting challenge here is to figure out how to have coding agents <i>demonstrate that the code works</i> without actually reading every line of it yourself - in the same way that I might ask an engineering team I haven't worked with before for a demo and then interrogate them about their testing strategy before relying on their work.

Hardware--physical hardware, not servers--could become commodities like the cloud: rent bipedal robots by the hour, lidar-equipped vans, and managed drone fleets.<p>The SaaS companies disrupting today could become utilities offering mechanized leases tomorrow.<p>With agents as a singular "swarm brain" (per machine, not a global hivemind) just seems like a natural course of abstraction.