<< The highly agentic are people who just do things. They don’t timidly wait for permission or consensus; they drive like bulldozers through whatever’s in their way.

I genuinely like the author's style ( not in the quote above; its here for a different reason ). It paints a picture in a way that I still am unable to. I suck at stories.

Anyway, back to the quote. If that is true, then we are in pickle. Claw and its security issues is just a symptom of that 'break things' spirit. And yes, this has been true for a while, but we keep increasing both in terms of speed and scale. I am not sure what the breaking point is, but at certain point real world may balk.

He writes an excellent blog: https://samkriss.substack.com/

I think there has always been some truth to that, long before AI. Being driven to get up and just do the thing is the most important factor in getting things done. Expertise and competency are force multipliers, but you can pick those up along the way - I think people who prefer to front-load a lot of theory find this distasteful, sometimes even ego-threatening, but it's held true in my observations across my career.

Yes, sometimes people who barrel forward can create a mess, and there are places where careful deliberation and planning really pay off, but in most cases, my observation has been that the "do-ers" produce a lot of good work, letting the structure of the problem space reveal itself as they go along and adapting as needed, without getting hung up on academic purity or aesthetically perfect code; in contrast, some others can fall into pathological over-thinking and over-planning, slowing down the team with nitpicks that don't ultimately matter, demanding to know what your contingencies are for x y z and w without accepting "I'll figure it out when or if any of those actually happen" - meanwhile their own output is much slower, and while it may be more likely to work according to their own plan the first time without bugs, it wasn't worth the extra time compared to the first approach. It's premature optimization but applied to the whole development process instead of just a piece of code.

I think the over-thinkers are more prone to shun AI because they can't be sure that every line of code was done exactly how they would do it, and they see (perhaps an unwarranted) value in everything being structured according to a perfect human-approved plan and within their full understanding; I do plan out the important parts of my architecture to a degree before starting, and that's a large part of my job as a lead/architect, but overall I find the most value in the do-er approach I described, which AI is fantastic at helping iterate on. I don't feel like I'm committing some philosophical sin when it makes some module as a blackbox and it works without me carefully combing through it - the important part is that it works without blowing up resource usage and I can move on to the next thing.

The way the interviewed person described fast iteration with feedback has always been how I learned best - I had a lot of fun and foundational learning playing with the (then-brand-new) HTML5 stuff like making games on canvas elements and using 3D rendering libraries. And this results in a lot of learning by osmosis, and I can confirm that's also the case using AI to iterate on something you're unfamiliar with - shaders in my example very recently. Starting off with a fully working shader that did most of the cool things I wanted it to do, generated by a prompt, was super cool and motivating to me - and then as I iterated on it and incorporated different things into it, with or without the AI, I learned a lot about shaders.

Overall, I don't think the author's appraisal is entirely wrong, but the result isn't necessarily a bad thing - motivation to accomplish things has always been the most important factor, and now other factors are somewhat diminished while the motivation factor is amplified. Intelligence and expertise can't be discounted, but the important of front-loading them can easily be overstated.