I’ve been spending a lot of time lately exploring what AI is actually good for beyond writing code… which is where most people seem to stop. One thing I’ve landed on that I think is genuinely useful is using it to maintain a structured research knowledge base on technical topics I care about. So I built one, and it now lives on this site.

It covers cutting-edge datacenters (cooling systems, power infrastructure, robotic server management, construction), energy (solar, SMR nuclear, batteries, grid resources), and robotics (actuators, sensors, edge compute, aerial and ground drones). These aren’t random topics. They’re pretty tightly interconnected when you start pulling on the threads, and that overlap is a big part of why I find them interesting.

I work at Amazon, which means I’m adjacent to infrastructure at a scale that makes you think differently about where things are heading. The question of how you build, cool, power, and automate a datacenter isn’t abstract to me. It’s the physical substrate underneath everything I work on. So I want to understand it at the component level. Who’s actually making the immersion cooling systems? Which SMR designs are closest to permitted? What startups are supplying the actuation systems that will eventually run the robotic logistics inside these facilities? That kind of thing.

What I found when I started digging is that most publicly available research in this space is either too high-level (analyst report fluff) or too narrow (a single company’s press releases). There isn’t a lot of good connective tissue between, say, perovskite solar cell efficiency breakthroughs and the energy sourcing decisions being made for the next generation of AI training clusters. But those things are connected, and if you’re trying to understand where the puck is going – for investing, for career positioning, or just because you find it fascinating – the connections are where the useful insight lives.

The research section is my attempt to build that connective tissue. Each entry is structured like a well-maintained Wikipedia stub: what it is, why it matters, recent developments, key people and organizations, sources. The focus is on smaller companies, university spinouts, and component suppliers, not the IBMs and Googles of the world, which already have plenty of coverage. The interesting stuff is one or two layers down from the obvious names.

It’s also AI-maintained on a schedule. I set up automated updates that keep the entries reasonably fresh as new developments happen. I do review things but I’m not manually rewriting every entry every time something changes. It’s an experiment in whether you can use AI not just to generate content but to curate and maintain a living knowledge base over time. So far the answer seems to be yes, with appropriate skepticism about any specific claim that would benefit from verification.

If you’re researching similar topics, hopefully this saves you some time. Why burn your tokens on what I’ve already gathered. And if you notice something wrong or missing, let me know.