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What does "playing politics" mean for software engineers? In defense of not understanding your codebase Blog about things you don't understand yet C2PA only works if everything is signed Text AI watermarks will always be trivial to remove Saying the obvious thing AI inference is obviously profitable AI GPUs probably live longer than three years Doing nothing at work Working with product managers Anti-AI nostalgia and the cult of the past Weird projects I shipped with AI Build agents, not pipelines The famous o3 "GeoGuessr" prompt did not work Prompts are technical debt too The just-say-no engineer was a ZIRP phenomenon How I use LLMs as a staff engineer in 2026 DeepSeek-V4-Flash means LLM steering is interesting again AI datacenters in space do not have a cooling problem Thinking Machines and interaction models The left-wing case for AI AI makes weak engineers less harmful Notes on incidents Why hasn't longer-horizon training slowed AI progress? Why I don't like the "staff engineer archetypes" Software engineering may no longer be a lifetime career Blood in the datacenter Many anti-AI arguments are conservative arguments Programming (with AI agents) as theory building Working on products people hate Engineers do get promoted for writing simple code Big tech engineers need big egos I don't know if my job will still exist in ten years Giving LLMs a personality is just good engineering What's so hard about continuous learning? LLM-generated skills work, if you generate them afterwards Two different tricks for fast LLM inference On screwing up Large tech companies don't need heroes Getting the main thing right How does AI impact skill formation? You have to know how to drive the car
Insider amnesia
2026-02-23 · via seangoedecke.com RSS feed

Speculation about what’s really going on inside a tech company is almost always wrong.

When some problem with your company is posted on the internet, and you read people’s thoughts on it, their thoughts are almost always ridiculous. For instance, they might blame product managers for a particular decision, when in fact the decision in question was engineering-driven and the product org was pushing back on it. Or they might attribute an incident to overuse of AI, when the system in question was largely written pre-AI-coding and unedited since. You just don’t know what the problem is unless you’re on the inside.

But when some other company has a problem on the internet, it’s very tempting to jump in with your own explanations. After all, you’ve seen similar things in your own career. How different can it really be? Very different, as it turns out.

This is especially true for companies that are unusually big or small. The recent kerfuffle over some bad GitHub Actions code is a good example of this - many people just seemed to have no mental model about how a large tech company can produce bad code, because their mental model of writing code is something like “individual engineer maintaining an open-source project for ten years”, or “tiny team of experts who all swarm on the same problem”, or something else that has very little to do with how large tech companies produce software1. I’m sure the same thing happens when big-tech or medium-tech people give opinions about how tiny startups work.

The obvious reference here is to “Gell-Mann amnesia”, which is about the general pattern of experts correctly disregarding bad sources in their fields of expertise, but trusting those same sources on other topics. But I’ve taken to calling this “insider amnesia” to myself, because it applies even to experts who are writing in their own areas of expertise - it’s simply the fact that they’re outsiders that’s causing them to stumble.


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Here's a preview of a related post that shares tags with this one.

On screwing up

The most shameful thing I did in the workplace was lie to a colleague. It was about ten years ago, I was a fresh-faced intern, and in the rush to deliver something I’d skipped the step of testing my work in staging. It did not work. When deployed to production, it didn’t work there either. No big deal, in general terms: the page we were working on wasn’t yet customer-facing. But my colleague asked me over his desk whether this worked when I’d tested it, and I said something like “it sure did, no idea what happened”.
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