FurtherAI's engineers now merge 5x more code, and a third of it ships with no human involved

This post is funny to me, because I feel it falls well short of what a proper, forward-thinking "AI-native" engineering org would actually look like. That is, the author is presenting this as kind of a radical departure from normal engineering, but I just see it as normal engineering with more AI. I don't see that as AI-native.

To me, true "AI-native" engineering will be one where questions regarding how humans will do things like review code are irrelevant. You explicitly throw away any concept of typical software engineering and, instead, differ to the AI. Your job, as a human, is solely to do what the AI can't physically do. That's not the same as closing gaps between what AI is currently capable of doing and what you'd need it to be capable of doing. It's literally enforcing that anything an AI is able to do is treated as correct in itself, and you, as a human, only support it by being a human.

If that sounds outlandish, that's because it is. We don't have such capable AI systems yet. It's possible we never will. But that's beyond the point. The reality is trying to make an "AI-native" engineering org by just layering AI on top of existing processes and systems is futile. If your expectation is that AI will continue to improve in capabilities at an accelerating rate, then these stop-gaps are pointless. You might as well just ignore AI altogether until it is able to completely take over, because that's the inevitable goal, right?

If you're willing to buy that will be possi […]

It this point, a whole lot of teams have been leaning into AI-first, not-entirely-reviewed code since around March this year.

That means we are already 6-8 months in, and not far from having a full year of experience of what that actually looks like, albeit weirdly influenced by the increase in model capabilities during that period.

I'm looking forward to those retrospectives. Did this turn out to work OK for the teams doing it? What were the biggest failings and/or surprises? With hindsight, did this produce good quality software?

I was just looking at some user-facing error messages Claude generated for me which contained unnecessary/irrelevant technical details while failed to provide clear instructions or fix.

Earlier, I reviewed code that does way more than specified scope, changes public API and introduces new behavior. I had to almost redo the whole thing.

I thought to myself, no way I am going to ship the code as written without actually reading through it (or as some people put it, only looking at the "intent"). I'll get more complaints from users while having absolutely no idea what's going on with the code or actual behavior.

It's a puzzle to me how these orgs can pull it off.

I see a lot of builders (myself included) falling into the trap of focusing on the wrong things. Development velocity doesn't matter per se, what matters are things like growth in profit or traction. It's really easy to conflate automation/systems/process with actual business results. Movement ≠ Progress.

https://xxcancel.com/sgondala2/status/2115832070274723875

Related: this article was very unilkely make it to the Hacker News homepage thanks to the clickbait headline, but I recommend reading it anyway: https://twitter.com/jsnnsa/status/2115623382721126826

It describes a team spending 30x payroll on tokens - $3,000,000 in token spend per month - building a multiplayer game where incidents and bug reports from users are passed directly to Fable 5/5.1 agents who then act on them and deploy the changes to production.

"We consider it an outage if we don't ship every hour"

It's worth reading because it's either a complete disaster or a glimpse of the future, or quite possibly both.

Spent few minutes reading the infomercial -- regretted it. Engineering principles form the bedrock first, and AI-tools are made to fit in them, not the other way around as this infomercial propounds. Its linkedin vibe is a big turnoff too.