[jillesvangurp]

In big software teams, the bottleneck is team communication. I've run big and small teams. If I want to speed things up, I remove people from the team. Everything gets easier. This has worked amazingly well every time I've done this over the past decades. Removing people doesn't have to mean firing them necessarily. Splitting teams is a good reflex. But of course the people you remove from a team are typically not the best performers. I was discussing this with a friend of mine who runs a small company. Exact same thing. He reduced the team size by 1 and the velocity went up almost instantly. This person was a bottleneck in the team and was slowing down people around him. After identifying the problem, solving it unblocked the rest of the team.

This was true long before AI. With AI the difference is just a lot bigger. It exposes team inefficiencies quite mercilessly. We have a big glaring issue with the current AI tools not being to suitable for usage by multiple users. All interactions are one on one. Which means hand offs between tools and people are bottle necked on people communicating with each other. So, any issues there with people delaying, gate keeping, etc. become very visible.

The sentiment of pushing back on AI is understandable but probably not a productive reflex. We need to find more effective ways on staying on top of massive amounts of changes. It's not going to slow down and insisting on manually reviewing all code is not going to be a long term sustainable way of developing software. It simply does not scale. I'd question the added value of manual PR reviews at this point. Are they finding real issues? Are we valuing those issues correctly? Could we come up with automated ways to find and fix those same issues? There are a lot of open questions about how we are going to do this. But no question about the notion that we need to up our game on this front.

[bxk76]

Efficiency is not magic. Its bounded. Above and below limits the environment can sustain it, systems will destabalize. If All the Great White Sharks magically get more efficient at hunting over night ecosystem will collapse. Individuals and teams have never scaled at this speed to the levels they have. And there is no signal at system wide level that a sustainable limit has been crossed. So People will happily believe things are getting more efficient at individual/team scale while at system scale things get more fragile. This is why we ended up with central banks deciding interest rates and controlling money supply. Before that any one could print cash. They all thought they were great efficient geniuses. The chimp troupe us not prepared for stuff that effects the entire system.

[ayewo]

> If I want to speed things up, I remove people from the team. Everything gets easier.

How did you land on this approach? From someone you learned from or from a seminal book like Mythical Man Month [1]?

> This was true long before AI. With AI the difference is just a lot bigger. It exposes team inefficiencies quite mercilessly. We have a big glaring issue with the current AI tools not being to suitable for usage by multiple users. All interactions are one on one. Which means hand offs between tools and people are bottle necked on people communicating with each other. So, any issues there with people delaying, gate keeping, etc. become very visible.

Shopify has also struggled with this and their solution is two-fold: move everything inside a monorepo they call World [2]. The do a number of things to make things legible for AI agents like e.g. having a comprehensive CI/CD system in place, documenting tribal knowledge in AGENTS.md which in aggregate turn out to also be good for humans new to the monorepo.

Then, they built an internal AI agent on top of this monorepo process that is useable from Slack. They call the AI agent River [3] and in this system all chatbot exchanges are public by default.

1: Fred Books was one of the first to point out that adding another team member to speed up a late project will produce the inverse effect of making that project later (because of coordination tax).

2: https://shopify.engineering/under-the-river

3: https://x.com/tobi/status/2053121182044451016

[ElFitz]

I’ve been making Codex and Claude get their work reviewed by most recent best performing model of their own family, and each other’s, for months.

On top of that, we have been running multi-model AI reviews on every PR through their respective GitHub integrations (Codex, Gemini, Copilot, Greptile, CodeRabbit).

They never fully overlap, and yet they somehow usually all miss the same things. The most significant improvement came from having agents commit their plan along with their work.

On the upside, it means I get to focus my reviews on different things.

[someothherguyy]

> I'd question the added value of manual PR reviews at this point.

Yeah, why not reduce the team size to zero while you are at it?

These generalizations about software engineering have never been useful, IMO. Context is everything, there is no flow chart for building a perfect software process.

Although, I'd say you are absolutely delusional if you think we are universally beyond the point where manual review of pull requests is required.

[z3t4]

Make the team size one person. Thats the fastest you can work. Zero means no work, and not doing anything is the quickest solution.

[shinryuu]

Honestly, we should make a world that is enjoyable and productive for humans. Not relentlessly optimizing for agents.

[sdevonoes]

We can also slow down (or keep old pace) and still ship quality.

A bit sick and tired of arguments like yours