Nope, it's not. You're also one of those guys who say, "Why did this stupid guy get promoted to management? He's the worst coder on our team." Yes, you might be right. But it's actually a good thing not to promote the best coder to management, because that's not the skill set required for the job. A manager relies on the best coders' opinions when it comes to code. The manager's job is to be the best at managing tasks, budgets, teams, and requirements. Linus isn't getting paid for writing code, not even for checking code in detail. Mostly this is done by other people supporting him. The outstanding thing about him is that he happens to be exceptionally good at both. But he's getting paid for managing this project. If he chooses to code, it's usually because he finds an idea particularly interesting intellectually and wants to get it done, or because a problem is exceptionally hard to solve and requires some groundbreaking decisions with a potentially large blast radius. That being said, it shows exactly the opposite of putting him in a bad light. He sees a tool that makes his work faster and more efficient while still allowing him to maintain quality. And your comment suggests you've never used Claude Code or any equally capable coding assistant in most recent time. Quality is no longer the issue you seem to think it is. The world has moved incredibly fast over the last 1–2 years. All the noise about AI bug fixing in the Linux kernel shows exactly this: maintainers aren't primarily complaining that the patches are bad or entirely wrong. They're complaining that there are too many (!) of them. That's a very important distinction. So AI is able to find and fix bugs much more quickly and, in many cases, just as reliably as human coders, potentially doing work that would otherwise require enormous amounts of developer time. Why would any intelligent person not use this tool to make much more efficient use of his precious $1.5-million-a-year time?

Kimi K2.6 autonomously overhauled exchange-core, an 8-year-old open-source financial matching engine. Over a 13-hour execution, the model iterated through 12 optimization strategies, initiating over 1,000 tool calls to precisely modify more than 4,000 lines of code.

The challenge with previous generations of tech—and the engineers who built them—is that they got stuck in the rigidity of systems.

By jumping into unfamiliar areas of code, even if you do not "solve" the bug, you can learn new areas of the code, tricks for getting up to speed quickly, and debugging techniques.

Applying Systems Engineering to Policy

System architects: equivalents to architecture and planning for a world of knowledge and data Both government and business need new skills to do this work well. At present the capabilities described in this paper are divided up. Parts sit within data teams; others in knowledge management, product development, research, policy analysis or strategy teams, or in the various professions dotted around government, from economists to statisticians. In governments, for example, the main emphasis of digital teams in recent years has been very much on service design and delivery, not intelligence. This may be one reason why some aspects of government intelligence appear to have declined in recent years – notably the organisation of memory.57 What we need is a skill set analogous to architects. Good architects learn to think in multiple ways – combining engineering, aesthetics, attention to place and politics. Their work necessitates linking awareness of building materials, planning contexts, psychology and design. Architecture sits alongside urban planning which was also created as an integrative discipline, combining awareness of physical design with finance, strategy and law. So we have two very well-developed integrative skills for the material world. But there is very little comparable for the intangibles of data, knowledge and intelligence. What’s needed now is a profession with skills straddling engineering, data and social science – who are adept at understanding, designing and improving intelligent systems that are transparent and self-aware58. Some should also specialise in processes that engage stakeholders in the task of systems mapping and design, and make the most of collective intelligence. As with architecture and urban planning supply and demand need to evolve in tandem, with governments and other funders seeking to recruit ‘systems architects’ or ‘intelligence architects’ while universities put in place new courses to develop them.

Linux Memory Management at Scale