Which management skills AI is genuinely changing, which stay non-negotiable, and how to tell the difference.
Cory Doctorow: The people who tell you ‘AI is changing everything’ are lying
It has become impossible to tell managers mesmerised by artificial intelligence that the tools are not, in fact, helpful. So employees just play along with the fiction to keep their jobs, writes our tech columnist
The Manager’s Path in the Age of AI
This week, I gave a talk at LDX3 New York. When they asked me to speak and asked me for a topic, I said that it was probably time I finally…
What we can’t measure about AI – yet | Aeon Essays
The costs of transformative innovations are immediately clear: it’s the longterm gains that are hardest to understand
Engineering managers have a new job description
Why engineering managers are expected to be hands-on again and how AI tools are making it possible to stay technically sharp while still leading.
Bring a Product Manager Mindset to Your Next Engineering Job
The age of AI demands strong ideas, data driven methods, and experimentation
AI CEO – Replace Your Boss Before They Replace You
Stop working for humans. AI CEO delivers algorithmic thought leadership, with instant decisions, and zero ego. Replace your boss before they replace you.
Why AI hasn’t replaced software engineers, and won’t
Arvind Narayanan and Sayash Kappor take on the question of AI job losses through the lens of a profession that is uniquely suited to AI disruption - software engineering. In …
AI Has No Wisdom and Neither Will You
Certainly the industry is transforming, however, the people and organizations falling into the trap of no longer reading and writing code only do so at their peril.
How building software is changing at Anthropic
A deepdive on what’s changed in how the leading AI lab makes software. Ever more code review and testing is done by AI, two-pizza teams very much alive, and more. Details from inside of Anthropic
AI Isn't Management. Try Explaining That to Matthew Prince
At last we have created the Corporation That Eliminates Middle Managers from classic management text Don't Eliminate the Middle Managers!
Arvind Narayanan (@aisnakeoil)
There’s a big, under-appreciated reason why people may have very different experiences and opinions about using AI for work — are they using it for tasks they’re already an expert at, or tasks they can’t do themselves? The former leads to a growth cycle and the latter leads to a dependence spiral. When I use AI to do something I’m an expert at, like coding, I treat it as a tool. I can build quickly, maintaining an understanding of the code, knowing that if necessary, I can fix the code myself. It feels empowering. It frees up my time to think about the complex, judgment-oriented parts of software engineering that I can’t or won’t delegate to AI. That means my own skills improve rapidly, and I get to climb the ladder of complexity and develop higher-level skills, much more so than when I write the code myself. I feel in control. I can lock in and achieve a flow state — when AI is working, I’m reviewing, building understanding, and planning the next steps. I never get the feeling that the tool is about to replace me. This is the growth cycle. (Of course, the growth cycle is not automatic. I still need to exercise agency to use AI responsibly. But it’s the same challenge with any productivity-enhancing technology, and those who’ve navigated such transitions before are well-equipped to navigate it with AI as well.) On the other hand, if I use it for tasks I don’t understand and haven’t learned to perform myself, I have no choice but to treat it as a superintelligence. If something breaks, the best I can do is ask AI to fix it and hope for the best. I generally can’t evaluate the quality of the output myself. The only way to find out if it's any good is if and when the work is ultimately reviewed by an actual expert. The experience is confusing, unsettling and disempowering. And forget about flow state. By over-relying on AI, I risk losing whatever skill I had at the task in the first place, even if it boosts productivity in the short term. This is the dependence spiral. It’s no wonder that entry-level workers and students preparing to enter the workforce find themselves in a bind. To compete with the AI-enabled productivity of more seasoned workers, they must adopt AI themselves, but doing so risks the dependence spiral. I have some thoughts on solutions that I will share in later posts, but I think having a clear diagnosis of the problem is a useful first step.
How the world's top AI models were revived
The personality clashes and industry confusion behind the 20-day saga.
When everyone has AI and the company still learns nothing
Are people using AI, or is the organization learning from it? What changed because we spent those tokens? And who moves discoveries from individuals to teams to organizational capabilities?
How AI is changing your mind
And here are six ways to protect yourself from a world view determined by AI.
Measuring Usage in the Age of AI - Research Information
Tasha Mellins-Cohen outlines COUNTER Metrics' new guidance for usage metrics associated with generative and agentic AI
