… Among my inner circle of engineering and data science friends, there is a lot of discussion about how long our competitive edge as humans will last. Will having good ideas (and lots of them) still matter as the agents begin having better ideas themselves? The human-expert-in-the-loop feels essential now to get good results from the agents, but how long will that last until our wildest ideas can be turned into working, tasteful software while we sleep? Will it be a gentle obsolescence where we happily hand off the reins or something else?

For now, I feel needed. I don’t describe the way I work now as “vibe coding” as this sounds like a pejorative “prompt and chill” way of building AI slop software projects. I’ve been building tools like roborev to bring rigor and continuous supervision to my parallel agent sessions, and to heavily scrutinize the work that my agents are doing. With this radical new way of working it is hard not to be contemplative about the future of software engineering.

Probably the book I’ve referenced the most in my career is The Mythical Man-Month by Fred Brooks, whose now-famous Brooks’s Law argues that “adding manpower to a late software project makes it later”. Lately I find myself asking whether the lessons from this book are applicable in this new era of agentic development. Will a talented developer orchestrating a swarm of AI agents be able to build complex software faster and better, and will the short term productivity gains lead to long term project success? Or will we run into the same bottlenecks – scope creep, architectural drift, and coordination overhead – that have plagued software teams for decades? – Read More

Every few months, we hear the same prediction:
“Software engineers will be obsolete in 6 to 12 months.”

This time, the warning came with a bold experiment.

The Cursor team — backed by billions in venture capital — decided to prove that AI agents could replace engineers. Instead of just talking about it, they launched a real test:
Hundreds of AI agents working nonstop for a week to build a web browser from scratch.

Building a browser is one of the hardest engineering challenges in modern software. Even Microsoft struggled with it for years. So if AI could pull this off, it would be a huge milestone.

But what happened next tells a very different story. Read More

I have written eight of these guides since ChatGPT came out, but this version represents a very large break with the past, because what it means to “use AI” has changed dramatically. Until a few months ago, for the vast majority of people, “using AI” meant talking to a chatbot in a back-and-forth conversation. But over the past few months, it has become practical to use AI as an agent: you can assign them to a task and they do them, using tools as appropriate. Because of this change, you have to consider three things when deciding what AI to use: Models, Apps, and Harnesses. Models are the underlying AI brains; Apps are the products you actually use to talk to a model, and Harnesses are what let the power of AI models do real work. Until recently, you didn’t have to know this. 

It means that the question “which AI should I use?” has gotten harder to answer, because the answer now depends on what you’re trying to do with it. So let me walk through the landscape. — Read More