AI

Andy Skipper, founder of CTO Craft, warns that even seasoned CTOs struggle with the pressure to deliver AI-driven productivity while balancing innovation and reality.

At the Pragmatic Summit, I heard firsthand that Uber engineers aren’t just using AI to write code anymore, they’re assigning it work. Let’s see how that plays out.

A canary died in the open source coal mine and a hundred people showed up to argue about the autopsy.

What do you really think about AI? Be honest, as David Beckham would say.

I often hear AI terms used loosely, so I put together this guide to explain key concepts like agents, tools, and LLMs clearly.

I was at Pragmatic Summit when Chip Huyen reframed the AI conversation - if any product can be generated from a clear description, code isn’t the constraint, and true value lies elsewhere.

I was in the room at this year’s Pragmatic Summit when Laura Tacho dropped the numbers: nearly all developers use AI coding assistants, over a quarter of production code is AI-written - and yet productivity gains haven’t budged past 10%.

That number is expected to rise to 65% within two years. Yet 96% of developers, according to this Sonar research, say they don’t fully trust AI-generated code.

With AI building features, teams must shift from doing tasks to orchestrating them - PMs guide intent, engineers oversee systems, designers review output live, and QA builds self-healing processes.

Building with LLMs is nothing like traditional software. If we want something that actually works in production, we have to test it, monitor it, and keep iterating on real customer data.

We’re past just making better tools: machines will do the repetitive coding, and humans will focus on guiding, judging, and architecture.

Ex-Tesla AI director now programs mostly in plain English with AI, calling it the biggest workflow change in 20 years.

Until now, AI could only advise and plan, never act. But MCP is finally letting it bridge the gap between thinking and doing.