Artificial Intelligence

Engineering teams are starting to resemble legacy Java apps hitting thread exhaustion. I believe change is unavoidable, and we need a better way to organize the work.

The fastest way to create technical debt in AI is still the oldest one: choosing a database because it feels comfortable.

The AI boom is no longer just a race to build faster machines. The real challenge now is making all the moving parts work together at scale.

At the AI Engineer conference in San Francisco, I heard Instagram co-founder Mike Krieger say developers no longer need to review every line of code by hand. The real job now is judging AI’s decisions and whether the product solves the right problem.

A new kind of builder is emerging inside organizations - one that works fast, ships often, and rarely follows the instincts of a software engineer. That raises a simple question: what happens to security?

AI can write the code. The harder question is whether anyone still understands what it was supposed to do six months later.

I went to Raise Summit in Paris to see whether Lovable is just another tool for non-technical founders - or something developers can actually use to build faster.

We asked engineers how far AI can really go in software delivery, and their answer was simple: it can speed things up, but people still have to make the decisions.

Anyone who’s clicked "always allow" on an agent knows the outcome: broad permissions, minimal oversight, and results that are correct on paper but strange in practice.

As AI tools reshape how software is built, the engineers in our new video say the job is shifting from writing every line by hand to guiding, reviewing, and orchestrating what AI produces.

When tech company ustwo assessed one AI product’s carbon footprint, they found most came from AI inference. It raised a question: if AI has a measurable environmental impact, why is it almost invisible to everyday users?

What economist Charles Goodhart said in 1975 is completely applicable in 2026: When a measure becomes a target, it ceases to be a good measure.

Watching AI product evolution from the sidelines makes you feel like things are going fast, but according to Anthropic's Gian Segato, that might not be the best metric.