Writing.

Token costs are climbing faster than productivity. The fix isn't spending less on AI. It's knowing which model actually deserves your money.

I built and ran an entire training deck without ever opening PowerPoint. The real unlock isn't the slides. It's the design system underneath them.

For years I measured my career by the size of my accounts and teams. Building with AI quietly changed the metric.

Adopting AI isn't an obligation. But sitting it out is a decision, and the gap is starting to compound.

I moved my whole app off a turnkey platform and rebuilt it from the ground up. The thing that finally made it possible wasn't a better prompt.

I automated the rest of the financial agent. The one piece I kept by hand is the one piece you'd expect to automate first.

I went to a networking event and ended up using Claude to redesign my Apple Watch face. The watch worked. The signal worked harder.

I burned tens of thousands of tokens building into a platform limit. Last week the limit dropped — and the third agent went live in 5 minutes.

How eighteen years of Google I/O led to a keynote where AI stopped being a feature.

Two hours a week of voice AI learning — and the call that always comes first.

Meta just opened its ad stack to Claude and ChatGPT. The next decade of marketing won't be won inside dashboards.

A simple curve every team sits on — and the gap that decides who actually compounds.

What a weekend project taught me about where agentic AI actually lands — and why the first 48 hours were the least interesting part.

Most people are adapting to AI. A smaller group is reinventing around it. The gap between them is growing fast.

Most teams are still debating which AI to use. The ones seeing results have moved on to something else entirely.

SaaS isn't dying. It's splitting in two — and the line between them is proprietary data.

The "AI replaces jobs" narrative is missing the most important variable.

I built 5 agents on a 15-year-old MacBook that was under a couch. Here’s why that matters.

The early data looks bad. But everyone’s asking the wrong question.