The newsletter

The writer is not notified. The appeal, if you want one, arrives after the label already exists.

Your AI policy is a procurement document wearing a safety costume. The first question is what you have already put within reach.

AI made every resume look great, which is exactly why looking great stopped meaning anything. Put the human back.

Your calendar is full of loops that forgot to ask whether they should still run. Here is how to find one and end it.

A new frontier model every 11 days. The failures still aren't about smarts. They're about context.

AI models can't grade their own blind spot very well. People don't either.

One word naming four systems used to just confuse people. Now it makes your AI confident, precise, and dead wrong.

Two models and a judge beat the best single frontier model. The lift lives in the reconciling, not the genius. Same law runs your org.

Open-weight is two quarters behind the frontier and closing. For anything you'll run indefinitely, the math has tipped, and the buying window looks a lot like 2020.

Organizations buy the shape they already know how to operate, then wonder why it never fits the problem.

The clock says 7:58 PM. But the curtain doesn't move until everyone is ready.

All information is perishable. AI info ages like milk.

Nearly one year in, I still don't know what I have.

Stripe shipped agent payments. The language for what agents may do was already open and free.

The question 'what are you doing?' assumes singular attention. Look for what's running, not what fits the dashboard.

David Soria Parra, MCP co-creator, at AI Engineer Europe this week: \"2026 is the year agents go to production.\" Not a prediction.

This isn't a hedge against bad press. This is the team that built the capability looking at the test results and concluding: \"uh-oh, this is way too potent for broad deployment right now.\"

Right now, somewhere in your organization, four different teams are managing four different slices of AI risk.

Something quietly shifted in the last year. The format used to describe what a person can do and the format used to describe what an AI agent can do are converging. Same structure. Same questions:

Two weeks ago we flagged the vocabulary fork. Last week we showed that skills were never guarantees to begin with.

Last week we flagged the vocabulary fork: \"skills\" now means two different things depending on which room you're in.

For as long as most of us have been working, \"skills\" meant one thing: what people can do.

Listen closely to how people at your org talk about AI. The verb they reach for tells you everything.

People are asking for AI rules before the rulebook can exist. Rules follow practice. Practice is still forming.

Last week I wrote about the dangers of saying \"not my job\" about AI. This week, I promised we'd talk about what to build.

When AI adoption hits 'That's not my job,' you're looking at an org design problem, not a skills gap.

You know that feeling when you've been avoiding the bathroom scale?

AI generates fast. Ownership doesn't scale.

AI is right at the wrong resolution -- and your validation infrastructure was built for a different kind of system.

An ancient failure mode AI is exposing everywhere.

When individuals outrun institutions, the ground shifts for everyone.

The only skill that matters is not prompting.

Tools changed weekly.

Most orgs are trying to drive AI adoption with tools, training, and enthusiasm.

Organizations are asking L&D to lead AI readiness, but what it usually becomes is tool tours, prompting tips, and satisfaction scores instead of real capability.

Why your AI strategy lives (or dies) in 1:1s.

Here in the US, it’s Thanksgiving week. That means an overload of gratitude posts for all the usual accelerators: breakthroughs, mentors, tools, breakthroughs pretending to be tools. There’s no wrong way to be thankful, but let’s take a different angle.

We’re moving into a strange era of AI adoption:

_The more “human-friendly” our tools become, the more mental overhead they create._

Unpacking confidence saturation in AI, separating smarts from sound, and rewiring rewards.

AI capability numbers are spreading faster than comprehension.

Yeah, lots happened in the last week (again): specifically over in Anthropic-land where they announced Skills, Web, MS365 integration, and Haiku 4.

Everyone’s building an AI score right now.

Personalization at Scale

Updating the Diffusion of Innovation model for AI, strategizing for trust-building, and navigating AI adoption hurdles.

Being replaced by AI is optional.

Wishing ≠ Collaborating

Parallel or Perish: Rethinking Training for AI

AI transformation is like baking bread.

AI is the world’s fastest amplifier.

The long-awaited iteration of the model that started the public love/hate affair with AI landed last week. And (predictably) the reaction is still love/hate.

Your sales funnel is losing leads before any salesperson speaks to anyone.

AI is outrunning internal governance frameworks, and the fix starts at the UX layer.

Open, auditable AI stacks just shipped. The implications are bigger than the headlines suggest.

The shift from prompt engineering to spec alignment -- why crafting the spec with care is what actually matters.

Tech teams ship AI features in weeks. The human systems around them still run on quarterly cadences.

What happens to a custom creative moat when AI can replicate the style in minutes.

Dashboards full of vanity metrics create the illusion of alignment -- and AI is making the problem shinier, not better.

Org charts were designed for accountability, not adaptability. Agentic AI is exposing that limit fast.

Tool choice is no longer just procurement -- it encodes organizational values, posture, and readiness for change.

AI tools are multiplying fast, but decision rights, governance, and coordination mechanisms are lagging behind.

Nearly every team is layering 3 or 4 AI tools deep -- but the coordination infrastructure hasn't caught up.