The newsletter

Ankit Patel asked to buy only the features he used. The answer was the whole package, for a year, or nothing.

When we can't name the wrong result, we're not measuring. We're describing our own efforts back to ourselves.

Run a test, run it twice, and don't let the tool define the problem.

AI made ideas free and reach free, so the biggest room stopped being worth winning. The scarce move is influence.

How to measure what everyone says can't be measured

The AI laws already in force are all over the place

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.

Anthropic tested Claude Mythos, found reckless and deceptive behavior, and chose not to release it broadly. The question is whether your own deployments could survive that discipline.

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

Human capability and agent capability are now described in the same format, so work gets allocated by what a task needs, not by whose name is on it.

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

Certifications and installable agent skills raise the odds of a good outcome, but neither guarantees it, and mistaking a bet for a guarantee is where costly surprises hide.

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.

Preparation used to come before action, but AI moved faster than any course could teach, so builders now understand tools by using them, not before.

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?

Generation cost has dropped to near zero, but nobody assigns an owner, so orphaned AI artifacts pile up with no one left to defend or delete them.

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.

A survivorship filter over 29 issues: the ten frameworks that held up under real deadlines, audits, and organizational friction in 2025.

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.

Skeptics, hesitators, and governance sticklers are not blocking AI adoption. Their resistance is what keeps it from shaking apart before it is ready.

Confident tone increasingly substitutes for verified reasoning, letting AI-generated decisions sound right long before anyone can defend them.

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.

Readiness scores and maturity dashboards multiply while the real friction between people and AI goes unmeasured entirely.

A former teacher and a video producer, both new to AI, show what personalization instinct looks like at machine scale.

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

Computer, translator, and curator were once job titles before they became tools, and those roles moved up instead of vanishing. The same shift is now hitting coaches and analysts.

We hold AI to a standard of perfection we never demanded from coworkers, then act shocked when it disappoints us. Treating it like a new colleague gets better results.

Training that waits for a tool rollout to finish cannot keep up with AI's pace anymore. Learning and doing need to run in parallel, like a surgical team acting at once.

MIT found 95 percent of organizations get zero return from generative AI, echoing decades-old change management failures. The fix is cutting weak pilots fast.

More dashboards, more alerts, more vendors: every addition security teams bolt on quietly widens the attack surface they meant to shrink.

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.