Blakeist

Every company must learn to turn the traces of its work into durable capability—or become training data for one that does.

Agents will become the internet's largest operational customers. Build products they can choose, use, and use at machine scale.

Long-running agents will cross human roles, collide with other agents, and produce the traces that train their successors.

Speed is not the reward for skill—it is the thing that builds it. Loop rate is the metric. When the cost of being wrong collapses, the whole logic of caution inverts. Swing.

Commerce spent decades persuading humans. Now it has to persuade the machines that decide for them. The discipline is Decision Intervention Orchestration; the metric is share of decision.

Capability used to be hired. Capability is now conferred. A yui is the basic unit of that shift — a bound, identified, accountable AI worker.

Every technology in history has flown trajectories inside physics we accept as given. Self-improving AI is the first one built to cross the line. The throttle does not interpolate. Sixty percent commitment buys an elliptical loop — and most institutions will mistake the loop for arrival.

The model was the engine. The unit is the car. Once local AI is good enough for most tasks, intelligence stops being something you reach for and becomes something already there.

The internet is full of doors that assume a person is on the other side. Agents need human credentials not because they're useful, but because every system they touch was built for people. That constraint turns out to be load-bearing.

A jar doubles every minute and is full at noon. At 11:55 it is three percent full. Exponential growth, doubling time, and what changes when AI helps build the next AI.

Every institution is a machine for making you legible. What changes when the inhabitants can build their own city?

The world can change in a week now. A mind still takes years. The gap between the two is era lock.

What got you here won't always get you there. The hardest thing isn't learning something new. It's setting down something old that cost you a lot to build.

Static permissions break at ten thousand steps. The next layer is regimes.

Software is learning to drive itself. At Level 2, your hands are still on the wheel. At Level 5, you're choosing the destination. The mechanics of getting there are completely consumed.

Choosing quality over velocity, form over rank, and refusal over optimization. The machines can have the infinite; we keep the finite, the flawed, and the finished.

If all your code was deleted tomorrow, would you be okay? The incantation was always the valuable part. We just couldn't see it when casting the spell took years of labor.

We build intelligences capable of solving problems that exceed our cognitive reach, and we ask them to plan our vacations.

While you're in your third meeting debating whether to prioritize the feature, a team of two just shipped it. They're moving at conversation speed. You're moving at ceremony speed.

Three signals point to a future where health becomes continuous: ChatGPT Health as a daily interface, SleepFM predicting disease from sleep patterns, and AI systems taking bounded clinical actions. The loop is closing.

When AI gains access to both people affordances and machine affordances, it doesn't just add capabilities—it multiplies them. This is the future of knowledge work.

Here's what's actually shocking about GLP-1s: they work by making the entire behavioral apparatus of weight loss obsolete. AI does the exact same violence to our model of intelligence.

The limiting factor shifts from cognition to governance and economics. The model is no longer the bottleneck. The system is.

Why the future of human-machine interface is already wired to our biology. Yet, we keep installing it backwards

People cannot see their own transformation.

Every week brings a new model release. Gemini 3 dropped in mid-November, GPT-5.1 days earlier. Opus 4.5 arrived late November. DeepSeek and Grok and Mistral in the mix. The discourse is endless: which

Most advice about work describes how work should behave, not how it does.

Experienced developers are getting slower with AI tools, not faster. Nineteen percent slower, according to . The juniors are speeding up. The experts are drowning.

I stood in front of Man Ray's rayographs at the MoMA this fall, studying the ghostly white silhouettes floating against dark backgrounds. A glass funnel, a coil of wire, objects I couldn't quite ident

TradTech opens the fridge and asks what can we make with what we have?

Every company is building an AI strategy by asking, "What can we add?"

Pharma marketing has spent two decades optimizing the wrong bottleneck.

ChatGPT hit one billion users in three years. The internet took thirty-six.

Your context engineering isn't an implementation detail. It's the whole product. Most AI products feel like wrappers because their prompts, retrieval, and evals deliver basic capability. The ones that