2026 is a record year for stock dispersion, but strip out the top 10 winners and it looks like any other year!

GenAI spend via cloud providers has surged 15–20x across every cohort in just 18 months!

Enterprise usage is shifting from asking AI questions to letting it do the work.

Energy storage is scaling faster than solar or wind ever did!

Four companies now spend at a staggering scale. Hyperscaler AI capex is on track for ~$733B in 2026, about 87% of the base U.S. defense budget.

The cost center of energy has become more about delivery than production.

We’re excited to see Base Power launch Base Core today and to keep backing Base Power and others reindustrializing the U.S. grid.

AI is changing the way we use Search.

As AI Overviews and AI Mode scale, users are asking longer, more conversational questions and spending materially more time in the Google app.

Better answers seem to be creating more questions.

The next scaling law: learning velocity "on the job".

AI has been defined by pre-training: spend more upfront, get a smarter model. But a shift is underway, from pre-training to continual, on-the-job learning.

A new study measured whether models improve when given time, feedback, and room to work. The data shows the newest models learn on the job almost twice as fast as models from just three months earlier, and the gains compound with iteration.

It turns out models may not be so different from us: given room to practice, they get better.

For more on this C:\Take, watch Max:

Major cloud providers are re-accelerating as AI adoption increases!

CPU is back from the dead.

Before AI, a human would think about what to do and then take action on their computer screen. Today the GPU plans the action by writing tokens, and the CPU executes it by writing code.

The workflow a lot of people can relate to is pulling data, analyzing it, and displaying a chart. Every single one of those steps can now be done by an agent using a computer driven by a CPU. That is massive CPU demand, because agents can move faster, longer, and deeper than a human possibly could.

Before, you had to click, read, and plan a task yourself. Now an agent runs that same sequence in a loop. The GPU decides the next move, the CPU carries it out, and the cycle repeats. Every cycle is a GPU-to-CPU round trip.

For more on this C:\Take, watch Frank and Nick: