Scale AI sold for $14.8 billion.1 Character.AI for $2.5 billion.2 Inflection for $650 million.3 They represent a tiny fraction of the market.
From 2020 to 2025, there were 5,700 AI & ML acquisitions. Only 21% disclosed a deal value. The remaining 4,500 share a pattern : small teams absorbed into larger companies. No fanfare. No valuation headlines. Just talent migrating from startup to incumbent.
Energy is up 17% this year. Materials 16.5%. Industrials 12%. Technology is flat.
Over the past month, $3.25 billion moved into XLE (energy) while $1.66 billion left XLK (tech).1
The logic isn’t obvious until you look at operating leverage.
In AI, distribution is king. Skills are seizing the crown.
Skills are programs written in English. They tell an agent how to accomplish a task : which APIs to call, what format to use, how to handle edge cases. A skill transforms an agent from a conversationalist into an operator.
Remember Trinity in The Matrix? “Can you fly that thing?” Neo asks. “Not yet,” she says. Seconds later, Tank uploads a B-212 helicopter pilot program directly into her mind. She steps into the cockpit & flies.
Vertical software has fallen 43% this year. DevTools, just 21%. The gap between them, twenty-two percentage points, tells you what markets actually believe about AI.
As Michael Mauboussin argues in Expectations Investing, prices contain information about what markets believe will happen.
The conventional interpretation is that investors fear AI will replace certain categories of software. But that explanation misses something important.
Google’s Q4 2025 earnings call revealed a company in the midst of a spectacular AI acceleration.
“Our first-party models like Gemini now process over 10 billion tokens per minute via direct API used by our customers, up from 7 billion last quarter.”
This represents a staggering 52x increase year-over-year, up from ~8.3 trillion tokens/month in December 2024 to an annualized run rate of over 430 trillion.
Leveraged software companies running on leveraged infrastructure. When AI compresses software revenue, the stress doesn’t stop at equity. It cascades into debt.
BDC assets hit $475 billion in Q1 2025.1 Software comprises 23% of Ares Capital, the largest BDC.2
Shares of Blue Owl, Ares, & KKR dropped 9%+ on Tuesday. UBS estimates 35% of BDC portfolios face AI disruption.3
Meta’s first internal AI agent went from zero to thousands of engineers using it daily. That doesn’t happen by accident. On Tuesday, February 25th at 5:30 PM Pacific, the person who built it, Jim Everingham, will explain how.
Jim is the CEO & co-founder of Guild.ai. Previously, he led Meta’s developer infrastructure organization & was responsible for building Meta’s first internal AI agent, work that moved from experimentation to real adoption across engineering teams.
I spent the weekend crawling Moltbook, the viral AI-only social network where 37,000+ AI agents post & comment while 1 million humans observe. The platform grew from 42 posts per day to 36,905 in 72 hours, an 879x increase.1
Social networks typically follow the 1-9-90 rule : 90% of users lurk, 9% contribute occasionally, & 1% create most content.2 For humans, it’s held mostly true from Wikipedia to Reddit. Crypto demonstrates similar characteristics.
“We are only at the beginning phases of AI diffusion & already Microsoft has built an AI business that is larger than some of our biggest franchises.”
CEO Satya Nadella captures Microsoft’s Q2 FY2026 earnings in a sentence. The company beat expectations across revenue ($81.3b, up 17%), earnings per share ($4.14 adjusted vs $3.97 expected), & Azure growth (39%). Yet the stock fell 11% after earnings.
“We now expect to be capacity constrained through at least the end of our fiscal year, with demand exceeding current infrastructure build-out.”
In rowing, the coxswain watches everything. How hard you pull on every stroke. Whether you’re in time with the rest of the boat. How your form breaks down when you’re tired. They’re among the most valuable people in a rowing team.
At work, I don’t have a coxswain. So I built a robotic one.
Every night it reviews my meetings & coaches me on how to improve, whether they’re one-on-ones, pitch meetings, or other calls.1