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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

Jeff Bezos famously said, “Your margin is my opportunity.”¹ Does this maxim apply in software & AI? Yes.

Software companies maintain 76% gross margins yet earn almost nothing. Sales, marketing, & research & development consume it all.

Among 69 publicly traded B2B software companies, the median net income margin hovers near zero.² Gross margins cluster tightly around 76%, yet almost none of that profit flows to the bottom line.

In 1908, 253 American automobile manufacturers competed for the market¹. By 1929, just 44 remained. The assembly line didn’t just change how cars were made. It changed who got to make them.

Ford’s Highland Park plant, operational in 1913, slashed the time to build a Model T from 12 hours to 93 minutes². That 90% productivity gain restructured an entire industry. Manufacturers who couldn’t match Ford’s efficiency faced a simple choice : adapt or exit.

Talented people get promoted to management. So do talented models. Claude manages code execution. Gemini routes requests across CRM & chat. GPT-5 can coordinate public stock research.

Why now? Tool calling accuracy crossed a threshold. Two years ago, GPT-4 succeeded on fewer than 50% of function-calling tasks. Models hallucinated parameters, called wrong endpoints, forgot context mid-conversation. Today, SOTA models exceed 90% accuracy on function-calling benchmarks¹. Performance of the most recent models, like Gemini 3, is materially better in practice than the benchmarks suggest.

Will designers design first in a world where AI can code software immediately, or just describe the design? Will large enterprises pay for premium observability when AI can migrate & monitor open source competitors?

As Michael Mauboussin writes, there’s information in price. These questions are priced in. It’s too early to see revenue erosion, but the market is pricing in the risk.

The median SaaS stock is down 14-17% year to date. 64% of software companies are down. Adobe has fallen 32%, HubSpot 57%, Atlassian 54%.

After a decade of success, the modern data stack has entered consolidation. What comes next?

The postmodern data stack is AI.

The modern data stack created more than $100 billion in market cap with a simple promise : move the data via ETL, transform it inside a cloud data warehouse, put a semantic layer on top of it to unify metric definitions, & analyze it through BI.

This worked brilliantly for structured data, numerical data found within tables. But AI & businesses thrive on unstructured data, such as call transcripts, status reports, web searches, & PDFs.

This week I chatted with an acquaintance who mentioned a board game. I caught half the title & looked for the full title & Amazon link using my AI in Asana.

The system tried with Gemini & failed. The failover to Claude also failed. Rather than continuously iterating with the AI until it worked, I created a Ralph Wiggum loop.