I’ve been using state-of-the-art models to teach small models running on my computer how I work.
My personal agent, based on Pi, runs my inbox, my deal pipeline, my blog publishing, my calendar, & my research. It looks less like a chatbot & more like a small operating system.
The first layer is QMD, a local markdown knowledge base of about eighty workflow files in ~/memories. Before answering any procedural question, the agent searches QMD for the right playbook.
When security practitioners become engineers, the mission changes from managing people to architecting the automated policies that govern an agentic world.
Jonathan Jaffe, CISO at Lemonade, joined me on Office Hours to discuss what this means for how we build, secure, & operate AI systems when both sides are automated.
The end of the software era is the beginning of the harness era.
AI outmoded SaaS managed databases with fixed workflows with intelligence. Like a mustang, AI is powerful but wild. Harnessing the power means domestication.
There are seven parts to this domestication :
If data gravity was the most important force in the Decade of Data, agent gravity will be the same in the Decade of Agents.
Agents are wonderfully powerful technologies & they require tremendous compute to power. That compute is big business & major platforms will fight to keep them on their platforms. The more agents & data running through a platform, the greater the agent gravity.
The most recent episode with a new Databricks feature on Microsoft’s platform :
Salesforce has gone headless : a sales person can update their deal sheet without ever logging into salesforce.com through AI. Many companies are following suit with MCPs. English as an interface to complex systems is a tremendous innovation.
And yet, some of the most sophisticated thinkers in AI are pushing more than markdown text, a format AI & computer systems use. These thinkers espouse richer UIs :
“Imagine using iMessage to do everything, when in fact every other app has a unique interface…With e-commerce, you want a very rich user interface.” - Brian Chesky, CEO of AirBNB
After 24 years as a private company, SpaceX filed its S-1 yesterday. The filing reveals an AI-era conglomerate. SpaceX has three distinct segments : Space, Starlink, and AI.
In 2025, SpaceX generated $18.7 billion in consolidated revenue with $6.6 billion in Adjusted EBITDA. But the real story lies beneath those top-line numbers.
SpaceX runs three businesses with fundamentally different economics :
Google’s AI triples in price each year.
OpenAI’s flagship model was seemingly subsidized for a while, before rising again.
Anthropic’s AI has been the same price for a little bit & decreased for the most powerful models.
As I was paging through Good Writing, Anne Lamott’s new book, I wondered what AI would say about twisting cliches & finding hidden metaphors (chapters 18 & 19).
Over the last 16 years of writing, I’ve read books about writing, hired an editor, & used AI. I’ve fine-tuned models to mimic my voice, tested more than 10 AI systems, & written many post with AI, with some Hindenburgs I’ve kept public as proof despite my embarrassment.
The fastest-growing companies in AI & software are either selling AI directly or reselling inference. At worst, they are the first derivative of inference.
Inference is the largest & fastest growing market in technology today, surpassing the database market & projected to be three times the size within seven years at $250 billion.1, 2 By selling inference or indexing a business to it, they grow at spectacular rates.
Anthropic has booked $9b & $10b in consecutive months.3 Google Cloud is growing 63% at an $80 billion run rate.4 Most businesses selling inference are exploding.
In yesterday’s post (which an agent pushed in raw outline form via email!), I wrote about the future of AI email. What does that future cost?
If you are using state-of-the-art model ranging, it costs between $22 to $130 per month. Would you pay for that? At work, I imagine, many would. Let’s take the middle case of $26/month raw cost.