Apparently, I Read S-1s Now
Reading Anthropic's S-1 as a non-finance person brought me back to questions about AI costs, changing jobs, productivity and the future of work.
Notes
What enterprise AI adoption actually looks like between the demo and the dashboard. One enterprise rollout at OneDigital, plus eight side projects I run after hours.
Reading Anthropic's S-1 as a non-finance person brought me back to questions about AI costs, changing jobs, productivity and the future of work.
AI can make software cheap to build while hiding the permanent job of owning it. How I decide what to build at SmartCue, OneDigital and Mark IT.
A conversation with Swati after reading a critique of business schools made me examine money, pride, and why my work at OneDigital feels meaningful.
A Harvard Business School discussion about Shelley and Ben made me question how we recognize experts whose work helps colleagues through AI.
My wife and brother adopted a new AI assistant in 30 minutes. What their experience taught me about useful delegation, familiar workflows and AI adoption at work.
AI can make every task faster while the whole process still waits. I'm starting to think enterprise AI needs a fourth measure: flow.
A few months after four AI agents started running my companies, I discovered the hardest person to protect my attention from was me.
A kite surfing trip in Sri Lanka went sideways, and I spent the last days watching four households live a version of a life nobody around me was showing me.
I spent months asking my own dev team to let me into my own codebase. They never said no. What I got instead was every excuse, and eventually a better idea.
A Tally dealership in Bombay, 15 people, nobody technical. Twice now the same dull instinct has saved it, and both times nobody lost their job.
We measure our AI program carefully, and the number we report is hours saved. Then I looked at what that number is made of, and at who is actually using these tools.
We run AI the way HR runs people: job descriptions, internships, probation, promotions, and sometimes terminations. What hiring and firing AI coworkers taught us.
I kept notes on wine for thirteen years to map my taste. What they actually recorded was people. On why I never open a good bottle alone.
I keep taking jobs I'm not qualified for on paper. It looks like nerve. It is actually a rule: cap the downside, and tell them exactly what you are not. The honesty is what makes the reach safe.
The two loudest stories about enterprise AI, that it deploys itself or that it replaces everyone, are both wrong. The real work is the boring middle. Notes from inside a 6,000-person AI program.
Meaning is retrospective. The operators who actually ship meaningful work didn't start by finding their why. They aimed at fun, new, smart people, and not letting down the people who bet on them. Here's the pattern up close.
I run an AI program for a 6,000-person company. I have tried three ways to measure whether people are using it well, and every clean metric measured something slightly off. Here is where my thinking has landed, for now.
Eight side properties as a curriculum, not a strategic portfolio. The GTM gap I'd lived with for fifteen years is closing, using the same AI-leverage that built the eight things in the first place.
The 2010s operator gospel (ideas are cheap, execution is everything) inverted sometime around 2023. Articulation became the bottleneck. Here's what that looks like up close.
Harvard published a case study on the AI program I run. I'm a self-described mediocre operator. I'm still trying to figure out what changed.