A practical six-rung ladder for AI agent autonomy: suggest, draft, execute-with-confirmation, execute-bounded, execute-with-rollback, execute-and-report. Each rung has different operational requirements. What promotes an agent from rung N to N+1, and what sends it back down.
Davos week. Amodei calls chip exports to China selling-nukes-to-North-Korea. Nadella warns the AI bubble is real if adoption stays inside big tech. Musk promises AGI by year-end. The labor displacement story hardens, and the pace is the part to watch.
AI is going to displace jobs, faster than I expected, slower than the doomers want you to believe. The honest conversation is about pace, incentives, where the lines are, and what human+AI collaboration actually looks like. I'd rather be wrong than caught off guard.
OpenAI flips the switch on ChatGPT ads, the DOJ's AI Litigation Task Force stands up to challenge state laws, xAI closes a $20B Series E, and the Q1 layoff wave starts taking shape. Davos opens Monday. What I make of the week.
Most organizations can't compete on AI spend. The strategic question isn't how to spend less than you'd like; it's how to extract disproportionate value from the smaller spend you actually have. Worth being plain about what frugal AI strategy looks like.
Three years past the original encoding-a-person framing, the question of what data actually encodes a person has gotten clearer. The answer is less about volume and more about the specific kinds of context that compound over time.
The cheapest way to train an LLM in 2026 isn't to spin up a giant cluster or to negotiate with a hyperscaler. It's to start from a pretrained base, optimize the data, and use the cheap-tier infrastructure that's matured around small-scale fine-tuning.
CES 2026 week. NVIDIA unveils Rubin and the DGX Spark/Station desktop AI line, AMD ships Ryzen AI 400 with a 60-TOPS NPU, Qualcomm pushes Snapdragon X2 to 80 TOPS, Samsung wires Bixby and Alexa+ into every appliance, and Mercedes ships Nvidia-powered L2 in the CLA. My take.
Three layers, top to bottom: applications (the AI features users see), platform (model registry, serving, observability, governance), substrate (K8s, GPUs, storage). Decisions as Code runs through every layer. Centralize the decisions. Project them everywhere.
Twelve specific predictions for 2026, calibrated against the patterns I missed in 2025. Less hand-wavy than the typical year-ahead piece. I'll grade them plainly at the next checkpoint.
The bridge week. SB 53 went live on January 1, the year-end retrospectives all said roughly the same thing about 2025, the 2026 prediction pieces from Sequoia and a16z dropped with surprisingly compatible takes, and CES 2026 is sitting on Tuesday's runway. What I make of the turn of the year.
End-of-year recap. Most of the year's narrative was hype layered over a smaller set of substantive shifts. Worth being explicit about what actually changed and what didn't, before the 2026-predictions piece tries to extrapolate from the noise.
