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Deep thinking on building businesses designed to own forever. Not how-to content. Decision logs, frameworks, and pattern recognition.
Startups in San Francisco raise $18 million just to afford the compute. We did the same work bootstrapped. The real gate on who gets to build AI isn't ability - it's who can afford to be in the room.
We had the money and we're a multi-year cloud customer. It still took two months, a flood of scammers, and a $14,000-a-week quote to rent a few chips. Here's how GPU access actually works.
Vinci ships August 8 with the methodology published in full - so competitors can copy it. The thing they can't copy is whether the model actually does what its constitution says, in public, under tests anyone can run.
For six months I was sure the answer was a from-scratch Canadian AI model. Seven days ago that broke. I had confused the model with the system - and the open-book version is the bigger bet.
On June 12 a government letter reached past Anthropic and switched off the best coding model I'd ever used. The shutdown wasn't the lesson - what it revealed about renting intelligence was.
Yesterday I published the post-mortem: we asked flash-1-mini ten questions any Canadian lawyer would consider basic, and it invented seven citations. That post was about what broke. This one is about what it changed. Because two weeks ago I was telling people we were building a Canadian legal AI model - and today we decided we're not. I want to walk through why, because the answer changed how I think about what "building AI" actually means. The original scope The original scope made sense
An honest post-mortem on our own model — and what anyone building or buying AI should take from it.
Google shipped local AI on the Mac this week. The weights are Apache 2.0. The experience is a walled garden. That's not a contradiction. That's the playbook.
Yesterday we released CBLRE — the Canadian Bilingual Legal and Regulatory Evaluation. The day before, we released flash-1-mini, a 4-billion-parameter bilingual Canadian legal AI model. Most of the launch coverage has focused on the model. That's the wrong artifact to focus on. The model is the proof. CBLRE is the moat. Here's why. The gap nobody had filled Before yesterday, no standard public benchmark existed for Canadian bilingual legal AI evaluation. That sentence is bigger than it so
Two AI infrastructure announcements landed in the same week. Ours was a model. We released flash-1-mini — a 4-billion-parameter bilingual Canadian legal AI — under Apache 2.0, alongside an open benchmark (CBLRE), an open training corpus, and the methodology behind both. The model runs on a MacBook. It runs on a Raspberry Pi. The weights are downloadable. You own the file. Perplexity's was an orchestrator. At Intel's Computex keynote in Taipei, CEO Aravind Srinivas demonstrated what the com
The model is the smallest part of the story. Here's what it is, what it isn't, and what comes next. Today we shipped flash-1-mini. It's a 4-billion-parameter open-weight model, fine-tuned for Canadian context, bilingual in English and French, that runs on a laptop with no cloud dependency. You can download it, run it offline, and own it. The weights are yours. I want to write about what it is, what it isn't, and what comes after — because the model itself is the smallest part of the story.
Over the past two years, Canada quietly rebuilt the question of who controls its artificial intelligence. Not in one announcement. That's the point. There was never a single moment loud enough to make you look up. The decisions arrived in fragments — a Christmas Eve letter, a contribution agreement with a file number, a press release at a university most people don't follow, an MOU with the important parts blacked out. Each fragment was, on its own, small enough to ignore. Together they red
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