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Why CBLRE Matters More Than the Model
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
Read storyFounder Reality is written by George Pu — $10M+ portfolio built by 27, no investors, no co-founders.
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How Cohere Wrote the Fund That's Now Funding Everyone Else
On Christmas Eve 2024, the Government of Canada issued a Letter of Intent to Cohere for a Strategic Innovation Fund contribution of $240 million. The timing is the first thing worth noticing. Christmas Eve is when governments send letters they want done quickly and quietly. The Trudeau government was already wobbling. Chrystia Freeland had resigned on December 16, eight days earlier, in a public letter that called out the Prime Minister's "costly political gimmicks." Less than two weeks lat
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Sovereignty Is Not About Ownership
A vice-president at a Canadian university called me last month. Her president had told her the institution needed to be on sovereign AI within the year. The provincial government had announced a funding program. Other universities were already applying. She wanted to know what she should be evaluating. I asked her what sovereign meant in this context. She paused for a long time. Then she said, "I think it means the data stays in Canada." I asked what about it staying in Canada specifically
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I Almost Told a Lawyer to Build His Own AI
A lawyer friend — call him Mark — called me this week. He and another friend had spent the weekend trying to run an 8 billion parameter language model on a 16 gigabyte laptop. Mark thought he was going to show his friend something impressive. The output was gibberish. Incoherent strings of text that no junior associate would have signed off on. He'd come to me because he wanted to know what hardware to buy next. On the call, I didn't know. What I did know was NVIDIA's software stack. I'd
Read essayFrom the series · 90-Day Action Plan to Surviving AI
The Untethering
"Who are you when the job goes away?"
More on Decision Log
Three essays from the archive on a different angle.
We're Shutting Down Our Cloud-Hosted Apps
Effective immediately, we're sunsetting the cloud-hosted versions of the following apps: * Founder Simulation Game * SimpleDirect Changelog * SimpleDirect Chat * SimpleDirect Roadmap SimpleDirect Financing, our legacy fintech application, is not affected. Let's be real These apps have no moat. We know that. AI can rebuild any of them in an afternoon. Pretending otherwise - keeping servers running, maintaining hosted vers
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I Almost Paid $4,000 a Year to Rent My Own Voice
I almost paid $4,000 a year to rent my own voice back to me. ElevenLabs. $330/month. The model that powers it? Open weights. The inference? Runs on 3GB of RAM. The voice cloning? A 30-second sample and a model you can download for free. The only thing between you and the exact same output is a setup guide nobody bothered to write. So I wrote one. Then I open-sourced it. The gap The gap between what AI costs to run and wha
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What You Kill > What You Build
OpenAI killed Sora Not sunsetted. Killed. The product they hyped as the next ChatGPT. The one Disney was about to invest a billion dollars around. The one that was supposed to turn OpenAI into the creative engine of the AI era. Gone in an afternoon. The reason is almost boring in how obvious it is. Sora was eating compute - the most expensive resource on earth right now - while Anthropic was running away with the market tha
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You might not have read this
A couple of older essays we think are worth a second look.
AWS and Google Cloud Are Designed to Keep You. Here's What We Do Instead.
When my startup was brand new, both AWS and Google Cloud felt like gifts. Our incubator had relationships with both. The credits were generous. The onboarding was smooth. We didn't pay a dollar out of pocket for months. It felt like they were investing in us. They weren't. They were investing in our dependency. I didn't understand that then. I do now. What You're Actually Paying For I want you to try something. Strip away
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Fine-tuning your own AI doesn't cost $35,000. It cost us about $50.
Two A100 graphics cards. Spinning quietly in a Google datacenter. Five hours of training. About $50 in compute. That's what it cost us to fine-tune our own 4-billion-parameter AI model this week. The base model went from 30% accuracy on the tasks we care about to 98%. Read any article on fine-tuning costs and you'll see numbers between $5,000 and $35,000. One blog called it a 'CFO conversation.' Another listed 'hidden expenses' that could double your initial estimate. A third quoted team
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Canada's AI hardware reality check — what's actually available vs. what founders think they can buy.
GPU shipping is the tell. If you can't physically own the compute, you don't own your AI stack.
I only write code when it's 10/10 important. Slowing down is the real productivity move in 2026.
Mac Studio supply is crunched. Apple's quietly rationing M3 Ultra — AI builders feel it first.
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