Ali Asaria

I have built three companies across three different technology waves. The thing I keep relearning is how little of the outcome is about the technology. I started at BlackBerry as a young engineer and wrote BrickBreaker on the side. It ended up on more than fifteen million devices — I was only 19 or 20 years old. That was the first lesson in how unevenly value gets attributed inside a big company. I founded Well.ca out of Guelph and grew it into one of the largest e-commerce businesses in the country. McKesson acquired us in 2017. The deal worked because of years of operating decisions made long before any banker was in the room. Customer trust. Category position. A team that could be handed the keys without the wheels coming off. I founded Tulip while I was still running Well.ca, because I could see what mobile was about to do to retail and nobody else was building for it. Mulberry, Coach, Kate Spade, Michael Kors, Salvatore Ferragamo. We raised over a hundred million in venture capital across the two companies and learned, the expensive way, what enterprise sales actually take. I am co-founder of Transformer Lab now. Open-source platform for AI model development. This is a different world, but it’s the same pattern underneath. Great products lose deals. Trust, timing and people decide more than the tech does. The founders who internalize that early build companies with more options when the moment arrives. Toronto in 2026 is in a strange place. More capital than it has ever had. More AI-native competition than most founders have priced in. More acquirer activity than the headlines reflect. The decisions founders make in the next eighteen months will quietly determine what the next decade of this ecosystem looks like. As Chair of @TechExitConf Toronto 2026, I am working with this steering committee to build a program for the Toronto founders who are inside that decision right now. Less narrative. More of what actually moves the outcome. Learn more: techexit.io/toronto/

This feature should launch to all users in the upcoming days

We built @karpathy autoresearch functionality to work natively inside @transformerlab . I see this type of harness as part of all future ML research work now -- what used to take me months of work is now happening automatically while I sleep.

@jedgar @MarkJCarney I also think this is awesome. What do you say to all the haters?

If you've been following me for long enough, you'll know I've been campaigning hard for a Canadian Sovereign Wealth Fund for years now. Today is my Christmas. Thank you @MarkJCarney!!

This is a great idea. For so many reasons: 1) The build community has been asking Canada to fast track and invest in major projects. This is one leg of that stool 2) This answers the biggest issue for the anti-oligarchy community: it allows ALL Canadians to win if the government fast tracks projects (not just wealthy insiders) 3) It's bold, and risky, and new. This is the kind of policy we need right now. Execution is everything, but this has the potential to be one of the smartest moves this government makes.

Tech world: don't let the startup CEOs who dominate our voices let you forget that the original promise science and engineering is about expanding humanity's knowledge and solving real problems for people.

“When companies stay private until they are worth a trillion dollars, public markets are no longer where value is created. They are where value is realized.”

@ariqbalkhan Now I'm reading the research on your website. So fascinating! I originally shared it with some friends on a Toronto tech community chat. Let's connect and stay in touch.

@aliasaria love this - is there a groupchat where folks can dissect and understand some of the ideas better?

I wrote an article on the intersection of Islam and modern AI. It’s called "Between Clay and Light: A Quranic Framework for the Age of Intelligence." I originally gave this as a private talk. It’s an attempt to bring together a few different areas I’ve spent time in -- merging technical AI concepts with the Islamic tradition. The ideas are an exploration, but I’m curious to see if this framing resonates with anyone else. aliasaria.ca/posts/between-…

We’ve spent so much time optimizing post-training that I forgot how humbling raw pre-training is. In RLHF/SFT, you see gains every hour. In pre-training, you’re just watching billions of calculations happen in the dark, hoping a signal emerges. It’s the most powerful technique we have, but the compute-to-progress ratio is soul-crushing.

Did you know the "million monkeys on a million typewriters" trope is actually impossible? Shakespeare’s shortest play has 88,361 characters. To have even a 1% chance of randomly typing it correctly, you would still need to exhaust roughly 0.01 x 50^88,361 combinations. Physics tells us this isn't just a matter of time, but of energy. According to Landauer’s Principle, there is a universal minimum energy requirement for any calculation or "bit flip." Even if you had microscopic monkeys operating at the absolute theoretical limit of efficiency, the energy required to attempt enough combinations for that tiny 1% gamble would exhaust the total energy of the observable universe roughly 10^149,000 times over.

Model on huggingface is here: huggingface.co/CohereLabs/coh…

Model Technical paper here: huggingface.co/blog/CohereLab… . Congrats cohere audio team. This is very impressive. A lot of folks trying to compete for SOTA on audio these days, really hard to do.

Cohere’s Transcribe model, announced on March 26, 2026, is impressive on a number of fronts. With a 5.42% average WER, it has officially claimed the #1 spot on the Open ASR Leaderboard. No easy task.

@garethdmm Yeah 3-6 months

@aliasaria I was thinking about this today. How fast do you think a small team could get to, say a llama3 8b caliber model today, starting from zero. Six months?

While there are real criticisms of the chart below there is something encouraging about it as we talk about sovereign and open AI in Canada. The idea is that a dedicated team with the right talent+budget can recreate state of the art results from trillion $ companies faster than you'd think.