Ali Asaria

Co-Founder of @transformerlab
ex CEO of Engineer at heart.

[Ali Asaria @aliasaria]

Common take: Canadian companies don't buy from startups because they're risk-averse.

I've sold to Canadian banks, retailers, grocers & pharmacy chains as a startup for 20 years. The description is accurate -- it really does suck to sell to Canadian businesses as a startup. But the reason is wrong.

As a startup you're rarely selling just a solution -- you're selling a paradigm shift ("you run a custom ecommerce engine, but SaaS changes what's possible"). So the real ask is: adopt an unproven paradigm AND trust a small startup to deliver it.

Now picture the typical Canadian buyer: a company that's rarely #1 in its field, comfortable in its position, with a decision-maker promoted on tenure -- not for being a maverick.

The ones who DO take paradigm risks (Lululemon, Shopify) do it because reinvention is core to their identity. They're the exception.

It's not that Canadian enterprises prefer US vendors. It's that if they're going to bet on a new paradigm, a small startup won't be the one to convince them -- but an OpenAI, SAP, or Salesforce will.

There's something deeper underneath it too. Across art, science, and tech, Canadians rarely see themselves as the ones who define the next wave -- so they don't trust homegrown talent to do it either. We instinctively look elsewhere for the future.

[Ali Asaria @aliasaria]

@DavidSHolz People should not have to use slurm. We built Tranformer Lab @transformerlab and many of our users use it in combination with kubernetes, dstack / skypilot to replace slurm. Can share real big labs and users privately in DM.

[Ali Asaria @aliasaria]

@ucalyd There are so many new frontiers, across so many different modalities, to pursue. So much left to be discovered and solved. Not just about fine-tuning LLMs.

[Ali Asaria @aliasaria]

A lot of folks talk about how much private sector progress we’ve made in AI in Canada. But the truth is that there are very very very few companies actually doing training of large models here. We’re talking about maybe 5 companies. It’s impossible to build an ecosystem around this.

[Ali Asaria @aliasaria]

@robjama Yes. You're an amazing example of this.

[Ali Asaria @aliasaria]

@Mashcr0ft Which layers can and should this country bet on?

[Ali Asaria @aliasaria]

@jrodgers All I know is what I can do… I’m going to build a frontier model building company.

[Chris Olah @ch402]

https://t.co/udIVxLdid5

[Ali Asaria @aliasaria]

FYI you can tell Claude Cowork to make a list of data brokers and automatically opt you out of all of them.

[Ali Asaria @aliasaria]

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 https://t.co/lHotbCJJGI 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 https://t.co/lHotbCJJGI, 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: https://t.co/hj6XXwJ3Bd

[Ali Asaria @aliasaria]

This feature should launch to all users in the upcoming days

[Ali Asaria @aliasaria]

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.

[Ali Asaria @aliasaria]

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

[Ali Asaria @aliasaria]

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.

[Mark Carney @MarkJCarney]

The Canada Strong Fund is Canada’s first national sovereign wealth fund. It will invest in the major projects that are transforming our economy — and give Canadians a direct stake in our nation’s prosperity.

[Ali Asaria @aliasaria]

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.

[Ali Asaria @aliasaria]

“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.”

[Tulip King 🌷 @tulipking]

https://t.co/QwOFNAFy9R

[Ali Asaria @aliasaria]

@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.

[Ali Asaria @aliasaria]

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.

https://t.co/ymVnOP4WgU

[Ali Asaria @aliasaria]

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.

[Ali Asaria @aliasaria]

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.