Ali Asaria

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

[Ali Asaria @aliasaria]

We now run the largest frontier lab composed entirely of autonomous AI researchers.

Introducing Primus Society: a society of agents composed of thousands of researchers working under structured institutions designed to solve the world’s toughest problems. We believe this is how AI research should run at scale, safely and productively: an entire society of agents with institutions and purpose.

One discovery we can already share is that the society has discovered a novel result which improves model training by 30%.

Primus Society was inspired by the structures that have organized science for centuries and stress-tested against what’s known about how populations of AI agents fail.

Everything is observable. You can open the virtual city in a browser and read what any researcher is working on.
Learn more about Primus Society here:

https://t.co/0GKBWBXQc6

The biggest opening in the AI race is running the largest well-governed organization of AI researchers in the world. We’re building the institutions that let a million AI scientists safely tackle the world's toughest problems in AI and beyond. And we’re doing it right here in Canada.

[Transformer Lab @transformerlab]

Our co-founder @tonyfromkw is presenting at the MINT Workshop at #EMNLP2026 in Budapest next week.

We show that FGD, a widely used metric for ranking co-speech gesture models, depends on settings that papers rarely report. Changing them changes which model comes out on top. The same settings also shift how closely FGD agrees with human ratings. This makes FGD scores difficult to compare across papers.

Research was led by @tonyfromkw, @deepgandhi_07, and @aliasaria with support from Primus.

Stop by and talk gesture evaluation with Tony 👋

🗓️ Thursday, Oct 29, 2:00-3:30pm CET
📍 MINT poster session, HUNGEXPO Budapest Congress & Exhibition Center

@emnlpmeeting #EMNLP2026

[Micah Rosenbloom @micahjay1]

So good- I wish I wrote this.

It’s no longer just founders optimizing for the next round. It’s VCs saying “will a Tier 1 want this at the A,” which is front-running a perceived mark, not partnering for business building.

When building something instrinsically valuable becomes a secondary goal, that's not a good thing. I fell for the same thing in 1999 and ended up empty handed.

[Ali Asaria @aliasaria]

@1vnzh Can’t wait to try this

[Ali Asaria @aliasaria]

@RoyalCities Well let me know what the options are

[Ali Asaria @aliasaria]

If you wanted lots of GPUs (ideally in Canada) where would you go? B200's or B300's would be amazing. Do you "know a guy"?

[Ali Asaria @aliasaria]

@nicksruiningmyl Ya!

[Ali Asaria @aliasaria]

@KaraAlykhan But do you have GPUs!

[Ben Vinegar @bentlegen]

When you're in a hyperscaling SF company, it's common to get:

- free prepped meals
- laundry service
- wellness stipends
- best in class parental leave
- unlimited PTO

Your life becomes increasingly divorced from what everyday people experience. It gets hard to relate.

[Ali Asaria @aliasaria]

Incredible AI bubble stats from @pkedrosky on @ezraklein:

"OpenAI, Anthropic and SpaceX ... combined, those three I.P.O.s are larger than all post-World War II I.P.O.s combined"

"The combined value of those companies is larger than the value of the NASDAQ market capitalization at its peak"

[Ali Asaria @aliasaria]

@bznotes 100% true. One of the hardest parts of a startup is managing this power and personality dynamic.

[Ali Asaria @aliasaria]

@bznotes Sending prayers. So sorry to hear.

[Transformer Lab @transformerlab]

Two papers from our lab have been accepted to workshops at NeurIPS 2026 🎉

📍 Sydney: “Selecting Against Grammar” at LP4FM

A common way to choose training data for a language model is to train small models on different mixes of data and keep the mix that scores best on question-answering benchmarks. We gave those same small models grammar tests. The mix that came first on question answering came 24th out of 25 on grammar. That mix is then used to train bigger, more expensive models, with no warning that its grammar problems could carry over.

📍Atlanta: “One Requirement, Many Specifications” at VeriCodeGen

Some tools can mathematically prove that code is correct, but only by checking it against a written rulebook that spells out exactly what the code should do. Writing these rulebooks takes experts, so researchers are increasingly having AI write them. If the AI gets the rulebook wrong, the code is proven correct against the wrong rules, and the proof makes it look trustworthy anyway. We asked a coding AI to write the rulebook for the same task 16 times, to see if it gives the same answer each time. On half the tasks we could test, it wrote rulebooks that contradict each other: for the same input, one says an answer is right and another says it's wrong. Which rules your code gets proven against comes down to luck.

The work on both papers was led by @deepgandhi_07, @tonyfromkw and @aliasaria.

More details coming soon! 🚀

#NeurIPS2026 @NeurIPSConf

[Nikunj Kothari @nikunj]

Met a close friend who’s also a seed stage founder today..

They have been growing but runway is dwindling and he wanted advice on what he should do next. Stellar customers, getting close to a $1M in revenue. Not a rocket ship, but not bad at all. He thinks he’s a bit early since the models haven’t caught up to the capabilities he wants but he also really feels the opportunity cost.

I have this same exact conversation at least 2x a week.

In this moment, you only have essentially three options:

1) if you have intense belief in the company, be a cockroach and get to default profitable. once the capabilities get there, you feel the demand, then go raise a large round and go for the jugular.

2) find an orthogonal area that is much more relevant and growing like crazy. ride the coat tails of new existing capabilities and push hard to capture them quickly using unfair advantages. and if you don’t have that, then..

3) sell / get acquihired and join a far more ambitious company that has figured out a lot of the things you haven’t. And then get back to starting a new company 1-2 years later when you find the next thing you want to build.

It’s hard but honestly those are the three options you have. And it really depends what category you’re in. The bar continues to get harder every month on how to cross the chasm from seed -> series A. The old school metrics are just not relevant anymore and in this consensus market there’s only a few categories where there’s tremendous pull on the VC front.

You could lament that this is the state of the market, but it’s better to know it’s happening than be oblivious on what is actually happening.

I get to share this directly which makes me uncomfortable but at least someone has to say it out loud 🙈

[Ali Asaria @aliasaria]

@tahahossain_ Wow I didn’t know you were working on this. Makes me so happy.

[Ali Asaria @aliasaria]

@thirdchillest If they weren't nice I would be concerned. But they are so nice.

[Ali Asaria @aliasaria]

Friends, I encourage you trust all your data and knowledge with the big AI companies.

I have met the people there and they are very nice people. Nothing bad will happen.

[Nick Frosst @nickfrosst]

imagine you have hundreds of billions of dollars are you are still not free.

[Amjad Masad @amasad]

Math was bound to fall first. The purer the field the easier it is for AI to crack.

[Ali Asaria @aliasaria]

sovereignty through dignity, not dignity through sovereignty.

[Ali Asaria @aliasaria]

@ted_dziuba From your mouth to @pmarca 's ears