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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 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:
lab.cloud/society
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.
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 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:
lab.cloud/society
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.
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Ali Asaria retweeted
On Canada creating access for sovereign AI compute, @aliasaria of @transformerlab says that, as getting GPUs right now in Canada is so difficult, "Ottawa helping data centres finance those purchases might sound crazy, but it might be crazy enough to work."
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How to get GPUs in Canada right now:
- Have one full time person talk to 50 vendors for weeks
- Have him go to dinners with vendors who take you to Michelin star restaurants but tell you not to ask for GPUs for now
- Say yes when random vendor says I have 2 B300's in India you have 24 hours to say yes
- Go to a conference where the famous Canadian GPU vendors are giving talks and put up your hand and say I don't believe you have GPUs
- Contact-us-form for their site doesn't ever respond but a guy shows up at your table after you spoke up at the conference and tells you how many GPUs they really have if you ask eight times
- Good luck
- Have one full time person talk to 50 vendors for weeks
- Have him go to dinners with vendors who take you to Michelin star restaurants but tell you not to ask for GPUs for now
- Say yes when random vendor says I have 2 B300's in India you have 24 hours to say yes
- Go to a conference where the famous Canadian GPU vendors are giving talks and put up your hand and say I don't believe you have GPUs
- Contact-us-form for their site doesn't ever respond but a guy shows up at your table after you spoke up at the conference and tells you how many GPUs they really have if you ask eight times
- Good luck
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Ethan is the future. I've never met someone like him before.
I remember the first time he told me about his academic journey ... it's the most amazing and inspirational story.
Let's make awesome, 🤯 science together.
I remember the first time he told me about his academic journey ... it's the most amazing and inspirational story.
Let's make awesome, 🤯 science together.
We’re thrilled to welcome Ethan Thompson to Transformer Lab as Lead Research Scientist in Quantum Science.
Ethan joins us from a rich background, including the Perimeter Institute, where he worked on nonlocal QFT with John W. Moffat, professor emeritus of physics at the Ethan will lead our work with physicists and quantum research groups applying Primus to some of the hardest problems in science.
Read the full announcement: lab.cloud/news/ethan-tho…
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Ali Asaria retweeted
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 t 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
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 t 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
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Ali Asaria retweeted
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.
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.
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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"?
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Ali Asaria retweeted
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.
- 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.
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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"
"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"
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Ali Asaria retweeted
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
📍 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
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Ali Asaria retweeted
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 🙈
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 🙈
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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.
I have met the people there and they are very nice people. Nothing bad will happen.
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Ali Asaria retweeted
Math was bound to fall first. The purer the field the easier it is for AI to crack.
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Ali Asaria retweeted
Your 'next cheque is hard' point shows up in Carta's data. Around 25 to 30% of startups that raised seed in 2018 reached Series A within 24 months. For the 2022 seed cohort it was about 17%. I'd plan a seed as 30 months of runway, not 18.
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Ali Asaria retweeted
The serious answer to how Shazam worked is it took the peaks of a spectrogram of short clips of every song, find the peaks from the highest amplitude bits, hash it with the value being (time stamp, track id) and then the actual recognition is a hashtable lookup.
We did this for he original paper is fantastic:
We did this for he original paper is fantastic:
Dude, how did Shazam work 15 years ago without AI!?
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