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@axiommathai

The Starting Point for Reasoning

Katılım Eylül 2025
21 Takip Edilen12.6K Takipçiler
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Axiom
Axiom@axiommathai·
1/ RELEASING AXLE: the Axiom Lean Engine ⚙️ We are serving our core Infrastructure for formal proving at scale. These are the same Lean metaprogramming tools that are behind AxiomProver, powering it to win Putnam and crack open research conjectures. Available to anyone today!
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Ben Bergman
Ben Bergman@thebenbergman·
Startups are raising at warp speed making it hard for us to keep up. At the Montgomery Summit, I asked @mattmcilwain, who has backed everything from Amazon to Axiom, how VCs can stay one step ahead of the competition.
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Axiom
Axiom@axiommathai·
Thank you to @Nasdaq for this shoutout at the Nasdaq Tower yesterday!
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Matt McIlwain
Matt McIlwain@mattmcilwain·
The gap between interesting AI companies and enduring ones is becoming visible. That was the defining question at this year's @MontySummit, and it shaped every conversation I had there. I joined @AxiommathAI CEO @CarinaLHong on stage to discuss innovation at AI's leading edge, and participated in the 2026 private capital outlook panel. Across both sessions and many more conversations, three themes kept surfacing: 1) Disruption vs. durability. Switching costs are low. Model commoditization is real. The companies that stood out — including several Madrona portfolio companies presenting at the Summit — had more than a product story. They had a thesis for why they become harder to remove over time. 2) National defense and American dynamism. More floor space than ever was given to dual-use technology and mission-critical infrastructure. This is where AI is being stress-tested in ways commercial applications cannot replicate. 3)Domain-specific AI. Life sciences, travel, and robotics were where the evidence was strongest — adoption accelerating, proof points accumulating, and researchers taking results seriously. The macro environment is complex. Capital markets are repricing risk. Geopolitical factors are reshaping where technology gets deployed. But complexity is not pessimism. The fundamentals of building a durable business have not changed. What has changed is the size of the opportunity for those who get it right. #MontySummit @thebenbergman @jai_das
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Constellation Research
Constellation Research@constellationr·
Verifiable AI startup Axiom raises $200M to prove AI-generated code is safe to use zurl.co/GiJwq @SiliconANGLE @Mike_Wheatley “The risk of LLMs hallucinating is not cute; it’s downright wrong and very often even dangerous when it comes to LLM-written code,” - @holgermu
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TBPN
TBPN@tbpn·
Axiom Math founder @carinalhong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”
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Madrona
Madrona@MadronaVentures·
There’s a certain kind of founder who doesn’t pick a problem because it’s tractable. They pick it because they can’t stop thinking about it, because the gap between what exists and what should exist is too important to solve. @CarinaLHong is that kind of founder. The company she and her team are building, Axiom, is tackling one of the most consequential problems in AI: how to verify the output of non-deterministic systems. And, specifically, how do you know when AI-generated code is provably correct, with enough rigor to stake a critical system on it? Last week, Axiom announced $200 million in new funding to deliver scalable verification solutions. The raise was covered in --> The New York Times: nytimes.com/2026/03/12/tec… And Carina spoke live on CNBC: youtube.com/live/hf_UlPABr… We could be more proud to continue supporting @axiommathai as the team goes after one of the most important unsolved problems in applied AI. Congratulations to Carina, @shubho, and the whole team! Read more about our investment here: madrona.com/axiom-math-car…
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Thomas Lin
Thomas Lin@7homaslin·
“Axiom has built technology that can formally prove whether an answer is right or wrong. It does this using a computer programming language called Lean, which was created more than decade ago as a way of proving mathematical statements.” cc @KSHartnett nytimes.com/2026/03/12/tec…
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Nima Rasekh
Nima Rasekh@nima_rasekh·
Interesting @nytimes article about @axiommathai using #Lean to verify #AI gen. code. Formalization in math is clearly growing, but coding seems more messy, so curious where this goes! A.I. Writes Buggy Code. A Silicon Valley Start-Up Wants to Fix It. nytimes.com/2026/03/12/tec…
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Matt Turck
Matt Turck@mattturck·
"We are on the threshold of a mathematical renaissance and massive scientific discoveries" - @CarinaLHong, CEO of @axiommathai, which just announced their $200M Series A Full episode 👇
Matt Turck@mattturck

Can AI be correct 100% of the time? Verification as the missing layer for reasoning superintelligence - my conversation with @CarinaLHong, the incredibly impressive CEO of @axiommathai. 00:00 Intro 01:25 Why the World Needs an AI Mathematician 02:57 Scoring 12/12 on the World's Hardest Math Test (Putnam) 04:05 The First AI to Solve Open Research Conjectures 06:59 Does AI Solve Math in "Alien" Ways? (The Move 37 Effect) 08:59 "Lean": The Programming Language of Proofs Explained 10:51 How Axiom's Approach Differs from DeepMind & OpenAI 16:06 Formal vs. Informal Reasoning (And Auto-Formalization) 17:37 The AI "Reward Hacking" Problem 20:18 Building an AI That is 100% Correct, 100% of the Time 23:23 Beyond Math: Verified Code & Hardware Verification 25:12 The Brutal Reality of Competitive Math Olympiads 29:30 From Neuroscience to Stanford Law to Dropout Founder 33:57 How Axiom Actually Works Under the Hood (The Architecture) 37:51 The Secret to Generating Perfect Synthetic Data 40:14 Tokens, Proof Length, and Inference Cost 42:58 The "Everest" of Mathematics: Scaling Reasoning Trees 46:32 Can an AI Win a Fields Medal? 47:25 "Math Renaissance": What Changes if This Works 55:47 How Mathematicians React to AI (And Why Proof Certificates Matter) 57:30 Becoming a CEO: Dropping Ego and Building Culture 1:00:42 Recruiting World-Class Talent & Building the Axiom "Tribe" youtu.be/DtD0ngZ5_bU?si…

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Ken Ono
Ken Ono@KenOno691·
Mahalo to Micky Iwamasa! 🌺 Thrilled to see him prove Knuth's Claude's cycle in Lean 4. Huge thanks for using Axle, Axiom's free Lean toolkit, to help make it happen. Seeing our tools tackle beautiful math is exactly why we build them! @leanprover @axiommathai Check it out: @mikito3/how-i-build-a-proof-on-the-prof-donald-knuths-claud-s-cycle-with-opus-4-6-and-lean4-skills-62b99a982f67" target="_blank" rel="nofollow noopener">medium.com/@mikito3/how-i…
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Axiom
Axiom@axiommathai·
"When formal verification becomes fast, cheap, and automatic, the addressable market is the right of first refusal to generate every line of AI-generated code that has any consequence." The TAM Is All AI Code's ROFR. If you share this dream, join us! menlovc.com/perspective/ai…
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Axiom
Axiom@axiommathai·
@MenloVentures @CarinaLHong Thank you very much for this feature and so excited to strengthen our partnership! 🚀
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Menlo Ventures
Menlo Ventures@MenloVentures·
We're proud to lead @axiommathai's $200M Series A at a $1.6B valuation! Mathematics is the right foundation for AI that can truly reason. Seven months in, @CarinaLHong and her team have proven it, and we're betting that verified, safe code will become as essential as generating it. Read more: mnlo.vc/axiom-series-a
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Keşfet

@mattmcilwain @Nasdaq @MontySummit @CarinaLHong @thebenbergman @jai_das @SiliconANGLE @Mike_Wheatley