Beyond Prediction Markets, Robinhood’s Vlad Tenev Is Fueling Millions Here

Robinhood:- When Robinhood founder Vlad Tenev’s name surfaces in headlines, it is almost always tied to Robinhood. Well that makes senses too. The company is emerging as the rapidly growing crypto trading app, expanding deeper into prediction markets recently.

From crypto trading to sports event contracts and international expansion into markets like Indonesia, its founder Vlad Tenev is aiming to make Robinhood ‘the financial superapp’.

But behind the familiar Robinhood narrative lies a quieter, far more radical bet.

Away from trading screens and market forecasts, Tenev has been pouring hundreds of millions of dollars into an obscure but ambitious frontier – “mathematical superintelligence”. These are the artificial intelligence systems capable not just of generating answers, but of formally proving mathematical truth.

At the center of this Vlad’s effort is Harmonic, an artificial intelligence startup co-founded by Tenev in 2023 with Tudor. Last month oonly, Vlad raised $120 million in fresh funding for his project, valuing the company at approximately $1.45 billion.

The startup’s mission is as bold as it is unconventional. It aims to eliminate hallucinations  in AI by teaching machines how to reason like mathematicians – and verify every step. Here’s How

Robinhood Founder’s Different Kind of AI Bet

Though Harmonic is not a crypto company in itself and founded by Vlad in 2023 with Tudor, its current emergence is significant.

This comes at a time when the crypto industry is already heavily betting on artificial intelligence. There is rapid development of crypto trading specific AI agents and trading bots to on-chain inference and data markets.

But what sets Robinhood founder Vlad Tenev apart, is that he is backing a very different class of AI systems.

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Rather than speculative applications or AI-branded assets, Tenev is investing in mathematical superintelligence. This is an AI designed to reason formally, generate machine-verifiable proofs, and eliminate ambiguity in logic-driven systems.

It is a quieter bet, but one that targets the foundational weaknesses of both AI and crypto: trust, correctness, and provability.

Instead of probabilistic text generation, Harmonic’s systems aim to produce machine-verifiable proofs – mathematical arguments that can be checked line by line by formal proof assistants such as Lean. If the proof compiles, it is correct. If it doesn’t, it fails – no ambiguity, no “almost right.”

This is a sharp departure from mainstream large language models, which are optimized for plausibility rather than certainty.

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Aristotle: The Engine Behind the Vision

Harmonic’s flagship AI system, Aristotle, has already demonstrated results that would have sounded implausible just a few years ago.

Traditionally, mathematical proofs rely on peer review – expert referees who manually check arguments for correctness. While effective, the process is slow, subjective, and prone to oversight.

However, in a recent auto-formalization experiment, mathematician Bartosz Naskręcki used Aristotle to translate an existing mathematical paper into over 5,000 lines of Lean code, correcting inconsistencies and filling logical gaps along the way. The compiled code became a proof certificate — a mechanically verifiable guarantee of correctness.

The implications are profound. Instead of trusting that a proof is correct because an expert says so, future mathematicians may rely on compiled verification. It is similar to how software engineers trust code that passes rigorous tests.

In another milestone, Aristotle autonomously proved Erdős Problem #481, an unsolved problem that had remained open for over 45 years. The proof was again fully formalized, meaning it could be validated without relying on human trust.

For mathematicians, this marks a potential turning point.

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Is Vlad’s Harmonic bet Good for Robinhood?

While Harmonic itself is not a crypto company in the conventional sense – it does not operate a blockchain, issue tokens, or offer decentralized finance services. But itss core technology has meaningful intersections with the crypto ecosystem.

The startup’s focus on formal verification and machine-checked mathematical reasoning addresses one of the most persistent risks in blockchain: smart contract vulnerabilities and protocol flaws.

Crypto protocols, zk-proof systems, consensus mechanisms, and tokenized financial contracts all depend on precise, unambiguous mathematics.

Tools that can auto-formalize specifications and generate proofs that compile in systems like Lean could greatly improve audit quality and reduce costly exploits.

Moreover, the philosophical alignment is strong: crypto communities prize mathematical guarantees and trust-minimization, and Harmonic’s work aims to convert ambiguity into verifiable certainty. This is a pursuit that resonates deeply with blockchain developers and security researchers and can be rewardinf for Robinhood in the long run.

And for Vlad Tenev, this may be the most consequential bet of his career.

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