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😺 🎙️ PODCAST: Can AI Solve Math's Biggest Mystery?

with Tudor Achim of Harmonic & Aristotle

Welcome, humans.

AI is getting better at math.

But Tudor Achim says the bigger shift is that AI may finally be able to prove what it says.

Tudor is the co-founder and CEO of Harmonic, the company behind Aristotle, a formal reasoning system built to generate mathematical proofs that computers can actually verify.

That sounds abstract. But it could matter a lot.

Because if AI can move from “trust me, this is right” to “check me, this is right,” it could reshape math, software, chip design, scientific computing, and maybe even how humans discover new knowledge.

Today’s Episode is Sponsored by Dell Technologies and NVIDIA

Plenty of companies can launch an AI pilot. Far fewer know how to make it stick. Explore this resource hub, sponsored by Dell AI Factory with NVIDIA, for strategies, decisions, and real-world lessons on turning AI into something scalable, useful, and worth the investment.

In our latest episode, Corey and Grant sit down with Tudor Achim to unpack what “mathematical superintelligence” actually means:

  • 00:00 AI That Proves Its Work

  • 02:00 What Mathematical Superintelligence Means

  • 03:22 A Clearer Bar Than AGI

  • 04:01 Are Today’s AI Systems Actually Creative?

  • 05:42 Math Is Not Just Arithmetic

  • 08:58 How AI Amplifies Mathematicians

  • 10:02 Why Verification Makes AI Useful

  • 11:45 Lean, Explained Simply

  • 12:15 What a Machine-Checked Proof Means

  • 12:42 Could AI Prove Riemann by 2028?

  • 15:24 Why Harmonic Opened Aristotle to Users

  • 17:33 Formal Math Becomes Practical

  • 21:01 Human Proofs vs. Machine-Verified Proofs

  • 23:11 GitHub for Mathematicians

  • 24:44 Compute, Limits, and Infinite Math

  • 27:29 The Moment That Changed Tudor’s View

  • 29:10 Trust Layers Beyond Math

  • 31:20 Tudor’s Aristotle “Aha” Moment

  • 33:49 Should AI Change Education?

  • 37:07 The Spec Problem in Verified Software

  • 39:10 How Aristotle Could Help Chip Design

  • 42:25 Will We Ever Run Out of Math Problems?

  • 46:03 The Problem Tudor Wants Solved

Bottom line: The future may not be AI replacing mathematicians. It may be mathematicians directing much more powerful tools, and finally being able to verify the results.

Listen now on YouTube | Spotify | Apple Podcasts

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Stay curious,

The Neuron Team
Corey & Grant

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🎙️ In Case You Missed It…

Four recent interviews you’ll definitely want to check out (pick whatever looks interesting to you and dive in!):

1. Interested in whether AI can actually design new drugs? Watch: Isomorphic Labs Is Trying to Turn AlphaFold Into Medicine. Here’s How.

TL;DW: Rebecca Paul, Head of Medicinal Drug Design at Isomorphic Labs, and Michael Schaarschmidt, Foundational AI Research Lead, explain why drug discovery is still brutally slow, expensive, and failure-prone and how foundation models could help scientists design better drug candidates faster. Their big point: “AI-designed drugs” are not one magic model. It takes many models working together across biology, chemistry, structure prediction, molecule generation, and human judgment.

Why you should watch: If you’ve ever wondered what comes after AlphaFold, this one gets into it. There’s a great section on how something that once could take an entire PhD to validate experimentally can now sometimes be predicted in seconds or minutes, and a wild bit about the dream of getting from a protein target to a drug candidate in one design cycle. Also: “undruggable” proteins may not stay undruggable forever.

2. Interested in what's missing before we hit AGI? Watch: This Company Mapped the Entire World in 3D. Here's Why.

TL;DW: Peter Wilczynski, CPO at Vantor (formerly Maxar), built a 3D model of the entire Earth at 50cm resolution and made it machine-readable. He argues spatial intelligence is the gap nobody's talking about in AI, and probably the missing piece before agents can actually operate in the physical world.

Why you should watch: If you've ever wondered why AI can write code and solve math olympiad problems but still can't reliably tell a drone where to go, this one answers it. Also, there's a wild bit about how the physical world becomes the new navigation layer for AI agents.

3. Curious how good AI music tools have actually gotten? Watch: This AI Just Made Our Podcast Theme Song

TL;DW: Corey sits down with Kendall Rankin, who left LinkedIn in 2024 to join Producer AI when it was a startup (advised by The Chainsmokers, no less). Google acquired the team in February 2026, and Kendall is now on the Flow Music team inside Google Labs. On the episode, they generate a garage rock song from a single sentence, build a custom synth in the "Spaces" feature, and walk through SynthID watermarking and one-shot music videos.

Why you should watch: Most AI music demos hand you a polished finished song and skip the part where things go sideways. This episode is the part where things go sideways. First pass fumbles, Corey asks for "more fuzz," second pass actually lands. That iteration loop is the whole story for anyone trying to figure out if these tools are actually usable.

Last thing: And if you haven’t subscribed yet, please do! Click the image below to go to our channel and hit “subscribe” to get notified right when new videos go live.

That’s all for today, for more AI treats, check out our website.

ICYMI: check out our most recent episodes below!