What AI Is Missing for Real Reasoning? Axiom Math’s Carina Hong on how to build an AI mathematician
Is math the ultimate test for AI reasoning? Or is next-token prediction fundamentally incapable of discovering new truths and discovering conjectures?Carina Hong, co-founder and CEO of Axiom Math, argues that to build true reasoning capabilities, we need to move beyond "chatty" models to systems that can verify their own work using formal logic.
In this episode of Inference, we get into:
Why current LLMs are like secretaries (good at retrieval) but bad at de novo mathematics
The three pillars of an AI Mathematician
How AlphaGeometry proved that symbolic logic and neural networks must merge
The difference between AGI and Superintelligence
Why "Theory Building" is harder to benchmark than the International Math Olympiad (IMO)
The scarcity of formal math data (Lean) compared to Python code
We also discuss the bottlenecks: the "chicken and egg" problem of auto-formalization, why Axiom bets on specific superintelligence over general models, and how AI will serve as the algorithmic pillar for the future of hard science.
This is a conversation about the structure of truth, the limits of intuition, and what happens when machines start grading their own homework. Watch it!
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*Guest:*
Carina Hong, co-founder and CEO of Axiom Math
https://www.axiom.xyz/
https://x.com/CarinaLHong
https://www.linkedin.com/in/carina-hong/
📰 The transcript and edited version at https://www.turingpost.com/carina/
Chapters:
0:53 Why LLMs Struggle with Basic Math
2:42 Building an AI Mathematician: The 3 Pillars (Prover, Knowledge Base, Conjecturer)
5:50 The Role of Human-AI Collaboration
6:34 Can AI Have Intuition? (Conjectures & AlphaGeometry)
10:16 A Hybrid Approach: LLMs + Formal Verification
11:24 Specialist Science Models vs. Generalist Giants
13:33 The Problem with Current AI Benchmarks
16:34 Practical Applications: Enterprise & Formal Verification
21:24 The Main Bottleneck: Data Scarcity
23:49 AGI vs. Superintelligence: The "Plate" Analogy
26:31 Book Recommendations (Math, Law, and Literature)
30:56 How to Use AI for Math Discovery Today
Turing Post is a newsletter about AI's past, present, and future. Ksenia Se explores how intelligent systems are built – and how they're changing how we think, work, and live.
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#AI #FutureOfAI #MathAI #FormalVerification #Lean #AxiomMath #Superintelligence #Reasoning
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32:45
Can We Control AI That Controls Itself? Anneka Gupta from Rubrik on…
Is security still about patching after the crash? Or do we need to rethink everything when AI can cause failures on its own?
Anneka Gupta, Chief Product Officer at Rubrik, argues we're now living in the world before the crash – where autonomous systems can create their own failures.
In this episode of Inference, we explore:
Why AI agents are "the human problem on steroids"
The three pillars of AI resilience: visibility, governance, and reversibility
How to log everything an agent does (and why that's harder than it sounds)
The mental shift from deterministic code to outcome-driven experimentation
Why most large enterprises are stuck in AI prototyping (70-90% never reach production)
The tension between letting agents act and keeping them safe
What an "undo button" for AGI would actually look like
How AGI will accelerate the cat-and-mouse game between attackers and defenders
We also discuss why teleportation beats all other sci-fi tech, why Asimov's philosophical approach to robots shaped her thinking, and how the fastest path to AI intuition is just... using it every day.
This is a conversation about designing for uncertainty, building guardrails without paralyzing innovation, and what security means when the system can outsmart its own rules.
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Guest: Anneka Gupta, Chief Product Officer at Rubrik https://www.linkedin.com/in/annekagupta/
https://x.com/annekagupta
https://www.rubrik.com/
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Subscribe to Turing Post: https://www.turingpost.com/subscribe
Chapters:
Turing Post is a newsletter about AI's past, present, and future. Ksenia Se explores how intelligent systems are built – and how they're changing how we think, work, and live.
Follow us →
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#AI #AIAgents #Cybersecurity #AIGovernance #EnterpriseAI #AIResilience #Rubrik #FutureOfSecurity
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26:58
Spencer Huang: NVIDIA’s Big Plan for Physical AI: Simulation, World Models, and the 3 Computers
When robots move into the real world, speed and safety come from simulation!
In his first sit-down interview, Spencer Huang – NVIDIA’s product lead for robotics software – talks about his role at NVIDIA, a flat organization where “you have access to everything.” We discuss how open source shapes NVIDIA’s robotics ecosystem, how robots learn physics through simulation, and why neural simulators and world models may evolve alongside conventional physics. I also ask him what’s harder: working on robotics or being Jensen Huang’s son.
Watch to learn a lot about robotics, NVIDIA, and its big plans ahead. It was a real pleasure chatting with Spencer.
*We cover:*
- NVIDIA’s big picture
- The “three computers” of robotics – training, simulation, deployment
- Isaac Lab, Arena, and the path to policy evaluation at scale
- Physics engines, interop, and why OpenUSD can unify fragmented toolchains
- Neural simulators vs conventional simulators – a data flywheel, not a rivalry
- Safety as an architecture problem – graceful failure and functional safety
- Synthetic data for manipulation – soft bodies, contact forces, distributional realism
- Why the biggest bottleneck is robotics data, and how open ecosystems help reach baseline
- NVIDIA’s “Mission is Boss” culture – cross-pollinating research into robotics
This is a ground-level look at how robots learn to handle the messy world – and why simulation needs both fidelity and diversity to produce robust skills.
*Chapters*:
0:22 The future of Physical AI begins here
1:00 Inside NVIDIA’s secret blueprint for teaching robots
3:46 Why safety is the hardest part of robotics
4:11 Simulation: the new classroom for machines
8:55 Can robots really understand physics?
13:55 How NVIDIA builds robot brains without a PhD
16:47 The plan to unify a fragmented robotics world
20:31 Why open source is NVIDIA’s biggest power move
21:21 What’s harder – robotics or being Jensen Huang’s son?
24:31 The one thing holding robotics back
27:56 The sci-fi books that shaped Spencer's mind
*Did you like the episode? You know the drill:*
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*Guest:* Spencer Huang, NVIDIA – a product line manager at NVIDIA leading robotics software product. His work centers on open-source simulation frameworks for robot learning, synthetic data generation methodologies, and advancing robot autonomy – from industrial mobile manipulators to generalist humanoid robots.
https://www.linkedin.com/in/spencermhuang/
*📰 Want the transcript and edited version?*
Find it here: https://www.turingpost.com/spencer
*Turing Post* is a newsletter about AI’s past, present, and future – exploring how intelligent systems are built and how they’re changing how we think, work, and live.
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#robotics #simulation #NVIDIA #Omniverse #digitaltwins #worldmodels #physicalAI #reinforcementlearning #syntheticdata
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28:23
Why do we need a special Operating System for AI?
When thousands of AI agents begin to act on our behalf, who builds the system they all run on?
Renen Hallak – founder and CEO of VAST Data – believes we’re witnessing the birth of an *AI Operating System*: a foundational layer that connects data, compute, and policy for the agentic era.
In this episode of Inference, we talk about how enterprises are moving from sandboxes and proof-of-concepts to full production agents, why *metadata matters more than “big data,”* and how the next infrastructure revolution will quietly define who controls intelligence at scale.
*We go deep into:*
What “AI OS” really means – and why the old stack can’t handle agentic systems
Why enterprises are underestimating the magnitude (but overestimating the speed) of this shift
The evolving role of data, metadata, and context in intelligent systems
How control, safety, and observability must be baked into infrastructure – not added later
Why Renen says the next 10 years will reshape everything – from jobs to the meaning of money
The ladder of progress: storage → database → data platform → operating system
What first-principles thinking looks like inside a company building for AGI-scale systems
This is a conversation about the architecture of the future – and the fine line between control and creativity when intelligence becomes infrastructure.
Watch the episode!
*Did you like the episode? You know the drill:*
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*Guest:* Renen Hallak, Founder & CEO, VAST Data
https://www.linkedin.com/in/renenh/
https://www.linkedin.com/company/vast-data/
*📰 Want the transcript and edited version?*
Find it here: https://www.turingpost.com/p/renen
*Chapters:*
*Turing Post* is a newsletter about AI’s past, present, and future – exploring how intelligent systems are built and how they’re changing how we think, work, and live.
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#agenticOS, #enterpriseAI, #metadata, #AIoperatingsystem, exabyte storage, GPUs, production AI
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25:58
The Future of Cancer Diagnosis: Digital Pathology and AI
This episode of Inference is dedicated to Breast Cancer Awareness Month. I’m talking with Akash Parvatikar – AI scientist and product leader in digital pathology and computational biology. He leads PathologyMap™ at HistoWiz, a digital pathology platform that turns whole-slide images into searchable, analyzable data with AI tools – streamlining research and accelerating insights for cancer and precision medicine.
Digital pathology is a very new field, but an important one, considering that the US is facing a large shortage of pathologists.
*What you’ll learn:*
- What “digital pathology” actually is – and why scanning glass slides changes everything
- Where AI already helps today and where it’s still just a very promising technology
- Why explainability, failure modes, and data standards decide clinical adoption
- What is the real bottleneck for using AI in pathology and diagnosis
- How agentic workflows might enter the lab in pieces first
- A practical timeline for digitization, FDA-type approvals, and hospital rollouts
- The human role that stays
*Big idea:* Digitize first. Validate carefully. Then scale tools that clinicians trust. Telepathology expands access. Good AI here speaks the pathologist’s language. Remember – AI that can’t explain itself in clinical terms won’t ship.
Watch the episode!
*Did you like the episode? You know the drill:*
📌 Subscribe for more conversations with the builders shaping real-world AI.
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*Guest:* Akash Parvatikar, AI Scientist, leading PathologyMap at HistoWiz
https://www.linkedin.com/in/akash007/
https://home.histowiz.com/pathology_map/
📰 Want the transcript and edited version?
Subscribe to Turing Post https://www.turingpost.com/subscribe
*Chapters:*
1:22 - The Current State and Future of AI in Cancer Diagnostics
2:27 - Real-World vs. Aspirational AI Breakthroughs in Patient Outcomes
3:36 - Evolution of AI Usage by Clinicians
4:47 - The Technical Challenges of AI in Pathology
7:22 - The Role of Generative AI in Diagnostics
8:42 - The Potential of Agentic AI Workflows in Pathology
9:50 - Key Bottlenecks in AI for Pathology
12:13 - About the Pathology Map Platform
13:49 - Navigating Regulations in AI-Powered Diagnostics
14:40 - The Human Impact of AI in Cancer Diagnostics
16:40 - What is Digital Pathology?
18:21 - Timeline for Mainstream Adoption of AI in Pathology
19:42 - The "Spotify for Precision Medicine"
20:20 - The Future Role of Humans in AI-Assisted Pathology
21:36 - The Economics of AI in Pathology
22:48 - Concerns and Excitations About the Future of AI in Pathology
24:43 - Book Recommendation
Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live.
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#DigitalPathology #AIMedicine #CancerDiagnostics #PrecisionMedicine #BreastCancerAwareness #EarlyDetection #AIForGood
Inference is Turing Post’s way of asking the big questions about AI — and refusing easy answers. Each episode starts with a simple prompt: “When will we…?” – and follows it wherever it leads.Host Ksenia Se sits down with the people shaping the future firsthand: researchers, founders, engineers, and entrepreneurs. The conversations are candid, sharp, and sometimes surprising – less about polished visions, more about the real work happening behind the scenes.It’s called Inference for a reason: opinions are great, but we want to connect the dots – between research breakthroughs, business moves, technical hurdles, and shifting ambitions.If you’re tired of vague futurism and ready for real conversations about what’s coming (and what’s not), this is your feed. Join us – and draw your own inference.
Ireland