The MAD Podcast with Matt Turck

Voice used to be AI’s forgotten modality — awkward, slow, and fragile. Now it’s everywhere. In this reference episode on all things Voice AI, Matt Turck sits down with Neil Zeghidour, a top AI researcher and CEO of Gradium AI (ex-DeepMind/Google, Meta, Kyutai), to cover voice agents, speech-to-speech models, full-duplex conversation, on-device voice, and voice cloning.
We unpack what actually changed under the hood — why voice is finally starting to feel natural, and why it may become the default interface for a new generation of AI assistants and devices.
Neil breaks down today’s dominant “cascaded” voice stack — speech recognition into a text model, then text-to-speech back out — and why it’s popular: it’s modular and easy to customize. But he argues it has two key downsides: chaining models adds latency, and forcing everything through text strips out paralinguistic signals like tone, stress, and emotion. The next wave, he suggests, is combining cascade-like flexibility with the more natural feel of speech-to-speech and full-duplex conversation.
We go deep on full-duplex interaction (ending awkward turn-taking), the hardest unsolved problems (noisy real-world environments and multi-speaker chaos), and the realities of deploying voice at scale — including why models must be compact and when on-device voice is the right approach.
Finally, we tackle voice cloning: where it’s genuinely useful, what it means for deepfakes and privacy, and why watermarking isn’t a silver bullet.
If you care about voice agents, real-time AI, and the next generation of human-computer interaction, this is the episode to bookmark.

Neil Zeghidour
LinkedIn - https://www.linkedin.com/in/neil-zeghidour-a838aaa7/
X/Twitter - https://x.com/neilzegh

Gradium
Website - https://gradium.ai
X/Twitter - https://x.com/GradiumAI

Matt Turck (Managing Director)
Blog - https://mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck

FirstMark
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap

(00:00) Intro
(01:21) Voice AI’s big moment — and why we’re still early
(03:34) Why voice lagged behind text/image/video
(06:06) The convergence era: transformers for every modality
(07:40) Beyond Her: always-on assistants, wake words, voice-first devices
(11:01) Voice vs text: where voice fits (even for coding)
(12:56) Neil’s origin story: from finance to machine learning
(18:35) Neural codecs (SoundStream): compression as the unlock
(22:30) Kyutai: open research, small elite teams, moving fast
(31:32) Why big labs haven’t “won” voice AI4
(34:01) On-device voice: where it works, why compact models matter
(46:37) The last mile: real-world robustness, pronunciation, uptime
(41:35) Benchmarking voice: why metrics fail, how they actually test
(47:03) Cascades vs speech-to-speech: trade-offs + what’s next
(54:05) Hardest frontier: noisy rooms, factories, multi-speaker chaos
(1:00:50) New languages + dialects: what transfers, what doesn’t
(1:02:54 Hardware & compute: why voice isn’t a 10,000-GPU game
(1:07:27) What data do you need to train voice models?
(1:09:02) Deepfakes + privacy: why watermarking isn’t a solution
(1:12:30) Voice + vision: multimodality, screen awareness, video+audio
(1:14:43) Voice cloning vs voice design: where the market goes
(1:16:32) Paris/Europe AI: talent density, underdog energy, what’s next

While Silicon Valley obsesses over AGI, Timothée Lacroix and the team at Mistral AI are quietly building the industrial and sovereign infrastructure of the future. In his first-ever appearance on a US podcast, the Mistral AI Co-Founder & CTO reveals how the company has evolved from an open-source research lab into a full-stack sovereign AI power—backed by ASML, running on their own massive supercomputing clusters, and deployed in nation-state defense clouds to break the dependency on US hyperscalers.

Timothée offers a refreshing, engineer-first perspective on why the current AI hype cycle is misleading. He explains why "Sovereign AI" is not just a geopolitical buzzword but a necessity for any enterprise that wants to own its intelligence rather than rent it. He also provides a contrarian reality check on the industry's obsession with autonomous agents, arguing that "trust" matters more than autonomy and explaining why he prefers building robust "workflows" over unpredictable agents.

We also dive deep into the technical reality of competing with the US giants. Timothée breaks down the architecture of the newly released Mistral 3, the "dense vs. MoE" debate, and the launch of Mistral Compute—their own infrastructure designed to handle the physics of modern AI scaling. This is a conversation about the plumbing, the 18,000-GPU clusters, and the hard engineering required to turn AI from a magic trick into a global industrial asset.

Timothée Lacroix
LinkedIn - https://www.linkedin.com/in/timothee-lacroix-59517977/
Google Scholar - https://scholar.google.com.do/citations?user=tZGS6dIAAAAJ&hl=en&oi=ao

Mistral AI
Website - https://mistral.ai
X/Twitter - https://x.com/MistralAI

Matt Turck (Managing Director)
Blog - https://mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck

FirstMark
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap

(00:00) — Cold Open
(01:27) — Mistral vs. The World: From Research Lab to Sovereign Power
(03:48) — Inside Mistral Compute: Building an 18,000 GPU Cluster
(08:42) — The Trillion-Dollar Question: Competing Without a Big Tech Parent
(10:37) — The Reality of Enterprise AI: Escaping "POC Purgatory"
(15:06) — Why Mistral Hires Forward Deployed Engineers (FDEs)
(16:57) — The Contrarian Take: Why "Agents" are just "Workflows"
(19:35) — Trust > Autonomy: The Truth About Agent Reliability
(21:26) — The Missing Stack: Governance and Versioning for AI
(26:24) — When Will AI Actually Work? (The 2026 Timeline)
(30:33) — Beyond Chat: The "Banger" Sovereign Use Cases
(35:46) — Mistral 3 Architecture: Mixture of Experts vs. Dense
(43:12) — Synthetic Data & The Post-Training Bottleneck
(45:12) — Reasoning Models: Why "Thinking" is Just Tool Use
(46:22) — Launching DevStral 2 and the Vibe CLI
(50:49) — Engineering Lessons: How to Build Frontier AI Efficiently
(56:08) — Timothée’s View on AGI & The Future of Intelligence

Dylan Patel (SemiAnalysis) joins Matt Turck for a deep dive into the AI chip wars — why NVIDIA is shifting from a “one chip can do it all” worldview to a portfolio strategy, how inference is getting specialized, and what that means for CUDA, AMD, and the next wave of specialized silicon startups.

Then we take the fun tangents: why China is effectively “semiconductor pilled,” how provinces push domestic chips, what Huawei means as a long-term threat vector, and why so much “AI is killing the grid / AI is drinking all the water” discourse misses the point.

We also tackle the big macro question: capex bubble or inevitable buildout? Dylan’s view is that the entire answer hinges on one variable—continued model progress—and we unpack the second-order effects across data centers, power, and the circular-looking financings (CoreWeave/Oracle/backstops).

Dylan Patel
LinkedIn - https://www.linkedin.com/in/dylanpatelsa/
X/Twitter - https://x.com/dylan522p

SemiAnalysis
Website - https://semianalysis.com
X/Twitter - https://x.com/SemiAnalysis_

Matt Turck (Managing Director)
Blog - https://mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck

FirstMark
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap

(00:00) - Intro
(01:16) - Nvidia acquires Groq: A pivot to specialization
(07:09) - Why AI models might need "wide" compute, not just fast
(10:06) - Is the CUDA moat dead? (Open source vs. Nvidia)
(17:49) - The startup landscape: Etched, Cerebras, and 1% odds
(22:51) - Geopolitics: China's "semiconductor-pilled" culture
(35:46) - Huawei's vertical integration is terrifying
(39:28) - The $100B AI revenue reality check
(41:12) - US Onshoring: Why total self-sufficiency is a fantasy
(44:55) - Can the US actually build fabs? (The delay problem)
(48:33) - The CapEx Bubble: Is $500B spending irrational?
(54:53) - Energy Crisis: Why gas turbines will power AI, not nuclear
(57:06) - The "AI uses all the water" myth (Hamburger comparison)
(1:03:40) - Circular Debt? Debunking the Nvidia-CoreWeave risk
(1:07:24) - Claude Code & the software singularity
(1:10:23) - The death of the Junior Analyst role
(1:11:14) - Model predictions: Opus 4.5 and the RL gap
(1:14:37) - San Francisco Lore: Roommates (Dwarkesh Patel & Sholto Douglas)

Sebastian Raschka joins the MAD Podcast for a deep, educational tour of what actually changed in LLMs in 2025 — and what matters heading into 2026.

We start with the big architecture question: are transformers still the winning design, and what should we make of world models, small “recursive” reasoning models and text diffusion approaches? Then we get into the real story of the last 12 months: post-training and reasoning. Sebastian breaks down RLVR (reinforcement learning with verifiable rewards) and GRPO, why they pair so well, what makes them cheaper to scale than classic RLHF, and how they “unlock” reasoning already latent in base models.

We also cover why “benchmaxxing” is warping evaluation, why Sebastian increasingly trusts real usage over benchmark scores, and why inference-time scaling and tool use may be the underappreciated drivers of progress. Finally, we zoom out: where moats live now (hint: private data), why more large companies may train models in-house, and why continual learning is still so hard.

If you want the 2025–2026 LLM landscape explained like a masterclass — this is it.

Sources:
The State Of LLMs 2025: Progress, Problems, and Predictions - https://x.com/rasbt/status/2006015301717028989?s=20
The Big LLM Architecture Comparison - https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparison

Sebastian Raschka
Website - https://sebastianraschka.com
Blog - https://magazine.sebastianraschka.com
LinkedIn - https://www.linkedin.com/in/sebastianraschka/
X/Twitter - https://x.com/rasbt

FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)
Blog - https://mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck

(00:00) - Intro
(01:05) - Are the days of Transformers numbered?
(14:05) - World models: what they are and why people care
(06:01) - Small “recursive” reasoning models (ARC, iterative refinement)
(09:45) - What is a diffusion model (for text)?
(13:24) - Are we seeing real architecture breakthroughs — or just polishing?
(14:04) - MoE + “efficiency tweaks” that actually move the needle
(17:26) - “Pre-training isn’t dead… it’s just boring”
(18:03) - 2025’s headline shift: RLVR + GRPO (post-training for reasoning)
(20:58) - Why RLHF is expensive (reward model + value model)
(21:43) - Why GRPO makes RLVR cheaper and more scalable
(24:54) - Process Reward Models (PRMs): why grading the steps is hard
(28:20) - Can RLVR expand beyond math & coding?
(30:27) - Why RL feels “finicky” at scale
(32:34) - The practical “tips & tricks” that make GRPO more stable
(35:29) - The meta-lesson of 2025: progress = lots of small improvements
(38:41) - “Benchmaxxing”: why benchmarks are getting less trustworthy
(43:10) - The other big lever: inference-time scaling
(47:36) - Tool use: reducing hallucinations by calling external tools
(49:57) - The “private data edge” + in-house model training
(55:14) - Continual learning: why it’s hard (and why it’s not 2026)
(59:28) - How Sebastian works: reading, coding, learning “from scratch”
(01:04:55) - LLM burnout + how he uses models (without replacing himself)

Will AGI happen soon - or are we running into a wall?

In this episode, I’m joined by Tim Dettmers (Assistant Professor at CMU; Research Scientist at the Allen Institute for AI) and Dan Fu (Assistant Professor at UC San Diego; VP of Kernels at Together AI) to unpack two opposing frameworks from their essays: “Why AGI Will Not Happen” versus “Yes, AGI Will Happen.” Tim argues progress is constrained by physical realities like memory movement and the von Neumann bottleneck; Dan argues we’re still leaving massive performance on the table through utilization, kernels, and systems—and that today’s models are lagging indicators of the newest hardware and clusters.

Then we get practical: agents and the “software singularity.” Dan says agents have already crossed a threshold even for “final boss” work like writing GPU kernels. Tim’s message is blunt: use agents or be left behind. Both emphasize that the leverage comes from how you use them—Dan compares it to managing interns: clear context, task decomposition, and domain judgment, not blind trust.

We close with what to watch in 2026: hardware diversification, the shift toward efficient, specialized small models, and architecture evolution beyond classic Transformers—including state-space approaches already showing up in real systems.

Sources:
Why AGI Will Not Happen - https://timdettmers.com/2025/12/10/why-agi-will-not-happen/
Use Agents or Be Left Behind? A Personal Guide to Automating Your Own Work - https://timdettmers.com/2026/01/13/use-agents-or-be-left-behind/
Yes, AGI Can Happen – A Computational Perspective - https://danfu.org/notes/agi/

The Allen Institute for Artificial Intelligence
Website - https://allenai.org
X/Twitter - https://x.com/allen_ai

Together AI
Website - https://www.together.ai
X/Twitter - https://x.com/togethercompute

Tim Dettmers
Blog - https://timdettmers.com
LinkedIn - https://www.linkedin.com/in/timdettmers/
X/Twitter - https://x.com/Tim_Dettmers

Dan Fu
Blog - https://danfu.org
LinkedIn - https://www.linkedin.com/in/danfu09/
X/Twitter - https://x.com/realDanFu

FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)
Blog - https://mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck

(00:00) - Intro
(01:06) – Two essays, two frameworks on AGI
(01:34) – Tim’s background: quantization, QLoRA, efficient deep learning
(02:25) – Dan’s background: FlashAttention, kernels, alternative architectures
(03:38) – Defining AGI: what does it mean in practice?
(08:20) – Tim’s case: computation is physical, diminishing returns, memory movement
(11:29) – “GPUs won’t improve meaningfully”: the core claim and why
(16:16) – Dan’s response: utilization headroom (MFU) + “models are lagging indicators”
(22:50) – Pre-training vs post-training (and why product feedback matters)
(25:30) – Convergence: usefulness + diffusion (where impact actually comes from)
(29:50) – Multi-hardware future: NVIDIA, AMD, TPUs, Cerebras, inference chips
(32:16) – Agents: did the “switch flip” yet?
(33:19) – Dan: agents crossed the threshold (kernels as the “final boss”)
(34:51) – Tim: “use agents or be left behind” + beyond coding
(36:58) – “90% of code and text should be written by agents” (how to do it responsibly)
(39:11) – Practical automation for non-coders: what to build and how to start
(43:52) – Dan: managing agents like junior teammates (tools, guardrails, leverage)
(48:14) – Education and training: learning in an agent world
(52:44) – What Tim is building next (open-source coding agent; private repo specialization)
(54:44) – What Dan is building next (inference efficiency, cost, performance)
(55:58) – Mega-kernels + Together Atlas (speculative decoding + adaptive speedups)
(58:19) – Predictions for 2026: small models, open-source, hardware, modalities
(1:02:02) – Beyond transformers: state-space and architecture diversity
(1:03:34) – Wrap