The MAD Podcast with Matt Turck

• Ex‑DeepMind Researcher Misha Laskin on Enterprise Super‑Intelligence | Reflection AI

  What if your company had a digital brain that never forgot, always knew the answer, and could instantly tap the knowledge of your best engineers, even after they left? Superintelligence can feel like a hand‑wavy pipe‑dream— yet, as Misha Laskin argues, it becomes a tractable engineering problem once you scope it to the enterprise level. Former DeepMind researcher Laskin is betting on an oracle‑like AI that grasps every repo, Jira ticket and hallway aside as deeply as your principal engineer—and he’s building it at Reflection AI.In this wide‑ranging conversation, Misha explains why coding is the fastest on‑ramp to superintelligence, how “organizational” beats “general” when real work is on the line, and why today’s retrieval‑augmented generation (RAG) feels like “exploring a jungle with a flashlight.” He walks us through Asimov, Reflection’s newly unveiled code‑research agent that fuses long‑context search, team‑wide memory and multi‑agent planning so developers spend less time spelunking for context and more time shipping.We also rewind his unlikely journey—from physics prodigy in a Manhattan‑Project desert town, to Berkeley’s AI crucible, to leading RLHF for Google Gemini—before he left big‑lab comfort to chase a sharper vision of enterprise super‑intelligence. Along the way: the four breakthroughs that unlocked modern AI, why capital efficiency still matters in the GPU arms‑race, and how small teams can lure top talent away from nine‑figure offers.If you’re curious about the next phase of AI agents, the future of developer tooling, or the gritty realities of scaling a frontier‑level startup—this episode is your blueprint.Reflection AIWebsite - https://reflection.aiLinkedIn - https://www.linkedin.com/company/reflectionaiMisha LaskinLinkedIn - https://www.linkedin.com/in/mishalaskinX/Twitter - https://x.com/mishalaskinFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:42) Reflection AI: Company Origins and Mission (04:14) Making Superintelligence Concrete (06:04) Superintelligence vs. AGI: Why the Goalposts Moved (07:55) Organizational Superintelligence as an Oracle (12:05) Coding as the Shortcut: Hands, Legs & Brain for AI (16:00) Building the Context Engine (20:55) Capturing Tribal Knowledge in Organizations (26:31) Introducing Asimov: A Deep Code Research Agent (28:44) Team-Wide Memory: Preserving Institutional Knowledge (33:07) Multi-Agent Design for Deep Code Understanding (34:48) Data Retrieval and Integration in Asimov (38:13) Enterprise-Ready: VPC and On-Prem Deployments (39:41) Reinforcement Learning in Asimov's Development (41:04) Misha's Journey: From Physics to AI (42:06) Growing Up in a Science-Driven Desert Town (53:03) Building General Agents at DeepMind (56:57) Founding Reflection AI After DeepMind (58:54) Product-Driven Superintelligence: Why It Matters (01:02:22) The State of Autonomous Coding Agents (01:04:26) What's Next for Reflection AI

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• AI Engineering Revolution: Winners, Chaos & What’s Next | FirstMark

  Welcome to a special FirstMark Deep Dive edition of the MAD Podcast. In this episode, Matt Turck and David Waltcher unpack the explosive impact of generative AI on engineering — hands-down the biggest shift the field has seen in decades. You’ll get a front-row seat to the real numbers and stories behind the AI code revolution, including how companies like Cursor hit a $500M valuation in record time, and why GitHub Copilot now serves 15 million developers.Matt and David break down the six trends that shaped the last 20 years of developer tools, and reveal why coding is the #1 use case for generative AI (hint: it’s all about public data, structure, and ROI). You’ll hear how AI is making engineering teams 30-50% faster, but also why this speed is breaking traditional DevOps, overwhelming QA, and turning top engineers into full-time code reviewers.We get specific: 82% of engineers are already using AI to write code, but this surge is creating new security vulnerabilities, reliability issues, and a total rethink of team roles. You’ll learn why code review and prompt engineering are now the most valuable skills, and why computer science grads are suddenly facing some of the highest unemployment rates.We also draw wild historical parallels—from the Gutenberg Press to the Ford assembly line—to show how every productivity boom creates new problems and entire industries to solve them. Plus: what CTOs need to know about hiring, governance, and architecture in the AI era, and why being “AI native” can make a startup more credible than a 10-year-old giant.Matt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckDavid WaltcherLinkedIn - https://www.linkedin.com/in/davidwaltcherX/Twitter - https://x.com/davidwaltcherFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCap(00:00) Intro & episode setup (01:50) The 6 waves that led to GenAI engineering (04:30) Why coding is such fertile ground for Generative AI (08:25) Break-out dev-tool winners: Cursor, Copilot, Replit, V0 (11:25) Early stats: Teams Are Shipping Code Faster with AI (13:32) Copilots vs Autonomous Agents: The Current Reality (14:14) Lessons from History: Every Tech Boom Creates New Problems (21:53) FirstMark Survey: The Headaches AI Is Creating for Developers (22:53) What’s Now Breaking: Security, CI/CD flakes, QA Overload (29:16) The New CTO Playbook to Adapt to the AI Revolution (33:23) What Happens to Engineering Orgs if Everyone is a Coder? (40:19) Founder opportunities & the dev-tool halo effect (44:24) The Built-in Credibility of AI-Native Startups (46:16) The Irony of Dev Tools As Biggest Winners in the AI Gold Rush (47:43) What’s Next for AI and Engineering?

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• GitHub CEO: The AI Coding Gold Rush, Vibe Coding & Cursor

  AI coding is in full-blown gold-rush mode, and GitHub sits at the epicenter. In this episode, GitHub CEO Thomas Dohmke tells Matt Turck how a $7.5 B acquisition in 2018 became a $2 B ARR rocket ship, and reveals how Copilot was born from a secret AI strategy years before anyone else saw the opportunity.We dig into the dizzying pace of AI innovation: why developer tools are suddenly the fastest-growing startups in history, how GitHub’s multi-model approach (OpenAI, Anthropic Claude 4, Gemini 2.5, and even local LLMs) gives you more choice and speed, and why fine-tuning models might be overrated. Thomas explains how Copilot keeps you in the “magic flow state,” how even middle schoolers are using it to hack Minecraft. The conversation then zooms out to the competitive battlefield: Cursor’s $10 B valuation, Mistral’s new code model, and a wave of AI-native IDE forks vying for developer mind-share. We discuss why 2025’s “coding agents” could soon handle 90 % of the world’s code, the survival of SaaS and why the future of coding is about managing agents, not just writing code.GitHubWebsite - https://github.com/X/Twitter - https://x.com/githubThomas DohmkeLinkedIn - https://www.linkedin.com/in/ashtomX/Twitter - https://twitter.com/ashtomFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:50) Why AI Coding Is Ground Zero for Generative AI (02:40) The $7.5B GitHub Acquisition: Microsoft’s Strategic Play (06:21) GitHub’s Role in the Azure Cloud Ecosystem (10:25) How GitHub Copilot Beat Everyone to Market (16:09) Copilot & VS Code Explained for Non-Developers (21:02) GitHub Models: Multi-Model Choice and What It Means (25:31) The Reality of Fine-Tuning AI Models for Enterprise (29:13) The Dizzying Pace and Political Economy of AI Coding Tools (36:58) Competing and Partnering: Microsoft’s Unique AI Strategy (41:29) Does Microsoft Limit Copilot’s AI-Native Potential? (46:44) The Bull and Bear Case for AI-Native IDEs Like Cursor (52:09) Agent Mode: The Next Step for AI-Powered Coding (01:00:10) How AI Coding Will Change SaaS and Developer Skills

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• Inside the Paper That Changed AI Forever - Cohere CEO Aidan Gomez on 2025 Agents

  What really happened inside Google Brain when the “Attention is All You Need” paper was born? In this episode, Aidan Gomez — one of the eight co-authors of the Transformers paper and now CEO of Cohere — reveals the behind-the-scenes story of how a cold email and a lucky administrative mistake landed him at the center of the AI revolution.Aidan shares how a group of researchers, given total academic freedom, accidentally stumbled into one of the most important breakthroughs in AI history — and why the architecture they created still powers everything from ChatGPT to Google Search today.We dig into why synthetic data is now the secret sauce behind the world’s best AI models, and how Cohere is using it to build enterprise AI that’s more secure, private, and customizable than anything else on the market. Aidan explains why he’s not interested in “building God” or chasing AGI hype, and why he believes the real impact of AI will be in making work more productive, not replacing humans.You’ll also get a candid look at the realities of building an AI company for the enterprise: from deploying models on-prem and air-gapped for banks and telecoms, to the surprising demand for multimodal and multilingual AI in Japan and Korea, to the practical challenges of helping customers identify and execute on hundreds of use cases.CohereWebsite - https://cohere.comX/Twitter - https://x.com/cohereAidan GomezLinkedIn - https://ca.linkedin.com/in/aidangomezX/Twitter - https://x.com/aidangomezFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (02:00) The Story Behind the Transformers Paper (03:09) How a Cold Email Landed Aidan at Google Brain (10:39) The Initial Reception to the Transformers Breakthrough (11:13) Google’s Response to the Transformer Architecture (12:16) The Staying Power of Transformers in AI (13:55) Emerging Alternatives to Transformer Architectures (15:45) The Significance of Reasoning in Modern AI (18:09) The Untapped Potential of Reasoning Models (24:04) Aidan’s Path After the Transformers Paper and the Founding of Cohere (25:16) Choosing Enterprise AI Over AGI Labs (26:55) Aidan’s Perspective on AGI and Superintelligence (28:37) The Trajectory Toward Human-Level AI (30:58) Transitioning from Researcher to CEO (33:27) Cohere’s Product and Platform Architecture (37:16) The Role of Synthetic Data in AI (39:32) Custom vs. General AI Models at Cohere (42:23) The AYA Models and Cohere Labs Explained (44:11) Enterprise Demand for Multimodal AI (49:20) On-Prem vs. Cloud (50:31) Cohere’s North Platform (54:25) How Enterprises Identify and Implement AI Use Cases (57:49) The Competitive Edge of Early AI Adoption (01:00:08) Aidan’s Concerns About AI and Society (01:01:30) Cohere’s Vision for Success in the Next 3–5 Years

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• AI That Ends Busy Work — Hebbia CEO on “Agent Employees”

  What if the smartest people in finance and law never had to do “stupid tasks” again? In this episode, we sit down with George Sivulka, founder of Hebbia, the AI company quietly powering 50% of the world’s largest asset managers and some of the fastest-growing law firms. George reveals how Hebbia’s Matrix platform is automating the equivalent of 50,000 years of human reading — every year — and why the future of work is hybrid teams of humans and AI “agent employees.” You’ll get the inside story on how Hebbia went from a stealth project at Stanford to a multinational company trusted by the Department of Defense, and why their spreadsheet-inspired interface is leaving chatbots in the dust. George breaks down the technical secrets behind Hebbia’s ISD architecture (and why they killed RAG), how they process billions of pages with near-zero hallucinations, and what it really takes to sell AI into the world’s most regulated industries.We also dive into the future of organizational design, why generalization beats specialization in AI, and how “prompting is the new management skill.” Plus: the real story behind AI hallucinations, the myth of job loss, and why naiveté might be the ultimate founder superpower.HebbiaWebsite - https://www.hebbia.comTwitter - https://x.com/HebbiaAIGeorge SivulkaLinkedIn - https://www.linkedin.com/in/sivulkaTwitter - https://x.com/gsivulkaFIRSTMARKWebsite - https://firstmark.comTwitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/Twitter - https://twitter.com/mattturck(00:00) Intro (01:46) What is Hebbia (02:49) Evolving Hebbia’s mission (04:45) The founding story and Stanford's inspiration (09:45) The rise of agent employees and AI in organizations (12:36) The future of AI-powered work (15:17) AI research trends (19:49) Inside Matrix: Hebbia’s flagship AI platform (24:02) Why Hebbia isn’t just another chatbot (28:27) Moving beyond RAG: Hebbia’s unique architecture (34:10) Tackling hallucinations in high-stakes AI (35:59) Research culture and avoiding industry groupthink (39:40) Innovating go-to-market and enterprise sales (41:57) Real-world value: Cost savings and new revenue (43:49) How AI is changing junior roles (45:55) Leadership and perspective as a young founder (47:16) Hebbia’s roadmap: Success in the next 3 years

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