The Superintelligence Podcast

Kim Isenberg & Peter Thum
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The people building the AI future — unfiltered. We sit down regularly to talk with founders, researchers, and operators actually doing it. No hype. Real conversations about what’s working, what’s breaking, and what’s coming next. From frontier labs to startups. This is Superintelligence. 

Episodes

  1. 3 days ago

    Can Cloudflare Make AI Companies Pay Creators? — Stephanie Cohen

    AI companies increasingly crawl and use content from the open web, while creators and publishers often receive neither meaningful traffic nor compensation in return. In this episode, Kim Isenberg speaks with Stephanie Cohen, Chief Strategy Officer at Cloudflare, about the company’s plan to reshape that relationship. They discuss why Cloudflare is giving website owners greater control over AI bots, the shift from Pay Per Crawl to Pay Per Use, and whether major AI companies are genuinely prepared to pay for the content their systems use. The conversation also explores Cloudflare’s new rules for distinguishing between search, training, and AI-agent traffic, the difficult relationship between Google Search and AI answers, and whether independent creators and small publishers can realistically benefit from this emerging market. Finally, Stephanie addresses the bigger questions: Should Cloudflare have the power to determine which bots can access the web? What happens if AI answers continue replacing clicks? And can the open web survive without a new economic model for original content? Topics include: How AI crawlers use online contentPay Per Crawl versus Pay Per UseGetting creators and publishers compensatedCloudflare’s new AI-bot controlsSearch, training, and agent trafficGoogle’s role in the changing web economyThe future of independent publishingWhether the open web can survive the AI transition

  2. 30 Jul

    Why AI Agents Break in Production | Alex Salazar (CEO of Arcade) on MCP, Permissions & Enterprise AI

    AI agents are rapidly moving from demos into production, but intelligence isn't the biggest challenge. Trust is. In this episode, Kim Isenberg and Peter Thum sit down with Alex Salazar, co-founder and CEO of Arcade and former VP of Product at Okta, to explore why permissions, governance, and identity are becoming the critical infrastructure for enterprise AI. Alex explains why today's agents often fail in production, why giving an AI agent unrestricted access is dangerous, how MCP differs from traditional APIs, and why guardrails alone aren't enough to secure autonomous systems. The conversation also covers how Fortune 500 companies are deploying AI agents today, why human–agent collaboration is outperforming full autonomy, and what still needs to be solved before AI agents can safely operate across enterprise software. Topics covered: Why AI agents fail in productionAuthentication vs. authorizationMCP vs. APIsEnterprise security and governanceLeast-privilege accessHuman + AI workflowsAgent hallucinations and risk managementFortune 500 AI deploymentsThe future of enterprise AI infrastructureGuest: Alex Salazar CEO & Co-Founder, Arcade Subscribe for more conversations with the builders shaping the future of AI, enterprise software, robotics, infrastructure, and frontier technology.

  3. 23 Jul

    Why AI Factories Need a New Data Layer — with Sven Breuner from VAST Data

    In this episode of Means of Production, Kim Isenberg speaks with Sven Breuner from VAST Data about one of the most underestimated bottlenecks in AI infrastructure: the data layer. As AI factories scale from thousands to tens of thousands of GPUs, performance is no longer just about having faster chips. The real challenge is often whether the underlying storage, metadata, networking, and data architecture can keep up with the demands of modern training and inference workloads. Sven explains why GPUs can sit idle while waiting for data, how AI workloads differ from traditional HPC, what VAST means by an “AI Operating System,” and why enterprises need to rethink their infrastructure before making massive GPU investments. A deep dive into AI infrastructure, data bottlenecks, GPU utilization, HPC, storage architecture, and what it really takes to build scalable AI systems.

  4. 9 Jul

    NVIDIA’s Quantum Computing Strategy with Sam Stanwyck (NVIDIA)

    In this episode, Kim Isenberg sits down with NVIDIA’s Sam Stanwyck at ISC to discuss one of the most misunderstood frontiers in technology: quantum computing. Sam leads NVIDIA’s quantum computing product team, where he focuses on how accelerated computing, GPUs, AI, and software tools like CUDA-Q can help move quantum computing from research toward practical applications. The conversation explores why NVIDIA is not building its own quantum computer, but instead working on the infrastructure around quantum systems: simulation, control, error correction, hybrid CPU/GPU/QPU workflows, and the software stack needed to make quantum computing useful. They also discuss where quantum computing stands today, what real scientific and product value could emerge first, and why areas like chemistry, materials science, energy, optimization, and fundamental research are central to the long-term promise of the field. A grounded conversation about quantum computing beyond the hype — and how NVIDIA sees its role in building the next generation of accelerated computing.

  5. 22 Jun

    Notion’s Co-Founder on the Rise of AI Agent Workspaces

    Description: In this exclusive Superintelligence interview, Kim Isenberg and Peter Thum sit down with Akshay Kothari, Co-Founder of Notion, to discuss how Notion is evolving from a notes and productivity app into an agent-first workspace. The conversation explores how humans, custom code, and AI agents could soon collaborate side by side inside the same operating layer for work. Akshay explains why Notion’s template ecosystem became such a powerful unlock, how AI agents can automate busy work without replacing human judgment, and why the future of work may be less about headcount and more about outcomes. We also discuss Notion Workers, internal AI agents like “Smilers,” self-improving knowledge bases, model optionality, and how specialized expertise could spread across entire organizations through shareable custom agents. A conversation about the next phase of software, the future of productivity, and what work looks like when AI becomes part of the team.

  6. 9 Jun

    Google DeepMind on Local Models, Open Source & the Future of AI Competition

    In this exclusive interview from Google I/O, I speak with Omar Sanseviero and Paige Bailey from Google DeepMind about the rapidly evolving AI landscape. We discuss the rise of local models, the growing importance of open source and open models, the role of developer communities, and how global competition — especially from China — is shaping the next phase of artificial intelligence. A conversation about where AI is heading next: from frontier labs to local inference, from closed systems to open ecosystems, and from model releases to real-world developer adoption.

  7. 28 May

    LTX CEO Zeev Farbman on Open AI Video Models, Local Inference, and the Future of Creative AI

    In this episode, Superintelligence Editor-in-Chief Kim Isenberg speaks with Zeev Farbman, CEO and co-founder of Lightricks/LTX, about the future of AI video, open foundation models, and local creative workflows. Farbman explains why LTX is betting on open weights, local inference, and efficient models optimized for Nvidia GPUs — and why closed API models may be a long-term problem for developers, studios, and enterprises. The conversation also covers Lightricks’ strategic restructuring, competition with Big Tech, the current AI hype cycle, upcoming LTX updates, and the vision that AI models could eventually replace traditional rendering engines. A conversation about open AI infrastructure, multimodality, creative production, and the next stage of generative video AI.

  8. 14 May

    "Your Calendar Is Leaking Revenue" — How SkipUp's AI Agent Kills Scheduling Forever

    Every company has a scheduling problem. Most just don't know how expensive it is. The coordination tax on mid-market companies runs up to $4,500 per employee per year, and up to 70% of inbound leads never even make it to a booked meeting. In this episode, Superintelligence Editor-in-Chief Kim sits down with SkipUp co-founders Dheer and Sasha to unpack why scheduling is still fundamentally broken in 2026 and how their AI agent is replacing the entire back-and-forth. SkipUp doesn't send a booking link and wait. It lives inside your email thread, reads context, proposes times across calendars and time zones, follows up autonomously, and books the meeting. No forms, no friction, no lost deals. We talk about why traditional scheduling tools like Calendly hit a ceiling, how a two-person team built an email-native AI agent in months, the hidden revenue impact of every meeting that doesn't happen, and what work looks like when the coordination layer is fully automated.

  9. 28 Apr

    Beyond LLMs: How Large Quantitative Models Are Curing Diseases and Reinventing Materials

    LLMs predict the next word. LQMs predict the physical world. In this episode, Kim sits down with Nadia Harhen, General Manager of AI Simulation at SandboxAQ — a company that spun out of Google's Moonshot Factory, raised over $950 million, and counts NVIDIA and Google among its investors. Nadia explains what Large Quantitative Models (LQMs) are, how they differ from the LLMs we all know, and why they could be the key to inventing new drugs, designing next-generation batteries, and tackling problems like rare genetic diseases and environmental waste. We talk about her journey from bench scientist at Johnson & Johnson to clearing cutting-edge AI medical devices to leading one of the most ambitious AI simulation teams in the world. We discuss SandboxAQ's work with Aramco on turning waste into valuable materials, why no AI-designed drug has passed Phase II clinical trials yet, and what breakthroughs she expects in the next five years. If you think AI is just about chatbots and text generation, this episode will change your mind. Topics covered: — What are Large Quantitative Models (LQMs) and how do they work? — LQMs vs. LLMs: Why language models can't invent new drugs — SandboxAQ's origin inside Google's Moonshot Factory — Drug discovery, battery chemistry, and catalysis breakthroughs — The case for rare genetic diseases — Why NVIDIA and Google are betting big on this technology Guest: Nadia Harhen — GM of AI Simulation, SandboxAQ Previously: Google, Johnson & Johnson | Harvard Medical School

  10. 14 Apr

    Inside Nemotron: NVIDIA’s Kari Briski on the Architecture Reshaping Enterprise AI

    NVIDIA’s Kari Briski joins Kim Isenberg live from GTC 2026 to break down Nemotron 3 Super — a 120B parameter model with a hybrid Mamba-2/Transformer/MoE architecture, 1M token context, and 5x throughput gains. They go deep on what makes it different, why NVIDIA released the full training recipe, and what the new Nemotron Coalition signals about where enterprise AI is heading.

  • 10 Episodes

About

The people building the AI future — unfiltered. We sit down regularly to talk with founders, researchers, and operators actually doing it. No hype. Real conversations about what’s working, what’s breaking, and what’s coming next. From frontier labs to startups. This is Superintelligence. 

Information

  • Creator
    Kim Isenberg & Peter Thum
  • Years Active
    2026
  • Episodes
    10
  • Rating
    Clean
  • Copyright
    © 2026 Kim Isenberg & Peter Thum
  • Show Website
    The Superintelligence Podcast

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