The Superintelligence Podcast

I sat down with Dr. Wieland Holfelder, Vice President of Engineering at Google and Site Lead for Google’s Engineering Center in Munich, to discuss sovereign AI for Europe—and what security means when AI agents can take action inside organizations.

Can an AI video model genuinely understand how the world works, or is it still just generating increasingly convincing pixels?

In this episode, Kim Isenberg and Peter Thum sit down with Yaron Inger, Co-Founder and CTO of LTX, to examine the technology and business behind LTX-2.5, the company’s latest open-weights video and world model.

We discuss what actually changed from LTX-2.3, how native multi-shot generation maintains consistency across cuts, where the model still fails, and whether its understanding of physics is strong enough to matter for robotics and physical AI.

We also challenge the business behind the technology: How fair are LTX’s speed benchmarks? What can creators realistically expect on affordable hardware? What are enterprise customers paying for when the weights can already be downloaded? And how does LTX build a durable advantage in an open ecosystem?

This is a direct conversation about whether AI video is evolving from a creative tool into real infrastructure for filmmaking, simulation, and robotics.

Learn more about LTX-2.5: https://ltx.io/

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In this episode, Kim Isenberg and Peter Thum sit down with DataCamp co-founder and CEO Jonathan Cornelissen and Chief AI Officer Yusuf Saber to explore how generative AI is transforming education and professional learning.

They discuss the development of DataCamp’s AI Tutor, how the company evaluates open-source models against proprietary frontier systems, and what it takes to turn rapidly improving AI capabilities into a reliable product for millions of learners.

Yusuf also shares lessons from building Optima, the AI-native learning platform acquired by DataCamp, while Jonathan explains how AI is changing DataCamp’s product strategy and the future of online education.

Topics include:

• Open-source models versus frontier models
• Building and evaluating AI tutors
• Personalization in online education
• Reliability, cost, and model selection
• DataCamp’s acquisition of Optima
• How AI is changing the way people learn

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In this episode of Means of Production, Kim Isenberg and Peter Thum sit down with PandaOS co-founders Philipp Türker and Marco Szeidenleder to examine what technological sovereignty really means in the age of AI.

The conversation explores Europe’s dependence on US frontier models, why hosting a foreign model in a European data center does not automatically create sovereignty, and whether European companies need to rethink how they control their data, infrastructure, and access to AI.

Philipp and Marco explain why sovereignty should not mean isolation. Instead, it means maintaining optionality: controlling your data and keys, being able to choose between different models, and switching providers “on a random Tuesday without everything breaking.”

They also discuss the potential of local inference and self-hosting, the role of smaller specialized models, the strengths and weaknesses of Europe’s AI ecosystem, and how PandaOS is building a local AI workspace that connects models, tools, agents, applications, and data while keeping control in the hands of the user.

A conversation about AI sovereignty beyond slogans—and what Europe must do if it wants genuine technological independence.

Topics include:

• Europe’s dependence on US AI companies
• What “sovereign AI” actually means
• Local inference and self-hosted models
• Data ownership, security, and control
• Switching models and providers without disrupting operations
• European AI models and infrastructure
• The AI Act and Europe’s regulatory strategy
• PandaOS and the future of local AI workspaces

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 content
• Pay Per Crawl versus Pay Per Use
• Getting creators and publishers compensated
• Cloudflare’s new AI-bot controls
• Search, training, and agent traffic
• Google’s role in the changing web economy
• The future of independent publishing
• Whether the open web can survive the AI transition

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 production
• Authentication vs. authorization
• MCP vs. APIs
• Enterprise security and governance
• Least-privilege access
• Human + AI workflows
• Agent hallucinations and risk management
• Fortune 500 AI deployments
• The future of enterprise AI infrastructure

Guest: Alex Salazar
CEO & Co-Founder, Arcade

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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.

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.

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.

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.