Air Street Press

At this year’s RAAIS, I joined Adam Satariano of the New York Times and Dimitrios Kottas, founder of Delian Alliance Industries and formerly Apple's Special Projects Group, for a candid conversation about one of the most taboo, yet increasingly urgent topics in tech: AI and defense.The timing couldn’t be more acute. The war in Ukraine has shown how low-cost drones and software can upend conventional military doctrine. More recently, the escalation of hostilities with Iran has further underscored the volatility of global security and the growing relevance of digital warfare and drone-enabled asymmetric tactics. In the U.S., a second Trump administration has accelerated European defense policy, with Germany, Poland, France, and others pushing military spending to levels unseen since the Cold War. The European Commission plans to deploy €800 billion toward defense by 2029.So: where does that money go? For once, not just to the legacy primes. There’s an opening for new entrants, namely startups building fast, iterating with operational focus, and pushing beyond the traditional defense hardware playbook.

At RAAIS 2025, Andreas Blattmann, co-founder of Black Forest Labs, shared a deep dive into FLUX.1 Kontext, the startup’s newly released generative model for controllable image and video generation. The talk gave a rare look under the hood of one of the most technically ambitious and commercially relevant AI-first companies to emerge in Europe.Andreas is no stranger to the field. He was among the original researchers behind Stable Diffusion and later worked with Stability AI before striking out on his own to build the model he always wanted: a unified, fast, and open infrastructure for pixel generation, whether from text, images, or combinations of the two.

What you need to know in AI across geopolitics, big tech, hardware, research, models, datasets, financings and exits over the last 4 weeks. 

Two years ago, I met Mati Staniszewski in a time before AI voices were good enough to fool anyone. ElevenLabs hadn’t launched yet, but their vision was clear: voice was broken, and they were going to fix it.Today, ElevenLabs is one of the fastest-moving companies in the agentic voice space. Their platform powers narration for authors, dubbing for studios, real-time agents for enterprises, and everything in between. At the 9th Research and Applied AI Summit in London, Mati and I recounted the story of how they got here, and where they're going next, and lessons learned for any founder building in AI.

If 2016 was the year AI shocked the world by mastering Go, 2025 is shaping up to be the year they learn to innovate. This is the thesis of Ed Hughes, long-time researcher at Google DeepMind and one of the few voices charting a credible path toward AI systems capable of doing science.In his closing talk at RAAIS this year, Hughes argued that we’re entering a new phase in the evolution of artificial intelligence: one where open-endedness becomes the central organizing principle. Not just solving problems, but defining them. Not just predicting the next token, but surfacing previously unknown unknowns. If he’s right, the next generation of AI systems won’t just be tools, they’ll be participants in the scientific process itself.

At this year's RAAIS, we convened a panel with Lionel Laurent (Bloomberg), Chris Yiu (Meta), and Benedict Macon-Cooney (Tony Blair Institute) to dissect the uneasy relationship between AI and the state. This conversation, grounded in experience across Whitehall, Big Tech, and Brussels, pulled no punches on where things stand and what still needs to change.

When poolside co-founder and CTO Eiso Kant stepped on stage at RAAIS 2025, he didn’t deliver the kind of slick, pre-baked keynote you might expect from the co-founder of one of the most ambitious AI companies in the world. Instead, Kant opted for a more experimental approach: a candid walkthrough of poolside’s beliefs, software systems, and bets on how to build AGI. The result was one of the most revealing sessions of the summit.Kant’s message was clear: if you want to compete at the frontier of model capabilities, the secret isn’t just scale, it’s iteration speed. And to iterate fast, you need infrastructure that matches the ambition of your research. This is the thesis behind poolside’s “model factory,” a production-grade system for turning ideas into results at industrial speed.

Discover the latest trends in AI compute usage with updated insights from the State of AI Compute Index as of June 2025. Explore the dominance of NVIDIA and the rising influence of AMD and startups in the chip market. Learn about the most and least commonly used chips across various research topics, showcasing the evolving landscape of AI technology. Get a glimpse into how these shifts impact research practices and the future of AI development.

The discussion dives into the pitfalls of the AI roll-up strategy, questioning its viability in transforming low-margin service businesses into software powerhouses. Experts critique the unrealistic expectations placed on these service models and stress the importance of understanding their core dynamics. The conversation further explores how private equity can aid in effectively integrating AI into professional services, but warns against traditional approaches that overlook ownership of the software necessary for genuine transformation.

At this year’s RAAIS, Paige Bailey of Google DeepMind delivered her talk on AI research to production with interactive demos and a clear thesis: “I’m going to show you how to automate significant parts of your work.” Generative models are now a co-author, a debugger, a lab assistant, a video editor. And increasingly, models are doing the work behind the curtain while you coach and edit. Here’s her talk and a narrative of the key insights: