Air Street Press

AI is the ultimate force multiplier on technological progress in our digital, data-driven world. Our mission at Air Street Capital has always been to back the most ambitious teams building breakthrough AI products that would have seemed like magic when I started investing over a decade ago. Key to these inflection points is the infrastructure that enables them: NVIDIA computing systems. I have long argued that NVIDIA is the defining company of the AI era, powering breakthroughs in science, industry, and national strategy. This is why I’m excited to share that we are deepening that story together: Air Street Capital is partnering with NVIDIA as part of its new £2B commitment to the UK AI ecosystem.

Clinical trials are the bottleneck of the pharmaceutical industry. They’re slow, expensive, and often fail, not only because the science frequently doesn’t pan out, but because the ops don’t either. That’s why we’re leading the $3.8M Seed round for Delfa. The team is building an AI-native operating system for clinical trials, starting with patient enrolment, the most broken part of the process.

We’re excited to announce Air Street Capital’s lead investment in Studio Atelico’s $5M Seed round, alongside friends including Chris Ré (Stanford), Thomas Wolf (Hugging Face), and Alex Ratner (Snorkel), to back a team on a mission to redefine the role of generative AI in games.

Welcome to the latest issue of your guide to AI, an editorialized newsletter covering the key developments in AI policy, research, industry, and start-ups over the last month.

Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology and human health. CRISPR-based gene editors derived from microorganisms, although powerful, often show notable functional tradeoffs when ported into non-native environments, such as human cells1. Artificial-intelligence-enabled design provides a powerful alternative with the potential to bypass evolutionary constraints and generate editors with optimal properties. Here, using large language models2 trained on biological diversity at scale, we demonstrate successful precision editing of the human genome with a programmable gene editor designed with artificial intelligence. To achieve this goal, we curated a dataset of more than 1 million CRISPR operons through systematic mining of 26 terabases of assembled genomes and metagenomes. We demonstrate the capacity of our models by generating 4.8× the number of protein clusters across CRISPR–Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. Several of the generated gene editors show comparable or improved activity and specificity relative to SpCas9, the prototypical gene editing effector, while being 400 mutations away in sequence. Finally, we demonstrate that an artificial-intelligence-generated gene editor, denoted as OpenCRISPR-1, exhibits compatibility with base editing. We release OpenCRISPR-1 to facilitate broad, ethical use across research and commercial applications.

No one is coming to save Europe. We must build new primes here and now.

At this year’s RAAIS, Max Jaderberg of Isomorphic Labs delivered a talk that felt like the spiritual sequel to AlphaGo, only this time the board isn’t 19×19, it’s the human body. The stakes? The future of drug discovery and human health.Isomorphic Labs, the biotech spinout from DeepMind, has declared a radical mission: solving disease. It's not a metaphor. It’s a systems-level wager that the same kinds of models that cracked Go can crack biology. Not only by digitizing wet labs, but by encoding the dynamics of biomolecules into machine-learnable substrates. If AlphaGo marked the start of AI systems inventing strategies never before seen in human play, Isomorphic wants AI to invent medicines we’d never stumble across in the lab.This is a moonshot, a bet that biology is information science, and machine learning is our best bet at learning its language.

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.