Latent Space: The AI Engineer Podcast

Alex Lupsasca, a theoretical physicist at Vanderbilt and OpenAI fellow, dives into AI at the edge of science. He talks about GPT tackling quantum field theory, black holes, gluons, and gravitons. There’s a wild story about ChatGPT cracking a year-old amplitudes problem before a plane landed. They also explore human steering, changing physics training, and whether models can pose the next big question.

Qasar Younis, Applied Intuition CEO and former Y Combinator COO, joins Peter Ludwig, the company’s CTO and autonomy systems engineer. They get into physical AI for cars, trucks, mining and defense machines. Expect talk on vehicle operating systems, simulation and RL, AI coding tools, hardware limits, safety metrics, public trust, and why deployment beats flashy demos.

Shawn “swyx” Wang, AI engineer, writer, and AI community builder, returns for a fast-moving tour of what matters now. They dive into conference signals, agent infrastructure, vertical vs. horizontal startups, and the rise of the agent lab playbook. Plus: coding wars, memory systems, open models, custom chips, security risks, and why small startups may still have room to win.

Mikhail Parakhin, Shopify CTO and former Microsoft exec behind Bing, Edge, and Windows, dives into Shopify’s AI surge. He unpacks near-universal internal AI adoption, why code review and deployment now matter more than code generation, and how Tangle, Tangent, and SimGym power reproducible experiments, auto-research, customer simulation, faster search, and smarter catalog intelligence.

Today, we explain this piece of “clickbait” from our guest!TL;DR: 95% of cancer treatments fail to pass clinical trials, but it may be a matching problem — if we better understood what patients have which tumors which will respond to which treatments, success rates improve dramatically and millions of lives can be saved — with the treatments we ALREADY have.See our full episode dropping today:Why Big Pharma is licensing AI ModelsTolstoy famously wrote, ‘All healthy cells are alike; each cancer cell is unhappy in its own way.’ Or something like that. Cancer might be the most misunderstood disease out there. It’s not one disease, it’s a family of diseases. Hundreds, maybe thousands, of unique diseases each with its own underlying biology. With this lens, saying you’ll “cure cancer” is like saying you’ll solve legos.We keep hearing AI will cure cancer, but sadly it may not be so easy. Today’s guests — Ron Alfa and Daniel Bear from Noetik — thinks they can use AI to break through a core bottleneck in the treatment development process.GSK recently signed a $50M deal for their technology that also includes an (undisclosed) long-term licensing deals for Noetik’s models like the recently announced TARIO-2, an autoregressive transformer trained on one of the largest sets of tumor spatial transcriptomics datasets in the world. Whole-plex spatial transcriptomics is the richest way to read a tumor, and approximately ~0% of cancer patients going through standard care ever get one — and TARIO-2 can now predict an ~19,000-gene spatial map from the H&E assay every patient already has. Most big AI plays in BioTech have focused on discovery, and usually result in an in-house development effort (meaning tools companies usually become drug companies). This deal stands out in that it is a software licensing deal, and represents a commitment to a platform rather than a drug. With attention on other software tools for drug development (see the Boltz episode and Isomorphic for example), it is starting to look like the appetite of Pharma for biotech tools has finally started to grow. Why the sudden interest?Cancer is hardBiology is hard, cancer is harder. But despite this, we’ve made incredible progress. So many cancers that would have been death sentences twenty years ago are routinely survivable. It used to be our main strategy was just chemotherapy — poison you and hope the tumor dies before you do. Now, there are many treatments that actually kill a tumor and leave the rest of you intact! Immune checkpoint inhibitors like Keytruda and Opdivo target the defenses of dozens of tumor types. CAR-T therapy adds modified T-cells to your blood that can target B-cell malignancies very accurately. Antibody Drug Conjugates such as Trastuzumab combine a drug with an antibody, allowing it to target very specific (cancer) cells. We truly live in marvelous times.With that said, we still have a long way to go. For every type of cancer with a miracle treatment, we have many more that are still death sentences. The world spends $20-30 billion a year trying to cure cancers, with hundreds of clinical trials yearly.Yet, progress is slow with a 95% failure rate in clinical trials.The lab doesn’t translate to the clinicAre we leaving something on the table? Enter Noetik and Ron Alfa. Ron’s core thesis is that many of these “failed” treatments actually work! But we’re not looking at the right patients with the right tumors. If only we had a way to really understand the unique types of cancer biologies and which patients will respond to which treatments, we might be able to show a much higher success rate. Millions of lives (and billions of dollars) may ride on this.The Hard part: Blind Faith in Data CollectionRon and Noetik had the conviction to spend almost two years just collecting data. Lots, and lots, and lots, of data. Noetik has acquired thousands of actual human tumors, and collects a large multimodal dataset of hundreds of millions of images that allows them to create a detailed map of the cell makeup in the local environment. These are real human tumors, not frankenstein mouse models or immortal cell lines.This data is then fed into a massive self-supervised model, creating a “virtual cell”. This model has a deep understanding of cancer biology — Noetik has worked carefully to show it can distinguish different types of tumors. Maybe even tumors we didn’t identify as distinct previously! More recently they figured out how to scale up their model and data, and see no limit in their scaling laws!Noetik’s models can simulate how a patient will respond to experimental treatments. They are working with partners to test promising drugs that were demonstrated to be safe, but not effective. If these models work as hoped, Noetik will bring new cancer treatments to patients without developing a new drug! Their models will also guide the discovery process towards drugs that are more likely to make it through clinical trials. You can imagine why this is so attractive to GSK.We’ll see…Ron and Dan make pretty persuasive arguments that their models will truly assist in cohort selection in useful ways and this seems valuable. And we think it’s pretty clear that* Translation from lab to clinic is the biggest bottleneck for drug development.* Better cohort selection using biomarkers is likely to improve translation from lab to clinic.Noetik has already had some success here. We’ll see if they’re able to translate that into a reliable advantage.Stepping back a bit from the technology, curing cancer is a pretty unambiguously positive application of AI. It is also a very hard problem to solve. Our guess is that most people have been impacted by cancer or will be at some point soon. And we hope that learning about the amazing work that companies like Noetik are doing will inspire a generation of AI engineers to work on the hardest and most exciting problems that society faces.Full Video Pod: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Sarah Sachs, Notion’s AI engineering lead, and Simon Last, Notion co-founder, unpack the long road to custom agents. They talk about five rebuilds, evals, low-ego teams, and why collaboration beats simple AI wrappers. Plus: software factories, meeting notes as data, agent setup in chat, and the MCP vs CLI debate.

Ryan Lopopolo, an OpenAI engineer building enterprise-scale agent systems, talks about harness engineering, running a 1M+ LOC codebase with no human-written or reviewed code, and spending billions of tokens a day. They get into Symphony, multi-agent orchestration, one-minute build loops, agent-led code review, and why teams should optimize for agent legibility over human coding habits.

Marc Andreessen, Mosaic co-creator, Netscape founder, and veteran investor, looks back at computing shifts and why AI feels fundamentally different now. He gets into scaling laws, edge inference, open source strategy, Pi and OpenClaw, crypto for AI payments, proof of human, and how AI could upend bureaucracy while institutions slow adoption.

Fan-yun Sun, Moonlake AI co-founder focused on interactive world models, joins Chris Manning, Stanford NLP pioneer, for a lively tour of AI worlds that are multimodal, interactive, and efficient. They dig into action-conditioned environments, symbolic structure over pure pixels, game engines as reasoning tools, persistent simulated worlds, programmable visuals, spatial audio, and why benchmarking should focus on real utility.

Pavan Kumar Reddy, Mistral AI’s audio research lead, joins Guillaume Lample, Mistral AI co-founder and chief scientist, for a fast tour of Voxtral TTS. They dig into multilingual speech generation, flow-matching audio design, real-time voice agents, privacy-minded enterprise deployment, brand voice personalization, long-context speech, open weights, and Leanstral’s formal proof direction.