Free Daily Podcast Summary
Latent Space: The AI Engineer Podcast: Daily Summaries Delivered
by Latent.Space
Latent Space brings together conversations with AI engineers, researchers, and founders about foundation models, AI agents, infrastructure, and scientific discovery. Hear interviews, news, and technical discussions from across AI.
Latest Episodes
The most recent episodes — sign up to get AI-powered summaries of each one.
- 3 days ago1h 46m
How Anthropic ships the #1 LLM in the World safely: Separating the Brain from the Hands
Katelyn Lesse and Angela Jiang join Latent Space to explain the engineering behind the Claude Platform: launching Opus 5.5, separating the model’s “brain” from the agent’s “hands,” scaling APIs, and building reliable managed agents. A new introduction ...
- 4 days ago1h 11m
Supabase’s Plan for a Billion AI-Built Apps
AI agents are creating more software—and that changes what a backend needs to do. Supabase co-founders Paul Copplestone and Ant Wilson join swyx to discuss running long-lived workloads alongside the database, making schemas easier for agents to underst...
- 4 days ago16 min
⚡ An Eval for AI Scientists That Knows the Answer Key - Ian Quigley, Leash Bio
Watch the full show on YouTube: https://www.youtube.com/watch?v=vhPCo-8K78U
- 1 weeks ago1h 25m
Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO
Warm welcome to Basil Chatha, our newest cohost from the Forward Deployed side of the AI Engineering world! We’ll be ramping up a new show with him and couldn’t pass up an interview with Modal’s CEO Eric on the occasion of their monster Series D!Two years ago, we spoke with Erik Bernhardsson about his vision for truly serverless AI infrastructure: developers should be able to run code without provisioning clusters, predicting load, or paying for idle resources. CTO Akshat then joined us to talk about Modal’s infra primitives and stack to offer those primitives and agent experience.A lot has changed in the past 5 months. Since their $5B series C, Modal now reports it crossed $1 billion in annualized run rate, raised a $750 million Series D at a $16B valuation, and has spun up nearly three billion sandboxes. In this conversation, Erik joins Vibhu to explain the infrastructure and economics behind that growth—and why demand for compute rarely looks like the contracts used to buy it.Watch the full conversation on YouTube →GPU demand isn’t rectangularA typical capacity contract gives a customer a fixed number of GPUs for a fixed period. Plot capacity against time and you get a rectangle. Real workloads behave differently: they grow, spike, and fall throughout the day. Buy enough for the peak and you pay for idle hardware; buy less and you risk missing demand.Erik describes Modal’s bet as a kind of duration transformation: pool demand across thousands of customers, secure capacity underneath, and let each customer use what they need when they need it. The smoother the aggregate demand, the more useful that pool can be. It’s a useful companion to our conversation with SF Compute’s Evan Conrad: Evan focuses on making capacity tradeable; Erik focuses on making it usable in smaller, elastic slices.There is a financial risk on the other side. If a provider locks in capacity at today’s prices and GPU prices later fall, it can be left holding expensive supply. Erik says he doesn’t expect an imminent collapse, but thinks the possibility matters when building the business. Jump to the GPU price discussion →The sandbox that almost didn’t make itModal launched sandboxes after Erik and cofounder Akshat Bubna noticed that language models could generate code—and that someone would need a place to run it. Early adoption was modest enough that the team discussed shutting the product down. Then coding agents, background agents, and reinforcement learning created new demand. Erik says sandboxes have grown to roughly a third of Modal’s revenue, a result he didn’t predict at launch.That growth also changes what infrastructure has to do. An agent may create an environment, run code, inspect the result, and try again many times. The latency of starting and restoring those environments becomes part of the experience. In our recent interview with Akshat, we explored this shift from developer experience to agent experience; here Erik adds the product and business story. Jump to the sandbox discussion →Fast startup is an infrastructure featureElasticity only works if capacity can arrive quickly enough to matter. Modal’s serverless GPU write-up describes the machinery: a buffer of available GPUs, a custom filesystem, process checkpointing, and GPU memory snapshots. Those pieces reduce the work needed to bring another model replica online. It is a concrete example of how infrastructure decisions show up in an application’s responsiveness and cost.We also discuss Erik’s path from Spotify’s recommendation system to Modal, how the company buys compute, alternatives to NVIDIA, forward-deployed engineers, and why code review is optional at Modal. Jump to engineering culture →Show notes* Modal — the AI
- 1 weeks ago26 min
You are the Team — Anekha Sokhal, Moshi
Anekha Sokhal, CEO of Moshi, joins swyx to discuss building a recommendation engine for commodity traders, turning market data into useful signals, and designing AI that fits users’ workflows.Watch: YouTube · Moshi · Anekha0:00 AI Engineer New York and “you are the team”0:54 Meet Anekha2:09 From mathematics to energy derivatives3:46 Fulbright and becoming a founder5:45 What Moshi does6:10 Market data9:03 Bloomberg and market workflows10:03 Bloomberg, Claude and Netflix12:03 From dashboards to workflows15:03 Trusted sources and signal17:13 Models and document extraction18:20 AI Engineer New York20:32 Customer meetings23:54 Hiring and customers25:55 swyx’s path into AI This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space
- 1 weeks ago43 min
AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess
Alex Lieberman and CJ Hess join swyx at AI Engineer New York to discuss Tenex’s approach to AI-native engineering, paying for outcomes, MetaHarness, enterprise AI adoption and rebuilding SaaS.Show notes:Tenex: https://www.tenex.co/Building an AI-native SDLC: https://www.tenex.co/blog/building-an-ai-native-sdlcEarlier Tenex episode: https://www.latent.space/p/10x-ai-engineers-with-1m-salariesKill My SaaS: https://www.youtube.com/watch?v=zlD-CkLVKtUFull video: https://www.youtube.com/watch?v=Vki6cklFqxo0:00 Cold open0:26 Introductions0:51 What Tenex does1:32 Why CJ joined Tenex3:17 Paying engineers for outcomes7:20 Inside MetaHarness10:25 Models and long-running tasks15:11 Token spending and ROI19:29 The enterprise AI adoption gap23:22 Why agent deployment is hard24:42 Vertical AI and enterprise software30:32 Kill My SaaS35:23 AI and jobs38:18 New York’s AI ecosystem42:28 Hiring at Tenex 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
- 1 weeks ago36 min
The Programmable Data Center — Evan Conrad, SF Compute
This was recorded on the sidelines of the new Compute Trading track at AIE NYC. Catch us next at AIE CODE, our last flagship for the top agentic engineers in the world.In the early days of cloud, former guest Martin Casado made something so disruptive that Cisco was threatened enough to pay $600m for it, and VMware outbid for $1.3B:That concept was software-defined networking, which virtualized the routing network by separating the control plane from the data plane. That revolution is now coming for GPUs - where previously running your own supercompute cluster required $50-100m upfront to get commitments and do the entire stack from hardware to firmware to kernels, SF Compute is now enabling you to not only buy or sell GPU hours now and in the future, but also to programmatically control everything from your network to memory.We last spoke with Evan about commoditizing compute. At AI Engineer New York, we went deeper: what does it take to physically deliver a GPU contract, and what has to be true before a market in compute can become liquid?From selling a room to running the hotelEvan describes SF Compute’s expansion with a hotel analogy. A customer wants a room, then asks for cleaning, room service, and security. Eventually you discover that delivering the room means operating the hotel. SF Compute began by trying to sublease a cluster; the company has since built much of the operating capacity of a cloud to deliver the compute traded on its market.That work is physical settlement: the buyer gets usable compute, rather than simply a cash payment against an index. It brings the physical constraints of a cluster into the market itself. Can a workload be interrupted? How quickly can it recover from storage? Which GPU generation, networking and software configuration does it need? Those details affect both the contract and the price.Making compute tradeableRather than waiting for every GPU cluster to become interchangeable, SF Compute matches orders with different constraints. Evan argues that this makes trading possible while the technology changes, at the cost of spreading liquidity across more variants. We also discuss spot and forward pricing, batch versus live inference, and why an index can become disconnected from the cost of actually buying compute.Independence is central to Evan’s case for a market operator. He argues that companies with large hardware or inference businesses have incentives that make it harder to run a neutral exchange. We also get into the unglamorous engineering: visiting sites, designing clusters, running the backplane, and making infrastructure programmable. Near the end, he describes the ability to subdivide InfiniBand fabric on the fly as one example.Guest: Evan Conrad, CEO of SF Compute. Show notes* Evan’s AI Engineer NYC talk: How Compute Markets Work* Our previous SF Compute conversation* Why oil futures went negative in 2020 — EIA* How SF Compute customers resell unused GPU capacity* SF Compute’s $245M compute sale* Abel: SF Compute’s settlement ledger* CFTC: Barclays and LIBOR manipulation* Elastic InfiniBand partitionsTimestamps* 0:00 — Why can’t NVIDIA build this?* 0:25 — Welcome back to Latent Space* 2:23 — From selling a room to running the hotel* 3:47 — Physical settlement: actually delivering compute* <a tar
- 2 weeks ago1h 49m
Lab-in-the-Loop at Compute Speed — Ian Quigley, Leash Bio
A few years ago I stumbled upon Leash Bio’s Substack, and was hooked. Be it their Kaggle competition that showed that no, we really haven’t solved protein-ligand binding, or their crazy discovery that knowing a molecule’s author is perhaps all you need to know to predict how good it is. Their substack — or at least Owl Posting’s deep dive on them — should be required reading for any scientist trying to do chemistry with AI.So I was super excited to finally be able to sit down with Ian Quigley from Leash Bio, and pick his brain. Leash specializes in DNA Encoded Libraries (aka “DELs”), a way of collecting ridiculously high-throughput interaction data between proteins and small molecules. Before I discovered Leash, I had heard nothing but horror stories from DEL users. And yet, Ian and friends somehow found a way to tame the beast, extracting clean and reproducible data that is just perfect for large scale modeling. Without teasing too much, the answer comes down to epistemic rigor, and an incredible conviction that you just have to Do The Thing.It gets even better! Near the end of this episode, Ian teases something his team was just playing with when we recorded: an AI playground for agents to do verifiable scientific reasoning. They discovered the data they had spent the last few years collecting could provide tens of thousands of “lab-in-the-loop” simulations, just what an agent needs for scientific RL. Two months later, it’s matured into Minos, and today they’re releasing a full preprint, blog, and open dataset. We’ll be releasing a lightning podcast in a few days with an update. Stay tuned!We had a blast with Ian, probably one of our most fun episodes to date. For both complete chemistry novices and expert AI x Bio practitioners, we think you’ll get something out of this.Enjoy!We covered:* Why DELs traumatize everyone who touches them, and how Leash puts dozens of barcodes on each molecule to catch proteins that bind the DNA instead of the drug.* Ian’s rule that biology only scales two ways: taking pictures and sequencing DNA.* The Kaggle competition: about 4B measurements from a basement and about 2,000 teams, with nobody beating chance on an unseen chemical library. Also why Leash quietly feared co-folding would make them obsolete.* Clever Hans in ChEMBL: models can guess a molecule’s chemist from its structure, and “who made it” predicts activity about as well as the structure does.* Hermes, trained only on Leash’s own data, keeping pace with Boltz on public benchmarks. Plus the BRD4 molecule Boltz had already seen in the PDB.* Breaking the pocket: engineering protein mutants at scale after catching Hermes memorizing “nicknames” for proteins instead of binding sites.* Lumacaftor-like hits as an accidental detector for misfolded proteins.* The agent sandbox: LLMs that reason about SAR in plain English, hunting for the best binders among 6.5M molecules on a budget of 100.* The Recursion mafia, and why its alumni companies all rhyme.* The Kosuri paradox: if you can collect the data, why do you need the model?* Going from a DEL hit to good mouse PKPD in six months on an inflammation program, and the case for pill
Get Latent Space: The AI Engineer Podcast summaries in your inbox
Free AI-powered daily recaps. Key takeaways, quotes, and mentions — in a 5-minute read.
Get Free Summaries →Free forever for up to 3 podcasts. No credit card required.
You Might Also Like
Listeners also like.
The AI Podcast
Explores artificial intelligence advancements, trends, and ethics for enthusiasts and professionals.
The Jaeden Schafer Podcast
Explores technology, startups, and the creator economy with clear analysis on AI, business, and emerging trends.
The AI XR Podcast.
Industry insiders interview top founders and executives on AI, spatial computing, VR/AR, and synthetic media.
Global News Podcast
Global News Podcast AI
This Week in AI
Four AI experts analyze the latest news and trends in artificial intelligence each week.
"The Cognitive Revolution"
Explores the transformative impact of artificial intelligence through interviews with innovators shaping its future.
80,000 Hours Podcast
Discusses artificial intelligence and global catastrophic risks with experts and researchers.
The AI in Business Podcast
Interviews with AI executives reveal practical applications and strategic insights for business leaders adopting AI.
AI Breakdown
Explores the latest advancements, ethical issues, and real-world impacts of artificial intelligence across industries.
The AI Power Podcast
Explores AI policy, regulation, and global power dynamics shaping artificial intelligence’s impact on security, economy, and geopolitics.
Training Data
Discussions with AI researchers and builders exploring the technological, business, and societal impacts of artificial intelligence.
Learn AI
Learn AI
About Latent Space: The AI Engineer Podcast
Latent Space brings together conversations with AI engineers, researchers, and founders about foundation models, AI agents, infrastructure, and scientific discovery. Hear interviews, news, and technical discussions from across AI.
Customized Recaps
AI-powered recaps with compact key takeaways, quotes, and insights.
Straight to Your Inbox
Get key takeaways from Latent Space: The AI Engineer Podcast in a 5-minute read.
Save Hours Every Week
Stay current on your favorite podcasts without falling behind.
Frequently Asked Questions
What is Podzilla's Latent Space: The AI Engineer Podcast daily summary?
It's a free AI-powered email that summarizes new episodes of Latent Space: The AI Engineer Podcast as soon as they're published. You get the key takeaways, notable quotes, and links & mentions — all in a quick read.
How does the Latent Space: The AI Engineer Podcast podcast summary work?
When a new episode drops, our AI transcribes and analyzes it, then generates a personalized summary tailored to your interests and profession. It's delivered to your inbox every morning.
Is this an official Latent Space: The AI Engineer Podcast product?
No. Podzilla is an independent service that summarizes publicly available podcast content. We're not affiliated with or endorsed by Latent.Space.
Can I get summaries of other podcasts too?
Absolutely! The free plan covers up to 3 podcasts, any in the catalog. Basic raises that to 15, Pro to 40 and Max to 100. Browse our full catalog at /podcasts.
How often does Latent Space: The AI Engineer Podcast release new episodes?
Latent Space: The AI Engineer Podcast publishes daily. Our AI generates a summary within hours of each new episode.
What topics does Latent Space: The AI Engineer Podcast cover?
Latent Space: The AI Engineer Podcast covers topics including Science, Technology. Our AI identifies the specific themes in each episode and highlights what matters most to you.
Start getting Latent Space: The AI Engineer Podcast summaries tomorrow morning.
Free forever for up to 3 podcasts. No credit card required.
Free forever for up to 3 podcasts. No credit card required.