Podcasts about foundation models

In this episode, Neil explores how agents, foundation models, and AI are set to transform the Computer-Aided Engineering (CAE) and Electronic Design Automation (EDA) landscapes. He shares a comprehensive historical perspective and predicts a near-future where AI-driven automation redefines engineering workflows, productivity, and innovation.Main Topics:The evolution of simulation codes from the 1960s to modern commercial softwareThe rise of cloud computing, GPUs, and their impact on CAE and EDA industriesThe integration of AI, surrogate modeling, and foundation models into simulation workflowsThe emergence of agentic AI systems capable of autonomously performing complex engineering tasksThe strategic responses of major software companies to AI and agent technologiesThe potential democratization and automation of engineering design through AI agentsCritical questions on model ownership, transparency, and industry adoptionTimestamps: 00:40 - Introduction: How agents and foundation models will disrupt CAE & EDA01:40 - Historical overview: From code writing in the 60s to commercial software03:10 - Growth of aerospace and automotive industry codes and commercialization04:40 - The impact of HPC, cloud computing, and hardware evolution06:25 - Rise of cloud SaaS models and "sassification" of simulation tools07:40 - Big tech entrance: AWS, Microsoft, and Google in CAE & EDA09:00 - GPU acceleration: Changed landscape in past three to four years09:10 - The role of AI startups offering surrogate models and real-time simulation10:40 - Industry consolidation: Mergers and acquisitions among software giants11:40 - The emergence of foundation models and surrogate systems in simulation13:00 - The significance of agents: Combining AI, models, and automation14:10 - Capabilities of autonomous AI agents in complex engineering workflows15:25 - Practical use cases: Running simulations, setting up experiments, and data analysis16:40 - How agent-driven automation could democratize engineering expertise16:10 - Questions about model ownership, open source codes, and licensing19:40 - The future of AI in engineering: Collaboration, transparency, and scientific rigor21:25 - Final thoughts: Opportunities, challenges, and the transformative potential of AI* Please note that this a personal opinion and not that of NVIDIA

In this episode, Ben Lorica sits down with Doris Xin and Moustafa Abdelbaky, co-founders of Disarray, to discuss why classical machine learning models remain essential despite the rise of foundation models and LLMs. Subscribe to the Gradient Flow Newsletter

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep learning. We begin with AI Virtual Cell, a multiscale effort to learn data-driven representations from proteins to cells to patients using single-cell RNA-seq data, protein language models like ESM, and structure models like AlphaFold—without hand-encoding biology. Jure then dives into relational deep learning, reframing enterprise databases as graphs and training neural networks directly on raw multi-table data. He explains Kumo's Relational Foundation Model (RFM2), which performs in-context learning over subgraphs to make accurate predictions on new databases and tasks with no training, and how this approach benchmarks against RelBench and other multi-table datasets. We also discuss real-world deployments at companies like Reddit, DoorDash, and Coinbase, explainability via attention over tables and columns, integration with agentic systems, deployment options, and practical limitations. The complete show notes for this episode can be found at https://twimlai.com/go/768.

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: Why are prior predictive checks so underused in practice, and how do simulations help?A: They're underused because researchers don't always think to run them before seeing data -- but also because doing them rigorously (in the style Michael Betancourt advocates, with prior push-forward checks on interpretable summaries) takes effort. Simulations make it cheap to generate thousands of “what-if world” datasets from your model and check whether they look plausible, catching bad priors before you ever touch real data.Q: How can generative AI help with prior elicitation?A: Rather than forcing a domain expert to choose a distributional family and parameterize it, you can use a generative model to translate their qualitative knowledge directly into a prior. The expert describes what realistic data should look like; the generative model produces synthetic datasets matching that description; those datasets are used to fit a prior distribution. It removes the assumption that experts can think in terms of parameters and replaces it with the more natural question: does this look like your data?Q: What would a foundation model for Bayesian inference actually look like?A: Stefan's bet is that it won't be a fine-tuned general LLM. The right analogy is chess: you don't fine-tune GPT to play chess, you teach it when to call Stockfish. For Bayesian inference, you'd want a semantic layer – an LLM that understands the analysis goal – calling specialized numerical engines (MCMC samplers, amortized inference networks) that do the actual computation. Agent skills are already a step in this direction; the longer-term vision is engines that have been trained from scratch to generalize across large families of models and priors.Full takeaways here.Chapters:00:00 How does amortized inference fit into modern Bayesian workflows?06:01 What role do simulations play across the full Bayesian workflow?12:12 How do you elicit priors from a domain expert who doesn't think in distributions?19:01 What would a foundation model for Bayesian inference actually look like?35:32 What is self-consistency in amortized inference and why does it matter?39:22 How does semi-supervised learning improve simulation-based inference?43:16 Why is sensitivity analysis so important yet so underused in Bayesian practice?47:40 What is multiverse analysis and how does it change how we report Bayesian results?51:32 How does amortized inference make sensitivity and multiverse analysis affordable?01:02:47 How do amortized inference and classical MCMC complement each other?01:10:08 What are the next major directions for BayesFlow and amortized inference research?Thank you to my Patrons for making this episode possible!Links from the show here.

Discover how indie developer Dani Devesa Derksen-Staats created RetroRapid, an accessible retro racing game, and Xarra, a flexible reading and listening app. Learn how thoughtful design, multiple input methods, and community feedback can shape more inclusive experiences across Apple devices. Shaun Preece talks to Dani Devesa Derksen-Staats, an indie developer based in London, about his transition from working at the BBC to building accessibility-focused apps in his spare time. His latest projects highlight how inclusive design can be applied across both gaming and productivity tools. RetroRapid is a retro LCD-style racing game available on iPhone, iPad, Mac, and Apple Watch. Originally a side project, it evolved into a fully released title following feedback at the ARCtic conference. The game was designed with accessibility in mind, supporting multiple input methods including taps, swipes, keyboards, game controllers, and the Apple Watch Digital Crown. Dani explains how audio cues—assigning musical notes to each lane—help players build a mental map of the road, alongside haptic feedback and direct touch controls. Community feedback from AppleVis also played a key role in refining the experience. Dani also introduces Xarra, a newly launched app designed to support reading and focus. It allows users to import text, PDFs, and web content, combining audio playback with synchronised on-screen highlighting at line or word level. Built with accessibility at its core, Xarra supports VoiceOver, Switch Control, Full Keyboard Access, and Voice Control, while preserving image descriptions in audio. It also uses Apple Intelligence and Foundation Models to convert code blocks into natural language when listening to technical content. Relevant Links
RetroRapid & Xarra: https://accessibilityupto11.com/apps
Accessibility Up to 11: https://accessibilityupto11.com ----Follow on:YouTube: https://www.doubletaponair.com/youtubeX (formerly Twitter): https://www.doubletaponair.com/xInstagram: https://www.doubletaponair.com/instagramTikTok: https://www.doubletaponair.com/tiktokThreads: https://www.doubletaponair.com/threadsFacebook: https://www.doubletaponair.com/facebookLinkedIn: https://www.doubletaponair.com/linkedinSubscribe to the Podcast:Apple: https://www.doubletaponair.com/appleSpotify: https://www.doubletaponair.com/spotifyRSS: https://www.doubletaponair.com/podcastiHeadRadio: https://www.doubletaponair.com/iheartAbout Double TapHosted by the insightful duo, Steven Scott and Shaun Preece, Double Tap is a treasure trove of information for anyone who's blind or partially sighted and has a passion for tech. Steven and Shaun not only demystify tech, but they also regularly feature interviews and welcome guests from the community, fostering an interactive and engaging environment. Tune in every day of the week, and you'll discover how technology can seamlessly integrate into your life, enhancing daily tasks and experiences, even if your sight is limited."Double Tap" is a registered trademark of Double Tap Productions Inc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Peter Walker brings Carta's proprietary private market data from 60,000 startups and 85% of US unicorns to expose the brutal realities of today's tech landscape. While Q1 saw record capital raised, the money is highly concentrated among foundation models. We review the harsh truth behind the 530,000 open tech jobs in the US and the widening talent divide separating top 10% performers from the rest of the market. The conversation covers why venture capital is squeezing operators, how private equity is no longer a guaranteed exit strategy, and the urgent need to optimize your GTM strategy for AI-native workflows. We also debate the death of long-term product roadmaps, the impact of AI on enterprise pipeline generation, and evaluate whether middle management will survive the next 24 months. Key Takeaways The traditional safety net for slow-growth SaaS companies has disappeared, with Peter Walker noting that "A lot of companies held out this PE route as like, this is my escape hatch if I don't grow that fast. And now they're finding it's like, actually, the PEs don't care about you either." Securing venture capital has never been harder for founders outside of the AI bubble, as Peter Walker states "it's definitely not the easiest time to raise money unless you are already in the legible cohort. And if you're in the legible cohort, you know who you are." Rapid execution is replacing traditional product planning, with Peter Walker emphasizing that "the companies that are moving fast… don't know what they're doing in three weeks... we have no idea what's going to happen in October." Artificial intelligence is fundamentally threatening traditional communication hierarchies, and Sam Jacobs is bearish on the future of people managers because "if the purpose of management is to facilitate decision making at certain executive levels, that is something that AI can do." Connect with the Hosts & Guests  Host: Sam Jacobs - https://www.linkedin.com/in/samfjacobs/  Host: AJ Bruno - https://www.linkedin.com/in/ajbruno3/  Host: Asad Zaman - https://www.linkedin.com/in/azaman1/  Guest: Peter Walker - https://www.linkedin.com/in/peterjameswalker/   Topline is more than a YouTube Channel! Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast  Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters:  00:00 The AI Funding Divide  02:36 Is It Harder to Raise Capital  04:37 VCs Only Care About Growth  06:44 The Death of the PE Exit  10:48 Transitioning to AI Native  14:50 Why Product Roadmaps Are (Kinda) Dead  19:19 Stop Defaulting to VC  29:11 Historic Tech Funding Rounds  31:59 Over 530K Open Tech Jobs  36:55 Hiring Market Concentration  46:29 The Growing Tech Talent Divide  49:11 VC Fund Performance Realities  54:28 Future of Foundation Models  58:29 The End of Middle Management  

As the AI landscape evolves, the methods we use to process structured data are undergoing a silent revolution. Join us to explore how Tabular Foundation Models (TFMs) are challenging the decade-long reign of tree-based algorithms, why the traditional "train and predict" workflow is being replaced by "in-context learning," and what this shift means for the future of resilient modeling.To help us, Christoph Molnar, renowned expert in machine learning interpretability and author of the Mindful Modeler newsletter, joins us to share his perspective on the emergence of tabular transformers, the surprising power of synthetic data, and how to maintain model safety in a world without parameter updates.The decline of the "fit and predict" paradigm in tabular dataTransformer architectures vs. traditional models like XGBoost and LightGBMIn-context learning: Predicting without traditional training stepsThe role of Structural Causal Models (SCMs) in generating training dataWhy models trained on "math and probability" succeed on real-world datasetsHardware accessibility and running foundation models on local MacBooksIntegrating SHAP values and conformal prediction for model interpretabilityThe future of the data science workflow: One tool among many or a total shift?This episode is full of technical insights and forward-looking predictions that are sure to change how you approach your next dataset. As we move into a new era of AI, it's the perfect time to explore the fundamentals of the next frontier!What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:LinkedIn - Episode summaries, shares of cited articles, and more.YouTube - Was it something that we said? Good. Share your favorite quotes.Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

In this episode of Data in Biotech, host Ross Katz sits down with Kevin Brown, co-founder of Standard BioModel, to explore one of the most ambitious projects in biomedical AI, building a multimodal foundation model that represents the full complexity of a patient across time.  Drawing on a career spanning brain-computer interfaces, computer-aided diagnosis at Siemens Healthineers, and oncology data science at Bristol Myers Squibb, Kevin shares the scientific and philosophical journey that led him to a single conviction: a patient is not a document. Rather than reducing a patient to clinical notes, ICD-10 codes, or isolated test results, Standard BioModel's approach maps every available modality - CT imaging, digital pathology, genomics, EKGs, longitudinal EHR data - into a shared latent space, and models how that patient moves through time.  The result is a framework designed not just for prediction, but for counterfactual reasoning, clinical trial matching, and personalized intervention, with open-source models already being validated across leading academic medical centers. What you'll learn in this episode:  >> Why reducing a patient to text - clinical notes, radiology reports, genomic assay summaries - and how mapping multimodal data into a shared latent embedding space preserves information that never makes it into the written record >> How Standard BioModel's temporal architecture models patients as trajectories through an abstract embedding space rather than static snapshots, enabling counterfactual reasoning about the likely impact of interventions on a patient's future health trajectory >> Why no single foundation model can own every clinical vertical and how building a highly generalizable base model that facilitates downstream fine-tuning is a more defensible and scalable strategy than building narrow, application-specific models >> How the model handles missing modalities in real-world clinical settings, and why the architecture is designed to function effectively even when not every data type is available for every patient >> Why Standard BioModel has chosen to open-source its models and why broad, institution-specific validation across diverse patient populations is not just a scientific priority, but a prerequisite for trustworthy clinical AI Meet our guest: Kevin Brown is the Founder and CEO of Standard Model Biomedicine, where he builds foundation models for biomedicine. He previously led AI work as Director of Artificial Intelligence at SimBioSys, and held data science and applied ML roles at Bristol Myers Squibb and Siemens Healthineers. With a neuroscience research background from New York University, Kevin's work spans generative AI and machine learning for biomedical and medical imaging applications. Connect with Kevin Brown on LinkedIn  About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn Connect with us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

In this episode, we dig deep into the evolving landscape of industrial AI, from April Fool's pranks to real advances in robotics and automation. We break down how the line between hype and reality is blurring, and why it's more challenging than ever to separate fact from fiction in the age of agentic AI. We welcome Jonas Messner from NEURA Robotics to unpack how their 'robot gym' is collecting real-world data, why new forms of memory and multi-modal sensing are critical, and how open platforms are redefining collaboration in physical AI. Join us as we connect industry history, current breakthroughs, and bold visions for the future—where robots learn, adapt, and even monetize their skills in dynamic environments.

These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?

What if the expertise that built foundation models could reshape how you think about AI's future? In this episode, Benjamin sits down with Soumya Batra, founder and CEO of WisePort AI and former safety lead on Llama 2 and Llama 3 at Meta, to explore how foundation models evolved from traditional NLP, why post-training holds the highest leverage for safety and controllability, and what natively agentic AI means for the next frontier of AI development. Whether you're curious about the model training lifecycle or wondering what comes after large language models, this conversation unpacks the technical strategies and vision shaping tomorrow's AI systems.

In this episode, we dive deep into the challenges facing time series AI model leaderboards, from hidden information leakage to the complexities of benchmarking foundation models. I sit down with Marcel Meyer to unpack why traditional approaches fall short and how our new TS Arena leaderboard is setting a new standard for fair, future-proof evaluation. We explore the pitfalls that plague current benchmarks, the surprising ways data contamination can skew results, and the innovative pre-registration protocol we've developed to keep evaluations honest. If you've ever wondered what it takes to build a truly trustworthy AI leaderboard—or why it matters for industry and research alike—this conversation is packed with insights you won't want to miss.

AI agents are everywhere at NVIDIA GTC. But here is what stood out in my conversation with Rudy Chetty from Amazon Web Services (AWS) on The Ravit Show. AWS is leaning heavily into this with Marketplace. A growing hub where you can search, purchase, and deploy AI solutions in minutes. From foundation models to vector databases to monitoring tools, everything is starting to come together in one place.We also talked about the role of partners. Because scaling AI is not a one-company job. It takes an ecosystem. AWS provides the infrastructure.More from the AWS Marketplace Kiosk at GTC.#data #ai #awspartner #nvidiagtc #nvidiagtc2026 #api #awsmarketplace #theravitshow

Tete Xiao, VP of Engineering and AI, Bot Auto joined Grayson Brulte on The Road to Autonomy to discuss the fundamental shift from virtual AI to the physical AI required for commercial autonomous trucking.Tete co-authored Segment Anything, the landmark paper that ushered in the era of specific models to an era of foundation models that generalize across large segments of data. This approach which he is implementing at Bot Auto, enables the company to move beyond the limitations of previous technology, treating autonomous trucking as a compute-driven challenge where the system learns to navigate the complex physics of driving a truck.To ensure safety, Bot Auto is utilizing a top-down redundancy architecture that mirrors aviation's triple autopilot systems. Including dual onboard computers and independent software stacks running parallel algorithms with deliberately different logic to prevent a single failure from propagating through the system.This spring, Bot Auto is planning to launch fully autonomous commercial operations with Ryan Transportation on the Houston to Dallas corridor. No safety driver. No safety observer. No human in the cab.Episode Chapters00:00 AUTNMY AI00:25 Segment Anything05:04 Virtual AI to Physical AI09:08 Redundancy and Aviation-Inspired Architecture13:40 Hardware and Software17:00 Launching Fully Autonomous Operations20:00 Foundation Models and Reinforcement Learning27:52 Compute Infrastructure35:22 Staying Ahead42:30 Building a Virtual Driver47:06 AGI48:36 Transportation Company53:59 Future of Bot Auto--------About The Road to AutonomyThe Road to Autonomy is the definitive media brand covering the Autonomy Economy™. Through our podcasts, newsletter, and proprietary market intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Subscribe today for free: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

258 | Im 19. Jahrhundert haben Robert Bosch, Werner Siemens, Gottlieb Daimler und viele andere Gründer die Basis für unseren heutigen Wohlstand gelegt. In dieser Solo-Folge besprechen wir, warum die Zeit heute ganz ähnlich ist zu damals.Partner dieser Folge:HolviFinanzen für kleine Unternehmen: Von Chaos zu Klarheit mit Holvi - Das kostenlos Holvi Flex Konto ist perfekt für Solopreneure, Freelancer und Unternehmen, die wachsen wollen. ⁠⁠www.holvi.com⁠/podcastMach das 1-minütige Quiz und finde eine Geschäftsidee, die zu dir passt: digitaleoptimisten.de/quiz.Kapitel(00:41) Gottlieb Daimler wird gefeuert und baut in seinem Gartenhaus den ersten Benzinmotor (02:52) Wie eine Handvoll Besessener die Deutschland AG aus dem Nichts baute (05:14) Sam Altman will Intelligenz wie Strom aus der Leitung verkaufen(07:56?) Gazprom, Starlink und die Frage, wem die AI-Infrastruktur gehört(08:46) Europa verliert vier Abhängigkeiten gleichzeitig und das ist eine gute Nachricht (12:34) Hades Mining bohrt mit Lasern nach Europas Rohstoffen der Zukunft (17:40) AI-Arbitrage: Mit Google Maps Scraping und GEO heute ein Business starten (21:23) Warum gerade jetzt der beste Moment seit 150 Jahren ist, zu gründenLearningsWandel früh sehen und handelnDaimler sah eine kommende Wende zu mobilen Motoren und setzte darauf, obwohl Otto skeptisch war. Drei Jahre später lief der erste schnelllaufende Benzinmotor; aus dem Gartenhaus entstand einer der größten Konzerne Deutschlands. Das zeigt, wie frühes Erkennen eines Umbruchs und entschlossenes Handeln disruptives Wachstum ermöglicht.AI wird zur GrundversorgungHypothese: AI wird zur Grundversorgung wie Wasser und Strom. Sam Altman hat gesagt, dass AI als Utility kommt, und OpenAI hat Infrastruktur-Deals über 1,4 Billionen Dollar abgeschlossen sowie rund 30 GW Rechenzentrumskapazität. Europa muss Abhängigkeiten vermeiden, sonst drohen geopolitische Risiken, wie Starlink oder russisches Gas zeigen.AI-Arbitrage als DenkregelHypothese: AI-Arbitrage ermöglicht, Lücke zwischen Machbarkeit und Marktkenntnis zu nutzen. Die Folge nennt Google Maps Scraping und GEO als Beispiele, die sich in realen Geschäften monetarisieren lassen (5.000 bis 10.000 Euro pro Monat). So entsteht eine konkrete Brücke zwischen Technik und Markt, ohne umfangreiche Finanzierung.Anwendungsfokus in KI investieren75 Prozent aller europäischen KI-Investitionen fließen in vertikale Anwendungen, nicht in Foundation Models. Dort liegt die Musik: Anwendungsebene für den deutschen Mittelstand, für Compliance und lokale Märkte. Gründer sollten konkrete vertikale KI-Lösungen entwickeln, statt in generische Modelle zu investieren.KeywordsAI-InfrastrukturAI-ArbitrageGenerative Engine OptimizationEuropäische SouveränitätDeep Tech EuropaWie AI zur Grundversorgung wirdEU Inc europäische DatenplattformAI-Arbitrage Geschäftsmodell für GründerGeothermie Seltene Erden EuropaLaser-Bohrsysteme Hades MiningEuropäische DatensouveränitätMittelstand KI-Lösungen

For the final installment of our LawNext on Location series, Bob heads across the bay, from San Francisco to Oakland, to the headquarters of e-discovery company Everlaw, where he sits down with founder and CEO AJ Shankar for a conversation about technology, AI and being in it for the long game.  AJ grew up in Connecticut, came west in 2002 for a computer science PhD at UC Berkeley, and has lived within a few blocks of the Berkeley campus ever since. He stumbled into the legal industry almost by accident — recruited to serve as a technical expert in litigation involving how the internet worked — and quickly realized that the legal world was home to some of the most technically fascinating and underserved problems he'd ever encountered. He never left. AJ had a prior startup, a computer vision company that was acquired, before launching Everlaw in 2011. The company was cloud-native and ML-infused from the start, built on the conviction, AJ says, that there's no single way to find the needle in a discovery haystack, and that building a genuinely useful litigation platform requires solving for collaboration, ease of use and scalability all at once.  The bulk of the conversation focuses on generative AI, and how Everlaw has approached it differently than much of the market. Rather than bolting on a chatbot, AJ says, Everlaw embedded AI deliberately throughout the platform — document summarization, coding suggestions, deposition analysis, fact extraction — always grounding responses in the actual documents at hand and citing sources so users can verify the work. The December launch of Deep Dive, which lets litigators pose a question and get a synthesized, cited answer drawn from an entire document corpus in about a minute, is the feature AJ calls a "new era" for discovery — one he genuinely believes represents a categorical shift. As Everlaw continues to grow, it also remains independent, with no private equity and no outside majority owners. As for AJ, he says he is in it for the long game, and has never included an exit slide in a fundraising deck.   Thank You To Our Sponsors This episode of LawNext is generously made possible by our sponsors. We appreciate their support and hope you will check them out. Paradigm, home to the practice management platforms PracticePanther, Bill4Time, MerusCase and LollyLaw; the e-payments platform Headnote; and the legal accounting software TrustBooks. Briefpoint, eliminating routine discovery response and request drafting tasks so you can focus on drafting what matters (or just make it home for dinner).   Chapters   00:00 Introduction and Setting the Scene 03:23 The Journey to Founding Everlaw 08:36 The Evolution of Everlaw's Technology 11:06 Incorporating Generative AI into Legal Processes 14:04 Deep Dive: A New Era in Discovery 19:17 Transformative Experiences in Legal Discovery 22:27 Previewing Innovations at Legal Week 25:03 Understanding AI's Limitations in Legal Contexts 28:11 Navigating Hype in Legal Technology 30:47 The Impact of Foundation Models on Legal Software 34:36 Future Vision for Everlaw and Legal Tech 38:13 Closing Thoughts and Company Philosophy   If you enjoy listening to LawNext, please leave us a review wherever you listen to podcasts.  

Send a textIf AI can detect patterns we cannot see, how do we know when its answers are clinically trustworthy?In this episode of DigiPath Digest #39, I explore a big-picture question in digital pathology and medical AI. Many models now match or even exceed human performance in specific diagnostic tasks. But most of that evidence comes from controlled or retrospective datasets. So what happens when we try to bring these tools into real clinical workflows?I review four recent papers that help frame this challenge and point toward the next steps for trustworthy AI in healthcare. You will hear about the role of prospective validation, real-world effectiveness, transparent reporting standards, and multimodal data integration as recurring themes across these studies.Key Highlights00:00 – Introduction What do we do when AI detects signals that humans cannot see? The core challenge is verifying those outputs before trusting them in clinical decision making. 03:32 – AI Across the Healthcare Continuum A narrative review shows AI achieving clinician-level performance in well-defined imaging tasks, including digital pathology. But most evidence comes from retrospective or controlled environments, and prospective validation remains limited. 08:34 – Multi-Omics and AI in Gastric Biopsy Diagnostics Morphology alone cannot fully capture molecular heterogeneity or predict disease progression. Integrating genomics, proteomics, metabolomics, and other omics with AI is shifting gastric pathology toward data-driven precision gastroenterology. 13:38 – Hyperspectral Imaging for Real-Time Surgical Guidance Spectral imaging can analyze tissue composition during surgery without staining, freezing, or contact with the tissue. Studies show promising sensitivity for detecting malignancy and supporting intraoperative decision making. 17:20 – REFINE Reporting Guideline for Foundation Models and LLMs An international consensus guideline introduces a 44-item reporting checklist to standardize how AI studies are described. The goal is transparent, reproducible, and comparable research in medical AI. 22:35 – Big Takeaway AI should be viewed as clinical decision support, not a replacement for clinicians. Real-world validation, ethical governance, and reproducible research standards will determine how these tools enter pathology workflows. References (Articles Discussed)Artificial Intelligence in Healthcare: From Diagnosis to Rehabilitation https://pubmed.ncbi.nlm.nih.gov/41755929/Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence https://pubmed.ncbi.nlm.nih.gov/41751306/From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging https://pubmed.ncbi.nlm.nih.gov/41750768/REFINE Reporting Guideline for Foundation and Large Language Models in Medical Research https://pubmed.ncbi.nlm.nih.gov/41762555/If you enjoy staying current with digital pathology and AI research, this episode will help you connect the dots between promising algorithms and practical clinical adoption.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Ben Lorica talks with Sudip Roy (Co-founder & CTO, Adaption Labs) about why enterprise AI adoption stalls in the “last 5%” of reliability — and why waiting for the next frontier model release is usually the wrong bet. They unpack “adaptation” as something broader than post-training, including gradient-free, inference-time techniques that can sit above models to route, combine, and continuously improve behavior.Subscribe to the Gradient Flow Newsletter

As foundation models, including large language models and multimodal models, are increasingly deployed in complex and high-stakes settings, ensuring their safety has become more important than ever. In this talk, I present a probabilistic perspective on AI safety: safety risks are treated as structured distributions to be discovered and controlled, rather than isolated failures to be patched. I first introduce probabilistic red-teaming methods that characterize distributions of failures, revealing systematic safety risks that standard evaluation often misses. I then describe probabilistic defense methods that control model behavior during deployment by adaptively steering generation toward constraint-aligned distributions. By unifying failure discovery and behavior control under a probabilistic perspective, this talk highlights a distributional approach for understanding and managing safety risks in foundation models. About the speaker: Ruqi Zhang is an Assistant Professor in the Department of Computer Science at Purdue University. Her research focuses on probabilistic machine learning, generative modeling, and trustworthy AI. Prior to joining Purdue, she was a postdoctoral researcher at the Institute for Foundations of Machine Learning (IFML) at the University of Texas at Austin. She received her Ph.D. from Cornell University. Dr. Zhang has been a key organizer of the Symposium on Probabilistic Machine Learning. She has served as an Area Chair and Editor for ML conferences and journals, including ICML, NeurIPS, ICLR, AISTATS, UAI, and TMLR. Her contributions have been recognized with several honors, including AAAI New Faculty Highlights, Amazon Research Award, Spotlight Rising Star in Data Science, Seed for Success Acorn Award, and Ross-Lynn Research Scholar.

On AI in Action, IBM researcher Campbell Watson explains how foundation models are accelerating discovery across Earth and space science. Moving beyond traditional numerical methods, his team applies concepts from large language models to multimodal satellite data to build powerful, open-source AI systems. In collaboration with NASA and the European Space Agency, they have developed foundation models for Earth observation, weather and heliophysics. They are using AI for sustainability use cases, such as flood detection, biodiversity monitoring and solar flare forecasting. Designed for hybrid cloud environments and even deployed in orbit, these models point toward a future where AI and quantum computing unlock deeper planetary insights.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ----In this episode, I'm joined by Jaime Sevilla, founder of Epoch AI; Hannah Petrovic from my team at Exponential View; and financial journalist Matt Robinson from AI Street. Together we investigate a fundamental question: do the economics of AI companies actually work? We analysed OpenAI's financials from public data to examine whether their revenues can sustain the staggering R&D costs of frontier models. The findings reveal a picture far more precarious than many assume; we also explore where the real infrastructure bottlenecks lie, why compute demand will dwarf energy constraints, and what the rise of long-running agentic workloads means for the entire industry. Read the study here: https://www.exponentialview.co/p/inside-openais-unit-economics-epoch-exponentialviewWe covered: (00:00) Do the economics of frontier AI actually work? (02:48) Piecing together OpenAI's finances from public data (05:24) GPT-5's "rapidly depreciating asset" problem (13:25) Why OpenAI is flirting with ads (17:31) If you were Sam Altman, what would you do differently? (22:54) Energy vs. GPUs; where the real infrastructure bottleneck lies (29:15) What surging compute demand actually looks like (33:12) The most surprising finding from the research (38:02) The race to avoid commoditization (43:35) Agents that outlive their models  Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem  Where to find Jamie: https://epoch.ai or https://epochai.substack.com Where to find Matt: https://www.ai-street.co  Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Lawyers have always relied on tools—but AI is different. It doesn't just assist with tasks; it makes decisions, applies judgment, and shapes outcomes. In episode #602 of the Lawyerist Podcast, Stephanie Everett talks with Damien Riehl about what ethical responsibility looks like when AI starts doing legal work on its own.  Their conversation examines how AI systems embed values, why verification matters more than transparency, and how lawyers can responsibly use tools they don't fully understand. They also explore what legal expertise looks like in an AI-powered future—and why intuition, trust, and integrity may matter more than ever as machines take over the “widgets” of legal work.  Listen to our other episodes on Ethics and Responsibility in AI.  EP. 582 Deepfakes, Data, and Duty: Navigating AI Ethics in Law, with Merisa Bowers Apple | Spotify | LTN  EP. 543 What Lawyers Need to Know About the Ethics of Using AI, with Hilary Gerzhoy Apple | Spotify | LTN    Have thoughts about today's episode? Join the conversation on LinkedIn, Facebook, Instagram, and X!    If today's podcast resonates with you and you haven't read The Small Firm Roadmap Revisited yet, get the first chapter right now for free! Looking for help beyond the book? See if our coaching community is right for you.    Access more resources from Lawyerist at lawyerist.com.    Chapters / Timestamps:   00:00 – Introduction  05:55 – Meet Damien Riehl  08:10 – Why AI Is a Different Kind of Legal Tool  11:05 – When AI Starts Doing Legal Work  14:30 – Ethics, Values, and AI Judgment  18:45 – Foundation Models vs. Legal-Specific AI  21:15 – The “Duck Test” and Trusting AI Output  24:45 – Trust but Verify: Reviewing AI Work  28:40 – What Lawyers Are Underestimating About AI  31:10 – What Still Requires Human Judgment  34:30 – Intuition, Trust, and Integrity in Law  37:40 – What This Means for Billing and the Future  40:40 – Closing Thoughts   

Drug development has long been a costly, trial-and-error effort, with nine out of ten clinical programs failing despite major scientific advances. One reason is that biological information remains fragmented in silos, and traditional R&D approaches often rely on narrow, task-specific datasets. Bioptimus aims to change this by using AI to build a foundation model that integrates multimodal, multiscale biological data into a single body of knowledge. The approach has particular promise for rare diseases, where patient numbers and data are scarce, preclinical models are poor, and development economics are challenging. We spoke with Jean-Philippe Vert, co-founder and CEO of Bioptimus, about the inherent messiness of biology, the potential to transform rare disease drug development with a foundation model, and how uncovering similarities between conditions could enable repurposing of existing drugs.

This is episode 3/3 with Alex Merose about his thoughts on weather foundation models. He is a member of the technical staff at Open Athena. In this episode, Alex steps through system characteristics of weather foundation models and how we can approach building them. Toward the end, the episode touches on an example of applying these approaches to a simulation of the Earth's weather over a period of 800 years. The approaches Alex has been talking about enable the use of a GPU to process this simulation in only one day. We conclude with the values that drive Alex's work.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years.Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic.To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/-----At Davos 2026, the mood was unlike any previous World Economic Forum gathering. With Donald Trump arriving amid escalating geopolitical tensions and European leaders sounding alarms about sovereignty, I recorded live dispatches from the ground. In this special episode, I bring together observations from four days at the annual meeting, tracking the seismic shifts in global order alongside the practical realities of AI adoption in the enterprise.Skip to the best bits:(00:38) Day one at Davos(02:10) Three recurring themes through the week(03:55) Day three at Davos(05:12) Mark Carney's stirring speech(05:52) Why European leaders are sounding the alarm(06:51) Why technological sovereignty just became urgent(09:31) Day four at Davos(12:59) What leaders really have to say on AI adoption(14:07) The case for only using open source modelsWhere to find me:Exponential View newsletter: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar/Twitter/X: https://x.com/azeemProduction by supermix.io and EPIIPLUS1. Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This is episode 2/3 with Alex Merose about his thoughts on weather foundation models. He is a member of the technical staff at Open Athena In this episode, Alex steps through ML weather models, their history and how they work.Links to items discussed:ECMWF Reanalysis v5 (ERA5): https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5I got fooled by AI-for-science hype—here's what it taught me: https://www.understandingai.org/p/i-got-fooled-by-ai-for-science-hypeheres

The Stanford PhD who built DSPy thought he was just creating better prompts—until he realized he'd accidentally invented a new paradigm that makes LLMs actually programmable. While everyone obsesses over whether LLMs will get us to AGI, Omar Khattab is solving a more urgent problem: the gap between what you want AI to do and your ability to tell it, the absence of a real programming language for intent. He argues the entire field has been approaching this backwards, treating natural language prompts as the interface when we actually need something between imperative code and pure English, and the implications could determine whether AI systems remain unpredictable black boxes or become the reliable infrastructure layer everyone's betting on. Follow Omar Khattab on X: https://x.com/lateinteractionFollow Martin Casado on X: https://x.com/martin_casadoCheck out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI is changing how companies are built and how venture firms operate, forcing faster decisions, clearer judgment, and new ways of working.In this exclusive conversation, Ben Horowitz shares how Andreessen Horowitz adapts to that shift. He explains why managing GPs is different from running a company, how investors are evaluated at the moment of decision rather than years later, and why verticalized teams help the firm scale without internal politics.Ben also breaks down the current AI cycle, from treating AI as a new computing platform to why application design and model orchestration matter more than raw model size. He discusses the return of M&A and why today's AI market reflects real demand, not just inflated valuations. Resources:Follow Ben on X: https://twitter.com/bhorowitzFollow Jen on X: https://twitter.com/jkhamehl  Read Justine's piece ‘There is No God Tier Video Model': https://a16z.com/there-is-no-god-tier-video-model-but-there-is-something-better/ Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X :https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This is episode 1/3 with Alex Merose about his thoughts on weather foundation models. He is a member of the technical staff at Open Athena. In this episode, Alex steps through the background on doing weather prediction, from early efforts around a century ago to numerical and physics based models. This prepares us for later episodes on machine learning or AI based approaches. Links to items discussed:Pangeo, a community for open, reproducible, scalable geoscience.Alex's Google Scholar profile.Episode with Sergei Nozdrenkov on a coral reef foundation model.Global Climate Data Collaboration: The Intentional Dream by George Dyson.Hurricane Melissa.Arham Ansari on GeoRiskAI.What can a technologist do about climate change.

Cofounders Jeremy Wohlwend and Gabriele Corso join the a16z podcast to discuss the launch of Boltz, a public benefit company building AI infrastructure for molecular biology. The conversation explains how breakthroughs following AlphaFold moved the field beyond protein structure prediction into modeling biomolecular interactions and binding strength, why open-source Boltz models saw rapid adoption across pharma and biotech, and how that work is now being productized. They outline the launch of Boltz Lab, a platform that brings protein and small-molecule design agents into scientist workflows, Boltz's decision to operate as an infrastructure company rather than a therapeutics company, and how AI could reduce early drug discovery bottlenecks by improving molecular design and speeding iteration between computation and the lab. Resources: Follow Gabriele on X: https://twitter.com/GabriCorso Follow Jeremy on X: https://twitter.com/jeremyWohlwend Follow Jorge X: https://twitter.com/jorgecondebio Follow Zak on X: https://twitter.com/zakdoric   Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X:https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://twitter.com/eriktorenberg](https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Pundits are screaming about the so-called “AI bubble.” But historically slow-to-adopt industries like medicine and law are actually embracing AI at an unprecedented speed. Sarah Guo and Elad Gil look ahead to 2026, breaking down the major trends that will define the next era of AI technologies. They explore the future of AI foundational models, predicting breakthroughs in solving complex scientific problems. They share competing views on the timeline for robotics and self-driving cars, debating whether startups have a chance for survival or if incumbents will dominate. Elad and Sarah also discuss the return of tech IPOs and M&As, forecast a new wave of AI consumer agent software, and explore why consumer product innovation has been slower than expected. Finally, the two offer bold non-AI predictions for the new year, including the acceleration of defense tech startups and the second-order underrated impacts of GLP-1 drugs on biohacking. Plus, stick around to hear predictions on what's next for AI in 2026 from some of tech's biggest names and industry leaders. We hear from Jensen Huang (Founder/CEO NVIDIA), Arvind Jain (Founder/CEO, Glean), Winston Weinberg (Founder/CEO, Harvey), Scott Wu (Founder/CEO, Cognition), Raiza Martin (Founder/CEO Huxe), Zach Ziegler (Founder/CTO, Open Evidence), Aaron Levie (Founder/CEO, Box), Misha Laskin (Founder/CEO, ReflectionAI), Noam Brown (Research Scientist, OpenAI), Joshua Meier (Founder/CEO Chai Discovery), Bryan Johnson (Living Man, Don't Die), Sholto Douglas (Member of the Technical Staff, Anthropic), Ben & Asher Spector (Stanford PhDs) and Dylan Patel (Founder/CEO SemiAnalysis). Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Chapters: 00:00 – Introduction 02:43 – AI Predictions for 2026 04:40 – Adoption of AI in Professional Fields 07:17 – Robotics and Self-Driving Cars 08:25 – Robotics: Incumbents vs. Startups 13:59 – Future of IPOs and M&A in AI 16:42 – Challenges in Consumer AI Innovation 21:08 – Funding of Neo Labs, RL Research 26:28 – Predictions for 2026 Beyond AI 26:44 – The Future of Defense and Technology 28:23 – Biohacking and Peptide Therapies 30:37 – 2026 Prediction from AI Industry Leaders 40:46 – Conclusion

In this episode, we dive into the world of responsible data annotation with Andreas Schachl, co-founder of Responsible Annotation Services. We explore how neurodivergent individuals—especially those on the autism spectrum—are bringing unparalleled focus and precision to the task of data labeling, raising the bar for AI model training quality. Andreas shares the origins of their company, the unique strengths of their team, and why European sovereignty and data protection matter more than ever. We discuss the practical steps involved in their annotation process, how they measure quality, and the real impact their work has on customers and the broader tech ecosystem. Join us for an insightful conversation that challenges assumptions and highlights how inclusive innovation is driving the next frontier in industrial AI.

Send us a textWhat if the biggest breakthrough in pathology AI isn't a new algorithm—but finally sharing the data we already have?In this episode, I'm joined by Jeroen van der Laak and Julie Boisclair from the IMI BigPicture consortium, a European public-private initiative building one of the world's largest digital pathology image repositories. The goal isn't to create a single AI model—but to enable thousands by making high-quality, legally compliant data accessible at scale.We unpack what it really takes to build a 3-million-slide repository across 44 partners, why GDPR and data-sharing agreements delayed progress by 18 months, and how sustainability, trust, and collaboration are just as critical as technology. This conversation is about the unglamorous—but essential—work of building infrastructure that will shape pathology AI for decades.⏱️ Highlights with Timestamps[00:00–01:40] Why BigPicture focuses on data—not algorithms[01:40–03:16] Scope of the project: 44 partners, 15–18 countries, 3M images[03:16–06:20] The 18-month delay caused by legal frameworks and GDPR[06:20–11:52] Extracting data from heterogeneous lab infrastructures[11:52–13:38] Current status: 115,000 slides uploaded and growing[13:38–18:39] Why LLMs and foundation models make curated data more valuable than ever[18:39–23:49] Industry collaboration and shared negotiating power[23:49–28:06] Data access models and governance after project independence[28:06–31:59] Sustainability plans and nonprofit foundation model[37:02–43:18] Tools developed: DICOMizer, artifact detection AI, image registration

In our season six finale, we dive deeper into how artificial intelligence (AI) is shaping the future of drug discovery and scientific research. With remarkable scale and speed, AI models parse through complex datasets and confirm or generate hypotheses, which can help scientists accelerate R&D. In this episode, co-host Danielle Mandikian welcomes Aviv Regev, Head of gRED, and Jure Leskovec, Professor of Computer Science at Stanford University, to talk about foundation models and autonomous agents. Together, they explore the opportunities and challenges of applying AI in drug discovery, including balancing innovation with scientific rigor and the evolving role of scientists. They also discuss how AI is reshaping the future of research — from building more biologically meaningful models to advancing agent-based systems and lab automation. Read the full text transcript at www.gene.com/stories/foundation-models-and-agents

In this meeting, Shu channels his inner David Ogilvy, pulling wisdom from the father of advertising as we unpack how bold individuals can often push progress faster than committees —while recognizing the moments when group consensus still matters. We pivot to the New York City mayoral race, and the strange new world of AI toys that occasionally say the wrong things to kids.  Our main discussion digs into foundation models in machine learning, the expanding economic frontier these models are creating, the staggering costs behind building them, and why thoughtful data collection matters more than ever. It's a conversation about hype, hard realities, and the discipline needed to build the future responsibly.

Carta's Peter Walker is back with the freshest data on what's actually happening at the early stage—and it's not what you're reading on X. While headlines scream about record-breaking rounds, the reality on the ground tells a different story. Seed deals are down. Time between rounds is stretching. And there's a brutal divide between the companies getting all the attention and everyone else. We dig into the exact valuations, graduation rates, team sizes and revenue you need for Seed and Series A... plus why the lowest-quartile seed rounds are failing at twice the rate. If you're raising or planning to raise, this is the episode.Why You Should ListenThe round size that cuts your Series A odds in halfWhy smaller teams are winning (and what that means for your hiring plan)The real median valuations at pre-seed, seed, and Series A right nowHow long it actually takes to get from seed to Series A in 2024When taking secondary as a founder makes sense (and when it doesn't)Keywordsstartup podcast, startup podcast for founders, seed round valuation, Series A fundraising, startup fundraising data, venture capital trends, pre-seed funding, startup metrics, founder secondary, seed to Series AChapters:00:00:00 Intro 00:02:46 Seed Valuations and Who Actually Graduates to Series A 00:06:58 What Founders Outside the Hot Cohort Should Do 00:11:44 Team Sizes Are Shrinking and Employees Are Getting Less 00:17:40 Crowded Categories and Competing with Foundation Models 00:24:47 Founders Starting Companies for the Wrong Reasons 00:33:32 When Founder Secondaries Make Sense 00:39:55 The Actual Median Valuations at Pre-Seed Seed and Series ASend me a message to let me know what you think!

Dr. Jure Leskovec is the Chief Scientist at Kumo.AI and a Stanford professor, working on relational foundation models and graph-transformer systems that bring enterprise databases into the foundation-model era.Relational Foundation Models: Unlocking the Next Frontier of Enterprise AI // MLOps Podcast #348 with Jure Leskovec, Professor and Chief Scientist, Stanford University and Kumo.AI.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractToday's foundation models excel at text and images—but they miss the relationships that define how the world works. In every enterprise, value emerges from connections: customers to products, suppliers to shipments, molecules to targets. This talk introduces Relational Foundation Models (RFMs)—a new class of models that reason over interactions, not just data points. Drawing on advances in graph neural networks and large-scale ML systems, I'll show how RFMs capture structure, enable richer reasoning, and deliver measurable business impact. Audience will learn where relational modeling drives the biggest wins, how to build the data backbone for it, and how to operationalize these models responsibly and at scale.// BioJure Leskovec is the co-founder of Kumo.AI, an enterprise AI company pioneering AI foundation models that can reason over structured business data. He is also a Professor of Computer Science at Stanford University and a leading researcher in artificial intelligence, best known for pioneering Graph Neural Networks and creating PyG, the most widely used graph learning toolkit. Previously, Jure served as Chief Scientist at Pinterest and as an investigator at the Chan Zuckerberg BioHub. His research has been widely adopted in industry and government, powering applications at companies such as Meta, Uber, YouTube, Amazon, and more. He has received top awards in AI and data science, including the ACM KDD Innovation Award.// Related LinksWebsite: https://cs.stanford.edu/people/jure/https://www.youtube.com/results?search_query=jure+leskovecPlease watch Jure's keynote:https://www.youtube.com/watch?v=Rcfhh-V7x2U~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Jure on LinkedIn: /leskovecTimestamps:[00:00] Structured data value[00:26] Breakdown of ML Claims[05:04] LLMs vs recommender systems[10:09] Building a relational model[15:47] Feature engineering impact[20:42] Knowledge graph inference[26:45] Advertising models scale[32:57] Feature stores evolution[38:00] Training model compute needs[42:34] Predictive AI for agents[45:32] Leveraging faster predictive models[48:00] Wrap up

In this episode, we catch up with John Paulett and Faris Siddiqui to talk about their 2025 SIIM Hackathon project, Agentic Vibes, which won first place. Have a listen to hear about hot topics like Foundation Models and Agentic AI, among others! You can find our podcast on Spotify, Apple Podcast, or anywhere else you subscribe to podcasts. Please help us out by leaving a review! Visit us at https://siim.org/page/siimcast Special Thanks to @RandalSilvey of http://podedit.com for editing and post processing support.

The Stanford PhD who built DSPy thought he was just creating better prompts—until he realized he'd accidentally invented a new paradigm that makes LLMs actually programmable. While everyone obsesses over whether LLMs will get us to AGI, Omar Khattab is solving a more urgent problem: the gap between what you want AI to do and your ability to tell it, the absence of a real programming language for intent. He argues the entire field has been approaching this backwards, treating natural language prompts as the interface when we actually need something between imperative code and pure English, and the implications could determine whether AI systems remain unpredictable black boxes or become the reliable infrastructure layer everyone's betting on.Follow Omar Khattab on X: https://x.com/lateinteractionFollow Martin Casado on X: https://x.com/martin_casado  Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Interview with Darren Shu Jeng Ting, MBChB, PhD, author of Performance of Foundation Models vs Physicians in Textual and Multimodal Ophthalmological Questions. Hosted by Neil Bressler, MD. Related Content: Performance of Foundation Models vs Physicians in Textual and Multimodal Ophthalmological Questions Mastering Ophthalmology in the Digital Age

What if we could train AI to understand how stem cells become any cell type in the human body? In this episode of the In Vivo Podcast, host David Wild sits down with Micha Breakstone (CEO & Co-founder) and Samantha Dale Strasser (VP of Strategy) from Somite.AI to explore how their company is using foundation models to revolutionize cell therapy development. Somite has pioneered a breakthrough approach that generates cell differentiation data at 1000x the efficiency of traditional methods using proprietary hydrogel capsule technology. By capturing millions of trajectories showing how cells respond to signals over time, they're building DeltaStem—a foundation model that could do for developmental biology what AlphaFold did for protein structure prediction. Topics covered: - How bringing cells to signals (rather than signals to cells) unlocks exponential scale - Why wet lab innovation is just as critical as AI models - Manufacturing optimization: improving purity, reducing variability, and cutting costs - From beta cells for diabetes to brown fat for metabolic disease—the therapeutic pipeline - Why even AI experts underestimate what's coming in the next decade - Lessons from building biotech companies from academic concepts to commercial ventures Whether you're in pharma, biotech, AI or just fascinated by the intersection of technology and human biology, this conversation offers a grounded look at how foundation models are moving from hype to real therapeutic impact.

Chip Huyen is a core developer on Nvidia's Nemo platform, a former AI researcher at Netflix, and taught machine learning at Stanford. She's a two-time founder and the author of two widely read books on AI, including AI Engineering, which has been the most-read book on the O'Reilly platform since its launch. Unlike many AI commentators, Chip has built multiple successful AI products and platforms and works directly with enterprises on their AI strategies, giving her unique visibility into what's actually happening inside companies building AI products.We discuss:1. What people think makes AI apps better vs. what actually makes AI apps better2. What pre-training vs. post-training is, and why fine-tuning should be your last resort3. How RLHF (reinforcement learning from human feedback) actually works4. Why data quality matters more than which vector database you choose5. Why high performers are seeing the most gains from AI coding tools6. Why most AI problems are actually UX issues—Brought to you by:Dscout—The UX platform to capture insights at every stage: from ideation to production: https://www.dscout.com/Justworks—The all-in-one HR solution for managing your small business with confidence: https://ad.doubleclick.net/ddm/trackclk/N9515.5688857LENNYSPODCAST/B33689522.423713855;dc_trk_aid=616485030;dc_trk_cid=237010502;dc_lat=;dc_rdid=;tag_for_child_directed_treatment=;tfua=;gdpr=$Persona—A global leader in digital identity verification: https://withpersona.com/lenny—Where to find Chip Huyen:• X: https://x.com/chipro• LinkedIn: https://www.linkedin.com/in/chiphuyen/• Website: https://huyenchip.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Chip Huyen(04:28) Chip's viral LinkedIn post(07:05) Understanding AI training: pre-training vs. post-training(08:50) Language modeling explained(13:55) The importance of post-training(15:20) Reinforcement learning and human feedback(22:23) The importance of evals in AI development(31:55) Retrieval augmented generation (RAG) explained(38:50) Challenges in AI tool adoption(43:19) Challenges in measuring productivity(45:20) The three-bucket test(49:10) The future of engineering roles(55:31) ML Engineers vs. AI engineers(57:12) Looking forward: the impact of AI(01:05:48) Model capabilities vs. perceived performance(01:08:23) Lightning round and final thoughts—Referenced:• Chip's LinkedIn post on what actually improves AI apps: https://www.linkedin.com/posts/chiphuyen_aiapplications-aiengineering-activity-7358971409227792384-y0mf/• Prediction and Entropy of Printed English: https://www.princeton.edu/~wbialek/rome/refs/shannon_51.pdf• Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody (CEO of Mercor): https://www.lennysnewsletter.com/p/experts-writing-ai-evals-brendan-foody•Inside the expert network training every frontier AI model | Garrett Lord (Handshake CEO): https://www.lennysnewsletter.com/p/inside-handshake-garrett-lord• First interview with Scale AI's CEO: $14B Meta deal, what's working in enterprise AI, and what frontier labs are building next | Jason Droege: https://www.lennysnewsletter.com/p/first-interview-with-scale-ais-ceo-jason-droege• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Why AI evals are the hottest new skill for product builders | Hamel Husain & Shreya Shankar (creators of the #1 eval course): https://www.lennysnewsletter.com/p/why-ai-evals-are-the-hottest-new-skill• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Stanford webinar—How AI Is Changing Coding and Education, Andrew Ng & Mehran Sahami: https://www.youtube.com/watch?v=J91_npj0Nfw• He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more: https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor• Anthropic co-founder on quitting OpenAI, AGI predictions, $100M talent wars, 20% unemployment, and the nightmare scenarios keeping him up at night | Ben Mann: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann• Lenny's vibe-coded app made on Lovable: https://gdoc-images-grab.lovable.app/• Story of Yanxi Palace: https://www.imdb.com/title/tt8865016/• Steve Jobs's quote: https://www.goodreads.com/quotes/427317-remembering-that-i-ll-be-dead-soon-is-the-most-important—Recommended books:• The Complete Sherlock Holmes: https://www.amazon.com/Complete-Sherlock-Holmes-Volumes/dp/0553328255• AI Engineering: Building Applications with Foundation Models: https://www.amazon.com/AI-Engineering-Building-Applications-Foundation/dp/1098166302• The Selfish Gene: https://www.amazon.com/Selfish-Gene-Anniversary-Introduction/dp/0199291152• From Third World to First: The Singapore Story: 1965-2000: https://www.amazon.com/Third-World-First-Singapore-1965-2000/dp/0060197765—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Are you making the #1 mistake in AI pricing? Many founders are using their old SaaS pricing model, but for AI, that's a huge error. As pricing expert Dan Balcauski explains, it's like showing customers your “underpants”, and it's killing your sales. In this guide to AI pricing, Dan breaks down why the game has changed. Unlike traditional SaaS, AI products have significant variable costs and unpredictable customer value, creating a new set of challenges for founders. We explore the psychology of value-based pricing, how to choose the right pricing metric, and the strategies companies like Google and OpenAI are using to price their AI tools. Check out the company: https://www.producttranquility.comBook a 1-on-1 advisory session with me to apply these principles to your business: https://calendly.com/wltb/advisory

Can your podcast app create shareable audio clips, skip silence with AI, or manage meds with just your voice? Dive into a hands-on comparison of Apple Podcasts, Overcast, Pocket Casts, and Downcast with the features that actually matter. • Deep dive on Apple Podcasts app features, transcripts, and syncing • Overcast's unique tools: smart speed, voice boost, personalized playback, and clip sharing • Pocket Casts overview: recommendations, playback customization, audio bookmarks, and listening stats • Downcast is a minimalist, video-supporting podcast downloader with Chromecast • News: Apple's new Foundation Models framework brings on-device AI to apps. Apple highlights apps leveraging Foundation Models for health, fitness, journaling, and content creation • Shortcuts Corner: Logging medications with Siri and Capsule, Apple Health limitations • App Caps: Incase AirPods lanyard & the Apple Sports app for live game updates Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Can your podcast app create shareable audio clips, skip silence with AI, or manage meds with just your voice? Dive into a hands-on comparison of Apple Podcasts, Overcast, Pocket Casts, and Downcast with the features that actually matter. • Deep dive on Apple Podcasts app features, transcripts, and syncing • Overcast's unique tools: smart speed, voice boost, personalized playback, and clip sharing • Pocket Casts overview: recommendations, playback customization, audio bookmarks, and listening stats • Downcast is a minimalist, video-supporting podcast downloader with Chromecast • News: Apple's new Foundation Models framework brings on-device AI to apps. Apple highlights apps leveraging Foundation Models for health, fitness, journaling, and content creation • Shortcuts Corner: Logging medications with Siri and Capsule, Apple Health limitations • App Caps: Incase AirPods lanyard & the Apple Sports app for live game updates Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Can your podcast app create shareable audio clips, skip silence with AI, or manage meds with just your voice? Dive into a hands-on comparison of Apple Podcasts, Overcast, Pocket Casts, and Downcast with the features that actually matter. • Deep dive on Apple Podcasts app features, transcripts, and syncing • Overcast's unique tools: smart speed, voice boost, personalized playback, and clip sharing • Pocket Casts overview: recommendations, playback customization, audio bookmarks, and listening stats • Downcast is a minimalist, video-supporting podcast downloader with Chromecast • News: Apple's new Foundation Models framework brings on-device AI to apps. Apple highlights apps leveraging Foundation Models for health, fitness, journaling, and content creation • Shortcuts Corner: Logging medications with Siri and Capsule, Apple Health limitations • App Caps: Incase AirPods lanyard & the Apple Sports app for live game updates Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Can your podcast app create shareable audio clips, skip silence with AI, or manage meds with just your voice? Dive into a hands-on comparison of Apple Podcasts, Overcast, Pocket Casts, and Downcast with the features that actually matter. Deep dive on Apple Podcasts app features, transcripts, and syncing Overcast's unique tools: smart speed, voice boost, personalized playback, and clip sharing Pocket Casts overview: recommendations, playback customization, audio bookmarks, and listening stats Downcast is a minimalist, video-supporting podcast downloader with Chromecast News: Apple's new Foundation Models framework brings on-device AI to apps. Apple highlights apps leveraging Foundation Models for health, fitness, journaling, and content creation Shortcuts Corner: Logging medications with Siri and Capsule, Apple Health limitations App Caps: Incase AirPods lanyard & the Apple Sports app for live game updates Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Mati Staniszewski is the Co-Founder and CEO of ElevenLabs, the world's leading AI voice platform. Since launching in 2022, ElevenLabs has raised over $350M, most recently at a $3.3BN valuation, making it one of Europe's fastest AI unicorns. The company counts Andreessen Horowitz, Nat Friedman, Daniel Gross, and Sequoia Capital among its backers. Today, Mati announces that the company has hit a staggering $200M ARR. ElevenLabs took 20 months to hit $100M ARR. 10 months to hit $200M ARR. Can they do $300M in 5 months… AGENDA:  [00:00] $100M in 20 Months?! ElevenLabs Untold Growth Story [12:20] Are AI Models Already Plateauing—or Just Getting Started? [14:00] Why OpenAI Can't Beat ElevenLabs  [17:30] The Talent Wars: How Do You Retain World-Class AI Researchers? [23:10] PR vs Product: Why Most Startups Botch Their Launch [36:00] Are U.S. VCs Playing a Different Game Than Europe? [44:00] The Real Cost of AI: Why ElevenLabs Built Its Own Data Centers [59:00] Voice Agents = Multi-Billion Dollar Business of the Future? [01:05:00] Buy OpenAI or Anthropic? Which Foundation Model Wins? [01:09:30] Europe: Strengths, Weaknesses and What Needs to be Done  

Anton Osika is the Co-Founder and CEO @ Lovable, the fastest growing company on the planet. In just 7 months, they have scaled from $0 to $120M in ARR. They have raised over $200M in funding from some of the best including Accel, Creandum and 20VC. Their latest round priced the company at a whopping $2BN.  Agenda for Today: 00:00 – Is AI an Arms Race… Or Just a Talent War? 03:45 – How Does Anton Compete with Zuck's $100M Packages for Talent 07:30 – Founder Mode vs. Structure: Can Chaos Scale? 10:15 – The Brutal Truth About Defensibility in AI Startups 13:20 – Unit Economics: Are AI Companies Doomed to Bleed Cash? 17:00 – GPT-5: Game-Changer or Overhyped Disappointment? 20:10 – How Lovable Hit $100M ARR in Just 7 Months? 25:15 – Replit, Figma, Bolt: Which Competitor is the Best? 30:00 – The Security Bombshells No One Talks About 36:40 – Should Anyone Still Study Computer Science? 40:30 – Work-Life Balance Is Dead: Inside Anton's 10x Culture 56:00 – OpenAI, Anthropic, or Grok: Who Wins the AI Wars?    

Martin Casado is a General Partner @ a16z where he leads the firms $1.25BN infrastructure fund. At a16z, Martin has led investments in companies like Cursor, dbt Labs, and Fivetran to name a few. Before joining a16z, he co-founded Nicira, acquired by VMware for $1.26B. At VMware, he served as CTO of Networking. Widely regarded as a visionary in enterprise infrastructure, Martin has helped shape the modern cloud computing stack. Agenda: 00:00 – Analysis of Current AI Investment Landscape 04:45 – Will Anthropic Kill the AI App Layer? 09:20 – “The Oligopoly Is Coming—Just Like Cloud” 12:50 – Are AI Models Actually Terrible Venture Investments? 15:40 – Why it is BS to Put Down AI Apps for Having Temporary Revenue 21:30 – “Open Source Is a National Security Weapon—And We're Losing” 26:40 – “Have the Foundation Models of the Future All Been Founded Already” 34:30 – Why it is BS to Denigrate AI Apps for Having Low Margins 38:40 – Does AI Make 1x Engineers 10x or 10x Becomes 100x 44:10 – “We're All Dead Wrong About AI and Job Loss” 50:30 – “The Only Sin in Venture: Backing the Wrong Winner” 55:10 – What People Think They Know About Wealth But Do Not