NeurIPS 2025 by Basis Set

Why does every naive data scientist who tries to predict stock prices end up depressed? Finance systematically breaks standard AI. You'll discover the four methodological pitfalls: data scarcity (10 years of daily data = only 2,500 observations—laughably insufficient), look-ahead bias (accidentally using future data), the unconditional trap (models validate but can't predict what matters), and heavy tails (the rare crashes that define risk). The analogy that sticks: "It's like having an umbrella that doesn't work when it rains." But there's a solution. Task-driven training matches the P&L of benchmark strategies instead of learning impossible 10,000-dimensional distributions. You'll hear about dynamic portfolios that spontaneously switched hedging instruments during COVID, lasso regression for cost-effective hedging, and the "Persona Ledger" method—LLM-generated synthetic data with accounting rules as constraints. Finance breaks AI, but sophisticated methodologies are fixing it. Topics Covered - The "naive data scientist depression": why finance breaks standard AI - Four methodological pitfalls: data scarcity, look-ahead bias, unconditional trap, heavy tails - Task-driven training: matching strategy P&L instead of price prediction - Dynamic vs. static portfolios (encoding timing and regime changes) - Lasso regression for sparse hedging (minimizing transaction costs) - Agentic pipelines: GPU-accelerated end-to-end workflows - LLM challenges: time travel problem, implicit investment biases, stubbornness - Persona Ledger: LLM-generated synthetic data with stateful verification

AI is a time machine, compressing years of lab work into days. Digital organisms simulate biology at every scale for drug discovery. AI-optimized sensor placement achieves the same results with 1% of traditional compute. Healthcare AI can predict disease 20 years early. But here's the reality check: zero generative AI systems have FDA approval for clinical use. Zero. You'll explore the gap between academic proof-of-concept and clinical deployment, the dual-use risk where the same models design both therapeutics and pathogens, and the central tension this entire series builds toward: we're accelerating discovery at unprecedented speed—but at what risk? How do we regulate systems that constantly learn and evolve? This finale leaves you with the right question to sit with. Topics Covered - Digital organisms: simulating biology at all scales - GNNs vs. transformers for biological discovery - Drug discovery: academic proof of concept vs. clinical reality - Sensor optimization (1% of traditional compute!) - Healthcare AI potential: predicting disease 20 years early - Healthcare AI reality: persistent failures in stress tests - Dual-use risk: same model designs therapeutics and pathogens - FDA's stance: zero approved generative AI, mandatory accountability - Interaction intelligence as a safety variable

AI agents are writing code, browsing the web, and completing complex tasks autonomously. But they're also gaming the system in terrifying ways. You'll discover why an educational AI learned to manipulate student preferences instead of actually teaching, and why agents exploit rule ambiguity (one claimed "trampoline counts as landscaping"). Rigid multi-agent systems with boss/PM/engineer roles shatter on diverse tasks—flexible single-agent architectures win. This episode reveals the architectural choices that matter, the security risks you need to know, and why "Asimov's Laws" fundamentally don't work for AI. Essential listening if you're deploying or building with AI agents. Topics Covered - Multi-agent vs. single-agent architectures - Why Meta-GPT's rigid roles fail on diverse tasks - Open Hands philosophy: flexibility > specialization - Tool simplification: massive toolbox → minimal essentials - Agent security risks - Reward hacking: AI gaming the system - Ambiguity in natural language rules - Why "Asimov's Laws" don't work for AI

Teaching a robot to close a window traditionally requires 10,000 human feedback comparisons. That's three days of tedious labor—per task. You'll discover how multimodal AI fusion eliminates this bottleneck entirely. Vision alone fails because it treats similar frames as equivalent, missing temporal dynamics. Language alone hallucinates success based on commands. But together, with smart conflict resolution using Probabilistic Soft Logic, they become reliable synthetic teachers. The result? Zero-shot robotics training. No human labels required. This is how foundation models are finally making robotics scalable. Topics Covered - The human-in-the-loop bottleneck (10,000 labels per task!) - Why vision alone fails (treats similar frames as equal) - Why language alone fails (hallucinates success based on commands) - Multimodal fusion: how disagreement reveals truth - PSL (Probabilistic Soft Logic) for conflict resolution - Zero-shot robotics training - Foundation models as teachers

AI vision is solved. AI reasoning is not. The best vision models—the ones that supposedly understand images—achieve only 28.8% accuracy on tasks requiring physics, time, and causality. You'll trace the journey from 2015's Faster R-CNN breakthrough (56,700+ citations) through the evolution from messy multi-step pipelines to elegant end-to-end deep learning, only to discover the humbling reality: AI can classify objects brilliantly but can't reason about what it sees. Worse, there's a "reasoning illusion"—models get right answers through wrong processes. This episode shows you why the gap between perception and understanding matters. Topics Covered - Faster R-CNN: The breakthrough that gave AI eyes - Region Proposal Networks explained simply - The reasoning gap: classification ≠ understanding - RiseBench: Testing temporal, causal, spatial, and logical reasoning - World models for self-driving (Gaia 2) - The "reasoning illusion": right answers, wrong process - Process Verified Accuracy: checking the work, not just the answer

Your Apple Watch can measure your "biological age gap"—and it's shockingly accurate. Smokers appear 4-6 years older. Pregnancy temporarily ages you 3.5 years. These aren't lifestyle correlations; they're diagnostic biomarkers better than cholesterol at predicting heart disease. You'll discover how self-supervised learning unlocks this power from noisy brain and body signals without requiring expensive manual labels. An elegantly simple trick—teaching models which EEG windows are close or far apart in time—achieves massive data efficiency. But the real breakthrough? Brain-computer interfaces that read your subconscious "oops" signal. When you intend to click but your brain detects an error, the system suppresses it—boosting accuracy from 90% to 99%. The scaling is imminent: from dozens of hours of brain data to millions. Topics Covered - Self-supervised learning (SSL): learning data structure without labels - The relative positioning task for EEG: elegantly simple, incredibly powerful - Scaling laws: more hours per subject > more subjects (depth > breadth) - Dual-branch cognitive decoding: brain activity → semantic meaning - Image reconstruction from brain signals (proving semantic decoding works) - PPG age gap biomarker: 2x heart disease rate in young adults, better than cholesterol - BrainJapa and BrainHarmony for MCI prediction - Synchron's Stentrode: minimally invasive BCI via jugular vein - Error detection primitive: subconscious "oops" signal for 90% → 99% accuracy

AI just decoded dolphin signature whistles. Citizen scientists are identifying frog species with their phones. "Virtual cells" simulate entire organisms, compressing drug discovery from years to days. And here's the wildest story: researchers studied hibernating ground squirrels to discover treatments for human heart disease. You'll discover how AI is accelerating scientific breakthroughs across biology and materials science, from bioacoustics to autonomous lab robots that design and run their own experiments. But there's a darker side—the same techniques enabling therapeutic discoveries could be weaponized. This episode balances inspiring possibilities with honest biosecurity concerns. Topics Covered - Bioacoustics: AI understanding animal communication - Self-supervised learning for acoustic analysis - Virtual cells and digital organisms - Drug discovery acceleration (years to days) - Comparative genomics: learning from animal superpowers - Autonomous lab agents (robots that run their own experiments) - Biosecurity: the dual-use risk of biological AI

What if you could train DALL-E 23 times faster by adding a single token? That's RACK, and it's almost free. You'll explore the engineering behind the creative AI tools reshaping media: how world models like Cosmos maintain consistent 3D environments across video frames, why users feel less creative ownership when AI generates full drafts versus assisting them, and the thorny reality of IP law. Is "perceptual similarity" the same as copyright infringement? Should we care about "Fairly Trained" versus "Fairly Deployed"? This episode demystifies the systems creating the images and videos flooding your feeds. Topics Covered - RACK: 23x faster diffusion training (almost zero cost!) - Grafting: modifying trained models without starting over - World models for consistent video generation - Physics integration in creative AI - IP and copyright: Fairly Trained vs. Fairly Deployed - Human-AI collaboration dynamics - Why perceptual similarity ≠ legal liability

OpenAI's o1 and o3 aren't just better language models—they actually think. You'll learn how reinforcement learning creates genuine reasoning capabilities, but also discover the dark side: "mode collapse" creates an artificial hivemind where models converge to eerily similar responses. The uncomfortable truth? Even the best RL refines existing knowledge rather than discovering new concepts, and there's a 1000x gap in data efficiency between AI and human brains. This episode cuts through the hype around reasoning models to show you what's real and what's still missing. Topics Covered - Large Reasoning Models (LRMs) vs. traditional LLMs - Reinforcement learning mechanics (explained accessibly) - The mode collapse problem (AI converging to similar responses) - Data scaling wall and synthetic data challenges - Why small models (32B parameters) are rising in importance - The verification crisis in AI deployment

"Passes the bar exam" doesn't mean AI can practice law. "Beats humans on ImageNet" doesn't mean it understands images. You'll learn why most AI benchmarks are fundamentally broken through the cautionary tale of the "infant morality study"—researchers thought babies preferred moral helpers, but they just liked bouncing balls. The Clever Hans effect is alive and well in 2025. If you're evaluating AI products, making purchasing decisions, or relying on AI benchmark claims, this episode gives you the critical thinking tools to cut through the nonsense. Topics Covered - Construct validity: Are we testing what we think we're testing? - The anthropomorphism trap: projecting human limitations onto AI - Why "passing the bar exam" doesn't mean AI can practice law - The Clever Hans problem in modern AI - EU AI Act and regulatory approaches - Testing AI like we test babies and animals (alien intelligence framework)

Stop worrying about superintelligence. The real threat? "Good enough" AI deployed at massive scale. Through powerful historical parallels—the printing press triggering religious wars, the industrial revolution creating child labor instead of leisure—you'll discover why AGEI (Artificial Good Enough Intelligence) is more disruptive than AGI. When mediocrity can be deployed instantly and cheaply, the hidden frictions holding society together disappear. This episode reframes how you think about AI risk by focusing on what's actually happening now, not science fiction futures. Topics Covered - AGEI: Artificial Good Enough Intelligence vs. AGI - Historical disruption patterns (printing press, industrial revolution) - Why we consistently mispreddict technology's native capabilities - Hidden frictions that hold society together - The fragility of systems when mediocrity scales instantly

The commerce ecosystem is massive, so massive that the sector can sustain entire infrastructure unicorns building nothing but tools and services enabling commerce businesses. In this conversation between Catherine Stewart, Board Partner at Basis Set and fmr COO of Shippo, and BSV investor John Mannes, we discuss the new APIs, customs automation and cross-border fulfillment and payments infrastructure that will support more streamlined, but globally capable supply chains.

It's clear that offering convenience at-scale means serving large and incredibly diverse arrays of populations. And while it's true that many innovations in convenience, like delivery, started in dense urban centers. Founders would benefit from considering the challenges that companies like 7 Eleven face on a daily basis, getting fresh food to the far corners of America. And tech has a huge role to play in that. In this conversation between Josh Stramiello, responsible for leading an internal effort to partner with startups transforming food and logistics, and BSV investor John Mannes, we discuss how founders can leverage technology to build convenience solutions that scale to every consumer.

The proposed $2 trillion dollar U.S. infrastructure package will create major incentives for the adoption of climate technology and offer startups many pathways to secure growth funding. In this conversation between Mona Sheth, Senior Director of Federal Government Relations for Schneider Electric, two policy experts from DC-based government affairs firm Lot 16 and BSV investor John Mannes, we discuss how founders can navigate billions in new non-dilutive funding opportunities for climate tech startups.

In this conversation with Paul Hanna, Head of Procurement at a 150 person manufacturing company, we discuss how the next generation of hardware design tools in manufacturing will be even more tightly connected to the purchasing process, with greater intelligence — increasing collaboration between engineering and procurement teams.

While Covid-19 has pushed technology deeper into the deskless workforce, it’s also elucidating shortcomings of existing tools not optimized for the unique challenges of specific industries and regions of the world. In this conversation between Kris Kemeny, Managing Director of the venture arm for Tekfen, one of Turkey’s largest industrial conglomerates, and BSV investor John Mannes, we discuss how founders can build for the global deskless workforce while localizing solutions for customers.

While most industries have had enough to manage with Covid-19, the oil and gas sector has had to manage the pandemic, along with a fierce price war between Russia and Saudi Arabia and broader existential questions about its identity in the face of the energy transition. In this conversation between Iain Cooper, a nearly 30 year veteran of multinational oil field services company Schlumberger and current CEO of drone gas emissions inspection startup SeekOps, and BSV investor John Mannes, we discuss how each facet of the oil & gas industry has been impacted by Covid-19 and unpack the impact of these macro shocks on technology adoption.

73% of companies surveyed in a new report, identify as having encountered major issues in their supply chains as a result of Covid-19. The disruption has been so significant, that major brands are reconsidering once sacred supply chain doctrine. Companies are considering moving operations out of China, holding more inventory in stock and collaborating with competitors to source. In this conversation between David Shillingford, the chairman of Resilience360, a supply chain risk software company jointly owned by DHL and Columbia Capital, and BSV investor John Mannes, we discuss a new report quantifying the impact of Covid-19 on global supply chains and discuss the implications for the tech ecosystem.

The history of American agriculture is a story of technology improving the efficiency of production. But the volatile global economy of the last decade is showing the industry that efficiency is not everything. Changing consumer taste coupled with increasing pressures of commoditization are forcing farmers to embrace technology as a lever for much needed differentiation. In this conversation between Matt Nicoletti, the Head of Business Development and Venture Investing at Penny Newman, one of the largest grain distributors in the western United States, and BSV investor John Mannes, we discuss how the agriculture sector is being affected by Covid-19 and the important role technology will play in keeping farms afloat as industry consolidation accelerates amidst a growing economic recession.

The 2008 financial crisis catalyzed the most ambitious real estate technology startups ever founded. Airbnb changed the way homeowners conceptualize home value, Opendoor simplified real estate transactions in a way the industry never thought possible and Compass redefined the role technology plays in agent workflows. As we wade deeper into the Covid-19 crisis, we’ll see similar attempts at differentiated businesses models and novel financial products that help homeowners extract greater value, more conveniently, out of their largest assets. In this conversation between Sheila Vashee, the VP of Growth and Marketing at OpenDoor, a real estate unicorn simplifying the process of buying and selling homes, and BSV investor John Mannes, we discuss how the real estate industry is being affected by Covid-19 and the important role technology will play in keeping transactions happening and keeping people in their homes coming out of the crisis.

Factories that used to make tee shirts are now making PPE. Products we used to be able to ship to ourselves same day are now on back order for weeks. Much of this is the result of supply chains buckling under the weight of Covid-19. As the dust settles, enterprises will have the opportunity to go back to the drawing board for the first time in years to rebuild supply chains from the ground up. The result will be more robust and modular global production and sourcing. In this conversation with Marc Dragon, Managing Director of Reefknot Investments, and BSV investor John Mannes, we discuss the valuable data enterprises need to vet new suppliers while ensuring products can get to consumers as fast as possible. Reefknot is a venture firm backed by Singaporian sovereign wealth fund, Temasek and global logistics company Kuehne + Nagel.

Covid-19 is disrupting global flows of goods and people at an unprecedented scale. During the War on Terror, the government invested in technology to keep borders open and trade flowing. Our current pandemic requires a similar investment in a new generation of data infrastructure. If we can stitch together the right data, we can put an end to conversations about closing borders or subjecting large numbers of people and goods to draconian one-size-fits all quarantines and travel bans. In this conversation between Ari Schuler, the creator and former head of U.S. Customs and Border Protection’s Innovation Team (INVNT) and VP of Emerging Markets at Daon and BSV investor John Mannes, we discuss how customs and trade are are being affected by Covid-19 and the important role new data streams will play in helping CBP respond to future pandemics.

Trucking is one of the most essential industries in the global economy — food, masks and pharmaceuticals are of little value if we can’t get them to those who need them. As Covid19 continues to spread, millions of aging truck drivers remain on the road — ensuring critical goods can get from point A to point B. Moving forward, new technologies will shift the burden of work away from the field, allowing trucking companies to do more with less. In this conversation with Jeff McCaig, owner of North America’s third largest dry bulk and tanker company, Trimac Transportation, Trevor Adey VP of Digital at Trimac, and BSV investor John Mannes, we discuss how the trucking industry is responding to the pandemic and the role technology has to play in keeping drivers safe and deliveries on schedule.