Podcasts about causal

Send us Fan Mail*How do you forecast an event that has never happened before?*How do you forecast an event that has never happened before?The recent closure and reopening of the Strait of Hormuz are unique events. For events like these, traditional risk models lose their statistical basis: repetition. Alexander Denev returns to the podcast to show how causal models (Bayesian networks) let us reason about rare events despite this limitation.In this episode, we cover:- Why value-at-risk and other correlation-based models break exactly when you need them most- How a causal structure can "hold in time"- Building scenarios with LLMs - benefits, drawbacks, and lessons learned- Historical analogy as a modeling tool: Bosphorus, Hormuz, and more- A three-way robustness test for any Bayesian network- How the model's call held up: a ceasefire, a still-closed strait, and lasting infrastructure damage keeping oil elevated"History doesn't repeat itself, but it rhymes."------------------------------------------------------------------------------------------------------Video version available on the Youtube: https://youtu.be/FzKy2ws-7qsRecorded on May 29, 2026 in London, UK.------------------------------------------------------------------------------------------------------*About The Guest*Alexander Denev works at the intersection of quantitative finance, causality, and AI. He's the CEO of Turnleaf Analytics and the author of two books on applying Bayesian networks and probabilistic graphical models to finance and scenario analysis.Connect with Alexander:- Alexander on LinkedIn: https://www.linkedin.com/in/alexander-denev-66a25824/- Alexander's web page: https://turnleafanalytics.com/*About The Host*Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4 ).Connect with Alex:- Alex on the Internet: https://bit.ly/aleksander-molak*Links*Web- Alexander's LinkedIn post, Bayesian-network scenario for the Strait of Hormuz / Israel-Iran-US conflict: https://www.linkedin.com/posts/alexander-denev-66a25824_when-modelling-the-impact-of-events-that-share-7442892381668048896-JDs5/- Risk.net article, "Iran confusion makes the case for causal modelling": https://www.risk.net/our-take/7963361/iran-confusion-makes-the-case-for-causal-modellingBooks- Rebonato, R. & Denev, A. - Portfolio Management under Stress: A Bayesian-Net Approach to Coherent Asset Allocation (https://amzn.to/3vE6Jc1)- López de Prado, M. - Advances in Financial Machine Learning (https://amzn.to/3PXD8kH)- Molak, A. - Causal Inference and Discovery in Python (https://amzn.to/3VVK4m3)- Denev, A. - Probabilistic Graphical Models: A New Way of Thinking in Financial Modelling (https://amzn.to/3VQeLJm)- Pearl, J. & Mackenzie, D. - The Book of Why (recommended entry point) (https://amzn.to/4e0ATrZ)- Pearl, J. - Causality: Models, Reasoning and Inference (for advanced readers) (https://amzn.to/49zBKf5)- Rebonato, R. - Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Stress (https://amzn.to/3RC411e)*Perks & resources*

Stacy had her car's belts tightened recently and more recently the car went up in flames. Coincidence or Causal? Find out on this episode of the Best of Car Talk.See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

In this episode of SERious Epidemiology, Hailey and Matt welcome guest host Dr. John Jackson to discuss Chapter 5 of Causal Inference: What If? This chapter focuses on explaining the concept of interaction. Together, they unpack the often-confusing distinction between causal interaction and effect measure modification. Throughout the discussion they go on (helpful) tangents to talk about factorial trials, risk stratification, DAGs, confounding control, and why students are right to find all of this a bit head-spinning. They also debate additive versus multiplicative interaction, sufficient component causes, causal pies, synergism, antagonism, and whether interaction terms can really tell us anything about mechanisms—or whether they mostly tell us where treatment effects may differ. Along the way, there are excellent examples involving surgery, vaccination, infectious disease. Also, in case you ever wondered, every academic has an ever-growing “papers to read” pile.

In this 50 minute healing session we work with the InterGalactics Level 4 healing frequencies called InterGalactic Earth.  In particular we cleanse and energize each of the seven subtle bodies of ourselves utilizing each of the InterGalactic Earth Frequencies: InterGalactic Earth Physical, Etheric, Emotional, Mental, Causal, Spiritual, and Dvine Frequencies.  This is a chance to make a deep connection to the Earth Consciousness as well as the Cosmic Consciousness of the InterGalactics to bring about balance and wholeness to any condition that we might be going through right now.  This can be the need to balance on the physical, energetic, emotional, mental, or spiritual levels.  Also, you can use the healing session to help advance your spiritual progress and connection to the earth energies at this time.

Is obesity truly reprogramming your brain and accelerating dementia, or is the link mostly genetics and family environment? We dive into Eric Topol's March 2026 Nature Metabolism perspective on brain-wide damage mechanisms versus the 2023 sibling study showing associations vanish within families—plus the key limitations of both studies. Balanced, evidence-based take on one of health's hottest controversies. (Ideal for longevity, metabolic health, and science podcast fans.)

In this episode, Dr. Catalina Fernández explains a new theoretical model of human growth and its opportunities for cross-sectional and diverse samples. Dr. Catalina Fernández is an Assistant Professor in the Department of Anthropology at Florida Atlantic University (United States). Her research focuses on the role of food and diet in human adaptation and evolution among contemporary populations. Drawing on evolutionary and biocultural frameworks and employing mixed methods, her work investigates how subsistence strategies, nutritional histories, and the environment shape genetic, physiological, and cultural adaptations. She is particularly interested in questions related to the consequences of global market integration for human health and well-being among rural and small-scale societies. She has experience working with rural and Indigenous communities in Latin America, addressing issues related to environmental and dietary adaptations, nutrition transition, chronic disease risk, and population genetics. Her most recent research project investigates the causes of variation in child growth trajectories among non-Western populations, aiming to better inform public health interventions using culturally and environmentally appropriate strategies. Building on this work, she is developing a research program that examines the life-course health outcomes related to water and food security resulting from the climate change–driven expansion of the mining industry among indigenous communities in Chile. Contact Dr. Fernández at catafernandezh@gmail.com ------------------------------ Find the paper discussed in this episode: John A. Bunce, Catalina I. Fernández, Caissa Revilla-Minaya; A causal model of human growth and its estimation using temporally sparse data. R Soc Open Sci. 1 August 2025; 12 (8): 250084. https://doi.org/10.1098/rsos.250084 ------------------------------ Contact the Sausage of Science Podcast and the Human Biology Association: Facebook: facebook.com/groups/humanbiologyassociation/, Website: humbio.org Chris Lynn, Co-Host, Website: cdlynn.people.ua.edu/, E-mail: cdlynn@ua.edu, X:@Chris_Ly Mecca E. Howe, Co-Host, E-mail: howemecca@gmail.com, LinkedIn: https://www.linkedin.com/in/mecca-howe/

Hello Interactors,It's been a while. Traveling for family, and a bit flooded by the relentless sneaker waves of unsavory world events — the kind that usually inspire me to write but lately threaten to pull me under.Spring in the northern hemisphere means Interplace turns to geographic information science and spatial analysis. How might we look at the complex unfolding of world events through this lens — and what happens when we push it further than emergence alone can carry it? That's what I attempt to explore here.PATTERNS PRECEDING PHYSICAL PLACESGeographic information science is a relatively recent field. It emerged from mid-20th-century cartography and land-use planning. Computer cartography and quantitative geography of the 1960s is often considered the first true digital Geographic Information Systems (GIS). It became a science (GIScience or GISc) in the late 1980s and early 1990s when Michael Goodchild questioned if there was a genuine scientific discipline lurking within the software.His answer was yes. He built an institutional home for that argument at the National Center for Geographic Information and Analysis at the University of California, Santa Barbara, my alma mater. Goodchild was my senior advisor in 1989 as UCSB was becoming a generative intellectual hub in the field. UCSB's geography department continues to push the question of what space means analytically, not just how to map it. I'm personally invested in better understanding how GISc may be a natural partner for complexity science, a field I've been attracted to since I started researching and writing.This partnership isn't new. GISc provides a powerful framework for dissecting the spatial dimensions of complexity, where systems defy reductionist analysis and emerge through nonlinear interactions. In the early 2000s, geographer David O'Sullivan, and others, articulated this as the study of “the behaviour of macroscopic collections of many basic but interacting units endowed with the potential to evolve in time” emphasizing these characteristic elements of complexity science: self-organization, path dependence, and the irreducibility of wholes to their parts. Around the same time, sociologist John Urry (and others) extended this to global scales, portraying globalization as co-evolving systems marked by unpredictability, irreversibility, and positive feedback loops that amplify disorder within pockets of order.These parings are a good start, but computational biologist Michael Levin offers what can be seen as a genuinely unsettling upgrade. His recent work on the origin of cognitive and morphological patterns suggests the dominant appeal to emergence as an explanatory endpoint may itself be, in his words, a “mysterian” position — one that “does not facilitate further advances.” When a surprising pattern appears in a complex system, the emergentist says “that's just what happens” and catalogs it.But Levin proposes these patterns are not random facts to be noted and admired. They are part of an ordered, non-physical space that physical systems, when configured the right way, ingress into. Ingression is a term Levin borrows from mathematician Alfred North Whitehead as a potential that timeless abstract objects possess to become actual concrete experiences. “Red” only becomes red when its potential is realized. These ‘ordered spaces' of potential are portals into what Levin calls a Platonic Space. Plato argued that the objects we encounter in the world are imperfect instances of perfect, eternal Forms that exist independently of any physical thing. The most primitive form being the triangle. Levin's argument is the triangle participates in a kind of Triangleness; it realizes it's potential to exist.Nature keeps arriving at triangles independently, across wildly different substrates, as if drawn by the same attractor. The triangle is the only polygon that is inherently rigid: push on any corner and the shape holds, which is why trusses, bridges, and bones all rely on triangular geometry for structural strength. Radiolarians, single-celled ocean organisms with no brain and no blueprint, construct intricate skeletal lattices of triangulated geometry at microscopic scales.In Levin's terms, nature is ingressing Triangleness — repeatedly, across billions of years and countless lineages — because the Form has properties that reward any physical system stable enough to express it. The truth that a triangle's angles sum to exactly 180 degrees owed nothing to the first organism that built one.Physical systems are, in this sense, less like containers and more like pointers — a term borrowed from computer science. Pointers are variables that hold the addresses that reference more information. Levin's framework requires a specific kind of pointer: not a pointer to stored data, which retrieves a static value, but a pointer to a subroutine that calls up a routine that executes complex actions and outputs beyond the pointer itself. The pointer is small, while the executed routine may be vast and behave unpredictably.Think of a street address. The address itself contains nothing — it is a short string of numbers and words that fits on an envelope — but hand it to the right system and it retrieves a house, a history, a neighborhood, everything that has ever happened inside those walls. This is Levin's claim about physical structures. A genome, a city, an institution doesn't contain its pattern so much as it points at one — and when the pointer is well-formed, you get considerably more out than you put in.What does this mean for GISc? It means that spatial configurations — cities, borders, trade corridors, migration routes — are not merely sites where local interactions produce global outcomes. They are interfaces into a latent pattern space. When a hub city emerges, when a colonial border persists for centuries past the empire that drew it, when a pandemic spreads exactly along the topology of air travel, we are not only witnessing the consequential mechanical emergence of patterns derived from local rules. We are watching physical structures act as pointers that summon — ingress — specific patterns of collective behavior, whose full complexity exceeds what was put in. Levin's core observation about biological morphogenesis translates here with uncomfortable precision.Consider one of his more unsettling tadpole experiments. The creation of its normal bulging eyes are suppressed (by microscopically manipulating cellular ‘software') and a replacement eye is instead induced — ingressed — on the tail. The optic nerve growing from that tail-eye doesn't connect to the brain — it terminates somewhere around the spinal cord. By any conventional account, the animal should be blind. It isn't. The tadpoles can still see and perform well in visual tasks. Somehow, the system routes around its own abnormal wiring to recover function. The pattern being pointed to — sight — was never housed in the eye itself, or in the specific neural pathway, or in any single component. The eye on the tail is a wildly improbable pointer, and yet it retrieves something far richer than its own structure contains. You get considerably more out than you put in.Some GISc tools — like agent-based models or network analysis — already detect this excess in a geography context. A single infected traveler tips a system toward chaos not because of arithmetic addition of local interactions described in the GISc analysis, but because that traveler's position in a network acts as an interface to a pattern of contagion whose scope was latent in the structure all along. The “geographic advantage” O'Sullivan, and crew, describes — GISc's relationship to multi-scalar processes and human-environment couplings — is, in Levin's vocabulary, a sensitivity to how physical arrangements act as pointers into a rich space of possible collective behaviors.This reframes world events not as linear narratives but as navigations of morphospace — the full landscape of forms a system could take, where some configurations are reachable and others are not, and where attractors pull trajectories toward specific patterns regardless of starting conditions.What pattern are current geopolitical configurations pointing toward? What is being ingressed by the particular architecture of today's global institutions, communication networks, and urban densities? While GIScience sharpens our sight on outcomes, it leaves uncharted the deeper question of what is the shape of the latent space these material forms slip into.BORDERS STORE WHAT BODIES KNOWLevin's work suggests at every scale of organization, we are dealing not with mechanical aggregation but with collective intelligence. To understand what he means by that, it helps to borrow an image from Einstein.Because nothing travels faster than light, any event you could possibly influence — or that could possibly influence you — is bounded by how far light could travel in the available time. Draw that boundary in spacetime and it forms a cone. Everything inside it is causally reachable, everything outside it is not. Levin borrows this image to describe the reach of any cognitive agent. A single cell's light cone is tiny — it can only sense and respond within its immediate chemical neighborhood, over milliseconds. A brain's light cone is vastly larger — it can model consequences years out and coordinate behavior across great distances. The cone is simply a measure of how far an agent's agency actually extends. And just as the body is a nested hierarchy of such agents — molecular networks, cells, tissues, organs — each operating within its own cone, pursuing goals whose scale its parts cannot perceive, so too is human society.A city is not simply a dense clustering of individuals whose local interactions produce urban dynamics. It is, in Levin's sense, a collective intelligence with a cognitive light cone that vastly exceeds that of any constituent. It pursues goals (economic growth, defense, habitability) across spatial and temporal horizons no individual cell — or individual person — can access. Institutions, legal codes, infrastructure, and cultural norms function as bioelectric memory — rewritable pattern memories that store the target morphology of the social body and guide error-correction toward it. Colonial borders, or the Great Wall of China, persist not merely through inertia but because they function like historic bioelectric setpoints. That is, they encode a spatial pattern that downstream processes continuously re-instantiate, even after the circumstances that produced them have dissolved.Levin's planarian flatworm experiments demonstrate this in biology. When bioelectric circuits are disrupted, the worm grows heads of other species — without any change to its genome. The pattern being expressed was latent in the space of possible forms, and a change in the interface (the bioelectric circuit) changed which pattern was ingressed. Geopolitical history offers analogies. How much of what we call a nation-state's “character” is not in its people but in the pattern stored in its institutional circuitry? When those circuits are disrupted — by revolution, invasion, or collapse — new patterns rush in from the adjacent possible, sometimes from regions of the latent space that are recognizable, sometimes shockingly novel.Pandemics also embody this scalar nesting. Viral replication is a molecular-scale process; its spread is topologically determined by the network of global mobility; its political consequences are mediated by institutional pattern memories about sovereignty, solidarity, and resource allocation. The COVID-19 pandemic did not merely “emerge” — it ingressed a set of patterns whose latency was already encoded in the physical architecture of 21st-century globalization. Competitive resource hoarding and cooperative vaccine-sharing were not just policy choices but different attractors in a landscape of a kind of “social morphospace”, pulling collective behavior toward different setpoints.GISc tools (like spatial game theory and network percolation models) map the surface of these landscapes. But Levin's framework asks us to go further. He wants us to not just map the attractors, but to ask what structured space those attractors are features of, and whether that space can be systematically explored.The scalar interplay extends outward. Local ethnic tensions, mapped via GIS hot-spot analysis, interact with what social theorist Zygmunt Bauman might term “global fluids” — arms, money, diasporas — to produce cascades that reflect not random chaos but path-dependent trajectories through a space of historical patterns. History's “nightmare on the brain of the living” becomes, in Levin's terms, a pattern-memory etched into the social substrate. Territorial borders, attempted genocide, human displacement are held as bioelectric setpoints, where trauma lingers as a morphogenetic field, quietly organizing the tissue of the present long after the original wound.MAPPING WHAT MATTER MERELY MISSESComplexity science, via GISc, forecasts world events as probabilistic landscapes rather than deterministic paths. Urry describes global systems as “adapting and co-evolving,” with attractors drawing trajectories amid chaos. GISc simulates this through fitness landscapes like agents navigate peaks and valleys of viability, local adaptations generating global patterns like economic booms or institutional collapses.Levin's framework intensifies this picture in two ways. First, it insists that the attractors are not randomly distributed. The latent space of possible social patterns — like the latent space of morphogenetic outcomes — has structure. Evolution, as Levin argues, progresses rapidly precisely because the space has “a relatively smooth character” in which “past interactions with it carry non-trivial information about the adjacent possible.” The same may be true of cultural and institutional evolution. The reason certain forms of governance, urbanism, or economic organization recur across independent civilizations is not purely because of convergent environmental pressures, but because they represent attractors in a structured space of collective intelligence patterns that sufficiently complex social interfaces tend to ingress.Second, and more provocatively, Levin's framework suggests that we do not simply make the social forms we inhabit. We invite patterns to temporarily inhabit our collective embodiments. To see why, consider one of his most uncontroversial and disarming experiments. Levin's lab studied simple sorting algorithms — the kind computer science students have used for decades. These are short deterministic procedures that take a jumbled list of numbers and rearrange them into sequential order. Nothing mysterious here but made for many an interview question at Microsoft!When Levin's team visualized the algorithm's progress as a movement through an abstract sorting space, unexpected behaviors emerged that nobody had noticed in all those decades of use. When the algorithm encountered a number that refused to move — a piece of broken data blocking its path — it didn't simply halt. It temporarily de-sorted the rest of the array, moved things around the obstruction, and then recovered its progress. It was exhibiting something resembling delayed gratification — the capacity to temporarily move away from a goal in order to reach it more completely later. Like a soccer player kicking the ball backwards to advance it forward.This ability was not written into the algorithm. Nobody put it there. Then, when the team ran a distributed version where each number ran its own variant of the algorithm, numbers sharing the same variant spontaneously clustered together — a kind of social behavior, emerging without a single line of code instructing any number to notice or prefer its own kind. The algorithm was doing something it was never designed to do, and had been doing it, unobserved, for decades.Now, imagine a democracy is not constructed from scratch by rational agents but an interface that, when configured appropriately, ingresses a pattern of distributed decision-making whose properties exceed what any designer or participant imagined or specified. Cities, constitutions, and international institutions become pointers. The patterns they summon may even surprise their architects — and may have been quietly surprising them and us all along.This has immediate consequences for how GISc could approach attempts at predicting futures. For example, prospective spatial modeling — Markov chains, scenario planning — maps the probability surface of possible trajectories. But a Levin-inflected GISc would ask this: what new pointers are being constructed right now, and what regions of the latent pattern space are they configured to access?The answers could become bewildering in a world of AI-mediated governance, hybrid human-machine urban systems, and the synthetic biological constructions Levin's team pursues. These are vehicles of exploration into regions of Platonic space we have not navigated before. “We are now fishing in regions of Platonic space we have never explored before,” he writes — with implications not only practical (”what will it do to us”) but ethical (”how do we fulfill the opportunities and duties of an ethical synthbiosis with beings who are not quite like us”).For GISc, this need not be merely philosophical. Spatial planning and governance literally configure the physical interfaces through which collective intelligence patterns are ingressed. Urban density fosters certain attractors of solidarity and innovation while sprawl ingresses different ones. Green civic infrastructure designed to buffer floods mechanically also reconfigures the relationship between human settlement and ecological pattern space which invites a whole different class of emergent resilience. The question is no longer only “what will happen here, probabilistically” but “what are we building a pointer toward?”Fatalists may see the latent space as already barring our options. Pessimists will amplify the risks of novel pointers we cannot control. Realists might attempt to quantify via more Monte Carlo simulations. And techo-optimists may try to engineer and configure interfaces to access and profit from whatever attractors emerge. But what I like most of all about Levin's framework is that it offers something more nuanced than any of these: structured humility. We do not know the full topology of the space we are pointing into. Every new city, every new institution, every new technological architecture is, in some sense, a bioengineering experiment — and like Levin's Xenobots and Anthrobots, it may manifest competencies and patterns nobody designed or predicted.If Levin's intuition is correct, we are but temporary self-organizing forms that hold together for a time, perform actions that exceed their physical composition, and then yield to the impermanence built into any pointer's relationship with the patterns it accesses. Humility does feel like the appropriate response. But more importantly, the recognition that mapping the structure of the space we are ingressing into is, at this moment, among the most important things we could do.The information embedded in Geographic Information Science has the potential to demystify fatalism, especially when death's certainty yields to spatial agency. Levin reminds us that information, at its Latin root, means to give form — to in-form. That is what geographic information has always done, long before it became a science. It did not merely transmit data, but impose structure on space, render the implicit geometry of human existence legible and actionable. Every map is an act of in-forming. The world is no doomsday script, but a co-evolving field — its attractors mappable, its interfaces legible, its vectors steerable — if we aim with care, with intent, and with the humility to know what we summon may exceed what we design.REFERENCESLevin, M. (2025). Ingressing minds: Causal patterns beyond genetics and environment in natural, synthetic, and hybrid embodiments. PsyArXiv. O'Sullivan, D., Manson, S. M., Messina, J. P., & Crawford, T. W. (2006). Space, place, and complexity science. Environment and Planning A: Economy and Space.Urry, J. (2003). Global complexity. Polity Press. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit interplace.io

La abogada Silvia Guevara Pérez, miembro de la Asociación Aklla Perú, informó que en algunas zonas del país se llegó a una cantidad de votos nulos que podría originar la nulidad de las elecciones realizadas el 12 de abril.

In this episode, I sit down with Bernard, co-founder of Ethon AI, to explore the seismic shift happening as machines move from simply reacting to truly understanding their own processes. We dive into how knowledge graphs, causal AI, and computer vision are transforming manufacturing—from chocolate factories in Switzerland to global giants like Siemens and Bosch. We discuss the real impact of connecting siloed industrial data, the journey from correlation to causation, and what it means for the people on the shop floor. Bernard shares concrete examples of AI-driven quality control, autonomous process optimization, and the evolving role of operators. Join me for a conversation that peeks into the future of manufacturing intelligence—and what it takes to build the digital quality engineers of tomorrow.

Hailey and Matt are joined by guest co-host Dr. Mabel Carabali to discuss Chapter 4 (Effect Modification) from Causal Inference: What If. We start off our discussion about heterogeneity of treatment effects, emphasizing that there is often no single causal effect but effects that vary across groups depending on population characteristics. Mabel helps to explain effect (measure) modification as variation in the exposure outcome effect across levels of a third variable. She also explains the concept of qualitative effect modification. We talk about how these concepts connect to transportability and generalizability. The end of the episode focuses on effect modification on the additive vs. multiplicative scales, continuing our (neverending?) debate about when and why we should care about effect modification on the relative scale versus the absolute scale.

We've been on a bit of a mini World Models series over the last quarter: from introducing the topic with Yi Tay, to exploring Marble with World Labs' Fei-Fei Li and Justin Johnson, to previewing World Models learned from massive gaming datasets with General Intuition's Pim de Witte (who has now written down their approach to World Models with Not Boring), to discussing the Cosmos World Model with with Andrew White of Edison Scientific on our new Science pod, to writing up our own theses on Adversarial World Models. Meanwhile Nvidia, Waymo and Tesla have published their own approaches, Google has released Genie 3, and Yann LeCun has raised $1B for AMI and published LeWorldModel.Today's guests have a radically different approach to World Modeling to every player we just mentioned — while Genie 3 is impressive, its many flaws demonstrate the issues with their approach - terrain clipping, noninteractivity (single player, no physics/no objects other than the player move), and maximum of 60 second immersion. Moonlake AI (inspired by the Dreamworks logo) is the diametric opposite - immediately multiplayer, incredibly interactive, indefinite lifetime, capable of MANY different kinds of world models by simulating environments, predicting outcomes, and planning over long horizons. This is enabled by bootstrapping from game engines and training custom agents: In Towards Efficient World Models, Chris Manning and Ian Goodfellow join Fan-Yun in explaining why their approach to efficiency with structure and casuality instead of just blind scaling is sorely needed:SOTA models still show physical or spatial understanding glitches, such as solid objects floating in mid-air or moving “inside” other solid objects.If the goal is to plan for the next action, how often is a high-resolution pixel view necessary for modeling the world? Our bet is that there is a disproportionately large share of economically valuable tasks where such detail is not required. After all, humans with a wide variety of sensory limitations have little difficulty doing almost everything in the world. Furthermore, for a large number of purposes, describing a scene or a situation in a few words of language (“the car's tires squealed as it cornered sharply”) is sufficient for understanding and planning.Experiments also show that humans only partially process visual input in a top-down, task-directed way, often making use of abstracted object-level modeling. In almost all cases, partial representations combined with semantic understanding are sufficient.…If the goal is to facilitate the understanding of causality in multimodal environments, then the world model—whether it is used in the virtual world or the physical world—must prioritize properties such as spatial and physical state consistency maintained over long time periods, and an ability to evolve the world that accurately reflects the consequences of actions. That's what Moonlake is building.Game engines are the right starting point abstraction to efficiently extract causal relationships, and building the interfaces and community (including their new $30,000 Creator Cup) to kickstart the flywheel of actions-to-observations.We were fortunate enough to attend their sessions at GDC 2026 (the Mecca of Game Devs), and were impressed by the huge variety and flexibility of the worlds people were building with Moonlake's tools already! Live videos on the pod.Full Video Pod on YouTube!Timestamps00:00 Benchmarking Gets Hard00:47 Meet Moonlake Founders01:26 Why Build World Models03:12 Structure Not Just Scale05:37 Defining Action Conditioned Worlds07:32 Abstraction Versus Bitter Lesson14:39 Language Versus JEPA Debate20:27 Reasoning Traces And Rendering Layer37:00 Gameplay Over Graphics38:02 Fiction Rules And World Tweaks39:15 Code Engines Beat Learned Priors41:10 Diffusion Scaling Limits43:23 Symbolic Versus Diffusion Boundary46:14 Platform Vision Beyond Games50:24 Spatial Audio And Multimodal Latents54:23 NLP Roots Hiring And Moon Lake NameTranscript[00:00:00] Cold Open[00:00:00] Chris Manning: Think this whole space is extremely difficult as things are emerging now. And I mean, it's not only for world models, I think it's for everything including text-based models, right? ‘cause in the early days it seemed very easy to have good benchmarks ‘cause we could do things like question answering benchmarks.[00:00:20] But these days so much of what people are wanting to do is nothing like that, right? You're wanting to get some recommendations about which backpack would be best for you for your trip in Europe next month. It's not so easy to come up with a benchmark, and it's the same problem with these world models.[00:00:41] Meet the Founders[00:00:41] swyx: Okay. We're back in the studio with Moon Lake's, two leads. I, I guess there's other founders as well, but, sun and Chris Manning. Welcome to the studio.[00:00:54] Fan-yun Sun: Thanks. Thanks, Chris. Thanks for having us.[00:00:56] swyx: You've got, you guys have, come burst onto the scene with a really refreshing [00:01:00] new take of mold models.[00:01:01] I would just want to, I guess ask how you, the two of you came together. Chris, you're a legend in NLP and just AI in, in, in general. You're, you're his grad student, I guess[00:01:10] Fan-yun Sun: Actually my co-founder.[00:01:11] swyx: Oh, yeah.[00:01:12] Fan-yun Sun: I should give a lot of credit to my co-founder, Sharon. Yeah. She was, she was actually working with Professor Fe Androgyn and then she ended up working with, Ron and Chris Manning here.[00:01:22] And then, so I got connected through to Chris initially, actually through my co-founder,[00:01:26] What is Moon Lake?[00:01:26] swyx: what is Moon Lake? What, what is, actually, I'm also very curious about the name, but like why going into world models?[00:01:33] Fan-yun Sun: So I was working a lot. With actually Nvidia research during my PhD years on essentially generating interactive worlds to train reinforcement learning agents or embody EA agents.[00:01:44] And then there's two observations. One in academia and one in industry. An industry like folks at Nvidia are actually paying a lot of dollars to purchase these types of interactive worlds, whether it's for the sake of evaluation or training the robots, or policies or models. And [00:02:00] then, in academia, same thing is happening.[00:02:02] And more specifically, when I was actually working with Nvidia on the synthetic data foundation model training project, we were actually generating a lot of these synthetic data and showing that, hey, you can actually, these synthetic data are actually as useful as real world data when it comes to multimodal pre-training.[00:02:16] But then, like I said, there's a lot of dollars being paid out to like external vendors or, or like. Other folks to manually curate these types of data. It was very clear to us that, okay, on our way to, let's call it embody general intelligence models need to learn the consequences behind their actions, which means that they need interactive data and the demand for those types of data are growing exponentially.[00:02:38] But everybody's sort of thinking about it from a pure, say, video generation perspective or something else. But we feel like the true actually opportunity is actually building reasoning models that can do these things, like how humans do these things today. So that's a little bit on the genesis of Moon Lake, and I think the reason I got into world models was partly.[00:02:59] A philosophical [00:03:00] take of the on the world where I like, believe the simulation theory and stuff like that. But on the other, on the other hand, it's really just like, oh, like there's an opportunity there that I feel like nobody's doing it the way I think should be done.[00:03:10] Structure, Not Scale: The Vision[00:03:10] Chris Manning: I can say a little bit about that.[00:03:12] Yeah. So of the overall goal is the pursuit of artificial intelligence and most of my career has been doing that in the language space and that's been just extremely productive. As we all know, the story of the last few years, I don't have to tell about how much we've achieved with large language models, but, uh.[00:03:31] Although they have been extremely effective for ramping language and general intelligence, it's clearly not the whole world. There's this multimodal world of vision, sound, taste that you'd like to be dealing with more than just, language. And then the question is how to do it. And despite, a huge investment in the computer vision space, right, as the research field computer [00:04:00] vision has been for decades, far, far larger than the language space, actually.[00:04:05] I think it's fair. Say that, vision, understanding sort of stalled out, right? You got to object recognition and then progress just wasn't being made right? If you look at any of these, vision language models, it's the language that's doing 90% of the work and the vision barely works. And so there's really an interesting research question as to why that is and at heart, the ideas behind Moon Lake are an attempt to answer that, believing that there can be a really rich connection between a more symbolic layer of abstracted understanding of visual domains, which aren't in the mainstream vision models, which are still trying to operate on the surface level of pixels.[00:04:50] swyx: I think one of your blog posts, you put it as structure, not scale. Is that, a general thesis?[00:04:57] Chris Manning: Yeah. Well, scale is good too.[00:04:58] swyx: Yeah. Scale is good. Too[00:04:59] lot,[00:04:59] Chris Manning: [00:05:00] lots of data is good as well and scale, but nevertheless, you want the structure Yeah. To be able to much more efficiently learn.[00:05:07] swyx: Yeah. The other thing I really liked also is you put out an example of what your kind of reasoning traces look like.[00:05:12] Right. Which you would distill is the word that comes to mind. I don't even think that's a good, good description, but it would involve, for example, geometry, physics, affordances, symbolic logic, perceptual mappings, and what, what have you. But like that, that is the kind of example that involves, let's call it spatial reasoning, role model reasoning as as compared to normal LM reasoning.[00:05:35] Yeah.[00:05:36] Defining World Models vs Video Generation[00:05:36] Vibhu: But also like taking it a step back. So how do you guys define world models? A lot of people see okay, you can do diffusion, you can do video generation. But, you guys put out quite a few blog posts. You put out a essay recently, we can even pull it up about efficient world models. You have a pretty like structural definition here, but for the general audience that don't super follow the space, right.[00:05:55] What's, what's the difference in what we see from like a video generation model to [00:06:00] a world gen A simulator? How do you kind of paint that last[00:06:02] Chris Manning: year? Yeah, so I think this is actually a little bit subtle because, people look at these amazing generative AI video models, SAWA VO three, one of these things, and they think Genie, they think, oh, this is amazing.[00:06:17] This is we've solved understanding the world because you can produce these generative AI videos, but. The reality is that although the visuals do look fantastic, those visuals actually are accompanied by an understanding of the 3D world, understanding how objects can move, what the consequences of different actions are, and that's what's really needed for spatial intelligence.[00:06:49] So I mean, a term we sometimes use is that you need action condition, world models. That you only actually have a world model if you can predict, [00:07:00] given some action is taken, what is going to change in the world because of it. And in particular, that becomes hard over longer time scales. So if you're simply, trying to.[00:07:12] Predict the next video frame. That's not so difficult. But what you actually want to do is understand the consequences, likely consequences of actions minutes into the future. And to do that, you actually much more of an abstracted semantic model of the world.[00:07:32] The Bitter Lesson & Data Abstraction[00:07:32] swyx: Yeah, the question comes where you want to have more structure than is available in just predicting the next token.[00:07:41] And typically, well, let's, let's call it the experience of the last five years has been that is just washed away by scale, right? So what is the right middle ground here that, you don't ignore the bitter lesson, but also you. Can be more efficient than what we're doing today.[00:07:57] Chris Manning: One possibility [00:08:00] is, look, if we just collect masses and masses and masses and masses of video data, this problem will be solved.[00:08:11] Under certain assumptions that could be true, but there are sort of multiple avenues in which it could not be true. The first is what's really essential is understanding the, the consequences of actions producing an action conditioned world model. And if you are simply, collecting observational video data, which is the easy stuff to collect, when you're sort of mining online videos, you don't actually.[00:08:41] Know the actions that are being taken to see how the video is changing. And so if you are never collecting directly actions and you are having to try and infer them from what happened in the observed video, that's not impossible. But it's very [00:09:00] hard and it's not really established that you can get that to work at any scale yet.[00:09:05] And so there's a lot of premium on collecting action condition video data, which is part of why there's been a lot of interest in using simulation so that you can be collecting data where you do know the actions, which isn't quite limited supply, but there's also in the limit of as much data as you could possibly have.[00:09:28] Maybe the problem is eventually solvable, but. Even though we collect huge amounts of text data is always at a great level of abstraction, right? Language is a human designed, abstracted representation where there's meaning in each token and it's representing and abstraction of the world, right?[00:09:51] As soon as you are describing someone as a professor, and as soon as you are saying that they're condescending, right? These are very [00:10:00] abstracted descriptions of the world. It's not at what you're observing as pixel level, and to get to that kind of degree of abstraction, starting from pixels is orders and magnitude of extra data and processing.[00:10:14] And so, although, we absolutely want to exploit, get as much data as possible, use the bitter lesson. Nevertheless, if there are ways in which you can work with five orders of magnitude less data than people working purely from pixels, you're gonna be able to make a lot more progress, a lot more quickly.[00:10:34] And that's the bet here. And so you could just say that's only wanting to be able to, do it more efficiently, do it more quickly, do it more cheaply. But I think it's actually more than that, I think. One should be making the analogy to how human beings work at one level. You know? Yes, we have these high [00:11:00] resolution eyes and we can look and see a scene like a video, but all of the evidence from neuroscience and psychology is that most of what comes into people's eyes is never processed.[00:11:13] Right. That you are doing fairly fine ated processing of exactly what you're focusing on. But as soon as it's away from that of yeah, there's another guy over there that you've sort of only processing top down this very abstracted semantic description of the world around you. And so, that's what human beings are doing.[00:11:33] They're working with semantic abstractions and so. I think it is just the right representation. ‘cause we also have other goals we want to be able to do, real time worlds. So that means there's a limit to how much processing you can do and we want to do long-term planning and consistency. And again, that favors abstraction.[00:11:55] I mean, I guess there was actually a recent. Blog posts that [00:12:00] came out from our Friends of physical intelligence and, they were sort of heading in the same direction they were saying Oh, to the pay[00:12:06] swyx: pay model.[00:12:07] Chris Manning: Yeah. Yeah. To maintain a long term memory of what's happening in the world. So we can, do longer term we actually storing text of what is, been happening in the world.[00:12:19] Right. It is not such a successful strategy of trying to keep it all at a pixel level.[00:12:24] Vibhu: And yeah, I mean, you can see it in video models like that Temporal consistency. We're at a scale of train on, all the video data we have. We have it for maybe 30 seconds, a few minutes. That's not the same as a game state played for half an hour.[00:12:37] Right. I thought you guys break it down pretty well. You have a, you have a blog post about. Building multimodal worlds with an agent. I dunno if you guys wanna talk about this. This is one of the things I read, I[00:12:48] swyx: thought, yeah, it's the thing I talked about with the reasoning chain. Yeah.[00:12:51] Vibhu: So there's like different phases to this.[00:12:53] It seems like it's more of an agent, a scaffold, very different approach than just, type in a prompt and you, you don't have the same consistency. [00:13:00] It also, like, for people that are listening, I, I would highly recommend reading it. It breaks down the problem in a different light, right?[00:13:06] So like, what do you need to consider when you're talking about video, like world game models, right? How would, what do you need to consider? What are the factors? What are the elements? What's the state? So I don't know if you guys have stuff to talk about for this one.[00:13:19] Fan-yun Sun: Yeah. Actually, I wanted to add on a little bit Yeah.[00:13:22] On our previous point, which is just like, change topics so quickly. I, I do feel like sometimes people confuse like, oh, like we're taking an an, an method with abstraction. That means they don't believe in bitter lesson. Like that's just false, right? Like we are believed is a bitter lesson. But then I feel like the question that we always discuss is like, what is the right abstraction level today?[00:13:42] The analogy I like to make is like, let's just say we can encode and decode. Represent all of images, videos, audio and bytes. Then the most bitter lesson approached is to train a next byte prediction model as opposed to the next token prediction model where it's just like, okay, it's natively multimodal, can just, but it's like, yeah, like [00:14:00] to, to Chris's point, it's like the scale and computing you need to achieve that.[00:14:03] So that's why we always come back to like, okay, what is the most efficient way to do it? And reasoning models to the point of this blog post is a showcase of like, Hey, we're actually just like reasoning about the world and reasoning about. The aspects of the world that CAGR that matter for me to learn what I want to learn from this role model.[00:14:21] swyx: Yeah, it's like you're improving the en encoder of whatever you're, trying to model. And like a better representation would just represent the important things in less space. Yeah. Which would just be more efficient.[00:14:33] Fan-yun Sun: Yeah.[00:14:34] swyx: So yeah, I, I, I fully agree that it is not, antagonistic to, bitter lesson.[00:14:38] I do wanna wanna mention one more thing. Is there any philosophical differences with the JPA stuff that, Yun is working on? I gotta go there. You, you, you, you're, you're imagining like some latent abstraction. I'm like, okay, fine. Let's, let's talk about it, right? Like it's an elephant in the room.[00:14:52] Chris Manning: Yeah.[00:14:53] JEPA & Philosophical Differences with LeCun[00:14:53] Chris Manning: There are philosophical differences. Jan Lacoon is a dear friend of mine, but. [00:15:00] He has never appreciated the power of language in particular, or symbolic representations in general. Yarn is a very visual thinker. He always wants to claim that he thinks visually and there are no words, symbols, or math in his head.[00:15:21] Maybe that's true of yarn. It's certainly not the way I think. Um. But at any rate, the world according to yarn is the basic stuff of the, the world and of intelligence is visual and language is just. This low bit rate communication mechanism between humans and it doesn't have much other utility and it's far inferior to the high bit rate video, that comes into your eyes.[00:15:53] And I think he's fundamentally missing a number of important things [00:16:00] there. Think of this evolutionary argument looking at animals, right? That the closest analogies, the things with chimps, right? So chimpanzees, have fairly similar brains to human beings. They have great vision systems, they have great memory systems.[00:16:18] They've got, better memory than we do of short term memories. They can plan, they can build primitive tools that, humans. Massively ahead in what we understand about the world, what we can plan, what we can build. And essentially what took off for us was that humans managed to develop language and that gave a symbolic knowledge, representation, and reasoning level, which just, okay if this sort of vaulting of what could be done with the intelligence in brains.[00:16:59] So the [00:17:00] philosopher Dan de refers to language as a cognitive tool and argues that, humans unique among the creatures in the world have managed to build their own cognitive tools and language is the famous first example. But other things like, mathematics and programming languages are also cognitive tools.[00:17:21] They give you an ability to. Think in abstractions, in extended causal reasoning chains. And that allows you to do much more. And we use that for spatial representation and intelligence and planning and gameplay as well. So we believe, and this is, underlying the specific technologies that Moon Lake is making, that symbolic representations are powerful.[00:17:50] And you want to use that in your understanding of the visual world when you want a causal understanding, when you want to maintain long-term [00:18:00] consistency and prediction. And as I understand it, that's just not in ya Koon's worldview. So I think that's the fundamental philosophical difference. Then there's the specific model.[00:18:11] He's been advancing jpa, that's a reasonable. Research bed is a direction as to, to head for building out a model of the visual world. To my mind, it's sort of one reasonable research bed. It's not really established. It's the best one that everyone should be following,[00:18:32] swyx: at least developed at scale, at Meta.[00:18:34] But it's not just vision, right? Like, I mean, JPA is a, just joint admitting prediction can be applied to anything really. And people have done it. The argument is that there is a latent representation or that is probably more. Suited to the task, then why not let machines do it for us instead of predefining it at all?[00:18:50] And isn't something like a JPA shaped thing the right answer? And if not, why not?[00:18:55] Chris Manning: So I think there's a part of jpa that's right, which is [00:19:00] you do want to have a joint. Embedding that gives you a consistent model of the world. And Jan's argument is you can never get that from auto aggressive language models ‘cause they're sort of left to right churning out one token at a time.[00:19:22] I guess this is where we're the research arguments of the field, I'm not actually convinced that's right. ‘cause although the token production is this auto aggressive, process that's heading, left to right, I guess don't have to be left to right. But anyway, in sequence of tokens we could have right to left Arabic.[00:19:40] But although that's true, all of the weights of the model that are internal to the transformer, they are a joint model of the model's understanding of the world. And so I think you can think of the weights of the model as a form of. Joint representation, [00:20:00] and therefore it is plausible to think that could be the basis of a world model, which avoids, ya's objections.[00:20:10] swyx: I think I follow, and obviously that would touch on what Moon Lake eventually ends up doing as well. Right. Like, which it's hard to tell because you put out the end results, but we don't know the inputs that go into it. So it's, it's, that's something that we have to figure out over time.[00:20:25] Vibhu: Yeah. I mean, I guess this kind of breaks down some of the outputs. Do you wanna walk us through it?[00:20:31] Reasoning Traces & Interactive Worlds[00:20:31] Fan-yun Sun: Yeah. So this, this really just walks us through the reasoning traces of like, okay. So that just say, if we wanna build a world in this context, it's really just a game demo that, that shows the, the variety of interactions that this world model can build.[00:20:45] And yeah, it's really just a reasoning traces of like, okay it prompted to create a bowling game. Like how did it achieve what you saw? That level of causality, interaction and consistency, right? So yeah, this is almost just like a, an example of [00:21:00] like a reasoning traces. Very[00:21:01] swyx: detailed.[00:21:01] Fan-yun Sun: Yeah.[00:21:01] Vibhu: Very, very detailed.[00:21:02] You gotta you don't even realize it, right? Like when a video is generated, what happens when a ball strikes a pin, right? So first, like you, there's audio in that, like audio triggers happens, score increments, the world changes. Like pins have to start dropping. There's a timer that goes on. It's just like very similar to how now we're used to reasoning for language models.[00:21:20] There's a whole state of what happens. So geometry, physics, all this stuff. And then yeah, there's kind of that single prompt. So asset, ation all this stuff. It's like a, it's a nice view to see what's going on.[00:21:32] swyx: I think Sun is also too polite to point out that, both like Google's genie, demos as well as world Labs is marble, do not have interactive worlds.[00:21:41] Fan-yun Sun: That's the benefit of having a reasoning model, right? Like, because you can, you can say, oh, like maybe in this particular context, I want to learn how to bowl. And then you can say, okay, then what is it important when it comes to learning how to bowl? Okay, maybe it's like I need to understand the, the basic of like, physics and I want to throw it over [00:22:00] them.[00:22:00] I wanna know that when I, when it resets it's a new game. So I know that yeah, basically, you know to pick up the ball, you know that ball's gonna cause the pins to fall down. You know that what's important to this particular bowling game is to score and you know that the score corresponds to the number of pins that fell down.[00:22:19] So it's just like, if it's a model that sort of knows what it. Looks like, knows what a bowling game looks like, but doesn't actually allows you to practice over and over again and to understand that, oh, like what it takes to actually get a high score. Then it sort of doesn't actually allow you to learn what you set out to learn within the world model.[00:22:38] And I think this is really just one example of showing like the advantages of the approach that we're taking over most the, let's call it the zeitgeist, is today, when people talk about clinical role models,[00:22:51] Chris Manning: right? So it sort of seems like the question to ask when there's a world model is.[00:22:58] Can I not [00:23:00] only just wander around the world and look at the beautiful graphics, can I interact with the objects in the world and see the right consequences of actions?[00:23:11] Vibhu: And you also understand what the consequences would be if you do something right. So it's not just like, okay, there's one thing if I pick it up, something will happen.[00:23:19] But, there's 50 options and I know I can expect, I can infer what would happen if I do any of them. Right. So very different when you can actually see it play around with it.[00:23:28] swyx: There,[00:23:28] Beyond Unity: Cognitive Tools for World Building[00:23:31] swyx: there's two cheeky elements of that. I mean, the, the, the I guess, less ambitious one is, let's really establish for listeners, why is this fundamentally different than writing Unity code, right?[00:23:40] Like just creating a model to translate a prompt into Unity code[00:23:44] Fan-yun Sun: so there is an underlying physics engine. Yeah. In that sense, there's some overlapping things to Unity, but the way we think about it is like physics engine. Tools or code are cognitive tools like borrowing Chris's term, right? Like tools [00:24:00] that the model can employ as means to an end.[00:24:04] So today maybe you say, okay, in this particular context we care about physics, we care about the long-term causality consequences. Then yes, we deploy it, employ physics engine, and then maybe tomorrow we say, okay, we're we're training that. Just say drones where we only care about really fluid dynamics and the visual aspect of the world.[00:24:25] Then, then yeah, maybe we don't actually, the model actually doesn't have to use a physics engine. Or maybe it employs other types of representation or physics engine to achieve the task. So yes, writing code for Unity is sort of similar to a tool that our A model can employ, but our goal is for a model to take a representation conditioned reasoning.[00:24:46] Approach or process.[00:24:47] swyx: Yeah,[00:24:47] Fan-yun Sun: internally.[00:24:48] swyx: Yeah. Using these things as just like general two calls. Right. Which I think is very interesting. The other more ambitious one is, some kind of recursive element where it becomes multiplayer, right? Like here, there's a single player element, you're not [00:25:00] modeling any other people involved.[00:25:01] And that is a whole other thing.[00:25:04] Fan-yun Sun: But in fact, we can really do multiplayers. Oh yeah, okay. I haven't seen any double situations. So just actually just like prompt our, our model to say, Hey, like configure to multiplayer. Then it'll do like this. You'll be able to configure multiplayer[00:25:16] swyx: great[00:25:17] Fan-yun Sun: persistency database for you.[00:25:18] Easy. Yeah.[00:25:19] Vibhu: So what, what are like some of the current limitations in where we're at? So there's one approach of like, okay, scale up video predictors. Obviously there's data issues. With approaches like this, is it data constraints? What are like the next steps? Is it real time? Like, so there's one side of, write an agent to write Unity code, but okay, I want to be streaming a game real time.[00:25:38] I want to have characters being also like agent, but where, where do we kinda see this scaling up? Right?[00:25:44] Fan-yun Sun: Yeah, there's definitely a data constraint. Like the more data, the, the better. This reasoning model can almost basically act as humans to like operate a variety of tools and softwares to build whatever's necessary.[00:25:57] And then there's a sort [00:26:00] of fidelity constraint, which we're actually solving with another model, which we can talk about later. But it's like, it's not as easy to get to photorealism with the approach that we're taking. But we think there are better solutions to that, which is we can dive into later.[00:26:14] Later.[00:26:15] Vibhu: The one one thing you note here is it's a diffusion model, right? So there's, there's a few approaches, diffusion caution, splatting, yeah, so Ry diffusion model, you guys wanna[00:26:25] Fan-yun Sun: Yeah.[00:26:25] Vibhu: Introduce,[00:26:26] Fan-yun Sun: yeah, totally.[00:26:26] Rie: Neural Rendering & Skins for Worlds[00:26:26] Fan-yun Sun: So within our world modeling framework, we think there are two models that we train, right?[00:26:31] Like, there's the multimodal reasoning model that we just talked about that essentially handles. Mainly the, the causality, the persistency and logic determinism of the world. And then RY is our bet on saying, okay, like while all those model, can take care of all these things that we just talked about, it's limitations compared to existing, say, video models, is that it doesn't have as high of a pixel [00:27:00] ality right off the gate, right?[00:27:02] And EE is to say, Hey, we can actually take whatever persistent representation that we generate with our multimodal reasoning model and learn to restyle it into photo photorealistic styles or arbitrary styles you want. So this model is almost to say, Hey, I'm going to respect the persistency and interactivity of the world that you created, but my only job is to make sure that its pixel distribution is close to what we want.[00:27:29] Vibhu: Yeah.[00:27:30] swyx: Great example right there. You kept the KL divergence.[00:27:33] Fan-yun Sun: Oh. Where,[00:27:34] swyx: no, no. I mean this, this is a, a classic like, how you don't stray too far from the source material as you, you kept the kl, which is Oh yeah. Kind of cool. Yeah.[00:27:43] Fan-yun Sun: Yeah.[00:27:44] swyx: I mean, and the[00:27:44] Chris Manning: difference is, and I mean sun was pointing at this, where sort of saying it's in one way a more difficult path, but a better path that, typically the diffusion models are producing the whole scene and it looks lovely, [00:28:00] but there isn't spatial understanding behind it, which is allowing for the real time graphics gameplay, the spatial intelligence, understanding the consequences of worlds where this is, taking a path where it is assuming an abstracted semantic model of the world's state.[00:28:20] And then the diffusion model is then being used on top of that to produce the high quality graphics.[00:28:27] swyx: Is there an intended practical, or business use for this, or is it like a, like a demonstration of capabilities?[00:28:34] Fan-yun Sun: We actually believe that this is gonna be the next paradigm of rendering. So it's gonna replace how ra raizer, it's gonna replace DLSS today because it not only has these pixel prior that's learned from the world such that you can literally play any game in photo realistic styles, which is a lot of people's desire when they do GTA, right?[00:28:51] Like,[00:28:51] Vibhu: all the mods, all the people adding perfect lighting and all this.[00:28:54] swyx: So[00:28:54] Fan-yun Sun: skins[00:28:55] swyx: for worlds, let's call it[00:28:56] Fan-yun Sun: skins, let's call it skin for worlds. I,[00:28:58] Vibhu: it's also like, you can call it skin, you can call it [00:29:00] customization. You can play it how you want, right?[00:29:01] Fan-yun Sun: Yeah, exactly. And I think another thing that we really pointed out specific specifically in this blog is the programmability of it, right?[00:29:09] So what this means is that this render historically render is always a derivative of the game state, right? You're saying, oh, here's the game state, I'm rendering out a frame. But here I'm saying actually this render can be part of the gameplay loop. I can say something along the lines of, if upon getting 10.[00:29:26] Apples, I'm gonna, my weapon of choice, my bullet's gonna turn into apples. And that's, that's possible because we can say, we can basically dynamically have certain game state trigger the, the preconditions to the render such that the rendering is now part of the game loop too. One thing is to just say, okay, it's, it's, it's the appearance.[00:29:47] But the second thing is also to say there's these novel interactions that are possible because this render now has actually priors of the world.[00:29:57] swyx: It is up to the artist to figure out what to do with it.[00:29:59] Fan-yun Sun: It [00:30:00] is up to the creators. Yes.[00:30:01] swyx: Yeah.[00:30:01] Fan-yun Sun: And I also think that's actually another big argument that we're making and the reason that we're picking, taking the bet we're baking is that a lot of the times, whether it's for embody AI gaming, like you want a layer where human can inject their intentions.[00:30:15] So, for example, let's just say in the context of gaming, it's obviously like my creative intent, but maybe in the context of embodied ai, it's like, oh, like I take this foundational policy and I want to actually fine tune it to deploy in my house. So you want to almost say, inject, have a layer where human can say, oh, here's the distribution of things I want to create to achieve my goal.[00:30:35] And I think 3D graphics as it as it is today, is basic, the layer for people to say, Hey, what do I care about in this world? And it allows, basically human intent to be expressed in these worlds much more explicitly and distributionally as opposed to just saying, Hey, I'm gonna generate like, arbitrary.[00:30:54] And it's like just prompts,[00:30:55] swyx: it's one of those things where like, I think you, you're going to build up a series of models, right? [00:31:00] This is just one of, this is probably like the highest utility or heaviest, frequency one, I don't dunno what to call this. Where like you Yeah. You can immediately drop this in on any game and you don't need anything else that.[00:31:10] That you guys do. But, I, I could see, I could see that I think the, the human intent is something that people are not even used to because we're so used to static worlds or, worlds that just don't react, or, I don't know. It's, it, you're kind of blowing my mind right now with like, I'm, I wonder if you've talked to people at GDC Hmm.[00:31:27] And what are they gonna do with it?[00:31:30] Fan-yun Sun: Yeah. Now the stance that we take on this front is like, we're not gonna be more creative than our users to ship[00:31:35] swyx: it out.[00:31:35] Fan-yun Sun: Yeah. But we wanna make sure that we're building things in a way that really allows them to express their intent.[00:31:41] swyx: The thing that you said about, here's the distribution that I want.[00:31:45] I think text may be too low of a bandwidth to. To really demonstrate, because I, I, there, I'm, I'm probably just gonna want to drop in a bunch of, reference assets and then you can figure it out from[00:31:58] Vibhu: there. But you probably wanna do a, a mixture of [00:32:00] both, right? Like you throw in a few images. I wanted this style.[00:32:02] Yeah. I want it to look like this. So it, it's, it's a mixture, right?[00:32:05] Chris Manning: I, I think it's a mixture. I mean, yeah, I mean there's clearly a visual component of this, and it's not that, everything can be text. ‘cause of course you want to give a visual look, but there's also a massive amount of giving the overall picture of the look of the world and the behavior of things that you can express in a few words of text.[00:32:32] And it be very time consuming and difficult to do via visual means. So I think, yeah, you want a combination of both.[00:32:40] Evaluating World Models[00:32:40] Vibhu: So one question I kind of have is, how do we go about evaluating world models? So like, there's many axes, right? One is like, okay. I have preferences. How well do we adhere to prompts? One is the simulation.[00:32:50] One is like do things, is there core logic that's broken? So coming from we know how to evaluate diffusion, there's fidelity, there's [00:33:00] stuff like that. But what are some of the challenges that most people probably aren't thinking about?[00:33:04] Fan-yun Sun: Yeah, I think this is like a great question and probably one of the hardest questions in role models because like, I think it always comes back to what are you building this role model for?[00:33:13] And depending on your end goal and purpose, the evaluation should defer. So in the context of games, then the most direct way of measuring is how much behind are people actually spending in this world that you create? And if your goal is to say, for example, in the context that we just talked about, like, hey, deploying, deploying action in body, a agent, then your, your end.[00:33:33] Metric is then, okay, after training in these worlds that you generate how robust it is to when you actually deploy to the target environment. But then, it's, it's hard to measure these end metrics. So today people have like these proxy metrics that I call that basically try to measure what we really care about, which is the end metrics, but then frankly it's different for every use case.[00:33:57] Yeah,[00:33:57] Vibhu: which seems like quite a challenge, right? Like in [00:34:00] in language models or video models. Image models, your benchmarks are proxies, right? People aren't actually asking instruction, following tool use questions. They're proxies of how well it will do downstream. But for this, so like, should teams, should companies have their own individual benchmarks outside of games?[00:34:16] If you think of stuff like, okay, video production, movies, stuff like that, that also want to use world models. Should, should they sort of internalize like. Their own proxy. Is this something you guys do? Where, where does that connect[00:34:28] Chris Manning: go? Yeah, I think this whole space is extremely difficult as things are emerging now.[00:34:35] And I mean, it's not only for world models, I think it's for everything including text-based models, right? ‘cause in the early days it seemed very easy to have good benchmarks ‘cause we could do things like question answering benchmarks and could you answer the question based on these documents and the various other kinds of, do pieces of logical reasoning or math.[00:34:58] But again, these are sort of. [00:35:00] And there were sort of visual equivalents of things like object recognition, right? For these small component tasks. These days so much of what people are wanting to do also with language models is nothing like that, right? You're wanting to, have an interaction with the language model and get some recommendations about which backpack would be best for you for your trip in Europe next month.[00:35:25] And it's not the same kind of thing, right? And it's not so easy to come up with a benchmark as to does this large language model give you an effective interaction for guiding you in a good way for shopping, right? So, and it's the same problem with these world models. So if we take the game design case, well success is that a game designer can.[00:35:57] Produce what they are [00:36:00] imagining in a reasonable amount of time. And that's really the kind of macro task. That's a very hard thing to turn into a benchmark and I think a lot of this is actually going to turn into people walking, walking with their feet. Right? I mean, I guess that's what's happening, at the large language model level, right?[00:36:23] When people are choosing to use, GPT five or Gemini or clawed, individuals are trying out these different models and deciding, oh, I like the kind of answers that GT five gives me, or no, I feel like I get more accurate detail from Claude, right?[00:36:43] Vibhu: It's a lot of[00:36:43] Chris Manning: vitech, a lot of people just using it.[00:36:45] It's vibe checking. I realize that, but it's actually whether. People feel it's giving them utility in what they want. Right.[00:36:52] Vibhu: And the the interesting thing there is like a lot of people prefer the visual, right? This looks pretty, which is not the objective of what this is [00:37:00] for, right? It's if a, if a game designer is working on something, they care about the game engine, right?[00:37:04] The state, it's, it can look whatever. You can fix that up later. Or you can have a really good game state and you can quickly edit it to 20. 20 different versions, like Keep State,[00:37:14] Chris Manning: right?[00:37:14] Vibhu: So[00:37:14] Chris Manning: that's a really important distinction, for and for speaking to Moon Lake strength, right? So, yeah, great visuals are lovely to look at for a few seconds, but gains are really all about the concept, the game play.[00:37:33] And a lot of the time that doesn't actually even require great visuals. I mean, there are just lots of very successful games which have relatively primitive visuals, and there are other games where people have spent millions producing photo realistic, visuals, and the game sucks, right? So, keeping those two axes apart is really important in thinking about what's important in a [00:38:00] world model for different uses.[00:38:02] swyx: This conversation is reminding me of some game review and fiction discussions I've, had in my sort of non-AI related life. Some, for some people might know Brandon Sanderson, who's a very famous, fiction author, had, is is a big game reviewer. And he, he's a big fan of video games where you change one thing about a normal what you might assume about, about the world.[00:38:22] For example, Baba is you, I don't know if you might have come across that, where like the rules change as you play the game. And also like where, you can do things like reverse time selectively or like change gravity selectively. And I think this is also reminds, reminds me of other kinds of world models that are created by authors.[00:38:38] Where Ted Chang is, is my typical example where he'll take the world that, you know today, but change one thing about it and, but then create a consistent world based on that. Which is long-winded answer of me to, of. For me to say is it's it easy to create alternative roles that don't exist, but you change one thing and then let's, let's run a whole bunch of people through it to see if it works.[00:38:58] Chris Manning: My first dance will [00:39:00] be, that seems a lot easier and more conceivable to do using Techn technology like Moon Lakes than with some of the other world models out there, where the sun can actually make it happen. I'll let him give a second answer.[00:39:15] swyx: If I guess for you, you're constrained by the game engine tool, right?[00:39:18] Like at the end of the day, that's the, that's the thought, partner that you have. If I ask for something where like, if it never is allowed to reverse time or if gravity only ever works one way, then well that's it. But sometimes gravity might change,[00:39:33] Fan-yun Sun: but it's a lot easier to change with code as opposed to a model that is learned primarily on data of.[00:39:42] Real world and virtual worlds that are, I guess, like for example, junior, like there's actually trained on a lot of real world data and a lot of virtual gaming data, and it's hard to say maybe it's easier to say, okay, I wanna change the visuals in like the time period of, of the world. Like, you can't change gravity, for [00:40:00] example.[00:40:00] Vibhu: I feel like you can to light bounds, right? Everything comes down to like, code is a better way to execute it, but the models aren't that diverse and creative, right? You can say, okay, make gravity slower. It can do that, but it's limited to your representation of how you text it out, right? Like they're, they're only gonna do a few iterations, whereas programmatically, if there's a game engine under the hood, you can kind of go wild, right?[00:40:22] So one of the, I dunno, one of the limitations of most models is that they're very overtrained to one style. Right. And extracting diversity is pretty difficult. At least that's something we've seen.[00:40:35] Fan-yun Sun: I mean, are there examples you have in mind where you Existing models? Yeah. Like it would be easier to do that's not using code.[00:40:43] Certain types of creative intent or like transition state transitions,[00:40:47] swyx: Clipping, other models, other wo models are very good at clipping through things. Clipping my, my, my legs clipping through a rock because it's, it's just, it's just bad. [00:41:00] Like, you would have to struggle very hard with your stuff to actually make that happen.[00:41:04] Which I think is maybe a topic that you actually prepared on, Gian Splatting versus, the other stuff.[00:41:09] Vibhu: Yeah. Yeah. It's just for those not super familiar, right? There's a, there's gian splatting, there is diffusion. Like what works, what scales up. I feel like in February when Soro one came out the blog post was literally titled like,[00:41:21] swyx: you bring it up.[00:41:22] You never know.[00:41:23] Vibhu: World, world, video generation models are world simulators. It's super bitter lesson pilled. Yeah, emer, a lot of it is emergence, right? So, not to go through their blog post, basically their whole thing was as you scale up all this consistency, all this stuff just kind of solves, it's a very simple premise, right?[00:41:41] They just scaled up, diffusion, and from there, this is, this is Feb 2024, how much can we, it's already been two years, which is basically five years. How much more in AI time do we need to just scale up or, or do we hit a data cap? But I think we already talked about this a lot, right? Like this is back to the beginning discussion of what's [00:42:00] appropriate for the time.[00:42:01] And that seems like your approach, right?[00:42:03] Fan-yun Sun: Yeah. The point I'm trying to make is that they're very many, many different types of world simulators and like having a world simulator that can produce pixel coherency is very, very useful for games and, marketing and all these things, but it's not as useful as people think when it comes to causal reasoning.[00:42:25] When it comes to embodied ai. Yeah, like it this title is true. We're not saying that it's, it's like, not a great world simulator, but actually in the blog that we, we, we, we wrote, the bet is more so that there are gonna be disproportionately large share of value of real world tasks or, and virtual tasks where high resolution pixel fidelity is not needed.[00:42:47] Yes. Video models have their values.[00:42:50] swyx: Yeah. This is at the absolute limit of my physics understanding, but one example that comes to mind is basically having to solve like ba the equivalent of a three [00:43:00] body problem in a deterministic Well, where the video models, which is approximated good enough. Yeah.[00:43:08] Right. Like there's, there's some point at which your approach kind of runs into like the you now have to simulate the world. Please, thank you very much. And like you're trying to do that, but only to the extent that the game engine lets you and like game engines cannot do some things.[00:43:23] Fan-yun Sun: Yeah, no, I mean, I think the interesting or more technical question here actually is where do you draw the boundary between.[00:43:32] What's handled with, let's say, diffusion prior and what, when? What's handled with symbolic priors?[00:43:38] swyx: Yes.[00:43:38] Fan-yun Sun: Okay.[00:43:38] swyx: Okay.[00:43:39] Fan-yun Sun: Right. Let's go there. Because this, this boundary can actually be fluid. Like I think like maybe what you're trying to get at is like, okay, people are saying pixel prior, everything. But what we're saying is, okay, there's a boundary that we draw where this is where we think provides the most economical value for the domains and things that we care about today.[00:43:59] [00:44:00] And I actually do think, and it's something that we do internally all the time, which is like, okay, given new equations that we learn or new elements of the world and that we, we learn, or maybe some other knowledge that we acquire in the process of developing the models. Should we still be maintaining this line exactly as it is today?[00:44:22] Or should we move it a little bit left or a little bit right? Right. Like sometimes that we realize that, oh, like maybe customers or, or folks like want certain things that are better handled with preop pryor as opposed to, symbolic prior than,[00:44:34] swyx: yeah. Your, your skin thing is a, is a example moving it, right.[00:44:37] Yeah.[00:44:37] Or left. Yeah,[00:44:37] Fan-yun Sun: exactly.[00:44:38] swyx: I dunno what the, the left right is.[00:44:39] Fan-yun Sun: Yeah, yeah, yeah. No the, the model.[00:44:42] swyx: Yes.[00:44:42] Fan-yun Sun: Actually we have a few iterations of them. They're actually at slightly different[00:44:45] swyx: I know boundaries. You should, you should do that. That's a cool dimension to show.[00:44:49] Fan-yun Sun: Yeah.[00:44:50] swyx: Is quantum mechanics the diffusion prior of our world?[00:44:55] Right. It's like that's the boundary of classical mechanics versus quantum. Right? Like, that's it. At one [00:45:00] point God plays dice and the other point doesn't.[00:45:02] Fan-yun Sun: I dunno if Chris, you wanna say it, but I think, I think generally I feel like physics is better with symbol P priors.[00:45:08] Chris Manning: Even quantum physics.[00:45:09] Fan-yun Sun: Even quantum physics.[00:45:11] swyx: Yeah. This is starts against to, MLST territory is, is what I call it, where, he, he likes to get philosophical. We, we we're quite friendly.[00:45:18] Vibhu: I mean, we need to get, we need to get singularity. I heard some of that.[00:45:23] swyx: No, no, I think that is actually really helpful and man, I just want you to productize this like, as a product guy, I'm just like, oh, also[00:45:32] Vibhu: a gamer, I[00:45:33] swyx: wanna, it's like a researcher, like, it's cool.[00:45:35] Like this is a, the theoretical, like you have a very good, I don't know, like the way of thinking about these things, but I just wanna see you like, express it. I do think like your fundamentally things when, when you leave open new tools, like, okay, use, use human intent to incorporate it into how you render.[00:45:52] Artists are gonna have to take like two to three years to figure out what to do with this. And you just don't know.[00:45:57] Chris Manning: Right. But I think, this is, [00:46:00] gives a much more approachable and controllable world for the society, which is the beauty, the beauty of, NLP, that that will enable it to be adopted and used.[00:46:10] And we are very hopeful about that. Yeah,[00:46:13] Fan-yun Sun: yeah. Yeah. I mean, we are, we are very focused actually on commercialization in the sense that like we do, we do really believe in the data flywheel app approach. Yeah. Where, we put this in the hands of the creators and the users and then they will teach us when, what capability our model should improve.[00:46:27] And that's why we are, we are actually, like products and beta[00:46:31] swyx: Yeah. Focusing on gaming. What, what's like the adjacent thing to gaming[00:46:34] Fan-yun Sun: embody adjacent, basically. So maybe we can, we can I'll maybe start with where we see the platform in three years. Yeah. Which is like, okay. The users would tell us what they want to achieve.[00:46:45] The end goal could be, Hey, I just, I wanna make something to teach my kids the value of humility. Or it could be, Hey, I wanna fine tune my, drones to be really good at rescue situations. I could be vacuum robots. I want to like train [00:47:00] my manipulation or like vacuum robot to be very robust to my office, right?[00:47:04] But it's like, whatever it is, scenario robust to[00:47:06] swyx: my office[00:47:07] Fan-yun Sun: or like navigate very robustly in my office. But then it's like, whatever end goal that you want, our role model will say, okay, given what you want to achieve, let me generate a distribution of environments such that I can train and evaluate whatever it is you want.[00:47:24] Yeah. Right. Maybe for the purpose of games, it's just the end simulation and that's the end product for certain policies. It's like I can train it within these environments and then help you see where your policy is failing or not. Yeah. And then, so I think,[00:47:37] swyx: so in that case, much more of a training tool.[00:47:40] Than in other training[00:47:41] Vibhu: evaluation? Both. Right?[00:47:43] swyx: Sure. Same. Same thing.[00:47:43] Fan-yun Sun: Yeah, same thing. I think it's just this role model that allows people to train any policy that can act in any multimodal environments.[00:47:51] swyx: Would it be harder to reward hack? Is there an angle here where it is harder to reward hack? Like it's just, I'll just put it generally because I think that's a, that's obviously a key [00:48:00] problem that a lot of people face when in training agents in these environments, and I don't know, can you solve it?[00:48:07] Chris Manning: I think not necessarily. To the extent that there's a mis specified reward that. It seems like it could be hacked in a more symbolic world or in a more pixel based world. I dunno if Sun's got any thoughts, but I don't think that's really being solved.[00:48:26] swyx: The other thing that comes to mind is just you could just build a better sawa as a video generator model, right?[00:48:31] Because then you, you would move the diffusion, side a bit more further to the right. I think if I got the directionality correct. And that's it.[00:48:40] Vibhu: It's better on domains, right? Like on consistency over now, or for sure it exists versus something doesn't, right.[00:48:46] Chris Manning: So[00:48:46] swyx: yeah. Yeah. Is[00:48:49] Vibhu: is a question more like, like[00:48:51] swyx: I'm just riffing on like, how do you, what can you build, you know?[00:48:54] Oh, with the stuff that you have. I do think that the minor, the academic does go immediately to training [00:49:00] and in eval evaluation, but like art tends to take unusual directions. Like you might end up,[00:49:06] Chris Manning: okay. Yeah. But the question is, can you use this piece of software to develop compelling gameplay and. I don't think you can take SOAR and produce compelling gameplay, right?[00:49:19] If you want to have a world that you can wander around in a bit, you are good. But what are your abilities to have gameplay mechanics implemented the way you'd like them to be and to have things stay, with the long-term history of your gameplay that influences future actions. I think there's just nothing there for that.[00:49:39] swyx: Yeah, I do tend to agree. I, I'm just trying to sort of test the boundaries. I would also make the observation that as AAA games industry has developed the line between what is a movie and what is a game has blurred. And you, you, you do end up basically producing a two hour movie as part of your game.[00:49:57] Fan-yun Sun: No, honestly, there, there's so many actually [00:50:00] applications in adjacent markets that our world model can go into. Yeah. But yeah, it, it's sort of fun to riff, riff on. Although on the execution side, we we, we need to stay focused with like, okay, what are the capabilities we want to unlock over time?[00:50:11] And there's a roadmap for that. But yeah, if we're just riffing on sort of like the possibilities, I feel like, whether it's endless Yeah, it's like classic[00:50:18] swyx: and the embedding for a possibility and endless in my mind, it's very close. Yeah. I do wanna, focus on one, like weird choice. I, I don't know if it's weird.[00:50:28] Maybe I'm, I got something here. Audio, right? You could have just said no audio And audio in my mind has a lot of recursion, whereas in video you can just do recasting and that's much computationally much simpler. Audio just seems way harder. I don't know if you wanna just comment on just the special 3D audio.[00:50:46] Problem. Did you really have to do it? I guess you do to be immersive, but like a lot of people do treat it as like, well, you just stick a, a tt S model on top of[00:50:57] Vibhu: Well, there's a lot more to game audio than [00:51:00] just speech. Right. It's not just[00:51:01] swyx: tts. Yeah. Tts. S Fxt, GM Spatial in my mind Echoes[00:51:06] Chris Manning: Yeah.[00:51:06] swyx: And reflections.[00:51:07] And I, I don't even know what's, what else? I don't know what, what other problems in this space.[00:51:13] Fan-yun Sun: Yeah, I think this point like the, it's sort of a more, more pointing to the benefits of using an game engine as a tool that's available to the model, right? Because like part of the spatial audio is from the code that is underlying the simulation.[00:51:32] And while we do give our model access to other types of audio models as. Tools.[00:51:39] swyx: None of them would be spatial, I think.[00:51:41] Fan-yun Sun: But that's exactly sort of more 0.2. We're giving our model an abstraction or a suite of tools such that it's able to achieve that. And you can argue that sort of spatial is like a, like a emergence out of the, the tools that we and abstraction that we provide to the agents.[00:51:59] And I think that's the beauty of [00:52:00] this, this, this approach is like there's a lot of things kind of like how human's built technology and they're like Lego blocks that build on top of each other. And it's the same thing here. There's gonna be things that sort of just sort of emerges from being able to put these things together in like combinatorially interesting ways,[00:52:14] Chris Manning: right?[00:52:15] So this integrated audio model exploits the understanding and semantics of the Moon Lake world, right? And whereas in general for the Gen AI video models. There's no actual integration across to audio at all, right? That someone might stick some music or stick a soundscape or whatever else on top of their video.[00:52:44] So it's not a silent video, but they're in no way connected into a consistent world model. And there's nothing that's okay. An action is happening in the video. Therefore there should be a sound that's [00:53:00] coming from this part of the visual field.[00:53:03] swyx: Yeah.[00:53:03] Vibhu: Is that different than Sora too? Does it not have audio?[00:53:06] Not to say it's not like[00:53:08] swyx: amazing[00:53:08] Vibhu: isn't a spatial[00:53:09] swyx: audio.[00:53:09] Vibhu: It doesn't,[00:53:10] swyx: no. I've played around it with it enough. It just sounds like someone put an 11 laps voice on top of it and just tried to do the lip sync.[00:53:18] Vibhu: Oh, yeah. I've seen, okay. Generate a dog at the beach and reactions to big wave and move[00:53:23] swyx: around.[00:53:23] It's definitely like, so have the dog, have the dog move away from camera and see if the, the song goes down. It doesn't. ‘Cause they don't have facial audio.[00:53:32] Fan-yun Sun: We do want to basically like we, our moral model, like the one we're training is basically towards the goal of having a combined latent representation across all these different modalities.[00:53:42] Right? Such that it can like reason across these different modalities. So for example, if I close my eyes and like you play a video, you play a sound of like a car skidding away from me. I almost can like, visually extrapolate that trajectory in my mind. And I think that type of capability, we want our model to be able to reason, right?[00:53:59] And that's the reason that [00:54:00] we're sort of taking this multimodal reasoning approach. It's like we want this combine late in space that can[00:54:05] swyx: Yeah. Oh, you said late in space. We like that. Here we have to play the, the bell Every time that someone says late in space, no, you gotta train daredevil one. Where you, you, you, it's only audio, but you have to work out.[00:54:15] Where everything is.[00:54:19] Cool. I I think that that was, that was about it for our Moon Lake coverage. I do think that we have like a couple of, Chris Madden questions on, on IR and, just any, any other sort of attention topics or n NLP topics.[00:54:31] Vibhu: Okay.[00:54:31] swyx: Go ahead.[00:54:32] Chris Manning's Journey: From NLP to World Models[00:54:32] Vibhu: Well, no, I mean, yeah, it's just fun. We talked a bit about how you guys met, but you basically, you, you were like the godfather of NLP per se, right?[00:54:39] You spent the whole career from early embeddings, early early attention. You did 2015 attention for machine translation, everything. You, you had information retrieval, so RAG before rag, we just wanna shout that out and admire a lot of that. Right? So what prompted the switch over to world models?[00:54:56] How, how'd all that come about?[00:54:58] Chris Manning: To some answer it [00:55:00] is, the enthusiasms and creativity of students, but there's a bit of a history there, right? So, yeah. So clearly most of my career has been doing stuff with language and how I got into research was thinking, ah, this is just so amazing how humans can produce speech and understand each other in real time.[00:55:21] And somehow they managed to learn languages from their kids. How could this possibly happen? And so, yeah, starting off I was very focused on language, but as it sort of got into the 2000 and tens, I started, going, I'd been working on question answering, and then I started to get, interest in visual question answering.[00:55:42] And that was an area where it was very noticeable. That the visual understanding was bad. Right. These were the days when like, it sort of seemed like there's almost no visual [00:56:00] understanding. You were just getting answers that came from priors. So, if you asked how many people are sitting at the table, it'd always answer two regardless of how many, how many people you could see in the picture.[00:56:11] And so it seemed like, oh, these models actually aren't able to get semantic information outta

Inflammation is a double-edged sword. It defends the body against infection and injury, yet when it becomes chronic, it can accelerate aging and fuel the very diseases that shorten human life. For decades, scientists have observed that people with higher levels of inflammatory markers like interleukin-6 (IL6) and C-reactive protein (CRP) tend to have shorter lifespans. But the critical question has always been: does inflammation cause mortality, or does it merely reflect underlying disease? A research paper, titled “Causal effects of inflammation on long-term mortality: A mendelian randomization study” was published in Volume 18 of Aging-US by an international team of researchers, provides a definitive answer by using a powerful genetic technique to untangle cause from effect. The team's investigation demonstrates that the IL6 inflammatory pathway has a direct causal impact on human survival—but with a surprising twist: two components of the same pathway pull in opposite directions. Full blog - https://aging-us.org/2026/04/il6-and-il6r-opposing-forces-of-inflammation-that-shape-human-survival/ DOI - https://doi.org/10.18632/aging.206352 Corresponding author - Eliano P. Navarese - elianonavarese@gmail.com Abstract video - https://www.youtube.com/watch?v=Br1A0jgU-4M Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.206352 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, mendelian randomization, inflammatory biomarkers, mortality, cardiovascular disease To learn more about the journal, please visit https://www.Aging-US.com​​ and connect with us on social media at: Bluesky - https://bsky.app/profile/aging-us.bsky.social ResearchGate - https://www.researchgate.net/journal/Aging-1945-4589 X - https://twitter.com/AgingJrnl Facebook - https://www.facebook.com/AgingUS/ Instagram - https://www.instagram.com/agingjrnl/ LinkedIn - https://www.linkedin.com/company/aging/ Reddit - https://www.reddit.com/user/AgingUS/ Pinterest - https://www.pinterest.com/AgingUS/ YouTube - https://www.youtube.com/@Aging-US Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc MEDIA@IMPACTJOURNALS.COM

Naftali Weinberger, Porter Williams and James Woodward on the role of worldly infrastructure. Read the full essay here

Summary: Tobi challenged marketing's fixation on prediction. He has built highly accurate LTV models, but accuracy alone does not move revenue. Marketing is intervention. Correlation shows patterns; causality tells you what happens when you pull a lever. That shift reshapes experimentation, explains why dynamic allocation can outperform static A B tests, and highlights how self learning systems can backfire or get stuck in local maxima. It also fuels his skepticism of unleashing agentic AI on historical data without a causal layer. If you want to change outcomes instead of forecast them, your systems need to understand levers and log decisions you can actually audit.(00:00) - Intro (01:22) - In This Episode (04:07) - Why Predictive Models Fail Without Causal Inference (09:49) - How to Validate Causal Impact on Customer Lifetime Value (13:04) - Reducing Uncertainty Around Causal Effects by Optimizing Levers, Not Labels (17:01) - Why Dynamic Allocation Works Better Than Fixed Horizon A B Testing (31:54) - The Boomerang Effect and Why Uninformed AI Sabotages Early Results (40:15) - Escaping Local Maxima and The Failure of Randomly Initialized Decisioning (44:04) - Why Agentic AI Trained on Data Warehouse Correlations Reinforces Bias (49:00) - The Power of Composable Decisioning (53:06) - How Machine Decisioning Transcends Marketing (01:01:41) - Why Clear Priority Hierarchies Improve Executive Decision Making About TobiasTobias Konitzer, PhD is VP of AI at GrowthLoop, where he's chasing closed-loop marketing powered by reinforcement learning, causality, and agentic systems. He's spent the past decade focused on one core problem: moving beyond prediction to actually influencing outcomes.Previously, Tobi was Chief Innovation Officer at Fenix Commerce, helping major eCommerce brands modernize checkout and delivery with machine learning. He also founded Ocurate, a venture-backed startup that predicted customer lifetime value to optimize ad bidding in real time, raising $5.5M and scaling to $500K+ ARR before its acquisition. Earlier, he co-founded PredictWise, building psychographic and behavioral targeting models that drove over $2M in revenue.Tobi earned his PhD in Computational Social Science from Stanford and worked at Facebook Research on large-scale ML and bias correction. Originally from Germany and based in the Bay Area since 2013, he writes frequently about causal thinking, machine decisioning, and the future of marketing.Why Predictive Models Fail Without Causal InferencePrediction dominates most marketing roadmaps. Teams invest months refining churn models, tightening confidence intervals, and debating which threshold deserves a campaign. Tobi built an entire company on that logic. His team produced highly accurate lifetime value predictions using deep learning and granular event data. The forecasts were sharp. The lift curves were clean. Buyers were impressed.Then lifecycle marketers asked a more uncomfortable question: what action should follow the score?A predictive model encodes the current trajectory of a customer under existing policies. It describes what will likely happen if nothing changes. Marketing changes things constantly. The moment you intervene, you alter the system that generated the prediction. The forecast reflects yesterday's conditions, not tomorrow's strategy.> “Prediction tells you the future if you do nothing. Causation tells you how to change it.”Consider the Prediction Trap.On the left, the status quo labels a person as high churn risk. The function is observation. The outcome is a description of what happens if you leave the system untouched. On the right, a lever gets pulled. The function is intervention. The outcome is directional change.That shift in function changes how you work.Prediction thinking centers on segmentation:Who is likely to churn?Who is likely to buy?Who looks like high LTV?Causal thinking centers on levers:Which incentive reduces churn?Which sequence increases repeat purchase?Which offer raises lifetime value incrementally?Tobi often uses an LTV example to expose the trap. Suppose high LTV customers frequently viewed a specific product early in their journey. A team might redesign the onboarding flow to feature that product more aggressively. The correlation looks persuasive. The causal effect remains unknown.Several alternative explanations could drive the pattern:The product may correlate with a specific acquisition channel.The product may have been highlighted during a limited campaign.The product view may signal prior brand familiarity.Only an intervention test can estimate incremental impact. Correlation can guide hypothesis generation, but it cannot validate the lever itself.Tobi also highlights a deeper issue. Acting on predictions introduces compounding uncertainty across multiple layers:The predictive model carries statistical variance.The translation from model features to campaign strategy introduces interpretation bias.The experiment introduces sampling error.Execution introduces operational noise.Each layer adds variability. When teams treat prediction accuracy as the goal, they lose visibility into where uncertainty enters the system. When teams focus on intervention impact, they concentrate measurement on the lever that drives revenue.Boardrooms already operate in causal language. Incremental ROI is causal. Budget allocation is causal. Executives care about what caused growth, not which segment looked promising in a dashboard. Prediction can inform prioritization. Causal inference determines what to scale.If you want to move in that direction, adjust your operating model:Start every initiative with a controllable lever.Define the action before defining the segment.Design experiments that isolate the incremental effect of that lever.Randomized or adaptive allocation both estimate causal lift.Report impact in revenue, retention, or contribution margin.Tie every experiment to a business outcome.Document assumptions and uncertainty.Build institutional memory around what caused change.Prediction remains useful. Intervention drives growth. Teams that understand that distinction build systems that learn through action instead of watching the future unfold from the sidelines.Key takeaway: Anchor your marketing engine in causal experiments. For every predictive score, define the specific action it informs, test that action against a control, and quantify incremental lift tied directly to revenue or retention. Replace segment rankings with lever performance dashboards that show effect size, confidence, and business impact. When every campaign answers the question “What did this intervention cause?” your team shifts from observing trajectories to shaping them.How to Validate Causal Impact on Customer Lifetime ValueMost teams treat high LTV segments as proof of where to spend. The model ranks customers. The top decile looks profitable. Budget flows upward. Tobi described asking the head of CRM at a billion dollar outdoor brand what he does when a model predicts someone will be high LTV. The answer came instantly: Spend more on them, no?That instinct feels responsible. It also confuses observation with intervention. Introducing the high LTV Fallacy:On the right side of the chart, you see a dense cluster labeled high LTV customers. Revenue increases with marketing spend. The correlation line slopes upward. It looks clean and convincing. They were going to buy anyway. That cluster may represent customers with higher income, stronger brand affinit...

On this episode, host Sima Vasa talks to Jason Cohen, Founder and CEO of Simulacra Synthetic Data Studio, about the limits of traditional research, the evolution of synthetic data, and why causal modeling matters more than larger sample sizes. Drawing on his experience building and exiting Gastrograph AI, Jason explains how real-world data gaps undermine decision-making and how synthetic data can support scenario-based predictions when applied responsibly. Key Takeaways: 00:00 Introduction.05:24 Most brands can't know in advance if research data is “correct.”​09:44 Generic LLM personas rarely represent any real population.​13:08 Cross‑coverage lets AI infer missing audience segments.​16:04 Synthetic data is real when it's actually used.​19:36 Diversity in base samples drives credible synthetic expansion.​23:28 Sample boosting alone doesn't fix bad research outcomes.​25:08 Synthetic data scales insights for hard‑to‑reach cohorts.​26:08 Misused synthetic personas can drive completely wrong decisions.​ Resources Mentioned: Simulacra Synthetic Data Studio | Website Thanks for listening to the Data Gurus podcast, brought to you by Infinity Squared. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show, and be sure to subscribe so you never miss another insightful conversation. #Analytics #MA #Data #Strategy #Innovation #Acquisitions #MRX #Restech

ON TODAYS SHOW ASB Polyfest kicks off today and Charlie chats about his oldest sons Feifai night. Are kids being too causal these days - what names are you getting called by them? For more, follow our socials: Instagram Facebook TikTokSee omnystudio.com/listener for privacy information.

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

Moral theologian Fr. Thomas Berg and philosopher and therapist Dr. Andrea Messineo take on the topic of personal conscience and parts work through a Catholic lens.  We explore the relationships among conscience, parts, the innermost self, the intellect, the will, impulses, and desires.  We address concupiscence and parts, and offer specific examples.  Join us for a fascinating exploration of conscience and parts.  Check out these other episodes:https://youtu.be/bw-zUp2h_TAhttps://youtu.be/f5MNCaCJLychttps://youtu.be/Isxmlx8pQAsDr. Peter's advanced group for Catholic formators: Relating Wholeheartedly with God in Prayer, Mondays from 10:00 AM to 11:30 AM Eastern starting on March 9, 2026.  Find out more here:  https://members.soulsandhearts.com/registrationDr. Gerry's advanced group for Catholic formators: Surviving, Healing, Thriving, and Flourishing - A Path To Greater Integration  Wednesdays from 10:00 AM to 11:30 AM Eastern, starting on March 11, 2026Fr. Thomas Berg's books: Hurting in the Church: A Way Forward for Wounded Catholics: https://www.barnesandnoble.com/w/hurting-in-the-church-fr-thomas-berg/1124597873Choosing Forgiveness: Unleash the Power of God's Grace: https://www.barnesandnoble.com/w/choosing-forgiveness-fr-thomas-berg/1140395384?ean=9781681926537Dr. Andrea Messineo's book, Alone in Church: https://www.amazon.com/ALONE-CHURCH-Andrea-Messineo/dp/1732054290Check out Dr. Messineo's website at andreamessineolpcc.comKey moments:16:15 What are the relationships among one's innermost self, one's parts, and one's conscience?21:25  St. Thomas Aquinas' emphasis on prudence23:30  How parts with emotions have a role in a well-formed conscience and the innermost self does not have a “localized omniscience.”  31:30 What are the relationships among parts and the faculties of the intellect and the will?37:00 Parts are closely connected with impulses and desires, driving agendas40:00  What about addictions, obsessions, and compulsions?45:40  Can a person possess a virtue, but parts of that person not have access to that virtue?56:20  Does the innermost self need any formation from others, or is it complete, as Richard Schwartz maintains?1:08:00 Causal chains that lead to morally problematic behaviors1:17:20  What is concupiscence and does it always need to be lodged in a part?

BUFFALO, NY — February 27, 2026 — A new #research paper was #published in Volume 18 of Aging-US on February 6, 2026, titled “Causal effects of inflammation on long-term mortality: a Mendelian randomization study.” Led by Eliano P. Navarese from Department of Life and Health Sciences, Link Campus University and SIRIO MEDICINE Research Network, Nicolaus Copernicus University, who is also the corresponding author — the study used large-scale Mendelian randomization (MR) to test whether genetically proxied levels of inflammatory biomarkers causally influence long-term all-cause mortality. The analysis combined genome-wide association instruments from more than 750,000 individuals and used FinnGen mortality data (median follow-up 11.7 years) to assess effects on overall survival and major cardiovascular endpoints. Using robust MR methods and multiple sensitivity analyses, the authors report that genetically higher IL6R (soluble IL-6 receptor) levels were associated with reduced all-cause mortality (odds ratio per 1-SD increase: 0.95; 95% CI: 0.91–0.98), and with lower risk of atrial fibrillation, coronary artery disease, stroke, and lung cancer. By contrast, genetically higher IL6 levels were associated with increased mortality (OR 1.05; 95% CI: 1.02–1.08). No significant causal effects were observed for CRP or GDF15, suggesting those markers more likely reflect disease burden than drive it. “These results support IL6R antagonism as a potential strategy for cardiovascular disease prevention.” The authors emphasize that the opposing directions for IL6 and IL6R point to distinct biological mechanisms: IL6 likely promotes chronic pro-inflammatory states that increase cardiovascular risk, while higher circulating IL6R (reflecting altered receptor shedding and signaling) appears to dampen harmful IL6 activity at the vessel wall and myocardium, yielding cardiovascular protection. Sensitivity and cis-MR analyses reinforced the IL6R protective signal and showed minimal evidence of directional pleiotropy. Together, the genetic evidence aligns with clinical trial data for IL6R antagonists in other settings and supports further evaluation of IL6R-targeted strategies for cardiovascular prevention. The paper also notes important limitations and next steps. Analyses were restricted to individuals of European ancestry, so results require replication in other ancestries. Translating genetic evidence into preventive therapies will need careful clinical evaluation, long-term safety assessment, and trials designed for primary prevention in high-risk populations. The authors also call for additional mechanistic work to map how IL6/IL6R modulation alters vascular inflammation and downstream disease processes. DOI - https://doi.org/10.18632/aging.206352 Corresponding author - Eliano P. Navarese - elianonavarese@gmail.com Abstract video - https://www.youtube.com/watch?v=Br1A0jgU-4M Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.206352 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, mendelian randomization, inflammatory biomarkers, mortality, cardiovascular disease To learn more about the journal, please visit https://www.Aging-US.com​​ and connect with us on social media at: Bluesky - https://bsky.app/profile/aging-us.bsky.social ResearchGate - https://www.researchgate.net/journal/Aging-1945-4589 X - https://twitter.com/AgingJrnl Facebook - https://www.facebook.com/AgingUS/ Instagram - https://www.instagram.com/agingjrnl/ LinkedIn - https://www.linkedin.com/company/aging/ Reddit - https://www.reddit.com/user/AgingUS/ Pinterest - https://www.pinterest.com/AgingUS/ YouTube - https://www.youtube.com/@Aging-US Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc MEDIA@IMPACTJOURNALS.COM

In this episode of Science of Reading: The Podcast, Susan Lambert is joined by Kristen McMaster, Ph.D., Guy Bond Chair in Reading and professor of special education in the Department of Educational Psychology at University of Minnesota. Together, they explore how reading comprehension isn't just about what's on the page—it's also about what's not there—and share practical insights on how to support students in developing inference skills. Susan and Kristen also discuss the dual processes of activation and integration when making inferences; the distinction between teaching students to process text actively versus teaching students to apply comprehension strategies; and different types of inferences, including causal, bridging, and elaborative.Show notes:Submit your questions to our listener mailbagAccess free, high-quality resources—including our recent Science of Reading: The Podcast Essentials "Comprehension" episode—at our companion professional learning page Download our Comprehension 101 bundle: Access free comprehension resources, including e-books, and on-demand professional learningConnect with Kristen McMasterLearn more about Kristen McMasterListen to Season 2 of Amplify's Beyond My Years podcastJoin our community Facebook groupConnect with Susan LambertQuotes:"Inferencing is really central to comprehension. We wouldn't comprehend if we didn't make inferences." —Kristen McMaster"I would encourage teachers not to underestimate the importance of supporting even the inferences that might seem obvious to us." —Kristen McMaster"Good comprehenders are often making very automatic inferences that they don't even realize." —Kristen McMaster"It helps to explicitly teach what an inference is in language that students will understand." —Kristen McMasterTimestamps*:00:00 Introduction: Filling in the gaps with inferences, with Kristen McMaster, Ph.D.05:00 Comprehension is how we make sense of the world around us09:00 The types of inferences: Causal, bridging, elaborative, and theory of mind17:00 How teachers can help students develop inference skills22:00 Creating an effective questioning strategy27:00 How teachers can preview a text and think about the inferences that might need to be made31:00 Supporting students who process texts in different ways37:00 The timing of comprehension questions40:00 The connection between oral language comprehension and text comprehension45:00 Final thought: Teacher's shouldn't underestimate the importance of inferences that might seem obvious.*Timestamps are approximate, rounded to nearest minute

Los Originales: 1. Además de ser el primer país en incluir el derecho al aborto, ahora las leyes de este país no conceden el divorcio solo por la falta de sexo en la pareja. 2. Conoce la manera adecuada de marcar el tarjetón en la consulta del 8 de marzo.

In a rare departure from our usual diet of online weirdos, this episode features an academic who is very much not a guru. We're joined by Julia Rohrer, a psychologist at Leipzig University whose work straddles the disciplinary boundaries of open science, research transparency, and causal inference. Julia is also an editor at Psychological Science and has spent much of the last decade politely pointing out that psychologists often don't quite know what they're estimating, why, or under which assumptions.We talk about the state of psychology after the replication crisis, whether open science reforms have genuinely improved research practice (or just added new boxes to tick), and why causal thinking is unavoidable even when researchers insist they are “only describing associations.” Julia explains why the standard dance of imply causality → deny causality → add boilerplate disclaimer is unhelpful, and argues instead for being explicit about the causal questions researchers actually care about and the assumptions required to answer them.Along the way we discuss images of scientists in the public and amongst the gurus, how post-treatment bias sneaks into even well-intentioned experimental designs, why specifying the estimand matters more than running ever-fancier models, and how psychology's current norms can potentially punish honesty about uncertainty. We also touch on her work on birth-order effects and offer some possible reasons for optimism.With all the guru talk, people sometimes ask us to recommend things that we like, and Julia's work is one such example!LinksJulia Rohrer's websiteThe 100% CI blogRohrer, J. M. (2024). Causal inference for psychologists who think that causal inference is not for them. Social and Personality Psychology Compass, 18(3), e12948.Rohrer, J. M., Tierney, W., Uhlmann, E. L., DeBruine, L. M., Heyman, T., Jones, B., ... & Yarkoni, T. (2021). Putting the self in self-correction: Findings from the loss-of-confidence project. Perspectives on Psychological Science, 16(6), 1255-1269.Rohrer, J. M., Egloff, B., & Schmukle, S. C. (2015). Examining the effects of birth order on personality. Proceedings of the National Academy of Sciences, 112(46), 14224-14229.BEMC MAY 2024 - Julia Rohrer - "Causal confusions correlate with casual conclusions"Dr. Tobias Dienlin - Less casual causal inference for experiments and longitudinal data: Research talk by Julia Rohrer

Gary Klein joins us to unpack the causal landscape of the 2025 World Series, exploring how decisions, context, and key moments shaped the final outcome.Gary Klein, Ph.D., authored Sources of Power: How People Make Decisions, and five other books plus three co-edited volumes. He is known for the cognitive models he described, such as the Recognition-Primed Decision (RPD) model, the Data/Frame model of sensemaking, the Management By Discovery model of planning in complex settings, and the Triple Path Model of Insight. He developed methods including the Pre-Mortem method of risk assessment, techniques for Cognitive Task Analysis, the ShadowBox training approach, and also helped Judith Orasau pioneer the Naturalistic Decision Making movement in 1989. Dr. Klein has decades of work experience in dozens of work domains, including military, healthcare, and emergency response.The company he started in 1978, Klein Associates, grew to 37 employees by the time he sold it in 2005. He kicked off his new company, ShadowBox LLC, in 2014. His hobbies include spending time with his grandchildren, watching movies, and beating John Schmitt at the Asian game of Go.You can find all of Gary's books, publications, and more at www.gary-klein.com________________________Where to find the hosts:Brian MoonBrian's websiteBrian's LinkedInBrian's TwitterLaura MilitelloLaura's websiteLaura's LinkedInLaura's Twitter

You've built a toolkit over the last several episodes. Logical reasoning. Causal thinking. Mental models. Serious intellectual firepower. Now the uncomfortable question: When's the last time you actually used it to make a decision? Not a decision you think you made. One where you evaluated the options yourself. Weighed the evidence. Formed your own conclusion. […]

You've built a toolkit over the last several episodes. Logical reasoning. Causal thinking. Mental models. Serious intellectual firepower. Now the uncomfortable question: When's the last time you actually used it to make a decision? Not a decision you think you made. One where you evaluated the options yourself. Weighed the evidence. Formed your own conclusion. Here's what most of us do instead: we Google it, ask ChatGPT, go with whatever has the most stars. We feel like we're deciding, but we're not. We're just choosing which borrowed answer to accept. That gap between thinking you're deciding and actually deciding is where everything falls apart. And there's a name for it. What Mindjacking Actually Is  Mindjacking. Not the sci-fi version where hackers seize your brain through neural implants. The real version. Where you voluntarily hand over your thinking because someone else already did the work. It's not dramatic. It's convenient. The algorithm ranked the results. The expert weighed in. The crowd already decided. Why duplicate the effort? Mindjacking is different from ordinary influence. You choose it. Every single time. Nobody forces you to stop evaluating. You volunteer, because forming your own conclusion is harder than borrowing someone else's. What exactly are you losing when this happens? The Two Skills Under Attack  Mindjacking destroys two distinct capabilities. They're different, and you need both. Evaluation independence is the ability to assess whether a claim is valid. Not whether the source has credentials. Not whether experts agree. Whether the evidence actually supports the conclusion. Decision independence is the ability to commit to a path based on your own judgment, without needing someone else to validate it first. Both skills need each other. Watch what happens when one erodes faster than the other. A woman researches her medical condition for hours. Journal articles. Treatment comparisons. She understands her options better than most medical students would. She walks into the doctor's office, lays out her analysis. It's thorough. Sophisticated, even. The doctor reviews it and says, "This is impressive. You've really done your homework." She nods. Then looks up and asks: "So what should I do?" She can evaluate. She can't decide. Now flip it. Think about someone who decides fast. Trusts their gut. Never waits for permission. How often does that person get burned by bad information they never verified? They can decide. They can't evaluate. Lose either ability and you're trapped. Lose both and you're not thinking at all. The Four Surrender Signals  How do you know when mindjacking is happening? It has a signature. Four internal signals that reveal the handoff in progress, if you know how to read them. Signal one: Relief. The moment you find "the answer," you notice a weight lifting. Pay attention to that. Relief isn't insight. It's the burden of thinking being removed. When you actually work through a problem yourself, the result isn't relief. It's clarity. And clarity usually comes with new questions, not a sense of "done." Signal two: Speed. Uncertainty to certainty in seconds? That's not evaluation. You found someone else's answer and adopted it. There's a difference between "I figured it out" and "I found someone who figured it out." One took effort. The other took a search bar. Signal three: Echo. Listen to your own conclusions. Do they sound like something you read, heard, or scrolled past recently? If your "own opinion" matches a headline almost word-for-word, it probably isn't yours. You're not thinking. You're repeating. Signal four: Unearned confidence. You're certain about a conclusion, but ask yourself: could you explain the reasoning behind it? Not where you heard it. The actual reasoning. If you can't, that confidence isn't yours. It came attached to someone else's answer, and you absorbed both their conclusion and their certainty without doing any analysis yourself. Once you notice these signals firing, you need a way to stop the pattern before it completes. The Interrupt  The interrupt is a single question: "Did I reach this conclusion, or just find it?" Six words. That's the whole thing. It works because it forces a distinction your brain normally blurs. "I decided" and "I adopted someone's decision" are identical from the inside, until you ask the question. Test it now. Think about the last opinion you formed. The last purchase you made. The last recommendation you accepted. Did you reach that conclusion, or just find it? The interrupt doesn't tell you what to think. It tells you whether you're thinking at all. Finding an answer isn't the same as reaching one. This matters more than you might realize, because the pattern is bigger than any single decision you make. The Aha Moment: The Illusion of Expertise  Researchers at Penn State looked at 35 million Facebook posts and found something remarkable: seventy-five percent of shared links were never clicked. Three out of four times, people passed along articles they hadn't read. But that's not the strange part. A separate study from the University of Texas discovered that the act of sharing content, even content you haven't read, makes you think you understand it. Sharing tricks you into believing you know. You didn't read the article about investing, but you shared it, so now you believe you understand investing. Worse: people act on that false knowledge. In the study, people who shared an investing article took significantly more financial risk afterward, even though they never read what they shared. They weren't pretending to know. They genuinely believed they knew, because sharing had become a substitute for learning. That's mindjacking at scale. Millions of people believing they're informed, acting confident, having never actually thought about any of it. The Feed Challenge  I want you to try something as soon as this video ends. Open your social media feed. Find a post where someone you know has liked or shared an article, an opinion, a hot take. Now ask: Did they actually think about this? Or did they just pass it along? Look for the signals. Is their comment just echoing the headline? Are they expressing certainty about something they probably spent ten seconds on? Did they add anything that suggests they read past the first paragraph? Or did they just click "like" and move on? Remember: seventy-five percent of shared links are never clicked. That like or share you're looking at? They probably never read what they're endorsing. You'll be shocked how easy this becomes once you start looking. It's everywhere. People confidently endorsing opinions they never examined. Certainty without evaluation. Expertise without effort. Why start with what others are putting in your feed? Because it's much easier to spot mindjacking in others than in yourself. Your ego doesn't interfere. Train your eye on what's coming at you first. Then turn it inward. Awareness precedes choice. You can't reclaim what you can't see. What's Next  Now you can see the handoff happening. That's the foundation. But seeing it isn't enough. Knowing the signals won't help you when you're exhausted and the algorithm is offering relief. Understanding the trap won't save you when everyone in the room disagrees and consensus feels like safety. Awareness alone won't protect you when the deadline is tomorrow and you don't have time to think. Those are the moments where mindjacking wins. Not because you lack the ability to think, but because thinking starts to look like a luxury you can't afford. That's the real battle. And that's what comes next. Next, we tackle the hardest version of this problem: acting before you're ready. What happens when you have to decide, the information isn't complete, and it never will be? Waiting for certainty feels responsible. But sometimes, waiting is the trap. If you're new here, check out the earlier episodes where we built the evaluation toolkit this series is built on. Watch the series on YouTube.  Don't Click Yet  Here's a thought: most people will finish this video and scroll to the next one. The algorithm already has a recommendation queued up. Relief is one click away. But you could do something different. You could stick with the discomfort for a minute. Actually, try the feed challenge before moving on. If you want to go deeper on mindjacking, the full breakdown lives at philmckinney.com/mindjacking. And if you want to support the team that helps me to produce this content, consider becoming a paid subscriber on Substack.  What's one opinion you realized might not actually be yours? Share this with someone who needs to hear it.   References Penn State University (2024). "Social media users probably won't read beyond this headline, researchers say." Analysis of 35 million Facebook posts published in Nature Human Behaviour.  Ward, A., Zheng, J.F., & Broniarczyk, S.M. (2022). "I share, therefore I know? Sharing online content – even without reading it – inflates subjective knowledge." Journal of Consumer Psychology, University of Texas at Austin McCombs School of Business. 

Can a digital replica of your heart save your life? In CXOTalk episode 902, Michael Krigsman talks with Dr. Joe Alexander, Director of the Medical and Health Informatics Lab at NTT Research, to explore the revolutionary world of Bio-Digital Twins.Discover how researchers are using mathematical modeling to build "computational replicas" of the human cardiovascular system. Dr. Alexander explains how these digital twins can predict heart failure, automate critical care in the ICU through closed-loop intervention systems, and pave the way for a future where personalized medicine is accessible to everyone.We dive deep into the science of treating the heart as an electrical circuit, the ethics of AI in medicine, and the "moonshot" goal of eliminating cardiovascular disease..

Ana Isabel Jiménez, alcaldesa de Alcalá de Guadaíra, en la inauguración de Pilatus: "La elección de Alcalá no es causal"

In this episode of Science of Reading: The Podcast, Susan Lambert is joined by University of Oregon College of Education Professor and Ann Swindells Chair in Education Gina Biancarosa, Ed.D., to explore how best to assess for comprehension. Gina elaborates on her extensive work developing more precise and informative measurements of reading comprehension and discusses think-aloud research, demonstrating how to infer for coherence, and examining how students who are struggling with comprehension tend to rely too heavily on making inferences or paraphrasing.Show notes:Submit your questions on comprehension!Access free, high-quality resources at our brand new, companion professional learning page.  Connect with Gina on LinkedIn.Read “Diagnostic and Instructionally Relevant Measurement of Reading Comprehension”Resources:Listen to Season 2 of Amplify's Beyond My Years podcast.Join our community Facebook group.Connect with Susan Lambert.Quotes:"A lot of what we know about reading comprehension comes from think-alouds where you ask someone to tell you what they're thinking as they read." —Gina Biancarosa, Ed.D"To model reading comprehension, [try] thinking aloud in front of a classroom of students in a way that is instructive for them, and also authentic to the reading process." —Gina Biancarosa, Ed.D."Students are making causal inferences in their daily lives, when they watch movies, and when they're hearing stories. And so what we're really trying to do is get them to generalize these behaviors that they engage in outside of the task of reading, during reading." —Gina Biancarosa, Ed.D.Episode Timestamps:02:00 Introduction: Gina Biancarosa, Ed.D. and comprehension assessment08:00 How do we assess comprehension?14:00 Think-aloud research21:00 MOCCA (Multiple-Choice Online Causal Comprehension Assessment)24:00 Causal coherence30:00 Paraphrasers and elaborators33:00 Comprehension assessment research39:00 Professional development and comprehension assessment42:00 Closing thoughts*Timestamps are approximate, rounded to nearest minute

As a practical matter, how much effort do you put into pinning down the causes behind daily occurrences? To developmental psychologist Frank Keil, who studies causal thinking, that answer is likely along the lines of 'not enough.' A lack of causal thinking is both endemic, and, to an extent, hurtful these days, he argues, suggesting that lacking even simplified causal models makes things like the black box of artificial intelligence a potential problem. In this Social Science Bites podcast, Keil, the Charles C. and Dorathea S. Dilley Professor of Psychology and Linguistics at Yale University, outlines for interviewer David Edmonds how causal thinking is a skill we seem to have at an early age, but which diminishes as we grow up. "[K]ids, by the time they approach elementary school, are asking up to 200 'why' and 'how' questions a day," he explains. "Within a year or two up to starting school, they're down to two or three, often none." Furthermore, Keil sees this diminishment continuing in society today – and this comes as a cost. "I think it's making kids today be pushed more towards surface understanding, being user interface understanders. I think it makes influences more influential. To just say 'This is cool' as opposed to 'This is how it works.' One of the negative consequences is that we can get fooled by misinformation more; one of the best ways to debunk an expert is to ask them to explain the mechanism." At Yale, Keil directs the Cognition and Development lab. He has written several books, from academe-oriented books like Developmental Psychology: The Growth of Mind and Behavior, to more general reader titles like Wonder: Childhood and the Lifelong Love of Science. His awards include the Boyd R. McCandless Award from the American Psychological Association (Developmental Psychology), the Distinguished Scientific Award for an Early Career Contribution to Psychology from the American Psychological Association, a Guggenheim Fellowship, a fellowship at the Center for Advanced Study in the Behavioral Sciences at Stanford University, a MERIT Award from the National Institutes of Health, and the Ann L. Brown Award for Excellence in Developmental Research.  

Dr Haidar Hobballah and Ali R. Khaki discuss the principle of sad al-dharāʾiʿ (blocking the means) and how Shīʿī legal thought approaches causal reasoning. They unpack the logic behind preventive rulings and explore their modern implications—from bioethics to environmental ethics—offering a rational framework for ethical decision-making in contemporary Islamic contexts.

Send us a textThe Causal Gap: Truly Responsible AI Needs to Understand the ConsequencesWhy do LLMs systematically drive themselves to extinction, and what does it have to do with evolution, moral reasoning, and causality?In this brand-new episode of Causal Bandits, we meet Zhijing Jin (Max Planck Institute for Intelligent Systems, University of Toronto) to answer these questions and look into the future of automated causal reasoning.In this episode, we discuss:- Zhijing's new work on the "causal scientist"- What's missing in responsible AI- Why ethics matter for agentic systems- Is causality a necessary element of moral reasoning?------------------------------------------------------------------------------------------------------Video version available on Youtube: https://youtu.be/Frb6eTW2ywkRecorded on Aug 18, 2025 in Tübingen, Germany.------------------------------------------------------------------------------------------------------About The GuestZhiijing Jin is a researcher scientist at Max Planck Institute for Intelligent Systems and an incoming Assistant Professor at the University of Toronto. Her work is focused on causality, natural language, and ethics, in particular in the context of large language models and multi-agent systems. Her work received multiple awards, including NeurIPS best paper award, and has been featured in CHIP Magazine, WIRED, and MIT News. She grew up in Shanghai. Currently she prepares to open her new research lab at the University of Toronto.Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

$37 billion. That's how much gets wasted annually on marketing budgets because of poor attribution and misunderstanding of what actually drives results. Companies' credit campaigns that didn't work. They kill initiatives that were actually succeeding. They double down on coincidences while ignoring what's actually driving outcomes. Three executives lost their jobs this month for making […]

$37 billion. That's how much gets wasted annually on marketing budgets because of poor attribution and misunderstanding of what actually drives results. Companies' credit campaigns that didn't work. They kill initiatives that were actually succeeding. They double down on coincidences while ignoring what's actually driving outcomes.   Three executives lost their jobs this month for making the same mistake. They presented data showing success after their initiatives were launched. Boards approved promotions. Then someone asked the one question nobody thought to ask: "Could something else explain this?" The sales spike coincided with a competitor going bankrupt. The satisfaction increase happened when a toxic manager quit. The correlation was real. The causation was fiction. This mistake derailed their careers.   But here's the good news: once you see how this works, you'll never unsee it. And you'll become the person in the room who spots these errors before they cost millions.   But first, you need to understand what makes this mistake so common—and why even smart people fall for it every single day. What is Causal Thinking? At its core, causal thinking is the practice of identifying genuine cause-and-effect relationships rather than settling for surface-level associations. It's asking not just "do these things happen together?" but "does one actually cause the other?"   This skill means you look beyond patterns and correlations to understand what's actually producing the outcomes you're seeing. When you think causally, you can spot the difference between coincidence, correlation, and true causation—a distinction that separates effective decision-makers from those who waste millions on solutions that were never going to work. Loss of Causal Thinking Skills Across every domain of professional life, this confusion costs fortunes and derails careers.   A SaaS company sees customer churn decrease after implementing new onboarding emails—and immediately scales it company-wide. What they missed: they launched the emails the same week their biggest competitor raised prices by 40%. The competitor's pricing reduced churn. But they'll never know, because they never asked the question. Six months later, when they face real churn issues, they keep doubling down on emails that never actually worked.   This happens outside of work too. You start taking a new vitamin, and two weeks later your energy improves. But you started taking it in early March—right when days got longer and you began going outside more. Was it the vitamin or the sunlight and exercise? Most people credit the vitamin without asking the question.   But here's the good news: once you understand how to think causally, these mistakes become obvious. And one of these five strategies can be used in your very next meeting—literally 30 seconds from now. Let me show you how. How To Master Causal Thinking Mastering causal thinking isn't about becoming a statistician or learning complex formulas. It's about developing five practical strategies that work together to reveal what's really driving results. These build on each other—starting with basic tests you can apply right now, and progressing to a complete system you can use for any decision. Strategy 1: The Three Tests of True Causation Think of these as your checklist for evaluating any causal claim.   The Three Tests:   Test #1 - Timing: Confirm the supposed cause actually happened before the effect. If traffic spiked Monday but you launched the campaign Tuesday, that campaign didn't cause it. The cause must always come before the effect.   Test #2 - Consistent Movement: When the supposed cause is present, does the effect reliably occur? When the cause is absent, does the effect disappear? Document instances where they occur together. Then examine situations where the cause is absent. If the effect happens just as often without the cause, you're looking at correlation, not causation.   Test #3 - Rule Out Alternatives: Think carefully about what else could explain what you're seeing. Actively try to disprove your idea rather than only looking for supporting evidence. If you can't eliminate other explanations, you don't have causation. Strategy 2: Ask "Could Something Else Explain This?" Here's a technique you can implement in the next 30 seconds that will immediately improve your causal thinking: whenever someone presents a causal claim, ask out loud: "Could something else explain this?"   This single question is remarkably powerful. It forces the speaker to consider hidden factors they ignored. It reveals whether they've actually done causal analysis or just noticed a correlation and declared victory. It shifts the conversation from assumption to examination.   Try it in your next meeting when someone says "We did X and Y improved." Watch how often they haven't considered alternatives. Watch how often their confident causal claim becomes less certain when forced to address this simple question.   Most people present correlations as causations without even realizing it. Your question makes that leap visible. Suddenly they have to justify it with evidence or back down. It's not confrontational—it's curious. And curiosity is the foundation of good causal thinking.   Use it today. Use it every time someone attributes an outcome to a cause without ruling out alternatives.   That question leads us naturally to our next strategy—learning to identify what those "something elses" actually are. Strategy 3: Hunt for Hidden Causes A confounding variable is a third factor that influences both your suspected cause and your observed effect. It creates the illusion of a direct relationship where none exists.   Here's a simple example: ice cream sales and drowning deaths both increase during summer months. Does ice cream cause drowning? Obviously not. The confounding variable is warm weather, which causes both more ice cream purchases and more swimming.   Now here's the business version: A retail company sees both customer satisfaction and sales increase after renovating their stores. Does the renovation cause higher satisfaction? Maybe—but both also increased because they renovated during the holiday shopping season when people are generally happier and spending more anyway. Same logical structure. Same expensive mistake if they conclude renovations always boost satisfaction.   Map the Relationship: When you observe a correlation, write down your suspected cause and your observed effect. This visualization helps you spot gaps in your logic immediately.   Ask "What Else Changed?": Think carefully about what other factors were present or changed during the same period. Make a written list so your brain doesn't skip over these hidden causes.   Search for Common Causes: Identify factors that could influence both variables at the same time. For instance, if both employee satisfaction and productivity increased, could several toxic managers have left the company?   Consider Time-Based and Environmental Factors: Examine seasons, business cycles, economic trends, reorganizations, leadership changes, and industry shifts that could affect multiple outcomes at once.   Test by Controlling Variables: If possible, create scenarios where you can control or account for potential hidden causes. Try analyzing subgroups where the hidden cause is absent, or run controlled A/B tests.   Once you can spot these hidden causes, you're ready to understand why your brain makes these mistakes in the first place. And this next one? It's probably happening in your head right now without you realizing it. Strategy 4: Outsmart Your Brain's Shortcuts Your brain is wired to see causal connections everywhere, even where none exist. This isn't a design flaw—it's a survival mechanism that kept your ancestors alive. But in the modern business world, this pattern-seeking instinct can mislead you.   Your brain wants simple causal stories. Reality is usually more complex. Once you know what to watch for, you can catch yourself before making these errors.   Catch Your Instant Explanations: When you observe a pattern, pause before declaring causation. Ask yourself: "Am I seeing causation because it's really there, or because my brain desperately needs an explanation?"   Fight Confirmation Bias: Actively search for information that challenges your causal idea, not just data that supports it. If you can't find contradicting evidence, you haven't looked hard enough.   Here's how this plays out: A manager believes remote work hurts productivity. She notices every time someone's late to a Zoom call. But she doesn't notice the three on-time people. She remembers the one missed deadline but forgets the five delivered early. Her brain is filtering reality to confirm what she already believes.   Question Your Compelling Stories: Be wary of explanations that sound too neat. If your causal explanation reads like a perfect success story, double-check it.   Don't See Patterns in Randomness: Three successful quarters in a row doesn't mean you've discovered a winning formula. It might just be a lucky streak. Always ask "Could this pattern occur by chance?"   Watch the 'After Therefore Because' Trap: Every time you catch yourself thinking "we did X and then Y happened," force yourself to consider alternative explanations. Ask yourself "What would I need to see to know this isn't causal?"   Now that you understand how your brain works, let's put this all together into a practical system you can use every time you need to make a high-stakes decision. Strategy 5: The Five-Question Causation Check Mastering causal thinking requires more than understanding principles—it demands a clear approach you can apply when the stakes are high and the pressure is on.   The Five-Question Causation Check:   Define the Relationship Clearly: Write out the specific causal claim you're evaluating with precision. "Social media advertising increases qualified leads by X%" is better than "marketing works."   Verify the Basics: Does the cause come before the effect in time? Are they consistently related across different contexts? Are there possible alternative explanations?   Look for or Create Tests: Find situations where the supposed cause varies while other factors stay constant. The goal is isolation—can you isolate the variable you're testing from everything else that's changing?   Check if More Causes More: Does more of the cause lead to more of the effect? If doubling your ad spend doubles your conversions, that's stronger evidence than if the relationship is erratic.   Test Reversibility: If you remove the cause, does the effect disappear? If you reinstate the cause, does the effect return? This is why pilot programs and controlled rollbacks are so valuable. Put It Into Practice You now have the complete framework for causal thinking—five strategies that work together to reveal what's really causing what.   But here's what separates people who learn this from people who actually use it—one simple practice you can do this week that makes this framework automatic. Practice Exercise: The Causation Audit A practical and effective way to internalize these strategies is through practice with real-world scenarios from your actual work.   Here's how to conduct your own causal analysis:   Identify a Correlation from Your Work: Choose a recent pattern or causal claim that affects budgets or strategy.   State Your Causal Hypothesis: Write out your causal claim explicitly. Be specific about the supposed cause and the supposed effect.   Brainstorm Alternative Explanations: List at least five alternatives. Force yourself beyond the obvious first three.   Apply Your Three Tests: Evaluate whether your idea meets all three tests for causation. Did the cause come first? Do they consistently move together? Have you actually ruled out alternatives?   Design a Simple Test: If possible, design a test to isolate the variable you're testing. For example, have some account managers follow one approach while others don't, with otherwise similar conditions.   Share Your Analysis: Explain your reasoning to a colleague or manager. Teaching forces clarity and demonstrates analytical rigor.   With practice, you'll become skilled at spotting false causation and identifying true cause-and-effect relationships. This skill compounds over time, making you more valuable with every analysis you conduct.   So what does this actually get you? Let me paint the picture of what changes when you master this skill. The Rewards The rewards of mastering causal thinking are well worth the effort and will compound throughout your career.   You become immune to the most expensive mistakes in business—the ones where you solve the wrong problem perfectly. When everyone else is celebrating a correlation as success, you'll be asking the questions that reveal what's really driving outcomes. Imagine being in a meeting where leadership is about to allocate $2 million to scale an initiative, and you're the one who asks the question that reveals a competitor's bankruptcy actually caused the results. That's career-defining value.   Your strategic recommendations carry weight because they're based on actual causation rather than hopeful patterns. Leaders who can distinguish between correlation and causation make decisions that actually work. When your predictions prove accurate while others' fail, your credibility compounds—you become the person everyone turns to when stakes are high.   You develop the intellectual humility that marks exceptional leaders. Causal thinking teaches you to question your initial judgments, seek alternative explanations, and change your mind when evidence demands it. These qualities don't just make you a better thinker—they make you someone others trust with important decisions.   So take these strategies and practice them. Apply them in your daily work. Question causal claims, hunt for hidden causes, check your biases, and use the systematic process. This makes you a more effective decision-maker, a more credible advisor, and someone who spots opportunities and avoids disasters that others miss entirely.   And you'll become the person in the room everyone listens to when the stakes are high. Your Thinking 101 Journey In Episode 1, "Why Thinking Skills Matter Now More Than Ever," we exposed the crisis: your thinking ability is collapsing, AI dependency is creating cognitive debt, and those who can't think independently will be left behind.   In Episode 2, "How To Improve Your Logical Reasoning Skills," you learned to distinguish deductive certainty from inductive probability, calibrate your confidence to match your evidence, and stop treating patterns as proven facts.   Today, you learned how to distinguish true causation from mere correlation—saving yourself from expensive mistakes where you solve the wrong problem perfectly.   Up next—Episode 4: "Analogical Thinking—The Power of Comparison." Your brain doesn't learn through pure logic—it learns by comparison. Every breakthrough idea came from someone who made an unexpected connection. You'll learn how to generate insights through analogy, recognize when comparisons break down, and spot when others use false analogies to manipulate you.   Hit that subscribe button so you don't miss future episodes. Also—hit the like and notification bell. It helps with the algorithm so others see our content. Why not share this video with a colleague who you think would benefit from it?   Because right now, while you've been watching this, someone just approved a million-dollar budget based on a correlation they mistook for causation. The only question is: will you be the one who catches it?       SOURCES CITED IN THIS EPISODE Pathmetrics – Marketing Attribution Waste 5 Common Marketing Attribution Mistakes to Avoid. (2025). Pathmetrics. (Citing Proxima research on global marketing waste) https://www.pathmetrics.io/attribution/5-common-marketing-attribution-mistakes-to-avoid/   Harvard Business Review – Correlation vs Causation in Leadership Luca, M. (2021). Leaders: Stop Confusing Correlation with Causation. Harvard Business Review. https://hbr.org/2021/11/leaders-stop-confusing-correlation-with-causation   The CEO Project – Correlation vs Causation in Business Correlation vs Causation in Business. (2024). The CEO Project. https://theceoproject.com/correlation-vs-causation-in-business/   Nature Communications – Causality in Digital Medicine Glocker, B., Musolesi, M., Richens, J., & Uhler, C. (2021). Causality in digital medicine. Nature Communications, 12, 4993. https://www.nature.com/articles/s41467-021-25743-9   Stanford Social Innovation Review – The Case for Causal AI Sgaier, S. K., Huang, V., & Charles, G. (2020). The Case for Causal AI. Stanford Social Innovation Review. https://ssir.org/articles/entry/the_case_for_causal_ai       ADDITIONAL READING On Causation and Decision-Making Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.   On Thinking Clearly Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.   On Statistical Reasoning Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.         Note: All sources cited in this episode have been accessed and verified as of October 2025.  

The Crisis We're Not Talking About We're living through the greatest thinking crisis in human history—and most people don't even realize it's happening. Right now, AI generates your answers before you've finished asking the question. Search engines remember everything so you don't have to. Algorithms curate your reality, telling you what to think before you've had the chance to think for yourself. We've built the most sophisticated cognitive tools humanity has ever known, and in doing so, we've systematically dismantled our ability to use our own minds. A recent MIT study found that students who exclusively used ChatGPT to write essays showed weaker brain connectivity, lower memory retention, and a fading sense of ownership over their work. Even more alarming? When they stopped using AI tools later, the cognitive effects lingered. Their brains had gotten lazy, and the damage wasn't temporary. This isn't about technology being bad. This is about survival. In a world where machines can think faster than we can, the ability to think clearly—to reason, analyze, question, and decide—has become the most valuable skill you can possess. Those who can think will thrive. Those who can't will be left behind. The Scope of Cognitive Collapse Let's be clear about what we're facing. Multiple studies across 2024 and 2025 have found a significant negative correlation between frequent AI tool usage and critical thinking abilities. We're not talking about a slight dip in performance. We're talking about measurable cognitive decline. A Swiss study showed that more frequent AI use led to cognitive decline as users offloaded critical thinking to machines, with younger participants aged 17-25 showing higher dependence on AI tools and lower critical thinking scores compared to older age groups. Think about that. The generation that should be developing the sharpest minds is instead experiencing the steepest cognitive erosion. The data gets worse. Researchers from Microsoft and Carnegie Mellon University found that the more users trusted AI-generated outputs, the less cognitive effort they applied—confidence in AI correlates with diminished analytical engagement. We're outsourcing our thinking, and in the process, we're forgetting how to think at all. But AI dependency is only part of the story. Our entire information ecosystem has become hostile to independent thought. Social media algorithms create filter bubbles that curate content aligned with your existing views. Users online tend to prefer information adhering to their worldviews, ignore dissenting information, and form polarized groups around shared narratives—and when polarization is high, misinformation quickly proliferates. You're not thinking anymore. You're being fed a carefully constructed reality designed to keep you engaged, not informed. The algorithm knows what you'll click on, what will make you angry, and what will keep you scrolling. And every time you accept that curated reality without question, your capacity for independent thought atrophies a little more. What Happened to Education? Here's where it gets personal. Schools used to teach you HOW to think. Now they teach you WHAT to think—and there's a massive difference. Research from Harvard professional schools found that while more than half of faculty surveyed said they explicitly taught critical thinking in their courses, students reported that critical thinking was primarily being taught implicitly. Translation? Professors think they're teaching thinking skills, but students aren't actually learning them. Students were generally unable to recall or define key terms like metacognition and cognitive biases. The problem runs deeper than higher education. Teachers struggle with balancing the demands of covering vast amounts of content with the need for in-depth learning experiences, and there's a misconception that critical thinking is an innate ability that develops naturally over time. But research shows the opposite: critical thinking skills can be explicitly taught and developed through deliberate practice. So why aren't we doing it? Because education systems reward compliance and memorization, not inquiry and analysis. Students learn to regurgitate information for tests, not to question assumptions or evaluate evidence. They're taught to accept authority, not challenge it. To consume information, not interrogate it. We've created generations of people who are educated but can't think. Who have degrees but lack discernment. Who can Google anything but can't reason through problems on their own. The Cost of Mental Outsourcing Let's talk about what you're actually losing when you stop thinking for yourself. First, you lose agency. When you can't analyze information independently, you become dependent on whoever controls the information flow. Political leaders, social media influencers, corporations, algorithms—they all shape your reality, and you don't even realize it's happening. 73% of Democrats and Republicans can't even agree on basic facts. Not opinions. Facts. That's what happens when thinking skills collapse—you can't distinguish between what's true and what you want to be true. Second, you lose adaptability. Repeated use of AI tools creates cognitive debt that reduces long-term learning performance in independent thinking and can lead to diminished critical inquiry, increased vulnerability to manipulation, and decreased creativity. In a rapidly changing world, the inability to think flexibly and adapt to new information is a death sentence for your career, your relationships, and your relevance. Third, you lose connection—to your work, your decisions, your life. 83% of students who used ChatGPT exclusively couldn't recall key points in their essays, and none could provide accurate quotes from their own papers. When you outsource thinking, you forfeit ownership. Your work stops being yours. Your ideas stop being original. You become a conduit for someone else's thinking, not a generator of your own. Research shows that partisan echo chambers increase both policy and affective polarization compared to mixed discussion groups. You're not just losing the ability to think—you're losing the ability to connect with people who think differently. You're trapped in a bubble where everyone agrees with you, which feels comfortable but leaves you intellectually brittle and socially isolated. The societal cost? We're becoming ungovernable. When people can't think critically, they can't solve complex problems. They can't compromise. They can't distinguish between legitimate disagreement and malicious manipulation. Democracy requires citizens who can reason, debate, and arrive at informed conclusions. Without thinking skills, democratic institutions collapse into tribal warfare where the loudest voices win, not the most rational ones. Why This Moment Demands Action Here's what makes this crisis urgent: we're at an inflection point. Researchers have identified a tipping point beyond which the process of polarization speeds up as the forces driving it are compounded and forces mitigating polarization are overwhelmed. Some political groups may have already passed this critical threshold. Once you cross that line, reversing cognitive decline becomes exponentially harder. Think about what's coming. AI is getting smarter, faster, and more persuasive. Deepfakes and AI-manipulated media are becoming increasingly sophisticated and harder to detect. Whether or not they've already influenced major events, the capability exists—and your ability to evaluate what's real becomes more critical every day. Social media platforms are optimizing for engagement, not truth. Educational systems are struggling to adapt. The information environment is becoming more hostile to critical thinking every single day. If you don't develop thinking skills now—if you don't reclaim your capacity for independent thought—you'll be swept along by forces you can't see and can't resist. You'll believe what you're told to believe. Buy what you're told to buy. Vote how you're told to vote. And you won't even realize you've lost the ability to choose. But here's the truth they don't want you to know: thinking skills can be learned. They can be developed. They can be strengthened through deliberate practice. You're not doomed to cognitive passivity. You can take back control of your mind. What Becomes Possible Imagine waking up every morning with the confidence that you can evaluate any information that comes your way. No more anxiety about whether you're being manipulated. No more second-guessing your decisions because you don't trust your own judgment. No more feeling like everyone else knows something you don't. When you master thinking skills, you become intellectually self-sufficient. You can spot logical fallacies in arguments. You can identify bias in news sources. You can separate correlation from causation. You can ask the right questions instead of accepting convenient answers. You can hold two competing ideas in your mind and evaluate them fairly without your ego getting in the way. You become harder to fool and impossible to control. Political propaganda bounces off you because you can see through emotional manipulation. Marketing tactics lose their power because you understand psychological triggers. Social media algorithms can't trap you in echo chambers because you actively seek out diverse perspectives and challenge your own assumptions. Your relationships improve because you can actually listen to people who disagree with you without feeling threatened. Your career accelerates because you can solve problems others can't see. Your decisions get better because you're working from logic and evidence, not fear and instinct. Research shows that innovative teaching methods like problem-based learning and interactive instruction significantly boost academic performance and cultivate critical thinking skills. These aren't just abstract benefits—they translate into real-world outcomes. Better grades. Better jobs. Better lives. Most importantly, you reclaim your autonomy. You stop being a passive consumer of information and become an active creator of understanding. Your thoughts become truly your own again. Your beliefs are chosen, not imposed. Your worldview is constructed through rigorous analysis, not algorithmic manipulation. The Path Forward This episode is the beginning of a journey. Over the coming weeks, we'll break down the specific thinking skills you need to master: logical reasoning, argument analysis, decision-making frameworks, cognitive bias recognition, and information evaluation.  Each episode will give you concrete tools you can use immediately. But before we get to the tactics, you need to understand why this matters. Why thinking skills aren't just nice to have—they're essential for survival in the modern world. Why the ability to think clearly is the ultimate competitive advantage. The thinking crisis is real. It's measurable. It's accelerating. But it's not inevitable. You have a choice right now. You can keep outsourcing your thinking to machines and algorithms, accepting a future where your mind grows weaker with each passing year. Or you can decide that your ability to think—to reason, to analyze, to question, to decide—is too valuable to surrender. The world needs people who can think. Your community needs people who can think. You need to be able to think. Not because it makes you smarter than everyone else, but because it makes you free. This is your invitation to reclaim your mind. Everything that follows will show you how. But first, you had to see what's at stake. Welcome to Thinking 101. Let's rebuild the most important skill you'll ever develop. Over the next eight weeks, we're building your thinking toolkit from the ground up. Logical reasoning. Causal thinking. Probabilistic judgment. Mental models that let you see what others miss. Each episode drops a specific skill you can use immediately—not theory, but weapons-grade thinking tools for the real world. Links to each episode will appear in the description as they're released, and you can find the full playlist on our channel. Subscribe now and hit the notification bell so you don't miss a single one. Because here's the truth: these skills compound. Miss one, and you're building on a shaky foundation. Watch them all, and you'll think circles around the competition. If you found this valuable, hit that like button—it helps more people discover this series. Drop a comment below: What's one thinking skill you wish you'd learned earlier? I read every single one. And if you want to go deeper, I write Studio Notes on Substack every Monday where I share the personal stories behind what I'm teaching here—the hard-won lessons, the mistakes that taught me why these skills matter, and what it actually looks like to rebuild your thinking from the ground up. The links in the description.  This week's post examines the education system's failure to teach students how to think. You can find it here - https://philmckinney.substack.com/p/the-worlds-best-test-takers  The crisis is real. The solution is here. Let's get to work.

In this episode of 'Loud and Clear,' host Francisco Cardenas discusses the complexities of programmatic marketing with Victoria Garcia Galarza, better known as VGG, from Causal. Together, they explore the importance of authentic brand voices, the perceptive capabilities of consumers, and the challenges of measuring business outcomes over media metrics. Victoria shares insights from her diverse cultural background and her journey in programmatic, from its early days to the current AI-driven landscape. They also discuss the role of AI in creative testing, the significance of transparency and trust with clients, and the evolving impact of retail media networks. The episode emphasizes continuous learning, adaptability, and prioritizing the consumer in digital marketing strategies.Guest: Victoria Garcia Galarza [VGG] Sales Director at CausalProducer:⁠⁠⁠⁠ Victor Cornejo Tell Me More StudiosHost: Francisco Cardenas Principal of Digital Strategy & Integration at LERMA/Music: Rolf Ruiz

For over three decades, Felix Finster has been developing a unique and ambitious reformulation of physics known as Causal Fermion Systems (CFS). Physicists usually describe the world in terms of fields defined on a spacetime manifold. Within this familiar framework, abstract quantities such as correlations between matter fields at different points in spacetime can be computed. In mathematical language, these correlations are captured by operators acting on a Hilbert space. What Felix realized is that this process can be reversed. If you start with a suitable collection of operators on a Hilbert space, satisfying certain mathematical properties, you can in principle reconstruct the underlying spacetime and fields that would give rise to those operators as operators of correlations. In this sense, Causal Fermion Systems  offers a dual description of reality. On the one hand, reality can be described in terms of symmetries, fields, and manifolds - the usual language of physics. On the other hand, CFS proposes that reality can just as well be described using abstract structures: Hilbert spaces, operators, and measures on sets of operators. Spacetime, matter, and everything we observe then emerges from these underlying mathematical quantities. The beauty of reformulating physics this way is that it opens up an entirely new framework in which to explore some of the deepest open questions in physics: What is spacetime like at the smallest scales? Why do we see precisely the particles we do in experiments? The hope is that within the CFS framework, answers to such questions might become more natural or even inevitable. Of course, we can't cover a 30-year research program in full detail in a single conversation. The goal here is to get a sense of the flavor of Felix's approach to physics. For the full details, you can explore Felix's books  (e.g. https://www.cambridge.org/core/books/causal-fermion-systems/CCA6DE1E1F4DA3AC0EF6729664A5D5B9 ). ►Watch on YouTube: https://youtu.be/qQl51qifus0 ►Find out more about Felix's work here: https://www.uni-regensburg.de/mathematik/mathematik-1/startseite/index.html https://causal-fermion-system.com/

Causal Inquiry in International Relations (Oxford UP, 2024) by Adam R. C. Humphreys and Hidemi Suganami defends a new, philosophically informed account of the principles which must underpin any causal research in a discipline such as International Relations. Its central claim is that there is an underlying logic to all causal inquiry, at the core of which is the search for empirical evidence capable of ruling out competing accounts of how specific events were brought about. Although this crucial fact is obscured by the ‘culture of generalization' which predominates in contemporary social science, all causal knowledge ultimately depends on the provision of empirical support for concrete claims about specific events, located in space and time.  Causal Inquiry in International Relations not only explores existing philosophical debates around causation; it also provides a detailed study of some of the most fundamental methodological questions which arise in the course of causal inquiry. Using examples drawn from philosophy and from the study of international relations, it demonstrates what is problematic about established ways of thinking, brings new clarity to both philosophical and methodological questions, and seeks to enhance collective understanding of the contribution that causal inquiry can make to empirically rich and critically aware scholarship about world politics. It concludes by situating ‘causal inquiry' in relation to other forms of inquiry employed in the study of world politics, emphasizing especially the often unnoticed dependence of causal inquiry on precisely the kind of knowledge of specific events which historians are well placed to provide. Adam Humphreys is Associate Professor and Head of Department in the Department of Politics and International Relations, University of Reading. He joined the University of Reading in 2013, having previously been a British Academy Post-Doctoral Fellow at the University of Oxford (2007-10) and Fellow in Politics at Brasenose College, Oxford (2010-13). His principal research interests are in International Relations theory and meta-theory, especially causation and causal explanation, realism and neo-realism, the English School, and the relationship between theory and history. He also has research interests in British foreign and defence policy, strategy, and the ethics of war.Hidemi Suganami studied International Relations at Tokyo, Aberystwyth, and London Universities. His first academic appointment was at Keele in 1975, where he later became Professor of the Philosophy of International Relations. In 2004, he moved to Aberystwyth, where currently he is Emeritus Professor of International Politics. His publications include: The Domestic Analogy and World Order Proposals (1989); On the Causes of War (1996); and, with Andrew Linklater, The English School of International Relations (2006). Over a number of years, he has been studying philosophical issues surrounding causation and explanation in International Relations. Stephen Satkiewicz is an independent scholar with research areas spanning Civilizational Sciences, Social Complexity, Big History, Historical Sociology, Military History, War Studies, International Relations, Geopolitics, and Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Causal Inquiry in International Relations (Oxford UP, 2024) by Adam R. C. Humphreys and Hidemi Suganami defends a new, philosophically informed account of the principles which must underpin any causal research in a discipline such as International Relations. Its central claim is that there is an underlying logic to all causal inquiry, at the core of which is the search for empirical evidence capable of ruling out competing accounts of how specific events were brought about. Although this crucial fact is obscured by the ‘culture of generalization' which predominates in contemporary social science, all causal knowledge ultimately depends on the provision of empirical support for concrete claims about specific events, located in space and time.  Causal Inquiry in International Relations not only explores existing philosophical debates around causation; it also provides a detailed study of some of the most fundamental methodological questions which arise in the course of causal inquiry. Using examples drawn from philosophy and from the study of international relations, it demonstrates what is problematic about established ways of thinking, brings new clarity to both philosophical and methodological questions, and seeks to enhance collective understanding of the contribution that causal inquiry can make to empirically rich and critically aware scholarship about world politics. It concludes by situating ‘causal inquiry' in relation to other forms of inquiry employed in the study of world politics, emphasizing especially the often unnoticed dependence of causal inquiry on precisely the kind of knowledge of specific events which historians are well placed to provide. Adam Humphreys is Associate Professor and Head of Department in the Department of Politics and International Relations, University of Reading. He joined the University of Reading in 2013, having previously been a British Academy Post-Doctoral Fellow at the University of Oxford (2007-10) and Fellow in Politics at Brasenose College, Oxford (2010-13). His principal research interests are in International Relations theory and meta-theory, especially causation and causal explanation, realism and neo-realism, the English School, and the relationship between theory and history. He also has research interests in British foreign and defence policy, strategy, and the ethics of war.Hidemi Suganami studied International Relations at Tokyo, Aberystwyth, and London Universities. His first academic appointment was at Keele in 1975, where he later became Professor of the Philosophy of International Relations. In 2004, he moved to Aberystwyth, where currently he is Emeritus Professor of International Politics. His publications include: The Domestic Analogy and World Order Proposals (1989); On the Causes of War (1996); and, with Andrew Linklater, The English School of International Relations (2006). Over a number of years, he has been studying philosophical issues surrounding causation and explanation in International Relations. Stephen Satkiewicz is an independent scholar with research areas spanning Civilizational Sciences, Social Complexity, Big History, Historical Sociology, Military History, War Studies, International Relations, Geopolitics, and Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/political-science

Causal Inquiry in International Relations (Oxford UP, 2024) by Adam R. C. Humphreys and Hidemi Suganami defends a new, philosophically informed account of the principles which must underpin any causal research in a discipline such as International Relations. Its central claim is that there is an underlying logic to all causal inquiry, at the core of which is the search for empirical evidence capable of ruling out competing accounts of how specific events were brought about. Although this crucial fact is obscured by the ‘culture of generalization' which predominates in contemporary social science, all causal knowledge ultimately depends on the provision of empirical support for concrete claims about specific events, located in space and time.  Causal Inquiry in International Relations not only explores existing philosophical debates around causation; it also provides a detailed study of some of the most fundamental methodological questions which arise in the course of causal inquiry. Using examples drawn from philosophy and from the study of international relations, it demonstrates what is problematic about established ways of thinking, brings new clarity to both philosophical and methodological questions, and seeks to enhance collective understanding of the contribution that causal inquiry can make to empirically rich and critically aware scholarship about world politics. It concludes by situating ‘causal inquiry' in relation to other forms of inquiry employed in the study of world politics, emphasizing especially the often unnoticed dependence of causal inquiry on precisely the kind of knowledge of specific events which historians are well placed to provide. Adam Humphreys is Associate Professor and Head of Department in the Department of Politics and International Relations, University of Reading. He joined the University of Reading in 2013, having previously been a British Academy Post-Doctoral Fellow at the University of Oxford (2007-10) and Fellow in Politics at Brasenose College, Oxford (2010-13). His principal research interests are in International Relations theory and meta-theory, especially causation and causal explanation, realism and neo-realism, the English School, and the relationship between theory and history. He also has research interests in British foreign and defence policy, strategy, and the ethics of war.Hidemi Suganami studied International Relations at Tokyo, Aberystwyth, and London Universities. His first academic appointment was at Keele in 1975, where he later became Professor of the Philosophy of International Relations. In 2004, he moved to Aberystwyth, where currently he is Emeritus Professor of International Politics. His publications include: The Domestic Analogy and World Order Proposals (1989); On the Causes of War (1996); and, with Andrew Linklater, The English School of International Relations (2006). Over a number of years, he has been studying philosophical issues surrounding causation and explanation in International Relations. Stephen Satkiewicz is an independent scholar with research areas spanning Civilizational Sciences, Social Complexity, Big History, Historical Sociology, Military History, War Studies, International Relations, Geopolitics, and Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/world-affairs

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Get early access to Alex's next live-cohort courses!Enroll in the Causal AI workshop, to learn live with Alex (15% off if you're a Patron of the show)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Causal inference is crucial for understanding the impact of interventions in various fields.ChiRho is a causal probabilistic programming language that bridges mechanistic and data-driven models.ChiRho allows for easy manipulation of causal models and counterfactual reasoning.The design of ChiRho emphasizes modularity and extensibility for diverse applications.Causal inference requires careful consideration of assumptions and model structures.Real-world applications of causal inference can lead to significant insights in science and engineering.Collaboration and communication are key in translating causal questions into actionable models.The future of causal inference lies in integrating probabilistic programming with scientific discovery.Chapters:05:53 Bridging Mechanistic and Data-Driven Models09:13 Understanding Causal Probabilistic Programming12:10 ChiRho and Its Design Principles15:03 ChiRho's Functionality and Use Cases17:55 Counterfactual Worlds and Mediation Analysis20:47 Efficient Estimation in ChiRho24:08 Future Directions for Causal AI50:21 Understanding the Do-Operator in Causal Inference56:45 ChiRho's Role in Causal Inference and Bayesian Modeling01:01:36 Roadmap and Future Developments for ChiRho01:05:29 Real-World Applications of Causal Probabilistic Programming01:10:51 Challenges in Causal Inference Adoption01:11:50 The Importance of Causal Claims in Research01:18:11 Bayesian Approaches to Causal Inference01:22:08 Combining Gaussian Processes with Causal Inference01:28:27 Future Directions in Probabilistic Programming and Causal InferenceThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad...

Why do we procrastinate, overthink, avoid, or hold back just when things matter most? Your social media feed at 2 am calls this self-sabotage, but is it?In this episode of the Scenic Route, we unpack fresh psychology research that flips the story of self-sabotage on its head. You'll discover three hidden patterns that shape how we repeat harmful choices:Sensitives: quick learners who adapt after mistakesUnawares: people who miss the cause-and-effect until it's explainedCompulsives: those who know better but can't break the cycleYou'll hear why common advice like “just use willpower” or “learn from your mistakes” fails so many of us, and what actually works instead.Because here's the twist: you're not broken, lazy, or your own worst enemy. What looks like sabotage is often safety in disguise. And once you see that, everything changes.Paper mentioned:Zeng, L., Park, H. R. P., McNally, G. P., Jean-Richard-dit-Bressel, P., et al. (2025). Causal inference and cognitive-behavioral integration deficits drive stable variation in human punishment sensitivity. Communications Psychology, 3, Article 103._____________________________________________________________________ Visit jenniferwalter.me – your cosy corner where recovering perfectionists, misfits, and those done pretending to be fine find space to breathe, dream, and create real change."

Patrick McKenzie (patio11) is joined by Emmett Shear, co-founder of Twitch, former interim CEO of OpenAI, who now runs Softmax AI alignment. Emmett argues that current AI safety approaches focused on "systems of control" are fundamentally flawed and proposes "organic alignment" instead—where AI systems develop genuine care for their local communities rather than following rigid rules. –Full transcript available here: www.complexsystemspodcast.com/ai-alignment-with-emmett-shear/–Sponsor: MercuryThis episode is brought to you by Mercury, the fintech trusted by 200K+ companies — from first milestones to running complex systems. Mercury offers banking that truly understands startups and scales with them. Start today at Mercury.com Mercury is a financial technology company, not a bank. Banking services provided by Choice Financial Group, Column N.A., and Evolve Bank & Trust; Members FDIC.–Links:Softmax - https://www.softmax.com/–Timestamps:(01:26) Understanding AI alignment(04:42) The concept of universal constructors(13:45) AI's rapid progress and practical applications(19:08) Sponsor: Mercury(20:19) AI's impact on work(34:59) AI's sensory and action space(42:10) User intent vs. user request(44:35) The illusion of a perfect AI(49:57) Causal emergence and system dynamics(55:19) Reflective and intentional alignment(01:01:08) Engineering challenges in AI alignment(01:04:15) The future of AI(01:26:40) Wrap

CP-3010 is the anomalous byproduct of a nearly undetectable entity, hereby classified as SCP-3010-1. SCP-3010 is characterized as a sensation of"being watched", similar to that of being intensely stared at or observed unwillingly by another human or sentient being.Content Warnings:  Unreality, nyctophobia, scopophobia.TranscriptPatrons May 2-10Joey,, Sebastian Rose, BlazoticPG, Ellie McCartney, James Good, Juice Man, Te Puhi Nathan, Jacob Byers, Henning, Fernando Tellez, Sienna Athy, Aaron Irvin, Stacy Krugger, Pyrelight, Zachary Hutchins, Jae Jump, and Diego Rivera!Cast & Crew SCP Archives was created by Pacific S. Obadiah & Jon GrilzSCP-3010  was written by iznarothScript by Daisy McNamaraComputer - Nichole GoodnightCap - Janine BowerMTF-066-7 - Jonathan McDonaldMTF-066-1 - Dexter HowardMTF-066-3 - Rebekah McLoughlinMTF-066-5 - Kit PatersonD-17729 - Russ MoreObrendt - Stephen IndrisanoSCP-3010 - Kayla TemshivArt - Eduardo Valdés-HeviaTheme Song - Mattie Roi BergerOriginal Music -  Newton SchottelkotteDialogue Editor - Nate DuFortSound Designer - Brad ColbroockShowrunner - Daisy McNamaraCreative Director - Pacific S. ObadiahExecutive Producer - Tom Owen Presented by Bloody FMwww.Bloody-Disgusting.comwww.SCParchives.com Patreon: https://www.patreon.com/scp_podStore: https://store.dftba.com/collections/scp-archivesInstagram: https://www.instagram.com/scp_pod/Bluesky: https://bsky.app/profile/scparchives.bsky.socialDiscord: https://discord.gg/tJEeNUzeZXTikTok: https://www.tiktok.com/@scppodYouTube: https://www.youtube.com/c/scparchives

Happy 801 Day, Salt Lake! Host Ali Vallarta, executive producer Emily Means, and Salt Lake Tribune reporter Andy Larsen count down their favorite things about our city. Plus, Heber residents push back against a new church, a cute new avalanche pup, and gourmet fast casual.  Resources and references: Residents to appeal Heber Valley temple decision to Utah Supreme Court [KPCW] Updated guidance on banned books in Utah schools: you own it, you can bring it [KUER] Join us for 801 Day at the Gallivan Center on Friday, Aug. 1. RSVP here! Become a member of City Cast Salt Lake today! It's the best way to support our work and help make sure we are around for years to come. Get all the details and sign up at membership.citycast.fm. Subscribe to Hey Salt Lake, our daily morning newsletter. You can also find us on Instagram @CityCastSLC.  Looking to advertise on City Cast Salt Lake? Check out our options for podcast and newsletter ads. Learn more about the sponsors of this episode:  Tracy Aviary Workshopslc.com - use code CITYCAST for 20% off. Live Crude - Get $10 off your first CRUDE purchase with promo code CITYCASTSLC Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's clip is from episode 137 of the podcast, with Robert Ness.Alex and Robert discuss the intersection of causal inference and deep learning, emphasizing the importance of understanding causal concepts in statistical modeling. The discussion also covers the evolution of probabilistic machine learning, the role of inductive biases, and the potential of large language models in causal analysis, highlighting their ability to translate natural language into formal causal queries.Get the full conversation here.Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Steph and Molly are here this week for the Weekly Dish to dish out the deal with "deep causal hosting."See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Causal assumptions are crucial for statistical modeling.Deep learning can be integrated with causal models.Statistical rigor is essential in evaluating LLMs.Causal representation learning is a growing field.Inductive biases in AI should match key mechanisms.Causal AI can improve decision-making processes.The future of AI lies in understanding causal relationships.Chapters:00:00 Introduction to Causal AI and Its Importance16:34 The Journey to Writing Causal AI28:05 Integrating Graphical Causality with Deep Learning40:10 The Evolution of Probabilistic Machine Learning44:34 Practical Applications of Causal AI with LLMs49:48 Exploring Multimodal Models and Causality56:15 Tools and Frameworks for Causal AI01:03:19 Statistical Rigor in Evaluating LLMs01:12:22 Causal Thinking in Real-World Deployments01:19:52 Trade-offs in Generative Causal Models01:25:14 Future of Causal Generative ModelingThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant...

In Hour 2, Isaac and Suke wonder why people dress so casually in the pacific northwest, congratulate the terrible Colorado Rockies for their first series sweep in over a year, and more.

How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlation is not causation. "Spurious correlations" can show, for example, how rising ice cream sales might statistically link to more drownings, not because one causes the other, but due to an unobserved common cause like warm weather. Our guest, Utkarshani Jaimini, a researcher from the University of South Carolina's Artificial Intelligence Institute, tries to tackle this problem by using knowledge graphs that incorporate domain expertise.  Knowledge graphs (structured representations of information) are combined with neural networks in the field of neurosymbolic AI to represent and reason about complex relationships. This involves creating causal ontologies, incorporating the "weight" or strength of causal relationships and hyperrelations. This field has many practical applications such as for AI explainability, healthcare and autonomous driving. Follow our guest Utkarshani Jaimini's Webpage Linkedin Papers in focus CausalLP: Learning causal relations with weighted knowledge graph link prediction, 2024 HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph, 2024