Zero to Singularity

Wilder Brooks

Zero to Singularity is a deep-dive podcast exploring artificial intelligence from first principles to the technological frontier. We break down how AI actually works, investigate the latest breakthroughs, separate evidence from hype, and explore where machine intelligence may be heading next.

  1. 2d ago

    The Hidden Committee: How Mixture-of-Experts AI Decides Which Brain to Use

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! Most people imagine an AI model as one enormous neural network using all of its intelligence for every question. But some of the most powerful modern systems work differently. Instead of activating every part of the model for every token, mixture-of-experts architectures can route different inputs through different specialized groups of parameters. One part may become useful for code. Another may respond more strongly to mathematics, language, or other patterns. A small routing system decides which experts should participate. In Episode 14 of Zero to Singularity, we investigate why AI researchers are building models that can contain enormous numbers of parameters without activating all of them at once. How does an AI decide which experts to use? Do experts genuinely specialize? Why can sparse models be cheaper to run than equally large dense models? What happens when the router makes a bad decision? And could future AI systems become less like one giant brain and more like a dynamically assembled society of specialists? The deeper question is: Does the path to larger intelligence require making every part of an AI bigger or teaching it which parts of itself are worth activating? Zero to Singularity — Understanding AI from the fundamentals to the frontier.

  2. Oct 25

    The Inference Revolution: Can AI Get Smarter Just by Thinking Longer?

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! For years, the dominant recipe for improving AI was straightforward: train larger models on more data using more compute. But another path is becoming increasingly important. Instead of spending all the computation before deployment, what if an AI can spend more computation after you ask the question? In Episode 13 of Zero to Singularity, we investigate inference-time and test-time compute the idea that AI performance can improve by searching, checking, revising, using tools, exploring multiple solutions, and allocating more computation to difficult problems. Why can the same underlying model sometimes produce a much better answer simply by being allowed to work longer? Is this genuinely better reasoning, sophisticated search, or both? And could future AI systems dynamically decide how much computation each problem deserves? The deeper question is: What if the next leap in intelligence comes not from building a bigger brainbut from giving the brain more time to think? Zero to Singularity — Understanding AI from the fundamentals to the frontier.

  3. Oct 19

    The Autonomous Scientist: Can AI Discover What Humans Haven’t?

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! For most of AI’s history, machines have learned from discoveries humans already made. But what happens when AI begins helping create the next discoveries? In Episode 12 of Zero to Singularity, we investigate the rise of AI systems that can generate hypotheses, design experiments, analyze results, operate automated laboratories, and decide what to test next. We explore autonomous labs, scientific agents, causal reasoning, drug and materials discovery, simulation, robotics, verification, and the difference between predicting something correctly and actually understanding why it happens. Can AI discover something genuinely new? Can it choose which scientific questions are worth asking? And if an AI proposes an experiment, robots perform it, and independent laboratories confirm the result who made the discovery? The deeper question is: What happens when machines stop merely learning from humanity’s scientific knowledge and begin helping produce knowledge that did not exist before? Zero to Singularity — Understanding AI from the fundamentals to the frontier.

  4. Oct 12

    The Verification Problem: How Do We Know When AI Is Actually Right?

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! AI can now write code, solve math problems, analyze research, plan multi-step tasks, and produce answers that sound remarkably confident. But intelligence creates a strange problem: What happens when producing an answer becomes easier than verifying whether the answer is correct? A model can generate thousands of lines of code in minutes. A human may need hours to inspect them. An AI can propose a mathematical proof almost instantly. Checking every step may be harder than creating the proof. An autonomous agent might perform hundreds of actions before a person ever sees the final result. So the next frontier of AI may not simply be building systems capable of generating better answers. It may be building systems capable of proving when those answers deserve to be trusted. Central question If AI eventually becomes better than humans at solving difficult problems, who or what will be capable of checking its work? That gives us a fantastic episode spanning AI reasoning, hallucinations, uncertainty, reinforcement learning, formal verification, code execution, scientific replication, AI judges, and the emerging idea of scalable oversight.

  5. Oct 5

    The Forgetful Machine: Can AI Build a Memory That Lasts?

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! Artificial intelligence can explain quantum physics, write software, analyze thousands of documents, and reason through complex problems. Then you start a new conversation... and it may not remember what happened yesterday. So what does it actually mean for an AI to remember? In Episode 10 of Zero to Singularity, we explore one of the most important problems standing between today’s AI assistants and truly persistent intelligent agents: long-term memory. We break down the difference between a model’s learned parameters and its context window, why a huge context window is not the same as permanent memory, and how modern AI systems use retrieval, embeddings, vector databases, summaries, and external memory stores to preserve information across time. Then we go deeper. How should an AI decide what is worth remembering? How does it know when a memory is outdated? What happens when two memories contradict each other? Can an AI learn from experience without retraining its entire neural network? And how can it keep learning without suffering catastrophic forgetting gaining new knowledge while damaging what it already knows? We explore: context windows, working memory, episodic memory, semantic memory, retrieval-augmented generation, vector databases, agent memory, continual learning, catastrophic forgetting, memory compression, personalization, privacy, memory poisoning, and the possibility of AI systems that accumulate experience over months or even years.

  6. Sep 30

    The Data Wall: What Happens When AI Runs Out of Human Knowledge?

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! Modern AI was built on an extraordinary resource: human knowledge. Books. Websites. Research papers. Code. Images. Video. Conversations. Billions upon billions of examples created by people. But what happens when frontier models have already consumed most of the useful, accessible human-generated data? In Episode 9 of Zero to Singularity, we investigate one of the biggest questions facing the next generation of artificial intelligence: Can AI keep improving if high-quality human training data becomes a bottleneck? This episode explores: how pretraining data powers modern AIscaling laws and diminishing returnsthe difference between more data and better datasynthetic training dataself-play and automated curriculum generationreinforcement learning and verifiable tasksmodel-generated reasoning examplessimulation and virtual environmentsmultimodal data from video, audio, robotics, and sensorsproprietary and expert-generated datasetsinference-time scaling and test-time computeretrieval, tools, agents, and external memorymodel collapse and synthetic-data contaminationwhy AI-generated internet content could become a training problemwhether machines can eventually generate their own useful learning experiencesWe also examine some of the biggest claims surrounding the future of AI training: Is the internet running out of useful data? Can synthetic data replace human-created knowledge? Does training on AI-generated content inevitably cause model collapse? Can self-play generate effectively unlimited training material? And could future AI systems eventually design their own increasingly difficult curriculum? The deeper question is no longer simply: How much human knowledge can AI absorb? It may become: Can intelligence eventually create the experiences it needs to make itself smarter?

  7. Sep 25

    The Machine Gets a Body: Is Physical AI the Next Frontier?

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! What happens when artificial intelligence stops living only on screens and starts acting in the physical world? In Episode 8 of Zero to Singularity, we explore Physical AI the convergence of advanced AI, robotics, and embodied intelligence. We break down how modern robots are learning to see, understand instructions, manipulate objects, move through unfamiliar environments, and connect language with physical action. We also examine why teaching a machine to operate safely and reliably in the real world may be far harder than teaching an AI to generate text, code, images, or plans. This episode explores: embodied AI and robotics foundation modelsvision-language-action systemshumanoid robots versus specialized machinesreinforcement learning and imitation learningteleoperation, simulation, and synthetic datadexterity, locomotion, spatial reasoning, and force controlthe robotics data problemwhy impressive demos do not automatically equal reliable deploymentsafety, recovery, and human supervisionthe economics of physical automationfactories, warehouses, and the much harder challenge of the homewhat Physical AI could realistically look like by 2030The central question: Was language actually the easy part? Because in software, intelligence only has to produce the right answer. In robotics, intelligence has to survive reality. Zero to Singularity explores artificial intelligence from the fundamentals to the frontier making complex technology understandable without oversimplifying the science.

    The Machine Gets a Body: Is Physical AI the Next Frontier?

  8. Sep 20

    The Vanishing First Rung: Is AI Rewriting the Career Ladder?

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! What happens when AI can do the work that used to train beginners? In Episode 7 of Zero to Singularity, we explore one of the most important questions in the future of work: whether artificial intelligence is beginning to reshape the traditional career ladder from the bottom up. The episode examines why entry-level workers may be feeling the effects of AI before the broader workforce, how reduced hiring can matter just as much as layoffs, and what happens when routine junior work is automated before young professionals have a chance to build experience. We break down: the difference between a task, skill, job, and occupationautomation versus augmentationwhy junior workers may be affected differently from senior workersthe growing “apprenticeship problem”codified knowledge versus tacit knowledgehow AI can both replace beginner tasks and accelerate learningchanges in software, finance, law, customer support, and creative workthe rise of AI agents and longer autonomous workflowswhy verification, judgment, and domain expertise may become more valuablewhether AI could broaden jobs instead of simply eliminating themhow education and early-career training may need to changesix possible futures for work through 2030The central question is simple: If AI removes the first rung of the career ladder, how do humans learn to climb? Zero to Singularity explores artificial intelligence from the fundamentals to the frontier making complex ideas understandable without oversimplifying the science, economics, or uncertainty.

    The Vanishing First Rung: Is AI Rewriting the Career Ladder?
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About

Zero to Singularity is a deep-dive podcast exploring artificial intelligence from first principles to the technological frontier. We break down how AI actually works, investigate the latest breakthroughs, separate evidence from hype, and explore where machine intelligence may be heading next.

Information

  • Creator
    Wilder Brooks
  • Years Active
    2026
  • Episodes
    14
  • Rating
    Clean
  • Copyright
    © 2026 Wilder Brooks. All rights reserved.
  • Show Website
    Zero to Singularity

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