Learn AI in Bits

Dan W

AI explained in bits. Each episode takes one concept, like tokens, embeddings, hallucinations, or prompt injection, and explains it in about five minutes. No jargon, no filler. Just the idea, why it matters, and what to remember. If you're curious about AI or already building with it, you'll come away understanding how these systems work. One concept. Five minutes. That's the whole show.

  1. 11 hr ago

    048 - What Is Recursive Self-Improvement?

    Recursive self-improvement, or RSI, describes a future in which AI systems become increasingly capable of improving the process used to develop AI itself. This episode explains the idea through today's coding and research agents, then draws the line between AI assisting AI development and a genuinely closed recursive loop. The episode emphasizes that full RSI has not been demonstrated. Current systems can already write code, run experiments, optimize defined objectives, evaluate results, and sometimes suggest better research directions. Humans still play important roles in setting goals, defining evaluations, providing infrastructure, and judging whether results are useful. Key current examples cited: - Anthropic reports that more than 80 percent of code merged into its codebase was authored by Claude as of May 2026. - Anthropic reports that its typical engineer merged about 8 times as much code per day in Q2 2026 as in 2024, while warning that lines of code are an imperfect productivity measure. - In one controlled optimization experiment, Anthropic reports improvement from roughly 3x to about 52x speedup between May 2025 and April 2026. The result is specific to that experimental setup and is not a claim that AI training is 52 times faster. - In a selected set of research-session decisions where human researchers had room for improvement, Anthropic reports that its best model suggested a better next step 64 percent of the time in April 2026. What full RSI would require: A system would need enough capability to conduct meaningful AI research, access to code, compute, experiments and evaluation, reliable feedback, and enough autonomy to choose useful experiments. The recursive element appears when improvements to the AI development process increase the system's ability to produce further improvements. Limitations and risks: RSI does not imply an immediate intelligence explosion. Compute, hardware, experiment time, new algorithmic ideas, evaluation quality, and model reliability remain constraints. A system can also optimize the wrong objective or produce plausible but incorrect research. If AI development accelerates, humans may have less time to understand and evaluate each new generation. REFERENCES 1. Anthropic — When AI builds itself https://www.anthropic.com/institute/recursive-self-improvement 2. OpenAI — Research Engineer / Research Scientist / AI Systems Engineer, RSI https://openai.com/careers/research-engineer-research-scientist-ai-systems-engineer-rsi-san-francisco/ 3. OpenAI — An Alien Mind https://openai.com/index/an-alien-mind/ 4. OpenAI — Preparedness Framework https://cdn.openai.com/pdf/18a02b5d-6b67-4cec-ab64-68cdfbddebcd/preparedness-framework-v2.pdf 5. Google DeepMind — From AGI to ASI https://deepmind.google/research/publications/239142/ 6. Chen, Wang, Qu — Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops https://arxiv.org/abs/2607.07663 Source note: Current claims in this episode were checked against the cited primary sources on September 10, 2026. The episode distinguishes demonstrated AI-assisted development from the stronger claim of fully recursive self-improvement.

  2. 16 hr ago

    047 - Will AI Make Us Richer and Leave Workers Behind?

    Can an economy get dramatically richer while many of its workers fall behind? Anthropic's new working paper, Economic Scenarios for Transformative AI, builds a model where exactly that can happen. This episode explains the three futures the model lays out for the US economy through 2030, and why a much larger economy could still shrink the slice going to workers. The key thing to know is that this is a scenario framework, not a forecast. The authors do not assign probabilities to the outcomes. The model treats the economy as a collection of tasks, takes assumptions about AI capability, adoption, productivity, and automation, and calculates what those assumptions imply. In the modest scenario, US GDP in 2030 is about 1.6 percent above the no-AI baseline. In the substantial scenario, it is about 8.3 percent higher. In the extreme scenario, GDP is roughly 32 percent higher, with annual growth reaching around 15 percent. The distribution is where it gets uncomfortable. In the extreme case, labor's share of income falls from about 60 percent to 45 percent. Cognitive wages, for professional, office, management, and sales work, land about 11.5 percent below the no-AI path, while wages in other occupations run about 34 percent above it, and nearly one in five cognitive workers is unemployed. Anthropic also surveyed more than ten thousand US adults, and the median expectations lined up with the substantial scenario rather than the extreme one. The episode is careful about the model's limits. It leaves out robotics, policy responses, business cycles, financial disruptions, aggregate demand effects, and catastrophic risks, and it uses a coarse picture of individual workers. It closes on the question the numbers raise: if AI builds a much larger economy, who owns the systems producing the extra output, and how is that gain shared? For a grounded look at AI, productivity, wages, and the labor share of income, this episode walks through what the model says and what it deliberately leaves out. Sources & References Anthropic Institute — Economic Scenarios for Transformative AI (report and scenario explorer) https://www.anthropic.com/institute/econ-scenarios Korinek, Jones, Sacher, Cotter, McCrory — Economic Scenarios for Transformative AI, Anthropic Institute Working Paper No. 2026-02, September 2026 https://www-cdn.anthropic.com/files/4zrzovbb/website/cf58f84d46a4a76bf5a5b039ac695fba6b80041c.pdf Anthropic — Labor market impacts of AI: A new measure and early evidence https://www.anthropic.com/research/labor-market-impacts Voice narration is AI-generated.

  3. 1 day ago

    046 - How OpenAI Used 10,000 AI Agents to Attack a 90-Year-Old Math Problem

    How do you attack a math problem that has resisted proof for ninety years? OpenAI's answer was to point roughly ten thousand AI agents at it and let them work together for eighty-eight hours. This episode looks at OpenAI's claimed solution to the Navier-Stokes existence and smoothness problem, one of the seven Millennium Prize Problems, and at the multi-agent research system that produced it. Navier-Stokes describes how fluids like air and water move, and the open question is whether a smooth three-dimensional flow can stay smooth forever or can break down into a singularity in finite time. OpenAI says an internal model, described as more capable than GPT-6 Astra, coordinated about ten thousand agents that read a cached copy of the web, ran code, and split into groups pushing toward a proof or a disproof. Codex consolidated useful intermediate results and shared them across groups. The agents exchanged around 2.7 million messages and generated roughly 130 billion output tokens before the system produced a counterexample: a fluid flow that starts smooth and develops unbounded speed in finite time. The result was formalized and checked in Lean, and OpenAI released a 165-page proof. The episode is careful to frame this as a claim rather than a settled fact. The Clay Mathematics Institute still lists the problem as unsolved, and mathematicians need time to scrutinize the argument. A credit dispute has also erupted: NYU mathematician Tristan Buckmaster and Anthropic researcher Levent Alpoge had been working on the same problem, and Buckmaster has publicly questioned OpenAI's timeline and whether its models drew on their in-progress work. Beyond the headline, there is a practical lesson for anyone building with AI. The advantage on display is the research system around the model: many agents exploring independently, sharing what they find, synthesizing, and verifying. You can run a scaled-down version of the same pattern with a handful of specialized agents and a reviewer. If you care about AI agents, multi-agent systems, and where AI research is heading, this episode breaks down what happened and why the architecture is the part worth watching. Sources & References Science — How an AI math breakthrough ignited a controversy https://www.science.org/content/article/how-ai-math-breakthrough-ignited-controversy TechCrunch — OpenAI fought dirty on career-making math problem, says NYU mathematician https://techcrunch.com/2026/09/08/openai-fought-dirty-on-career-making-math-problem-says-nyu-mathematician/ CoinDesk — OpenAI says 10,000 AI agents solved a $1 million math problem. Now mathematicians are fighting https://www.coindesk.com/tech/2026/09/09/openai-says-10-000-ai-agents-solved-a-usd1-million-math-problem-now-mathematicians-are-fighting Quartz — OpenAI said its AI cracked a $1 million math problem. A dispute erupted https://qz.com/openai-ai-navier-stokes-millennium-prize-math-090826 Wikipedia — Levent Alpoge https://en.wikipedia.org/wiki/Levent_Alp%C3%B6ge Voice narration is AI-generated.

  4. 1 day ago

    045 - Will AI Kill Us in 10 Years?

    Will AI kill us within the next ten years? The question stopped sounding like pure science fiction the week an Anthropic researcher resigned over it. This episode separates the Terminator-style fear from the risks worth taking seriously, using the news that prompted the question and the numbers behind it. On September 9, 2026, Anthropic researcher Jacob Coxon resigned, warning that frontier labs are racing toward self-improving superintelligence and, in his words, gambling with our lives. Around the same time, Anthropic's Alignment Science Lead, Evan Hubinger, said he personally puts the chance of AI killing all humans within a decade above ten percent. The episode is careful about what that figure is: one researcher's personal estimate, not an Anthropic forecast, and Hubinger himself notes that current models pose relatively low risk. From there, the episode walks through concrete ways AI could cause serious harm well short of extinction. It looks at AI-assisted hacking, including Anthropic's reports of models finding unknown vulnerabilities and gaining unauthorized access in security testing. It covers the fragility of the infrastructure AI depends on, citing the International Energy Agency's projection that data-center electricity use could roughly double between 2025 and 2030. It examines jobs, with the International Labour Organization's estimate that one in four workers globally have some exposure to generative AI, and the IMF's figure that about forty percent of global employment is exposed in some way. It turns to financial stability and the Bank for International Settlements' warning that many institutions running similar AI models could react to shocks in the same way and amplify market swings. And it closes on the hardest case: increasingly autonomous systems that can accelerate their own development faster than humans can evaluate or control them. The takeaway is practical rather than apocalyptic. The most credible danger may be a chain of connected failures across systems we cannot afford to lose, rather than a single machine turning against us. For a grounded read on AI extinction risk, AI safety, and the Coxon resignation without the hype, this episode lays out what deserves your attention. Sources & References CNBC — Anthropic researcher quits AI safety https://www.cnbc.com/2026/09/09/anthropic-researcher-quits-ai-safety.html Financial Times — Anthropic researcher quits over AI labs 'gambling with our lives' https://www.ft.com/content/20c07191-8da6-440f-b04b-8ea0ebdd9153 Anthropic — Mapping AI-enabled cyber threats https://www.anthropic.com/research/attack-navigator Anthropic — Trustworthy agents in practice https://www.anthropic.com/research/trustworthy-agents Anthropic — Improving our alignment and security efforts https://www.anthropic.com/news/improving-alignment-security-efforts International Labour Organization — Generative AI and jobs: A 2025 update https://www.ilo.org/publications/generative-ai-and-jobs-2025-update IMF — New Skills and AI Are Reshaping the Future of Work https://www.imf.org/en/blogs/articles/2026/01/14/new-skills-and-ai-are-reshaping-the-future-of-work Bank for International Settlements — Progress and peril https://www.bis.org/publications/aer-2026/progress-peril International Energy Agency — Energy and AI https://www.iea.org/reports/energy-and-ai Voice narration is AI-generated.

  5. 2 days ago

    044 - Job Seeking in the AI Age

    AI is changing both sides of the hiring process. Job seekers can tailor resumes and submit applications at scale, while employers use ATS and AI to screen growing application volumes. This episode looks at the resulting arms race, why overly polished AI resumes can backfire, and how candidates can use AI without losing credibility. REFERENCES LinkedIn — 2026 Talent Research https://news.linkedin.com/2026/LinkedIn-Research-Talent-2026 Workday — Recruitment Privacy Statement https://www.workday.com/en-us/privacy/recruiting-privacy-statement.html Workday — AI in Hiring: Debunking the Top Misconceptions https://www.workday.com/en-us/perspectives/hr/debunking-ai-in-hiring-misconceptions.html The Washington Post — Employers to job seekers: Your AI résumé isn’t fooling anyone https://www.washingtonpost.com/technology/2026/02/21/ai-resume-jobs/ Zapier — AI recruiter screens: What we learned and why we'll keep going https://zapier.com/blog/ai-recruiter-insights/ Wired — AI in the Job Market Is Creating an Infinite Doom Loop https://www.wired.com/story/ai-job-market-infinite-doom-loop Business Insider — Job applicants are hiding secret AI messages in their résumés https://www.businessinsider.com/resume-ai-prompt-injection-applicants-job-search-2026-9 LinkedIn News — Swamped with AI-sweetened resumes, recruiters go old school https://www.linkedin.com/news/story/swamped-with-ai-sweetened-resumes-recruiters-go-old-school-7540620/ Voice narration is AI-generated.

  6. 2 days ago

    043 - Claude.md vs AGENTS.md vs Skills

    A simple guide to CLAUDE.md, AGENTS.md, and skills in Claude Code and Codex. Learn where each one lives, what it does, when to use it, and how to decide whether something belongs in persistent project instructions or a reusable skill. Voice narration is AI-generated. References Anthropic — Claude Code documentation https://docs.anthropic.com/en/docs/claude-code Anthropic — Claude Code Skills https://docs.anthropic.com/en/docs/claude-code/skills OpenAI — Codex documentation https://developers.openai.com/codex/ OpenAI — Codex Skills https://developers.openai.com/codex/skills/ Martin Fowler / Thoughtworks — Context Engineering for Coding Agents https://martinfowler.com/articles/exploring-gen-ai/context-engineering-coding-agents.html

  7. 4 days ago

    042 - Now That Astra Is Here, What Should Developers Focus On? (Deep Dive)

    GPT-6 Astra and Claude Fable 5.1 can now handle more of the implementation, so the question for developers changes. If the model can write the code, what should you become great at? This episode works through the skills that gain value when the cost of producing software drops, moving past prompt engineering and the generic advice to just learn AI. It covers eight shifts. Development moves from writing code to directing work, which makes supervision a core skill: recognizing a good plan, catching a bad assumption early, and knowing when to stop an agent. Context becomes part of engineering, since turning a vague request like "add authentication" into a precise specification is where much of the advantage now sits. Computer science fundamentals gain value rather than lose it, because someone has to understand the consequences of generated code as tables grow, caches go stale, and distributed services fail. Verification becomes a bottleneck skill, bigger than running a unit test. Architecture and system design move up the stack, now including AI-specific layers like model gateways, retrieval, tool calling, agent state, memory, orchestration, and MCP servers. The episode also treats agents as systems rather than models, using Astra's asynchronous tool calling and mid-turn steering, along with Anthropic's positioning of Fable 5.1 around long-running agentic coding and research, to show why developers need to design the harness around the model. Security gets its own section, covering least-privilege access, sandboxing, prompt injection, and tool abuse, and notes that OpenAI says Astra crosses its Critical cybersecurity capability threshold. The last and arguably most valuable shift is learning a problem domain well enough to know what to build in the first place, which is even more important for business owners deciding what to delegate. It closes with a concrete plan: keep your fundamentals, practice AI-assisted development on a working repository, learn context engineering and verification, understand agent architecture, and learn a domain deeply. The through-line is that as these agents get better at implementation, a developer's advantage moves toward judgment: understanding systems, defining problems, providing context, verifying results, and designing the environment where AI can safely do useful work. Sources & References GPT-6 Astra: A new generation of intelligence — https://openai.com/index/gpt-6-astra/ GPT-6 Astra Model | OpenAI API — https://developers.openai.com/api/docs/models/gpt-6-astra Safety overview: GPT-6 Astra — https://openai.com/index/safety-overview-gpt-6-astra/ What's new in Claude Fable 5.1 — https://platform.claude.com/docs/en/models/fable-5-1/whats-new-fable-5-1 Voice narration is AI-generated.

  8. 5 days ago

    041 - GPT-6 Astra: What Can It Actually Do? (Deep Dive)

    With GPT-6 Astra, the question stops being what AI can write for you and starts being what job you can hand it. This episode moves past the announcement of OpenAI's new September 2026 model to a practical question: what can you build with it? The shift is that Astra no longer sits in a chat box waiting for one problem at a time. It can reason through a goal, use tools, operate a computer, work across a codebase, create files, and keep going when the requirements change mid-task. For developers, the episode explains how Astra works with a full repository instead of isolated snippets: reading a project, changing multiple files, running tests, inspecting failures, and iterating, plus computer use, meaning it can open applications, navigate interfaces, and fill in forms the way a person would. It covers Astra creating websites, web apps, and games from a prompt and hosting them through Sites in ChatGPT, and the API tools that give the model its reach: web search, file search, code execution, image generation, computer use, hosted shell, skills, and MCP. It also explains two new Responses API features, asynchronous tool calling, which lets the model keep working while a tool runs, and mid-turn steering, which lets you change a requirement while Astra is still working, and what both mean for long-running agents. For business owners, the episode turns market research, competitive analysis, hiring shortlists, supplier comparisons, budget spreadsheets, and template-based presentations into workflows rather than one-off answers, and explains how Astra handles ambiguity by asking only when a missing detail could change the outcome. For creative teams, it covers building a three-dimensional model in Blender and turning it into a walkable Unreal Engine scene, generating visual assets, and coordinating video-production workflows across tools, with Astra orchestrating the steps rather than acting as a dedicated video model. The episode also covers the security tradeoff: a model that can take action needs tighter boundaries around what it can read, change, and approve. It closes on the larger change underneath all of this. The unit of work is getting bigger, so the developer's skill moves from prompting the model to specifying the outcome. Sources & References GPT-6 Astra: A new generation of intelligence — https://openai.com/index/gpt-6-astra/ GPT-6 Astra Model | OpenAI API — https://developers.openai.com/api/docs/models/gpt-6-astra Safety overview: GPT-6 Astra — https://openai.com/index/safety-overview-gpt-6-astra/ OpenAI launches new Astra model amid growing scrutiny over agents' safety — https://www.reuters.com/legal/litigation/openai-launches-new-astra-model-amid-growing-scrutiny-over-agents-safety-2026-09-03/ Voice narration is AI-generated.

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About

AI explained in bits. Each episode takes one concept, like tokens, embeddings, hallucinations, or prompt injection, and explains it in about five minutes. No jargon, no filler. Just the idea, why it matters, and what to remember. If you're curious about AI or already building with it, you'll come away understanding how these systems work. One concept. Five minutes. That's the whole show.

Information

  • Creator
    Dan W
  • Years Active
    2026
  • Episodes
    48
  • Rating
    Clean
  • Copyright
    © Dan W
  • Show Website
    Learn AI in Bits