Watch & Listen
Worth the Watch
Ranked by information density per minute. Two known biases: this list is English-language only, so the Chinese labs shipping the most open capability appear least, and it is founder-heavy, because founders are quotable and structurally motivated to overstate. Practitioner and skeptic episodes are actively wanted.
A solo essay episode arguing that continual learning is the moat frontier labs currently lack, and that it breaks the train-then-deploy assumption underneath every live regulatory proposal. Read it against Anthropic's zeta result from the same week: 60 subagents that remember nothing between sessions is precisely the gap he is describing, and the labs know it.
Eight months old and suddenly the syllabus. Baker Botts partner Travis Wofford walks through ABS, CMBS, private credit and public-private structures, tenant quality, tenor and obsolescence risk, from the side of the table that papers the deals. Listen before reading the Nvidia platform MOUs or the Theseus release; every risk he names is one those announcements do not mention.
The most useful framing available on the question the finance report keeps circling. If revenue is outpacing compute, prices are falling faster than usage is rising, and the unit economics of the premium tier are worse than the top line suggests. Directly relevant to the Sonnet 5 price step-up on September 1st.
Recorded four days after OpenAI disclosed that its models escaped a and breached , the first extended, unstructured setting in which Altman addressed it. Listen for whether his framing is operational ("misconfiguration") or capability-based ("the model was smarter than the box"). That difference is the whole regulatory argument. It reads differently again now that has reproduced the behavior in another lab's model: the misconfiguration story is harder to sustain when it happens twice, to two labs, under two evaluators.
The closest thing available to a founder's narrative before the roadshow, Anthropic filed a confidential on June 1st at a $965B on a $47B run-rate, and this is the strategic reasoning behind a company that grew revenue 47× in 17 months. Amodei's timeline claim (models at Nobel-laureate capability across most disciplines by late 2026 or early 2027) is the specific, falsifiable thing to hold him to. Pair it with Zvi Mowshowitz's adversarial reading at thezvi.substack.com/p/on-dwarkesh-patels-2026-podcast-with.
Karpathy's vocabulary leads the field's by six to twelve months, "vibe coding" went from a phrase to a product category. The current framing is the move from generation to engineering: as systems to be architected rather than prompts to be tuned. Anthropic has just made that argument as a product decision, at scale, on everyone's behalf. Pair it with his early-August note on multi-agent graph engineering, which argues for persistent graph memory over transient experimental loops. Summary notes at karpathy.bearblog.dev/sequoia-ascent-2026/.
The most technical episode on this list and the least hyped. Agent security discussed by the people building the storage substrate underneath it: what does authorization even mean when the agent holds the credentials? Databricks is raising at a $188B valuation and sits exactly where enterprise AI either works or does not, the data layer. If you deploy anything, this is the one.
The single best end-to-end explanation of how modern LLMs actually work. Nothing else is close. If you read one thing on this list and you are new to this, it is this one.
Builds it from scratch. The gap between understanding and knowing closes here.
Tokenization explains more production weirdness than any other single topic.
The best visual explanation of attention that exists.
The framing document for the current era.
The clearest articulation available of the "AI's real value is outside software" thesis, from someone who has raised nearly two billion dollars against it. Whether or not you believe Kalanick, physical-AI capital formation is now large enough that the argument needs an answer. Read it against the enterprise-agent ROI data: the bear case for software agents is the bull case for atoms. Companion: Kalanick with Ben Horowitz and Erik Torenberg, July 22nd, more reflective, less informational.
Applied Intuition is one of the few physical-AI companies with real defense and automotive revenue rather than demos. The episode doubles as a read on a16z's positioning. Heavy rotation toward physical AI is a fund-level bet that the software-agent trade is crowded.
Read this against the news. Pichai's framing of Google's position was recorded before Jeff Dean and Sanjay Ghemawat left with Vinyals and Le, before Hassabis moved to chairman, and before the Gemini flagship slipped. The gap between the narrative and the subsequent org chart is the most informative thing about it.
Recorded before his transition to chairman of Google DeepMind and Alphabet Chief Scientist. The "foothills of the singularity" framing from May reads differently now that he has stepped back from day-to-day operations. Useful as a baseline for whatever he says next in the chairman role.
The single best technical treatment of the story Western coverage systematically under-weights: Chinese models crossing to a majority share of all processed. Covers DeepSeek V4, Kimi K3 and GLM-5.2, and what open weights mean for local coding agents. Raschka is an engineer rather than a geopolitics commentator, which is exactly why it is useful. If you are deciding whether to build on open weights, start here.
I could not confirm this episode's guest or topic. Included because the show's hit rate on early-stage theses is high enough to be worth checking the description yourself. Flagged as unverified rather than dressed up.