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Learning podcast

Lessons I create to understand AI, energy, and housing. Each starts with a question and works toward a useful explanation, with research and excerpts from people who know the subject. Episode notes are public; listening access is shared individually.

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Start with a question

Jul 18, 202612:06

15 — YIMBY — How does “luxury” housing become ordinary housing?

Filtering, moving chains, and why market-rate supply and subsidy solve different problems.

Available in the private feed.

Episode notes & sources

The impetus

Nolan Gray on ‘luxury’ housing becoming affordable ↗

The one-line intuition prompted the deeper question: through what mechanisms, on what timeline, and under which market conditions does filtering work?

Source audio in this episode

Evidence and further reading

Jul 17, 202612:24

14 — YIMBY — Does building housing really lower rents?

How supply absorbs demand, starts moving chains, and changes rents across a market.

Available in the private feed.

Episode notes & sources

The impetus

Nolan Gray: build lots of housing and rents fall ↗

The claim is simple but politically contested. The episode begins with the harder question: what causal evidence connects a new market-rate home to rents elsewhere in the market?

Source audio in this episode

Evan Mast on moving chains of affordability ↗

Evan Mast, in a video produced by Pew. 13 seconds, about 2:44 into this episode.

More source audio

Evidence and further reading

Jul 16, 202612:07

13 — Energy — Is permitting an energy technology?

Why process design can change deployment as much as a better turbine, reactor, or well.

Available in the private feed.

Episode notes & sources

The impetus

Thomas Hochman’s core permitting-reform reading list ↗

The reading list treats process design as a field worth learning in its own right—the premise behind asking whether permitting can function like an energy technology.

Source audio in this episode

Evidence and further reading

Jul 15, 202611:23

12 — Energy — Why build the next nuclear plant at an old coal plant?

Reusing transmission, industrial land, power-sector workers, and community relationships.

Available in the private feed.

Episode notes & sources

The impetus

TerraPower at a closed Wyoming coal plant ↗

The project makes the energy transition look less like replacement and more like reuse. The question is which parts of an industrial place—grid connection, workforce, land, and trust—retain value.

Source audio in this episode

Evidence and further reading

Jul 14, 202611:51

11 — Energy — Why is nuclear expensive if the technology already works?

Nuclear cost through construction finance, project delivery, standardization, and repetition.

Available in the private feed.

Episode notes & sources

The impetus

The 88–2 Senate vote to speed nuclear licensing ↗

The unusually broad vote sharpened the puzzle: if the technology works and political support exists, why is new nuclear still so difficult to deliver?

Source audio in this episode

Evidence and further reading

Jul 13, 202612:38

10 — Energy — What changed in geothermal?

How oil-and-gas drilling capabilities may turn underground heat into scalable clean power.

Available in the private feed.

Episode notes & sources

The impetus

Fervo’s 70% reduction in drilling time ↗

The drilling result suggests a sharper question than whether underground heat exists: can oil-and-gas learning curves make much more of it economically recoverable?

Source audio in this episode

Tim Latimer on geothermal’s cost curve ↗

Tim Latimer, speaking with Bill Loveless on Columbia Energy Exchange. 17 seconds, about 3:08 into this episode.

More source audio

Evidence and further reading

Jul 12, 202612:30

09 — Energy — Will AI break the grid—or finance the next one?

How data-center demand could shift costs, bypass the grid, or underwrite new supply.

Available in the private feed.

Episode notes & sources

The impetus

Eli Dourado on off-grid solar data centers ↗

The proposal reframes AI load as a design choice: wait for the grid, shift costs onto it, or build a new power system around the data center.

Source audio in this episode

Evidence and further reading

Jul 11, 202612:31

08 — Energy — Why can’t new power plants just connect to the grid?

Grid studies, upgrade costs, project withdrawals, and the interconnection queue.

Available in the private feed.

Episode notes & sources

The impetus

Jesse Jenkins on the interconnection queue ↗

The deceptively simple problem is that hundreds of gigawatts of proposed clean power cannot connect. The episode asks what the queue is actually doing—and why projects keep falling out of it.

Source audio in this episode

Evidence and further reading

Jul 10, 202613:20

07 — AI Technical — Can an AI model stand in for a customer?

What synthetic consumers reproduce, what they miss, and why aggregates hide segment failures.

Available in the private feed.

Episode notes & sources

The impetus

The striking claim about synthetic purchase intent ↗

The dramatic ‘90% accuracy’ framing creates the real question: what did the study actually measure, and when can aggregate resemblance conceal segment-level failure?

Source audio in this episode

Evidence and further reading

Jul 9, 202615:26

06 — AI Technical — Why will AI costs feel more like cloud costs than software seats?

Why tokens create variable infrastructure spend—and what finance teams need to manage.

Available in the private feed.

Episode notes & sources

The impetus

Ramp’s emerging pattern in enterprise AI spend ↗

The pattern connects the cloud-cost analogy to a wider market shift: AI spending spreads into monitoring, integration, and use-specific infrastructure rather than staying a predictable software seat.

Source audio in this episode

Evidence and further reading

Jul 8, 202612:40

05 — AI Technical — How do you know an agent works?

Task evals, trajectory inspection, production monitoring, and business outcomes.

Available in the private feed.

Episode notes & sources

The impetus

Ara Kharazian on AI trust and observability spend ↗

A Ramp spending observation suggested that enterprise value may accrue around proving and monitoring performance, not only buying model access.

Source audio in this episode

Evidence and further reading

Jul 7, 202612:07

04 — AI Technical — What belongs in the model, the context, a skill, or a tool?

An architectural rule based on change rate and authority.

Available in the private feed.

Episode notes & sources

The impetus

Anthropic Skills and the emerging agent stack ↗

The architectural question underneath Skills is more durable than the announcement: what belongs in a reusable procedure, and what should remain in context, a model, or a tool?

Source audio in this episode

Evidence and further reading

Jul 6, 202613:27

03 — AI Technical — Why does Claude Code feel different from chat?

How repositories, tools, tests, and reversible diffs close the agent feedback loop.

Available in the private feed.

Episode notes & sources

The impetus

Danielle Morrill: make Claude Code your chief of staff ↗

The chief-of-staff framing raises the central problem: why does an agent become more capable when work is organized inside a repository with tools and feedback?

Source audio in this episode

Boris Cherny on why Claude Code began in the terminal ↗

Boris Cherny, speaking with Dan Shipper on AI & I. 10 seconds, about 3:03 into this episode.

More source audio

Evidence and further reading

Jul 5, 202614:41

02 — AI Technical — What is an AI agent, actually?

The model, tools, state, permissions, feedback, stopping rules, and ownership inside an agentic loop.

Available in the private feed.

Episode notes & sources

The impetus

Aaron Levie on the scarcity of people who can deploy agents ↗

The hard part is not saying ‘agent’; it is understanding the loop well enough to deploy one inside a real organization.

Source audio in this episode

Evidence and further reading

Jul 4, 202615:09

01 — AI Technical — How does a language model learn anything?

Tokens, prediction error, parameters, gradient descent, attention, post-training, and inference.

Available in the private feed.

Episode notes & sources

The impetus

A rigorous, accessible route into how ChatGPT works ↗

Karpathy’s from-scratch approach suggests that language-model training can be explained without treating the system as either a database or a mysterious mind.

Source audio in this episode

Evidence and further reading

Jul 3, 20260:15

Welcome to the learning podcast

A short setup trailer for a private feed about AI systems, energy, and housing.

Available in the private feed.