17 August 2026
9 new episodes (7.3 hours listening) published by the 38 podcasts on the list. 9 highlight clips below (23 mins total).
Investment 17 August 2026 · 9 clips
Merryn Talks Money
Duncan Lamont, Head of Strategic Research at Schroders
Head of Strategic Research at Schroders, working from US series that run back to the 1920s because that is where the longest data sits. He has published on adapting asset allocation to stagflation risk and has two unpublished pieces in progress, on 100 years of credit spreads and on US pension flows.
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1 of 3 contrarian
Schroders' Duncan Lamont points out that the top holdings of the main US value indices are Alphabet, Meta, Apple, Microsoft and Amazon, so a passive value allocation bought to hedge AI risk owns the thing it is meant to hedge.
He ran every quarter since 1996 in which semiconductors fell, using them as a proxy: the average fall was 12% against zero for value, and among falls beyond 5% it was 15 against 1. A cheapness screen applied to a market this concentrated keeps re-selecting the same megacaps, which is why he says the hedge has to be run actively.
Audio streamed from the publisher.
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2 of 3 contrarian
Schroders' Duncan Lamont finds that an investor who switched to cash whenever US stocks hit an all-time high, and back in when they did not, would have finished a century more than 90% poorer than one who stayed put.
An index that rises over time sits at a record in more than 30% of all months, so the rule parks the money in cash through most of the compounding. On the same data back to the 1920s, buying at a record beat buying at any other moment over the following twelve months, and at two and three years the difference disappeared.
Audio streamed from the publisher.
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3 of 3 framework
Duncan Lamont is modelling what happens to the monthly 401(k) bid under US stocks if AI displaces accountants, lawyers and financial services staff rather than low earners.
Past technology waves hit lower earners, whose contributions are small in dollar terms, so the flow into US equities held up regardless of who lost work. This cohort is smaller in number and carries a disproportionate share of the dollars, and the work is unpublished, so it is a scenario being mapped rather than a result.
Audio streamed from the publisher.
Invest Like the Best
Ben Thompson
An independent technology analyst who has been publishing on the sector since at least 2013, when he was already arguing that Intel had to build a foundry business. He wrote aggregation theory up in 2015, spent years based in Taiwan before moving back to the US, and runs a subscription content business of his own.
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1 of 3 framework
Ben Thompson reads Google's equity issuance as a deliberate trade of search's margins for the far larger absolute profits available in AI, the same swap Berkshire made when it put See's Candies money into BNSF.
Search scales in every direction at almost no incremental cost, which caps what it can ever earn in dollars rather than in percentage terms. A shareholder diluted into a much bigger pie has not lost anything, and that is the argument for spending through free cash flow, then the debt markets, then stock, in that order.
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2 of 3 framework
Ben Thompson says the payback periods being used to justify data centre spending were all calculated inside a shortage, and container shipping shows what happens to them when the capacity lands.
Once the shell is built the money is spent, so an operator runs the asset at whatever covers marginal cost and the price falls to the cost of the marginal supplier. Freight went from three or four thousand dollars a container to seventeen or eighteen during COVID and then collapsed as the two-year build cycle delivered, and memory has repeated that pattern for decades.
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3 of 3 current issue
Ben Thompson puts the near-term constraint on the AI build-out at money rather than compute or power, with the industry through free cash flow, debt and equity issuance in roughly a year.
Google's 500 billion dollar structure reaches past all three into pension funds and insurance floats, and Thompson's question is what sits below that on the capital curve. He takes the 1870s railways as the precedent: the track kept running and kept contributing to GDP, but the financing ran out first, and the gap is where the damage happened.
All-In
Dario Defends Himself, Datacenter Panic, AI Doomer Trap, Senate Toss-Up
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framework
All-In ties the data centre backlash and the rising risk-free rate together, and puts the frontier labs at the sharp end because their balance sheets cannot carry the compute their own growth requires.
Abbott in Texas and Shapiro in Pennsylvania have both moved against new build, and Axios reported a GOP memo asking the AI companies to stop inflaming voters before the Ohio Senate race. Neither restriction is the binding one: a higher risk-free rate pulls capital out of everything speculative first, and growth then raises the compute bill faster than a sub-investment-grade borrower can fund it.
AI 17 August 2026 · 9 clips
All-In
Dario Defends Himself, Datacenter Panic, AI Doomer Trap, Senate Toss-Up
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prediction
All-In maps a route to banning open models that nobody has to vote for: a safety standards body the closed labs fund, then a fairness rule open weights cannot technically meet.
Amodei told the Senate, on a date the show hedges at 2023, that sufficiently advanced open models are dangerous because nobody can centrally monitor, control or roll them back. Those properties are technological and do not move, so a uniform pre-release testing requirement lands on downloadable weights as an impossibility instead of a cost.
Machine Learning Street Talk
Ilia Shumailov and Alexander Panfilov
Shumailov is an AI and security researcher, formerly at Google DeepMind, who completed his Cambridge PhD under Ross Anderson. Panfilov is a PhD researcher at the ELLIS Institute Tuebingen and the Max Planck Institute for Intelligent Systems, working on adversarial machine learning and LLM red-teaming. Their paper drew roughly three million views in 40 hours.
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contrarian
Shumailov and Panfilov find that seeding two tokens of Opus reasoning into Kimi changes the wording of its visible answer, an effect they see in none of the other open models they tried.
Both authors stop short of calling it distillation: the sample is small, the analysis post hoc, and a shared data vendor or shared environments would explain it as well. Harder for them to account for is the length effect, where the same seed shifts Kimi's whole reasoning-length distribution toward the source model's.