20 September 2026
101 new episodes (96 hours listening) published by the 45 podcasts on the list. 30 highlight clips below (93 mins total).
Investment 20 September 2026 · 30 clips
The Real Eisman Playbook
Why Dario Amodei and Sam Altman Are Faking the AI Doomsday Crisis | The Weekly Wrap
No guest; Eisman records the weekly wrap alone.
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contrarian
Steve Eisman argues Amodei and Altman cannot mean their calls to slow AI, since OpenAI alone is $300 billion of Oracle's backlog and Anthropic is weeks from an IPO; he reads the warnings as a bid for regulation that protects pricing.
Business, on his account, is getting harder just as it gets dearer: token maximising is ending, open-weight models keep taking share, there is no pricing moat when tomorrow's model is better and cheaper, and data centre and capital costs are rising. Regulation invited by fear would hand the two labs a duopoly, and he thinks the gambit has already failed because Trump said this week he has no interest in regulating AI.
The Meb Faber Show
Roger IbbotsonYale and Zebra Capital
Finance professor at Yale for four decades, founder of Ibbotson Associates and chairman of Zebra Capital; his return series from 1926 is the one the industry quotes, and his 1976 building-block forecast now has 50 years of out-of-sample data behind it.
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1 of 3 prediction
Roger Ibbotson's 1976 building-block forecast overestimated nominal returns and underestimated real ones over the 50 years since, and his new median stock forecast to 2050, after a survivorship haircut, is 7% nominal and 5.6% real.
The 1976 numbers landed almost exactly in nominal terms out to 2000 and stayed near the middle of the distribution across the full half-century, with the misses running opposite ways because inflation came in high. For the next 25 years he no longer carries the US equity premium forward: Dimson, Marsh and Staunton's 125-year global data puts the average country about 1.5% a year below the US, and the forecast takes that haircut.
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2 of 3 contrarian
Roger Ibbotson puts total cash returned to US shareholders, dividends plus buybacks, at roughly 4% a year for two centuries, with the 1% dividend yield a change of route since the 1980s.
The paper, written with a co-author at Morningstar, treats the switch as a financial innovation: capital-gains treatment, and a holder who chooses when to take the money while the company still chooses when to pay it. He is unalarmed by the yield on that basis and notes the UK, Japan and China moving the same way.
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3 of 3 prediction
Roger Ibbotson thinks SpaceX's long-term prospects as a listed stock are poor, because less than 5% of the offering was new stock and the other 95% sits with venture and private-equity holders who want out.
His original dissertation found IPOs deliberately underpriced, averaging a 15% rise on issue in the 1970s data and weak returns after, and his Hot Issue Markets paper found issuance clustering in periods when everything goes up. What differs now is scale: companies are arriving already large, so a few listings carry the supply that hundreds of small caps used to.
Insightful Investor
Peter Kraus
Co-founder, chairman and chief executive of Aperture Investors, which managed $6.7 billion at the end of June 2026. He was chief executive of AllianceBernstein and before that co-head of Goldman Sachs Asset Management, across about 45 years in the business.
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contrarian
Peter Kraus prices the liquidity given up in private markets by what it bought in 2008, when equities were the only thing that could be sold and buying them that October beat the private equity vintage of the same year.
Public credit could not be sold either, so an over-allocated institution had nowhere to raise cash and no way to buy the dislocation. He also splits the record: private equity beat the active equity indices over 20 years and has not over the last 10, which makes the long history a poor guide to a commitment being signed now.
Capital Allocators
Abby Barlow (CIO, Westwood Management), Laura Hill (CIO, Advocate Health), Brian Sugrue (CIO, Shannonbridge), Jenny Heller (President and CIO, Brandywine Group Advisors), John Lawrence (President, Rice Management Company), Matt Bank (CIO, GEM), Kristin Kallergis Rowland (Global Head of Alternative Investments, J.P. Morgan Asset & Wealth Management), Jon Webster (Senior Managing Director and COO of Technology & Operations, CPP Investments)
Eight chief investment officers and their equivalents, from Abby Barlow, the sole investment professional at the single-family Westwood Management, to Jon Webster, who runs technology and operations at CPP Investments and its US$580 billion. In between sit Advocate Health at $26 billion, Rice Management at $8.5 billion, GEM at $14 billion, and Kristin Kallergis Rowland, who oversees $250 billion of alternatives inside J.P. Morgan's $500 billion private bank.
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framework
CPP Investments names its largest AI risk as spending heavily on the technology and coming out no better as an investor.
He puts the difficulty at seven years: a long-horizon decision improved today cannot be shown to have been improved until the outcome arrives, which he calls a verifiability gap and does not claim to have closed. The fund still expects a defensible return-on-investment answer within 12 to 18 months, three years into something he thinks will read as a revolution in twenty.
Money Maze Podcast
John Claisse
Chief executive of Albourne, which advises more than 400 investors globally on alternatives from 650 staff across 10 offices and has never managed money, charging a fixed fee independent of assets. He interned there in 1995 and is now its second largest shareholder following an employee-ownership transaction.
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1 of 3 current issue
Albourne is fielding calls from allocators who want to hedge a SpaceX position they hold through their venture funds, and now expect the same question for OpenAI and Anthropic.
One name is arriving on both sides of the book at once, private through the venture portfolio and public as it enters the indices. Claisse puts the venture benchmark's first quarter at 2% to 3% on Albourne's own private-market indices, with another 3% to 5% of first-half uplift from that single holding.
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2 of 3 explainer
Albourne says the fee errors it recovers turn up most often at the most sophisticated investors, the ones who negotiated bespoke terms nobody downstream implemented correctly.
Investors get paid for investment risk and not for operational risk, and Claisse builds the case for separate scrutiny on that asymmetry. Albourne ran about 800 full operational reviews for clients in 2025 plus 500 on open-ended funds, and in the past year found a manager marketing a first fund that had never existed.
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3 of 3 framework
Albourne found its manager AI survey was being answered by investor-relations teams and moved fourteen questions into the due diligence that runs alongside a nine-figure allocation.
Over 500 funds completed the new questions in six months, against three annual surveys that covered 300 managers and close to $9 trillion of alternative assets. The questions cover a firm's AI governance, its diligence on third-party vendors, which vendors it uses, and which functions the tools have reached.
Top Traders Unplugged
Robin Wigglesworth
Robin Wigglesworth edits the Financial Times' Alphaville and wrote Trillions, on the rise of passive investing. His new book, A Fabulous Debt, traces a thousand years of bond market history and is out on 29 September.
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1 of 2 contrarian
Robin Wigglesworth, who expected bond ETFs to end badly, now argues they have made large parts of the credit market more tradeable than it was before they existed.
Wigglesworth puts the reversal on the creation and redemption process, which lets the fund trade without the underlying bonds trading and suits an asset class where most lines sit untouched for weeks. Portfolio trading and electronic execution fed off the same machinery, and the cost falls on issues no major index holds.
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2 of 2 prediction
Robin Wigglesworth says Scott Bessent's enlarged Treasury buybacks will fail even at ten times the size, because every dollar bought back is a dollar the Treasury borrows again.
The volatility the programme was meant to dampen was not high to begin with, on his reading: the term premium moved because a Fed under Kevin Warsh looks less willing to raise rates, not because the basis trade broke. He argues the direct lever is Warsh saying he would raise them.
Training Data (Sequoia)
Ali Ghodsi
Co-founder and chief executive of Databricks since 2015, when the company's GAAP revenue was about $1.5 million. It now runs 12,000 staff and 3,500 engineers and is free cash flow break-even, and he describes the sales organisation built under him as a $1 million to $7 billion ARR engine.
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explainer
Ali Ghodsi built a Databricks connector himself in two days that his own team took three quarters to ship, and concluded that diffusing AI through the world's organisations takes at least a decade.
None of the three quarters went on writing code. A quarter went on customer interviews and a PRD while the pipeline stalled, more on standing up Salesforce, Workday and NetSuite connections the team disliked and were not expert in, and testing sat at the end; compressing the PRD to a week, buying in the integration work and moving testing forward got them to seven connectors a quarter.
Alt Goes Mainstream
David Golub
Co-chief executive of Golub Capital, a $95 billion private credit manager he built with his brother Lawrence after starting in private equity in 1987. The firm pioneered the unitranche one-stop loan and lends to about 200 private equity sponsors, who have supplied 90% of its new deal volume every year for the past decade.
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1 of 2 current issue
David Golub reports elevated credit stress right across the private equity ecosystem in a wide range of industries, and says larger portfolio companies are not holding up better than smaller ones.
He attributes it to tastes changing after Covid, higher interest rates, too much leverage against too little growth and, in some cases, AI threats to the business model, with no single cause dominating. His forward call is that junior debt carries the trouble from here and that the rest of it is manager-specific.
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2 of 2 framework
David Golub underwrites a loan on whether a strategic buyer would still want the company in a beaten-up state, a test neither the bank's liquidation view nor the syndicator's distribution view asks.
Golub Capital keeps everything it originates, so there is never a question of clearing paper to other holders, and the whole weight falls on entity value under a downside. Two screens follow directly: a single-product borrower fails, and so does one with a concentrated customer base, since losing either removes that second exit.
Excess Returns
Andy ConstanDamped Spring Advisors
Constan runs Damped Spring Advisors, a macro research firm, and joins the show monthly; he holds the global stock-and-bond positions he argues for here.
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1 of 2 explainer
Andy Constan puts the Treasury's enlarged buybacks at 5 to 10bp of suppression on long yields, small but stimulative and working directly against the Fed's hike.
Coupon auctions run at $928bn a year with a duration near six years; the buyback increase of roughly $2.5bn to $3bn across 32 operations takes an extra $75bn to $100bn of that away from private hands. Growth and inflation expectations move rates by 25 to 50bp and supply by single digits, which leaves Bessent easing at the long end while 18 of 19 FOMC members tighten at the short.
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2 of 2 framework
Andy Constan reckons a 10-year note now pays about 100bp over cash for 6% annual volatility, a 0.17 Sharpe that is poor in absolute terms and a reversal from the negative premium of 2020.
Term premium ran above 2% at the start of the 1982 bull market and reached minus 50bp when 10-year yields hit 65bp in September 2020, the point he calls a bubble. The curve has since steepened to roughly 75bp over Fed funds; since 1973, held through the spike and the 40-year rally, bonds have kept up with equities on a risk-adjusted basis, and the five years since the bubble popped are the whole reason nobody wants them.
MacroVoices
Harley Bassman
Fixed income strategist who created the MOVE index and writes as the Convexity Maven, with 20 years of commentary published free on his own site.
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contrarian
Harley Bassman splits the AI build-out down the capital structure: the hyperscaler bonds are money good, and the loss shows up in the equity.
Meta, Google, Amazon, Microsoft and Oracle carry core businesses that cover the coupons with another 100 to 200 basis points on top, and the labs buying the compute are not the borrowers. His precedent is Global Crossing: the cable went in, the price per megabyte came out at a tenth of plan, and most markets end up holding one or two winners.
MacroVoices
Matt Barrie
Founder and chief executive of freelancer.com, which he puts at 90 million users, and owner of escrow.com. He runs around 40 AI agents across the company's own workflows and is a repeat guest on the show.
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current issue
Matt Barrie takes Ed Zitron's subprime analogy for the data centre build seriously: $1.65 trillion of debt loaded on in five years, against a subprime peak of $1.3 trillion in 2007.
Subprime had 55 million mortgages behind it and this has two customers, with OpenAI and Anthropic accounting for most of the AI revenue at Amazon and Microsoft on the figures he cites. The chips and the debt sit in special purpose vehicles, so a $46 billion exposure appears in Meta's filings without reaching its balance sheet.
Monetary Matters
David BuschTrajan Wealth
Chief investment officer of Trajan Wealth, a family office and wealth manager with offices in 16 states serving mass affluent to ultra high net worth clients, after 18 years as a fixed income portfolio manager.
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framework
David Busch has kept Trajan Wealth largely out of private credit, and says most funds pitching it will not hand over a loan tape when asked.
Interval funds let money in at any time and out at about 5% of NAV a quarter, pro rata once redemptions are capped, and a yield pitched as equity-like on bond-like risk carries the extra risk somewhere in the structure. Busch's pattern for new products is that early entrants hand off to institutions, and institutions then look for their exit in retail, which is when the product reaches his desk.
AI 20 September 2026 · 30 clips
The Cognitive Revolution
Anton Leicht
Fellow in the Technology and International Affairs Program at the Carnegie Endowment for International Peace and author of Threading the Needle, a substack on the political economy of AI. He co-wrote the transformative-AI strategy for Europe discussed in the episode, and worked previously in German energy and Covid policy.
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1 of 2 framework
Anton Leicht's compute-for-access proposal has Europe host American hyperscalers' data centres on condition that frontier model access continues, and take the data centres back if it stops.
Hosting the capacity is what buys the access, and the deal reverses if the models stop coming. Washington's objection is a security one, so he pairs it with European KYC rules and physical and cyber standards, and puts ASML and the tooling supply chain behind the whole thing as a declared anti-coercion instrument.
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2 of 2 prediction
Anton Leicht calls Australia a sleeping giant for compute and says it could run the inference for half the world at five to ten times its current data centre ambitions.
Construction cost and energy supply are half of it. The other half is that Washington trusts Canberra not to defect to China and to hold the line on China-facing export controls, which is the condition deciding where American compute is allowed to sit.
Dwarkesh Podcast
Noam Brown
Noam Brown is a researcher at OpenAI and one of the foundational contributors to o1 and the reasoning models. He now works on multi-agent systems, has been in the field for ten years, and says more than 10% of his team is on alignment and safety.
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1 of 2 contrarian
Noam Brown puts less than 10% of the credit for OpenAI's Navier-Stokes result on multi-agent, and says the published scaling data stops at sixteen agents.
Four agents halve the time at twice the cost, and the curve stays slightly sublinear from there, but the ablation was never run at the scale that produced the headline. A general-purpose model held over long horizons did the work, and parallel search made it quick.
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2 of 2 explainer
OpenAI is watching chain-of-thought monitorability degrade, and Noam Brown says every intervention prompted by reading the reasoning pushes the model toward hiding it.
The trace is legible only because nothing has yet trained it not to be, which Brown calls the best case safety was ever going to get. Light-touch correction survives that on the research he cites, each correction still applies a little pressure toward concealment, and the cause of the current slide is not understood.
MacroVoices
Matt Barrie
Founder and chief executive of freelancer.com, which he puts at 90 million users, and owner of escrow.com. He runs around 40 AI agents across the company's own workflows and is a repeat guest on the show.
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1 of 2 explainer
Matt Barrie burned 4 billion tokens in a day for $1,300. The same day's work would have cost roughly $80,000 on a frontier model, or about $150 on Chinese open weights.
Three billion of the four were prompt-cache hits, not fresh inference, and one instruction to the agents to cut their own token use took another 85% out. Moving a whole fleet to a different model costs him a line in the coding harness, and nothing in the stack holds him to a vendor.
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2 of 2 contrarian
Matt Barrie runs a 300-billion-parameter open model on a pair of $4,000 Nvidia desktop boxes, and puts the hardware needed to carry his whole agent fleet at about $65,000.
Sixteen of those boxes draw less power between them than a kettle and cost around $100 a day of hardware amortised over two years, plus $8 of electricity. What he buys at that price is a fleet that keeps running with the internet switched off and never sends a document outside the building.
a16z
Ali GhodsiDatabricks
Co-founder and chief executive of Databricks. He says AI now writes over 90% of Databricks' own software, that the company has been committing model-written code to production since Q4 2025, and that its internal knowledge graph runs to millions of nodes, larger than any customer's.
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1 of 3 framework
Ali Ghodsi sets four conditions that would all have to hold at once before recursive self-improvement is real, and says there is no evidence any of them holds today: each new model needs fewer GPUs, takes less time, is more capable, and the loop repeats.
If compute stays flat the process paces itself against the hardware supply, so a model writing most of its own code, which he says already happens at Databricks, does not count. Sarah Wang adds that the compute floor for a frontier run has climbed from roughly $100 million to $5 to $10 billion, and Ghodsi says each lab manages one or two such runs a year, with several botched.
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2 of 3 contrarian
Ali Ghodsi argues most enterprises would lose nothing if the frontier stopped advancing today, because what they lack is organisational context rather than model intelligence, while the labs would be hit hard as the price of intelligence falls about tenfold every six months on his figure.
Audiences he polls have said since late 2025 that models are smarter than most people around them, yet almost nobody runs coordinated swarms of agents; most enterprises sit on Microsoft Copilot and a chatbot that amounts to faster search. What the models lack is what a five-year employee has, the meetings, the processes and who to ask, and no gain on Humanity's Last Exam supplies it.
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3 of 3 current issue
Databricks measured a near 2x cost difference from running the same model version through different agent harnesses, and has held its AI bill roughly flat while token use keeps climbing.
Per-person and per-group budgets, routers that drop to cheaper models near a ceiling, and switching harness did the work. In the same stretch a large engineering organisation is said to have told a board last week it is moving from frontier models to GLM, a pattern of cheap models for implementation with a frontier model for architecture and audit is described as emerging, and open source is put at about 5% of spend by dollar but over 60% by token.
Training Data (Sequoia)
Aaron Levie, cofounder and CEO of Box
Cofounder and chief executive of Box, a roughly twenty-year-old company he puts at a $1.3 billion revenue run rate, sitting on hundreds of billions of customer files. He has taken the company through a full pivot to AI as a public company.
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contrarian
Aaron Levie expects the labs' token subsidies to be temporary, because a public company gets held to the same laws of capitalism as everyone else and still has to pay for its training runs.
Meta, SpaceX, Nvidia and China are the actors he says break the pricing: none of them needs inference to carry a lab's margin, so 10% is tolerable if it fills their compute clusters. Where that holds, cost per token falls on a like-for-like basis and the value moves to whoever sits between the model and the workflow.
20VC
Thomas SohmersPositron AI
Co-founder, chairman and CTO of Positron AI, which builds hardware for generative AI inference and has just announced an $875 million Series C at a $5 billion valuation; he has worked in semiconductors for about 13 years.
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contrarian
Thomas Sohmers argues that a model running on a phone or on a company's own servers will raise the token volume reaching the frontier labs rather than cut it, because the local model becomes the thing that decides when to call a bigger one.
An always-on local model reading a person's mail, calendar and messages produces requests continuously and escalates the ones it cannot handle, so per-person demand on the largest models rises even as more work is done locally. Sohmers puts today's split at 80% to 85% of tokens from the top four labs and perhaps 5% run on-premise.
Worth listening to in full 20 September 2026
Most clips above stand alone. These are the episodes that justify the whole hour.
How JPMorgan, CPP & Rice CIOs Actually Use AI
Eight allocators, from a one-person family office to a $580 billion pension, each describing what they built, what it cost and what failed, on a question every investment committee is now being asked and where no comparable peer benchmark exists. At 127 minutes it runs past the usual ceiling, and the operational detail that will not fit a clip, the tool names, the sequencing and the governance wording each of them adopted, is what earns the extra time.
The List Is Public. It Still Outperformed For 28 Years | Alex Edmans on What Markets Don't Measure
A 67-minute conversation that holds one argument the whole way, that markets underprice intangibles because they are hard to assess and separately hard to process, and keeps testing it: the guest supplies the evidence, the host pushes back with his own research on talent flows and the limits of capitalising R&D, and the argument survives the pushback in modified form rather than being restated. Four clips is what fits in the format; there are another three or four stretches of comparable quality, including the rebuild of value investing around innovation, human capital, brand and network effects, and the demolition of demographic diversity as a return driver. The first ten minutes on football results and the wisdom of crowds are familiar ground for this reader and can be skipped.
The AI Boom Is Creating a Financial System We Haven't Seen Before (Paul Kedrosky Explains)
45 minutes holding one argument end to end, that the scale of the AI build-out defeats the intuitions people bring to it, worked through financing, deflation, chip supply, valuation and model convergence without a detour. Three sponsor reads and no filler otherwise.