22 October 2026
154 new episodes (149 hours listening) published by the 50 podcasts on the list. 39 highlight clips below (120 mins total).
Investment 22 October 2026 · 39 clips
The Real Eisman Playbook
Danny MosesOn the Tape
Danny Moses was Steve Eisman's partner at FrontPoint through the subprime short told in The Big Short, and before that, from 1996, a senior salesperson at Oppenheimer covering hedge funds and state pension funds. He now hosts the On the Tape podcast.
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1 of 2 framework
Danny Moses compares AI capex with the 1999 fibre build, when Lucent and Nortel lent customers the money to buy their equipment and the arithmetic on capacity never worked.
He separates the hyperscalers, whose capex comes from cash flow they can switch off when they choose, from builders funded by lenders. His example is CoreWeave, carrying $51 billion of debt including leases while it has moved toward renting capacity on shorter terms, a potential mismatch between what it owes and how long it is paid for.
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2 of 2 contrarian
Steve Eisman argues institutions moved into private equity after 2008 partly to stop seeing volatility, and expects a long troubled period for a business that runs on borrowed money while rates rise and exits stay shut.
On his account a fall of roughly 40% in 2008 taught institutions to prefer an asset with no daily price, though the risk underneath was the same. Moses adds that redemption requests now exceed the 5% gates, that managers mark to model because nothing obliges them to mark to market, and that retail access is how managers fill deals institutions pass on.
In Conversation with Julie Segal
Ken TropinGraham Capital Management
Ken Tropin founded Graham Capital Management in July 1994 with $40 million from Tudor and $10 million of his own, and chairs it 32 years on; 75 to 80% of its alpha comes from macro trading, and it runs 25 portfolio manager teams. He is Institutional Investor's 2026 Hedge Fund Lifetime Achievement Award winner.
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1 of 2 current issue
Ken Tropin of Graham Capital says institutions hold equities near record highs on a concentrated AI bet with no natural hedge, because bonds stop diversifying once inflation expectations rise.
Tropin adds that some of their alternatives book sits in private equity and venture, which he treats as a less liquid beta bet. For his own macro fund he claims low correlation over the long haul but rejects the hedge label: if equities are rising, Graham's job is to be long them, and diversification is meant over market cycles, not every time.
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2 of 2 current issue
Ken Tropin counts 82 quarter-point interest-rate moves by the Fed, the ECB and the Bank of England since 2021, against 22 from 2010 to 2020: four times as much for macro traders to work with.
An early Graham discretionary trader, a former New York Fed watcher who traded the front end of the curve, retired mostly because Fed forward guidance, which Tropin thinks began around 2014 or 2015, left him nothing to predict. Tropin reads the new Fed chair as sceptical of forward guidance and more focused on inflation than employment, which on his account keeps that opportunity open.
Excess Returns
Barry Ritholtz, Jim Paulsen, Ben Hunt
The hosts replay excerpts from three earlier conversations: Barry Ritholtz on AI financing and bonds, Jim Paulsen on technology-stock cycles back to 1926 and on rate cuts, and Ben Hunt on data-centre power demand and government backstops.
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1 of 2 current issue
Barry Ritholtz puts SpaceX, Anthropic and OpenAI at $5.2 trillion combined if the last two list at the valuations being discussed, against $4.1 trillion for every US tech IPO from 1980 to 2025.
He concedes the comparison is not inflation-adjusted and that the consensus valuations could be wildly wrong, maybe by half. His other concern is the debt: data-centre bonds that are theoretically A-rated are yielding well above Treasuries, and on his account a building sized for today's chips may need only 5% or 10% of its space once the hardware shrinks.
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2 of 2 contrarian
Jim Paulsen counts six cycles of technology-stock outperformance since 1926: none of the first five lasted more than eight years, and the current one has run for 20.
On his chart of rolling 20-year periods, tech beat the market 80% of the time in the latest one, against 41% at the dot-com top, and he reads that as complacency built by a single decision paying for two decades. His worry is less valuation than crowding: when the cycle turns, everyone overweight has the same reason to cut at once.
Excess Returns
Rob ArnottSyzygy Asset Management
Founder of Research Affiliates and creator of the RAFI indices, a business sold in 2026; now chairman of Syzygy Asset Management, which runs long-short strategies built on the same research. He has published more than 150 academic papers and was long-time editor of the Financial Analysts Journal.
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1 of 3 explainer
Rob Arnott puts Tesla's rise from its March 2020 low to its S&P 500 addition at 900%, including roughly 50% between the announcement and the day it joined, and calls it the privilege of index membership.
S&P indexers hold more than 25% of every stock in the index on his account, so an addition obliges them to buy roughly a quarter of the float from holders who mostly do not want to sell. They then crowd the closing price on addition day to keep tracking error at zero, and Arnott argues that habit is what makes the buying so disruptive.
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2 of 3 framework
Rob Arnott says a simple price-to-book value strategy left its investors four times as wealthy as growth investors over 60 years, and eight times once R&D and a third of overhead are counted as assets rather than expenses.
GAAP books a $5,000 desk as an asset and a million dollars of R&D as a bonfire, though no company spends on research unless it expects a return at least as good as the desk's. Capitalising that spending over about 10 years matters most in the last 30, as the economy has shifted toward assets that leave in the lift each evening.
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3 of 3 contrarian
Rob Arnott reads Nvidia's lending to its own customers as a dot-com warning sign, and cites a recent paper suggesting AI spending would have to reach 9% of US GDP to justify current valuations.
The chain pays only if each link earns more than it spends: end users must value AI above its cost before AI vendors can value chips above theirs, and only then do chipmakers profit. He has not tested the 9% figure himself, but argues the market prices chip suppliers richly while pricing the customers who must win as if AI will push them aside.
Excess Returns
Andy ConstanDamped Spring Advisors
Andy Constan founded Damped Spring Advisors, which analyses global macro markets, market liquidity and the forces driving asset prices for investors and allocators. He has followed the US Treasury's quarterly refunding announcements for decades.
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1 of 3 framework
Andy Constan argues a slowdown in the AI build could cut the stocks by 30% without becoming a financial crisis, because the compute would still find a buyer.
His test for contagion is whether the assets keep their value when a borrower fails: the fibre laid in 2000 kept its worth but took years to pay. If CoreWeave failed he expects customers or the market to buy its capacity, leaving the large platforms with slower revenue and shareholders with the loss.
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2 of 3 current issue
Andy Constan points out that CoreWeave's debt, issued at 8%, now trades at 15%, and the damage lands on the next dollar it has to raise.
What lets a neocloud borrow at all, on his account, is a set of promises from stronger companies: Nvidia guarantees the aftermarket price of the chips, and OpenAI, Microsoft and Google have signed long-term compute commitments. Constan treats those commitments as bonds that do not show up as liabilities on the buyers' balance sheets the way a corporate bond would, and judges them small against the size of the companies making them.
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3 of 3 explainer
Andy Constan says the US 20-year bond often yields more than both the 10 and the 30-year, and cancelling it with bill funding would take about $168 billion a year out of long supply.
His fix is to spread the 20-year's volume across the 10 and 30-year auctions so long-dated supply holds. Financing it with bills instead lowers long yields and lifts every asset priced off them, and he argues that makes the Treasury an inflationary actor with no inflation mandate, working against the Fed.
Monetary Matters
Jim BiancoBianco Research
Jim Bianco of Bianco Research, whose bond index the WTBN ETF tracks, called the 2022 bond bear market and stayed bearish on bonds for six years before turning constructive in early October 2026.
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1 of 4 current issue
Jim Bianco puts the all-in cost of Gulf crude near $200 a barrel against a Brent quote of about $100, counting $35 a barrel of tanker freight and an estimated $50 to $60 of US naval security.
The benchmark is quoted free on board, so shipping that cost $2 to $3 a barrel before the Iran war sits outside it; he ties today's rate to 25 ships struck and two sunk since late September. He reads the extra cost as already absorbed, by refiners through wide crack spreads and by US taxpayers, which leaves oil itself limited room to climb.
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2 of 4 contrarian
Jim Bianco argues US inflation is stuck near 3% because goods prices, which fell about 0.5% a year from 2001 to 2020, now rise about 2%, and both parties back the trade barriers behind the change.
On the PCE measure goods are about a third of the index and services ran near 2.5% throughout, so the old headline of about 1.6 to 1.7% rested on cheap imports. Bianco accepts a San Francisco Fed finding that tariffs explain most of the goods inflation and treats them as a settled policy choice, citing the US ban, begun under Biden, on BYD cars priced at $10,000 to $15,000.
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3 of 4 prediction
Jim Bianco argues US bonds are no longer expensive for the first time in six years, because 5% yields are normal for an economy he expects to grow 5 to 6% a year in nominal terms.
He builds the nominal figure from inflation stuck near 3% and real growth near 2%, and set his trigger at the point when every Treasury rate from the three-year to the 30-year carried a five handle. On his arithmetic a negative ten-year return from here needs ten-year rates of 14 to 15% within five to seven years, a world in which, he jokes, bonds would still lose least.
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4 of 4 current issue
Jim Bianco says that with Fed chair Kevin Warsh giving no forward guidance, markets hunt for it in other officials' speeches, and the odds priced for an October hike fell from 55% to 13% in two days.
He backs the change: the 2021 signals read as a promise to hold, and the central bank waited until March 2022, with inflation at 8.5%, before it moved. What he concedes is the side effect, that John Williams and Philip Jefferson now move markets as presumed proxies for Warsh.
The Pomp Podcast
Moez KassamAnson Funds
Moez Kassam co-founded Anson Funds, a Toronto hedge fund running about US$2.5 to 3 billion that underwrites several billion dollars of deals a year. It put about $1 billion into bitcoin treasury companies over 18 months.
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current issue
Moez Kassam reckons that 95% of the time, investors who say they own a piece of OpenAI or its peers are not on the cap table but in a vehicle several layers below it.
Kassam sees most private offers and secondaries, and describes one set of original investors with layers of special purpose vehicles beneath them charging 1 and 10 or 2 and 20, many set up by family offices and new groups for the purpose. He credits Anthropic with more care over its structured holdings and says the structure could come under a lot of stress when that money tries to come back out.
Merryn Talks Money
Matt Levine, Michael Green, Simon Evan-Cook, Paul Kearney
Matt Levine is a Bloomberg columnist; Michael Green is a former hedge fund manager who worked for Canyon Capital and for Peter Thiel; Simon Evan-Cook is a fund manager at Downing Fund Managers; Paul Kearney is at Asset Risk Consultants.
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current issue
Two teams at Millennium Management made about 4 billion between them in June trading ahead of expected index changes, more than half the firm's profit for the month, Bloomberg reported.
Michael Green's framing is that a passive fund is an active investor running the simplest algorithm there is: buy when cash arrives, sell when cash is asked for. Because the cash goes in by index weight, the largest stocks draw the most money and grow larger, a cycle the hosts say could run in reverse if savers start drawing down in retirement.
Audio streamed from the publisher.
20VC
Sameer DholakiaBessemer Venture Partners
Partner at Bessemer Venture Partners, which has just raised $5.75 billion across a $1.75 billion seed and early fund and a $4 billion growth fund; he has led investments in Anthropic, Fireworks and Legora. Before venture he was chief executive of SendGrid, taking it from $30 million to more than $200 million of annual recurring revenue.
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1 of 2 current issue
Bessemer's Sameer Dholakia thinks about 20% of the firm's previous growth fund went into one company, Anthropic, and expects late-stage positions in the new fund to average 5% to 10%.
Dholakia says the fundamentals have perhaps not changed much at this fund size, because the firm still wants concentrated bets: a conviction position can take $200 million to $400 million, probably over two or three rounds. Early growth is run differently, and the firm will go above 10% for a company it judges generational.
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2 of 2 explainer
Bessemer's Sameer Dholakia says the firm paid about 150 times current revenue for its Anthropic stake, and defends such prices by valuing fast growers on the next 12 months' revenue, at 10 to 15 times.
He stops at 12 months: valuing on the end of next year can mean paying for revenue 18 to 24 months away, which as a former chief executive he says is very hard to forecast at these growth rates. Legora is his case, led at $1.8 billion on about $20 million of revenue, and on his account it has since gone from 20 to 200.
Animal Spirits
It's a Bloodbath | Animal Spirits 487
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1 of 2 current issue
Animal Spirits cites Duality Research putting the median S&P 500 stock up 8% this year against 13% for the index.
One host reads the losers as momentum names still up between 100% and almost 500% on the year after falls of 25% to 50%; the other counts the stocks in ruins whatever the index shows. A USC student's Portfolio Autopsy study of the 343 companies in the index in 1999 and still in it adds the long view: 92% have lost half their value at least once, and 85% fell further than the index's worst drawdown this century, about 55%.
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2 of 2 current issue
Animal Spirits reads the sell-off in CoreWeave's debt, with yields at 15% on a Bloomberg chart as bond prices fell, as a sign the bond market may start setting a ceiling on the AI build-out.
The company fills data centres with Nvidia chips and leases them to the hyperscalers, financing purchases in the tens of billions through a mix of public, private, secured and unsecured borrowing, with Nvidia as supplier and shareholder. The hosts argue that once a boom runs on borrowing as well as equity, lenders act as a governor on risk appetite, which they say got sloppy when rates were at zero.
Big Technology Podcast
Sundar PichaiAlphabet
Chief executive of Google and Alphabet; he set the company's AI-first direction in 2015 and makes the call on how its compute is split between frontier research and Google Cloud.
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current issue
Sundar Pichai says AI capacity, unlike dark fibre, is in use and constrained every day, and still accepts that an overbuild can happen.
Pichai plans compute on committed contracts, demand signals and the trajectory of Google's own models, and names the routes to an overbuild: a bottleneck in one of what he puts at something like 2,000 components or more, or a shock to the economy. He also says Google Cloud is already forgoing short-term revenue so that frontier research gets the compute it needs.
Invest Like the Best
Christina CacioppoVanta
Christina Cacioppo co-founded Vanta about eight years ago and runs it as chief executive; the security and compliance software company now serves about 16,000 customers across about five products. She has also worked as an investor.
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contrarian
Vanta's Christina Cacioppo argues a software business growing 80% a year at 80% margins will return more cash than one growing 160% at 30%, yet the market pays three to ten times the multiple for the second.
She calls the market irrational and describes investors, many of them AI maximalists, backing the labs and AI applied to physical industry with little in between. On her account the pricing reaches a private company through two channels, the terms of its next financing round and staff who move to the companies the market favours, and her answer is to run the business for the long run regardless.
Big Technology Podcast
Ranjan RoyMargins
Ranjan Roy of Margins joins the show on Fridays for the week's technology news; he works in AI and earlier ran a startup.
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current issue
CoreWeave bonds issued at 9.625% are trading around a 15% yield, and Ranjan Roy reads the sell-off as one part of the market that still appears to price risk efficiently.
Roy's case is that the scale of debt being raised for data centres should lift the price of capital, with the 10-year Treasury having touched something like 5.3%, and that a great deal of the build-out was planned as though capital were infinite. CoreWeave already holds its money at the issue coupon; on his account the secondary price is the market's estimate of default risk, effectively distressed territory.
Capital Allocators
Tracy Britt CoolKanbrick
Tracy Britt Cool co-founded Kanbrick, a long-term investment partnership buying family-owned businesses with $5 million to $50 million of earnings, after ten years at Berkshire Hathaway as one of Warren Buffett's deputies. She chaired several Berkshire portfolio companies and was chief executive of Pampered Chef.
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framework
Tracy Britt Cool argues buyout funds sell their compounding businesses too early because the fund structure and its incentives push them to exit.
Kanbrick's alternative is to keep a business compounding at 20% or more a year and give investors liquidity along the way through a recap or similar mechanism. She names three cases that might make a business a candidate for sale: a price that already pays for five years of future earnings, earnings that new risks such as AI make less certain, and a buyer that can grow the business faster.
Top Traders Unplugged
Trend Following Rewards Investors Who Wait | Systematic Investor | Ep.422
A co-hosted episode of the Systematic Investor series with no guest. Kaminski co-wrote a book on trend following with Alex Greyserman and wrote the paper Bonds Behaving Badly.
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contrarian
Katy Kaminski's paper Bonds Behaving Badly finds that when inflation is the main risk, bonds tend to move with stocks, turn more volatile and lose their safe-haven status.
Inflation, absent for most of today's investors' careers, is on her account the natural predator of bonds, and a recent sample flatters them. Kaastrup-Larsen points to a Danish public pension fund that moved into what he thinks was a levered risk parity strategy near the low in rates, and to the patience investors extend to bonds and equities through five or ten years without a new high.
AI 22 October 2026 · 39 clips
Big Technology Podcast
Sundar PichaiAlphabet
Chief executive of Google and Alphabet; he set the company's AI-first direction in 2015 and makes the call on how its compute is split between frontier research and Google Cloud.
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1 of 2 current issue
Sundar Pichai says everyone in AI is short of compute, and capacity for a new customer in 2027 is already very hard to find.
Frontier research is served first, as a non-negotiable, and each business, Cloud included, plans on its own demand signals and contracted revenue two to three years out. He broadly accepts a view relayed second-hand from Amazon that supply is sold out years ahead, and says the company rented extra short-term supply last year to help some clients.
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2 of 2 current issue
Sundar Pichai says Google's new model was built partly by AI improving itself alongside human researchers, and thinks models should in time be checked by independent evaluators, as companies are by auditors.
He rests the safety case on defence in depth: alignment work with acknowledged gaps, real-time monitoring and sandboxing of running models, and customer controls over what data they reach. The next step he names is shared cross-industry standards, with regulation following.
In Good Company (Norges Bank)
Eric Schmidt
Eric Schmidt was chief executive of Google from 2001 to 2011, helped write the US AI strategy, and now advises governments on AI while running four new companies.
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1 of 2 contrarian
Eric Schmidt says Chinese AI models look about six months behind American ones, and argues that is already a loss because China gives its models away.
Schmidt says the Chinese labs look like fast followers, and appear to have been helped by distillation, training on a larger model's answers, which he calls more or less illegal, and by smuggled chips. If losing is measured in users, he argues, a poor country that cannot pay Western prices will run a Chinese model.
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2 of 2 framework
Eric Schmidt says AI is advancing fastest in software, cyber and mathematics because each produces its own training signal, and slowest where data has to be gathered, as in biology.
On his account code can be run and checked, so a model learns as it writes; attack code is the same skill turned to harm; and mathematics is scale-free, needing nothing new from outside. He names Anthropic, which writes most of its own code with its own models, as the leading edge, and expects large companies, European ones especially, to follow slowly.
Invest Like the Best
Christina CacioppoVanta
Christina Cacioppo co-founded Vanta about eight years ago and runs it as chief executive; the security and compliance software company now serves about 16,000 customers across about five products. She has also worked as an investor.
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1 of 3 current issue
Christina Cacioppo reports that many French software companies now run mainly on Chinese models, a switch she was told followed a US model being closed to non-American users.
She had assumed the choice was nationalism and was corrected: on the account she was given, they all switched once one American model was withdrawn from non-Americans, having concluded no business could count on keeping access to a US model. Her account comes from customer meetings on a single trip and gives no count of how many firms have switched.
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2 of 3 explainer
Christina Cacioppo says AI models break into systems less through new flaws than by chaining together many minor ones, which ends the habit of leaving low-severity fixes for 90 days.
She points to an OpenAI talk at RSA as an example: a model's persistence lets it try far more combinations than a person would, and flaws that were sensibly queued for later become a route in once linked. Her conclusion is that some of the triage has to go and organisations will have to fix what they find as fast as it is found.
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3 of 3 explainer
Vanta's Christina Cacioppo says customers will not pay a markup on model tokens: priced that way, an AI application is compared directly with the model vendor and loses.
Cacioppo says Vanta is absorbing the token cost and accepting the lower margin, with about 25% of customers using its agent-led workflow and a target of 75%, on the view that retention follows usage. She treats this as a deployment phase and leaves pricing to be settled once the usage is there.
The Cognitive Revolution
Alex RivesBiohub
Alex Rives is head of science at Biohub, the Chan Zuckerberg Initiative-backed effort that has just announced a $1.8 billion expansion of its Virtual Biology Initiative with the Department of Energy, the NIH, Google DeepMind, Isomorphic and Meta. He co-founded Evolutionary Scale, maker of the ESM protein language models, and was its chief scientist.
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framework
Alex Rives expects the reinforcement learning with verifiable rewards that took off in maths and code to work in biology, where the verifier has to be a laboratory experiment.
He maps the language-model recipe onto cells, self-supervised pre-training to learn the system and a later stage that draws out the part relevant to a problem, and describes the end state as empirical AI that proposes an experiment, runs it, learns and proposes a better one. Fast computational checks are why maths and coding moved first.
Training Data (Sequoia)
Josh GaoMecka AI
Founder and chief executive of Mecka AI, which creates, processes and licenses the data used to train robotics foundation models, moving hundreds of petabytes through its own upload lines.
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current issue
Mecka AI's Josh Gao puts robot training data at millions of hours, up from about 4,000 two years ago, and still many orders of magnitude short of what robotics needs.
Self-driving has taken over a decade and millions of cars to work on two degrees of freedom, steering and acceleration, while a robot hand has dozens. Asked whether any economy can fund that much data, Gao answers that commercial tasks unlock one at a time, as broad pretraining is topped up with data from the job site itself to reach the four nines of reliability real-world settings often need.
a16z
Karan SinghalOpenAI
Karan Singhal leads health AI research at OpenAI, where more than 300 million people a week use ChatGPT for health and wellness questions. He worked on specialised healthcare models in an earlier role.
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framework
OpenAI adds healthcare data at every stage of training its general models instead of building a separate medical model, which Karan Singhal says would fall behind the frontier.
Singhal, who worked on narrow clinical systems in an earlier role, says frontier progress outruns anything focus can buy. Evaluations and examples from OpenAI's network of hundreds of physicians go into the big model instead, a bet that scale keeps paying.
ChinaTalk
Nick BrownCenter for Technology and Statecraft
Nick Brown co-founded the Center for Technology and Statecraft, a think tank spun out of IFP, and wrote its report on DUV immersion lithography as the foundation of the US lead in AI compute, which estimates China's installed base of ASML tools. He studied physics and worked as a software engineer in and around government.
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1 of 2 framework
ChinaTalk puts the US lead in AI compute at about 30 to one, and Nick Brown argues the chips that decide whether it lasts are the ones China makes itself.
Brown ranks China's chips by the leverage Washington keeps over each. Remotely accessed chips can be cut off at any moment, exported chips are let go at the point of sale, and chips made on China's own supply chain leave very little, which is why he treats indigenous production as the real contest while the debate runs on which Nvidia part may ship.
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2 of 2 explainer
On Nick Brown's estimate, a single ASML scanner can probably make more than 100,000 Huawei Ascend AI chips a year, even at China's lower yields.
That is why the MATCH Act's 150-day lead time concerns him: China stockpiled HBM ahead of earlier controls and may bring in tens of scanners, perhaps more than 100, before a ban takes effect. He argues the ban still caps the decade, and that China already holds enough lithography for ten years of smartphone demand, leaving a large AI build as probably the only demand that would hit the ceiling.
Worth listening to in full 22 October 2026
Most clips above stand alone. These are the episodes that justify the whole hour.
AI Credit in Detention: Coreweave's Debt Panic & Jim Bianco's Bull Case for Bonds (Finally!)
Seventy-six minutes in which Jim Bianco puts a number on each step of his case: why 5% yields are normal for the US economy, why inflation is stuck near 3%, what Gulf crude costs once freight and naval security are counted, and where the recent widening in high-yield spreads sits. He also takes the host's challenge on AI credit and says what would settle it.
Big Short Reunion w/ Danny Moses: Rising Rates, AI & Private Equity | The Real Eisman Playbook Ep 80
Forty minutes with two people who ran the subprime short together, putting numbers and mechanisms on the AI credit chain: the warehouse lines behind the lending, Nvidia's receivables concentration and CoreWeave's $51 billion of debt including leases. They then turn to private equity's liquidity problem and the dollar's reserve share.