29 October 2026
144 new episodes (136 hours listening) published by the 50 podcasts on the list. 40 highlight clips below (110 mins total).
Investment 29 October 2026 · 40 clips
Hidden Forces
Paul Kedrosky
Paul Kedrosky has run venture funds with a partner for almost 15 years and covered the 1990s technology boom as an equity research analyst at a major brokerage. He now sells independent market and technology research to a small number of clients, mostly hedge funds and large institutional investors.
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1 of 2 current issue
Paul Kedrosky estimates that AI capital spending has supplied 25% to 60% of US quarterly GDP growth over the last seven or eight quarters.
That, he argues, hides weakness everywhere else and flatters policies such as tariffs, and it distorts the inflation data behind higher rates. In markets, on his figures, the Magnificent Seven supplied almost 45% of the S&P 500's gains over five years, and the Dow Jones created its transport index in the 19th century when railroads made up about half of the main average.
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2 of 2 framework
Paul Kedrosky estimates that close to $10 trillion of data centre spending over four or five years needs $4 trillion to $6 trillion of annual revenue by the early 2030s to earn a reasonable return.
Money arriving that fast mostly replaces existing outlays, he argues, and the outlay in reach is white-collar pay: the sum is about 40% of the US white-collar market and a little under a quarter of the global one. He calls that at least a quarter of white-collar work repriced, with or without layoffs, and argues the rest cannot keep its old price beside it.
In Good Company (Norges Bank)
Bill AckmanPershing Square
Bill Ackman founded Pershing Square, which runs one concentrated portfolio of 12 to 14 companies with nine investment professionals in a firm of 48. The host puts its compounding at 16% over a long record.
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1 of 2 current issue
Bill Ackman says a high-quality company that disappoints can now lose 30% to 40% on a single miss, where it once might have fallen 3% or 4%.
He puts it down to indexation shrinking the float that trades and to more short-term money running on borrowed funds, which together make the occasional durable growth company cheap for a while. Activism has changed alongside it on his account: Pershing Square's last campaign was in 2016, and CEOs now generally write to thank it for taking a stake.
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2 of 2 framework
Pershing Square pays its nine investment professionals on the whole portfolio's result and on no individual P&L, so nobody gains from getting an idea into the book.
Ackman sets it against the usual hedge fund, where an analyst's only incentive is to get capital allocated to their ideas, which leans them towards wanting an idea in. At Pershing Square two people do the deep work on a name, two oversee it, and the rest have done none before the team decides together.
Insightful Investor
Jim CareyStone Point Capital
Co-chief executive of Stone Point Capital, an alternative investment firm focused on financial services with more than $80 billion under management at the end of July 2026. He and his partners have built it over nearly 30 years, from about 10 people to more than 200.
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1 of 3 framework
Jim Carey of Stone Point Capital treats exceptionally high growth in a financial services business as a warning sign, because it often means a product is being underpriced.
He says buying for growth in the sector has led to some bad investment outcomes, and Stone Point looks for complexity instead: assets others will not spend the work on, where fewer bidders let it, in many cases, either earn a higher return for understanding them or build structure around the downside.
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2 of 3 framework
Stone Point Capital credits the owner's control of when to sell for turning deals it judged overpaid, once underwater with debt trading at 50 cents, into very good money.
Jim Carey calls that control an option private markets offer that is hard to value, and the result a holding period bailout. What keeps the option, on his account, is a capital structure light enough that lenders cannot take the keys at the wrong time; Stone Point still uses debt as a key part of its acquisitions and runs the whole portfolio to the same test.
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3 of 3 contrarian
Stone Point Capital, an active investor in wealth management, calls the 20-times-EBITDA multiples wealth firms have commanded for three to five years hard to sustain.
Jim Carey would be more cautious about building them into exit models, and puts the assumption down to recency bias, the same habit that has people expecting mortgage rates now above 7% to return to the 3% to 4% of the last 15 years, when his own first mortgage cost 8%. He sees fear of missing out in the 2020 to 2022 software rush, which left buyers owning good businesses at prices that, in retrospect, were probably too high.
Monetary Matters
Citrini
Citrini runs Citrini Research, which publishes investment research and has an institutional side, and was among the first to argue for the AI hardware trade in 2023, naming SK Hynix before most people had heard of it. His 2028 Global Intelligence Crisis piece was viewed about 100 million times.
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1 of 3 current issue
Citrini argues that probably every incremental dollar of AI infrastructure spending now comes from equity and debt raised in the markets, where for roughly the trade's first three years most of it came out of hyperscaler operating cash flow.
On his account that is what made earlier drawdowns safe to ignore: tariff scares and the yen carry unwind came and went while the companies receiving the capex kept improving. A sentiment turn can now shut the funding itself, and he names the number it hangs on: lab revenue underwritten at $500 billion that arrives at $300 billion would be a disaster.
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2 of 3 current issue
Citrini puts the AI lab revenue built into current estimates at about $1 trillion a year by the end of 2028, a path that needs about $50 billion of new annualised revenue added each month against roughly $13.6 billion now.
White-collar work has to become the main source of token demand for the next few years, he argues, and its buyers pay per task and take the cheapest model that does the job. Open models' share of net new revenue has gone from about 1% a year ago to 18% on his figures, which is how he reaches the view that too much capex may already be priced in.
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3 of 3 framework
Citrini found that AI compressed months of drug-design work into days, while synthesising and testing the designs at contract labs still took months.
The data that would make models better at biology can only be generated at the speed cells grow, he argues, so the time from idea to experiment compresses while the experiment itself does not.
Animal Spirits
Why Baby Boomers Have All the Money | Animal Spirits 488
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1 of 2 current issue
US stocks would be up almost 30% this year on earnings growth alone if the forward P/E had held where it began the year, a chart on Animal Spirits shows.
The hosts suspect the falling multiple is the market declining to believe the AI earnings will last, and note on Goldman Sachs charts that profits sit about as far above their 30-year trend as at any point since the 1990s. Their reason the old reversion may not come: median net margins have risen from under 6% in the 1990s to 13%, because competing with today's dominant firms takes a trillion dollars of capital.
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Animal Spirits finds no sign yet of AI in US aggregate labour data, with initial jobless claims at 194,000, the fourth-lowest reading since the 1960s.
The first traces sit below the national figures: Microsoft has offered early retirement to 60,000 of about 220,000 staff, roughly what the hosts reckon it hired this decade, and Goldman Sachs reports a statistically significant negative correlation between the share of US firms adopting AI and employment growth. The hosts' explanation for the gap is firms doing more with the same headcount, revenue outgrowing staff, which has yet to reach national productivity figures.
Unsupervised Learning
Erik BernhardssonModal
Co-founder and chief executive of Modal, the AI compute platform, which has grown from about $2 million of annual run-rate revenue to more than $1 billion in three years. He spent seven years at Spotify.
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1 of 2 current issue
Erik Bernhardsson puts the upfront payment on a GPU commitment at about 30%, so a lab that raised $1 billion can sign for roughly $3 billion of compute.
The rest falls due month by month over the following years, which he calls implicit leverage: the lab depends on raising again to meet its GPU bills. If venture funding pulls back he expects a secondary market, with labs reselling capacity or turning into neoclouds as Mistral has to a degree, and notes that buyers justify large purchases by assuming the chips can always be resold.
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2 of 2 framework
Erik Bernhardsson calls the neoclouds sound but undifferentiated leasing businesses whose growth is held back by a high cost of capital.
On his account they lock in customer contracts, borrow against them and pay the debt down, and in many cases GPUs have lasted longer and resold for more than expected, which adds a residual profit; the heavy leverage is why their equity swings with GPU prices. He points out that the three successful hyperscalers, AWS, Azure and Google Cloud, all sit inside cash-generating public companies with cheap capital.
Monetary Matters
Vincent DeluardStoneX
Director of global macro strategy at StoneX, where he writes a weekly report for clients; he had just returned from meetings with pension fund clients in Latin America.
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1 of 3 contrarian
Vincent Deluard counts a bit more than $300 billion of fiscal support running into US corporate margins this year, about 10% of roughly $3 trillion in profits.
Deluard names three channels: tariff refunds that companies kept after raising prices when the tariffs hit, about $150 billion of individual tax refunds spent over the summer, and corporate tax collections down 50% in two years under full expensing of capital spending. On his figures perhaps half of the roughly 30% third-quarter earnings growth estimate came from policies he expects to lapse after the midterms.
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2 of 3 current issue
Vincent Deluard of StoneX counts about two-thirds of US stocks below their 200-day moving average with the index about 2% from its all-time high.
Consumer names including Nike, Harley-Davidson, Lennar and McDonald's are down 50% to 60% or more, damage he doubts has been seen in a Fed hiking cycle, and after six years arguing nominal growth was underrated he now thinks the market too sanguine on earnings, on the economy and on the Fed's ability to raise rates.
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3 of 3 explainer
Vincent Deluard puts France's extra interest bill at about €20 billion a year, as roughly €400 billion of debt issued near zero rolls into yields close to 5%.
That is half the €50 billion deficit cut the budget targets, a budget he doubts will pass, and with growth that is mostly inflation, rising rates widen the deficit and the deficit pushes rates higher. On his account France traded only 30 to 40 basis points over Germany for years, on the size of its bond market and its core status, despite metrics closer to Italy's or Spain's.
The Real Eisman Playbook
Benn SteilCouncil on Foreign Relations
Director of International Economics at the Council on Foreign Relations and author of histories of Bretton Woods, the Marshall Plan and Henry Wallace's vice presidency, the archival work behind them including 13,000 pages of FBI files on Harry Dexter White obtained under freedom of information.
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1 of 2 current issue
Benn Steil puts the share of the Treasury market held by rate-insensitive buyers, mostly central banks, at 43%, down from 76% 20 years ago.
Each extra dollar of deficit now has to be placed with buyers who need paying for it, so Steil expects an accelerating effect on yields as the investor base worsens alongside the deficit. France is further down the same road: almost all its buyers are rate-sensitive, and nobody holds French bonds by reflex the way banks worldwide hold Treasuries for overnight repo.
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2 of 2 current issue
Iran's demand to be paid its strait tolls in renminbi or crypto moved payments from SWIFT to China's CIPS, and they moved back when the ceasefire was announced, Benn Steil says.
On Steil's account, central banks wanting a reserve asset beyond the reach of US dollar sanctions have bought gold rather than other currencies, with China and Russia building and re-domesticating their stocks and the Dutch bringing theirs home from the New York Fed. Gold has proved about as good as the dollar in a crisis, he says, which leaves it the only alternative on offer.
Top Traders Unplugged
Mark Rzepczynski
Mark Rzepczynski co-hosts the show's Systematic Investor series and researches trend following, reading markets through who trades them and the rules they trade under.
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1 of 2 current issue
Mark Rzepczynski cites research showing major hedge funds' 13F stock positions growing more alike as their quants adopt the same machine-learning methods.
A survey he cites puts that toolkit in use at maybe over 90% of hedge fund managers, many taught transformer and boosting techniques at a few universities, so each signal is crowded out sooner and firms spend more compute looking for the next. He argues trend following may have no comparable half-life, because it waits for dislocations and accepts the dry spells.
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2 of 2 framework
Mark Rzepczynski says commodity prices go parabolic on inventories rather than output, because a buyer facing a stockout will pay almost any price to keep running.
He points to European winter inventories below the levels of the last five years, where a cold winter brings that risk, and to cocoa a couple of years ago. Hoarding ahead of a feared shortage creates it, and the markets most exposed are those with no close substitute, as diesel is for a diesel truck.
Big Technology Podcast
Alex StamosCognition
Alex Stamos is chief information security officer at Cognition, which helps large companies refactor old software, and has taught at Stanford.
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1 of 2 current issue
Google Cloud grew quarterly revenue 82% year on year to $24.8 billion, against 42% growth for Azure on $29.4 billion.
If those rates persist for maybe another year or two, the show argues, second place changes hands, with AWS still ahead at $42.2 billion growing 37%. The catch it names is that capacity sold to customers is capacity taken from the company's own research, on build decisions made two years before.
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2 of 2 current issue
Meta warned in a regulatory filing that rivals' capital raises have made fundraising increasingly hard, so it will raise early before competitors absorb the market's available capital.
The show reads it as the first sign that money, not chips, may become the binding limit on the AI build, with Meta's infrastructure spending guidance now $825 billion against $700 billion in July and its shares down 9% on the week. The hosts add that SpaceX found the yields on a $40 billion debt raise higher than expected.
Big Technology Podcast
Ethan MollickWharton School
Ethan Mollick is a professor at the Wharton School, writes the One Useful Thing newsletter and is the author of Co-Intelligence and the new Coexistence: The Next Phase of AI. He says he talks regularly to chief executives of large companies and takes no money from the AI labs.
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1 of 2 current issue
Ethan Mollick cites a company that renegotiated a $200 million mainframe data migration plan down to $20 million after breaking up teams around AI.
He ties it to pulling apart standing teams, putting a senior engineer with a subject expert and a marketing and strategy person on near-impossible projects; other firms add large token budgets. His broader claim is that firms have lost the habit of experimenting with how they organise, after 30 years of leaving that thinking to software vendors and consultants.
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2 of 2 explainer
Ethan Mollick says code output rose about twentyfold in the studies he cites while products shipped rose about 30%, because organisations have not yet rebuilt around AI.
His reading is that AI is a single-player tool inside multiplayer organisations: a coder producing a hundred times more code still works in two-week sprints with daily stand-ups and a marketing team, so the extra output queues. The host's Dallas Fed figures show high-exposure jobs gaining more than low-exposure ones, but less than they did around COVID.
Invest Like the Best
Cliff Sosin
Cliff Sosin runs a concentrated portfolio of about five positions, roughly 80% of it in Carvana and about 5% in Meta.
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1 of 2 framework
Cliff Sosin argues that once intelligence is cheap and everywhere, the businesses worth owning are those holding its complements: data, attention, physical proximity, scale and regulatory capture.
His test is to assume every competitor and customer has excellent intelligence and ask what is left of the business. Agents that optimise around the clock hurt firms whose margin depends on customers not bothering to shop around, and software that amounts to crystallised intelligence, while a small firm with data and attention but a thin engineering team gains.
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2 of 2 framework
Cliff Sosin sketches AI agents handing whole streets to the lowest-cost garbage collector, because an agent will switch a household to save 50 cents a month where its owner would not bother.
His spectrum runs from decisions people will happily delegate, such as chasing the best insured deposit rate, to ones they will not, such as choosing a sweater. Where judgement can be delegated the first effect is a move to the cheapest provider; the second, he suggests, is that route density lets that provider cut price again and roll up the street.
Merryn Talks Money
Steve HouSilicon Data
Head of research at Silicon Data, a firm founded in 2024 that publishes benchmark indices of GPU rental rates and AI token prices, with CME futures and options planned pending CFTC approval. Previously a quantitative researcher on Bloomberg's index research team, working on equity and multi-asset strategy research.
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current issue
Silicon Data's index of the effective price paid for AI tokens has fallen by almost half since about the end of May, while GPU rental rates have kept rising.
Hou's explanation is that everyday use has migrated from frontier models to cheaper ones, as car buyers might move from Mercedes to Corollas, and every agent running on them still consumes compute. He ties the timing, tentatively, to a slowdown some estimates show in Anthropic's revenue growth, now at a $60 billion annualised run rate, and argues that slower growth for competitive reasons would test the trillion-dollar valuations discussed for the labs' IPOs.
Audio streamed from the publisher.
20VC
Satya NadellaMicrosoft
Satya Nadella is chief executive of Microsoft, where he has worked for 34 years; the company was worth $300 billion when he took over and $3.9 trillion now.
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framework
Satya Nadella says $100 billion training runs will be paid for the way R&D is, out of revenue, so the AI build ultimately depends on world GDP growth inflecting.
Heavy R&D companies spend roughly 15% to 20% of revenue on it, he notes, and income statements will still show revenue, cost of revenue and profit; power, chips and fabs are long-lead items and nobody knows when supply meets demand. He hopes AI lifts growth above about 2%, with health care, and software, whose demand has proved elastic, as early cases.
Alpha Exchange
Randy CohenPEO Partners
Professor of Finance and Entrepreneurship at Harvard Business School and co-founder of PEO Partners, which builds liquid private equity portfolios. A Chicago PhD of 1998, he has taught investment management at HBS and MIT and first argued for replicating buyouts with public factors in a 2000 talk at HBS.
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1 of 2 explainer
Randy Cohen argues the companies a buyout fund can borrow against carry, by necessity, the five Fama-French factors: high profit, high payout, low multiple, low risk and small size.
On his arithmetic, roughly two-thirds debt at something like 9 to 12% costs 6 to 8% of enterprise value a year in interest, which a company bought at 30 times earnings cannot pay and a company reinvesting everything for growth would have to divert that money to meet. Cohen adds that even a buyout veteran who scorns academic finance is placed on all five factors by the financing model itself.
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2 of 2 contrarian
Randy Cohen expects a listed copy of buyout funds to trail them by 3 to 6% a year before fees and argues it can still finish level or ahead after them.
On his account a public replicator gives up private equity's inside information and its operational gains net of the control premium, and pays for hedges so a 20% market fall does not show as 25 to 30% beside private equity's reported 10%. Against that, Cohen cites a CalPERS study putting private equity's fees above 700 basis points a year, which leaves a vehicle charging about 100 room for a gross shortfall of up to 6%.
AI 29 October 2026 · 40 clips
Big Technology Podcast
Dan PrimackAxios
Dan Primack is the business editor at Axios and writes its Pro Rata newsletter.
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current issue
Anthropic earned $2.28 of revenue for every dollar of compute on the latest figures, up from 40 cents in the first quarter of 2025.
Primack argues the spend side decides whether these become viable businesses: if chips and models get much more efficient, Anthropic's compute bill falls as a share of revenue even while it keeps rising in dollars. He adds that the company is still unprofitable and is listing because it needs the money.
Invest Like the Best
Cliff Sosin
Cliff Sosin runs a concentrated portfolio of about five positions, roughly 80% of it in Carvana and about 5% in Meta.
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contrarian
Cliff Sosin argues falling prices eventually shrink a market: Cogent Communications, he believes, carries 30% to 40% of internet traffic on a market value of a few billion dollars.
Jevons paradox, on his account, describes one stretch of an ordinary demand curve: a market grows as prices fall from very high, peaks, then shrinks once further cuts stop pulling in enough volume. He does not say the market for intelligence will shrink as far as bandwidth's did, only that the build-out case assumes the curve stays on its rising stretch.
Hidden Forces
Paul Kedrosky
Paul Kedrosky has run venture funds with a partner for almost 15 years and covered the 1990s technology boom as an equity research analyst at a major brokerage. He now sells independent market and technology research to a small number of clients, mostly hedge funds and large institutional investors.
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contrarian
Paul Kedrosky says something like 70% to 80% of frontier model usage has come from software engineers, an early-adopter group that tells little about how everyone else will use or pay for AI.
Coding is expansive, an eight-word prompt returning a large body of code, while much white-collar use is compressive: a 30-page research report cut to what matters. In his blind tests ordinary users could not tell a frontier model from a lesser one, so a premium for the newest model loses its buyers just as China sells tokens near cost about eight months behind the US frontier.
20VC
Satya NadellaMicrosoft
Satya Nadella is chief executive of Microsoft, where he has worked for 34 years; the company was worth $300 billion when he took over and $3.9 trillion now.
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prediction
Satya Nadella expects most AI token usage to run on commodity models, open-weight or cheap closed ones, once a task's frontier saturates.
His reasoning is that a firm training on its own traces does not need the latest release for a problem that has not changed, and may pay top prices only while its evaluation keeps moving. Microsoft's Copilot already routes work to cheaper proprietary systems alongside downloadable ones, and he expects heavy price competition.
Unsupervised Learning
Erik BernhardssonModal
Co-founder and chief executive of Modal, the AI compute platform, which has grown from about $2 million of annual run-rate revenue to more than $1 billion in three years. He spent seven years at Spotify.
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1 of 3 prediction
Erik Bernhardsson now expects GPU rental prices to be higher in 12 months, a view he changed after reading Anthropic's S-1.
He had expected Anthropic to slow training spending once public; the filing showed him a company profitable enough to have no reason to slow its buying of capacity, and he assumes OpenAI is in a similar position. As the two largest buyers of compute, their continued buying is his case for prices rising.
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2 of 3 current issue
Erik Bernhardsson says Nvidia's older Hopper H100s are still the most common GPU type on Modal, even as it ramps up Blackwell chips aggressively.
Only frontier language models need a full node of the newest chips, on his account; smaller and older models, speech, audio and biology workloads run more cost-efficiently on Hoppers, A100s or smaller cards such as the RTX Pro 6000. Memory decides the match, and the usual rule is to pick the cheapest card that holds the model.
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3 of 3 framework
Erik Bernhardsson argues that coding agents entrench incumbent software, because models go on recommending the tools in their training data.
Modal sees it from both sides: retiring a feature now takes far longer, since agents trained on old code still generate calls to it, and Modal tracks which tool an agent picks when asked for help without being named, much as firms once tracked search rankings. He hears of developers choosing React for everything as agents write it well, and calls the result a flywheel.
Latent Space
Katelyn Lesse, Angela JiangAnthropic
Katelyn Lesse and Angela Jiang are Anthropic's heads of engineering and of product, whose teams run the Claude API and the platform infrastructure behind the company's products; the two worked together earlier at Stripe.
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current issue
Anthropic can no longer buy enough ordinary CPU capacity on demand, and now plans it in advance the way it plans AI accelerators.
The demand comes from agent sandboxes, the API and product services, and the CI runs that multiply as more code gets written, all drawn from one CPU allocation negotiated with the cloud providers and split inside the company between research and product. The guests say every firm they talk to reports the same.
Big Technology Podcast
Alex StamosCognition
Alex Stamos is chief information security officer at Cognition, which helps large companies refactor old software, and has taught at Stanford.
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contrarian
Alex Stamos says open-weight models would have found the decade-old PHP flaw if OpenAI and Anthropic had not.
He says he can now reverse-engineer and write exploits on a desktop machine running a modified open-weight model that leaves no logs, so blaming the labs misreads where discovery comes from. His answer is to rewrite much of the internet's software in memory-safe languages, impossible five years ago and now a question of money, model access and the open-source community's willingness.