8 October 2026
125 new episodes (119 hours listening) published by the 50 podcasts on the list. 30 highlight clips below (78 mins total).
Investment 8 October 2026 · 30 clips
The Real Eisman Playbook
Krishna GuhaEvercore
Krishna Guha is Evercore's Fed watcher, the analyst Steve Eisman says he reads every day or two.
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current issue
Evercore's Krishna Guha attributes the 10-year Treasury yield's climb from something like 4.5% in July to around 5.25% to AI hyperscalers borrowing alongside a deficit of 6% of GDP, investors expecting a higher normal rate, and oil.
Bond investors, on his account, now see a multi-year capex boom competing for a limited pool of savings where there used to be too much saving chasing too few investments, so the normal Fed funds rate needs to sit a little higher for several years. Crude benchmarks understate the energy shock: diesel has moved roughly as if oil were closer to $200.
Top Traders Unplugged
Cem Karsan
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1 of 2 contrarian
Cem Karsan, citing a Carlyle report, puts all US earnings growth in the AI complex and traces it to mark-ups on private AI stakes and to capex, with nothing from AI efficiency.
When Anthropic and companies like it go from $380 billion to $2 trillion, on his figures, the gain is marked through holders' earnings without cash; the money raised then funds capex that is depreciated over years, and operating cash flow diverges from reported income. Karsan argues a falling market would run the same machinery in reverse, with valuations against the 10-year yield at or near tech-bubble levels.
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2 of 2 current issue
Cem Karsan reads the roughly 45% of S&P 500 members carrying a negative beta as the plainest measure of how far the market rests on one AI trade.
Growth this narrow depends on the equity market and on the investment flowing into one complex, so on Karsan's account the market can rise further and still be fragile, and the useful response is to position across the range of outcomes instead of betting on direction. Hyperscaler spending is put at $800 billion this year, rising past $1 trillion within a few years.
Insightful Investor
Jeffrey ShermanDoubleLine Capital
Deputy chief investment officer of DoubleLine Capital, which managed about $97 billion at 30 June 2026.
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1 of 2 prediction
Jeffrey Sherman expects the bond market to decide how far the AI build-out goes, because estimates put next year's capex at $900 billion to $1.5 trillion, which has to be financed through corporate bonds, asset-backed debt, bank loans or free cash flow.
Sherman applies the same supply arithmetic to the Treasury's buybacks: $6 billion will not do it against a $30 billion auction, and only net purchases would, which would look like Fed and Treasury managing the curve together. He thinks the bond market would reject that because it leads to higher inflation, leaving two outcomes: growth turns AI profits into reinvestment, or lenders set the terms.
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2 of 2 framework
Jeffrey Sherman says private credit, bank loans and CLOs are the same trade, floating-rate lending, held as three line items that look like diversification.
All three worked because they carry little duration, much of it floating rate, and enough yield; they sit at different points in the capital structure but are all lending into one narrow market. He finds the same doubling-up in equities: tech is about 35% of the US market, against what he recalls as the low 20s for tech and telecom combined at the telecom peak.
Training Data (Sequoia)
Amin VahdatGoogle
Amin Vahdat is Google's Chief Technologist for AI Infrastructure, named head of its AI infrastructure at the end of last year, at a company the host expects to spend more than $200 billion on capital expenditure this year, most of it on data centres.
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current issue
Google's Amin Vahdat says its seven- and eight-year-old TPUs are still running at 100% utilisation, against a depreciation life of about six years.
Google retires hardware a pod at a time, which might be 9,600 chips across about 150 racks, once it is fully depreciated and newer generations' power efficiency makes replacement worthwhile. Fitting a new pod into an old pod's footprint is a retrofit it cannot plan years ahead.
Monetary Matters
J MintzmyerValue Investors Edge
J Mintzmyer founded Value Investors Edge in 2015, a 13-person research shop covering about 50 shipping companies, and wrote his PhD on using AIS ship-tracking data to map sanctions and trade flows.
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1 of 2 current issue
J Mintzmyer of Value Investors Edge puts recent Middle East Gulf to China VLCC fixtures at around $1 million to $1.2 million a day, against a normal $20,000 to $40,000 or so, with one-year charters recently at about $150,000 to $170,000.
A round trip takes about 60 days, so a voyage adds roughly $30 of freight to every barrel of a 2-million-barrel cargo, a record. Owners still take term business well below spot because, he suggests, a ship may not reach the Gulf in time or the owner may want a year of certain revenue; ship values now exceed their 2007 to 2008 peak.
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2 of 2 explainer
J Mintzmyer of Value Investors Edge argues the tanker boom runs on inefficiency: the Hormuz closure trapped ships inside and scattered the rest, and ton miles, cargo times distance, is what owners actually sell.
Ships that had queued for the Gulf scrambled to the US Gulf and West Africa, 20 to 30 days of repositioning, and when protected lanes opened the fleet was in the wrong places. With crude consumed at $100 to $110 a barrel, refiners can absorb $20 to $30 a barrel of freight in some cases and still buy.
In Good Company (Norges Bank)
John ArmitageEgerton Capital
John Armitage started Egerton Capital in 1994, one of Europe's early hedge funds, and has run its investments for about 30 years. Before that he ran a unit trust at Morgan Grenfell for six years that beat its index by 10% a year and ranked first of 85 funds over the period.
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1 of 4 contrarian
John Armitage of Egerton Capital calls valuing companies on EV to EBITDA meaningless, and points to neoclouds valued on EBITDA when depreciation is their biggest cost.
Wearing out and writing down assets are real charges, he argues, because a business has to replace its capital, and serial acquirers quite often put more cost into intangible assets than they should, only for the amortisation to be added back. His own measures are price to earnings, free cash flow multiples and perhaps operating profit against enterprise value.
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2 of 4 explainer
Egerton Capital's John Armitage says the numbers that matter most at Airbus and at a reinsurer, the price on each aircraft order and the terms of each treaty, are private, so his models are built by inference.
For a reinsurer the substitute is the history of reserve development, which shows how well it has estimated losses before. For Nvidia, on 75% gross margins, it means reconstructing the bill of materials and the timing of memory contracts to test how far a further memory price spike would cut margin; Visa, at 67% margins with clear costs, needs little of that.
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3 of 4 framework
John Armitage counts the sale of Altice among the best he made, and it was prompted by one slightly light quarter that barely touched his medium-term numbers.
Altice ran on little revenue growth, heavy cost cutting, leverage and acquisitions, and he is always suspicious of a company with little top-line growth because it leaves little room for a mistake. A high margin gives the opposite protection: a sales shortfall then does not wreck the profit and loss account.
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4 of 4 framework
John Armitage says Egerton Capital does badly in violent markets, and that in 2008, sitting in London, he heard US house prices had compounded at 7% a year, thought that quite low, and missed how they linked into the wider economy.
Egerton does best, by his account, in trending markets and trending stocks; violent moves tend to be emotional and often come before a big shift driven by geopolitics or the economy. A stock picker anchors on the spreadsheet and on a company conversation a few weeks old, and can be far too late to see that a holding rests on supports in the wider economy.
20VC
Venky GanesanMenlo Ventures
Venky Ganesan is a partner at Menlo Ventures, a three-time Forbes Midas List investor and a former chair of the National Venture Capital Association, with 28 years in venture. His own record includes Palo Alto Networks, Upwork, Poshmark and Rover, and Menlo's portfolio includes Anthropic.
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1 of 2 framework
Venky Ganesan of Menlo Ventures argues venture now has to beat a no-fee, no-carry index by 1,000 basis points of IRR, because every venture company pays a toll to Nvidia, the hyperscalers and possibly the model labs, all listed or soon to be.
When he started 28 years ago, venture investors watched cash-on-cash multiples and let IRR take care of itself. Today, he says, no venture company succeeds without the Magnificent Seven taking part in everything it does, and since there is no IRR without DPI, the hurdle turns on how fast a fund returns cash.
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2 of 2 framework
Venky Ganesan of Menlo Ventures tells LPs that a venture firm's track record lags by five to seven years, and suggests they ask successful AI founders which partners they respect, even where they did not take the money.
Returns, in his words, are the rear-view mirror: good at describing what a firm did, silent on what it will do next. His windshield is a partner's standing with the founders building the companies that matter now, and a firm with few such partners in the mix is, in his reading, showing where it is headed.
Animal Spirits
Sudden Wealth Syndrome | Animal Spirits 485
A two-host episode: Michael Batnick and Ben Carlson, both of Ritholtz Wealth Management, work through the week's markets, rates and private-markets charts.
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1 of 2 current issue
Animal Spirits points to the FT's history of capex booms, across railways, 1920s utilities, Japan, dotcom and shale, in which stocks rolled over first and capital spending slowed months later.
One host argues the fall in prices is a cause as well as a signal, because it raises the cost of funding the next round; someone at Barclays, quoted in the New York Times, adds that if a less rate-sensitive part of the economy drives growth, the Fed has to squeeze the rate-sensitive parts led by housing. So far the money keeps coming: Paramount sought $52 billion of debt and drew about $150 billion of orders.
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2 of 2 current issue
Animal Spirits puts Invesco's high-beta S&P 500 fund up 260% this decade against 40% for its low-volatility sibling, and reckons the gap is about the widest in 30 years.
Assets have gone the other way, with roughly seven times as much money in the defensive product. Rising yields hit that dividend-heavy end of the market twice, through what the businesses pay to borrow and through a 3% income stream that now competes with bonds.
Alt Goes Mainstream
Josh PristawClarion Partners
Josh Pristaw is president of Clarion Partners, the $73.3 billion real estate investment firm within Franklin Templeton's alternatives platform, with about 95% of its capital in open-end funds and $42 billion in US industrial property. He has invested in real estate since 1997, including 17 years running a Brazil business and a period at a single-family rental operator with 100,000 homes.
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1 of 3 explainer
Clarion Partners finds home sales the strongest driver of self-storage rent growth, and expects the roughly 25 million to 27 million US homes owned by seniors to sell within 5 to 10 years as their owners age out of living alone.
About 80% of Americans over 75 own their home, roughly 10 percentage points more than the previous generation. Pristaw corrects the host on what releases those homes: owners who can no longer live alone will sell whether or not new senior housing gets built, which ties the timing to age.
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2 of 3 explainer
Clarion Partners, the third-largest owner of US industrial property, ties warehouse demand to e-commerce sales, expected to run $1 trillion a year higher in a decade, and expects robotics to change which warehouses hold their value.
Its research finds a dollar of online sales the strongest driver of demand per square foot. Robots picking from height keep pushing single-storey buildings taller and need flatter floors that carry more weight, and Pristaw can see two warehouses that look alike in ten years differing widely in value on access to power.
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3 of 3 explainer
Clarion Partners' Josh Pristaw names the hardest part of running an evergreen real estate fund as matching flows to strategy: institutional money arrives quarterly with long notice, while wealth-channel money can move every day.
Asked for the most underappreciated skill, he pairs active portfolio management with a capital structure that allows it. His name for the alternative is computer real estate: a spreadsheet shows a sale and reinvestment earning an extra 10 basis points a year, and a debt covenant, someone's consent or a concentration limit blocks it.
Excess Returns
Tom MaherHilton Capital Management
Tom Maher manages Hilton Capital Management's small and mid-cap opportunity strategy, which holds 50 to 75 stocks and takes its size range from the Russell 2500.
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1 of 2 current issue
Hilton Capital's Tom Maher says Sandisk was still classed as a mid cap at more than $300 billion until late June, because the Russell index was reconstituted only once a year.
Maher defines Hilton's range by the Russell 2500, with the top at roughly $25 billion to $30 billion today and about 20% of the portfolio below $4 billion. Russell is changing how often it rebalances, which should shorten the time a winner can sit in the index far above its size band.
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2 of 2 explainer
Tom Maher of Hilton Capital says a small cap placed in a thematic ETF beside large caps can move with the basket for six to twelve months, whatever its own results.
Flows buy or sell every constituent, and the less liquid small cap may move furthest because it trades in smaller value. When a holding behaves oddly, Maher checks whether its fundamentals have changed or a basket it sits in is being sold, and treats the second as a possible chance to buy before the business pulls away from its cohort.
AI 8 October 2026 · 30 clips
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Kevin MandiaArmadin
Kevin Mandia is founder and chief executive of Armadin, which attacks customers' systems with AI to find exploitable vulnerabilities before adversaries do. He has spent 30 years in security and built Mandiant, which he started self-funded in 2004 and which ended up inside Google.
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1 of 4 framework
Kevin Mandia thinks AI turns cyber attack from the nation-state sniper round, a few targets hit deep, into a drone swarm that is noisier and sloppier but covers far more ground.
States have tended to pick a short list, 30 defence contractors for instance, and go deep, and an AI agent cannot yet work that quietly without heavy post-training and, he suggests, a person in the loop. Swarming hands that reach to less capable, less technical attackers, who will look far more successful on volume alone, and makes attribution a little harder: defenders will have some clue, but not always whether a nation or an individual is behind an intrusion.
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2 of 4 current issue
Kevin Mandia says AI favours attackers now and defenders later, and he knows of no large enterprise that is not already scanning itself for exposure in real time.
Attackers in Iran or Russia feel pressed to get in now, while defenders try to patch every window, and inside companies the response has become a war room across the CIO, the security team, product teams and business lines. One of the most sophisticated companies discussed moved a large share of its engineers and researchers onto fortifying its own systems.
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3 of 4 prediction
Kevin Mandia expects AI to run prevention, detection and response in security operations, because a human in the detect-and-respond loop will be too slow.
Once inside a network with internal command and control, Armadin's own agents spread at a speed he calls shocking, a thousand actions at once where a human operator types one lateral move at a time. Each window keeps narrowing until events move too fast for a person to act on them, and he thinks whole processes in the SOC will go away.
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4 of 4 contrarian
Armadin ran open-weight models and the most advanced closed models through 24 kill chains drawn from real intrusions and found every model stalled at the same point.
The closed models got there faster and the open ones caught up when left to run longer, so what separated them was speed and cost. Red teamers and exploit developers write most of Armadin's evaluations, on Mandia's argument that a team without a cyber background will miss what an attacking agent does until experienced attackers have looked at the evals.
Training Data (Sequoia)
Amin VahdatGoogle
Amin Vahdat is Google's Chief Technologist for AI Infrastructure, named head of its AI infrastructure at the end of last year, at a company the host expects to spend more than $200 billion on capital expenditure this year, most of it on data centres.
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1 of 2 current issue
Google's Amin Vahdat says Google must double its effective token-serving capacity roughly every six months, and expects as much or more of that to come from software as from new chips.
He has no exact split, but says that in Google's experience most of the gain in intelligence per watt comes from model improvements, with systems software making sure installed hardware is used. Hardware can still deliver 2x or more a year, a multiplier every layer above it can count on.
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2 of 2 framework
Google split its TPU line into separate inference and training chips for the first time in 2026, once it judged inference might reach 30% to 60% of the market over the chips' lifetime.
Vahdat says a chip twice as fast for serving might not have been worth building had inference been projected at 2% or 5% of demand, because one general-purpose design buys uniformity. The split works partly because each chip can still run the other's workload, so a wrong forecast over six years of hardware life leaves capacity usable.
The Cognitive Revolution
Woodson MartinOutSystems
Chief executive of OutSystems since 2025, after 18 years at Salesforce. OutSystems, founded in 2001, runs mission-critical applications for customers including Petrobras, Vodafone and Toyota.
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1 of 2 current issue
Woodson Martin says OutSystems went from four major feature releases a quarter to 26 by moving its engineering onto AI, then took token spend below forecast with its own harness and a model router.
Martin's explanation is that most of the work never needed a frontier model: on his view enterprise workloads run well on models three years old, on open weights or on plain deterministic code. He dates the industry's reckoning to February and March, when CFOs got the bill and companies began capping per-person use and routing jobs to cheaper models.
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2 of 2 current issue
Woodson Martin says OutSystems customers have agentic systems built and tested that sit in compliance queues waiting for approval of the model underneath, some of them doing no more than turning PDFs into structured data.
One question holding them is provenance: whether the data the model was trained on was legally acquired. Martin reads much of the caution as prudent, because a bank takes a different risk on allocating IT resources to staff than on loan origination, where regulators can contest each decision and every step has to be auditable.
Unsupervised Learning
Yash PatilApplied Compute
Yash Patil runs Applied Compute, a 16-month-old company that post-trains and serves models for AI application companies, after working at OpenAI on Codex.
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current issue
Yash Patil of Applied Compute says OpenRouter statistics show usage spiking whenever a model's price is cut, and that he underestimated how much cost would outweigh capability in what buyers choose.
Patil reads it as Jevons paradox at work. He would say most open models can now roughly do what customers want, so once teams finish experimenting they consolidate on the right model for each task, though he concedes frontier models still lead on frontier work.
Worth listening to in full 8 October 2026
Most clips above stand alone. These are the episodes that justify the whole hour.
Google's Head of AI Infra, Amin Vahdat, on the Architecture of Intelligence
Vahdat leads one of the most capital-intensive AI build-outs under way, and the hour holds one argument the whole way: how failure rates, power, agent workloads and the forecast mix of inference and training now shape what gets built, with numbers at each step (failures several times a day at 100,000 accelerators, capacity doubling every six months, inference projected at perhaps 30% to 60% of the market, two gigawatts needed for every one if Google generated its own power at 99.99%, seven- and eight-year-old chips fully used). The stretches between the clips, on co-design with DeepMind, optical switching and orbital compute, carry the same substance. The open-standards segment is the one stretch of positioning.
5% Rates, Bessent's Failed Gamble & What Comes Next w/ Krishna Guha | The Real Eisman Playbook Ep 78
Forty-nine minutes with Evercore's Krishna Guha working one question through: why the 10-year Treasury yield has climbed to around 5.25% and where it starts to bite. He attributes the move to AI borrowing alongside a deficit of 6% of GDP, a higher normal rate and oil; argues that Kevin Warsh dropping the broader explanation of the Fed's reaction function has helped long yields overshoot; reads Scott Bessent's buybacks as having failed to cap yields; and puts the strain on the non-AI economy first, with AI investment holding until long rates reach something like 6%.