15 October 2026
145 new episodes (137 hours listening) published by the 50 podcasts on the list. 39 highlight clips below (122 mins total).
Investment 15 October 2026 · 39 clips
Flirting with Models
Philip Maymin and Allan MayminJanus Henderson
Philip and Allan Maymin co-head global asset allocation at Janus Henderson, in the group Myron Scholes founded there more than a decade ago, and draw risk signals from option prices across more than 4,000 assets. Philip started at LTCM in the 1990s; Allan came through high-frequency trading, sell-side derivatives and prop trading.
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1 of 2 framework
Janus Henderson's Maymins read the 60/40 portfolio as a disguised drawdown budget: 60% in equities that might fall 50% in a crisis, with bonds assumed fairly uncorrelated, gives roughly the 30% loss an institution might reckon it can claw back from.
Once the drawdown is named as the real constraint, a static mix stops being the only way to meet it, and 2022 showed the bond leg falling alongside equities. Over 100 to 150 years stocks and bonds have more often moved together, which the team argues is what two asset classes with positive expected returns should do, so exposure is managed dynamically against the budget.
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2 of 2 framework
Janus Henderson's asset allocation group treats risk as the lever on compounding, citing a 1980 Merton result that an average return can take decades of data to tell apart from zero.
Compound return is roughly the average less a drag from risk, so enough noise on any positive alpha eventually loses money. Risk estimates sharpen as the same period is cut from monthly to daily to hourly, with no need to reach back to a 1970s IBM that no longer exists, and knowledge of managing changing environments accumulates where an alpha source can vanish.
Conversations with Institutional Investors
John PearceUniSuper
John Pearce was chief investment officer of UniSuper when this 2021 conversation was recorded, running a fund of over $100 billion with about 70% managed in-house. He started in the back office of Westpac Investment Management and earlier headed global asset management at Ping An.
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1 of 2 explainer
UniSuper held a portfolio insurance position with a face value of $9.6 billion on its defined benefit fund, bought in 2019 at around 1%, and did not cash it in when COVID marked it up into the billions.
Pearce compares cashing it in to surrendering a house insurance policy with the fire across the road. With the defined benefit fund in surplus and protected, the team could tell members to rest easy and buy more risk assets at the bottom of the market.
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2 of 2 framework
UniSuper found that running money itself turned manager meetings from an hour on the manager's favourite stocks into a two-way exchange where bluffing gets called, and a couple of managers have missed out on mandates because they were not comfortable sharing information.
Pearce keeps external manager selection inside the same team that runs the in-house portfolios, so the people questioning a manager are managing money too. The same capability lets UniSuper tailor mandates, such as a high-yield book barred from energy and an Asian bond portfolio barred from property developers before Evergrande.
Monetary Matters
Ben InkerGMO
Co-head of asset allocation at GMO, which manages more than $80 billion. GMO's Q3 2026 quarterly letter, the subject of this episode, argued that equity supply will be the catalyst that brings down the AI bubble, playing out over the next 12 to 18 months.
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1 of 4 prediction
Ben Inker puts the SpaceX listing and, if they follow, Anthropic's and OpenAI's at maybe 6% to 7.5% of US market cap reaching buyers within about 12 months, when no year on record has brought more than 4%.
Historically the market has done about 4% worse for each percentage point of capitalisation issued by IPO in a given calendar period, so 6% implies a return 24% below normal, and the three heaviest issuance years, 1999, 2000 and 2021, were each followed by difficult markets. Staggered lock-up expiries avoid a single 180-day cliff, but almost every early holder still gets the chance to sell.
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2 of 4 current issue
Ben Inker says high-yield and investment-grade spreads sit around the 13th percentile of history while AI borrowers are already paying a premium because lenders are running out of room to diversify.
A lender makes some money if things go fantastically well and loses a lot if they go badly, so credit cannot make the all-in bet on AI that an equity investor might. If all the slated investment arrives, AI's share of corporate debt would exceed anything in the modern history of credit markets; only in the railroad era was nearly all debt railroad debt.
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3 of 4 prediction
Ben Inker sketches how a cut in training spend could leave the world awash in compute, idle five-year-old data centres and take the market down 35% to 50%, which he is not sure will happen but says would be easy to.
His illustration, a number he says may be wrong, has half of today's spend going to training new models; if that falls to a tenth because the return on the spend is not coming in, the world goes from short of compute to awash in it. Defaults on the credit side and failures on the equity side follow, with the hyperscalers' return on investment turning out lousy.
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4 of 4 prediction
Ben Inker notes that the internet bubble rolled over before anyone could see the scale of the malinvestment, and says AI could easily follow the same order because data centres are planned years ahead of uncertain demand.
Inker says AI is already pulling the economy in: with rates where they are, housing and most non-residential construction are close to dead and investment outside AI is falling, while data centres are unaffected. He prefers to invest where others are not, because scarcity is what earns a return.
Capital Allocators
Erlina Yeo TerwelpColumbia Investment Management Company
Managing Director at Columbia Investment Management Company, where she has spent about nine years and now oversees the endowment's private markets portfolio, on a 15-person investment team. She came to it through investment banking at Citi from 2006, private equity, growth equity and four years at a Houston family office.
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1 of 4 framework
Columbia's endowment ran its portfolio by risk factors years before total portfolio approach was common vocabulary, managing to an equity beta or a range much as a hedge fund would.
Every opportunity was reduced to its cleanest form: its equity beta, its liquidity and the cost of that liquidity, and its leverage, with very little filling of asset-class buckets. Erlina Yeo Terwelp says the data let the team move quickly, particularly on the public side.
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2 of 4 framework
Columbia's Erlina Yeo Terwelp reads a manager's carry allocation as a sign of where the firm sits in its life cycle, and treats carry that does not reflect the talent in the organisation as grounds to leave.
Fund size growing beyond what the strategy can absorb is the most common reason she gives, treated as arithmetic and analysed afresh even when a fund doubles or triples; the others are a partnership nearing the end of its run and, rarely, information from the manager that proved inaccurate. Staying too long is among the mistakes she owns.
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3 of 4 current issue
Columbia is still growing its private markets programme, with little venture exposure and comfortable unfunded commitments, and is spreading venture across vintages because many 2026 rounds are pricing at multiples of six months earlier.
Erlina Yeo Terwelp argues that companies staying private longer keeps private markets attractive, and that public markets outrunning private ones leaves Columbia room to add. She expects fortunes to be made in the 2026 vintage and wants to be in 2027 and 2028 as well, since the next Nvidia may not be built until 2028.
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4 of 4 framework
Erlina Yeo Terwelp, who oversees Columbia's private markets portfolio, names higher interest rates as the largest left-tail risk in buyouts, and re-underwrites managers on how much of their value creation came from operating margins and growth.
Dearer debt makes the arithmetic harder and leaves revenue and margin expansion to carry returns, so the question is whether a firm's playbook repeats. Her second worry is groupthink in venture, where many investors hold the same companies; the endowment offsets it with buyout funds in high-asset, low-obsolescence (HALO) businesses, a sleeve it set out to build five years ago.
Merryn Talks Money
Adam RozencwajgGoehring & Rozencwajg
Co-founder and portfolio manager at Goehring & Rozencwajg, which runs a natural resource equities strategy; he has run it for about 20 years, alongside a partner with 35 years in the sector.
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1 of 2 current issue
Adam Rozencwajg argues that the 6 million barrels a day of oil demand said to have vanished since the Strait of Hormuz closed is lost refining capacity, not lost consumption.
Demand is modelled mainly from refinery intake, and the war has removed 6 million barrels a day of refining: 2 million in Russia from Ukrainian strikes, 2 million in the Gulf and 2 million in China by policy. A real fall that size would rival COVID while GDP prints stay positive almost everywhere, so he reads a crude shortage that has yet to reach the price.
Audio streamed from the publisher.
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2 of 2 contrarian
Adam Rozencwajg says US shale has peaked, with growth slowing every year from 2020 and now close to an outright fall, after the firm's own neural network on well productivity called the slowdown in 2019 and 2020.
The firm had used the same Hubbert-style basin analysis in the early 2000s to call the roll-over of the North Sea and Cantarell. The 1970s set the precedent he leans on: oil rose 15-fold and US producers drilled four times as many wells, yet the country still lost 30% of its production.
Audio streamed from the publisher.
Invest Like the Best
David VélezNubank
David Vélez founded Nubank in 2013 and runs it as chief executive. It serves 140 million customers with 10,000 employees, including close to 120 million in Brazil, upwards of 65% of the adult population, and he worked as an investor before starting it.
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1 of 2 current issue
Nubank's David Vélez puts the cost of US banks' deposit pricing to savers at about $600 billion a year: roughly $15 trillion of retail deposits earning an average 40 basis points while policy rates sit near 4.5% to 5%.
He calls it a wealth tax that pays for branch networks and falls hardest on lower-income savers, since high-income customers do get paid a rate. Remittance fees of 5% to 10% on about $100 billion leaving the US are the same kind of profit pool, and he expects both to be competed towards zero once moving money is simply moving bits.
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2 of 2 current issue
Nubank's David Vélez says the bank missed the December 2025 jump in coding models by 60 days, then set about rebuilding 120 workflows from compliance to regulatory capital around AI and cut its management layers from 10 to five.
Its engineers had written the models off because they failed on Clojure, a language the bank uses heavily, until tests run after a trip to California showed they now handled it well. Nubank built evaluation infrastructure to measure each workflow at every step of its reinvention, and the redesign allowed wider spans of control, to the point that Vélez went from one direct report to 20.
Excess Returns
Barry Ritholtz and Dave NadigRitholtz Wealth Management; etf.com
Barry Ritholtz is with Ritholtz Wealth Management and writes the blog The Big Picture at ritholtz.com. Dave Nadig's work appears at etf.com.
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1 of 2 current issue
Excess Returns' Click Beta panel cites 83% of S&P 500 members down 10% from their 52-week highs in the third quarter, and 42% down 20%, with the index itself at all-time highs.
The panellist reading the figures is untroubled by Mag 7 concentration as such, since those seven grew through 891 acquisitions over 20 years and each could be split into 10 or 20 index members. The narrowing participation underneath is what he reads as a warning, though not as the end of the bull market.
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2 of 2 current issue
Excess Returns' Click Beta panel puts SpaceX, Anthropic and OpenAI at a combined $5.2 trillion if the next two list at the valuations now discussed, against $4.1 trillion for every US tech IPO from 1980 to 2025.
One panellist thinks absorbing trillions of dollars of new AI and tech market cap reaches beyond the buyers, since those prices become collateral and anchor large indexes, and that businesses without 100% forward growth rates will be starved of capital. The panel concedes the comparison is not inflation-adjusted and that the listings could come at half those valuations.
Hidden Forces
Jim BiancoBianco Research
Founder of Bianco Research and Bianco Advisors, a shop of fewer than ten people that writes on bonds, the economy and inflation for professional investors. Here he explains why he has turned bullish on bonds for the first time in six years, after arguing they were overvalued.
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current issue
JPMorgan's list of 41 AI-related companies makes up 49% of the S&P 500 by value, and Jim Bianco says the last time a single theme held more than half the US market was probably the railroad boom of the 19th century.
Adding SpaceX, OpenAI and Anthropic once they float and qualify would take the share to about 55%. On his count 70% of US stock gains from ChatGPT's launch on 29 November 2022 came from those companies, and foreign capital keeps arriving because neither Asia nor Europe offers the same exposure.
Excess Returns
Ed Yardeni, Chris Davis, Robert Hagstrom, Katie Stockton and Tom Maher
Each appears in an excerpt from a longer interview the show ran this week. Chris Davis of Davis Funds and Robert Hagstrom have been friends for some 40 years; Tom Maher manages small and mid-cap stocks.
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contrarian
Chris Davis suspects, on what he calls a tinfoil-hat theory, that venture investors keep companies private longer so that index funds become an automatic buyer of half the company at any price.
Indexing, on his account, multiplies momentum: the more a stock rises the more weight it takes, and money moving from active to passive amplifies that, so at the end of a bull market the index is in a way a momentum fund. Venture owners holding 80% to 90% of SpaceX, Anthropic or OpenAI would meet a ready buyer for 50% to 60% once they list.
All-In
Martin Shkreli
Martin Shkreli started at Jim Cramer's hedge fund at 16, later started his own fund, and built two drug companies on molecules bought from large pharma; he named sparsentan, now FDA-approved. He has since served a prison sentence, which the episode discusses.
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explainer
Martin Shkreli says big pharma sold molecules far below their worth, and that his first drug company started on a $2 million purchase from Bristol-Myers Squibb and is now worth about $6 billion.
He says the sellers did not care: Pfizer would part with a drug worth a billion dollars for a million. He found the alpha in that market for molecules rather than in public companies, and the constraint was finding people to vet the assets.
Top Traders Unplugged
Andrew Beer
A regular on Top Traders Unplugged's Systematic Investor series who runs a managed futures ETF. He graduated from Harvard Business School in 1994 and has started four asset management businesses, the current one run by seven people.
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framework
Andrew Beer says capital market assumptions amount to 40 market calls plus their correlations, anchored on recent history, and that rebalancing at the end of 2021 had allocators buying bonds at the top of a 20-year bubble.
A late-1990s academic study found commodity futures gave equity-like returns with negative correlation to equities, and that correlation flipped positive by the mid-2000s. The 60/40 portfolio rests on a similar window, when stocks and bonds moved opposite ways and bonds rarely fell; at end-2021 equities were up 28% for the year while bonds were flat and yielding close to zero, after a year of talk about inflation.
Big Technology Podcast
Ranjan RoyMargins
Ranjan Roy of Margins joins the show every Friday for its weekly discussion of technology news.
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current issue
A Financial Times report putting OpenAI's annualised revenue this year at $50 billion rather than $70 billion knocked roughly 3% to 8% off Nvidia, Oracle, CoreWeave and AMD, and the hosts put the gap down to accounting method.
The earlier $70 billion was some OpenAI investors' tally, counting sales through cloud vendors such as Google Cloud the way Anthropic counts its own; OpenAI's own method leaves them out. The hosts argue those partner sales belong in sales and marketing expense, not top-line revenue, which leaves the two labs' revenue figures on different bases.
The Investor's Podcast Network
Michael MoritzSequoia Capital
Sir Michael Moritz spent about 38 years at Sequoia Capital, leading or co-leading the firm for around 16 of them, and worked on its early investments in Google, YouTube, PayPal, Airbnb, LinkedIn and Stripe. Before that he was at Time magazine from about 1979 to 1984, latterly in San Francisco covering Apple and Microsoft.
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framework
Michael Moritz says that when someone suggests selling a stock because it trades at an all-time high, he asks how many days Amazon has traded at one in its 30 or so years as a public company.
Charlie Munger, as the host reads him, said Moritz was not the leading intellect among Sequoia's early partners but did better than all of them because he held on to everything. Moritz replies that most investors, Berkshire Hathaway aside, are congenitally unable to do nothing.
Insightful Investor
Al ChuMan Group
Al Chu is a natural resources portfolio manager at Man Group, a $253 billion alternatives manager as at June 2026, which he joined in 2023 after senior investment roles at BNY Mellon, Citadel, SAC Capital and Caxton. He has covered energy for 26 years.
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1 of 2 explainer
Man Group's Al Chu traces tight natural gas, with outages from Iran bidding up LNG, through to next season's crops: in a scarce winter the gas would go to heating before fertiliser plants, and crops short of fertiliser in spring would yield less.
Chu puts energy under food as well as power, citing a figure of about three tablespoons of diesel to grow a single cherry tomato. Links between assets that stay hidden while supply is ample show through once it tightens, and he finds some of the best investment ideas there.
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2 of 2 explainer
Man Group's Al Chu shows how a bullish gold call can be right by $1,000 an ounce and still barely pay through futures rolled in contango, while an unhedged miner earns more as it sells into the higher curve.
Physical bullion avoids the roll but carries storage and insurance, and returns one bar for one bar. A miner owns metal in the ground, is often lightly hedged and turns a rising curve into cash flow, dividends and debt repayment while it acquires, discovers or is acquired; the cost is stock-specific risk, such as a mine that disappoints while the metal rallies.
Alt Goes Mainstream
Jeb AltonagaArcesium
SVP of business development for private markets at Arcesium, after 13 years in hedge funds including Citadel, a seat at the family office Blue Pool Capital, bank roles and building a boutique investment bank.
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contrarian
Arcesium's Jeb Altonaga believes the BDC run was about exits rather than the loans: investors ran when somebody shouted fire and there were not enough exits.
His remedy is matching the lock-up to the life of the paper, because a retail buyer drawn by a 14% yield rarely reads what a 5% gate means. A Hong Kong event-driven fund seeded with $250 million by Blackstone grew to $1 billion on an 18-month lock against a five-year horizon, fell back to $250 million when unlocked investors ran during a drawdown, and still hit its target over five years.
Excess Returns
Brent KochubaSpotGamma
Brent Kochuba of SpotGamma, which sells options-flow and dealer-positioning analysis, joins Excess Returns each month ahead of options expiration to read how options hedging is moving the stocks underneath the index. By his account a third of SpotGamma's members are outside the US.
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current issue
SpotGamma's Brent Kochuba puts zero-day options at something like 70% or more of combined S&P 500 and Nasdaq index options volume, and expiries of five days or less at 85% or more of QQQ volume.
Kochuba names retail sellers of index options, algorithmic strips he thinks come from pod shops or high-frequency firms, and dispersion trades of index against single-stock options. He finds it remarkable that none of this flow has broken the market, the tariff drama included.
Animal Spirits
Are Higher Rates Worse Than ZIRP? | Animal Spirits 486
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1 of 2 current issue
Animal Spirits reruns a July 2018 count: the five largest US stocks then equalled the bottom 282 members of the S&P 500 by market value, and today they equal the bottom 434.
Profits have kept pace with the weights: the top five's trailing 12-month net income equals that of the bottom 436 companies. The hosts add Michael Mauboussin's finding that wide breadth, with many stocks beating the index, tends to arrive in bear markets, as it did in 2001, and point out that small caps, mid caps and the equal-weight index are all up double digits this year.
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2 of 2 current issue
Jason Furman's chart has US real construction spending on data centres up 65% over the year while almost every other category fell, manufacturing by 24%.
Animal Spirits suggests AI may be crowding out the rest of the build-out, and doubts the other categories recover quickly if AI investment slows, since building work cannot hand over leadership the way stocks can. Manufacturing is the category the tariff programme set out to bring back.
AI 15 October 2026 · 39 clips
Big Technology Podcast
Ranjan RoyMargins
Ranjan Roy of Margins joins the show every Friday for its weekly discussion of technology news.
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current issue
The Information reports that Microsoft has cut its planned internal Claude spend of at least $1 billion by more than a third, and Meta's Claude Code users have fallen from about 60,000 to about 30,000.
Saving cost and moving staff onto homegrown tools drove the Microsoft reduction, while Muse Code, built on Meta's own models, has passed 6,000 employees using it even as 28-day spend there still topped $105 million. The hosts note that two customers made up 25% of Anthropic's revenue at the end of 2025, and argue its IPO will turn on projected growth.
20VC
Mike Cannon-BrookesAtlassian
Co-founder and chief executive of Atlassian, the company behind Jira, Confluence, Trello and Loom, which generated $6.5 billion in ARR in 2026 and serves more than 350,000 customers, including over 85% of the Fortune 500.
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1 of 2 prediction
Mike Cannon-Brookes says most companies announcing their own model have fine-tuned a Chinese open-weight one, and a model said to cost a million dollars to train is usually someone else's billion-dollar model, tailored.
His test for a specialised model is whether it keeps beating the latest general model on price performance, which needs a continuing proprietary data set and a problem the general models do not absorb. Many startups that built their own models two or three years ago have, he thinks, been overtaken by the foundation models; harness design matters now, but better base models lift a lot of harnesses with them.
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2 of 2 contrarian
Atlassian keeps Chinese models out of production because some customers push back, though Mike Cannon-Brookes reports none against open models.
Atlassian runs Nvidia's Nemotron, Mistral and other open models instead, since the objection is to provenance and not openness. He doubts the line holds: a lot of US models are increasingly trained on Chinese ones without saying so, enterprises already run Linux with a large Chinese share of contributions, and turning down a cheaper, better model whose contents are known looks to him a hard long-term choice.
The Real Eisman Playbook
Gary Marcus
Gary Marcus is an AI critic and recurring guest on the show, the author of Taming Silicon Valley, who briefed senators and their staff on AI in 2023 and gave the framing remarks at a UN event on AI policy days before this recording.
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contrarian
Gary Marcus argues AI doomers have worked practically as a marketing arm for OpenAI and Anthropic, because every warning that the technology could kill everyone tells investors it is powerful.
Investors hear the power and discount the danger, and the safety groups raise their own money on the same story, so warnings that look independent end up validating the labs. Marcus thinks the funding Sam Altman and Dario Amodei have raised would have been smaller without them.
Latent Space
Valentin De Matos, Yazin and Marko Kraemer
The three finalists among 72 teams in Latent Space's Kill My SaaS contest, judged by AI Engineer's events team under its head of operations, Kelsey.
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current issue
AI Engineer was quoted $43,000 a year for software to manage speakers across its four conferences, and asked builders to clone it with coding agents instead.
The team used maybe a third of what the vendor sold, with no discount for the rest, and change requests often took something like three to six months to ship, or never shipped. After seven AI Engineer events, the team already knew how bad this category of software is.
a16z
Dhaval Adjodah and Owen HeMakerMaker.AI
Dhaval Adjodah and Owen He co-founded MakerMaker.AI, which is building a recursively self-improving AI system. Adjodah did a PhD in reinforcement learning at the MIT Media Lab; He did his PhD on continual learning and meta-learning and spent a few years as a research scientist at DeepMind.
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framework
MakerMaker's founders apply Amdahl's law to self-improving AI: where the digital step is a fifth of the work, even a millionfold speed-up on it saves at most that fifth, so drug discovery, they expect, stays paced by wet labs and clinical trials.
Coding and mathematics, where an idea can be checked quickly and cheaply, are where they expect gains to compound. Anywhere evaluation means moving atoms, the slowest physical step sets the pace, which they read as intelligence behaving like a vector with different growth rates by field.
a16z
Nathan LambertTrillium
AI researcher and founder of Trillium, a new nonprofit research lab, who previously co-led post-training at AI2. He writes the Interconnects newsletter.
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1 of 3 explainer
Nathan Lambert relays that an average OpenAI researcher has about 5,000 H100-equivalent GPUs on call, a figure that stuns top Chinese pre-training researchers, and puts the gap between Chinese labs and the closed frontier labs mainly down to compute, with talent close to even.
A model's outputs are a function of the compute behind them, which makes distilling a stronger model a way of buying compute indirectly. Lambert's evidence that the effect is modest comes from open post-training work: training on a strong model's reasoning traces has not reliably lifted results, and fully open recipes are still stuck on an outdated Qwen teacher, so he credits distillation with a few months of progress.
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2 of 3 framework
Nathan Lambert argues open and closed AI models run on different clocks: closed labs reach the high-value markets first, as coding revenue has gone from zero towards $100 billion, while the open economy builds slowly towards companies owning their models.
Closed labs own the whole chain, serving at scale and seeing what customers need, while in the open economy the model builder, the inference host and the fine-tuning shop are separate firms, customer information does not flow back, and margins are thinner. What comes out the other end is weights a company runs on its own hardware with no terms and conditions.
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3 of 3 explainer
Nathan Lambert argues much of the frontier progress outside maths and code comes from labs buying high-quality data and building worlds for models to practise in, and expects years of it, sector by sector.
Maths, code and distributed engineering are verifiable, so models can drive their own costs down there; biology, healthcare and law are not, and each needs someone at a lab to define the target behaviour and buy the data for it, which Lambert calls very capital intensive.
Worth listening to in full 15 October 2026
Most clips above stand alone. These are the episodes that justify the whole hour.
145: From the Archives – UniSuper's John Pearce
Forty-four minutes with the CIO of an Australian super fund of over $100 billion that runs about 70% of its assets in-house, holding one line throughout: what building internal capability changed, from manager meetings and mandate terms to asset-class buckets, a $9.6 billion tail hedge and the regulator's performance test. It dates from 2021, so the low-yield, China and stapling sections read as a record of that year's thinking; the in-house and governance material has not dated.
Why the AI Bubble Will Pop Before the Fundamental Cycle Turns | Ben Inker | GMO
Ben Inker holds one falsifiable argument for 70 minutes, that record IPO supply in a price-inelastic market ends the AI bubble before the overbuild is visible, and the host walks him through each support in turn: issuance history, passive ownership, credit concentration, profitability by company size and a crash scenario with numbers attached. The only padding is a short sponsor read and a teaser, and the whole bear case from a large allocator arrives in one sitting.