Episode 98 and another unmissable Sunday Show! The AI trade is getting flushed. But is this the big one, or just another crowded-position unwind? We unpack the widening cracks in AI financing, why booming compute demand does not automatically mean booming revenue, and whether the S&P equal weight’s comeback signals a genuine market rotation.
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Pod Summary
The AI Unwind in Numbers
* The week’s equity weakness = a sharp reduction in crowded positioning, particularly around AI, rather than clear evidence of a broader market collapse.
* This is probably not “the big one”.
* Net leverage has been aggressively reduced, with positioning measures reaching the bottom percentiles
* Similar to the deleveraging seen from late June into late July.
* The damage within AI-related trades has been much more severe than the headline indices suggest
* The central distinction is between a modest-looking index drawdown and a substantial collapse in individual speculative trades.
AI financing: competition for capital becomes the constraint
* Only a small number of players can compete effectively for credit-market funding.
* Is access to financing is straightforward even for the largest companies?
* Borrowers are having to pay more.
* Beyond a certain point, a higher interest rate may not solve the problem because lenders hit capacity limits.
* This creates a potential dividing line within AI: the ability to fund infrastructure may matter as much as the ability to generate demand for it.
AI capex: compute demand is not external revenue (Minute 4)
* Is the bullish argument that extraordinary demand for compute, including strong Hopper rental rates, is sufficient to justify AI investment.
* Compute demand is not equivalent to revenue.
* The relevant question is how much revenue must ultimately come from customers outside the AI industry to support the infrastructure spending.
* Discussion of Paul Kedrosky’s AI capex revenue hurdle calculator
* It translates AI capital expenditure into a required level of external revenue.
* It then expresses that requirement relative to GDP, global software revenue and US white-collar salaries.
* The purpose is to test whether the economic assumptions behind the investment cycle are plausible.
* “Unlimited demand for compute” becomes less useful as an investment proposition unless it translates into real customer spending outside the sector.
* Are credit markets are beginning to confront precisely this issue?
AI usage versus spending (Minute 7)
* Rapidly rising usage need not produce equally rapid growth in expenditure.
* Better routing, system design and optimization can allow usage to grow geometrically while spending flattens or declines.
* The key issue is whether customer spending will grow enough to justify the capital already committed.
* The strategic motive for continued infrastructure investment.
* Large operators may be trying to become indispensable, establish a toll-road position or price competitors out of the market.
* In that scenario, the infrastructure owners could retain value even if the model developers face difficult economics.
* Own the compute rather than be Anthropic or OpenAI but compute ownership only works if the owner can continue accessing capital markets. That narrows the investable universe substantially.
* The hosts compare the possible outcome with a utility-like market in which a small number of dominant providers control access and capture the profits.
What happened to Anthropic’s S-1?
* Anthropic’s expected S-1 filing did not appear on Friday.
* Delayed disclosure may help preserve the AI narrative.
* Once detailed financial information becomes public, investors must assess the actual economics rather than an open-ended story.
* Differentiation in private debt markets between OpenAI-backed and Anthropic-backed data-centre projects.
* Some OpenAI-linked deals are reportedly not getting done.
* Where financing is available, he has heard of differences of approximately 200 to 300 basis points.
* He explicitly qualifies these observations as individual data points, not a comprehensive market survey.
The sell-off spreads beyond direct AI beneficiaries (Minute 10)
* The latest week’s weakness has extended further down the AI-related supply chain.
* Copper miners fell nearly 10%. Iron ore miners were also coming under pressure.
* The damage is moving from direct AI winners into second- and third-order beneficiaries.
* Even traditional value investors are beginning to feel the effects.
* He also sees increasing concern about slowing growth and a more stagflationary backdrop.
* But do these apparently macro-driven moves simply just instead reflect the mechanics of crowded-position unwinds.
Equal weight S&P versus Nasdaq (Minute 12)
* The ratio has staged a striking rebound over past five weeks.
* For it to extend, the semiconductors probably need to break down - but the weekly chart still in primary uptrend:
* Betting against semiconductors means betting against currently booked revenue and earnings, not just a speculative narrative.
* The hosts agree that “cheap” and “expensive” are difficult labels for highly cyclical businesses.
* A bullish memory-stock thesis requires a clear view on:
* The timing and scale of additional Chinese supply.
* How that supply affects pricing.
* Whether strong earnings can persist for another year or longer.
* Very low multiples could produce attractive returns if current profitability lasts.
* However, the thesis ultimately requires confidence in earnings further out, including 2028 and 2029.
* The broader point is that the durability of earnings matters more than the apparent cheapness of a single year’s multiple.
Industrials ‘20-for-20’ (Minute 18)
* The traditional portfolio role for industrials is challenged in current pricing environment.
* Industrials should ideally provide a steady base of compounding businesses.
* Many now carry substantial premiums associated with AI infrastructure, electricity constraints or defence spending.
* He revisits a hypothetical portfolio built from a 2006 starting point.
* A portfolio extrapolating then-current narratives might have owned auto companies, handset manufacturers and GE.
* A more successful hindsight portfolio could have included FANUC, Caterpillar and Schneider Electric.
* The lesson is not simply to identify good businesses, but to distinguish business quality from the price paid.
* Caterpillar is a classic case study - an “accidental bottleneck stock”.
* He sees a similar pattern across industrial businesses connected with physical power constraints or the broader AI theme.
* Defense stocks present a related problem.
* Their thematic narratives are powerful.
* That does not automatically make their starting valuations attractive for a portfolio assembled in 2026.
Gold miner cash generation (Minute 23)
* Popular narrative is that gold miners are “printing money” at $4,000 gold.
* Some companies may be generating substantial cash.
* That does not justify assuming the same economics across the sector or buying GDX indiscriminately.
* Discussion of Respeculator research on ASX-listed gold miners.
* The spread between gold prices and reported all-in sustaining costs can suggest margins of approximately $2,000 per ounce.
* Yet those apparent margins are not consistently appearing as incremental cash on company balance sheets.
* A major reason is spending classified as “growth capex”.
* Some growth spending, including mine extensions needed to keep mills supplied, is economically necessary rather than genuinely optional.
* He draws a direct comparison with AI accounting.
* Ignoring model-training costs (‘EBTIT’) flatters the economics of AI labs.
* Ignoring recurring mine-development requirements can similarly flatter the economics of gold producers.
* The analytical requirement is to identify which companies actually convert high gold prices into cash after all necessary spending.
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