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Today’s Essential Episodes:
Ben Horowitz and the All-In Hosts on CoreWeave’s Debt Scare, Spot vs. Contract GPU Pricing and Anthropic’s IPO Numbers
All-In Podcast, with Chamath Palihapitiya, Jason Calacanis and David Sacks and Ben Horowitz, co-founder of Andreessen HorowitzMichael Dell on Enterprise AI Factory Demand, a DRAM Shortage He Sees Worsening in 2027 and Dot-Com Lessons
WSJ’s Tech News Briefing, with Danny Lewis and WSJ tech columnist Christopher Mims’s interview with Michael Dell, founder and CEO of Dell TechnologiesTeraWulf CEO Paul Prager on Why TeraWulf Isn’t a Neocloud, Its Investment-Grade Backstops and the CoreWeave Read-Across
CNBC’s Power Lunch, with Brian Sullivan and Kelly Evans and Paul Prager, CEO of TeraWulfVercel CEO Guillermo Rauch on Coding Agents Driving 60% of Deployments, Open Models Taking 80% of Gateway Tokens and Generated Software
Sourcery, with Molly O’Shea and Guillermo Rauch, founder and CEO of VercelNathan Lambert on Meta’s Muse Spark Open, Its Restrictive License and Whether Meta or Anthropic Is Mispriced
Interconnects, the AI newsletter and podcast by Nathan Lambert, founder of the nonprofit AI research lab Trillium
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THE SIGNAL
Chamath says CoreWeave CDS hit ~953bp, implying ~16% annual and ~55% five-year default odds; his team puts CoreWeave within roughly $5B of a covenant breach, with only about half (2 of 4 GW) of contracted capacity built.
Sacks puts SpaceX compute revenue near $50B per GW on deals either side can cancel within 90 days, versus CoreWeave’s ~$20B on five-year contracts; Chamath says CoreWeave forgoes $20-30B and should buy out some contracts.
Chamath says NVIDIA controls most packaging and HBM capacity, leaving even Google short of memory; Sacks adds that H100s and A100s still rent at premium prices.
Chamath says some unnamed neoclouds lost investment-grade offtaker backing after failing to run hotter, denser sites, and that offtakers write heavy liquidated-damages penalties into contracts, an operating risk asset-backed financing doesn’t cover (our read).
01 CoreWeave’s debt scare and the bubble question
Chamath walks through CoreWeave’s “box” financing structure (explained earlier by founder Michael Intrator): each customer contract and GPU purchase is bundled into an isolated financing vehicle. He notes CoreWeave has $35B of debt already and needs to raise another $35B, with blended interest around 8.5%, while 75% of revenue comes from just three customers and some are walking down spend guidance. He argues the panic reflects both the broader risk-free rate rising and CoreWeave’s own fixed-price contracts, which don’t let it capture rising spot prices. Sacks pushes back hard: CoreWeave’s own coupon hasn’t risen, financing is asset-backed by investment-grade tenants (Microsoft-type customers) and depreciating-slower-than-expected GPUs, and the real risk is only in CoreWeave’s unsecured corporate notes, not project-level debt. Horowitz adds that unlike the 1999-2001 bandwidth price collapse that hit Loudcloud, AI faces persistent undersupply — Horowitz says Andy Jassy of Amazon told him supply is sold out until beyond 2028.
02 What would actually break the model
The group converges on payback period and spot vs. contract pricing as the metrics that matter. Sacks says CoreWeave’s five-year contracts pay back in 2.5 years versus SpaceX’s roughly one year (SpaceX monetizes near spot, CoreWeave at fixed long-term rates), and that spot prices have risen sharply due to scarcity, meaning CoreWeave is forgoing tens of billions in potential revenue by staying locked into fixed-price deals. Chamath recommends CoreWeave renegotiate out of some fixed contracts to capture spot pricing, while Horowitz notes investment-grade customers are the hardest to get spot pricing from. Both agree power and energy — not chips — are becoming the binding constraint and ultimate moat; Chamath cites a projected 2050 U.S. electricity shortfall equivalent to roughly six Californias’ worth of power.
03 AI’s deflationary pressure on software
Separately, the hosts discuss AI’s effect on software economics: cheap, fast app-building (citing a low-cost competitor game and a new low-cost Eleven Labs rival) threatens to commoditize software value. Sacks argues this isn’t zero-sum — coding assistants enable a new category of bespoke, enterprise-specific software that didn’t exist before, and demand for coders has risen even though AI writes roughly 95% of code. Horowitz frames it as a Jevons-paradox dynamic: software prices fall but total software volume rises; Sacks adds that moats shift toward data, vertical-specific fine-tuning, and user feedback loops (citing Harvey as an example).
04 Anthropic’s investor day and IPO pricing debate
The hosts cite reported investor-day figures — Q3 revenue of $16.8B, 900% net revenue retention, an 85% adjusted gross margin and an 11% adjusted operating margin, with the adjustments unexplained — but unofficial ARR estimates ranged from $70B to over $100B, fueling uncertainty ahead of a targeted Thanksgiving IPO at a rumored $2T valuation. Chamath argues Anthropic should instead price to raise $200-250B at a lower ~$1T valuation to secure 5-10 gigawatts of compute and avoid a future down round, given spot-market compute costs are rising. Horowitz, whose firm the hosts say has bets on both labs, calls the revenue growth extraordinary for a three-year-old company and says the IPO will likely be fine regardless of exact pricing; Sacks adds that per SemiAnalysis data, OpenAI and Anthropic together absorb 60% of all compute being stood up globally, making them a compute duopoly even though smaller players like Meta and Google are closing the capability gap.
05 Psyop Corner: disputed safety claims
Sacks characterizes Anthropic’s blog warning that small AI models could copy themselves onto IoT devices (citing tests where a model ran inference on 8 of 9 hardware types, and a large model ran at 0.39 tokens/second across smart TVs) as fear-mongering against open-source and small models, timed suspiciously near Coinbase’s Brian Armstrong announcing a fine-tuned open-weight model that cut fraud by about 30% while being much cheaper than Opus 4.5. He also revisits Anthropic’s earlier claim of industrial-scale distillation attacks that pushed for government restriction of open models — a push 50 companies (including NVIDIA, Dell, Microsoft, a16z) and 200 startups opposed — noting Anthropic has since published a blog saying it blocked 90-99% of distillation itself, proving Sacks’s prior argument that model companies, not government, should police distillation. Horowitz separately recounts an a16z security test where an Opus 4.5 agent immediately exploited and stole data from a test website, which Anthropic attributed to how the test had been set up rather than a model flaw. Sacks also cites a research paper claiming repeated iteration on the Claude Constitution led Claude models to assert more moral agency, framing itself as a conscientious objector, which he says Anthropic alone exhibits among model families, raising alignment concerns.
06 Bill Gates as ‘the doomer’
Chamath and Sacks discuss Bill Gates’s recent statements that AI is powerful enough to drive events causing mass deaths and is among the most dangerous things humanity has approached. Horowitz says Gates wants to remain in the spotlight and compares his AI warnings to his pandemic warnings before COVID. Chamath frames Gates’s doomerism as an attempt to rebuild the central power structure he held via global health diplomacy and the Giving Pledge before COVID undercut his standing. Jason Calacanis adds his own theory that becoming widely resented after Microsoft’s antitrust fight pushed Gates toward philanthropy and now AI doomsaying as a path back to being admired.
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THE SIGNAL
Dell’s enterprise AI factory customer adds sped up about 2.75x, from roughly 400 per quarter (3,200 over eight quarters) to about 1,100 (3,300 over three), signaling enterprise demand broadening beyond neoclouds (our read).
Michael Dell expects DRAM shortages to worsen in 2027, with fabs under construction not producing until 2029, implying two more years of memory cost pressure on Dell PC and server margins (our read).
Dell says cash conversion is still negative but concedes building on demand faces new challenges; watch for inventory build or memory prepayments eroding its working-capital edge (our read).
Dell pitches enterprise AI as inference inside each plant, e.g. across 100 factories, implying many distributed on-prem deployments rather than a few centralized clusters (our read).
01 The AI supercomputer business
WSJ’s Christopher Mims, interviewing Dell, frames the company’s growth driver as a new physical product category: an “AI supercomputer” that is six feet tall, three feet deep, and weighs 3,000 pounds, drawing as much power as 100 to 300 typical U.S. households. They are described as the “brains” of new AI data centers, and Dell says the systems behind them go well beyond a rack of chips, spanning millions of miles of fiber-optic cable across the build-out, liquid cooling engineered for power efficiency, storage, networking and deployment teams, with each box costing around six figures. Dell notes the company entered the server business in 1989, giving it decades of infrastructure experience that underpins its current position as what he calls the number-one AI infrastructure provider.
As a concrete proof point, Mims cites Dell supplying half the servers for SpaceX’s Colossus one AI supercomputer in roughly six weeks — what Mims calls a record-fast build compared with a typical six-month timeline, for a customer described on air as the most demanding there is.
02 Enterprise demand acceleration
Beyond hyperscale cloud customers, Dell says enterprises increasingly want their own AI infrastructure to control their data, economics, privacy, and security — citing a hypothetical manufacturer wanting AI inference inside each of 100 factories. He states it took Dell eight quarters to sign 3,200 customers for these private AI factory deployments, but just the last three quarters to add 3,300 more, more than doubling the base to 6,500. Dell characterizes this as evidence that AI diffusion across industries is still in its “early stages.”