David Cahn
@DavidCahn6
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Partner at Sequoia
Palo Alto
Joined June 2022
6/ A lot of people have been talking about how the AI CapEx buildout is debt financed, but in reality, what’s interesting about the circular deals is precisely that they are not debt financed. For now, debt investors are unwilling to bet on data center demand 15-years out
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China’s influence in Latin America is deeper than ever. Since joining the WTO in 2001, China’s trade with the region has surged, reaching $518 billion in 2024 and overtaking the U.S. as South America’s top trading partner. But it’s not just trade: China has financed over $286
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5/ All of these deals are quoted in GW, which obfuscates just how massive they are. And how much money is still needed to a) Pay for them, and then b) Generate an ROI. Below is a table that converts from GW to $:
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4/ But these companies, even combined, are much smaller than Microsoft and Amazon, and so now Nvidia, AMD and Broadcom are stepping up to absorb some demand risk, too. There is also a sense in which these deals are “priming the pump” to attract further investment.
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3/ At that time, the hyperscalers were funding everything and taking on all the risk, driving the supply chain to greater and greater output. What’s changed this year is that Microsoft and Amazon have stepped back, leaving Oracle and Coreweave to fill the void.
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2/ Last year, in “The AI Supply Chain Tug of War”, I wrote about pairwise game theoretic dynamics up and down the supply chain. “Each player wants to maximize profit while minimizing risk. This creates supply chain conflict, which lurks behind the scenes”
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New Post: Circular Deals & Supply Chain Dynamics 1/ The big story of 2025 has been the direct transfer of data center demand risk from hyperscalers to NeoClouds and chip companies ("the demand hot potato"). Circular deals crystallize this dynamic.
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In the context of AGI, Deus ex machina now has a double meaning: the literal creation of a new godlike intelligence, but also the only path forward to resolve an increasingly tricky ROI problem.
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Deus ex machina (“god from the machine”) refers to a plot device where an unsolvable problem is suddenly resolved by an unexpected intervention...
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There are a few reasons for this, but the biggest is that PhD programs are being starved for talent, and it's very hard to make true low probability, divergent research bets. But this type of divergent search is required for scientific progress.
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This is surprising, since AI luminaries have been walking back their AGI timelines. If anything, overinvestment in compute may reduce the probability of reaching AGI over a longer time horizon (ten to twenty years).
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New post: AI CapEx Now Hinges on Deus ex machina With talk of 100 GW or 250 GW of energy buildout, a really big plot twist would be needed to make the math pencil out. Nothing short of AGI will be enough to justify the investments now being proposed for the coming decade.
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Bootstrapped to $1M ARR with 4 people. Now: $10M ARR, 2,500+ customers. Juicebox is more than a tool, it’s a new way of recruiting. Proud to back @davepaffenholz and @ishangpta—their grit & hustle is next-level.
Excited to announce Juicebox has raised $36M in funding, including our Series A led by Sequoia Capital (@sequoia) - to help the world’s leading teams win the talent war. More below 🧵
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Thousands of recruiters, including our own team, are already obsessed with @davepaffenholz and @ishangpta’s Juicebox. We’re excited to be leading their Series A. https://t.co/vYhIQoYsx0
techcrunch.com
The recruiting startup has over 2,500 customers, including recruiters at Perplexity, Ramp, and OpenAI.
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No one has more than ~5 years of LLM experience. That means young people can leapfrog — and “AI generalists” (native + versatile in AI) will define the next decade.
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AI is that change. Jobs are increasingly seen as either AI-good (on the right side of history) or AI-bad (on the wrong side). Big Tech & Wall Street are losing ground. AI-native startups are pulling talent in.
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For years, most grads followed what I call the Algorithm: 👉 Work wherever the smartest people 1–2 years ahead of you went. It’s mimetic, simple, and surprisingly effective. Until the world changes.
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I meet with 200–300 new grads every year. Their #1 question: Where should I work? In the age of AI, the old algorithm (follow the smartest people a year ahead of you) is breaking. Here's what I'm seeing 👇
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