Alex Sacerdote

Sacerdote’s highest-conviction position is Anthropic, bought in the August 2025 round at the $180 valuation after passing on the $60B round (“the gross margins were negative and frankly we hadn’t seen coding explode”). The revenue ramp — “100 to a billion on the way to 9” — was “like nothing we’d ever seen before,” and the coding math alone underwrites it: people inside Anthropic spending $100/day on tokens annualizes to $20-30K per coder, times 20 million coders worldwide = “a half a trillion dollar market just from coding alone,” on 7-9 month old technology.

The foundational model layer began to look like a three-horse race and somewhat an oligopoly — Anthropic (enterprise), OpenAI (consumer), Gemini — echoing how three clouds came to underpin all of SaaS. Models are not commodities: “there’s tremendous differentiation within” (Anthropic for private equity and finance, Google for ingesting PDFs), and open-source can approach the frontier but “can’t leapfrog it, and then they kind of falter.”

Enterprise AI is “less than 1% penetrated — we call this an L curve, just straight up.” Only ~10 bips of knowledge workers truly use AI (per Sunder), heading to 1-2% or 3%, then 5%, then 15% within four years — yet compute is already sold out, with “Anthropic has half of what they need right now” before the take-up even starts. Mark Andre’s one certainty for the next four years: not enough compute.

The Whale Rock framework — S-curve + competitive advantage + underappreciated earnings power — bought Nvidia in 2023 at 4x earnings, Tesla in 2019 at 5x, and AWS “for free,” because “the world doesn’t think exponentially.” At around 30-40% penetration, exponential growth ends, the sell side catches up, and beats stop.

Whale Rock went from 40-50% of the portfolio in application software to net short software entering this year: AI products weren’t good enough to charge for, software fell down every CIO’s priority list (“they’re spending it on Anthropic tokens because there’s faster ROI there”), annual price hikes are now risky, and job cuts hurt seat counts. Half-baked offset: agents operating inside Slack or CRM could entrench the systems of record.

The infrastructure trade is the “decommoditization of the hardware industry”: AI workloads growing 10x/year push every server component to physical limits — Celestica (bought at 8x earnings as sole Google TPU server supplier, 50-60% of cloud Ethernet switching), 40-layer PCBs, Corning fiber (scale-up moving from copper to fiber “two to three X’s Corning’s opportunity”), power ASPs up 40% for the next four years. “We’re already 30% short” DRAM, NAND, and PCB supply.

Chief risk: if the leaders hit a wall, open source catches up and it might be a race to the bottom — probably won’t be good for model stocks, could be good for chips (“chip companies don’t care who wins”).

🔗 Original source & video: Why the AI Boom Is Just Getting Started

Leon’s macro sequencing: AI is “long-term… a highly deflationary force” — healthcare alone is ~18% of GDP and ripe for LLM disruption — but the short-term is sticky inflation first: chip/memory/infrastructure input costs are skyrocketing and software-engineer hiring rose 18% last month as the old economy staffs up to implement LLMs. Add robotics and “the labor market can look very different a few years from now” — “I do not envy” the Fed.

Alex’s adoption math is the core bull case: only ~10 basis points of 1 billion white-collar workers use AI in the advanced agentic way, and Claude Code has just 14 million DAUs — headed to 500 million — while those power users burn “a thousand times as much compute.” This isn’t S-curve adoption, “this is an L-curve, straight up,” and “we have half of what we need from a compute standpoint before it’s even started.”

Alex’s foundation-model economics put the capex-ROI debate to bed: Anthropic has gone 100M → 1B → 9B → $45B run rate, Anthropic + Open AI could hit “$200 billion of revenue” by year-end, and because they locked up compute early, incremental margins are enormous — “you could be looking at something that’s like 18 times earnings.”

Leon’s boldest sector call: WFE spend goes from ~$120-130B to $300B over 3-4 years as the industry shifts from one under-spending buyer (Taiwan Semi) to multiple spenders (Intel, Samsung, Hynix, Micron, SanDisk), with customer margins at 70-80% and LTAs reducing cyclicality. “Estimates are like 50 to 70% too low… I don’t know what the next 10% is, but I’m guessing the next 50 to 100 is up.”

Alex’s “golden age of hardware”: 40 years of commoditized $2,000 x86 servers are giving way to $300,000 racks that must be reinvented yearly — networking jumping 400G → 800G → 1.6T → 3.2T — yielding units +50%, ASPs +20-100%, gross margins +300-500bps, and “growing earnings 100% for the next 4 years.” Efficiency gains won’t kill the trade: tokens grow ~14x/year vs. chips improving 100-200%.

Picks: Alex owns TTMI (complex PCBs going from 10 to 120 layers, just won Nvidia, 40% defense incl. Iron Dome) and Google (“they’ve won AI… could easily be up 50%, I don’t see very much downside”). [Speaker?] likes Lam Research ($55B revenue potential on the memory boom, a mid-‘27/‘28 story) and tight analog — likely Infineon, Texas Instruments, likely Renesas.

On software, both are cautious but split by vertical: horizontal application layer has “a lot of trouble,” but data/infrastructure names like Datadog benefit — Anthropic uses its tools. Alex: the decline is “largely justified”; AI now tops the CIO’s list and token spend is taking software budget.

🔗 Original source & video: Intelligence as Infrastructure: How AI Is Rewiring the Economy