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Invest Like the Best with Patrick O'Shaughnessy

Alex Sacerdote - How to Invest Through Technology Cycles - [Invest Like the Best, EP.477]

71 min episode · 3 min read
·
Alex Sacerdote

Episode

71 min

Read time

3 min

Topics

Remote Work, Investing, Startups

AI-Generated Summary

Key Takeaways

  • S-Curve Timing Framework: Technology adoption follows a predictable flat-then-vertical pattern. Barriers must be eliminated before the "tornado of demand" triggers. iPhone needed touch screen, 3G, and sub-$200 pricing simultaneously. Investors can miss the first 100% gain and still profit enormously if the S-curve top is large enough — AWS reached $600B TAM from near-zero penetration over a decade.
  • AI Penetration Baseline: Sundar Pichai estimated only 10 basis points of global knowledge workers currently use AI in a meaningful, agentic way. Anthropic has roughly 14–15 million DAUs, but few use it deeply. Sacerdote projects penetration rising from 10bps to 2–5% within four years, calling the trajectory an "L-curve" — essentially vertical — rather than a traditional S-curve.
  • Modified Rule of 40 for AI Exposure: To evaluate companies in the AI era, Sacerdote uses a new metric: add the percentage of revenue derived from AI to the company's market share in that AI category. A company with 30% AI revenue and 30% category share scores 60 — strong. Most legacy software companies score under 5, signaling structural vulnerability despite high valuations.
  • Hardware Decommoditization: AI workloads grow 10x annually versus 25–40% in the cloud era, pushing every hardware component to physical limits. This creates pricing power across previously commodity markets: high-bandwidth memory, 40-layer PCBs, liquid-cooled server enclosures, and fiber optics. Celestica, for example, trades at 8x earnings while holding 60% share of cloud Ethernet switching — a critical AI networking bottleneck.
  • Private Market Access Strategy: To secure allocations in Anthropic's $180B round, WhaleRock built a 90-page research deck using Claude Code to analyze the coding market, then presented directly to management. The firm conducts 2,500–3,000 face-to-face management meetings annually, with 10–15% involving private companies. Long-term holding signals to VCs — who prefer buyers that stay through IPO — help secure above-weight allocations.

What It Covers

WhaleRock Capital founder Alex Sacerdote explains his three-part investment framework — S-curves, competitive advantage, and underappreciated earnings power — applied across AI infrastructure, foundational models, and enterprise software. He details his highest-conviction position in Anthropic at a $180B valuation, the decommoditization of hardware, and why enterprise software faces structural headwinds from AI disruption.

Key Questions Answered

  • S-Curve Timing Framework: Technology adoption follows a predictable flat-then-vertical pattern. Barriers must be eliminated before the "tornado of demand" triggers. iPhone needed touch screen, 3G, and sub-$200 pricing simultaneously. Investors can miss the first 100% gain and still profit enormously if the S-curve top is large enough — AWS reached $600B TAM from near-zero penetration over a decade.
  • AI Penetration Baseline: Sundar Pichai estimated only 10 basis points of global knowledge workers currently use AI in a meaningful, agentic way. Anthropic has roughly 14–15 million DAUs, but few use it deeply. Sacerdote projects penetration rising from 10bps to 2–5% within four years, calling the trajectory an "L-curve" — essentially vertical — rather than a traditional S-curve.
  • Modified Rule of 40 for AI Exposure: To evaluate companies in the AI era, Sacerdote uses a new metric: add the percentage of revenue derived from AI to the company's market share in that AI category. A company with 30% AI revenue and 30% category share scores 60 — strong. Most legacy software companies score under 5, signaling structural vulnerability despite high valuations.
  • Hardware Decommoditization: AI workloads grow 10x annually versus 25–40% in the cloud era, pushing every hardware component to physical limits. This creates pricing power across previously commodity markets: high-bandwidth memory, 40-layer PCBs, liquid-cooled server enclosures, and fiber optics. Celestica, for example, trades at 8x earnings while holding 60% share of cloud Ethernet switching — a critical AI networking bottleneck.
  • Private Market Access Strategy: To secure allocations in Anthropic's $180B round, WhaleRock built a 90-page research deck using Claude Code to analyze the coding market, then presented directly to management. The firm conducts 2,500–3,000 face-to-face management meetings annually, with 10–15% involving private companies. Long-term holding signals to VCs — who prefer buyers that stay through IPO — help secure above-weight allocations.
  • Enterprise Software Structural Risk: Legacy software companies face four compounding pressures: AI products generating under 2% of revenue, budget displacement as CIOs prioritize AI tokens with faster ROI, reduced pricing power, and potential seat reduction from AI-driven headcount cuts. The only partial offset is agents potentially operating inside incumbent platforms like Slack or Workday, solidifying them as data repositories rather than displacing them entirely.

Notable Moment

Sacerdote describes how coding became the decisive unlock for Anthropic's business. Within Anthropic itself, employees were spending $100 per day on tokens — roughly $30,000 annually. Multiplied across 20 million global coders, that implies a $500B market from coding alone, on technology that was only seven to nine months old at the time of calculation.

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Episode Transcript

I know firsthand how complex the tech stack is for asset managers, and seemingly every new tool and data source makes the problem even worse, adding more complexity, more headcount, and more risk. Ridgeline offers a better way forward, one unified platform that automates away all that complexity across portfolio accounting, reconciliation, reporting, trading, compliance, and more, all at scale. Ridgeline is revolutionizing investment management, helping ambitious firms scale faster, operate smarter, and stay ahead of the curve. See what Ridgeline can unlock for your firm. Schedule a demo at ridgelineapps.com. OpenAI, Cursor, Anthropic, Perplexity, and Vercel all have something in common. They all use Work OS. And here's why. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, SCIM, RBAC, and audit logs. That's where WorkOS comes in. Instead of spending months building these mission critical capabilities yourself, you can just use WorkOS APIs to gain all of them on day zero. That's why so many of the top AI teams you hear about already run on WorkOS. WorkOS is the fastest way to become enterprise ready and stay focused on what matters most, your product. Visit workos.com to get started. Felix by Rogo is a personal finance agent that turns a single prompt into finished client ready work using your firm's own templates, context, and standards. Send Felix an email like, take these comments and turn them for me, or update my tracker with the context of these emails, or run the ability to pay math on this buyer, and Felix sends back finished PowerPoint decks, Excel models, and sourced research. Felix works the way your team already does, delivering work quickly and accurately around the clock. Learn more at rogo.ai/felix. Hello, and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best. This show is an open ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. If you enjoy these conversations and wanna go deeper, check out Colossus, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus along with all of our podcasts at colossus.com. Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc. My guest today is Alex Sacerdote, founder of WhaleRock Capital Management. WhaleRock is a technology focused investment firm that manages more than $17,000,000,000 across hedge fund, loan only, and hybrid strategies. Over the past few years, it's been one of the best performing funds compounding at roughly 44% per year. Alex invests through a single lens that he has refined over twenty years. He looks for technology …

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Books, tools, and gear mentioned in this episode

SignalCast may earn commission on purchases via these links. As an Amazon Associate, SignalCast earns from qualifying purchases.

Gear

  • by Apple

    Barriers must be eliminated before the 'tornado of demand' triggers. iPhone needed touch screen, 3G, and sub-$200 pricing simultaneously.

Products

  • by Anthropic

    WhaleRock built a 90-page research deck using Claude Code to analyze the coding market, then presented directly to management.
  • The only partial offset is agents potentially operating inside incumbent platforms like Slack or Workday, solidifying them as data repositories rather than displacing them entirely.
  • The only partial offset is agents potentially operating inside incumbent platforms like Slack or Workday, solidifying them as data repositories rather than displacing them entirely.

company

  • He details his highest-conviction position in Anthropic at a $180B valuation, the decommoditization of hardware, and why enterprise software faces structural headwinds from AI disruption.
  • WhaleRock Capital founder Alex Sacerdote explains his three-part investment framework — S-curves, competitive advantage, and underappreciated earnings power.
  • AWS reached $600B TAM from near-zero penetration over a decade.
  • Celestica, for example, trades at 8x earnings while holding 60% share of cloud Ethernet switching — a critical AI networking bottleneck.

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