Why the AI Boom Is Just Getting Started
When evaluating AI investments like Anthropic, focus on coding productivity as the unlock metric. Investors discovered that power users inside Anthropic were spending $100/day on tokens—translating to $20-30K annually. With 20 million coders worldwide, that's a half-trillion dollar market from codin
1h 20mKey Takeaway
When evaluating AI investments like Anthropic, focus on coding productivity as the unlock metric. Investors discovered that power users inside Anthropic were spending $100/day on tokens—translating to $20-30K annually. With 20 million coders worldwide, that's a half-trillion dollar market from coding alone. The key insight: when barriers to adoption disappear (no installation, just open a browser), adoption doesn't follow a traditional S-curve—it goes vertical like an L-curve. Look for moments when the fundamental friction evaporates.
Episode Overview
Alex discusses his firm's investment in Anthropic at a $18B valuation, explaining how they identified the coding market as a massive unlock for AI. He breaks down their investment framework based on S-curves, competitive advantages, and underappreciated earnings power, while explaining why foundational AI models have stronger moats than initially expected and how the enterprise AI market is less than 1% penetrated despite explosive growth.
Key Insights
The Coding Market Represents AI's First Killer App
While early AI models were impressive, the breakthrough came when coding tools evolved from basic assistants to truly agentic systems. Power users began spending $100/day on tokens, and developers like Andrej Karpathy completely stopped writing code except in English. With 20 million coders worldwide at $20-30K annual spending potential, coding alone represents a half-trillion dollar market before considering non-coders who can now build software.
AI Adoption Follows an 'L-Curve' Not an S-Curve
Unlike B2B technologies that require installation and integration (like dishwashers that must be 'plugged in'), AI can be accessed by simply opening a browser. This eliminates traditional adoption barriers, creating what they call a 'backwards L-curve'—nearly vertical growth rather than gradual S-curve adoption. The enterprise AI market is less than 1% penetrated despite this rapid growth trajectory.
Foundational Models Have Stronger Moats Than Expected
Initially, many predicted AI models would become commodities. Instead, significant differentiation emerged: different models excel at different tasks (Anthropic for finance/PE, Google for PDF ingestion), companies build 'harnesses' and ecosystems around their APIs creating lock-in, and the quality gap between leading-edge and open-source models (80% vs 85% on benchmarks) represents a huge unlock that open-source can't bridge due to compute limitations.
Infrastructure Investment Precedes Application Winners
The firm initially focused on chips and infrastructure (the 'picks and shovels') because those winners were identifiable early and would benefit regardless of which applications won. As clarity emerged on the foundational model layer over 2-3 years, they could then invest in companies like Anthropic. This staged approach reduced risk while maintaining exposure to the broader AI wave.
Exponential Thinking Enables Multi-Year Predictions
Most investors focus on the next quarter or year and don't believe you can predict 2-4 years out. But by understanding S-curves, competitive moats, and how to model exponential growth, you can accurately predict long-term trajectories. This allows buying exceptional companies at extremely low multiples (4x P/E for Nvidia in 2023, 5x for Tesla in 2019) because the market underestimates exponential earnings power.
Notable Quotes
"When you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, your earnings don't grow linearly, they grow exponentially. You know, the world doesn't think exponentially. Very few people believe you can accurately predict 2 3 4 years out. But if you follow and understand the S-curve and you know the moats and you know how to model, you really can predict these great things."
"The enterprise AI or enterprise application AI market is less than 1% penetrated and we've never seen, you know, we talk about S-curves, we call this an L curve, just straight up."
"We could see just on the coding market alone that Anthropic had a tremendous opportunity ahead of it. We made the investment at the 180 valuation. And we said, and I think they were hoping to get to a nine billion—one to nine. And the numbers were like nothing we'd ever seen before. 100 to a billion on the way to 9."
"Karpathy said you know last year's code tools could write 20% and 80% would be handwritten that flipped when the latest model came out and now he hasn't written a line of code not except in English."
"It's okay to miss the first 100%. Peter Lynch, I started at Fidelity and he loved to mentor the young kids. So, I got some time with him. He said, 'White out the chart. It's all about the future.'"
Action Items
-
1
Study Technology Adoption Through Visual Pattern Recognition
Don't rely solely on data when evaluating new technologies. Visit conferences, watch how people use products in real life, and look for 'standing room only' moments that signal explosive demand. Combine left-brain analysis with right-brain visual intuition to spot inflection points before they appear in the data.
-
2
Map the Full S-Curve Before Investing
Determine how tall the S-curve is (total addressable market), how steep the adoption curve will be (based on barriers to entry), and where you are on the curve (% penetration). This allows you to underwrite 2-4 years out and buy great companies at low multiples because you can predict exponential growth the market doesn't see.
-
3
Identify Competitive Advantages Beyond Traditional Moats
In technology, look for network effects, scale advantages, platform dynamics, critical IP, and brand power. Companies can achieve Walmart-scale advantages in 5 years versus 40, making these digital moats potentially stronger than offline businesses. Map which specific moats each company possesses and how durable they are.
-
4
Invest in Infrastructure During Uncertainty, Applications Once Winners Emerge
When a new technology wave begins, start with infrastructure investments (chips, cloud, foundational layers) where winners are identifiable and demand is certain. As the market matures and application-layer winners become clear (typically 2-3 years later), shift capital to those pure-play opportunities with clearer competitive positioning.