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WTT: AI: Fundamentals, Valuation, and the Next Allocator Dilemma

from Capital Allocators – Inside the Institutional Investment Industry

8m 16s

WTT: AI: Fundamentals, Valuation, and the Next Allocator Dilemma

This piece examines the current AI investment landscape through the lens of the dot-com bubble of 2000, drawing parallels between the two periods of technological revolution and market exuberance. The author recalls sitting in a classroom during the spring of 2000, confused by a market that valued companies on clicks and eyeballs rather than revenues and cash flow. Shortly after, the dot-com valuation bubble burst, yet the internet-powered economy continued to grow, proving that both enthusiasts and skeptics were partially right. Today, AI occupies a similar position as the next revolutionary technology and the centerpiece of every investment conversation. The AI supply chain is experiencing unprecedented adoption, revenue growth, and capital expenditure, with fundamentals across frontier models, compute infrastructure, and energy demand off the charts. However, valuations of both public and private companies imply these trends will continue indefinitely. Two podcast guests capture the debate: Gavin Baker views AI as one of the most extraordinary moments in capitalism, while Rajiv Jain avoids hyperscalers due to downside risk from massive capex without free cash flow follow-through. Both may be correct, as AI may transform businesses while markets have already priced in a decade or two of progress. The allocator dilemma has shifted from how to gain AI exposure to how to position portfolios when access is no longer an obstacle, especially as mega-cap private winners prepare to go public. The power law in venture capital is more pronounced than ever, creating allocation imbalances across public and private markets. The hard part comes after being right: deciding what AI is worth today and how much exposure to own.

Transcription

1230 Words, 7581 Characters

English
Speaker 1this what ted's thinking ai fundamentals valuation and the next allocator dilemma takes on a high level assessment of ai companies as late stage private winners prepare to go public and the next big challenge allocators face as a result i'm sitting in my classroom in disbelief five years of training and value investing and a year and a half at business school led me to a class called managing the market space the old marketplace of revenues margins and cash flow driving shareholder value had suddenly been replaced by clicks and eyeballs shortly thereafter i attended a wedding and sat next to someone working at a technology company the business had just gone public sporting a three billion dollar market cap and three million dollars in cash flow driving shareholder value had suddenly been replaced by clicks and eyeballs the more questions i asked the more confused i became with the answers eventually that recent warden graduate turned to me in frustration and said you just don't get it he was right i couldn't see the future or understand the present it was the spring of 2000 a few months later the dot-com valuation bubble burst but the internet powered economy roared on maybe i was proven right or maybe i was early and wrong that's what made the period so difficult to navigate the enthusiasts were right about the technology and the skeptics were right about prices here we are again with ai ai is the next revolutionary technology and the centerpiece of every investment conversation i won't pretend to know how the technology business models or capital markets will play out but it's hard not to think about it about ai these days i tend to see the world through probabilities rather than certainties consistent with that thread i see two sides of the ai discussion across investment prospects winners and losers and the next big allocator challenge fundamentals versus prices the ai supply chain is experiencing unprecedented adoption revenue growth and capital expenditure the fundamentals of frontier models compute infrastructure energy demand and capital formation are off the charts at the same time the valuations of both public and private companies imply these trends will continue creating unprecedented growth returns on invested capital and future profits two recent podcast guests capture the two sides of this debate gavin baker from atreides describes the ai revolution as one of the most extraordinary moments in the history of capitalism he sees real demand constrained by the supply of watts and wafers compelling returns from productivity gains and a market that underestimates the durability of ai spending on the other hand rajiv jain from gqg is avoiding hyperscalers and ai related businesses he worries about the downside risk from massive capex without free cash flow follow-through lack of pricing power and extreme valuations while he agrees that ai is a revolutionary technology he's skeptical that today's business fundamentals just don't justify today's prices both may be correct much like the internet ai may transform businesses and become ubiquitous throughout the global economy but it's also possible that markets have already priced in a decade or two of progress just as happened with amazon and microsoft in 2000 those companies ultimately fulfilled enormous expectations but investors who bought before the bubble burst still endured years of disappointing returns the question isn't whether ai matters it's how much of that future is already reflected in today's prices winners and losers during the internet boom investors didn't have to distinguish winners from losers everything went up the hard work started after the boom amazon became one of the most valuable businesses in history pets.com and hundreds of other online retailers vanished the internet transformed the economy and the economy of the world while simultaneously destroying enormous amounts of capital ai may prove similar today capital is abundant for private ai companies at every stage and in every layer of the stack models infrastructure applications tooling and services but capitalism eventually forces distinctions not every company can be a winner technology concedes spectacularly while many investments fail to meet expectations from access to positioning the allocator dilemma for allocators this distinction matters for another reason the biggest winners increasingly sit inside institutional portfolios creating a different challenge than simply deciding whether ai is real allocators are moving from how to gain exposure to ai to how to position portfolios when access is no longer an obstacle the power law phenomenon in venture capital is more pronounced than ever the 10 largest financial venture-backed winners are worth around two and a half trillion dollars and represent over 50 percent of venture capital value many institutional investors participated in these businesses through early mid and late stage private investments a few fortunate endowments are reported to have 10 to 15 percent of their entire pools in spacex for years private for longer created allocation imbalances across public and private markets lp's road expansion and expansion of private markets have been a key factor in the development of private and public markets as a result of the expansion of private markets as a result of the expansion of private markets as a result of the expansion of private markets as a result of the expansion extraordinary winners without much agency to adjust their portfolios ed greffenstedt spoke about this setup on a recent podcast articulating the unsatisfying options of selling in the secondary market at a discount slowing venture commitments or adjusting asset allocation targets to adjust for mega cap venture winners but as mega cap private companies begin offering meaningful liquidity to public investors allocators face a new challenge what do you do when you're in a position where you're in a position where you're in a position where you're when yesterday's private market winners become tomorrow's public market index constituents. A few years ago, in Active Management Today is a Single Decision, I wrote that active management came down to the choice of how much exposure to hold in the MAG-7. Replace MAG-7 with AI, overlap acknowledged, and the dynamic isn't much different. AI may be real, valuations may be high, and allocators still have to decide what and how much to own without hiding behind the constraints of long-ago-seeded private investments. The next decade may not only determine the winners and losers of AI, it may determine how allocators perform relative to each other. That's a question of technology, economics, and market structure all at once. The hard part comes after being right. AI technology may exceed our expectations, while many investments fail to meet them. The challenge is now deciding how much of that future is already reflected in today's prices, and how much exposure to own when access is no longer a bottleneck. 26 years after that wedding conversation, I suspect the lesson is the same. The latest and greatest technology will exceed our lofty expectations, many investments will not, and both can be true at the same time. The future is no longer the hard part to imagine. The hard part is deciding what it's worth today. Thanks for listening to the show. If you like what you heard, hop on our website at capitalallocators.com, where you can access past shows, join our mailing list, and sign up for premium content. Have a good one, and see you next time.

Podcast Summary

Key Points:

  1. The author reflects on the dot-com era of 2000, drawing parallels to today's AI investment landscape where revolutionary technology and inflated valuations can coexist.
  2. AI is experiencing unprecedented adoption, revenue growth, and capital expenditure across frontier models, compute infrastructure, and energy demand.
  3. Two contrasting views are presented
  4. Both perspectives may be correct, as AI could transform the economy while markets have already priced in decades of future progress, similar to Amazon and Microsoft in 2000.
  5. During the internet boom, all companies rose together, but afterward only true winners survived while many investments failed to meet expectations.
  6. The power law in venture capital is more pronounced than ever, with the 10 largest venture-backed winners worth roughly $2.5 trillion and representing over 50 percent of venture capital value.
  7. Allocators face a new dilemma as mega-cap private AI companies prepare to go public, shifting from how to gain AI exposure to how to position portfolios when access is no longer a bottleneck.
  8. The core challenge is determining how much of AI's future is already reflected in today's prices and how much exposure to own, which will determine relative allocator performance.

Summary:

This piece examines the current AI investment landscape through the lens of the dot-com bubble of 2000, drawing parallels between the two periods of technological revolution and market exuberance. The author recalls sitting in a classroom during the spring of 2000, confused by a market that valued companies on clicks and eyeballs rather than revenues and cash flow. Shortly after, the dot-com valuation bubble burst, yet the internet-powered economy continued to grow, proving that both enthusiasts and skeptics were partially right.

Today, AI occupies a similar position as the next revolutionary technology and the centerpiece of every investment conversation. The AI supply chain is experiencing unprecedented adoption, revenue growth, and capital expenditure, with fundamentals across frontier models, compute infrastructure, and energy demand off the charts. However, valuations of both public and private companies imply these trends will continue indefinitely.

Two podcast guests capture the debate: Gavin Baker views AI as one of the most extraordinary moments in capitalism, while Rajiv Jain avoids hyperscalers due to downside risk from massive capex without free cash flow follow-through. Both may be correct, as AI may transform businesses while markets have already priced in a decade or two of progress. The allocator dilemma has shifted from how to gain AI exposure to how to position portfolios when access is no longer an obstacle, especially as mega-cap private winners prepare to go public.

The power law in venture capital is more pronounced than ever, creating allocation imbalances across public and private markets. The hard part comes after being right: deciding what AI is worth today and how much exposure to own.

FAQs

Allocators must decide how much of AI's future is already reflected in today's prices and how much exposure to own when access is no longer a bottleneck.

Like the internet, AI may transform the economy while many investments still fail to meet expectations, and both can be true at the same time.

One side sees extraordinary demand, constrained supply, and durable AI spending; the other worries about massive capex without free cash flow, weak pricing power, and extreme valuations.

During the internet boom everything went up, but afterward many companies vanished while a few became hugely valuable, so capital was destroyed even as the technology succeeded.

The 10 largest venture-backed winners are worth about $2.5 trillion and represent over 50% of venture capital value, making a few companies dominate returns.

They must decide what to do when yesterday's private market winners become tomorrow's public market index constituents, which changes portfolio positioning.

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Transcription

1230 words