How to Trade Crypto Cycles with Raoul Pal

This episode reveals why the convergence of AI and crypto is the greatest macro trade of all time, driven by a single, dominant factor: global liquidity.

Macro Liquidity Squeeze and Market Volatility

• Raoul Pal opens by dissecting the current market weakness, attributing it directly to a macro-level liquidity mismatch. He explains that a combination of the US Treasury building up its general account, drained reverse repos, and ongoing Quantitative Tightening (QT) is creating a "sucking of liquidity" from the system. This squeeze disproportionately affects the highest-risk assets like crypto. While technology stocks have been volatile, they have been partially propped up by buybacks and fund managers chasing performance after underestimating the AI trend.
• Pal emphasizes that global liquidity is the single most dominant macro factor for investors to watch.
• "Liquidity we found in our work... explains 95% or 97% of NASDAQ's price movements over time since 2012 and 90% of bitcoins," he states, highlighting its critical importance.

Deleveraging and the Business Cycle Outlook

• Despite the recent market downturn and Nvidia's stock falling after strong earnings, Pal maintains a positive forward-looking view. He notes that the current business cycle has been subdued due to high interest rates, but he anticipates a much stronger environment heading into the 2024 election year. This optimism is based on expected fiscal and liquidity stimulus, along with the probability of rate cuts.
• He points to a pervasive sense of fear in the market, which exaggerates price movements and encourages profit-taking, especially ahead of the holidays.
• Strategic Implication: Investors should look past the current fear and liquidity air pocket, as forward-looking indicators suggest a strengthening business cycle that will favor risk assets.

Crypto's Underperformance and Market Structure Issues

• Pal explains that crypto has been hit harder than equities due to a combination of factors. Beyond the macro liquidity drain, the crypto market is facing internal headwinds. These include profit-taking from long-term holders, a pervasive belief in the rigid four-year cycle prompting selling, and, critically, impaired balance sheets of market makers following the October 10th flash crash.
• This impairment means market makers cannot provide as much liquidity, which exaggerates price moves.
• Additionally, forced selling from DATs (Decentralized Autonomous Trusts)—investment vehicles holding crypto assets—is adding significant supply pressure to the market.

Navigating the Crypto Risk Spectrum

• When approaching portfolio strategy, Pal advises focusing on full business cycle gains rather than short-term performance. He personally avoids going too far out on the risk curve, preferring to stick with Layer 1 blockchains because their metrics and drivers are more definable. He shares a personal anecdote about losing nearly 100% on a meme coin, treating it as a learning experience.
• "Every time you think you're really being smart out the risk curve your probability of success is pretty small so I tend to stick with layer ones generally," Pal advises.
• Actionable Insight: A small allocation (under 10%) to highly speculative assets can be educational, but the core of a crypto portfolio should be in more established infrastructure plays to avoid catastrophic losses.

Valuing Crypto Networks: Metcalfe's Law vs. Traditional Finance

• Pal argues that traditional valuation models like Discounted Cash Flow (DCF) are a "fool's errand" for crypto networks. Instead, he insists they should be valued using Metcalfe's Law, which posits that a network's value is proportional to the growth of its user base and the economic activity they generate.
• The key metrics are the number of active users and the total value transacted per user.
• Using this framework, he believes Ethereum is positioned for significant growth as more financial activity, such as stablecoins, moves on-chain, massively increasing the value transacted even if user count remains stable.

The L1 vs. L2 Value Accrual Debate

• Addressing the concern that Layer 2 solutions might drain value from Layer 1s like Ethereum, Pal uses an analogy of the United States federal system. He argues that states (L2s) generating their own economic activity and revenue ultimately make the entire nation (the L1) more valuable.
• He asserts that L2 activity should be counted as part of the total Ethereum network's value.
• Strategic Implication: For researchers and investors, the growth of L2 ecosystems should be seen as a bullish indicator for the underlying L1, as it expands the total economic footprint of the network.

Portfolio Positioning for the Next Cycle

• Pal distinguishes between his personal strategy and a general recommendation. He is personally 100% allocated to crypto, a position he has built over many years. However, for a typical macro-oriented portfolio, he has recommended a split between crypto and technology stocks since the market lows of December 2022.
• As a business cycle investor, he plans to hold this allocation until he sees clear signs that the cycle is peaking.
• His approach underscores the power of extending one's investment time horizon to align with multi-year macro cycles, which increases compounded returns.

The K-Shaped Economy and Its Impact

• The conversation highlights a major disconnect in the economy. Pal points to the ISM (Institute for Supply Management) survey, a key indicator of manufacturing health, which has remained below 50 (signaling contraction) for a historically long period. This reflects a K-shaped recovery where the AI and hyperscaler sectors are booming, while Main Street struggles with low real wages and lacks disposable income.
• Actionable Insight: This lack of broad economic health explains why retail investors have not yet entered the crypto market in force this cycle. A key catalyst for the next wave of adoption will be an improvement in Main Street's financial condition, likely driven by future rate cuts.

The Systemic Risk of DATs

• Pal expresses strong reservations about the structure of DATs, calling them "over-financialized structures" that create misaligned incentives. He explains that their model, which involves selling underlying crypto assets to fund share buybacks, creates a dangerous negative feedback loop in a down market.
• This dynamic leads to forced selling, which can crash the price of the underlying assets, creating a "death spiral."
• He views this as a structural mess that should never have been allowed, as it introduces significant, non-fundamental selling pressure into the market.

Thesis Validation: Liquidity and Network Adoption

• Pal reveals that his entire investment framework is built on two core, testable hypotheses:
1. Global liquidity is the primary driver of the overall market.
2. Network adoption is the primary driver of an individual crypto asset's value.
• He spends the majority of his time testing these two pillars. "If that explains 90% of the price movement of Bitcoin, I need to get that right because it's the single most dominant factor we've ever had in financial markets," he emphasizes.

The Real Impact of Crypto ETFs

• While many anticipated ETFs would trigger a massive price rally, Pal offers a more nuanced take. He notes that overall liquidity has been lackluster this cycle compared to previous ones. Furthermore, a large portion of ETF volume comes from arbitrageurs playing the basis trade (buying spot and selling futures), not from net new institutional or retail investment.
• True, sticky inflows are dependent on the health of the broader business cycle, which dictates whether investors have free cash flow to deploy.

The AI Revolution: A Civilizational Shift

• Pivoting to AI, Pal describes it in stark terms as the "greatest discovery humanity will ever make and the last discovery humanity will make." He views it as a profoundly disruptive, civilizational-level technology that will act as a "nuclear bomb when it comes to deflation."
• He argues that the race for AI supremacy is the most important geopolitical contest, ensuring that governments in the US and China will provide whatever backing is necessary for their industries to succeed.
• Strategic Implication: The immense scale of the AI race guarantees sustained, massive capital investment from both public and private sectors, creating a powerful, long-term tailwind for the entire sector.

The Energy-Compute-Intelligence Flywheel

• Pal outlines the core growth engine of AI as a flywheel: energy creates compute, which creates intelligence, which in turn demands more energy and compute. The most pressing bottleneck in this cycle is energy.
• He identifies solar as the fastest and cheapest energy source to scale, followed by natural gas.
• Nuclear energy is the most efficient long-term solution but has a much longer development cycle (8-9 years).
• Actionable Insight: The exponential growth of AI creates a non-negotiable, massive demand for energy. This presents a clear investment thesis for the energy sector, particularly in solar and the surrounding infrastructure.

The Crypto-AI Nexus

• Pal connects the two revolutionary technologies, explaining that blockchain provides the essential infrastructure for the emerging AI economy. As billions of AI agents are deployed, they will need payment rails to transact for energy and compute. Blockchain offers the instantaneous, decentralized rails required for this.
• Furthermore, in a digital world where AI can create infinite copies of anything, blockchain solves the critical problem of digital scarcity.
• This makes crypto a key enabling technology for a future dominated by AI.

A Framework for Profit-Taking

• To conclude, Pal offers a practical framework for managing risk and taking profits. He advocates for taking "lifestyle chips" off the table—selling a portion of your holdings (e.g., 25-30%) well before a cycle peaks to fund personal goals and reduce psychological stress.
• The primary macro signal to begin de-risking more significantly is when the business cycle shows clear signs of peaking, which can be tracked using indicators like the ISM.
• He suggests a simple rule: start adding risk when the ISM is below 50 and start taking profits when it moves above 57-58.

Conclusion

• This conversation reveals the business cycle, driven by liquidity, is the primary force shaping crypto and AI markets. Investors and researchers should use macro indicators like the ISM to align their investment horizon with this cycle, timing risk-on and risk-off decisions for optimal returns.