Investors should maintain a 2% to 3% allocation to Bitcoin (BTC) as a long-term strategic reserve, but avoid aggressive buying until it stabilizes and breaks back above its 200-day moving average. For broader crypto ecosystem exposure, use the current price dip to dollar-cost average into Ethereum (ETH) over the next 12 months. Eli Lilly (LLY) is a high-conviction "Application AI" play that could become the world's largest company by using proprietary data and NVIDIA hardware to "program" human biology. Shift your AI investment focus away from commoditized hardware and chips toward specialized software companies that own their data and build private, edge-based models. For tactical traders, use MicroStrategy (MSTR) as a high-volatility vehicle to gain leveraged exposure to Bitcoin's price swings while holding the underlying asset for the long term.

Bitcoin (BTC)

• Current Market Status: Bitcoin is currently down approximately 50% from its all-time high. Visser notes that it is technically in a bear market as it failed to hold its 200-day moving average. • Uncoupling from Equities: For the first time in its four-year cycle, Bitcoin is moving lower while the stock market moves higher. Visser views this lack of correlation as a necessary step for Bitcoin to mature into a legitimate alternative to the fiat system. • The "Agentic" Play: A primary long-term thesis is that AI agents will eventually dominate commerce. Visser believes Bitcoin is the native currency for AI agents, making it a direct play on an AI-dominated future. • Institutional Churn: The launch of ETFs and political acceptance (e.g., the President launching a meme coin) were "sell the news" events that led to the current price stagnation and "churn" among long-term holders (OGs).

Takeaways

• Portfolio Allocation: Visser suggests a 2% to 3% allocation for almost every investor, regardless of their bullishness, while warning that 100% allocation is "very stupid." • Entry Strategy: Avoid "momentum" buying. Wait for the asset to "base" (stabilize) and break back above long-term moving averages (like the 200-week moving average) before aggressive entry. • Long-term Resilience: View Bitcoin as a "strategic reserve" for the next generation, similar to high-end real estate or luxury assets (e.g., Patek Philippe), focusing on its durability over a 20-50 year horizon.

Eli Lilly (LLY)

• The AI/Healthcare Convergence: Visser argues Eli Lilly could become the largest company in the world and the top AI play within five years. • Specialized AI Models: Unlike general models (ChatGPT), Eli Lilly is building a specialized model using 150 years of proprietary clinical trial data. They have built their own data centers with NVIDIA Blackwell GPUs to keep their IP private. • Peptides as "API Keys": Visser describes peptides (like those in GLP-1 drugs) as "API keys for the human body," allowing scientists to "program" biological responses to treat obesity, diabetes, and potentially addiction. • Strategic Moat: The company has stopped disclosing Phase 1 trial data to prevent competitors from using AI to reverse-engineer their intellectual property.

Takeaways

• Sector Rotation: Investors should look for a rotation from "Hardware AI" (chips/infrastructure) to "Application AI," with Eli Lilly being the prime example of a "Human Software" company. • Second-Order Effects: Look for businesses benefiting from the "GLP-1 economy" (e.g., Victoria's Secret reporting sales growth because customers are losing weight and buying new clothes).

Ethereum (ETH)

• Ecosystem Proxy: Visser is currently buying Ethereum during the price drop, viewing it as a better proxy for the broader crypto ecosystem and "network effects" than Bitcoin. • Correlation: Visser’s research shows that a diversified index of 40 crypto-related assets is highly correlated to Bitcoin, but Ethereum remains the primary bet for those wanting exposure to the "financial guardrails" and tokenization.

Takeaways

• Accumulation Phase: Visser is "happy it's going down" as it allows for incremental buying (dollar-cost averaging) in anticipation of a 12-month growth cycle driven by network adoption.

AI Infrastructure & Software

• Hardware Peak: There are signs of a "top" in the hardware trade (NVIDIA, Micron). Data center costs are inflating rapidly (from $50B to $100B per gigawatt), making it harder for companies to see an immediate ROI on general-purpose AI. • Commoditization of LLMs: Large Language Models (LLMs) like Claude and ChatGPT are becoming "commoditized." The value is shifting from the model itself to how it is applied to specific workflows (e.g., CFO Silvia for finance). • The Rise of DeepSeek: Mention of DeepSeek as a disruptive, cheaper, and potentially more accurate alternative to Western models like Claude, signaling a race to the bottom for token pricing.

Takeaways

• Investment Shift: Move focus from the "Five-Layer Cake" bottom (chips/energy) to the top (specialized applications). • Corporate Efficiency: Companies are actively trying to reduce "token consumption" to save costs. Invest in companies that own their data and can build their own "edge" models rather than relying on expensive third-party APIs.

MicroStrategy (MSTR)

• Trading Vehicle: Visser distinguishes between "owning" Bitcoin and "trading" MicroStrategy. He uses MSTR as a high-volatility tool to play the upside and downside of the Bitcoin market while holding the underlying BTC long-term.

Takeaways

• Tactical Use: For investors with higher risk tolerance, MSTR remains the primary equity vehicle to gain leveraged exposure to Bitcoin price movements.