AI isn't a bubble but the real upside is shifting

Trader Talk Summary – Featuring Daniel Newman (April 26, 2024)

Key Concepts:

• Magnificent Seven: The seven large-cap tech stocks (Apple, Microsoft, Alphabet, Amazon, Nvidia, Tesla, Meta) that have driven market performance.
• TAM (Total Addressable Market): The total market demand for a product or service.
• GLPs (Glucagon-Like Peptide-1s): A class of peptides gaining prominence in health and wellness, initially known for weight loss but with broader applications.
• Peptides: Short chains of amino acids that signal the body to perform specific functions.
• Enterprise AI: The application of AI within businesses, utilizing internal data sources.
• Super Cycle (AI): A prolonged period of significant growth and innovation driven by Artificial Intelligence.
• Quantum Computing: A type of computing that utilizes quantum mechanics to solve complex problems.
• Market Breadth: The number of stocks participating in a market rally.

I. Market Shift & Broadening Participation

Kenny Pulcari opened the show highlighting a shift in market dynamics. For years, the “Magnificent Seven” tech stocks have dominated market gains, but this dominance is waning. He noted a measurable broadening of market participation, with the S&P 500 rally in early 2024 being driven by more than just the top handful of stocks. Specifically, he stated that 493 out of 500 stocks are contributing to gains, indicating investors are looking beyond mega-cap tech.

This is supported by the strength of small-cap stocks, with the Russell 2000 reaching record highs while tech giants paused. Wall Street strategists, like those at Wells Fargo, are predicting a wider rally encompassing sectors like financials, materials, and energy. Pulcari cautioned against solely focusing on the largest names, suggesting diversification is becoming crucial. He quoted Michael Burry’s warning that relying solely on revenue growth driven by AI hype is misleading and that returns on invested capital (ROIC) need to catch up. The core takeaway is that the “peak crowded trade” of owning only mega-cap tech is loosening, and alpha (outperformance) is increasingly likely to be found elsewhere.

II. The AI Super Cycle – Early Innings

Daniel Newman, CEO of Futurum Group, was introduced as a leading tech analyst. The central theme of their discussion was the current state of AI and its potential. Newman emphatically stated that we are in the “very early stages of a multi-decade super cycle” in AI, and that it is not a bubble. He used the analogy of Netflix shipping DVDs to illustrate that current LLMs (Large Language Models) represent a foundational step, akin to the initial novelty of DVD delivery.

Newman emphasized that the total addressable market (TAM) for AI is far larger than just the current subscription models like OpenAI. He cited a $1 billion deal between Nvidia and Eli Lilly to accelerate drug discovery as an example of AI’s expanding applications. He highlighted the importance of Enterprise AI, which leverages the 95% of data that resides behind corporate firewalls. This data, owned by banks, manufacturers, and pharmaceutical companies, represents a massive untapped potential for AI-driven innovation. He noted that companies like Google and Meta have already successfully utilized publicly available data for optimization, and now enterprises can apply the same principles to their proprietary data.

III. AI & Data Privacy – Opportunities & Risks

The conversation addressed the potential risks associated with utilizing private enterprise data for AI applications. Newman acknowledged the need for data anonymization, encryption, and adherence to industry regulations (HIPAA, FINRA). He clarified that the focus isn’t solely on personal data, but also on ERP (Enterprise Resource Planning) and CRM (Customer Relationship Management) data, which contain valuable transactional and customer information.

He explained how AI can optimize processes, shorten development cycles (e.g., drug discovery), and improve product delivery. He emphasized that AI isn’t always about disruption, but often about iterative improvements that can lead to significant gains in efficiency and profitability.

IV. The Role of Quantum Computing & Infrastructure Bottlenecks

Newman discussed the emerging role of quantum computing in solving specific, complex problems that traditional AI struggles with, such as optimization challenges in supply chains and manufacturing. He noted that while current quantum machines lack sufficient “qubits” (quantum bits), a hybrid approach combining quantum and traditional computing is likely for the foreseeable future.

He also highlighted a critical bottleneck in the AI expansion: infrastructure capacity. He pointed to shortages in wafers, memory, and energy as constraints on AI’s growth. He agreed with Michael Burry that regulatory delays in approving new infrastructure projects (fabs, turbines, nuclear power plants) pose a significant risk. He stated that energy is the primary bottleneck, and streamlining the approval process for energy projects is crucial.

V. Peptides & the “AI of Human Evolution”

Newman then transitioned to discussing his investment in Optimize Health and Performance, a peptide clinic. He described peptides as “short chains of amino acids that send a signal” to the body, helping to restore functions lost with age or lifestyle factors. He positioned peptides as the “AI of human evolution,” enabling humans to live healthier, higher-quality lives.

He explained that peptides can address a wide range of health concerns, from gut health and inflammation to cognitive function and recovery from injury. He cited examples like BPC-157 for tissue repair and GLPs (Glucagon-Like Peptides) for weight management and metabolic health. He emphasized the potential for peptides to reduce reliance on traditional pharmaceuticals.

VI. Investment Implications & Future Trends

Newman predicted a surge in AI-security partnerships, as increased data and applications necessitate stronger cybersecurity measures. He recommended following his “Cyber 7” list and “Future AI 15” list for investment ideas outside the Magnificent Seven. He reiterated his belief that the AI super cycle is just beginning and that the demand for AI solutions is “insatiable.”

Conclusion:

The conversation painted a picture of a rapidly evolving technological landscape. The market is shifting away from concentrated reliance on the Magnificent Seven, with broader participation and emerging opportunities in sectors like small caps, financials, and energy. AI is not a bubble, but rather the beginning of a multi-decade super cycle, driven by both public and, crucially, private enterprise data. Infrastructure constraints and regulatory hurdles pose challenges, but the potential for innovation and growth remains immense. Finally, emerging fields like peptides and quantum computing offer exciting new avenues for investment and advancement. The key takeaway is to diversify, embrace the broader market, and recognize that we are in the very early innings of a transformative technological era.