Open AI vs. Nvidia: Who really has the AI moat?

Key Concepts

• Moat: A sustainable competitive advantage protecting a business from competition. In AI, this is shifting from model superiority to distribution, workflow integration, and demonstrable ROI.
• ROIC (Return on Invested Capital): A measure of how efficiently a company generates profit from its investments. Crucial for evaluating AI spending effectiveness.
• Agentic AI: AI agents designed to perform tasks autonomously, currently limited by high computational costs.
• Bifurcation: The increasing divergence in performance between different AI-related companies based on strategic positioning.
• Terawatt Hours (TWh): A unit of energy, highlighting the massive electricity consumption of data centers.
• Space-Based Computing: Utilizing satellites and space infrastructure for data processing and storage, offering potential solutions to power constraints.

The AI Trade Heading into 2026: A Deep Dive

I. Shifting Dynamics of the AI Trade

The AI trade is entering a new phase, moving beyond the broad-based rally of 2022-2023. A significant “bifurcation” is occurring, with performance diverging based on strategic alignment and positioning within the AI ecosystem. This trend is expected to continue for the next 3-5 years, emphasizing the importance of identifying companies poised to capture upside. The focus is shifting from simply investing in AI to evaluating who is effectively monetizing it. Ivana Dlesca, founder of Spear Alpha (SPX), emphasizes that unlike previous years where all AI stocks moved in tandem, differentiation is now key.

II. The Importance of “Moats” in the AI Landscape

The concept of a “moat” – a lasting competitive advantage – is central to evaluating AI companies. Traditionally, moats were defined by brand recognition, established networks, or proprietary products. However, in AI, the landscape is more fluid. While a superior model was initially considered a moat, the decreasing cost and commoditization of models are shifting the advantage to those controlling distribution, workflow integration, and demonstrable return on investment (ROI). Currently, no single company possesses an “untouchable” moat, as the adoption cycle is still early.

• Google: Benefits from a large installed base, allowing for rapid deployment of AI applications.
• Nvidia: Holds an 80% market share in GPUs, representing a significant, though potentially challenged, advantage. However, proof of sustained dominance is still required.
• OpenAI: While initially a leader, its reliance on Microsoft creates vulnerability as Microsoft diversifies its AI partnerships. OpenAI’s ability to attract enterprise clients and demonstrate immediate ROI will be crucial for building a lasting moat. A potential IPO could provide access to capital needed for moat-building, but requires demonstrating consistent results.

III. ROIC: The Key Metric for AI Investment

The central question for 2026 is not simply who is spending the most on AI, but who is generating a return on that investment. ROIC (Return on Invested Capital) is presented as the critical metric for evaluating AI spending effectiveness. The discussion highlights a clear distinction:

• Hardware Component Suppliers: Currently exhibit the highest ROIC, as they require minimal additional investment in capex or R&D.
• Model Companies: Require significant capital expenditure and currently demonstrate lower ROIC, with potential for improvement in 3-5 years.

Recommended Investments (based on discussion):

• Networking: Asterolabs, Arista Networks, Coherent, Lumenum (focus on improving chip performance through networking).
• Power Generation: Constellation Energy (large nuclear fleet with capacity for expansion), Applied Digital, Terra Wolf (Bitcoin miners transitioning to data center operations).

IV. The Electricity Constraint & The Rise of Space-Based Computing

The massive electricity consumption of data centers (448 terawatt hours globally in 2025) is identified as a significant constraint on AI growth. This “speed limit” highlights the importance of infrastructure considerations alongside technological advancements. The discussion introduces the emerging trend of space-based computing as a potential solution to power limitations.

• Space-Based Advantages: Higher power intensity in space, reduced regulatory hurdles compared to building terrestrial power plants.
• Challenges: High launch costs, the need for specialized chip design, and a 5-10 year timeline for large-scale deployment.
• Investment Opportunities: Rocket Lab (launch capabilities), Echoar (proxy for SpaceX ownership), AS SpaceMobile (building a satellite constellation with Google partnership).

Investment Criteria for Space-Based Companies: Focus on companies with a solid base of either military contracts or commercial broadband revenue to provide downside protection, with data center applications offering potential upside.

V. Agentic AI & Cost Reduction

The rollout of AI agents has been slower than anticipated due to high computational costs. The deployment of Nvidia’s Blackwell systems and Blackwell Ultra is crucial for reducing these costs by a factor of ten, making agents more accessible for widespread consumer use. Currently, the cost of using AI agents (estimated at $3,000-$5,000) is prohibitive for most consumers.

VI. OpenAI’s Future & Market Impact

OpenAI’s potential IPO is discussed in the context of its need for capital to build a sustainable moat. While OpenAI’s IPO was a major event in 2025, its long-term success hinges on attracting enterprise clients and demonstrating ROI. The market has broadened beyond OpenAI, with Google and other players emerging as significant competitors. A negative outcome for OpenAI (failed funding round or disappointing IPO performance) is less likely to tank the entire AI trade due to this diversification.

VII. Notable Quotes

• Ivana Dlesca: “We’re just starting to see companies starting to build moats, but there is really nobody that is untouchable at this point in time because we’re so early in the adoption cycle.”
• Ivana Dlesca: “The moment they roll out a tool or an AI application, they’re rolling it out to a much wider audience than somebody starting from scratch having to build these customers.” (referring to Google’s advantage)
• Ivana Dlesca: “AI requires a lot of capital. So, being able to access the public markets could offer them access to capital and that would allow them to build a moat.” (regarding OpenAI’s potential IPO)

Conclusion

The AI trade in 2026 is characterized by increasing complexity and differentiation. Success will depend on identifying companies with sustainable competitive advantages (“moats”), demonstrating strong ROIC, and navigating the challenges of power constraints. The discussion highlights the importance of a holistic view, considering not only technological innovation but also infrastructure, energy, and strategic positioning within the evolving AI ecosystem. The emergence of space-based computing represents a long-term opportunity, while the near-term focus should be on companies capitalizing on the current infrastructure build-out and demonstrating tangible returns on AI investments.