AI investing in 2026: Volatility, valuations, adoption, and risks

Key Concepts

• AI Infrastructure vs. Adoption Mismatch: The current rapid investment in AI infrastructure (data centers, semiconductors) is outpacing actual enterprise adoption of AI technologies.
• Enterprise Readiness for GenAI: Only approximately 30% of the Global 2000 companies are currently prepared to implement Generative AI (GenAI).
• Free Cash Flow & Capex: A preference for companies with positive free cash flow due to the capital-intensive nature of AI infrastructure buildout.
• Rising Component Costs: Increasing prices of key components like DRAM (Dynamic Random-Access Memory) are a significant, yet under-recognized, risk to margins and demand.
• Geopolitical Risk (US-China): Escalating tensions between the US and China, particularly regarding AI and defense, represent a growing economic and strategic risk.
• Equity Liquidity Concerns: Potential strain on equity markets if multiple AI companies simultaneously seek large capital raises.
• Agentic AI & Software Spend: Focus on software companies, particularly those involved in "agentic" AI (AI agents automating tasks), as indicators of enterprise adoption.

AI & Tech Outlook for 2026: Volatility, Adoption, and Risks

Introduction

The discussion centers on the outlook for the technology sector, specifically Artificial Intelligence (AI), moving into 2026, following a strong performance in 2025. The primary theme is a predicted increase in volatility driven by a disconnect between infrastructure investment and enterprise adoption, coupled with emerging financial and geopolitical risks.

I. The AI Trade: Infrastructure Buildout vs. Enterprise Adoption

The speaker anticipates “tremendous amount of volatility” in 2026. A key observation is the current imbalance between the rapid expansion of AI infrastructure and the slower pace of enterprise adoption. Investment in infrastructure is growing at a rate of 40% annually, while the semiconductor index (SOX) is up nearly 48% year-to-date, contrasting with the software index (IGV) which has only risen 7.8%. This disparity suggests the market has largely priced in the infrastructure build, but the benefits of enterprise AI implementation are yet to materialize.

II. Enterprise AI Adoption Timeline & Indicators

Currently, only around 30% of the Global 2000 companies are prepared to transition to Generative AI (GenAI). The remaining 70% are still focused on data cloud migration and data cleansing – prerequisites for leveraging Large Language Models (LLMs). The speaker estimates full enterprise adoption won’t occur until 2027-2028.

To gauge genuine AI modernization (beyond marketing hype), investors should monitor:

• Cloud Titan Token Growth: Growth in usage-based pricing models from major cloud providers.
• Software Spend (Agentic Plays): Robust net new Annual Recurring Revenue (ARR) in software companies specializing in “agentic” AI – those automating tasks through AI agents (e.g., CRM, ServiceNow, HubSpot).
• AI-Driven Security Sector Growth: Increased investment and adoption of AI within the cybersecurity industry.

III. Financial Risks: Free Cash Flow & Capital Raises

The speaker’s clients prioritize companies with positive free cash flow, given the capital-intensive nature of the current AI infrastructure cycle. Microsoft and Google are highlighted as the only major tech companies currently capable of self-funding their substantial capital expenditures (capex). Google’s year-to-date performance is up 87%.

A significant concern is the potential need for next-generation AI companies (e.g., Anthropic, OpenAI, SpaceX) to raise capital. The debt market may be insufficient to cover the necessary funding, forcing these companies to seek equity financing. The speaker warns of a potential “drain on the equity liquidity lake” if multiple companies attempt to raise large sums simultaneously, potentially forcing portfolio managers to sell existing holdings. OpenAI is rumored to be seeking $100 billion in funding.

IV. Rising Component Costs & Demand Elasticity

A largely overlooked risk is the increasing cost of key components, particularly memory (DRAM). Micron’s recent report showed a 20% sequential increase in DRAM Average Selling Prices (ASPs). The CEO of Dell has indicated potential component shortages and price increases in Q4. This is expected to impact the PC and handset supply chains, potentially limiting product availability due to AI-driven demand for memory. The speaker anticipates “elasticity of demand” – meaning higher prices will likely curb overall demand.

V. Geopolitical Risks: US-China Economic Warfare

The speaker views the recent Chinese government sanctions on 20 US defense companies (retaliation for US weapon sales to Taiwan) as a manifestation of broader economic warfare. He emphasizes that the geopolitical landscape is often underestimated by the market. The speaker believes that success in AI is critical for both economic development and national defense, leading to increased competition and potential conflict. He warns that without a “happy medium” in trade relations, the US and China could be entering a “new cold war.”

VI. Defense Sector Pivot to AI

Defense budgets are rapidly shifting towards AI-driven technologies, including drones and AI weaponry. This pivot is accelerating the economic warfare aspect of the US-China relationship.

Conclusion

The outlook for the tech sector in 2026 is characterized by significant volatility. While AI remains a key growth driver, investors should be cautious about the mismatch between infrastructure investment and enterprise adoption, the financial health of AI companies, rising component costs, and escalating geopolitical tensions. A focus on companies with strong free cash flow, monitoring key indicators of enterprise AI adoption (software spend, cloud growth), and acknowledging the growing risks associated with US-China relations are crucial for navigating this complex landscape.