The rapid growth of artificial intelligence (AI) investment is outpacing actual advancements in research, according to experts. As the sector heads into 2026, a disconnect between investor enthusiasm and the reality of AI development raises concerns about overvaluation and missed opportunities. Jenny Xiao, a former researcher at OpenAI and now the head of Leonis Capital, highlights the “years-long lag” in the AI hype cycle, cautioning that many investors are operating on outdated assumptions about the technology.
Xiao, who founded Leonis Capital in 2021 after obtaining a PhD from Columbia University, emphasizes a significant gap in understanding. While leading AI labs are innovating with new technologies, such as multimodal models and autonomous agents, investors remain fixated on concepts that were groundbreaking three to five years ago. “There is a massive disconnect between what researchers are seeing and what investors are seeing,” she stated in an interview with Business Insider.
Investment Trends and Emerging Concerns
The AI investment landscape has experienced explosive growth, with global spending on AI infrastructure projected to surpass $500 billion in 2026. However, this surge often reflects excitement surrounding outdated technologies. For example, large language models (LLMs), which were the focal point of discussions in 2023 and 2024, are now recognized by researchers as limited tools, leading to diminishing returns.
Despite this shift, investors continue to funnel resources into LLM-focused startups, neglecting emerging technologies, such as agentic AI systems capable of executing complex tasks autonomously. Industry observers are beginning to predict a significant transition, with forecasts suggesting that 2026 could be a pivotal year for agentic AI, potentially seeing up to 40% of enterprise applications incorporating these technologies.
A recent report from Capgemini highlights a transition from hype to practicality, with organizations focusing on infrastructure and workforce training to derive long-term value from AI investments. This ongoing hype lag mirrors patterns seen in past technological revolutions, yet the stakes are particularly high in AI due to its potential to disrupt various sectors, including healthcare and finance.
Challenges in Valuation and Investment Strategies
The disconnect between research advancements and investor understanding leads to significant valuation mismatches. Major companies, such as Microsoft, Google, and Meta, have increased their AI-related capital expenditures dramatically, with estimates suggesting they could spend over $500 billion in 2026 on data infrastructure. Yet, this spending is outpacing profit growth, raising concerns about potential market corrections reminiscent of previous bubbles.
Xiao calls for a new breed of investors—those with a deep technical background—to navigate this complex landscape. She argues that the current investment climate suffers from a herd mentality, with funds often directed toward familiar technologies rather than innovative startups that offer advanced solutions. A recent article from Forbes Middle East echoes this sentiment, warning that rising stock prices may not align with earnings.
Geopolitical factors further complicate the investment landscape. A report from the Atlantic Council outlines the impact of AI on global affairs, emphasizing how nations like the U.S. and China are competing for dominance in AI technology. Investor strategies often lag behind these rapid developments, creating additional challenges.
To bridge the gap between research and investment, leaders like Xiao advocate for increased education and collaboration. Leonis Capital, for instance, conducts workshops and publishes insights aimed at equipping venture capitalists with the tools necessary to evaluate startups effectively. This initiative is gaining traction, evidenced by a growing number of AI-focused venture funds led by former researchers.
As the industry evolves, the demand for investments in nuanced areas such as embodied AI or robotics is expected to rise. However, startups in these fields often struggle to secure funding amid the noise surrounding more popular technologies. Xiao’s firm is positioning itself to capitalize on these opportunities, as outlined in their recent predictions newsletter.
The unpredictable nature of AI development complicates investment strategies. Breakthroughs often occur in bursts rather than following a linear path, which can be challenging for investors accustomed to steady technological progress. As we approach 2026, industry stakeholders must adjust their focus from hype-driven investments to those promising tangible returns.
In summary, the AI investment landscape is at a crucial juncture. By addressing the disconnect highlighted by experts like Jenny Xiao, the sector can move towards a more balanced and innovative future where capital supports genuine advancements rather than echoes of previous trends. As AI continues to integrate into essential sectors, the need for informed investment strategies has never been more critical.
