In 2026, a growing disconnect between investor enthusiasm and actual advancements in artificial intelligence (AI) research is raising concerns among industry experts. As billions of dollars flow into the sector, figures like Jenny Xiao, a former researcher at OpenAI and current leader of Leonis Capital, emphasize a “years-long lag” in the AI investment hype cycle. This lag may lead to overvaluation, potentially jeopardizing the future of many promising technologies.
Xiao’s insights underscore a significant issue: while AI research is advancing rapidly, particularly in areas like multimodal models and autonomous systems, many investors remain anchored to concepts that are now outdated. Xiao, who founded her firm in 2021 following her PhD from Columbia University, points out that the investment community is often several years behind the cutting-edge innovations being developed in leading AI labs.
Understanding the Investment Landscape
The landscape of AI investment has evolved dramatically, with global spending projected to exceed $500 billion in 2026 alone. However, experts warn that much of this excitement is based on older technologies, such as large language models (LLMs), which dominated discussions in 2023 and 2024. Researchers now view these models as foundational yet limited, while investors continue to pour funds into startups focused on them, often at the expense of emerging technologies like agentic AI systems.
Recent discussions on social media platforms have highlighted a potential turning point, with predictions that 2026 could be the “breakout year for agentic AI.” According to forecasts from firms like Gartner, as much as 40% of enterprise applications could integrate these advanced technologies. This call for a more informed investment strategy aligns with Xiao’s push for increased technical expertise among venture capitalists (VCs).
The Capgemini report reinforces this shift, suggesting organizations are beginning to prioritize infrastructure and workforce training to unlock the long-term value of AI investments. The ongoing hype cycle is reminiscent of past technological revolutions, where initial excitement often preceded a period of market correction. In the case of AI, the stakes are heightened due to the technology’s potential impact across numerous sectors, including healthcare and finance.
The Need for Technical Expertise
Xiao argues that the disconnect between researchers and investors is exacerbated by a lack of deep technical expertise among many VCs. “There is a massive disconnect between what researchers are seeing and what investors are seeing,” she stated. This gap leads to inefficiencies in the market, where innovative startups struggle to secure funding because their advancements are too complex for many investors to understand. As a result, funding often gravitates toward safer, more familiar technologies.
The investment frenzy around AI stocks has raised concerns about a potential bubble. Analysts suggest that large tech companies, such as Microsoft, Google, and Meta, could see their capital expenditures in AI exceed $500 billion in 2026. Yet, as noted by a market analyst on social media, this expenditure is growing faster than associated profits, echoing fears of a market correction.
Xiao emphasizes the need for a “new breed of technically savvy VCs and founders.” The industry currently faces a shortage of investors with hands-on research experience, which can lead to herd mentality funding. This is evident in the current exuberance surrounding AI stocks, which have driven market highs but also sparked discussions about the sustainability of such valuations.
The Atlantic Council recently outlined how AI will shape global affairs in 2026, highlighting the competitive landscape between nations like the United States and China in the race for AI dominance. The rapid pace of technological advancement often outstrips investor strategies, further emphasizing the need for alignment between research and investment approaches.
Bridging the Gap
To address these challenges, industry leaders like Xiao advocate for enhanced education and collaboration between researchers and investors. Leonis Capital hosts workshops and publishes insights aimed at demystifying frontier AI, equipping VCs with the necessary tools to evaluate startups more accurately. This strategy is gaining traction, as the number of AI-focused venture funds led by former researchers continues to grow.
Xiao’s firm has outlined predictions for 2026 in their recent newsletter, emphasizing the importance of diversifying portfolios to include local AI growth and embodied systems. As the technology matures, experts believe that understanding its nonlinear progress will be essential. Breakthroughs in AI often do not follow a predictable path, leading to unpredictability that complicates investment strategies.
As 2026 unfolds, the call for a more informed funding ecosystem could redefine the landscape of AI investment. By addressing the lag identified by experts like Xiao, the industry may be able to cultivate a more balanced and innovative future—one where capital is directed toward genuine advancements rather than past trends.
The implications for startups are significant. Founders in niche areas, such as robotics and embodied AI, may find it increasingly challenging to secure funding amid the prevailing noise of more popular technologies. However, firms like Leonis Capital are betting on these often-overlooked sectors, advocating for a shift in focus toward non-linear AI advancements.
Ultimately, 2026 presents a critical juncture for AI investments. As experts call for greater alignment between research and funding, the future may depend on whether investors can adjust their strategies to keep pace with the evolving technological landscape. Ignoring the lag could result in missed opportunities, while those who heed the advice of experienced voices like Xiao may find success in navigating the true frontiers of AI.
