The gap between investor enthusiasm and actual advancements in artificial intelligence (AI) is widening, according to experts. As the sector prepares for **2026**, concerns are mounting that venture capitalists are investing based on outdated information rather than current research developments. This disconnect raises alarms about overvaluation and the potential for missed opportunities, particularly in an industry that has experienced remarkable growth since the introduction of models like **GPT-3** five years ago.
Jenny Xiao, a former researcher at **OpenAI** and now the leader of **Leonis Capital**, has emerged as a prominent voice in this discussion. In a recent interview, she highlighted a “years-long lag” in the AI hype cycle. Many investors, she argues, are relying on outdated assumptions about the technology’s capabilities. Xiao, who founded her firm in **2021** after earning a PhD from **Columbia University**, notes that while leading AI labs are innovating in areas such as multimodal models and autonomous systems, the investment community is still focused on concepts that were cutting-edge several years ago.
Xiao points to a significant gap in understanding between researchers and investors. “There is a massive disconnect between what researchers are seeing and what investors are seeing,” she stated. Her insights stem from her experience at OpenAI, where she worked on foundational models, and her current role as an investor targeting startups that can bridge the divide between laboratory advancements and commercial applications. Leonis Capital prioritizes “frontier AI” ventures, emphasizing the necessity for investors who can assess technologies beyond mere surface-level hype.
The Roots of the Hype Lag
The AI investment boom has been striking, with global spending on AI infrastructure projected to surpass **$500 billion** in **2026**. Despite this surge, Xiao and others caution that much of this excitement is based on past innovations. For example, large language models (LLMs), which attracted significant attention in **2023** and **2024**, are now seen by researchers as foundational but limited tools. Investors continue to funnel resources into LLM-centric startups, overlooking emerging paradigms such as agentic AI systems capable of executing complex tasks autonomously.
Industry observers on social media platforms have indicated a potential shift, predicting that **2026** could be the “breakout year for agentic AI,” with as much as **40%** of enterprise applications incorporating these technologies. This prediction supports Xiao’s call for a more technically adept investor base, as the current funding environment often favors buzzworthy pitches over rigorous technical validation.
A recent report from **Capgemini** further reflects a shift from hype to realism, underscoring the need for organizations to prioritize infrastructure and workforce training to derive long-term value from AI investments. This lag in the hype cycle is not unprecedented; similar patterns have emerged during past technology revolutions, such as the dot-com boom and blockchain developments. However, the stakes are higher in AI due to its potential to transform industries ranging from healthcare to finance.
Xiao emphasizes that this delay can create inefficiencies. Promising startups might struggle to secure funding because their innovations are too advanced for many venture capitalists to comprehend, while safer, familiar investments often attract more capital. Her firm’s newsletter recently critiqued **2025** for its missteps and highlighted opportunities in areas like non-linear AI progress.
Investor Blind Spots Exposed
The implications of this lag extend to valuation mismatches. Hyperscalers like **Microsoft**, **Google**, and **Meta** have significantly increased their capital expenditures for AI. Analysts estimate that these corporations could spend over **$500 billion** in **2026** on data centers and chips, a figure that has risen sharply from earlier projections. This spending trend raises concerns about potential market bubbles, as profits are not keeping pace with capital infusion.
Xiao argues for a new breed of technically savvy venture capitalists and founders. The industry currently suffers from a lack of investors with PhDs or hands-on research experience, leading to herd mentality funding. This trend is evident in the enthusiasm surrounding AI stocks, which has driven market highs but also sparked fears of a downturn. Reports from **DNYUZ** and **Yahoo Finance** highlight the need for AI investments to transition from hype to measurable returns.
Geopolitical factors further complicate the landscape. The **Atlantic Council** has identified eight ways AI will influence global affairs in **2026**, emphasizing a race between the **U.S.** and **China** to dominate the AI sector. Despite these rapid developments, investor strategies often fail to keep pace.
To address the hype lag, industry leaders like Xiao advocate for improved education and collaboration between researchers and investors. Leonis Capital is actively working to demystify frontier AI through workshops and publications aimed at equipping venture capitalists with the necessary tools to assess startups more effectively. This approach is gaining traction, as reflected by the increase in AI-focused venture funds led by former researchers.
As the landscape evolves, Xiao’s predictions for **2026** emphasize the importance of diversifying portfolios to include local AI growth and embodied systems. Her firm is particularly focused on areas poised for growth, such as robotics, which are expected to flourish by **2028**.
The future of AI investment remains uncertain, but as experts like Xiao continue to advocate for a more informed funding ecosystem, the industry may find pathways to mitigate bubbles and foster sustainable growth. Emphasizing the need for alignment between research and investment could lead to a more robust and innovative AI sector, one where capital supports genuine progress rather than echoes of past trends.
