In the fast-paced world of artificial intelligence, where billions of dollars chase the next big breakthrough, a growing chorus of experts is sounding alarms about a disconnect that’s reshaping how money flows into the sector. As we enter 2026, venture capitalists and tech insiders are grappling with a reality where investor enthusiasm often outpaces the actual progress in AI research. This mismatch isn’t just academic—it’s fueling concerns about overvaluation and missed opportunities in an industry that’s seen explosive growth since the launch of models like GPT-3 half a decade ago.

Jenny Xiao, a former researcher at OpenAI who now leads Leonis Capital, has emerged as a key voice in this debate. In a recent interview, she highlighted a “years-long lag” in the AI hype cycle, arguing that many investors are operating on outdated assumptions about the technology’s capabilities. Xiao, who founded her firm in 2021 after earning a PhD from Columbia University, points to a fundamental gap: while frontier AI labs are pushing boundaries with innovations in areas like multimodal models and autonomous agents, the investment community is still catching up to concepts that were cutting-edge three to five years ago.

This lag, according to Xiao, stems from a lack of deep technical expertise among many venture capitalists. “There is a massive disconnect between what researchers are seeing and what investors are seeing,” she told Business Insider. Her perspective is informed by her time at OpenAI, where she worked on foundational models, and now as an investor betting on startups that bridge the gap between lab discoveries and commercial applications. Leonis Capital, under her guidance, focuses on “frontier AI” ventures, emphasizing the need for backers who can evaluate technologies beyond surface-level hype.

The Roots of the Hype Lag

The AI investment boom has been nothing short of meteoric, with global spending on AI infrastructure projected to exceed $500 billion in 2026 alone. Yet, as Xiao and others note, this surge often reflects excitement over yesterday’s news. For instance, concepts like large language models (LLMs), which dominated headlines in 2023 and 2024, are now viewed by researchers as foundational but limited tools, with diminishing returns setting in as scaling laws hit plateaus. Investors, however, continue pouring funds into LLM-centric startups, sometimes overlooking emerging paradigms like agentic AI systems that can execute complex tasks autonomously.

Echoing this sentiment, posts on X (formerly Twitter) from industry observers suggest a brewing shift. One user, drawing on forecasts from firms like Gartner, predicted that 2026 would mark the “breakout year for agentic AI,” with up to 40% of enterprise applications incorporating such technologies. This aligns with Xiao’s call for more technically savvy investors, as the current wave of funding often favors buzzworthy pitches over rigorous technical validation. Meanwhile, a Capgemini report released just days ago underscores a move from hype to realism, with organizations prioritizing infrastructure and workforce upskilling to realize long-term value from AI investments, as detailed in their press release.

The hype cycle’s lag isn’t new—it’s a pattern seen in past tech revolutions, from the dot-com era to blockchain. But in AI, the stakes are higher due to the technology’s potential to disrupt everything from healthcare to finance. Xiao argues that this delay creates inefficiencies: promising startups struggle to secure funding because their innovations are too advanced for most VCs to grasp, while safer, more familiar bets soak up capital. Her firm’s newsletter, co-authored with partners Jay Zhao and LJW, recently outlined predictions for 2026, critiquing what 2025 got wrong and betting on areas like non-linear AI progress, as shared on Substack.

Investor Blind Spots Exposed

Delving deeper, the lag manifests in valuation mismatches. Take the hyperscalers—companies like Microsoft, Google, and Meta—whose capital expenditures for AI have ballooned. Estimates from analysts indicate that these giants could spend over $500 billion in 2026 on data centers and chips, up from earlier projections. Yet, as one X post from a market analyst noted, this spending is accelerating faster than profits, raising bubble fears reminiscent of past market corrections. Forbes Middle East recently highlighted this dynamic, warning that stock prices are rising ahead of earnings, in a piece available here—though the exact thread links to broader market insights.

Xiao’s critique extends to the need for a “new breed of technically savvy VCs and founders.” In her view, the industry suffers from a shortage of investors with PhDs or hands-on research experience, leading to herd mentality funding. This is evident in the exuberance around AI stocks, which has driven market highs but prompted fears of a correction. A DNYUZ article echoed these concerns, noting that investor exuberance has sparked bubble talk, with details in their report. Similarly, Yahoo Finance analysts predict that 2026 will force AI investments to “foot the bill,” shifting focus from hype to tangible returns, as explored in this analysis.

Beyond domestic markets, geopolitical factors are amplifying the lag. The Atlantic Council recently outlined eight ways AI will influence global affairs in 2026, from supply chain disruptions to international competition in chip manufacturing. Their dispatch emphasizes how nations like the U.S. and China are racing to dominate AI, yet investor strategies often lag behind these rapid developments. Xiao, in her Substack contributions, has touched on this, advocating for bets on resilient, innovative firms that can navigate such uncertainties.

Bridging the Gap Through Expertise

To address the lag, industry leaders like Xiao are pushing for more education and collaboration between researchers and investors. Leonis Capital, for example, hosts workshops and publishes insights to demystify frontier AI, aiming to equip VCs with the tools to evaluate startups more accurately. This approach is gaining traction, as evidenced by a surge in AI-focused venture funds led by former researchers. One X post from a crypto-AI enthusiast framed the current frenzy through tech-cycle frameworks, projecting a “golden age” post-2025, with massive capex investments signaling sustained growth despite short-term bubbles.

Critics, however, warn that the lag could lead to painful corrections. MIT Sloan researchers, cited in recent X discussions, foresee a 2026 deflation in AI valuations as hype outstrips ROI. This mirrors sentiments in a Stephen’s Lighthouse post, which referenced Xiao’s interview and stressed the disconnect, linking back to the original Business Insider piece without redundancy. Meanwhile, AOL’s coverage reinforces the call for understanding latest studies, in their article.

The implications for startups are profound. Founders in niche areas like embodied AI or robotics—poised for dominance by 2027, per some X forecasts—are finding it harder to attract funding amid the noise. Xiao’s firm is betting big here, as outlined in their predictions newsletter, which critiques overhyped areas and highlights undervalued opportunities in non-linear AI advancements.

Navigating Nonlinear Progress

AI’s development isn’t linear, a point Xiao emphasizes repeatedly. Breakthroughs often come in bursts, defying predictable scaling. This unpredictability exacerbates the hype lag, as investors accustomed to steady tech progress struggle with AI’s fits and starts. For instance, while 2025 saw pilots and experiments, 2026 is dubbed the “show me the money” year on X, where physical AI in logistics and agentic workflows will demand proven value.

Broader market trends support this shift. Threads from Business Insider on social platforms, like this one, amplify Xiao’s message, calling for savvy players amid AI’s unpredictable nature. Similarly, a Conch Shell Capital podcast, referenced in X posts, discusses massive investments with uncertain returns, using capital cycle frameworks to predict caution.

As 2026 unfolds, the push for alignment between research and investment could redefine the sector. Xiao’s vision—of a more informed funding ecosystem—might mitigate bubbles and foster sustainable growth. Yet, with capex revisions upward and geopolitical tensions rising, the path ahead remains fraught. Investors ignoring the lag risk being left behind, while those heeding voices like Xiao’s could capitalize on the true frontiers of AI.

Emerging Bets and Future Trajectories

Looking ahead, Leonis Capital’s predictions for 2026, co-authored by Xiao and her team, bet on corrections to 2025’s missteps, such as overreliance on compute-heavy models. They advocate for diversified portfolios that include local AI growth and embodied systems, aligning with X sentiments forecasting robotics cycles by 2028. This forward-thinking stance is crucial as AI integrates deeper into critical sectors.

Geopolitical dispatches from the Atlantic Council further contextualize these bets, predicting AI’s role in shaping global power dynamics. For insiders, this means scrutinizing investments through a lens of both technological merit and international viability.

Ultimately, the AI investment arena in 2026 stands at a crossroads. By addressing the hype lag, as championed by figures like Jenny Xiao, the industry could move toward a more balanced, innovative future—one where capital chases real progress, not echoes of the past.