AI investment continues to accelerate, but the market’s tone is clearly changing. In the latest episode of Lead-Lag Live, Melanie Schaffer sat down with Kai Wu, Founder and CIO of Sparkline Capital, to unpack what the AI boom looks like once the initial euphoria fades and harder questions around returns, sustainability, and concentration come into focus.
From Euphoria to Scrutiny
Wu noted that much of 2024 and early 2025 was defined by near-automatic enthusiasm around AI capital spending. Announcements of large data-center builds or AI infrastructure commitments were routinely rewarded with sharp stock price gains. Over time, however, that enthusiasm has become more selective.
High-profile examples highlight the shift. Some companies that once rallied aggressively on AI headlines have since retraced much of those gains, while credit markets have begun to price in greater uncertainty around the durability of AI-driven spending plans. Recent pullbacks in AI-linked infrastructure names suggest investors are no longer willing to underwrite unlimited capital expenditures without clearer visibility into eventual returns.
The result is a market that is no longer uniformly bullish on AI spending, but increasingly discerning about who is deploying capital effectively and who may be overextending.
Concentration Risk Beneath the Surface
A major theme of the discussion was concentration risk. Even before the AI boom, a small group of mega-cap technology firms dominated equity benchmarks. Today, those same firms also represent the largest investors in AI infrastructure.
Wu emphasized that this creates a double layer of risk for investors, including those who believe they are diversified through passive index exposure. A significant share of capital is effectively riding on the judgment of a handful of CEOs making long-duration, capital-intensive bets on AI platforms. If those bets deliver strong productivity gains, the payoff could be substantial. If not, the downside risk is more systemic than many investors appreciate.
The ROI Question Still Looms
Despite rapid growth in AI usage, Wu argued that the central question remains unresolved: will AI generate enough revenue to justify the scale of investment underway?
Current AI application revenues remain small relative to the trillions of dollars being deployed into data centers, chips, and cloud infrastructure. While usage metrics continue to rise, monetization is far less certain. Consumer adoption has outpaced enterprise adoption, and even among large user bases, only a fraction are paying customers.
Wu stressed that enterprise adoption cycles are typically slow, which means it may simply be too early to draw firm conclusions. Still, as markets look ahead, the burden of proof is shifting. Investors are increasingly focused on whether AI meaningfully improves productivity and whether corporations are willing to pay for those gains at scale.
Why Intangible Assets Matter More Than Ever
One area where Wu’s conviction has only strengthened is the growing importance of intangible assets. Traditional valuation frameworks tend to emphasize physical capital and underweight assets such as data, intellectual property, and proprietary algorithms.
The rise of generative AI, in Wu’s view, reinforces the idea that a larger share of corporate value now resides in these intangible inputs. Companies with durable data advantages and scalable models may ultimately prove more valuable than those simply spending the most on hardware and infrastructure.
Learning From the Dot-Com Cycle
To frame where markets may be headed next, Wu drew parallels to the dot-com era. Early in that cycle, infrastructure providers and “picks-and-shovels” firms delivered outsized returns. Over time, valuations became stretched, and leadership shifted away from the most capital-intensive players.
Wu suggested AI may now be entering a similar phase. After strong gains in infrastructure-focused and high-beta AI names, the next opportunity could lie with AI adopters. These are companies using AI to enhance efficiency, margins, or competitiveness without bearing the full burden of massive capital investment.
Importantly, many of these firms trade at little to no premium relative to the broader market, offering what Wu described as “free AI exposure.” They also span multiple sectors and geographies, potentially offering diversification benefits alongside AI-driven upside.
Positioning for the Next Chapter
The takeaway from the conversation was not that AI’s promise has faded, but that the easy phase of the trade may be over. As expectations rise, markets are likely to reward demonstrable outcomes rather than ambition alone.
For investors, that means thinking beyond headline spending figures and focusing instead on where AI adoption translates into measurable business improvements. The next phase of AI investing, Wu argued, is likely to be defined less by who builds the biggest data center and more by who uses AI most effectively.
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DISCLAIMER – PLEASE READ: This is a sponsored episode for which Lead-Lag Publishing, LLC has been paid a fee. Lead-Lag Publishing, LLC does not guarantee the accuracy or completeness of the information provided in the episode or make any representation as to its quality. All statements and expressions provided in this episode are the sole opinion of Sparkline Capital and Lead-Lag Publishing, LLC expressly disclaims any responsibility for action taken in connection with the information provided in the discussion. The content in this program is for informational purposes only. You should not construe any information or other material as investment, financial, tax, or other advice. The views expressed by the participants are solely their own. A participant may have taken or recommended any investment position discussed, but may close such position or alter its recommendation at any time without notice. Nothing contained in this program constitutes a solicitation, recommendation, endorsement, or offer to buy or sell any securities or other financial instruments in any jurisdiction. Please consult your own investment or financial advisor for advice related to all investment decisions.
