Jeremy Siegel: The biggest risk on AI investing is that it can be done much more cheaply

"The biggest risk on AI investing is that it can be done much more cheaply." This provocative statement from Jeremy Siegel, Professor Emeritus of Finance at The Wharton School and Chief Economist at WisdomTree, cuts to the core of a pervasive sentiment in the current market. Siegel spoke with CNBC's 'Squawk Box' about equity markets, the bull-to-bear ratio, and the broader economic landscape, offering a nuanced perspective on the AI investment frenzy.

Siegel's central thesis revolves around the potential for AI's transformative power to be realized at a significantly lower cost than many anticipate. This challenges the prevailing narrative that equates AI dominance with massive capital expenditure on infrastructure, such as the construction of vast data centers. While acknowledging the undeniable revolutionary potential of artificial intelligence, Siegel posits that the true disruption might come from efficiency gains and cost reductions rather than solely from scale. He implies that the market may be overestimating the capital intensity required for widespread AI adoption.

The historical context of technological revolutions provides a valuable lens through which to view Siegel's argument. He draws parallels to previous technological shifts, noting that while these were indeed transformative, they often became more accessible and cost-effective over time. For instance, the internet's initial infrastructure build-out was substantial, but the subsequent proliferation of affordable internet access and services democratized its use. Siegel suggests that AI could follow a similar trajectory, where initial heavy investments in foundational models and hardware might give way to more efficient, distributed, and ultimately cheaper methods of leveraging AI capabilities.

A key insight from Siegel is the potential for AI to optimize existing processes and resources, thereby reducing the need for incremental, large-scale capital outlays. Instead of building new, massive data centers, companies might find ways to deploy AI more efficiently within their current operational frameworks. This could involve software optimization, the use of more efficient algorithms, or leveraging distributed computing power. "I think it's different this time," Siegel stated, implying that the current enthusiasm might be overlooking the inherent efficiency-driving nature of AI itself. This perspective suggests that the companies best positioned to benefit might be those that can effectively integrate AI into their existing business models, rather than those solely focused on building the underlying infrastructure.

Furthermore, Siegel's commentary hints at a potential recalibration of investment strategies. Rather than betting solely on the companies building the most powerful AI models or the most extensive hardware infrastructure, investors might consider those that can harness AI to deliver superior products and services at a lower cost. This could include companies that are adept at optimizing AI deployment, developing innovative AI applications, or those whose business models are inherently enhanced by AI-driven efficiencies. The implication is that the true value creation in the AI revolution might not be in the sheer scale of deployment but in the intelligence and cost-effectiveness with which it is applied.

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The market's current exuberance surrounding AI is palpable, with valuations for companies perceived as leaders in the field soaring. However, Siegel’s perspective serves as a crucial reminder to temper this enthusiasm with a pragmatic assessment of cost dynamics. The potential for AI to be implemented more cheaply than anticipated could mean that the market is currently overvaluing certain aspects of the AI ecosystem. This doesn't diminish AI's transformative power, but it does suggest that the path to widespread adoption and profitability might be more nuanced and less capital-intensive than commonly believed.

Siegel's overarching message is one of cautious optimism, urging a more granular analysis of how AI's economic impact will manifest. The focus on cost efficiency as a primary driver of AI's market penetration offers a compelling counterpoint to the narrative of ever-increasing infrastructure spending. This perspective is vital for founders, VCs, and AI professionals seeking to navigate the complex landscape of this rapidly evolving technology.