Last week’s tech stock sell-off sent ripples through Wall Street, underscoring the dual-edged nature of hype surrounding artificial intelligence (AI). While the broader tech sector experienced some recovery after the dip, concerns linger among investors regarding potential future downturns. The hardest hit were software stocks, with many analysts suggesting that this shakeup represented a necessary correction in a market eager for tangible results.
Daniel Newman, CEO and chief analyst of Futurum Group, characterized the market’s reaction as a “healthy, overdue rotation.” He emphasized the importance of looking beyond hype to assess genuine growth and the underlying constraints in the sector. According to Newman, the key to identifying promising AI investments lies in a structured framework.
One critical factor is the connection between AI spending and revenue generation. Not all companies investing in AI are reaping the benefits, and many analysts point to the struggle as reflective of the technology’s nascent state. Newman noted that those companies demonstrating returns on significant investments—estimated at over $470 billion this year by major players—are more likely to be rewarded by the market. For instance, he highlighted Amazon and Microsoft, whose cloud services, AWS and Azure, have seen revenue growth driven by AI initiatives.
Another point Newman raised was the competitive edge enjoyed by companies developing their own silicon chips. With demand for chips surpassing supply, firms such as Amazon, Alphabet, and Microsoft are well-positioned to benefit from vertical integration and capacity advantages. This can translate into better profit margins, giving them an edge in the evolving AI landscape.
Newman also pointed to a trend in enterprise monetization, where companies successfully capitalizing on AI spending are primarily focused on enterprise solutions. Key players in this space include cloud giants like Alphabet, Microsoft, and Amazon, alongside firms such as Palantir, which stands out among retail traders. Noteworthy mentions also included ServiceNow and IBM, both active in driving AI workflows that contribute to effective monetization.
The future of AI may well lie in its physical applications. Elon Musk, during a recent earnings call for Tesla, suggested that their humanoid robot, Optimus, could unlock as much as $10 trillion in long-term revenue. Mendoza echoed this sentiment, suggesting that the burgeoning market for physical AI—distinct from the data center focus—could provide a multi-trillion-dollar opportunity, with Amazon and Tesla leading the charge.
However, not all software companies will thrive in the AI era. Newman warns against seat-based software solutions that fail to evolve with AI capabilities. He does not foresee AI completely taking over software but insists that firms must adapt to fulfill the new demands of enterprise clients for AI-driven insights and productivity improvements. Companies like Salesforce and ServiceNow, which are collaborating with AI technologies, could better navigate this transition.
Lastly, investors are advised to exercise caution with companies that lack a clear return on investment narrative for their AI strategies. Overreliance on debt to finance AI infrastructure is another potential red flag, as it positions such stocks as riskier bets in volatile market conditions. Newman suggests that savvy investors should focus on companies capable of sustainable investment and consistent cash flow generation with minimal risk, a necessity in today’s uncertain economic environment.
