AI spending is not all equal. Wall Street rewards hyperscalers, punishes DoorDash and Duolingo
News Summary
This earnings season, tech companies broadly announced increased spending on artificial intelligence. Wall Street has shown a bifurcated response: rewarding hyperscalers like Alphabet, Amazon, and Microsoft for their boosted capital expenditure forecasts, viewing these investments as necessary to meet surging demand for AI services and infrastructure and as possessing long-term strategic value. In contrast, smaller companies such as DoorDash, Duolingo, and Roblox saw their stock prices slump by double digits after announcing increased AI-related investments. Analysts largely expressed skepticism about the returns on these investments, fearing they would erode near-term profit margins and questioning whether they would generate substantial new revenue. DoorDash's stock fell 17%, Duolingo's 25%, and Roblox's nearly 16%. Notably, Meta stood out as an exception among megacaps, with its stock declining 11% post-earnings. Its substantial capital expenditure (projected at $72 billion) combined with a lack of a cloud service business prompted investor concerns about the clarity of revenue generation from its AI investments.
Background
As the artificial intelligence boom continues to accelerate in 2025, U.S. tech companies are under immense pressure to invest to maintain competitiveness in the AI sector. Hyperscalers like Alphabet (Google Cloud), Amazon (AWS), and Microsoft (Azure) are heavily investing in data centers, AI chips (including proprietary silicon), and AI services to meet robust demand from enterprises and developers. Concurrently, companies in other sectors and those with smaller market capitalizations are also attempting to integrate AI into their products and services for growth and efficiency. However, these companies often face greater market scrutiny, with investors demanding higher and more stringent assessments of the short-term returns and long-term profitability of their investments.
In-Depth AI Insights
Does Wall Street's bifurcated response to AI spending reveal an inherent bias in its approach to 'AI value capture'? - Wall Street's reward for hyperscalers is essentially an endorsement of their "infrastructure" and "platform" moats. These companies, by providing computing power, cloud services, and AI models, serve the foundational layers of the AI economy. Their investments are seen as necessary steps to capitalize on the AI mega-trend, expected to yield certain returns through economies of scale and network effects. - The punishment meted out to application-layer companies like DoorDash and Duolingo reflects market uncertainty regarding the monetization path of their AI investments. Investors worry that their AI spending might not be on core competencies but rather defensive or exploratory, potentially facing higher failure risks, longer payback periods, and difficulty in creating exclusive advantages. - This suggests that when evaluating AI investments, the market favors underlying providers who "sell the picks and shovels" and "build the highways," rather than the application players "driving on the highway," unless the latter can clearly demonstrate unique, scalable business models and profitability from their AI applications. Why are Meta's massive AI investments still questioned by the market, despite its hyperscaler status? How might this relate to the Trump administration's tech policies? - Meta's AI investments are primarily focused on improving the precision of its core advertising business and advancing its metaverse vision (e.g., AI-driven avatars and experiences), rather than providing AI infrastructure or cloud services like its competitors. Market skepticism stems from the metaverse still being in a highly uncertain phase and the direct revenue contribution of its AI investments being unclear. - Against the backdrop of the Trump administration's "America First" and emphasis on technological leadership, Wall Street may be more inclined to support companies that directly enhance U.S. leadership in global AI infrastructure and computing power (such as cloud providers and chip manufacturers). Meta's metaverse investments, while involving cutting-edge technology, might not be as favored in terms of geopolitical "urgency" and "certainty" compared to investments directly related to national computing backbone. - Furthermore, continued antitrust scrutiny and data privacy concerns from the Trump administration for big tech could subject Meta, as a social media giant, to additional regulatory pressures on its data-intensive AI applications, further impacting investor confidence. How should smaller tech companies adjust their capital allocation and market communication strategies to avoid stock price penalties amid surging AI investments? - Smaller companies need to articulate a clearer direct link between AI investments and improvements in core business growth and profitability. For example, quantifying how AI specifically improves customer acquisition costs, retention rates, ARPU, or operational efficiency, rather than just emphasizing "future growth" or "experimental" investments. - Consider adopting more incremental, controllable investment strategies rather than announcing large, one-off capital expenditures, to mitigate short-term margin impact. Simultaneously, set clear milestones and measurable success metrics, and regularly report progress to the market. - Explore partnerships with large AI infrastructure providers instead of attempting in-house R&D in all AI domains, to utilize resources more efficiently. For instance, focus on AI innovation in specific product features and user experience optimization, outsourcing heavy asset investments like underlying computing power to cloud service providers. - In market communications, positioning AI investments as "efficiency enhancements" and "existing business optimization" rather than "disruptive new ventures" might better manage investor expectations, especially in the current market environment where profitability is highly scrutinized.