Microsoft, Amazon, Meta and Google face scrutiny as valuation professor warns Big Tech is “collectively overinvesting” in AI

Big Tech’s rush to build and deploy artificial intelligence is drawing renewed skepticism from one of Wall Street’s most prominent valuation voices. As reported by Yahoo Finance via Benzinga, Aswath Damodaran, a professor at NYU Stern widely dubbed the “Dean of Valuation,” said companies including Microsoft, Amazon, Meta and Google are “collectively overinvesting” in AI.

Damodaran’s central critique, as described in the report, is that the spending wave is not clearly tied to reliable, near-term returns. He characterized much of the current effort as “betting, not investing,” implying that the decision-making may be driven as much by fear of falling behind as by a demonstrated path to economic payoff.

The professor’s comments come as AI has become a centerpiece of corporate strategy across cloud, advertising, consumer services and enterprise software. In that environment, large companies face pressure to keep pace with rivals that can claim leadership in AI models, tools and infrastructure. According to the way the remarks were framed in the Benzinga report, that competitive dynamic can produce synchronized spending decisions across multiple firms.

For Microsoft specifically, the article frames the issue as a valuation question rather than an operational one. The key concern highlighted in the report is whether the scale and speed of AI expenditures can be supported by durable revenue growth or profit improvements that show up with enough clarity to justify the cost. Damodaran’s view, as summarized by the publication, suggests that investors and executives may be underappreciating how difficult it is to translate AI build-outs into cash flows that meet valuation hurdles.

The report also points to a broader question that the market has wrestled with repeatedly since the AI boom accelerated: how to separate AI projects that become economically embedded in products from those that primarily expand capabilities without immediately monetizing. Damodaran’s framing, “betting,” aligns with the risk that companies can keep funding AI capacity even when the precise timing and magnitude of monetization remain uncertain.

While the Benzinga/Yahoo item focuses on Damodaran’s opinion, it does not provide a detailed breakdown of any company’s AI budget, spending totals, or ROI assumptions. It also does not spell out whether Damodaran is targeting specific segments within each company, such as cloud services, advertising products, or enterprise software. As a result, the remarks should be read as a valuation critique of the overall competitive posture rather than a quantified forecast tied to any single operating metric.

Still, the argument has potential implications for how markets interpret corporate AI announcements and capital plans. If the “betting, not investing” framing catches on, analysts may demand clearer evidence that AI spending is converting into sustained margin expansion, not just technical progress. For companies, it may also raise the bar for management communications, pushing executives to articulate measurable outcomes and time horizons for commercialization.

For investors and observers, the immediate “watch next” item is whether major AI adopters begin providing more explicit linkage between AI infrastructure spending and business results. In particular, attention will likely turn to guidance language and disclosures that show monetization progress, cost discipline, and the durability of demand for AI-enabled products. The Benzinga report offers Damodaran’s perspective, but it does not, by itself, resolve the question it raises: whether Big Tech’s AI expenditures are becoming investments with predictable returns or bets with uncertain payoffs.