2) Earnings leverage from AI is coming into view, elevating the value of monetization over mentions:
According to Morgan Stanley Research’s most recent mapping of 3,600 stocks for AI exposure, 21% of S&P 500 companies mentioned at least one AI benefit (up from 10% in 2024). The catch: the market isn’t paying for “AI mentions” alone.
However, AI adopters are seeing results, with cash-flow margin expansion outpacing the global average by 2x. Morgan Stanley Investment Management found that second-order AI beneficiaries show similar efficiency gains and margin expansion.
Markets are paying for evidence that adopters can monetize—and punishing uncertainty. That’s why Morgan Stanley Research flags the recent drawdown in software sector stock prices as a “peak uncertainty” moment, with group enterprise value/sales back near levels last seen during prior disruption scares.
3) AI financing is reshaping markets—and rewarding discipline:
AI’s scale means balance sheets matter again. As AI capex rises, Morgan Stanley Research expects debt financing to follow, especially for infrastructure-heavy projects. But markets are adapting. Issuers are increasingly diversifying their sources of capital. The full spectrum of credit markets—secured, unsecured, structured and securitized across both public and private realms—now play a role in financing AI-related infrastructure. This dynamic was on display in 2025, when Morgan Stanley advised Meta on the $27 billion structured JV for the U.S. AI data-center campus.
4) AI is accelerating M&A and capital reallocation:
Competitive pressure to leverage the benefits of AI is pulling forward strategic decisions. Morgan Stanley’s Investment Banking team notes that AI is increasingly driving M&A as firms seek expertise and client base/market penetration.
This acceleration isn’t limited to corporates. Demand from individuals and family offices to participate directly in the AI build-out has also been strong. Morgan Stanley has originated investment opportunities in private AI companies and data center assets—14 in 2025 alone — through its Wealth Management Private Markets platform.
5) Risks are real—but they shape opportunity:
AI’s upside is big—but markets are stress-testing who benefits, who gets disrupted and global strains along the way.
Debate on the nature of business model disruption will keep shifting investor preferences. History suggests disruption cycles are volatile, not linear. In prior cycles, stocks perceived as disrupted experienced sharp drawdowns and rallies. The recent selloff in software and other services perceived as at-risk is emblematic.
Geopolitical overreach is the macro overlay. As the U.S. and China compete for AI leadership—across chips, compute, energy and data—tighter export controls, higher tariffs and localization pressures could fragment supply chains and raise costs. Those are risks to global growth even as they accelerate domestic buildout.
Labor disruption adds another layer. AI may impact the demand for existing work even as it creates new roles and productivity gains.
Each risk creates a response lever.
- The risk of AI-driven business model disruption drives faster AI adoption and innovation, with a focus on assets that have enduring value in a world of more powerful AI capabilities.
- The impact of geopolitics increases the value of secure, domestic infrastructure.
- Labor disruption increases returns for firms that redeploy workers into higher-value roles.
