By 10:47 a.m. on Wednesday morning, billions of dollars had evaporated from wealth management stocks. There had been no earnings miss. No regulatory shock. No fraud. Just a press release from a startup announcing an AI-powered financial planning tool that could analyze tax returns, generate scenarios, and personalize investment strategies in minutes. Within hours, asset managers in London were sliding in sympathy. Brokerage firms in New York were down sharply. Days earlier, legal publishers and data providers had suffered similar fates after the launch of new AI research tools. A wealth manager and a legal publisher have very little in common. Yet, as reported by the FT, their stocks fell for the same reason in the same week.
Investors were no longer analyzing industries. They were scanning for automation exposure.
This is the early shape of a new valuation regime. Markets are beginning to price artificial intelligence risk everywhere, but they are doing so bluntly. Entire segments are being discounted not because revenues have collapsed, but because someone somewhere might automate part of what they do. Software companies are punished because AI agents could reduce seat licenses. Insurance brokers are sold because an app can compare policies. Professional services firms are repriced because generative models can draft, summarize, and create advice. In each case, the reaction is category-level. The assumption is that if AI can do a task, then firms built around that task must be structurally impaired.
In the short to medium term, this pattern is understandable. Equity valuation depends on assumptions about margins, growth rates, and the durability of competitive advantage. AI destabilizes all three at once. It threatens fee structures by lowering the cost of delivering knowledge work. It compresses barriers to entry by making sophisticated capabilities widely accessible. It makes long-term forecasts harder because productivity gains are nonlinear and unevenly distributed. Faced with this uncertainty, analysts default to caution. They lower multiples, raise discount rates, and trim guidance. When in doubt, they sell first and revisit later.
But this transitional phase of broad devaluation should not be confused with long-term structural decline. We are witnessing the first-order reaction to a general-purpose technology. History suggests that when a foundational technology emerges, markets initially punish exposure to perceived risk before they learn to differentiate between those who will be disrupted and those who will be transformed. The current wave of repricing reflects anxiety about automation, not yet insight into configuration.
This distinction matters. The dominant valuation gap of the last two decades separated technology companies from traditional firms. Software commanded premium multiples because it was asset-light, scalable, and defensible. Industrial, financial, and service firms were valued more conservatively because they were labor-intensive and capital-bound. AI is beginning to dissolve that divide.
As I argue with my co-author, Nitin Mittal in our new book, Abundant Intelligence: How Digital Labor Will Rewrite the Rules of Business - the next valuation frontier will not be tech versus non-tech. It will be cognitively leveraged versus cognitively constrained. Cognitive leverage is the ratio of useful intelligence applied to a problem relative to its cost. Digital labor, in the form of AI agents, adaptive robots, and machine reasoning systems, dramatically reduces the unit cost of cognition and getting things done.
When intelligence becomes abundant, the economic question shifts from access to configuration. The firms that will command premium valuations are not simply those that deploy AI tools, but those that redesign their operating models around scalable intelligence. They will reallocate work between humans and machines deliberately, increase the speed of decision cycles, and expand the scope of what each employee can accomplish.
In the interim, however, we should expect volatility and value destruction. As AI tools improve, business models built on selling standardized cognitive outputs will come under pressure. Subscription software priced per user may face headwinds if autonomous agents can perform tasks across platforms.
Professional services firms that bill by the hour may struggle if clients expect AI-augmented productivity gains to translate into lower fees. Education platforms that monetize answers will compete against free generative tutors. These shifts can compress margins and reduce growth rates before companies adapt.
Yet the longer-term effect is more nuanced. Digital labor does not simply eliminate work; it redistributes and reconfigures it. When routine analysis, drafting, or coordination becomes machine-augmented, human effort can be redirected toward higher-order judgment, creative synthesis, and system design.
Organizations that treat AI as a bolt-on efficiency tool may realize incremental savings. Those that redesign workflows, governance structures, and incentive systems around blended human-machine intelligence can unlock operating leverage that traditional metrics struggle to capture.
This is where equity markets will eventually refine their lens. Rather than discounting entire sectors based on automation exposure, investors will begin to assess how firms configure intelligence. Do they own proprietary data that improves their models? Have they redesigned processes to eliminate redundant human friction? Are they able to scale output without proportional headcount growth? Can they increase revenue per unit of cognition, not just revenue per employee? These questions cut across industry boundaries.
Consider two wealth management firms. Both face AI-driven planning tools. One treats them as back-office assistants to reduce paperwork. The other integrates digital agents into client onboarding, portfolio construction, risk modeling, and continuous engagement, enabling each advisor to serve twice as many clients with higher personalization. The first experiences margin compression. The second expands capacity and improves outcomes. From a sector perspective, both are “wealth managers.” From a cognitive architecture perspective, they are fundamentally different enterprises.
The same divergence will emerge in insurance, law, engineering, healthcare, and manufacturing. Some firms will cling to labor-based models and see multiples compress. Others will build operating systems around digital labor, improving speed, scale, and scope while reducing error and latency. The equity market will eventually recognize that intelligence configuration, not industry label, determines sustainable advantage.
In the near term, markets are pricing fear. The volatility in software, financial services, and professional content reflects a rational awareness that the old economics of knowledge work are unstable. But indiscriminate selloffs obscure a more strategic reality. AI is not simply an automation wave; it is a redefinition of how organizations create and capture value. The abundance of intelligence shifts scarcity toward design, orchestration, and governance.
For CEOs and boards, this is not merely a technology strategy question. It is a capital markets question. If valuation increasingly reflects cognitive leverage, then leadership must measure and manage it explicitly. They must understand where human judgment is essential, where machine autonomy adds speed and precision, and how the two interact. They must move beyond pilot projects toward systemic redesign. Because the market will not wait for perfect clarity. It will continue to scan for vulnerability and reward adaptability.
The recent selloffs across unrelated sectors are an early signal of this shift. Investors are searching for a new organizing principle in an AI-shaped economy. They have not yet found the right metric. When they do, the valuation gap that defined the digital era will be replaced by a new one. It will not separate technology firms from traditional industries. It will separate those who scale intelligence from those who merely consume it.
