The Future Is Shrouded in an AI Fog

Confidence in a specific vision of the future is the foundation for long-term investment. You purchase a 30-year bond when you believe in the solvency of the issuer. You spend a decade to become a radiologist because you believe you’ll be compensated for your hard-earned expertise for the duration of your career. You invest in a software company when you’re confident it will generate many years of recurring revenue. There’s always some uncertainty, but when you’re confident about your ability to see 30 years ahead, you build a skyscraper and a railway. When you can see months ahead, you pitch a tent and buy a bicycle. AI has catapulted us into an era I can best describe as extreme opacity about the future. Given all the things that might change because of AI, it feels like a fog has descended that occludes our ability to see the future. And right now, that’s its most important—and perhaps most underappreciated—economic effect. Think of how uncertainty about basic economic realities keeps multiplying. Dario Amodei or Sam Altman forecast the arrival of super-intelligent AI systems in the next few years. The potentially looming “jobpocalypse” and the very-much-in-the-present “SaaSpocalypse” have revealed how vulnerable our current economic situation may be to AI-powered disruption. The same goes for the large gyrations in public market valuations seemingly caused by new feature releases from the major AI companies. And then there’s advances in physical AI, perhaps best exemplified by the surreal feeling many of us have had watching the world pass by from the backseat of a Waymo, which can make it seem like we’ve crossed the line into science fiction. It’s impossible to work out the full ramifications of ubiquitous machine intelligence harnessed to our most capable software and embodied in different robotic systems. Will AI eliminate the need for almost all software engineers, or will it counterintuitively create more of these jobs? How capable will robots be in a couple years, and what will that mean for the job market? Is the traditional customer service center actually toast? Every question like this reveals the limits of our visibility into the short-term future, and this extreme uncertainty challenges the criteria we use to commit to forward-looking investments. Staring into this fog, leaders will be tempted to trade the potential future gains from skyscrapers and railways for the temporary utility of tents and bicycles. Without the ability to confidently predict where we will go from here, and considering the high cost of premature commitment in volatile environments, I believe that the best approach is to optimize for the unknown. As professor of entrepreneurship who also advises leading companies, I argue that success in an era of extreme opacity often belongs to those who master optionality; those who learn to stage-gate capital, to remain agile in their identities, and to build adaptable organizational systems. The Human Capital Dilemma Consider education, which is the largest long-duration investments many of us make. We commit years and take on substantial debt for college and advanced degrees and the opportunity to step onto a career ladder with predictable rungs. If you are young and ambitious, you might invest in an expensive medical degree in return for 30- to 40-year income of a rewarding, high-earning career. Or enroll in an MBA program to sharpen your analytical and managerial skills for a decades-long career leading a P&L. These are bets on the long-term value of human capital. Today, the decision to invest in human capital is harder to justify. Who will attend medical school if we can’t articulate what a “doctor” even is in 2035? I don’t mean this as a cheap provocation. I mean it literally. Will the doctor be a diagnostician? A bedside counselor? A procedure performer? An overseer of an AI triage system? The signatory on liability forms? None of the above? Who knows. Likewise, who can feel confident that consulting, banking, and tech firms will be hiring MBAs from the 2029 graduating class? Even generalist undergraduate degrees that train students to think, write, analyze, strategize, and communicate may become questionable investments, if AI systems can do these things at near-zero marginal cost. If this sounds alarmist, the data have begun to slowly pile up. Jobs for college grads appear to be much harder to come by than in the recent past (though the outlook may be looking a little better this year), drawing into question the ROI on undergraduate education. Although the political landscape also plays a role, applications to MBA programs have sharply declined this year, too. The argument turns on a distinction economists rarely apply to human capital: the difference between risk and uncertainty. Risk is quantifiable; one can assign probabilities that allow us to price a bet. Uncertainty describes environments where the probability distribution itself is unknown, which is the precise consequence of invisibility. The range of plausible outcomes for a radiologist completing a residency in 2035 spans from “indispensable” to “professionally obsolete.” That’s where the chilling effect of the AI fog sets in. When people face an outcome distribution that is wide and unknowable, they often walk away from costly bets. Corporations sit on the other side of individuals’ investments in human capital. For the companies that depend on skilled workers, the AI fog poses an unprecedented workforce planning problem. Leaders must decide how many people to hire, in which roles and at what salaries, against a backdrop in which the shelf life of any given skill set is unknowable. The same uncertainty that deters individuals from investing in education also makes it harder for leaders to hire and invest in their people, particularly in regions with regulatory constraints on headcount reductions. Of course, this may be why the job market for recent college grads has been shaky. If AI agents can draft memos, write code, perform basic administrative work, conduct research, and build financial models faster and cheaper than junior employees, then companies will adjust hiring plans accordingly. Simply put, expectations about corporate hiring plans and individual human capital investments are two sides of one coin. Corporate Math The haze of the future also throws into question how companies are valued, and therefore if and how they raise capital and communicate their worth to investors and other constituents. For leaders, this means the AI fog will influence the cost of capital, the terms on which growth is financed, and whether a strategic plan passes muster with analysts and investors. The mechanics of this fog-induced change are straightforward, but its implications are radical. Equity values are determined by how much cash a company will generate in the future. Analysts project a company’s free cash flow for the next decade, add an earnings stream that captures long-horizon performance, and discounts the cash flows back to establish a present value. At current public market multiples, a typical company’s terminal value might account for 60% to 80% of its total market capitalization. A critical assumption here—the sole justification for terminal value—is that we believe the business is durable. If AI fog brings into question the long-term viability of a company’s core product or service, then we must also question whether it will earn its terminal value. In turn, if we doubt the terminal value, the valuation collapses. The company is simply worth less. Another way to think about this consequence of the fog is that AI is challenging the preservation of corporate moats. Any business whose competitive advantage is mostly software-, process-, human capital-, or content-based is now vulnerable to a competitor that can replicate features faster and cheaper. This is what we’ve seen in the B2B software-as-a-service (SaaS) sector. SaaS companies have been darlings of the equity markets for decades because they have benefitted from high switching costs, low churn, and fat margins. But if AI substitutes for existing software functionality, commoditizes software creation, shifts the revenue model from high per-seat subscription fees to on-the-margin token consumption, or fundamentally disrupts the enterprise software stack, what becomes of these businesses? Can the market still justify valuation multiples of 50 times free cash flow or higher like we currently see at companies such as Samsara, Cloudflare, and others? Inside the Firm, the Same Fog The enveloping fog also affects internal capital markets. Nearly every large organization runs a stage-gate resource allocation process. A team proposes a new initiative that requires a capital outlay. Leaders evaluate the proposal against an ROIC hurdle. Project advocates forecast over a five- or 10-year time horizon to calculate an expected return. The undertaking gets funded if the numbers clear the cost of capital hurdle and it is on the company’s strategic path. But what happens to this approach when the future is opaque? Take a hypothetical mid-sized contract manufacturer that produces precision-machined components for aerospace and defense customers. The company’s operations team proposes a $30 million investment in a new production line, which would require advanced CNC machines, clean-room finishing, and automated inspection systems. The facility expansion is sized to handle the company’s forecasted order book. The finance team models a decade of projected revenue against the capital outlay. The IRR clears the hurdle rate comfortably. Should leadership greenlight the investment? As the fog rolls in, they must ask whether, within two to three years, a new generation of AI-controlled robots learn tolerances from a few sample runs rather than from months of setup will allow competitors (or even their own customers) to bring this production in-house at a fraction of the capital cost? With its dedicated tooling, long production runs, and skilled machinists, the new line is optimized for today’s manufacturing paradigm. But if general-purpose robotic systems collapse the setup costs that have always protected specialized manufacturers, the competitive moat guarding the company’s 10-year forecast isn’t reliable. The original revenue forecast goes up in smoke. Optimizing for the Unknown We have entered a period in which conviction in the prospects of many long-term investments has declined. The more opaque the future becomes, the harder it is to justify long-duration bets. That is true for companies, for individual, for investors, and even for governments. Opacity pushes up the cost of capital because high uncertainty is hard to price. There are a few predictable outcomes of this unpredictability: risk premia will rise and projects with distant payoffs will lose appeal. It’s straightforward that if you can’t see the future, it’s hard to build for it. This kind of opacity is unfamiliar to many of us in the West. For decades, we have enjoyed an unusual combination of geopolitical insulation, institutional stability, and technological changes that, while eventually disruptive, have arrived slowly enough to not dislodge long-range planning. That stability made it rational to invest in long-lived infrastructure projects, in specialized career tracks, in sprawling supply chains, and in corporate structures designed for evolutionary—but not revolutionary—change. We have had the luxury of investing as though tomorrow would be a slightly updated version of today. In the face of the AI fog, however, the sudden invisibility of the future means that the only compelling option is optionality itself. For individuals, this will require us to cultivate psychological and professional agility. By these, I mean the ability to abandon a plan despite having invested in it, to reskill often, to refocus on opportunities as they arise, and to let go of our professional identities so we can adapt them as circumstances evolve. Corporate leaders need to embed optionality into both their capital allocation models and their organizational designs. First, it is time to shift from multi-year capital commitments to staged investment with explicit decision points. Likewise, it is time to (re)consider zero-based budgeting systems, to remove some of the built-in inertia in resource allocation. It’ll be necessary to replace the question, “What is the 10-year return on this investment?” with “What is the smallest commitment we can make now that buys us information and the right, but not the obligation, to follow on with more capital?” Venture capital has always understood this logic. Large corporations now need to practice it too. Second, rather than locking in organizational structures optimized for a stable future, think about how to organize for the arrival of agentic AI. If the future “operating model” of the firm is uncertain, as is perhaps foreshadowed by companies like Block, which laid off roughly 40% of its staff as part of what CEO Jack Dorsey claimed were AI-related changes, and if doubt surrounds the product portfolio and unit economics, then leaders can’t hard-wire the company for a single forecast. Modular teams, frequent changes to organizational processes and designs as innovative companies discover new and more efficient approaches, job design with built in flexibility, and thin and adaptable layers of coordination (whether humans or AI agents), will all help to preserve maneuverability. Third, leaders need better sensing systems. We are witnessing a period of constant, unprecedented change in the scale and scope of enterprise-grade AI capabilities. Even passive awareness of all that is happening is difficult to achieve, and passive awareness is not enough. Leaders need to put in place a small team whose full-time job is to monitor frontier AI capabilities and translate technical progress into managerial implications for the firm, including what to adopt, when to adopt it, how to implement it, and what it all means for the company. This team should be asking uncomfortable questions early, before the market asks the same questions of leaders, but with greater force. What new, technologically enabled opportunities are coming down the pike? Which parts of the product portfolio will be commoditized by technical advances? Which offerings are genuinely defensible, and which remain prosperous only because no one has yet aimed a capable system at them? Which workflows can now be unbundled from labor? . . . Markets and firms will surely adapt to the fog. Investors will recalibrate. AI-native companies will be founded. Existing firms will restructure, although many may fail. None of this forecloses a generally optimistic outcome. An enormous amount of capital is already flowing into areas in which there is collective confidence in long-term viability, such as energy infrastructure, foundation model development, robotics, and the expanding ecosystem of companies building the physical and computational substrate that intelligent systems require. Productivity gains will likely materialize, and for many companies, will enhance unit economics. But the fog means that explosive growth in some sectors will coexist with wrenching dislocation in others, and it is premature to know which ones yet. In short, we are entering a paradigm shift in how we place bets on the future. The long-duration investments that define modern economic life—mortgages, law degrees, high-CapEx product development—emerged in an era in which the future was legible enough to justify committing to it. AI has made that future opaque in many areas, and it has done so faster than our institutions have adapted. Equity markets have responded mostly quickly to the change, because capital can usually be reallocated with a few keystrokes. Of course, repricing the value of a corporation is far simpler than restructuring it. But leaders are still running 10-year DCFs. Universities are still selling expensive four-year degrees as lifetime assets. Governments are still debating whether AI will be disruptive at all. The fog is here. It’s no longer a question whether it will reshape how we invest, hire, build, and plan. The truly pressing question is: Will we redesign our institutions for opacity before investment of all sorts seizes up?

The Future Is Shrouded in an AI Fog

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