Investment process: progress, probabilities, and patience
Beyond aligning with AI’s progress, two further principles belong in the investment-process drawer.
First, think probabilistically. Investment decisions should resemble branching decision trees, not single-point forecasts. Each AI breakthrough creates multiple future states: on one axis, the march of time; on another, the firm’s competitive position. Shareholders, to invoke Warren Buffett’s instruction to “think like owners”, must ask how every branch reshapes the moat over successive years, not just the next quarter, and compare to the expectations embedded in market valuations.
Second, exploit time arbitrage. Pinpointing the exact moment AI reshapes an industry is futile; grasping the direction is not. We believe markets routinely underrate long-run technological shifts. The patient investor collects the spread between early insight and eventual consensus. As quarters pass, the odds that reality converges with informed expectations on AI only grow.
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In sum, three Ps capture the investment process:
- Progress — Assume AI’s capability curve keeps steepening and back the firms that gain as costs fall and performance rises.
- Probabilities — Model outcomes branch by branch and invest where the odds and payoffs skew in your favor.
- Patience — Profit from the gap between early insights into AI and the market’s slow recognition — focus on if, not when.
Stock-picking strikes back
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AI works on probabilities, and AI is a general-purpose technology. We believe in combination, those traits mean AI is no plug-and-play panacea. Its value diffuses unevenly and must be tailored to each task, then knitted tightly to the right complementary assets.
That makes stock selection indispensable. From a distance everything appears calm; at ground level each firm is fighting a distinct battle, evident to anyone who listens to operators and management commentary. If AI’s imprint escapes you, you are surveying the situation at 10,000 feet while the disruption is playing out at street level.
Single-stock considerations
Where, then, should stock-pickers focus? Here is an indicative (but not exhaustive) set of checks.
Multi-order effects
A general-purpose technology reaches full stride only after products, infrastructure, and an ecosystem take shape — a process that unfolds in waves. Expect concentric ripples. First-order change is raw capability — the compute and the models. Second-order change comes from the complements that channel that capability. Further multi-order change follows when those complements reorder incumbents and spawn entirely new markets, producing third- and fourth-order shifts.
Tracing the knock-on effects takes discipline. Many analysts stop at the first round; skepticism and hazy understanding about AI’s promise drains the will to think further, often leaving these analysts’ research reactive.
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Evolving competitive frontiers
Every business’s competitive edge is a stone arch: countless blocks press together in silent complexity, yet one keystone converts weight into stability. How the introduction of AI affects the structure is uncertain. Nudge a peripheral block and little shifts; jar the keystone and the arch collapses. This is proof that complexity, whether in a stone arch or competitive moat, conceals nonlinear chokepoints. Whether AI has negligible or seismic effect on a business depends entirely on where it strikes the true source of advantage.
That task begins with first principles. AI alters how every firm ingests, parses, and acts on information. One should analyse, therefore, how those new flows reshape the organisation’s innards (costs, efficiency, error-rates, product improvement, R&D) and outward stance (customer intimacy, pricing, competition against rivals).
Nor are businesses anchored in the physical world automatically safe. Their competitive arches rest on many blocks: plants, supply chains, warehouse networks, distribution, information networks, and, increasingly, code. Moreover, multi-order effects matter, affecting moats that AI may stretch or shrink.
Timing and scale add another layer of fog. First movers may reap data loops; fast followers may dodge early missteps. Precision is elusive, but stock-pickers can earn their keep by tracing these firm-by-firm dynamics.
Incumbents vs disruptors, old vs new ecosystems
As AI pushes out the competitive frontier, investors face the perennial choice: back the incumbents or side with the insurgents.
I believe what happens at the margins and what is priced in matters most. The incremental shareholder value may migrate to an AI-native ecosystem at the expense of today’s infrastructure, even if it doesn’t get disrupted entirely — an asymmetry seen in every major technological shift. Equally, a misunderstanding of AI and mispricing of incumbent competitive advantages will likely provide alpha opportunities too.
Customers: proximity and personalisation
Because AI is probabilistic, and inherently fuzzy, it never ships as a one-size-fits-all package; every deployment may need tailoring.
That reality puts a premium on proximity to the customer. With direct access to user data, businesses can potentially fine-tune models, sharpen personalisation, and, in the process, gain bargaining power over upstream suppliers. The further a business sits from the consumer, the greater its risk of being squeezed out by a tighter, AI-mediated relationship.
Data: is it important?
The notion that mere ownership of vast amounts of data guarantees safety or competitive advantages from AI is naïve.
Vast datasets confer no moat if they are duplicated elsewhere, poorly labelled, or irrelevant to the decisions that create value. Data also ages. Moreover, much of the critical context and insight is absorbed into models during pre-training, leaving datasets less defensible than they appear.
Evolving business models
The new compute paradigm brings a marked rise in variable costs. We have moved from an era of near-zero marginal costs to one in which running AI workloads incurs meaningful expense. Even if inferencing ultimately approaches negligible cost, that hinges on the level of capability delivered at that price point. Whether incremental marginal costs settle lower remains an open question.
Management and culture
Qualitative factors, chief among them management and culture, may prove the single biggest determinant of success or failure. A business honed for stability often stumbles when disruption arrives, whereas one built for change can pivot swiftly as AI reshapes the field. Scale can cut both ways, either creating an inertia of bureaucracy and bogging down decision-making or supplying the data critical for seizing AI’s promise.
Incentives matter. If executives are rewarded for preserving the status quo, they may resist upheaval; if bonuses hinge on innovation, they may lean into AI’s disruptions. Similarly, lengthy reporting hierarchies can stifle responsiveness, whereas flatter chains of command allow leadership to pivot quickly as the landscape shifts.
AI is reshaping industries beyond what any single metric can capture. Progress demands that we assume AI’s capabilities will keep compounding; probabilities require that we map the branching outcomes with cold realism; patience insists that we let insights mature while the market chases noise. None of this can be captured by a factor screen. I believe the exercise is, and will remain, one of stock-picking company by company, moat by moat, culture by culture. This is the only way, in my opinion, to separate the businesses that harness AI from those that are humbled by it.
Gurvir Grewal is global research analyst on William Blair’s global equity team
