Picture the old Pac-Man screen for a moment: a yellow circle moving through a maze, consuming dots one by one.
Now imagine that circle is artificial intelligence.
The dots? Business sectors. Public companies. Entire professions. Possibly even your own job.
The comparison is imperfect, but it captures something real. What we’re watching unfold is not just the rise of AI in isolation. The more consequential shift is the convergence of three technologies: robotics, artificial intelligence, and quantum computing. For simplicity, call them RAQ: Robotics. AI. Quantum computing.
Individually, each is disruptive. Together, they have the potential to change how industries operate at a structural level. This is not about marginal efficiency gains. It is about redesigning production, analysis, and decision-making from the ground up. In some cases, it may mean certain functions simply stop existing in their current form.
RAQ and the Nature of Technological Change
Technological revolutions rarely arrive quietly, and they rarely coexist peacefully with legacy systems.
They replace them.
Horses did not gradually coexist with automobiles for long. Film did not maintain dominance once digital imaging became viable. Brick-and-mortar retail did not hold its position once e-commerce achieved scale. Each shift created new fortunes while rendering older business models obsolete.
RAQ carries similar potential.
Robotics reduces dependence on human labor for physical tasks. AI reduces the need for human processing in analytical tasks. Quantum computing, if it becomes commercially practical, could dramatically accelerate complex simulations, optimization, and pattern recognition.
When those forces converge, the result is not incremental improvement. It is structural change.
Industries built on routine human analysis, documentation, and advisory services may discover that what they believed to be durable competitive advantages were in fact temporary inefficiencies.
Early Signals in the Market
This is not purely theoretical. We are already seeing early indications.
AI has begun pressuring traditional software providers by driving the marginal cost of analysis and customization toward zero. That alone challenges subscription-based business models built on information processing.
More recently, the financial services sector received a preview of what this shift might look like.
A privately held company, Altruist, introduced an AI platform called Hazel. Hazel can instantly analyze client financial documents and portfolios and generate personalized tax strategies. Work that previously required hours of professional time can now be produced almost immediately.
Markets reacted.
Stocks of firms such as LPL Financial, Charles Schwab, Raymond James, and Stifel Financial declined following the announcement. Investors understood what was at stake. When automation begins encroaching on billable expertise, revenue assumptions must be re-evaluated.
Hazel sounds benign, even friendly. The branding is intentional. But the economics are not gentle. If complex financial analysis becomes scalable and inexpensive, pricing power changes. And when pricing power changes, valuations adjust.
The Question of Expertise
Consider the professionals potentially affected: CPAs, financial planners, tax attorneys, consultants.
These are not low-skill roles. They require years of education and credentialing. Historically, that investment translated into scarcity, and scarcity translated into economic premium.
Markets, however, do not reward effort. They reward scarcity.
If AI reduces scarcity in certain forms of analysis, the premium compresses. That does not mean every professional becomes obsolete. It does mean the value chain shifts, and margins come under pressure.
Some futurists argue that work itself could become optional, reduced to something closer to a hobby. That prediction may be overstated. Then again, many technological shifts seemed exaggerated before they became obvious in hindsight.
We do not need certainty to recognize direction.
The cost of both physical and cognitive labor is declining. That trend alone has significant implications for wages, business models, and long-term equity returns.
For Investors, This Is About Capital Allocation
This is not a science-fiction narrative. It is a capital allocation problem.
Every major technological transition produces the same dual outcome: incumbents struggle while new leaders emerge. Entire sectors reprice. Some companies adapt quickly and integrate new tools. Others resist until resistance becomes irrelevant.
The transition is rarely smooth. Volatility is part of the mechanism.
The important question is not whether robotics, AI, and quantum computing will influence markets. They already are.
The more relevant questions are structural:
Which companies depend on forms of human expertise that may no longer be scarce?
Which firms control proprietary data, distribution channels, or infrastructure that AI cannot easily replicate?
Which businesses are building the underlying tools rather than defending against them?
Capital tends to move before certainty arrives. By the time outcomes are obvious, repricing has often already occurred.
Pac-Man or Dot
In Pac-Man, there are only two roles. You are either consuming territory or being consumed.
RAQ will not eliminate every sector. Many businesses will integrate these technologies successfully. Others will be gradually displaced.
Investors face a similar decision.
You can dismiss these developments as temporary hype. Many will.
Or you can examine the incentive structures, study the structural shifts underway, and position capital accordingly.
Technological revolutions do not eliminate opportunity. They redistribute it.
Markets remain understandable. Incentives remain analyzable. Structural change, while disruptive, is not random.
The dots do not choose their fate.
Investors do.