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The recent “Global Intelligence Crisis” scenario has amplified concerns that AI-driven displacement of white-collar labor could undermine consumption and destabilize economic growth. While these fears are understandable, history suggests that transformative technologies have repeatedly reshaped—not collapsed—economic systems.
At Sands Capital, we approach this moment as rational optimists: rigorously assessing downside risks while positioning portfolios to participate in long-term productivity expansion and increased dispersion across business models.
Key Points
- Technological disruption is not new — and neither are doomsday predictions.
- AI introduces real transition risk, but history argues against systemic demand collapse.
- Our portfolios reflect disciplined participation and active capital allocation.
Citrini Research’s “2028 Global Intelligence Crisis” outlines a dystopian but logically constructed pathway in which rapid advances in AI compress the economic value of human intelligence, displace high-income white-collar labor, and trigger cascading second-order effects: declining consumption, weakening tax bases, margin pressure across services, and ultimately a structural slowdown in GDP.
The core fear is intuitive. Knowledge workers represent a disproportionate share of income, consumption, and asset ownership. If AI meaningfully erodes the intelligence premium embedded in modern economies, what happens to aggregate demand?
This concern is not emerging in a vacuum. From unexpected breakthroughs such as DeepSeek and other rapidly advancing open and closed model releases, to accelerating enterprise experimentation, to questions around capital intensity and competitive durability, investors are grappling with how AI could go wrong. Could margins compress faster than revenue expands? Could labor reallocation lag technological progress? Could capital be misallocated at historic scale?
These are serious questions.
They are also deeply familiar.
Every major technological inflection point has arrived accompanied by a version of this same fear: that the innovation in question would destabilize labor, undermine economic structures, and permanently impair demand.
AI is not the first technology to provoke existential anxiety. It is simply the latest.
When Machines Were Going to End Work
In 1811, skilled textile workers in England began destroying mechanized looms. The Luddites were not irrational; they were responding to real wage compression and job loss. Mechanization did eliminate certain artisanal roles. The fear was straightforward: machines would permanently erode the economic value of human skill.
Instead, industrialization expanded output dramatically, lowered prices, increased accessibility of goods, and ultimately raised real incomes. The composition of work changed. The scale of economic activity expanded.
A century later, in 1930, John Maynard Keynes warned of “technological unemployment”—the possibility that machines would outpace society’s ability to create new uses for labor. This was not fringe commentary. It was a sober assessment from one of the most influential economists in history.
Yet the decades following World War II saw one of the most powerful expansions of middle-class employment in modern history. Aerospace, pharmaceuticals, consumer electronics, professional services—industries that barely existed in Keynes’ era became pillars of growth.
The pattern repeated in the computer age.
In the 1970s and 1980s, office automation was widely expected to hollow out white-collar work. Word processors would eliminate administrative staff. Spreadsheets would replace accountants. Databases would compress analytical roles. In 1983, Nobel laureate Wassily Leontief compared humans to horses displaced by tractors, suggesting computers could render human labor economically obsolete.
Instead, finance grew more complex. Consulting expanded. Enterprise software scaled. The IT services industry flourished. Knowledge work did not disappear. It evolved, and often expanded in scope.
Even the internet, now foundational to global commerce, faced both underestimation and post-crash fatalism. In 1998, it was compared to the fax machine in terms of economic impact. After the dot-com collapse, many concluded the digital revolution had been largely illusory. Yet the excess infrastructure built during that volatile period became the backbone of the next two decades of growth.
Across railroads, electricity, mechanization, computing, and the internet, the sequence has been remarkably consistent:
- Breakthrough technology promises productivity gains
- Capital floods into the opportunity
- Fears of labor displacement and structural instability intensify
- Volatility and overcapacity emerge
- Weak participants fail
- The technology diffuses broadly and expands economic scope
Disruption is real. Capital is often misallocated. Certain jobs and companies do not survive.
But the dystopian outcome, permanent aggregate demand collapse due to productivity improvement, has not historically materialized.
What Makes the AI Debate Different
AI directly touches cognitive labor, which is why the current fear feels more acute. Previous waves automated physical effort or narrow computational tasks. AI has the potential to augment, and in some cases substitute for analytical, creative, and decision-making functions.
That increases dispersion risk. It may accelerate adjustment timelines. It may compress certain business models faster than prior cycles.
The Global Intelligence Crisis scenario is intellectually coherent: if high-income cognitive workers lose earning power en masse, consumption declines; if consumption declines, service-heavy economies contract; if contraction feeds back into employment, a negative loop emerges.
For this outcome to become structural rather than cyclical, however, we believe several unprecedented conditions must hold:
- Automation must eliminate more value than it creates.
- New industries and complementary tasks must fail to emerge at sufficient scale.
- Productivity gains must not translate into lower costs, broader access, or new demand pools.
- Capital must fail to reallocate effectively.
History suggests that general-purpose technologies do not behave this way.
They compress certain forms of labor while expanding others. They destroy specific business models while enabling new categories that were previously uneconomic. They generate volatility, sometimes severe, but ultimately broaden productive capacity.
The more probable outcome is reweighting, not collapse.
Asking the Right Question: What Could Go Right?
As growth investors, we are rational optimists. That does not mean dismissing risk. It means acknowledging uncertainty while grounding expectations in historical base rates.
In periods of fear, it is natural to focus on downside pathways. But investing requires asking a symmetric question: what could go right?
What if AI meaningfully enhances productivity across healthcare, scientific research, logistics, manufacturing, and software development?
What if it lowers the cost of knowledge work, expanding access rather than compressing aggregate demand?
What if entirely new industries—currently difficult to imagine—emerge around AI-native services and capabilities?
Railroads enabled national markets. Electricity enabled mass production. The internet enabled digital commerce and cloud computing.
AI could enable:
- Accelerated drug discovery and personalized medicine
- Autonomous industrial systems
- Radical improvements in enterprise productivity
- New consumer categories built around intelligent interfaces
- Entirely new forms of digital labor and entrepreneurship
The magnitude and timeline are uncertain. But the direction of capability expansion is clear.
Portfolio Positioning at Sands Capital
Our portfolio construction reflects a balanced view of the current moment: participation in structural opportunity, paired with rigorous evaluation of business model durability and opportunity cost.
Technological transitions do not impact all companies equally. They widen dispersion. Our task is to distinguish between structural beneficiaries, adaptable incumbents, and businesses at risk of long-term economic erosion.
- Meaningful, Diversified Exposure to AI Infrastructure Beneficiaries
Historically, durable value creation in general-purpose technology cycles has often accrued at the infrastructure layer — rail networks, electric grids, semiconductor platforms, cloud architecture.
In the current cycle, AI infrastructure spans the full stack, from advanced semiconductors, lithography, and high-bandwidth memory to power management, networking, data center buildout, and global cloud platforms. These businesses enable compute creation, orchestration, and distribution. While individual applications and model leaders may evolve, the underlying demand for performance, connectivity, and energy efficiency is likely to expand with broader AI adoption.
We maintain diversified exposure across the AI stack. Technological evolution is nonlinear and competitive dynamics shift. Diversification reduces fragility associated with concentrated architectural or monetization assumptions.
Our objective is not speculative exposure to a single outcome, but participation in the expanding productive capacity that AI enables across industries.
- Heightened Scrutiny of Potential “Terminal Value Purgatory”
Technological transitions often create what we describe as “terminal value purgatory” (TVP) — companies that may appear stable in the near term but face uncertainty around the durability of their long-term economics.
Importantly, TVP is not a binary label, nor does it imply automatic exit.
Our analysis focuses on three core questions:
First: Why might the business be in purgatory?
Is the pressure cyclical, competitive, technological, regulatory — or structural? Is AI compressing pricing power, altering customer workflows, lowering barriers to entry, or changing the value chain in ways that could impair long-term returns?
Second: What would allow the company to exit purgatory?
Does it possess credible reinvention pathways—product evolution, platform expansion, cost restructuring, balance sheet flexibility, or strategic repositioning—that could strengthen or restore investor confidence?
Third: How long could that transition take?
Even where adaptation is plausible, the timeline matters. Multi-year repositioning efforts can carry significant opportunity cost in an environment where capital deployed toward structural beneficiaries may compound more effectively.
Based on this work, outcomes vary.
In some cases, valuation already discounts structural risk and offers attractive asymmetric return potential if adaptation succeeds. In others, the duration and uncertainty of recovery justify trimming or exiting in favor of clearer long-term compounders.
The discipline lies not in avoiding all businesses facing transition, but in being explicit about durability, reinvestment requirements, and time horizon.
- Embracing Dispersion and Opportunity Cost
Periods of technological acceleration increase dispersion across sectors, business models, and competitive positioning. They reward adaptability, scale advantages, and capital discipline—and they expose fragility.
Rather than positioning portfolios for systemic contraction, we position for differentiation.
This means:
- Concentrating capital in businesses with clear structural tailwinds and durable competitive advantages
- Continuously re-underwriting long-term growth assumptions as AI reshapes industry structures
- Allowing position sizes to reflect conviction, durability, and evolving risk-reward dynamics
- Concentrating capital in businesses with clear structural tailwinds and durable competitive advantages
At the portfolio level, the goal is resilience through selectivity — balancing exposure to long-term innovation with a willingness to reassess when facts change.
While economies historically adapt to transformative technologies, individual companies must demonstrate that they can adapt as well. We believe our portfolio construction reflects that distinction.
A Measured Conclusion
But history counsels against extrapolating technological disruption into permanent economic decline.
General-purpose technologies have repeatedly reshaped economies — sometimes painfully, often unevenly—yet ultimately expanded productive capacity and living standards.
We believe AI will likely follow that broader historical arc: volatility, dispersion, reallocation, and ultimately long-term expansion.
Our role is not to predict utopia or dystopia. It is to allocate capital thoughtfully across that transition.
Disclosures:
The views expressed are the opinion of Sands Capital and are not intended as a forecast, a guarantee of future results, investment recommendations, or an offer to buy or sell any securities. The views expressed were current as of the date indicated and are subject to change.
This material may contain forward-looking statements, which are subject to uncertainty and contingencies outside of Sands Capital’s control. Readers should not place undue reliance upon these forward-looking statements. There is no guarantee that Sands Capital will meet its stated goals. All investments are subject to market risk, including the possible loss of principal. Recent tariff announcements may add to this risk, creating additional economic uncertainty and potentially affecting the value of certain investments. Tariffs can impact various sectors differently, leading to changes in market dynamics and investment performance. There is no guarantee that Sands Capital will meet its stated goals. Past performance is not indicative of future results.