It’s hard to keep up with the evolution of “value” in the artificial intelligence business as scarcities that create value keep shifting.
Between 2017 and early 2024, for example, scarcity and value in the AI value chain were heavily concentrated at the top of the stack:
high-quality training data
frontier model development(research talent, algorithms like transformers, and initial large-scale training runs).
Compute, in the form of Nvidia graphics processing units, was important, but a dominant bottleneck:
inference was relatively cheap
models were mostly accessed using APIs or research prototypes
real-world deployment at scale was limited
So value accrued to pioneers in data curation, model architecture, and cloud providers.
By 2026, constraints have shifted with mass deployment:
compute infrastructure (GPUs/accelerators, high-bandwidth memory/HBM, advanced packaging) remains scarce
energy is emerging as a new scarcity (data center electricity, grid capacity, and permitting delays)
physical infrastructure (data centers, land in power-rich locations, cooling) lags demand
data scarcity is resurfacing as high-quality public data exhausts and regulations tighten
model weights and foundational capabilities have commoditized somewhat
supply chain crunches extend to materials like indium phosphide for optics and memory chips.
Overall, value has "inverted" toward the bottom of the stack, a shift from past decades where value accumulated in applications:
infrastructure and physical hardware is scarce (chips, GPUs and accelerators, compute as a service, utilities, and energy firms)
application-layer value (SaaS, agents, enterprise workflows) is growing but often depends on cheap/reliable inference, and therefore infrastructure
consumer surplus from gen AI has risen sharply, but producer value capture is uneven.
Among the key shifts so far in 2026:
value has moved downstream from "intelligence creation" (models/data) to "intelligence delivery and scaling" (inference and infrastructure)
compute shortages have evolved into broader supply-chain and energy issues including
power contracts
tier-2 locations
inference efficiency
energy consumed per token.
Future scarcities could develop in the future:
embodied AI (robotics, sensors, actuators, energy storage, and unstructured environment handling)
orchestration and decision-making (supply chains, logistics)
regulatory compliance
valuable applications that leverage abundance (as physical constraints lessen)
geopolitics, materials, and talent for physical AI.
And, by definition, we don’t know what we don’t know. So we cannot predict what unknown issues might arise.
"Known unknowns" in the AI value chain refer to recognized uncertainties or risks whose existence we acknowledge, even if we cannot precisely quantify their timing, magnitude, or full impact.
These are issues we can model, debate, plan for, and partially mitigate through investment, policy, redundancy or research and development.
In contrast, "unknown unknowns" are the true blind spots:
risks
emergent behaviors
systemic shifts we do not yet realize exist.
Unknown unknowns arise from emergent properties and non-linear interactions across the value chain:
unpredictable model optimization for objectives not explicitly intended
systemic supply chain compromises or cascading failures, such as AI agents acting as unpredictable "insider threats"
transformative capability jumps or self-acceleration if AI begins automating large parts of its own R&D, training, or infrastructure design at unexpected speeds or in unforeseen directions
disruption of labor markets, trust mechanisms, legal systems, or global power balances
AI amplifying or interacting with unrelated disruptions or introducing fragility.
By definition, it is virtually impossible to plan for unknown unknowns, except to retain as much flexibility and adaptability as possible.
