Thesis
The demand for intelligence has no natural ceiling.
Directional begins with one observable direction: AI capability, usage, model-lab revenue, token demand, and compute infrastructure are all bending from gradual to near-vertical. The firm invests in the second-, third-, and fourth-order effects of that acceleration.
If you think AI is overhyped, you do not have to take it from us. Read what the people building and underwriting it are saying.
One direction
The same line keeps showing up.
Model capability, time horizon, user adoption, and frontier-lab revenue are not the same business metric. They should not naturally produce the same chart. Right now they do. That is what Directional means: not a precise endpoint forecast, but a compounding direction visible across the AI stack.
Every curve is exponential. The direction shows up everywhere.
The mechanism
The flywheel keeps the curves coupled.
Better models create more use cases. More use cases create more usage. More usage underwrites more hardware. Better hardware enables better models. The flywheel is what turns separate hockey sticks into one reinforcing system.
Capability · Context · Modality
Coding · Agents · Research
Consumer · Developer · Enterprise
Backlog · ARR · Tokens
GPUs · Datacenters · Power
Advanced packaging · EUV · Optics
What the loop forces
Hardware must scale in quality and quantity.
Bandwidth, density, cooling, interconnect, and cluster scale.
Memory, accelerators, racks, power, data centers, and equipment.
Where forced change meets scarce capacity or technical control.
Value migration
We are early because the stack is still base-heavy.
Hardware came first because the stack had to be built: chips, memory, networking, data centers, power, cooling.
A developed AI market inverts. Revenue migrates up to the applications that monetize intelligence directly. That migration is beginning, and it is rapid: Anthropic has roughly 10× its revenue every year since 2023, with year-end 2026 run-rate estimated near $100 billion.
- Hardware. Training clusters, chips, memory, fabs, power, and cooling. Last year's gains were here.
- Infrastructure. The clouds and data centers that turn trained models into inference capacity. Google Cloud grew 63% in Q1 2026.
- Applications. The products built on top. Anthropic scaled from roughly $9 billion at the end of 2025 to roughly $44 billion as of April 2026.
Most visible value so far came from making frontier models possible.
As intelligence is used, value migrates toward products and workflows.
Directional filters
Three filters. We don't need to stretch beyond them.
Atoms over bits
AI makes software abundant. Atoms stay scarce. We default to the physical.
Narrow gates
Some companies hold decades of accumulated knowledge that no competitor can recreate. ASML is the canonical case.
Pace of the curve
AI compounds at multiples per year. The use cases keep widening. We are not settling for a 12-15% IRR.
Scale check
We are early.
Frontier AI lab revenue should reach roughly $200 billion annualized by year-end 2026. That is just over 1% of US white-collar compensation, under 1% of global white-collar compensation, and a fraction of a percent of global GDP.
2026E annualized
annual
annual
2026E nominal
The larger question is not whether AI revenue can be bigger; it is what ratio of economic activity intelligence ultimately captures. White-collar productivity is only the first denominator. Drug discovery, materials science, iterative model improvement, research, and other invention-heavy domains expand the pie rather than merely lowering the cost of existing work. The bars above should be read as distance from the beginning of infinity, not as a ceiling.