Thesis
The flywheel is the starting point.
Directional begins with one observable direction: AI usage rises as models improve, and model improvement requires the physical substrate to keep scaling. The firm studies where that pressure creates second-, third-, and fourth-order effects.
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.
Mechanism
Better models create their own demand.
Each generation of models unlocks use cases that were previously too brittle, too slow, or too expensive. As those use cases become useful, usage rises. Higher usage pulls more compute, memory, networking, and power. The resulting hardware investment enables the next generation of models.
This is why Directional treats AI as a compounding system rather than a single product cycle. The question is not whether one model release is overhyped. The question is where repeated turns of the flywheel force the real economy to change.
Memory as example
Some problems are both frontier and scale.
Memory shows the shape of the problem. Higher-bandwidth memory is a capability frontier because models need to move data faster. Total memory supply is also a capacity frontier because adoption can pull far more volume than a historically commodity-like market expected to provide.
Context windows are expanding, KV cache becomes a first-order constraint, and HBM4 consumes far more wafer capacity per bit than commodity DRAM. The same pattern can appear elsewhere: making the thing is hard; making enough of it, at scale and at an acceptable cost, can be equally hard.
Scale is not illustrative here: the units are HBM capacity. Rubin NVL72 is arithmetic from NVIDIA's per-GPU Rubin memory disclosure.
Value migration
The trade should not stay in one layer forever.
Early in a platform shift, the scarce tools can capture extraordinary value. Over time, value may migrate upward as infrastructure becomes more available and the application layer finds economically important uses. Cloud followed that pattern. AI may rhyme with it, but the path will be shaped by the physical constraints of intelligence at scale.
Directional criteria
The filter is narrow by design.
- Atoms over bits
- Narrow gates over broad themes
- AI and AI derivatives only
- Asymmetry over consensus comfort
- Strong convictions, loosely held
These are not slogans for a broad thematic fund. They are constraints on what earns attention. Directional looks for places where the flywheel is forcing change and where the market has not fully priced the consequences.