Diffused AI strategies yield marginal, replicable gains. Deep concentration in one domain—chosen via three filters: infrastructure needs, workflow centrality, and measurability speed—creates competitive moats. Commitment to execution matters more than perfect selection.
Language models trained on text learn how intelligence sounds, not how it works. VL-JEPA offers an alternative: predict continuous meaning rather than discrete tokens. Smaller model, better physical understanding. Constraint abstraction beats compression.
Language models trained on text learn how intelligence sounds, not how it works. Written language is compressed reasoning residue, stripped of exploration and failure. Real progress requires constraint discovery through interaction, not pattern prediction.
AI sovereignty isn’t about replacing vendors, it’s about controlling intent. In a world where models evolve every six months, enterprises must own the logic that governs decisions while treating execution as swappable. Control the architecture, not the tools.
Enterprises face a false choice: vendor lock-in or costly rip-and-replace. The third path: sovereignty-first architecture. Own the logic that defines strategy, delegate execution to vendors through interfaces that preserve inspectability, overridability, and portability.