The Case for Owning Your AI | & Token Economics of bringing AI In-house

The signal is that AI infrastructure spend is beginning to fragment rather than pool. The consensus trade has treated inference as a tide lifting a handful of hyperscaler balance sheets. The repatriation thesis implies that a portion of that spend redirects toward server original-equipment manufacturers, edge-hardware vendors, and the colocation operators who would house on-premise factories that do not fit inside corporate walls.

None of this displaces the hyperscalers at the frontier, where training and the most capable models remain centralised. But it complicates the cleaner version of the hyperscaler-margin story, in which every incremental AI dollar accrues to the same few providers. The more interesting exposures may be the picks-and-shovels names positioned across both deployment modes, and the operators whose economics improve whether inference centralises or disperses. The standing caution is that this remains, for now, a vendor-led narrative; the buy-side evidence of repatriation at scale is still largely anecdotal.

#AIInfrastructure, #InferenceEconomics, #TokenFactory, #DataSovereignty, #CapitalAllocation, #EdgeAI, #Hyperscalers, #EnterpriseAI, #JevonsParadox, #PunjabCapital, #CapitalInsights