Desk
Analysis of the strategic shift by businesses from proprietary large language models to open-source alternatives or in-house solutions.
As cloud AI pricing climbs, founders are rerouting to open-source models, edge inference, and niche accelerators — and investors should take note.
Enterprises are shifting to locally hosted, open LLMs to cut costs and control data. Big clouds won't vanish, but margins will compress—and opportunities will follow.
From quantized weights to private GPU clusters, companies are moving AI workloads off pricey APIs. Here's what that means for cloud, chipmakers and startups.
On-device and private models are moving from experimental to production. Here is why US companies are choosing local LLMs over public APIs — and what it means for costs, compliance and control.
Rising API bills, compliance headaches, and data risk are pushing enterprises toward self-hosted and open models. Expect GPU vendors, cloud gatekeepers, and MLOps firms to profit.
As API bills climb and data risk grows, American companies are betting on in-house, open-source models for cost control, privacy and product differentiation.
From Wall Street shops to hospitals, a quiet migration to on-prem and open-source large language models is reshaping the AI vendor map—and the winners won’t be who you expect.
A cost-driven migration to open-source LLMs and in-house inference is reshaping venture bets, cloud demand, and who wins the next phase of artificial intelligence.