Source: "The value has to live somewhere: In fine-tuned weights the vendor owns. In a generic model wrapped in your memory and context layer. In your own application logic and data... That's a choice, not a law of physics. Open-weight models like Llama, Mistral and Qwen are now good enough for most enterprise work, and they run on hardware you own... Banks, hospitals and defense already run this because regulation forces it. For everyone else it's on the table." ## Atomic Insight Bringing in a Forward Deployed Engineer gets a company outcomes, but does not settle where the resulting intelligence ends up living: in fine-tuned weights the vendor owns, in a generic model wrapped in the client's own context layer, or in the client's own application logic and data. A vendor tends to concentrate value in the part it keeps unless the client makes an explicit infrastructure choice. Open-weight models such as Llama, Mistral, and Qwen are now capable enough for most enterprise work and can run on owned hardware, including a fully air-gapped box, so routing the engagement through a vendor's proprietary model is a choice, not a technical necessity. Regulated industries already default to on-prem for this reason; the same option exists elsewhere but is rarely exercised. ## Cross-Domain Connections [[Infrastructure Ownership Principle]] states the same principle, that owning the stack preserves portability while dependency creates lock-in, applied there to managed API services. [[Enterprise AI's Bottleneck Has Shifted From Model Quality to Deployment Capacity]] is a sibling note: that one covers why Forward Deployed Engineers get hired, this one covers what happens to the intelligence once they are. ## Source - [[Four of the biggest companies in tech just made the...]] — Vaibhav Sisinty, tweet, 2026-07-04 — https://x.com/vaibhavsisinty/status/2073378306343465298