Writer documents experience learning to run AI models locally

By Splitfeed EditorialLast updated Oct 11, 2026, 11:10 UTC

On October 11, 2026, The Verge published a first-person account by a staff writer documenting their initial attempt to use AI models locally rather than through cloud-based services. The piece frames the experience with three words: exciting, overwhelming, and frustrating. The writer cites privacy concerns about sharing personal data with cloud services as the main reason for making the switch, and flags the high cost of RAM-intensive computers and laptops as a significant financial barrier to entry. The article is presented as the opening installment of an ongoing series.

The story matters because it captures a growing tension in how everyday users are beginning to relate to AI tools. Cloud services offer convenience but require users to send personal data to third-party servers, a tradeoff that has quietly become a point of friction for a widening audience. Local models sidestep that concern but introduce a new one: the hardware required to run capable models is expensive, and the setup process is evidently steep enough that a writer at a major technology publication found it genuinely difficult. That combination of privacy motivation and hardware cost positions local AI as something that is technically possible for more people than before, but not yet frictionless or cheap.

None of the three camps had published reactions to this story at the time of writing, so what follows reflects what each camp would typically argue about a piece like this. The Pro-AI camp would likely point to the writer's engagement as evidence that local AI is maturing and that motivated non-specialists can now realistically explore it. The Anti-AI camp would probably seize on the cost and complexity as proof that AI adoption imposes real burdens on ordinary people and that the benefits remain overstated. The Middle Ground camp would most likely echo the article's own framing, acknowledging both the genuine privacy advantage of running models locally and the barriers that still make it inaccessible to most users.

The natural thing to watch is the continuation of the series itself. As the writer publishes follow-up installments, the practical verdicts they reach on cost, usability, and privacy will give readers a grounded answer to the tradeoff question the first piece deliberately leaves open.