Sam Altman Called Local Inference a Poor Allocation of Resources
On October 30, 2026, Altman stated that running models on one's own computers is inefficient from the standpoint of global memory allocation. We break down the argument.
On October 30, 2026, Sam Altman (head of OpenAI) spoke about local inference (running an already trained model on one's own device). His position: keeping part of the world's DRAM (dynamic memory, which makes up RAM) supply in personal computers and using it at a few percent is a poor allocation of a limited resource. According to him, from a global standpoint, maximum utilization in the cloud is more beneficial.
This is not a new technical thesis but a clarification of an old debate. Proponents of local inference usually cite privacy, independence from the network, and control over the model. Altman shifts the frame: the question is not whether it is convenient for the user, but how scarce memory is consumed. Previously, the argument about open models sounded like "now you can run it yourself." Here he examines it specifically as resource economics, not as a question of access.
In practice, this means that for tasks where throughput matters, the cloud remains the primary option. Local running makes sense where data cannot be taken outside or where there is no stable network. But if memory is counted as a shared limited supply, then a computer sitting idle most of the day is a loss. For developers, this is an argument in favor of hybrid schemes: heavy computation in the cloud, sensitive data on the device.
It is unknown exactly what utilization figures and memory volumes Altman has in mind: the quote contains no specific measurements. There is also no data on how this relates to DRAM prices and the availability of cloud capacity at the end of 2026.