What becomes free and abundant stops being where the money is and becomes less important. Tap water is essential and it is not an industry. If Jason is right that Deepseek and Other Open Source AI are indistinguishable for 95% of queries. The consequence is not that corporations buy hardware and run open source. 95% of what we are doing becomes worth roughly nothing per token, and all the remaining value concentrates in the 5% where reliability, liability, and the last few points of per-step accuracy decide outcomes.
It’s happening
Default will be local model by next year for most corporate workers https://t.co/wcTL8RFKLd
— @jason (@Jason) July 31, 2026
All companies would the same problem that AI cloud providers have. Capture enough value for return on the hardware spending before it is deflated. There is a 10X annual deflation in the tokens.
The deflation trap and the agent-chain panel are really the same wall viewed twice. Commoditization is what makes local capex unrecoverable, and it’s simultaneously what pushes the money into exactly the regime where local can’t compete. His mechanism is right; it just doesn’t terminate where he says it does.
What is Likely to Happen
1. Open weights on rented cloud / neoclouds (~50–55%)
Model quality and price pressure play out exactly as Jason describes. MIT-licensed (or similar) weights dominate token volume and cap closed pricing. Almost none of the volume runs on customer-owned hardware. This is already the base case and continues.
2. Cascade / router hybrid (~20–25%)
Small local or ultra-cheap cloud models handle classification, simple generation, redaction, autocomplete, and the easy majority. Frontier (open or closed) handles escalations. Local tokens can grow 10–50× while local spend share stays small. This absorbs 100× local tokens without requiring primary local dominance or solving the memory/fit problem for frontier models.
3. Local arrives as invisible OS feature (~10–15%) — Apple, Google, Microsoft, etc., ship competent small-to-mid models inside the OS / on-device for private, low-latency, offline tasks. Users never switch. It just works for the 80% easy cases. Currently blocked by falling memory content per device and the quality gap on anything agentic. Becomes more plausible if architecture efficiency delivers 5–10× memory-per-useful-token gains.
4. Sovereign / regulated / air-gapped islands (~5–8%) — Real, growing, and defensible for specific sectors. Structurally capped at low single-digit percent of total tokens and higher (but still limited) share of high-value regulated spend.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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