The Infinite Demand for Tokens, Claude Mythos, and Supply Constraints (Invest Like the Best)
Source
Patrick O'Shaughnessy
Author
Patrick O'Shaughnessy
Date
Terms in this piece · Glossary
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
Why it matters
If you are budgeting for inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → at scale, Patel's breakdown of where compute and memory supply actually binds — and why tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → demand keeps outrunning it — is useful context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition → for why capacity and pricing move the way they do.
Key quotes
“If you don't use more tokens,
you'll never escape the permanent underclass.”
Dylan Patel
“Mythos is potentially the biggest step up
in model capabilities in two years.”
Dylan Patel
“Now ideas are cheap and plentiful,
but execution is very easy.”
Dylan Patel
“DRAM will double or triple from here still
because that's how much capacity is required”
Dylan Patel
“People hate AI.
AI is less popular than ICE,
less popular than politicians.”
