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Jack Morris (@jxmnop) on 2026-06-12 argued that while looping Fable across…

2026-06-12 · 18:48 UTC ·@jxmnop ·1 min read

Brief

Jack Morris (@jxmnop) on 2026-06-12 argued that while looping Fable across massive datasets is poor compute use, Fable (and Mythos) are valuable for high-leverage data work—eval design, rubrics, error analysis, and fixing noisy pretraining data. He warned xAI could have Fable inspect every pretraining row to repair typos, and Anthropic cannot block this without crippling its models.

Source evidence

to clarify, looping Fable over huge datasets wouldn't be a good use of compute for many reasons

but much of what goes into better models is DATA WORK: eval design, rubrics, error analysis, repairing noisy data

Fable can do this!
and Mythos will do this too

Jack Morris (@jxmnop)

An underrated part of this discussion is that
(a) there's huge leverage in improving data, and
(b) there's no way Anthropic could safeguard this

xAI could instruct Fable to look through EVERY row of pretraining data and fix any typos and errors. this probably the single highest-leverage activity for a lab playing catchup

and it's not possible for Anthropic to prevent this without completely kneecapping the model itself, because data quality work looks like any other kind of knowledge work ("check this text for errors", "rewrite this in a formal tone")

— https://nitter.net/jxmnop/status/2065495499566989675#m