Story · Semantic Scholar
What Happens When the Model Eats the Stack? Rethinking the Research Agenda for Data Agents to Withstand the Bitter Lesson (Semantic Scholar)
paper · Story page
A position paper argues that as models improve they absorb the system layers built to cover their limits, and that what stays worth building is persistent semantic context about the data environment, served as a first-class abstraction.
In plain words
- Researchers argue that better artificial intelligence could make some tools built around today's systems unnecessary.
- Those tools make up for limitations that future systems may overcome on their own.
- The researchers propose storing reusable background information that helps the software understand the data it works with.
- The researchers argue this background information could help software answer questions about huge, complicated collections of data.
Appeared in
- Per-phase model routing cuts agent cost, RASER on Slurm, Amp steers mid-run
Sep 09, 2026 · in the sections
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