Question: if the models can already do knowledge work pretty well (and models that are smarter/bigger become unbearably expensive), are massive improvements to the harness the next frontier?
Author (@bran_don_gell) asserts that current LLMs already perform knowledge work…
Brief
The post argues that because larger, smarter models face rapidly rising cost and inefficiency, progress should shift from scaling model size to improving the surrounding harness. The harness here implies improvements in prompting, retrieval, tool use and orchestration, runtime efficiency, evaluation/feedback, and user-facing workflows as the viable next frontier for boosting knowledge-work performance.
Why it matters
Author (@bran_don_gell) asserts that current LLMs already perform knowledge work 'pretty well' and that making models 'smarter/bigger' is becoming unbearably expensive.
Key details
- Author proposes that the next frontier may be 'massive improvements to the harness' — i.e., better prompting, retrieval-augmented workflows, tool/runtimes orchestration, evaluation/feedback loops, and UI/workflow integrations rather than raw model scale.