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LLMs lower the cost of optimizations, yet software slowness persists due to other factors
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Engineers can achieve certain performance improvements more cheaply with LLMs, but overall software speed is still limited by non-technical factors such as budget constraints and tolerance for slowness.
Lowering the cost of optimizations makes it easier to implement JITs, indexes, and workload-specific tweaks. However, the same latency introduced by LLM-based tooling shifts work to asynchronous models, which can affect teams that expect immediate feedback. Ultimately, budget limits, cost-center mindsets, and increased tolerance for slowness can prevent these gains from translating into faster user-facing software.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
LLMs reduce the cost of certain optimizations like JITs and indexes.
Increased tolerance for slower software shifts the desirability of performance improvements.
Budget constraints and cost-center mindset limit investment in performance work despite technical feasibility.
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LLMs have lowered the cost of producing specialized solutions such as JIT compilers and search indexes.
This makes workload-specific optimizations more accessible to engineering teams.
The same capability also enables faster auto-completion models in editors.
However, using LLMs often introduces latency from agentic loop iterations that shifts work to an asynchronous model.
Adopting these LLM-based optimizations requires integrating LLM-based components and handling the added latency.
Teams must spend engineering time to design asynchronous workflows around LLM calls.
Written by elseif from the cluster below · checked for specifics the sources never containedTHE CLUSTER