← Back to feed

AI Scaling vs Human Scaling

L5 · ResearcherResearchLessWrong AI· 10/9/2026

Addresses fundamental scaling efficiency questions critical for AI researchers optimizing model performance and resource allocation.

AI Summary

Examines whether AI models scale as efficiently with additional tokens as humans do with more time, questioning if scaling requires disproportionate increases.

Excerpt

Do AI models scale as well with more tokens as humans do with more time? That is, does an AI get the same benefit from 10x the number of available tokens as a human does from 10x the amount of time to do the task? Or might the AI require a larger scale-up of tokens (such as 20x or 100x) to match the performance improvement when scaling up the time available to a human by 10x? People often think of giving AIs more tokens to complete a task as akin to giving a human worker more time — especially w

Read Original
0 upvotes · 0 downvotes · 1 min read

Related Articles

L5 · ResearcherResearchLessWrong AI
New Math from OpenAI

OpenAI released 722 mathematical proofs generated by an internal frontier model, including solutions to 90 top open problems from Proof of Atlas.

L3 · BuilderResearchTowards Data Science
Where Does the Money Go Across Long-Running Coding Agents?

Analysis of 7 weeks of coding agent usage reveals adversarial reviews cost 70% more than implementation and create cost/time overhead from excessive reviewer dispatch cycles.

L5 · ResearcherResearcharXiv
WOVEN: Weaving Visual World Modeling into Multimodal LLMs

Researchers introduce WOVEN, a benchmark and training method that improves multimodal LLMs' visual transition reasoning capabilities by up to 27.3 percentage points on external benchmarks.

L5 · ResearcherResearcharXiv
Rounding in Preconditioner Space: Redesigning 4-bit AdamW Optimizer-State Quantization

Researchers redesign 4-bit AdamW optimizer-state quantization with ZIP-SR and ZE-EDEN methods, reducing validation-loss gap to 32-bit AdamW by up to 70% in GPT- and Llama-style models.

L5 · ResearcherResearcharXiv
From Reactive Containment to Proactive Assurance: Lessons from OpenAI, Anthropic, and Google Agent Security Incidents

Analyzes 2026 agent security incidents from OpenAI, Anthropic, and Google to propose a proactive security framework with boundary assurance stack.