Cognitive Burden (kau.sh)

The author argues that the real, often-overlooked benefit of AI tools is reducing cognitive burden rather than strictly being faster or producing better output. Using a custom agent trained on their voice, they feed in bullet points and creative touches, have the model draft a post, and then review every word — not because the AI replaces skill, but because it removes the mental grind of assembling sentences. That lower friction makes publishing less exhausting and more frequent even if turnaround isn’t dramatically quicker. The same logic applies to coding: the author still writes and reviews code, but AI lowers the cognitive tax of context‑switching, meetings, requirement wrangling, and repetitive detail, letting them maintain 3–4 features in flight (with docs, tests, and variants) and spend more time on product quality. For the AI/ML community, this reframes how success should be measured and where to invest: prioritize tools that reduce mental load across workflows (requirements, design, documentation), not just raw code generation speed. Practically, that means building customizable agents, integrating review workflows, and evaluating tools by how much cognitive bandwidth they free up — enabling engineers and writers to focus on higher‑value thinking and creativity while retaining final control. The takeaway: cognitive friction, not throughput alone, is the key metric for human–AI productivity.