lo & slo: a summer series

starting something this week. a clarai series called lo & slo, 5 essays across summer 2026 about a single gap: the modern set of ai tools moves at a speed human biology was never built for, and the people getting the most out of it are often the most fried.

if you've felt the version of it in your own week (the laptop you can't quite close, the workload that keeps expanding, the strange experience of being more productive and more exhausted at the same time) you are not doing it wrong. the system around the tools is.

3 studies from early 2026, published in harvard business review and as an nber working paper, confirm the gap is real and measurable.

uc berkeley haas¹ (HBR, feb 2026): aruna ranganathan and xingqi maggie ye spent 8 months inside a 200-person tech company, interviewing and observing staff using ai daily. HBR runs it as in-progress research. 83% said ai increased their workload. exposed workers put in an extra 3 hours 15 minutes per week on average. the researchers named the dynamic the ai burnout paradox: workers voluntarily expanded their workloads because the tool made more feel achievable. by month 6, exhaustion and decision paralysis set in.

bcg (HBR, march 2026)²: julie bedard and colleagues surveyed 1,488 us employees at large companies. acute cognitive fatigue concentrated in the heaviest ai users, especially those managing several agents at once, with mental fog, headaches, and slower decisions. they called it "brain fry." high performers got hit hardest.

mit / nber (february 2026)³: daron acemoglu, an mit economist who won the 2024 nobel memorial prize, and 2 coauthors built a formal economic model of how ai-driven cognitive offloading erodes society's shared "knowledge commons." their conclusion: at a high enough level of ai accuracy, the stable steady-state is what they call complete knowledge collapse.

3 research methods, 3 timescales, and the same direction of travel.

stanford psychiatrist anna lembke⁴, chief of stanford's addiction medicine clinic, laid out a useful frame for what sits underneath the data in her 2021 book dopamine nation. she calls it the pleasure-pain balance: pleasure and pain are processed in the same brain region, and they oppose each other. every dopamine hit creates a counterweight of low-grade pain. the only short-term relief is another hit. it's pop psychiatry more than rigorous neuroscience, but the shape matches the architecture behind slot machines, social feeds, junk food, doomscrolling, and what knowledge workers report feeling at the end of an ai-heavy day.

for knowledge work specifically, ai is a different kind of hit. it does not deliver pleasure the way instagram or potato chips do. it delivers the feeling of being productive: a draft in seconds, an answer in seconds, a deliverable in minutes, 10 variations of a strategy memo before lunch. each one is a real, small accomplishment, and each one is also a real, small dopamine kick from a tool that rarely gets named as addictive.

so you keep prompting. you can't quite close the laptop because closing it means losing the next hit. and the pain underneath, that productive-and-exhausted feeling, keeps growing. it is like an ai hangover: restlessness when the screen is off, mental fog when it's on.

every dopamine hit your ai tools deliver is borrowed against your future cognition. when a biology evolved for scarcity meets an engineered system that never stops, the brain pays for that friction in measurable ways.

the debt is paid in acute burnout, knowledge collapse, and measurable cognitive decline. each essay in this series walks through one of those bills, and one angle on how to think about it.

the lo & slo series begins and ends with a challenge: we close the laptop on weekends.