Burning out: An essay on overworking in AI (www.interconnects.ai)

An essay by an AI researcher spotlights a growing, often performative but increasingly real culture of overwork in the LLM era — from “996” grind cycles to Wall Street Journal reports of 100-hour weeks — arguing that this pressure is driven by a shrinking window to stay competitive as model quality expectations accelerate. Using athletics as an analogy, the author explains why elite, sustained focus can feel rewarding but also why it leads to burnout: mental acuity, creativity and the ability to spot technical dead-ends deteriorate without rest, turning one-more-experiment behavior into a long-term liability rather than a short-term edge. Technically, the piece emphasizes that model development now demands three core ingredients — internal tools/recipes, resources (compute/data), and personnel — and that progress has become more sensitive and resource-intensive. Training and post-training pipelines are far more complex than a few discrete stages: dozens of checkpoints, merges and sequencing steps require large coordinated teams and deep context, making “starting from scratch” an uphill battle for new labs despite more capital. The implication for AI/ML is a human-capital bottleneck: faster hardware and funding won’t substitute for sustainable culture, tooling and management. The author urges recognition of this trade-off and highlights ecosystem efforts (e.g., ATOM Project) to address both competitiveness and healthier work practices.