A controversial work rhythm that once defined swaths of China’s tech scene — the so-called “996” schedule (9 a.m. to 9 p.m., six days a week) — is showing signs of being repackaged for Silicon Valley, where a renewed rush to dominate artificial-intelligence projects and data-center buildouts is lifting appetite for ever-longer workweeks. Startups and some investors say the frenetic pace helps compress timelines and chase breakthroughs; critics warn it risks deep burnout, legal headaches and a rollback of decades of gains in workplace protections.
Silicon Valley’s culture of long hours is nothing new. What’s different now is a clearer, sometimes explicit push to replicate 996-style intensity as an option — or an expectation — inside AI teams and cash-strapped startups racing to ship models and services. Reporting and industry observers say a mix of factors is driving the shift: the winner-take-all economics of AI, investor pressure for rapid scaling, and executive voices publicly endorsing tougher trade-offs between growth and work-life balance.
For some employers, the approach is framed as voluntary: engineers can opt into a “sprint” track with steeper hours in exchange for higher pay, extra equity or faster promotion. Proponents argue that in small teams racing to product-market fit, concentrated bursts of effort can deliver outsized results. Opponents call that model an “opt-in illusion,” saying the career incentives effectively coerce participation and widen inequalities between those who can afford to work long hours and those who cannot.
Employees report real consequences. Burnout, disrupted personal lives and mental-health strains have followed earlier waves of intense startup schedules; advocates for labor protections warn that normalizing 70-plus hour weeks could raise health risks and long-term costs for companies in the form of attrition and lawsuits. Meanwhile, the cultural optics matter: younger workers — especially Gen Z — are pushing back with demands for boundaries, the “right to disconnect,” and work measured by output rather than clock time.
The revival of 996 echoes an ongoing debate about what innovation should cost. Supporters point to historical examples where grueling schedules coincided with rapid scale and breakthroughs; detractors point to regulatory differences, different social safety nets, and the legal landscape in the U.S., where overtime rules and employment law create friction with any wholesale adoption of 72-hour workweeks. Some analysts say the visible embrace of 996-like practices in the Bay Area stems from a psychological shift: after a decade of remote-friendly work and talent churn, a slice of leadership believes concentrated, in-person intensity is the competitive edge for the AI era.
Policymakers, labor advocates and workplace scientists are watching closely. If the practice spreads beyond isolated teams, regulators and courts could be forced to grapple with new enforcement questions: are employees truly exempt, is overtime being properly compensated, and how do gig-economy and contractor relationships figure into the calculus? Meanwhile, companies experimenting with “choice” tracks may face reputational and retention risks if long hours are seen as the only route to advancement.
For workers, the choice is rarely purely philosophical. Pay packages, career timing, family needs and geographic mobility all shape whether someone signs up for a 996-style stretch. The result is a patchwork labor market where some teams promise fast career acceleration for those willing to grind, while others double down on sustainable practices to attract talent who prize balance. How broadly 996 takes hold in the Valley will depend as much on workers’ collective responses and regulatory pushback as on startup boardrooms and venture-capital timelines.
Bottom line
The reappearance of 996 in Silicon Valley is less a simple copy-paste than an ideological shift: a belief among some leaders that extreme dedication — even at legal or ethical margins — is necessary to win the AI race. Whether that bet pays off for companies, and at what human cost, is about to become a central question for tech’s next chapter.
