Google tells employees they need to double their work every 6 months to keep up with AI (www.techradar.com)

Google executives told staff the company must roughly double its AI serving capacity every six months to hit an ambitious goal of ~1,000x scale within 4–5 years while keeping “essentially the same” cost and energy per unit of work. AI VP and others outlined this at an all‑hands, and CEO Sundar Pichai warned 2026 would be “intense” as compute demand and competition accelerate. To meet the target Google plans multi‑pronged moves: expand data‑center and cloud infrastructure, push more in‑house silicon (seventh‑gen TPUs, codenamed Ironwood, are touted as ~30× more power‑efficient than 2018 models), and reduce dependency on third‑party chips amid Nvidia supply shortages that have already slowed industry rollouts. Significance: this public roadmap signals a near‑term industry sprint where raw capacity growth must be matched by steep efficiency gains, forcing changes across chip design, datacenter cooling/power, software serving stacks, and procurement strategies. For AI/ML teams it means faster model deployment expectations, tighter latency/cost envelopes, and greater emphasis on hardware‑aware model engineering and system optimizations. For the broader ecosystem it raises stakes for chip vendors, cloud providers and startups—underscoring that failing to invest in scalable, efficient compute now could be riskier than riding out potential market corrections.