What a day can do

Team-level AI infrastructure can precede and contain individual training. The cost of encoding how a team works into shared reusable tools just dropped from hours to minutes with Gen 2 tools (Claude Code + transcripts). A small jewellery company built thirteen shared skills in a day. Step two doesn't just follow step one, it can contain it. What happened this week: * Claude Code now writes 4% of all GitHub commits, doubled in six weeks; Anthropic run rate $30B (up from $9B at end of 2025), Claude Code alone $2.5B; projected 20% of commits by December * Goldman Sachs quantified AI's net labour market drag: -25k jobs substituted + 9k augmented = 16k net monthly loss; entry-level-to-experienced wage gap widened 3.3pp. But CFO surveys put genuine AI ... * Meta's internal tokenmaxxing leaderboard: 85k+ employees, 60T tokens in one month, Zuckerberg not in top 250. Rewards orchestration over outcomes. Incentivise use yes, incentivise maxxing no What to try: * Start with critique, not creation. Brand voice evaluator was diagnosis-only; teams fear proofreaders less than replacements. Nobody fights the spellchecker * Ask what keeps people up at night, not what they want AI to do. First question surveys existing habits; second surfaces unmet needs. Almost nothing appears on both lists * Show your team how others use AI. 515-startup field experiment: case studies alone led to 44% more AI usage, 1.9x revenue, 39% less capital needed ('the mapping problem') Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-04-11