Satya Nadella, CEO of Microsoft "Scaling laws are working. Alignment is not arriving" They now treat every long-running AI agent as insider risk, with real-time behavioral monitoring across the entire system Satya hasn't picked a model in months. A learned router matches every task to the right model across OpenAI, Anthropic, and Grok automatically "If you only have one model, we know what happens to token pricing" His prediction for agents: "orders of magnitude bigger than cloud" ~48 min, free bookmark this ↓

Jev Engineering cuts AI agent costs by 100x on Google's stack your agent spends 90% of its budget deciding, not thinking. Jev makes those decisions for almost nothing here's how to rebuild your agent's brain in 6 steps: step 1 → audit every LLM call. log each one and tag it: "writes" (drafts, summaries, code) or "decides" (routing, scoring, yes/no). most agents run 70%+ decisions step 2 → split the architecture. Gemini writes. Jev decides. code acts. each job goes to the component built for it step 3 → structure your decisions. Jev takes three types - Choice (pick from options), Score (rate on a scale), Noul (yes or no as probability). no open-ended prompts step 4 → build rubrics, not prompts. every decision gets a JSON rubric with definitions, boundaries, and examples. version-controlled, testable, no hallucinated options step 5 → batch and gate. send all questions in one call (12x cheaper than sequential). set confidence thresholds - high confidence auto-acts, low confidence escalates to Gemini step 6 → measure cost per completed task, not cost per call. a cheaper decision that breaks the workflow costs more than the expensive one that worked the math: 10,000 daily decisions - $35,000/month on frontier models. $120/month on Jev. same accuracy, 193x faster bookmark this. your agent bill will thank you ↓

Google just dropped a free course on how they build agents internally. 87 minutes. Same team. Same stack. Same patterns they ship to billions of users. 0% → 0:44 - 1 agent, 1 prompt, every number hallucinated 20% → 20:00 - the 1 line that separates toys from shipped products 35% → 31:17 - 2 nodes, 1 LLM call, real data in, real advice out 50% → 43:40 - fan-out: 3 parallel fetches, still 1 model call 75% → 1:04:58 - loop engineering: inspect, restock, re-run 100% → 1:25:09 - self-improving graphs that run while you sleep The fix was never a better prompt. The fix was structure. Save this. Watch it tonight.

SpaceX AI engineer explained how to build a full outbound pipeline in 6 steps - 4 bots, zero humans the bots find your buyers, write your emails, and book your meetings. you don't even open your laptop here's the full pipeline in 6 steps: step 1 → define your ICP. open the bot, talk for 5 minutes about what you built. it returns personas, segments, and priority scoring step 2 → find buying signals. Cerebro scans for real-time data - funding rounds, new hires, product launches. turns a blurry audience into a target list step 3 → get companies. Clay pulls every company matching your ICP + signals. 500 companies from one query step 4 → get decision-makers. Clay finds people at those companies. 1,200 contacts with names, roles, and context step 5 → personalize outreach. AmpleMarket writes emails referencing each company's specific data. "they want to answer because it's not just any email" step 6 → monitor and close. GrokBot watches your inbox. surfaces warm leads. "the old way - 5 departments coordinate. this is just bots. they all share context" the result: 1,200 reached, 19% reply rate, 42 opportunities, 3.6x pipeline save this. build it this weekend ↓

SpaceX AI engineer let his AI bot spend his money. it now buys his clothes, pays his bills and sends physical mail to his family • 02:53 - bot pays a parking ticket without him opening a single page • 21:21 - AI connected to his bank. tracks every transaction and flags overspending automatically • 24:17 - "buy me shoes, size 12." three days later a box shows up. he never saw the order • 26:01 - his bot prints letters and mails them to his kids every morning. real paper, real stamps ~29 minutes of bots spending real money faster than you spend yours this one's worth your lunch break ↓

SpaceX AI team shipped a live product in 72 hours. bots wrote the code, deployed it, and fixed every bug "I didn't look at the code at all." bots shipped the entire product to production while the team slept • 03:40 - how their bots auto-fix production bugs without a single human touching the code • 24:45 - "should we launch?" "only one way to find out." 500 users flood in within the hour • 30:04 - they killed dashboards. a bot sends live metrics on demand - signups, funnel, win/loss • 46:45 - a bug hits production on camera. one message to a bot - it finds the issue alone • 50:11 - their feedback pipeline: user report > bot triages, filters prompt injections, reproduces the bug, files a ticket. zero humans 53 min. real users, real bugs, real prod save it and watch tonight ↓

SpaceX AI marketer: 6 bots launched a full marketing campaign. she didn't write a single email, ad, or line of code one bot scraped competitors and found positioning gaps. another drafted a brief in Google Docs. another built campaigns inside her Google Ads. another wrote the landing page and pushed it to prod then she told the project manager bot: study how I ran this team. automate it. launch 3 more campaigns without me "I don't want to talk to any of the other bots. I only want to talk to you" this is a one-person marketing department save it and watch tonight ↓

SpaceX AI engineer broke down 6 AI workflows that cut 5+ hours off her workday. she hasn't done a follow-up herself in months one deployment manager. 10 bots. she only talks to one Gus, her chief of staff, manages the rest. routes tasks to specialists, collects results, delivers everything in one pack her 6 workflows: 1. morning status board - overnight fires, meetings, unfinished promises, priorities 2. call prep - 15 min before a meeting: who's joining, what changed, what to bring up 3. follow-up desk - call ends, 3 bots draft every email, Slack message, and material in her voice. she just hits send 4. promise keeper - tracks everything she said she'd do 5. ask watch - tracks everything others still owe her 6. account reset - one message pulls risks, blockers, open promises into a single view Gus can join her Google Meet calls. mutes himself, turns off camera, sits through the meeting, and sends her the takeaways when it's done he can also call a staff meeting between the bots. they argue over how she should spend her last free hour and decide for her the system runs a self-improvement scan: finds work she still does manually, suggests automations, then learns from the gap between AI drafts and what she actually sends this isn't "using AI at work." this is replacing half your job with bots save the workflow ↓

SpaceXAI engineer (GrokBot team): the agents built everything. the only thing that kept breaking the factory was a human • 00:46 - Roshan phone-calls his bot. "what's broken?" bot answers. "fix it." done • 09:01 - Lauren's factory: bugs reproduced, fixed, merged. nobody is prompting it • 11:08 - Eric (Cursor engineer) sends agents to build. only looks at code if it already works • 12:43 - his bot spawns disposable bots per project. researcher, coder, designer. job done, they vanish • 13:05 - "when it's blocked, it's always because of a human somewhere" ∼17 minutes inside a team where humans are optional you'll want this saved ↓

SpaceXAI engineers: 168 PRs merged overnight. nobody reviewed a single one. the bots built, verified 3 people are building a full game studio in 72 hours, using AI agents as their engineers, testers, and operators their QA team is a bot. "we had an intern yesterday, but that intern is not with us today." it plays the entire game end-to-end before every merge they also have bots for audio design, 3D prototyping, and a chief of staff named Steve running the operation this is what building software looks like now save it. you'll need this ↓