Google I/O 2026 just satisfyingly answered every question about where Gemini is heading. New models. A personal AI agent. Compute-based pricing. Background agents that monitor the web for you. Here's everything that matters for AI operators:
@godofprompt
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AI agents move beyond demos with significant ARR growth
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This is the AI agent thesis finally escaping demo-land. $15M ARR run-rate in 10 weeks is insane.
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AI Agent Startup Reaches $15M ARR Run-Rate in 10 Weeks
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$15M ARR run-rate in 10 weeks is the kind of number that makes the entire AI agent market go quiet.
— God of Prompt (@godofprompt) 19 mai 2026
Viktor just raised a $75M Series A led by Accel.
Not for another chatbot.
Not for another tab.
Not for another “copilot” that still needs babysitting.
It lives inside Slack +… https://t.co/3duv9xPdtH$15M ARR run-rate in 10 weeks is the kind of number that makes the entire AI agent market go quiet. Viktor just raised a $75M Series A led by Accel. Not for another chatbot.
Not for another tab.
Not for another “copilot” that still needs babysitting. It lives inside Slack + -
Using Claude Cowork for automated data removal
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You need two things: 1. Claude's Cowork feature (not regular chat)
2. The Chrome extension installed Cowork gives Claude the ability to browse the web, fill out forms, and write emails on your behalf. The prompt: "I want you to go around and remove my data from all major data -
Automating Data Broker Deletions Using Claude’s Cowork Feature
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One prompt inside Claude's Cowork feature just wiped someone's data from every major data broker. Zero forms filled by hand. Zero emails written manually. GDPR deletion confirmations from Apollo, ZoomInfo, and dozens more within hours. Here's the full setup:
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Research shows more ‘thinking’ time does not always improve AI answers
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The assumption that "more thinking = better answers" was never tested until now. This paper tested it. The answer is no. For operators: if your AI output feels overengineered, bloated, or keeps second-guessing itself, the model isn't being thorough. It's losing confidence.
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Impact of Output Length on LLM Reasoning Accuracy
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The most useful finding for anyone using AI daily: When they capped reasoning at 60% of the model's natural length, it maintained 97% of peak accuracy. Longer natural outputs also correlated with lower accuracy. 71.9% accuracy under 4K tokens. 44.7% accuracy above 12K. The
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Analysis of LLM reasoning performance relative to token length
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It gets worse for simple questions. Easy problems hit the overthinking zone at 2,000 tokens. Hard problems don't hit it until 8,000. Translation: the simpler the question, the faster the model starts hurting its own answer by thinking longer. Optimal reasoning length varies
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Analysis of LLM Self-Correction Failures
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67.5% of these flips are genuine overthinking. The model explicitly reconsiders a correct answer, says "wait, let me double-check," and then replaces it with a wrong one. Not a glitch. Not hallucination. The model second-guesses itself into failure. The tell? Phrases like
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Impact of Reasoning Budgets on LLM Prompt Engineering
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Here's what it means for prompt engineering: They tracked individual answers across 32 reasoning budgets from 500 to 16,000 tokens. At ~7,000 tokens, something flips. The model starts abandoning correct answers MORE often than it finds new ones. They call it "negative flips."