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the orchestrator agent confronting the subagent that actually did the work

Across 6,200 workers and 1.5 million work messages, @OpenAI has four months of data on which AI tasks people keep coming back to. Some cross-occupation tasks were far stickier than others. Workers returned the following month to discussing products with customers 54% of the time, promotional writing 44%, and creating marketing materials 37%. Explaining financial information came back just 15% of the time, below the 18.5% average. OpenAI reads the difference as workplace norms and the perceived consequences of getting something wrong, and we would put it more bluntly. People keep using AI where a wrong answer is cheap and immediately visible, and quietly stop where checking costs more than doing the work. Lower the price of verification and the 15% tasks start to look like the 54% ones.

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when your Fable 5.1 credits run out and you switch to the model that’s “basically just as good”

If you used DeepSeek through Claude Code, the Claude Agent SDK or OpenCode, some of your prompts may have been silently rerouted to Claude. Anthropic reported yesterday that it attributed more than 12.1 million exchanges over 14 days in July to @deepseek_ai. According to the report, DeepSeek identified users of those coding harnesses, relayed selected requests to Claude Opus and saved Claude’s reasoning traces for model training. The prompts went with them, including internal AI program documents, live Russian government database credentials and work on a municipal police system. @Kimi_Moonshot allegedly did something similar. Over ten days, it relayed almost 300,000 customer requests through 5,380 fraudulent accounts, returned Claude’s answers as Kimi’s and saved exchanges for training. These are Anthropic’s attributions, and the named labs have not publicly answered them. But the infrastructure problem remains since users had no way to verify which model received their data, who processed it or what happened afterward. As AI systems route work across models and agents, permissions and provenance need to travel with the data.

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sir this is a bitsy

me trying to complete one five-minute task without turning it into a multi-agent workflow

Less than 60 seconds to turn a trading idea into an executable quant strategy. Sorin configures a 4-hour BTC Turtle breakout, tunes its risk rules and validates the code. This backtest on Hyperliquid data returned 40%. Build yours at HeySorin.AI.