AI can generate hundreds of ideas without repetition, allowing brainstorming to shift from being about individual creativity to becoming a curation process.
AI is most effective when paired with expert knowledge to quickly assess its outputs, especially in complicated and exacting work where the expert can identify errors or inaccuracies.
If you're already highly skilled at a task, you're likely better than AI at it, so testing AI on such tasks may not reveal its true potential, which lies in assisting with tasks you're less skilled at or dislike doing.
AI doing tasks better than humans is likely the fastest-growing category, with many of these tasks being automated by AI agents in the coming years.
There is a lower tolerance for AI errors compared to human errors, with customers often accepting human mistakes 5% of the time but expecting AI accuracy to be over 99%.
AI can hallucinate, persuade you it's right, or become sycophantic, so understanding its failure modes is crucial to avoid being misled by incorrect outputs.
Effort and struggle are often necessary for deep understanding and breakthroughs, as shortcuts can prevent reaching vital 'aha' moments that come from sustained engagement with a topic.
AI summaries, like those from Notebook LM, are likely to become the first point of consumption for academic papers, helping learners get over the initial hurdle before diving into full readings.
How to actually get value out of AI at work right now. A reading and discussion inspired by https://www.oneusefulthing.org/p/15-times-to-use-ai-and-5-not-to
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