Show Notes
Running more AI agents can look productive without moving the work forward. Nate explains how to give agents useful assignments, recognize what deserves your attention, and stop recurring jobs once their purpose has passed.
The practical test is whether the results help you make a decision or solve the central problem. From checking a painting estimate to questioning a weekly update nobody reads, the examples show why more output is not always more progress. Hosted on Acast. See acast.com/privacy for more information.
More from AI News & Strategy Daily with Nate B. Jones
For deeper playbooks and analysis: https://natesnewsletter.substack.com/ What does Anthropic’s S-1 reveal about the market for intelligence after a company starts using AI? Nate examines the gap between winning a customer and becoming important to that customer. The filing offers evidence of expansion while leaving major questions about deployment costs, workflow complexity, and future growth.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/
For deeper playbooks and analysis: https://natesnewsletter.substack.com/
Can better understanding of AI make more powerful models safer? Nate explains why Goodfire’s research on interpreting neural networks gives him reason for optimism, while separating demonstrated findings from the products and safeguards that still need to be built.
Before buying a bigger computer for AI agents, find out what is consuming the machine you already have. Nate explains how to diagnose memory and storage pressure, give agents a stopping condition, and schedule demanding work around the resources available.