HBTop post: @herbiebradley “Takes on Claude Tag: 1. A *team-level* AI like Claude Tag inherently causes higher switching cost, because the memory is less accessible. It's only fully viewable by admins, and requires much IAM management. 2. Continual learning & agents taking more async actions across surfaces & apps will only increase the complexity of access management issues. 3. Currently, admins have responsibility for managing which knowledge team-wide or org-wide Tag agents have access to—a high burden of complexity which probably will lead to some embarrassing internal exposes of confidential information. This probably can be done by the model, but will it be 100% reliable? 4. With just MD file memory, we can deterministically manage access because it's explicit. But true continual learning in the enterprise seems like it would require agents to have the judgement to decide themselves what to expose. If agents could construct their own RL envs from a company's context, and post-train, then they would also need to be able to somehow limit their use of in-weight knowledge. 5. In theory, we could unlock better capability from enterprise AI by letting agents always access all information, but decide themselves how to abstract that to preserve confidentiality. But this seems to require much more reliable agents at information control. 6. The token cost could balloon as team-level async agents do more and more work. It's higher friction to control the team-wide model choice in the Slack interface, so you can't restrict eg Opus use to save $ so easily. Eventually we might get a good router, but labs are not incentivized to give you a good one! CISOs and CTOs of companies should stay wary of letting a single vendor's pricing dynamics eat up their spend, especially given the risk of government intervention in model access. 7. Just as companies have stores of tacit/process knowledge in humans, they will also accumulate this as the collective memory of their team/org-level agent swarms. They will not be perfect substitutes, because much tacit knowledge is about relationships or not easily expressible in text. 8. Satya's recent piece on an AI ecosystem was timely, and he clearly sees the potential here for continual learning & "as many models as there are firms". 9. Presumably all major AI labs will have some sort of "async org-level agent swarm" product, and some companies will have multiple running at once. Someone will sell the model wrapper version (including OS models & routing), which could be cheaper.”