Town/Platformer - automating the organisational knowledge substrate
An article in Platformer recently covered Town, which is building something I think is more strategically interesting than another AI assistants I've seen.
Town's software connects to a user's email, calendar and other data, then builds a living wiki about that person - effectively creating the context layer its AI assistant, Townie, needs to be useful. The initial build of that personal knowledge base can cost around US$100 per user.
The really interesting part, however, is what comes next. Town is developing a team version that will assemble a shared company knowledge base from what individual Townies know. In other words, it is attempting to automate the construction of the organisational knowledge layer on which future AI agents will operate.
That is a much bigger proposition than automating scheduling, meeting preparation or email.
It also exposes what I think is one of the central challenges of enterprise AI. We are racing to deploy agents because the marginal cost of automating a task can appear almost trivial, while paying far less attention to the much harder question of what information, processes, procedures, rules, permissions, exceptions and organisational norms should become part of the underlying fabric those agents are built on.
You cannot simply combine everyone's knowledge because someone's private information might inadvertently become part of the company knowledge base. Greze describes the failure mode as "egg on face", but the consequences can be considerably more serious than embarrassment.
His longer-term proposition is that we will eventually trust AI models to enforce company policies about what information can enter a shared knowledge base. I am much more sceptical. Not because I doubt the technology will improve, but because organisations are at risk of outsourcing to AI a decision that should precede automation: deciding what the organisation itself believes, permits, protects and values.
This is where the economics of AI can become misleading. When an agent can perform a task for cents, automation looks almost free. The real cost often only becomes visible later, when a bad assumption has been embedded into a process and the organisation pays through reputational damage, customer attrition, employee distrust, regulatory remediation or the expense of rebuilding what it automated too quickly.
The most important AI capability an organisation may therefore be building is not another agent. It is the organisational substrate that tells those agents what they know, what they can do, what they must not do and when they should stop and ask a human.
Town is interesting because it is trying to automate that substrate.
The question for every organisation adopting agents is whether it has thought carefully enough about what should be automated before it starts automating it.