Mediation Is AlreadyAs AI keeps working after the conversation ends, reusing approved judgments and returning decisions to people when needed has become a problem real products must solve. Early user accounts of ChatGPT Dots show what has been realized and what remains.
AI that keeps working has arrived
When we asked AI a question and got an answer, a human started every conversation. We explained what to do, checked the result, and typed the next request. Always-on agents such as ChatGPT’s Dots keep working after the conversation ends, maintain the state of several tasks, and ask a human for judgment when needed. The relationship between AI and people is shifting from a single request and response to ongoing delegation and collaboration.
This shift means mediation is no longer a problem for a distant future. For AI to keep working, it has to remember and reuse people’s purposes and judgments. At the same time, when a past judgment is no longer valid or a new choice is needed, it has to stop, hold the affected parts, and return the decision to the person. What to keep doing and when to ask again has become a present-day problem that real products must solve.
What Dot has realized
Dot has turned part of this problem into a real product. The user sets goals and permissions, and Dot uses its own computer and connected tools to keep working between conversations. It remembers work in progress, delegates separate tasks, and asks a human before actions that need approval. The user can check work in progress, change priorities, or stop a task and resume it later.
This persistence shows up in early user accounts. One user described a multi-day task reviewing hundreds of PDFs in which Dot kept its place, handled questions along the way, and returned to the original task. While waiting on actions that required permission, it kept going with other work it could do. The user judged that this structure takes people out of much of the work of coordinating tasks.1
This overlaps substantially with the operating direction of Mediation: AI keeps executing within the scope a person has approved, and returns the decision to the person where a new judgment is needed. There is no basis for saying OpenAI applied TYPE’s Mediation System, but we can see that the problems the two structures address are converging.
What has not been realized yet
Persistent memory and execution do not by themselves mean appropriate mediation. Dot remembers what I said and keeps working, but I cannot tell how it took what I said. With the features Dot has made public, it is hard for a user to see clearly which statements were simply opinions and which were approved judgments, which tasks a judgment applies to, and how long it remains valid. Having memory is a different problem from accurately reusing the judgment that fits the current situation.
One user found Dot useful when it organized 93 files and set aside, without deleting, the items the user needed to check. In another task, however, after waiting 72 minutes, hidden classification data had been applied incorrectly and had to be verified again. The fact that the work kept going did not mean the result was implemented appropriately.2
Another user found that even simple actions took a long time and that work stalled on permission and browser problems.3 On the other side, one user said they would not use Dot because its memory was too broad and its information boundaries unclear. Information from many areas goes into one persistent memory, and it is hard for the user to inspect that memory in detail and manage it selectively.4
Dot has implemented a form of continuous execution and Human Return, but it is still hard to say whether it surfaces the status, scope, validity conditions, and change history of approved judgments at a level users can understand and control. This is not just a missing feature. People do not always speak in clear rules. The same words can be a correction for one task, a standard to keep going forward, or an exception allowed only once. What makes mediation hard is that it must structure judgments expressed in natural language without fixing people to their past judgments.
Is the felt burden getting lighter?
Early reviews also report a lighter felt burden. One user told Dot about tasks whenever they came to mind, and Dot updated the task list. At the start of each day, the user set priorities and split the work between themselves and Dot. Dot then flagged where it was blocked, and the two checked progress at regular intervals. They felt their productivity had doubled on the first day.
In the comments on the same post, though, someone asked how to tell whether AI actually reduces work or simply creates more documents and processes for managing it.5
The change we can see now is less that AI does everything faster, and more that people may carry less of the burden of staying present to resume work and coordinate several executions. If work continues after the conversation, AI remembers its progress, and it asks only for the interventions it needs, people can gradually step away from the job of keeping execution going.
But once we count the costs of initial setup, permission management, checking progress, verifying results, and correcting wrong memories, we do not yet know whether the total burden has actually gone down. New management work may grow as much as repeated instructions shrink. Early impressions of productivity or convenience are important observations, but they should not be stretched into evidence that Mediation works.
So where are we going?
We are moving from an era in which AI answers human requests to one in which it takes over human judgments and keeps acting on them. AI will remember more, work longer, and carry out more on our behalf. The core problem left to people then shifts away from the ability to do the work directly and toward what they want and what they will choose.
But people are not beings who command AI with clear purposes and standards from the start. We learn what we want by seeing options, experiencing results, and discovering possibilities we did not expect. Choosing is not only expressing a will that is already complete. It is a process of forming and revising purposes and values through experience.
AI can help with this process. It can show a wider range of possibilities, explore the consequences of a choice in advance, and present evidence and counterexamples people had not seen. At the same time, it can make people choose only within the options AI produced, or fix past choices as present tastes and identity. Having less to handle directly does not mean people are choosing better. Reducing the burden of choice is not the same as removing the person who chooses.
So the direction we need is neither to keep people in every step of execution nor to leave them only as a final approval button. While AI takes on execution, people need to understand their own purposes and standards, review the possibilities AI presents, and be able to choose a different direction again. What matters is not formally holding decision rights, but understanding for ourselves what matters, choosing a direction as our own, and being able to change it again when needed.
What should TYPE research, then?
The arrival of Dot shows that AI remembering human judgments and executing on them continuously has become a problem for real products. This does not weaken the need for the Mediation TYPE has been working on. The more continuous AI execution becomes real, the more we also need to study where human judgment begins and how it changes.
TYPE’s Mediation research addresses a structure that connects approved human judgments with AI execution and returns the decision to the person when a new judgment is needed. That research is still needed. But TYPE’s broader direction does not end with the procedure of returning judgment. We need to study what people can base their choice on when a judgment is returned to them, how those standards are formed and revised, and whether AI’s exploration and results widen people’s self-understanding in their next choice.
The better AI remembers people’s past choices, the less they may need to repeat themselves. But if the same memory treats past choices as fixed present traits or identity, people’s room to change shrinks. TYPE therefore needs to distinguish the conditions under which personalization helps AI understand people better from those under which it fixes them to their past selves. We also need to examine whether showing only highly relevant options actually helps people choose, or removes unfamiliar possibilities and counterexamples.
What TYPE needs to study is not only how well AI carries out human decisions. It is whether people, together with AI, can discover their purposes and values, understand the reasons for their choices, and review and change those choices through experience. The object of study should not be the act of pressing one of the options AI offers, but the whole process of considering possibilities and forming what to adopt as one’s own direction.
Mediation connects human judgment with AI execution. Choice deals with how that judgment forms and changes. TYPE should connect the two and study not a system in which AI makes choices on people’s behalf, but one that helps people understand more broadly, choose more autonomously, and realize those choices as real outcomes.
References
- Reddit r/OpenAI 사용 후기 — I spent a day poking dots with sticks Source
A personal user account, not evidence of general performance. - David Proctor (2026-10-01). OpenAI dot: why would I start a new chat? Trilogy AI (Substack). Source
A personal usage log recording 93 organized files, a task that took 72 minutes, and wrong classification data. - Reddit r/codex 사용 후기 — ChatGPT dots so far too slow to be useful Source
A personal user account. - Reddit r/OpenAI 사용 후기 — Why I will not use dots: insufficient visibility Source
A personal user account. - John Walter. LinkedIn 게시글과 댓글 토론 (ChatGPT Dot 사용 첫날). Source
A personal account; the productivity figure is self-reported.