I went back to ChatGPT Plus because my tests of Pro had not shown enough additional value for the work I needed to do. More capacity for scheduled tasks or Codex was not what I was looking for. In my own workflows, I had not found the step up in intelligence that would justify staying on Pro. I wanted useful work to progress without having to manage every step.
In October, instructions I had established over the last months became less reliable around the switch from Sol 5.6 to Sol 6.1 in ChatGPT Work. These covered using current sources, preserving knowledge, obtaining approval and checking results. One failure captured the problem: when I asked whether I could delete a conversation, the assistant said yes without first running the agreed process to preserve its useful knowledge.
The frustration: more capability, more supervision
In regular ChatGPT and ChatGPT Work, even basic execution started failing: saving a document became a handoff problem, required fixes stopped at diagnosis, and parts of requested tasks were forgotten while the result was still described as complete.
Over the past months, I had set up workflows for writing, multi-source research, automated tasks for my own websites and knowledge-file maintenance in my Second Brain, my personal knowledge library. Every workflow taught different lessons. The difficulty was knowing which workflows and the capabilities behind them would reliably make my day easier.
The governance rules mattered. Yet having them written down was not the same as having them applied. When I had to remind the assistant to run the process, inspect the right version or finish the agreed fix, I was still carrying the responsibility for execution.
The change: work started moving with me
While trying to resolve these problems, I decided to try dot. A dot is OpenAI’s persistent AI agent for carrying work forward between conversations, using connected tools within its permissions. I upgraded back to Pro to get access and see whether it could make a difference.
When I began using dot, the first days required coaching, and some familiar problems remained. But I started to experience continuity. By continuity, I mean the assistant carrying the agreed work and its context forward while I move between conversations, devices and other things I need to do.
The mechanism was practical: explain the outcome, provide the relevant sources and access, agree the boundaries, then let dot prepare and carry out the work while bringing decisions back to me. Because the work could continue between our conversations, I no longer had to keep a laptop session at the centre of every task.
For teams running websites, CRM and commerce, the comparable opportunity is to reduce the manual coordination between a request, an authorised action and a usable result.
During a tram ride in Kraków, dot submitted ten pages from my website to Google Search Console for indexing, and all ten requests were accepted. I followed progress on my Apple Watch, opening my phone when a decision or approval needed me. That was a real change in how the work fitted into my day. But dot did not turn those successes into a dependable routine. Subsequently, another failed run led me to pause the automation and take the submissions back as manual work.
The usefulness: help across everyday life
Voice was valuable because it let me stay involved in the thinking. I could challenge a recommendation, explain why a distinction mattered or change the emphasis without typing another long prompt. Dot could then continue the preparation. The useful relationship was between my judgment and work that kept progressing.
Travel comparisons helped me make decisions and book two trips. A reminder became useful when an alert actually reached me. These were modest outcomes, but they belonged to the same experience: help across different parts of life, rather than a demonstration confined to one impressive task.
It also made existing knowledge more useful. Material I had already developed about search visibility (answer engine optimisation (AEO) and generative engine optimisation (GEO)) became a German agency brief for a friend, adapted to his website and stripped of my private information. My Mac was off. The knowledge did not need to be recreated; it needed selecting, translating and shaping for someone else to act on.
That breadth, with one assistant carrying context between different tasks, made the idea of artificial general intelligence feel tangible to me.
For a content or CRM team, that suggests a practical direction to test. Can existing research, decisions and approved material become a usable brief without someone repeatedly transferring the context? The value would be in a better handoff and less preparation effort, not simply in producing more documents.
The trust: useful does not mean effortless
Dot did not make every problem disappear. I still had to repeat preferences and correct work that had been called ready too soon.
Dot also helped me resolve problems in my earlier ChatGPT workflows. ChatGPT Work had started saving my instruction to preserve useful knowledge as if the instruction itself were new knowledge. Dot helped tighten the rules so that only meaningful additions should be saved.
The real question is how much useful work is finished, and how much effort still falls back to the person.
I judge the value by the balance of dependable help against the checking and repair it asks of me. Making a consequential decision is a legitimate part of that relationship. Catching a missed instruction should not become a permanent new job. The same distinction applies to authority: preparing work does not automatically permit sending, publishing or sharing it.
The lesson: delegate responsibility in useful increments
For a team, useful delegation has to survive ordinary work, changing requirements and the occasional failure. Judge the whole handoff, including the effort needed to get a result ready for use.
Start with one recurring workflow and a named owner. Agree the desired outcome, the sources the assistant may use, the actions it may take and the decisions that must come back. Compare usable results and total human effort with the current way of working, including setup, review and repair. Expand the responsibility when repeated experience shows a genuine improvement.
Since I started using dot, I haven’t used regular ChatGPT chat or ChatGPT Work. I now work with dot through iMessage or the ChatGPT dot interface. It still needs coaching, but it changed the subscription decision. I returned to Pro to test dot, and I stayed because the value became tangible.
A few fast answers before you act
What is a ChatGPT dot?
A ChatGPT dot is OpenAI’s persistent AI agent for ongoing work across conversations and connected tools. It can continue assigned work, prepare results and bring decisions back to the user. Its access and actions remain subject to permissions and safeguards.
Why can dot make AGI feel tangible?
ChatGPT dot can make the idea of general-purpose AI feel concrete by combining different kinds of help with continuity between conversations. In my experience, it helped with travel decisions and adapting existing knowledge for a friend, while other tasks progressed when I was away from the desk.
Why did ChatGPT Pro become worthwhile for you?
My earlier tests had not given me a clear enough personal reason to keep Pro over Plus. I upgraded again to access dot and test whether it could make a difference. Its voice-led, mobile way of working then made the benefit tangible: I could discuss decisions on the move while useful preparation continued in the background.
What did dot achieve in Google Search Console?
During a tram ride in Kraków, dot submitted ten pages from my website to Google Search Console for indexing, and all ten requests were accepted. I followed progress on my watch and used my phone for decisions or approvals. A later failed run led me to pause the automation and resume manual submissions.
What still required human attention?
In my experience with ChatGPT dot, decisions, approvals and sharing boundaries remained with me. I also had to correct missed instructions and work called ready too soon. That repair effort counts against the benefit of delegation. Preparing a draft does not automatically authorise sending or publishing it, and important outputs still need checking where they will be used.
How should a team test this way of working?
To test AI delegation, choose one recurring workflow with a clear owner, permitted actions and an observable completion standard. Compare usable results and total human effort with the existing workflow across repeated runs, including setup, review and repair. Expand the assistant’s responsibility when those results demonstrate a genuine improvement.
