Tag: ChatGPT Pro

ChatGPT dot makes AGI feel tangible

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.

My AI Finished. I Wasn’t Done.

An AI workflow updated 125 files in my knowledge system. Then the chat broke.

The files had changed, but I had no completion message. Fortunately, I was already using two chats: one to develop the strategy and prompt, the other to execute with a more capable model. While the execution chat was unavailable, I used the first to inspect the saved results without repeating the update.

For leaders putting AI into recurring work, the practical question is how much work a team can safely hand over. My recent experiments show why the answer depends on verified delivery, clear ownership and the effort that comes back when a run fails.

The handover: 125 files and a broken chat

This was a controlled update to my Second Brain. My Second Brain is a structured collection of knowledge files that AI can read and update within agreed limits. I used ChatGPT Work with Astra at Extra High to add four YAML metadata fields to each of 125 Markdown files. These are structured properties at the top of a file.

A streaming error prevented a usable completion message, and the Work chat became inaccessible on my phone. Some hours later, back at my desk, I opened a support ticket with OpenAI. Independent of the open ticket, after a six-hour wait, the chat became accessible through my desktop browser, but its visible history was blank. When I asked, the AI said it could still access the history and confirmed completion.

Because the updated files were saved outside the failed conversation, the other chat could inspect them. It confirmed that all 125 contained the four requested fields.

For teams maintaining shared content and reporting, the equivalent requirement is that completed work remains verifiable when the conversation that produced it becomes unavailable.

Consider a regional content update handed from one team to another. The recipient needs to know which records changed, which checks passed and what remains unresolved before deciding whether anything needs to be repeated. Otherwise, even a successful update can leave the next team investigating instead of using it.

The update succeeded. The handover failed. I still had to spend time checking that the work was complete.

The report: plenty of output, more auditing

My weekly campaign-intelligence task exposed a different problem.

One report had 16 campaign cards and six AI items. The audit still rejected its coverage claim. Some video links were marked as usable without confirming that readers could actually watch the campaign videos.

I went through three audit passes on the campaign workflow. A later weekly run still needed more corrections.

The brief sounded straightforward: find relevant campaigns, establish what launched and when, and provide working links to the sources and relevant campaign videos. The report arrived. Then I had to check whether it had met that brief.

An unchecked campaign claim can become an agency briefing error. Catch it after work has started, and brand and agency teams may have to redo the research, recommendation and brief.

Updating the runbook gave the next run better instructions. But I still had to check subsequent reports to see whether the same failures returned.

The real question is how much work remains with the person who supposedly delegated the task to the AI.

The fallback: ten URLs, back on my desk

My website-indexing workflow made that burden concrete.

The intended daily routine was to inspect eligible Ramble pages in Google Search Console, request indexing for up to ten URLs and record the outcomes. This meant submitting requests for Google to consider.

In a batch of ten selected URLs, ChatGPT Work successfully submitted one before Google Search Console returned an error. That left nine unsubmitted. I submitted those manually.

I subsequently defined a recovery path. On the next daily occurrence, the ChatGPT Work cloud browser could not be opened or run for the task. ChatGPT advised me to open a support ticket. But repeated checking and manual execution left too much work with me. So I stopped the workflow without opening a support ticket for this failure.

The technical cause remains unresolved. The operating outcome was clear: this workflow had failed to take the recurring task off my hands.

In a content operation, someone still has to notice the missed work, clear the backlog and decide whether the workflow should continue. A manual fallback can be sensible. It still needs an owner, time and a place in the business case.

The infrastructure: delivering a checked result

My LinkedIn post-performance workflow showed what a more complete delivery could look like. It used authenticated browser access to open the analytics, export the native spreadsheet and inspect its contents. It then recorded the metrics in my maintained Second Brain source, read them back to check that everything had been saved, deleted the exact temporary export and verified its removal.

That sequence of connected access, execution and verification completed successfully. The result was available in the record for subsequent analysis, with the temporary file removed after the checks.

For shared marketing reporting, the same approach would give the next analyst a saved result they could inspect and reuse. Source retrieval, recording and verification would be part of delivery, reducing the work each recipient has to reconstruct.

This chain produced useful work. Reliable repetition and net time saved still need demonstrating.

The model: one part of the service

Comparing ChatGPT Plus and Pro pushed me to separate three questions:

  • Can the model do the thinking the task requires?
  • Can the environment complete the required actions?
  • Does the workflow deliver usable work with an acceptable review burden?

A more capable model may help with difficult reasoning. It does not establish that a browser session will work, a saved result will be checked or a missed delivery will reach the right person.

The same separation belongs in an enterprise buying decision. Model quality, tool access, capacity and operating effort each need assessment. A successful run is evidence for that task and setup; attributing the benefit to a higher subscription tier requires a separate comparison.

The economics: count the work that comes back

I would judge an AI workflow by accepted output and the total human effort needed to obtain it. By accepted output, I mean a result that meets the agreed requirements and is usable for its intended purpose.

If a report needs two repairs before acceptance, it is one delivered report with three attempts behind it. A scheduled run that produces nothing usable still belongs in the record. So does time spent maintaining instructions, recovering access and finishing the task manually.

For a team pilot, three measures would make the business case more useful: acceptance on first delivery, human review and recovery effort, and whether the output was actually used. Missing and abandoned runs must stay visible. Before claiming time savings, count the time spent running, checking and fixing the AI workflow, then compare it with the time the same task took before.

Ownership matters just as much. Someone needs responsibility for missed runs and recovery. Someone needs authority to accept the result. In a small team, those may be the same person. Both responsibilities still need to be explicit.

Controls also take time. If every harmless step needs approval, the task keeps returning to its owner. Checks should sit at consequential decisions, while already-authorised work continues. The result must be worth the money and staff time spent producing, checking and correcting it.

The next step: test the AI handover

The next test is whether a colleague can use the result and handle a failed run without calling the person who built the workflow. That shows whether the team can operate it when its creator is unavailable.

Choose one recurring workflow and define what its recipient needs to accept the result. Name who handles failures, run it through its normal schedule and test a handover to another team member. Record missing and repaired deliveries, actual use and total human effort against the existing process. Expand when the evidence supports the extra scale; narrow or stop when the work keeps coming back.

A few fast answers before you act

Is a scheduled task the same as an AI agent?

No. A schedule determines when work starts. An AI agent uses a model, instructions and tools to pursue a goal and choose its next steps. In ChatGPT Work, a scheduled task can run this kind of workflow. Setting the schedule alone does not make it reliable.

What should count as successful AI delivery?

Successful delivery means the result meets its agreed requirements and is available where the recipient needs it. Record any review, correction or recovery required. A useful result can still have failed its first delivery.

Why keep AI workflow records outside the chat?

Saved results and decision records support continuity when a conversation becomes unavailable. Recovery depends on what was preserved and can be inspected. Another AI’s assurance of completion should be checked against the saved result.

Will a more expensive AI plan make workflows reliable?

A higher price does not establish reliable execution. Evaluate model quality, tools, access, capacity and recovery separately, using comparable work. Attribute a subscription benefit only when the evidence supports it.

How much governance does an AI workflow need?

Match controls to the consequences. Name who accepts the result and who handles failures. Use mechanical checks where possible, reserve approval for decisions that require it and let already-authorised work continue. Measure the review burden alongside errors caught.

What should an enterprise AI pilot measure?

Measure acceptance on first delivery, human review and recovery effort, and practical use against the existing process. Include missing runs, abandoned work and instruction maintenance. Test whether another team member can operate the workflow.