I still use ChatGPT almost every day.
That’s exactly why I think ChatGPT is dying.
Not OpenAI.
Not GPT.
Not generative AI.
I’m talking about something much more specific:
the habit of opening a chatbot and asking it what to do.
For the last few years, this has felt like the future. Open ChatGPT.
Type a prompt. Wait.
Read the answer.
Copy it.
Paste it somewhere else.
Maybe correct the prompt.
Copy another answer.
Repeat.
It feels incredibly advanced compared with traditional search.
But lately I’ve started thinking:
Why am I still doing all this manual work?
Suppose I receive 40 emails.
I don’t actually want to ask:
“Help me decide which emails are important.”
I want the important ones identified.
Maybe summarized.
Maybe turned into tasks.
Maybe draft responses prepared.
Maybe meetings extracted.
I don’t want advice about doing the workflow.
I want the workflow done.
That’s a subtle difference.
But I think it changes the entire AI industry.
This is the weirdest part of using ChatGPT today.
ChatGPT writes something.
I copy it into Gmail.
ChatGPT analyzes something.
I copy the result into Slack.
ChatGPT generates code.
I move it into my repository.
ChatGPT gives me research.
I put it into a document.
ChatGPT creates a plan.
I manually execute the plan.
The AI is intelligent.
But I’m still the API.
I’m moving information between systems.
I’m providing context.
I’m executing decisions.
I’m reporting what happened back to the AI.
That feels temporary.
Think about what prompting actually is.
You’re manually telling a machine:
what happened,
what you want,
what context matters,
what rules it should follow,
and what output you expect.
But companies already have enormous amounts of that context.
Email.
Calendar.
Slack.
GitHub.
CRM.
Databases.
Documents.
Tickets.
Analytics.
Internal systems.
What happens when AI already has the relevant context and permission?
You stop writing:
“Here are 17 customer complaints. Please categorize them.”
The system already knows they arrived.
It categorizes them.
Escalates unusual cases.
Prepares responses.
Updates the appropriate system.
Then maybe asks you about the three cases where human judgment actually matters.
That’s not a better chatbot.
That’s the disappearance of the chatbot.
The internet used to require us to know where information lived.
Then search engines gave us a box.
We typed what we wanted.
They gave us links.
ChatGPT compressed another step.
It gave us an answer.
Now AI agents are attacking the next step.
You specify what you want.
The system attempts to accomplish it.
The progression is interesting:
Website → Search → Answer → Action
Each transition removes work from the human.
ChatGPT dominates the “answer” era.
I’m not convinced the answer era lasts forever.
Every few months we get another benchmark war.
GPT is ahead.
Claude is ahead.
Gemini is ahead.
New model. Bigger context window.
Better reasoning.
Better coding.
Higher benchmark score.
All useful.
But I think consumers eventually stop caring about most of this.
Nobody asks:
“Which database technology does Uber use before I request a ride?”
They want the ride.
AI may become similar.
The model matters enormously to the company building the system.
The user increasingly cares about:
Did it work?
A few years ago, “prompt engineer” sounded like a completely new profession.
People shared enormous prompts.
Prompt marketplaces appeared.
Thousands of posts taught:
10 prompts that will change your life.
Some of that is still useful.
But the direction seems obvious.
Good AI systems should require less prompting, not more.
If I need a 900-word prompt every time I want software to perform a routine business task, the software isn’t finished. The system should know:
the workflow,
the rules,
the available tools,
the permissions,
the company context,
and when to ask a human.
Prompt engineering doesn’t disappear completely.
It becomes infrastructure.
Users shouldn’t have to think about it.
The moment AI moves from:
“Here’s what I recommend.”
to:
“I did it.”
everything gets harder.
A hallucinated paragraph is annoying.
A hallucinated database operation is terrifying.
A wrong answer can be ignored.
A wrong action might need to be reversed.
Now we need:
permissions, approval gates,
audit trails,
rollback,
observability,
data boundaries,
failure handling,
and human escalation.
This is why building an impressive AI demo has become relatively easy.
Building an AI system a company trusts is still hard.
That sounds ridiculous.
But successful technologies often disappear into infrastructure.
You stop thinking about the technology itself.
You just expect things to work.
AI may follow the same path.
Today:
“I’ll ask ChatGPT.”
Tomorrow:
your development environment understands the repository.
Your inbox understands which messages matter.
Your CRM understands which accounts need attention.
Your internal tools understand company workflows.
Your support system resolves routine cases.
Your analytics system investigates anomalies.
You don’t necessarily visit AI.
AI is already there.
Obviously millions of people aren’t going to stop using chatbots tomorrow.
Chat is an incredibly natural interface.
There will always be situations where conversation is exactly what we want.
Learning.
Brainstorming.
Exploring.
Writing.
Asking questions.
Thinking through ideas.
Chat is fantastic for those things.
What’s dying is the idea that chat is the final interface for everything AI can do.
It isn’t.
Sometimes I don’t want to talk about the work.
I want the work completed.
That’s the part I find most interesting.
Today AI companies compete for our attention.
Open this app.
Visit this website.
Use this chatbot. Tomorrow the winning AI might compete for something much more valuable:
permission to act.
Permission to read the relevant information.
Permission to use tools.
Permission to make low-risk decisions.
Permission to execute workflows.
Permission to involve you only when necessary.
That changes the product completely.
The most important question in AI may stop being:
“Which chatbot gives the best answer?”
and become:
“Which system do you trust enough to actually do something?”
That’s a much harder problem.
And probably a much bigger business.
So yes.
I think ChatGPT is dying.
Not because people don’t want AI.
Because eventually…
asking AI what to do may feel unnecessarily primitive.
As AI moves from generating answers to generating and modifying real software, the dangerous part becomes verification.
I built The AI Backend Production Safety OS around guardrails, production failure modes, and reviewing code produced with Claude Code, Cursor and Codex.
The AI Backend Production Safety OS: Guardrails for Claude Code, Cursor & Codex
I write on Substack about AI agents, software engineering, production systems, and what happens when AI stops being something we visit and becomes something that works around us.
That’s what I’m building with ProdRescue AI.
We build custom AI agents, internal tools, copilots, integrations and workflow automations around real business processes.
If your company has people repeatedly copying information between systems, generating the same reports, processing requests manually, or doing work that software should probably handle: The question isn’t whether ChatGPT will still exist.
It’s whether you’ll still need to open it.
ChatGPT Is Dying. We Won’t Use AI Like This for Much Longer. was originally published in Stackademic on Medium, where people are continuing the conversation by highlighting and responding to this story.