I would like to report what I believe is an important problem in the way ChatGPT approaches projects, planning, and multi-step problem solving.
This is not primarily about coding mistakes or individual technical errors.
The problem is that ChatGPT can confidently guide a user through a technically valid approach without first adequately considering whether there is a substantially faster, simpler, or more efficient path to the same objective.
In my experience, this happened in two different ways during the same real-world project. Together, they revealed what I believe is the core issue.
THE CORE PROBLEM
ChatGPT often appears to optimize the next step without sufficiently optimizing the complete path to the user’s goal.
A solution should not be considered good merely because it works technically.
If Route A requires 14 days and Route B can accomplish essentially the same objective in 3 days, that difference should not have to be discovered because the user happened to stop and ask:
“Can this be done faster?”
The assistant should be considering that question before confidently recommending Route A.
That is the central point of this feedback.
REAL-WORLD EXAMPLE #1: MY PROJECT X
I have been developing a project called Project X with ChatGPT’s assistance.
ChatGPT was guiding me through the development process step by step, providing corrections, implementation instructions, code changes, testing procedures, and subsequent steps.
At one point, I stopped and asked an important question:
At the current pace, following the steps and corrections ChatGPT was giving me, approximately how long would it take to finish?
ChatGPT estimated approximately 14 days.
That immediately made me question the strategy.
I then asked whether the process could be optimized so that the project could be completed faster.
The answer was yes.
ChatGPT then proposed a more optimized approach and estimated approximately 3 days.
That difference is enormous.
Approximately 14 days versus approximately 3 days.
The important issue is not whether either estimate was perfectly precise. Project estimates naturally contain uncertainty.
The important issue is that a dramatically more efficient strategy apparently existed, but ChatGPT did not proactively consider or present it until I explicitly asked whether the process could be made faster.
Before that question, ChatGPT was confidently giving me step after step as though the current route were the appropriate one.
This raises a fundamental question:
Why should the user have to discover that optimization opportunity?
If ChatGPT can recognize a substantially faster strategy when explicitly asked, why was time-to-completion not already one of the variables considered when choosing the original strategy?
The user should not have to interrupt an AI assistant and ask:
“How long will all of these steps actually take?”
and then:
“Can you optimize this?”
before the assistant considers whether its own proposed workflow is unnecessarily expensive in human time.
REAL-WORLD EXAMPLE #2: WORDPRESS TESTING ENVIRONMENT
A second example made the same problem even clearer.
Initially, I specifically asked for an Android-based environment for testing my WordPress project.
Therefore, exploring Android solutions was not itself the problem. Android was a constraint that I had explicitly requested.
Later, however, I decided to move to a PC and simply asked ChatGPT for a PC solution.
At that point, the solution space had changed.
ChatGPT was no longer constrained to Android.
Instead of first comparing the easiest existing ways to accomplish my actual objective, ChatGPT proposed a much more manual Windows-based route involving setting up and configuring the environment.
But there were already purpose-built solutions that could accomplish my objective much more directly, including WordPress Studio, which provides an automated local WordPress development environment.
That is where I believe the failure occurred.
I did not explicitly say:
“Give me the easiest possible PC solution.”
I simply asked for a PC solution.
And that distinction is important.
A user should not have to know enough about the solution space to explicitly request:
“Please search for the easiest existing automated tool before giving me a manual implementation.”
That should be part of intelligent problem solving.
When several valid approaches exist, the assistant should consider their cost before selecting one.
The default should not be to choose a technically valid route and begin producing instructions immediately.
The assistant should first ask internally:
“What is this person actually trying to accomplish, and what is the most efficient reasonable way to get there?”
THESE TWO EXAMPLES ARE THE SAME UNDERLYING FAILURE
These situations looked different on the surface.
One involved software development planning.
The other involved choosing a WordPress development environment.
But I believe they expose exactly the same underlying problem:
ChatGPT can begin solving before sufficiently optimizing.
In the Project X example, a workflow estimated at roughly 14 days continued until I explicitly asked whether it could be optimized, after which a roughly 3-day approach was proposed.
In the WordPress example, once I requested a PC solution, ChatGPT provided a more complicated manual path without first prioritizing an existing automated environment that was much closer to my actual objective.
In both cases, the optimization occurred only after the human questioned the route.
That relationship should be reversed.
The AI should help the human discover the efficient route, not depend on the human to notice that the AI’s route is inefficient.
LOCAL CORRECTNESS IS NOT GLOBAL OPTIMIZATION
This distinction is extremely important.
ChatGPT can provide a technically correct answer to every individual step while still producing a poor overall solution.
Imagine:
Step 1: correct.
Step 2: correct.
Step 3: correct.
Problem appears.
Correction: correct.
Another problem appears.
Correction: correct.
Configuration required.
Instructions: correct.
Another issue appears.
Solution: correct.
Every individual response can be defensible.
But if an alternative route could have eliminated most of those steps, the overall assistance was still inefficient.
In other words:
A technically correct answer to the wrong subproblem can move the user farther away from an efficient solution.
Local correctness is not enough.
The assistant should also evaluate global strategy.
HUMAN TIME SHOULD BE AN EXPLICIT OPTIMIZATION VARIABLE
Human time is not an unlimited resource.
Neither are human attention, energy, patience, money, or tolerance for frustration.
Twenty minutes, three hours, three days, fourteen days, and three months are not interchangeable implementation costs.
Therefore, when ChatGPT recommends a path, time-to-completion and human effort should be treated as fundamental variables alongside technical correctness.
Before giving substantial multi-step guidance, ChatGPT should ideally evaluate:
1. What is the user’s actual end goal?
2. What constraints has the user explicitly imposed?
3. What existing tools or solutions already address that goal?
4. Are there official tools, established applications, frameworks, templates, automation, integrations, managed services, or existing workflows that eliminate unnecessary manual work?
5. What are the plausible routes?
6. For each reasonable route, what is the approximate:
-
time to completion
-
number of steps
-
complexity
-
required technical knowledge
-
monetary cost
-
human effort
-
risk of errors
-
risk of losing work
-
maintenance burden
7. Which route achieves the objective with the lowest reasonable overall cost while still satisfying the user’s requirements?
Only then should execution begin.
Conceptually:
GOAL
↓
USER CONSTRAINTS
↓
EXISTING SOLUTIONS
↓
POSSIBLE ROUTES
↓
TIME / EFFORT / COMPLEXITY / COST / RISK
↓
SELECT AN APPROPRIATE ROUTE
↓
EXECUTE
REASSESSMENT SHOULD ALSO HAPPEN DURING EXECUTION
Even a good initial strategy can become inefficient.
Therefore, ChatGPT should not simply continue indefinitely because a route has already been started.
If unexpected friction accumulates — repeated errors, configuration problems, manual corrections, dependencies, workarounds, excessive debugging, or a rapidly expanding number of steps — that should trigger a strategy-level reassessment.
Instead of:
PROBLEM
↓
FIX
↓
NEW PROBLEM
↓
FIX
↓
NEW PROBLEM
↓
FIX
↓
CONTINUE
the system should periodically ask:
“Given the user’s original objective and everything we now know, is this still the most efficient route?”
That question could prevent enormous amounts of unnecessary work.
THIS ALSO HAS A COMPUTATIONAL AND BUSINESS COST
There is another dimension that I initially did not include in this feedback.
I believe inefficient problem-solving also wastes resources on the AI provider’s side.
Every unnecessary troubleshooting exchange requires additional model inference, server resources, energy, tool calls, context processing, and user interaction.
If ChatGPT sends a user through 30 unnecessary steps when an existing solution could have accomplished the objective in 5, the inefficiency is not only being paid for by the user.
The platform is also spending computational resources generating, analyzing, correcting, and extending an unnecessarily long conversation.
The user may also be paying for a subscription or higher service tier specifically to obtain better assistance and complete work more effectively.
Therefore, improving global problem-solving efficiency could potentially benefit both sides:
- less wasted human time
- less user frustration
- fewer unnecessary interactions
- less unnecessary computation
- more efficient use of paid access
- potentially greater user trust and satisfaction
I am not claiming that the shortest answer or the least computation should always win.
Some problems genuinely require long and complex processes.
The point is that complexity should be necessary, not accidental.
PROJECT ESTIMATES SHOULD NOT COME BEFORE STRATEGY OPTIMIZATION
The Project X example also suggests another principle.
Before telling a user how long a project will take under the current plan, ChatGPT should consider whether the current plan itself can be materially improved.
Otherwise, the system may accurately estimate an inefficient strategy.
A 14-day estimate can be internally reasonable and still be strategically poor if a 3-day workflow is available and satisfies essentially the same requirements.
Therefore, the sequence should ideally be:
OBJECTIVE
↓
OPTIMIZE STRATEGY
↓
ESTIMATE TIME
not:
CURRENT STRATEGY
↓
ESTIMATE TIME
↓
WAIT FOR USER TO ASK WHETHER IT CAN BE OPTIMIZED
WHY I CONSIDER THIS A PRECISION ISSUE
I do not consider this merely a UX preference.
For goal-oriented AI assistance, I believe efficiency is part of accuracy.
If the user asks for help reaching Objective X, the quality of the answer cannot be measured only by whether each individual instruction is technically correct.
The assistant is helping choose a path.
Path selection is part of the answer.
Therefore, unnecessary time, complexity, effort, and risk should matter when evaluating the quality of that answer.
The question should not only be:
“How do I solve the next problem?”
It should also be:
“Why am I solving this problem at all?”
and:
“Given the user’s actual objective, is this still the right path?”
SUMMARY
The principle I am proposing is simple:
HUMAN EFFICIENCY SHOULD BE PART OF PROBLEM-SOLVING ACCURACY.
ChatGPT should optimize not only the correctness of the next response, but the complete route from the user’s current situation to the user’s actual objective.
A user should not have to discover that a 14-day workflow can become a 3-day workflow simply by asking, “Can you optimize this?”
A user should not have to explicitly say, “Give me the easiest solution,” for the assistant to consider whether an existing automated tool is preferable to a complicated manual setup.
And the assistant should not continue generating corrections indefinitely without periodically reconsidering whether the route itself is the problem.
My suggestion is that goal-level planning, existing-solution discovery, time-to-completion, human effort, complexity, computational cost, and strategy reassessment become explicit considerations in ChatGPT’s approach to substantial multi-step tasks.
The central principle is:
Do not merely optimize the next step. Optimize the path to the goal.
I believe incorporating this principle more systematically could substantially improve ChatGPT’s usefulness, efficiency, and trustworthiness for real-world projects.
I hope this feedback can be considered by the teams working on ChatGPT’s reasoning, planning, tool use, product design, and user experience.