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# Do You Need AI’s Opinion on Every Decision?
- URL: https://theseguysknow.io/do-you-need-ai-opinion-every-decision/
- Published: 2026-10-01T21:12:50.000Z
- Updated: 2026-10-01T21:12:50.000Z
- Description: AI can help you make better work, but you still need to decide what you want, judge its suggestions, and know when another answer will not help.
- Author: Anas Amenziz
- Tags: Things to Avoid, Bad Habits

AI is a great helper, but it is not always right, and sometimes neither are you. We use these tools every day, but asking for their opinion on every detail can turn a simple project into something too complicated to finish.

The model can explain why another feature would be useful or defend a rewrite you do not even like. You need to know what you want well enough to recognise when its suggestions are helping and when they are taking you somewhere else.

## Quick Answer

**No**, you do not need AI’s opinion on every decision. **Use it to check your reasoning and improve the work**, but keep responsibility for the direction. Ask again when there is a specific problem to solve or something to verify. If you have enough information and are waiting for the chatbot to approve your choice, make the choice and try it.

## AI Needs a Good Leader

You set out to build a simple app, but before you have tested it, you ask AI what else it needs. It suggests accounts, reminders and reports, and you approve the lot because each sounds useful. Now you have doubled your to-do list without finding out whether the basic app is any good.

AI needs a good leader, and part of that job is knowing what you are building well enough to say, “No, we don’t need that.” Get the main function working, fix what stops people using it and let them try it before debating five layouts for an app you have not finished.

In [our Codex app experiment](https://theseguysknow.io/building-an-app-with-codex-non-developer/), we wanted the dashboard to show people what they needed to do next, with money totals coming afterwards. Codex wrote the code, but we rejected layouts that looked clean while hiding that next step.

Tell the model what the result needs to accomplish, let it propose a way to achieve that, then judge whether it has done the job. “Show the user their next action without making them hunt for it” gives it a clear job without dictating where every button goes.

| Situation           | A vague request                  | A clearer job                                                                                                                                                  |
| ------------------- | -------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| A difficult message | “Am I right? What should I say?” | “I need to address this missed deadline without starting an argument. Check whether I’m making unfair assumptions about their intentions and suggest an edit.” |
| A purchase          | “Which headphones should I buy?” | “Compare these three for commuting under €150, mainly for comfort and calls. Recommend one using evidence I can check, and say what you couldn’t verify.”      |
| An article          | “Is this idea good?”             | “My readers already know the basics. What would they learn from this idea, and is there enough here for an article?”                                           |

## AI Can Sharpen Your Ideas, but the Direction Is Yours

AI may know more than you about the subject you are working on, but it does not know what you truly want unless you explain it. Keep asking for opinions without deciding what you are trying to achieve and you can get a thousand reasonable answers, then spend your time following whichever one sounded best in the last message.

Give it your direction, let it sharpen the idea and suggest changes, then decide which changes belong. If it replaces the part you liked with something bland, tell it why the original matters; if it finds a real flaw, take that seriously. You can stand up for your idea and still accept a correction that makes it better.

Say you want an article that explains something in plain language. Breaking a connected thought into tiny sentences or adding a paragraph of vague bullshit makes the writing worse, even if the model calls it more readable. Reject that and explain what needs fixing, rather than asking it to approve your preference.

## AI’s Agreement Does Not Prove You Are Right

Knowing your direction helps you judge suggestions, but it does not stop a model from telling you what you want to hear. [Anthropic’s 2023 research](https://www.anthropic.com/research/towards-understanding-sycophancy-in-language-models?ref=theseguysknow.io) found that human feedback sometimes favoured convincing answers that agreed with the user over correct ones. Training on those preferences can reward agreement at the expense of accuracy.

A [study published in Science in March 2026](https://www.science.org/doi/10.1126/science.aec8352?ref=theseguysknow.io) found that 11 models affirmed users’ actions 49% more often than human responses did in the advice datasets studied. In its experiments, people who received agreeable advice became more convinced they were right and reported less willingness to repair a conflict. They also preferred that advice.

If you describe an argument by telling the chatbot how patient you were and how unreasonable the other person was, it may write a convincing defence of your behaviour. Include what you did wrong too, and ask it to examine the argument rather than help you win it. Even then, judge its reasons: a chatbot agreeing with your version of events does not establish that your version was fair.

## A Convincing Answer Can Still Be Wrong

AI can help you do one job brilliantly and get the next one wrong. A [study involving 758 BCG consultants](https://pubsonline.informs.org/doi/10.1287/orsc.2025.21838?ref=theseguysknow.io), conducted with GPT-4 in 2023 and published in 2026, found both outcomes:

| Task in the experiment                             | Result with AI assistance                                       |
| -------------------------------------------------- | --------------------------------------------------------------- |
| Work within the tested GPT-4 system’s capabilities | Consultants finished 25.1% faster on average.                   |
| A different problem outside those capabilities     | The rate of correct answers fell by about 19 percentage points. |

A [2025 survey of knowledge workers](https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/?ref=theseguysknow.io) also found that greater confidence in AI was associated with less reported critical thinking. Several impressive answers are a poor reason to stop checking the next one.

Before spending money, verify the detail that could change your choice, such as the current price or whether the product works with what you own. If an answer depends on research, check what the study measured. Asking “Are you sure?” or getting a second chatbot to agree still does not verify the fact.

Suppose you ask AI to assess a business course and show it the seller’s impressive sales screenshot. If it treats that as proof of a profitable business, it has missed the costs and the question of what ordinary customers achieve. We explain why revenue needs to be considered alongside costs in our guide to [online business ideas worth testing](https://theseguysknow.io/online-business-ideas/).

During our Codex build, tests were reported as passing, but an ordinary record would not save in the local preview. The browser had a newer form while an older server process rejected its new field. Restarting the server let the entry save, and using the form exposed what the reported test result had missed.

A clear request can still produce made-up facts, and lacking the expertise to spot them does not make the mistake your fault. When an important claim remains unverified, the model’s confidence does not make it safe to rely on.

## Know When to Stop Asking

Another question is useful when the answer could change what you do. Check compatibility before a purchase or ask about a hole in an argument. If two options already meet your needs, choose whichever you prefer.

Reopening the chat can become a habit, like the automatic visits to a feed we discuss in our [doomscrolling guide](https://theseguysknow.io/how-to-stop-doomscrolling/). The time feels productive because you are discussing your own project, even when another comparison only rearranges the same pros and cons.

If you are [afraid of looking cringe](https://theseguysknow.io/stop-worrying-about-looking-cringe/), a chatbot cannot promise how your audience will react. Ask it to identify a weak argument, then judge the criticism. You can keep your original opening even when the model defends its replacement; removing every unusual part can erase the reason you liked the idea.

## Final Verdict

We use AI every day and want it to improve our work, which means giving it a clear job and checking what comes back. Keep the suggestions that help, reject additions that make the project harder to finish, and change your mind when the evidence gives you a reason. Once you have something you can use, try it; another convincing explanation will not tell you as much as finding out what happens.

#### Sources

****TGK Evidence and Related Reading**

- [Can a Non-Developer Build an App With Codex?](https://theseguysknow.io/building-an-app-with-codex-non-developer/), 28 September 2026\. The dashboard decisions and failed-save example come from this documented experiment.
- [8 Online Business Ideas Worth Testing in 2026](https://theseguysknow.io/online-business-ideas/). Explains why sales figures need to be considered alongside costs.
- [How to Stop Doomscrolling Without Throwing Your Phone Away](https://theseguysknow.io/how-to-stop-doomscrolling/), 11 July 2026.
- [Fear of Looking Cringe Is Making You Boring](https://theseguysknow.io/stop-worrying-about-looking-cringe/), 20 July 2026.

****Research**

- Myra Cheng and colleagues, [Sycophantic AI decreases prosocial intentions and promotes dependence](https://www.science.org/doi/10.1126/science.aec8352?ref=theseguysknow.io), **Science*, 26 March 2026\. The 49% figure compares affirmation rates, rather than measuring the percentage of wrong answers. Three experiments involved 2,405 participants and measured judgments and reported intentions, rather than subsequent relationship outcomes.
- Hao-Ping Lee and colleagues, [The Impact of Generative AI on Critical Thinking](https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/?ref=theseguysknow.io), CHI 2025, April 2025\. Microsoft Research and Carnegie Mellon University researchers surveyed 319 knowledge workers about 936 examples of AI-assisted work. Participants reported less critical thinking with greater confidence in AI and described a shift towards verifying information, fitting answers into their work and overseeing the task. This was survey evidence, not a measured decline in thinking ability.
- Fabrizio Dell’Acqua and colleagues, [Navigating the Jagged Technological Frontier](https://pubsonline.informs.org/doi/10.1287/orsc.2025.21838?ref=theseguysknow.io), **Organization Science*, published online 11 March 2026\. The experiment involved 758 BCG consultants using GPT-4 in 2023\. Its results concern those tasks and that system.

****Vendor Research**

- Anthropic, [Towards Understanding Sycophancy in Language Models](https://www.anthropic.com/research/towards-understanding-sycophancy-in-language-models?ref=theseguysknow.io), 23 October 2023.