I Don’t Publish a GPT Until It Survives an Investor-Style Review

The evaluation process that changed how I decide which GPTs deserve to exist.

Before I publish a GPT, I imagine explaining why it deserves to exist.

When I first started building custom GPTs, I had a simple rule.

If I finished it, I published it.

That felt reasonable at the time. Building a GPT took effort, so publishing it felt like the natural next step.

It didn’t take long to realize those were two completely different decisions.

Finishing a GPT doesn’t mean it’s a good product.

It only means you’ve stopped working on it.

Some of my GPTs solved genuine problems.

Some were interesting experiments.

Some existed simply because I thought the idea was clever.

The trouble was that I couldn’t tell the difference anymore. Every new GPT felt valuable simply because I had built it.

That’s when I realized I was asking the wrong question.

Instead of asking, “Is this finished?” I started asking something much harder.

If someone else had built this GPT, would I invest my own time and money in it?

That single question changed how I decide what gets published.

Build Like a Creator. Review Like an Investor.

Most creators review their work like builders.

Does it work?

Are the instructions polished?

Did I cover every feature I wanted?

Those are important questions. They just aren’t the first ones that matter.

An investor doesn’t care how many evenings you spent refining a prompt.

They care whether the product deserves to exist.

That became the philosophy behind my review process.

Before I publish any GPT, I imagine walking into a room where someone is deciding whether to fund it.

They aren’t interested in the effort behind it.

They’re interested in its future.

A great product starts with better questions, not better prompts.

The Questions an Investor Would Ask

The review isn’t about grammar.

It isn’t about prompt engineering.

It’s about the business.

Imagine you’re presenting your GPT to someone who has never met you and has no emotional attachment to your work.

What would they want to know?

They’d probably ask questions like these:

• Who is the intended user?
• What specific problem does it solve?
• Why would someone choose this GPT instead of simply opening ChatGPT?
• Is there evidence that people actually want something like this?
• How crowded is the space?
• What makes this GPT meaningfully different?
• Could this become a sustainable product, or is it simply an interesting experiment?

Those questions are surprisingly uncomfortable.

They force you to stop defending your idea and start evaluating it.

Sometimes the answers are encouraging.

Sometimes they’re not.

Occasionally, the review leads to an unexpected conclusion.

The best decision isn’t to improve the GPT.

It’s to stop building it altogether.

That isn’t failure.

It’s time saved.

Adding more features isn’t always the same as building a better product.

When the Review Changed My Mind

One conversation stands out.

I was refining a GPT called *Look Like You*. Its purpose was to help people explore realistic changes to their appearance before making them in real life.

As I worked on it, I started thinking bigger.

Why not include clothing advice?

Maybe it should recommend hairstyles.

Then I wondered if it should include travel packing suggestions, too.

The GPT was slowly becoming an all-in-one personal style assistant.

The investor review challenged that instinct.

Instead of asking how many features I could add, it asked whether adding those features actually made the product better.

Its recommendation surprised me.

Keep Look Like You focused on appearance.

Build separate GPTs like *Dress Like You or [Pack Like You](https://chatgpt.com/g/g-6a3ec3f22eec81919dc7a2c627da05a3-pack-like-you)* if those ideas deserve to exist on their own.

I still don’t know whether that’s the right business strategy.

But it was unquestionably the better product conversation.

Sometimes the smartest expansion is building a family of focused products instead of one oversized product.

The Hardest Decision Is Sometimes Saying No

The biggest benefit of this review isn’t that it helps improve GPTs.

It’s that it gives me permission to abandon weak ideas before I invest even more time in them.

As creators, we’re naturally attached to what we’ve built.

We remember every prompt we refined. Every feature we added.

An investor has none of that emotional investment.

They evaluate what is in front of them.

Adopting that perspective has made me far more disciplined.

Some GPTs survive the review unchanged.

Some are renamed.

Some are split into multiple products.

A few never make it any further.

Looking back, those were probably the best decisions of all.

A Habit That Applies Far Beyond GPTs

This way of thinking isn’t limited to AI.

It applies to software, books, courses, newsletters, startups, and almost any creative project.

Before asking whether something is finished, ask whether it’s worth backing.

If someone else had created it, would you invest your own time, money, or reputation in helping it succeed?

That’s a much harder question.

It’s also a much more honest one.

I’ve found Kill or Scale Your GPT to be one of the best filters for deciding which ideas deserve to become products and which ones should remain experiments.

The products you don’t build are often as important as the ones you do.