Could Your Mom Do This on ChatGPT Instead?

How to Actually Build a Product in the Age of AI

I’ve been sitting on this one for a while. Every week, someone sends me a deck for a startup that’s basically ChatGPT+ UI+ vibes. And every week, I gently, and sometimes not-so-gently, ask:

“…Couldn’t your mom just do this on ChatGPT instead?”

That’s it. That’s the test.
If your startup doesn’t pass it, it’s not a product. It’s a prompt.

Let me explain.

We’re Not in 2022 Anymore

Back then, building with AI meant you were early, cool, visionary. Now? Everyone and their uncle is an “AI founder.” You’ve got Claude for writing, Copilot for coding, Midjourney for stunning visuals, Veo 3 for video, and GPT-4o for literally everything else.

The moat isn’t “we use AI.”
The moat is: we use AI better: deeper, faster, smoother, and smarter than anyone else.

So here’s the updated version of “The Mom Test” for 2025:

Could your mom, with zero context and a free account, achieve the same thing on ChatGPT with a little copy-pasting and a few prompts?

If yes, your product’s in danger.
If not? You might have something worth building.

Let’s dig into how to figure out which side you’re on.

Step 1: The Substitution Test

“Could your mom do this on ChatGPT instead?”

This question sounds snarky, but it’s deadly serious. It's the modern founder's equivalent of the "product-market fit" gut check. In 2025, your product isn’t competing with a few other scrappy startups. It’s competing with a hundred other generalist AI tools that are free, powerful, and frictionless.

So here’s the test: Take the core task your product promises to do (generate a pitch deck, write marketing copy, summarize a legal document, create a website wireframe, whatever) and try to do it in ChatGPT (or the relevant tool). Now, watch closely.

• How many steps did it take?

• Did the output meet the bar?

• Was it faster, slower, messier, cleaner?

• Did you have to prompt-engineer your way through it, or did it just work?

Better yet, ask a friend who’s not “techy.” Your mom, your cousin in HR, your lawyer friend. Give them the same task and ask them to try it on ChatGPT without your help. Then compare it to your product’s flow.

If they can do it quickly, without breaking a sweat, and the output is “good enough”, you’ve got a substitution risk.

And substitution risk is death.
Because why would anyone pay ₹499/month for your startup when they can do the same thing in GPT for free?

Most AI-native products today aren’t solving problems. They’re solving UI friction. A little form here. A fancy font there. But underneath, it’s all the same API call. Users can tell. And more importantly, they will switch once they realize it.

So use this test early and often. Be ruthless. If your product flunks it, that’s okay. It just means you have to go deeper. Specialize more, integrate harder, differentiate better.

The goal is to build something that doesn’t just do the thing, it does it better, faster, smarter, and more reliably than any general-purpose AI can. That's when you go from a wrapper to a real business.

Step 2: Audit Your Moats

Because AI isn’t your product. It’s the plumbing.

Okay, so maybe your product passes the Substitution Test. A user can’t just type the same prompt into ChatGPT and get the same result. That’s great. But now comes the next hard question:

Why you?
Why this product?
Why now?
Why should someone stick with your solution, even when newer, shinier models pop up every six months?

This is where moats come in.

In the pre-AI era, moats were things like distribution, pricing power, proprietary tech, and user lock-in. Some of that still applies. But in an AI-saturated world, moats look a little different, and you need to audit yours honestly.

Let’s break down what you should be looking for:

1. Proprietary Data
   Do you have access to data no one else does?
   Think: customer usage data, niche domain knowledge, vertical-specific datasets, feedback loops, etc.
   This kind of data is gold. It lets you train better models, deliver more relevant results, and tune your product in ways a generic tool never could.

2. Workflow Integration
   Are you embedded in the way people already work?
   If your product lives inside VSCode (like Copilot), inside Google Docs (like Grammarly), or hooks into Slack + Notion + Jira (like Glean), you’re harder to rip out. That’s a moat.
   Be the tool that’s just there when users need it. Invisible, frictionless, indispensable.

3. Speed & UX
   If you can get users from “I have a problem” to “I have a solution” in fewer steps than a general AI tool, that’s a win.
   Don’t underestimate good onboarding, snappy results, and zero-thinking-required defaults.
   Remember: the average user doesn’t want to prompt engineer. They want outcomes.

4. Trust
   Do your users trust that your output won’t hallucinate?
   Do they know you have guardrails, human review, or guarantees?
   Trust is a serious moat in regulated industries (healthcare, finance, legal), but also just in life. People will pay for peace of mind.

5. Community & Brand
   Are people talking about you? Sharing use cases? Creating content?
   Is there a Slack group, a Discord, a fanbase?
   A community is hard to replicate. A strong brand is even harder.

Do this audit. Put it on a Notion page. Color-code it. Be brutal.

Because if your only moat is “we’re early” or “we’re using GPT,” it’s not a moat, it’s a moment. And it’ll pass.

Step 3: 10x or Bust

Because no one’s switching for a 10% improvement.

Let’s say you’ve built something cool. It passes the Substitution Test. You’ve got a moat or two.

Great. But now ask yourself the question that makes most founders squirm:

Is this 10x better than what already exists?

Not 10% faster. Not 10% prettier.
Ten. Times. Better.

That’s the bar in the age of AI.

Why? Because the baseline is already amazing. ChatGPT, Claude, Midjourney, these tools are fast, powerful, and either free or dirt cheap. They don’t need onboarding. They don’t require loyalty. And they’re improving at warp speed.

So if your product doesn’t drastically outperform a general AI tool in some meaningful way, users won’t switch. They won’t even try it. They’ll blink once, yawn, and go back to what they already know.

Here’s what 10x could look like:

• 10x faster: Your product does in 30 seconds what takes 5 minutes on ChatGPT with prompt fiddling.

• 10x easier: No prompt engineering, no trial and error. Just click-click-done.

• 10x more accurate: You serve a domain where precision matters, like legal, medical, enterprise, and your output is provably better.

• 10x more delightful: The experience is so seamless, beautiful, or emotionally satisfying that users enjoy using it, not just tolerate it.

• 10x more trusted: Your results are verifiable, audited, human-reviewed, or explainable in a way generic AI isn’t.

• 10x more integrated: You’re not an app. You’re a utility. Always-on, embedded, invisible.

Even if your product isn’t 10x better yet, you still need to be on the path to it.

Because AI is not just a wave, it’s a compounding one. Every new release raises the floor. The things that felt magical in 2023 are table stakes now. And the things you think are “good enough” today? GPT-5 will eat them for breakfast.

So if you’re not actively building toward that 10x delta, toward undeniable, oh-my-god-I-need-this value, then you’re building for irrelevance.

Let that scare you a little.
Then let it focus you.

Because 10x isn’t a nice-to-have.
In the AI era, it’s the only kind of win that matters.

Step 4: Build for Use, Not Just Users

In the early 2010s, the playbook was: get the user, figure out what they’ll do later.
Today, that’s a death sentence, especially in a world where AI tools are increasingly default utility.

You’re not building for users anymore.
You’re building for use cases: specific, repetitive, high-friction jobs that people want off their plate now.

Ask yourself:

• What’s the exact moment someone thinks, “God, I wish this was easier”?

• Can your product slot into that moment so naturally that it feels like a reflex?

• Does your product deliver value before someone fully understands how it works?

That’s the bar now.
Because the best AI-native products don’t require onboarding. They don’t need a sales funnel. They’re used because they’re useful, instantly.

Think about tools like Otter, Notion AI, and Gamma. People don’t “adopt” them because of fancy positioning. They adopt them because they need to transcribe, summarize, or generate a doc right now, and these tools do that with minimal friction.

Three questions to pressure-test your product’s “use” value:

1. Is the core job to be done happening in under 60 seconds?
   If not, you’ve built a workflow, not a product. Slice until only the “job” remains.

2. Would a person use this even if it had no brand, no logo, no loyalty?
   If yes, congrats. You’ve built utility, not just identity.

3. Does your product get better with every use, or just exist with use?
   AI-native products have feedback loops. Static ones get commoditized.

In the age of AI, ease is default. Use is king.

You can’t just rely on signup hacks, design porn, or “cool idea” energy.
You have to build something that solves a job better than anything else, including ChatGPT.

If your product isn’t being used on Day 1, it won’t be remembered on Day 30.

Don’t optimize for adoption. Optimize for need

Step 5: Don’t Wrap the Model. Own the Workflow.

This is the hard truth:
Anyone can hit the OpenAI API.
What they can’t do as easily?
Understand the messy, real-world workflow that surrounds a problem, and design a product that owns it end-to-end.

Your product is not the model. It’s the experience.

In the age of AI, “wrapping the model” is the new MVP trap. It feels like you’re building something: slick interface, smart prompts, maybe even fancy branding. But if all you’ve done is slapped a UI on ChatGPT, you’ve made a feature. Not a company.

The winners in this wave will be the ones who go deeper.
They won’t just offer a faster way to complete a task.
They’ll offer a complete way to own the entire user journey, from problem awareness to final output, and every decision in between.

Let me show you the difference.

A wrapper says:

“Look, we can generate tweets!”

A real product says:

“Here’s your entire social media calendar, based on brand voice, market trends, and this week’s campaign goals. Click to schedule.”

See the leap?

Ask yourself:

• Where is the real friction in your user’s day? (Hint: it’s rarely in the thing AI is solving. It’s in the 5 steps before and after.)

• Are you solving just the task? Or the context around the task?

• Could your product become the place where this entire workflow happens?

Take examples:

• Notion AI doesn’t just help you write. It helps you think, draft, organize, and share, all in one place.

• Runway doesn’t just help you generate videos. It builds pipelines for creative teams.

• Figma + AI don’t replace designers, they cut the busywork out of design collaboration.

These tools don’t just “integrate” AI, they orchestrate it.

So stop thinking “How do I add AI to this?”

Start asking: What painful, high-context workflow can I remove entirely?
What process can I own, not just accelerate?

So how do you avoid becoming just the AI?

Ask yourself:

• What’s the system around the model?

• Are you guiding users through a process or just giving them a blank prompt box?

• Is there memory, feedback, correction, versioning, collaboration?

• Does your product improve as people use it, or is it static?

• Are you solving a workflow or selling a novelty?

Your moat isn’t the model.
Your moat is knowing the user’s chaos better than they do, and taming it beautifully.

People don’t pay for tools.
They pay for clarity.

Step 6: Adapt or Die Trying

Because AI isn’t standing still. And neither can you.

You could be winning today, and completely irrelevant six months from now.
That’s not pessimism. That’s just what building in the age of AI feels like.

OpenAI, Anthropic, Google, Meta, they’re all in a race to obliterate the concept of “edge.” What was once a novel feature is now an OpenAI plugin. What felt like innovation is now a menu option. The things you raised money for in 2023? Probably in GPT-4o for free now.

So what do you do?

You adapt. You move fast. You zoom out, then double down.

Every founder I respect in this space builds with a little paranoia and a lot of grace. They expect that whatever they launch will be copied. Or outmoded. Or absorbed into the foundation models. So they bake change into their DNA. They don’t build temples. They build trampolines.

And that starts with one golden question:

“If OpenAI or Google releases [insert scary feature here] tomorrow, will my product still be valuable?”

If the answer is no, that’s your blueprint for where to build next.

Here’s what adaptation looks like in practice:

• You go from feature to workflow.

• From tool to platform.

• From clever AI trick to deep, daily utility.

• From selling outputs to owning outcomes.

Adaptation also means ruthlessly watching what users are hacking together with general AI tools, then turning that into the product.

If your users are chaining prompts, that’s a product.
If they’re copy-pasting into Canva or Notion or Jira, that’s a native integration.
If they’re hiring VAs to QA ChatGPT outputs, that’s a trust moat waiting to be built.

AI is not going to slow down for you to catch up.
So don’t try to “keep up.” Try to see around corners.

You’ll win not by building the biggest thing, but by building the thing people can’t live without, no matter how good the models get.

Stay flexible. Stay obsessive. Stay two steps ahead.
Because if you don’t evolve, someone else will.

And in this game?
The first one to stagnate loses.

Still Facing a Crisis of Imagination?

A few examples:

GitHub Copilot: Integration Is the Moat

Copilot didn’t win because it had better AI. It won because it lives in your IDE, autocompleting code as you type, without breaking your flow. Developers didn’t need to context switch or prompt-engineer anything; it just worked, where they already worked.
Insight: Embed your product inside the user’s core workflow, not outside it. Become invisible, not extra.

Midjourney: Design + Community = Stickiness

Midjourney consistently produced beautiful, stylized images with minimal prompting. But its real genius was hosting everything in a Discord server where users could watch, learn, compete, and share. The tool became a scene.
Insight: Don’t just build a tool, build a vibe. If you can’t own the algorithm, own the atmosphere.

Glean: Solve the Bigger Pain, Not Just the Prompt

Glean went beyond “AI Q&A” and tackled the real issue: employees wasting hours finding internal knowledge across Slack, Notion, Jira, Drive, etc. By integrating everything, it offered precision that general LLMs couldn’t.
Insight: Frame your product around the whole workflow pain, not just the part AI can solve. Be the glue, not the gimmick.

Jasper: From Copycat to Contextual Command

Jasper started as a generic AI writing tool and nearly got obliterated by ChatGPT. Survival came through reinvention: focusing on brand voice, team collaboration, and end-to-end marketing content workflows. Now it’s a marketer’s control room.
Insight: When general AI catches up, go upstream. Own more context, brand fidelity, and decision-making.

You Don’t Win by Fighting AI. You Win by Outlasting It.

Founders who get this don’t build wrappers.
They build rituals.
They build tools people use without even thinking twice.

You don’t need to fight AI.
You need to dance with it and lead.

Founders who understand this don’t just survive; they set the rhythm.
They don’t chase novelty. They chase usefulness. And they win because they know that in the age of infinite tools, the only thing that matters is making something people actually need, again and again and again.

So, ask yourself with ruthless honesty:
Could your mom just do this on ChatGPT instead?

If the answer is no? Beautiful. Now earn that “no” every single day.
If the answer is yes? Then this is your call to evolve before you become yet another abandoned wrapper.

Because in this new world, building around AI isn’t enough.
You have to build beyond it.