Stop Building What GPT Just Made Easy

I killed a feature on a live coaching call the other week.

It wasn't a bad idea, and the team could absolutely build it. Because GPT made it irrelevant overnight - and the founder I was talking to hadn't updated his roadmap to reflect that.

Building AI-adjacent products right now means you might spend six months building something that OpenAI ships as a default feature in their next update.

Let me walk you through exactly what happened, because I think there's a framework buried in this conversation that I keep applying in my SaaS coaching calls.

The Feature That Died Mid-Call

The product in question is an AI chatbot platform - think customer support, lead capture, inbound sales. The team had a whole roadmap. One of the bigger items on it was a landing page builder. Fully featured, AI-powered, drag-and-drop. The pitch was: build your chatbot AND the page it lives on, all in one tool. Like ClickFunnels, but smarter.

It sounded good on paper. Solid market. Clear use case. The team was capable of building it.

I killed it.

Why? Because GPT changed my mind. Chatbot integration is now so simple, so fast, so cheap to wire up through the API that the landing page builder doesn't add the moat we thought it would. Anyone with a basic webpage can drop in a snippet and be live in minutes. You don't need to own the page-builder layer to win the chatbot layer.

But more importantly - building a landing page builder is building a whole separate business. You're talking about an entire product category. If you do it, you now have to market two things and support both of them. Hiring needs double too. Your positioning gets blurry, and eventually the team loses focus.

I told the team straight: this splits your marketing completely. Right now, if you're just doing lead generation and chatbots, you can point every marketing dollar at one message. The second you add website building, you're fighting in a different arena. You'd be competing against ClickFunnels, Carrd, and Leadpages - companies that have spent years refining their page editors. You're walking into their territory.

That pivot will probably kill you.

Your Roadmap Should Shrink When AI Improves

Founders treat new AI capabilities as an opportunity to add more to their roadmap. A new model drops, they brainstorm five new features they could build on top of it.

Wrong direction.

Every time a foundation model levels up, your first question should be: what just got commoditized? Check which features the model can now handle natively. What's on our roadmap that customers will be able to get for free in six months?

Kill those features. Kill them fast, before they eat up engineering sprints, sales conversations, and product positioning that should be pointed at the stuff that protects you.

This is how you win. It's how you stay sharp. The teams that keep adding features every time AI improves end up with bloated products that do fifteen things poorly. The teams that subtract - that ruthlessly cut whatever just got commoditized - end up with products that do one thing better than anyone.

The landing page builder wasn't cut because it was a bad idea in a vacuum. It was cut because the moat it was supposed to create evaporated. The integration complexity that made it valuable as a bundled feature got solved by the underlying models getting smarter. So the feature died. Good riddance.

What Doesn't Get Commoditized

Here's what I told the team to focus on instead - and this is where the roadmap conversation got interesting.

The things that don't get commoditized are the things that require distribution, relationships, or proprietary data. The model can get smarter all day long, but it can't replicate your integration with a specific platform, your existing customer relationships, or the workflow you've embedded yourself into.

In this case, the product we were building has a clear lane: AI chatbots that live inside sales and customer support workflows. Not just on websites - inside Slack, Discord, Instagram DMs, WhatsApp, abandoned cart flows, email follow-up sequences. Anywhere a conversation can happen, this bot can live.

Distribution and integration depth take time to build and take time for a competitor to copy, even if the underlying model is freely available to everyone.

We talked about the inbound side first: someone hits your website, interacts with the bot, gives you their contact info. Then the bot chases them down across every channel - email, text, even phone calls eventually. Building that requires the integrations, the workflows, the channel connections - GPT doesn't ship any of that.

Then we talked about the outbound side: using AI to find conversations happening about your product category on Reddit, in forums, in communities - and respond in a way that's helpful but also drives people back to your product. There were some tools floating around doing versions of this, some more aggressive than others. The legitimate version of this is content-aware community engagement at scale. That's a standalone product. That's not something the base model does for you.

Both of those require serious engineering work. Neither of them are going to get shipped as a free ChatGPT feature next quarter. That's where you put your sprints.

The Shopify Example

One of the best ideas that came up in this conversation - and I want to credit the thinking here because it's sharp - was Shopify integration.

If you're building an AI chatbot for lead gen and customer support, and you're thinking about abandoned cart flows... Shopify is sitting right there. It's a massive market. Every Shopify store has abandoned carts. Every abandoned cart is a conversation that didn't get closed. If your bot can plug into Shopify, detect an abandoned cart, and start a conversation to recover that sale - across email, SMS, or even WhatsApp for international merchants - that's a vertical integration with recurring revenue attached to it.

More importantly, it's not something GPT-4 or Claude or whatever the next model is will do natively. It requires a Shopify app listing, an API integration, a workflow builder, merchant onboarding. That takes distribution work and relationship-building, which compounds into a moat.

That kind of feature belongs on your roadmap after you've cut the stuff that got commoditized.

The Channel Expansion Principle

We spent a chunk of the call talking about channels - where the chatbot lives - and this idea applies beyond chatbots.

Right now, the default for AI chat tools is website-first. You put a widget in the bottom corner, someone clicks it, conversation happens. That's fine. That's table stakes. But it's also saturated - every chatbot startup already has a website widget.

Where else can conversations happen that haven't been properly addressed yet?

Instagram DMs are one. You've seen the play - DM me a keyword and I'll send you something. There's a whole workflow that can live inside that interaction, and it maps perfectly to what a sales chatbot does: qualify, capture info, follow up. That's not a crowded space yet.

Email is another. Inbound support and sales email. Someone emails your business. Instead of it sitting in a queue waiting for a human, your AI handles the first response, qualifies what they need, maybe closes the conversation entirely. That's a channel chatbot companies keep ignoring because they're too focused on the website widget.

The more channels you can be present in, the harder you are to replace. A competitor can clone your website widget. They can't easily replicate the fact that you're already embedded in a merchant's Shopify store, their Instagram DMs, their email support queue, and their abandoned cart flow simultaneously.

That's defensibility. That's what a roadmap should be optimizing for.

The LLM Question - And Why It's Not the Priority

One thing that came up that I want to address directly: the question of whether to build your own LLM.

The honest answer is: probably not yet, but keep the door open.

My thinking on this, which I shared in the call, is that you should architect your product so that you're not hardcoded to OpenAI. You want to be able to swap out the underlying model without rebuilding everything. That means abstracting the API layer so you're not wrapping GPT so tightly that you can't swap it out.

Why? Because LLMs are going to fragment. Every major enterprise will eventually have their own. There will be specialized models for different verticals. The market in five or ten years looks nothing like it does today. If you're a sales AI company, you might end up with a sales-specific LLM that dramatically outperforms general-purpose models for your use case. You want to be able to plug that in.

But - and this is important - you can build a multiple-million-dollar-a-year business without touching the LLM layer at all. The value you're creating is in the product layer: the integrations, the workflows, the UX, the distribution. The LLM is an input, not the product.

So don't get distracted building an LLM when you haven't finished building the product that uses one. Get to revenue first. Build the moat in distribution and integration. The LLM question is a year-three problem, not a day-one problem.

What This Means for Your Roadmap Right Now

If you're building anything in the AI-adjacent space, here's the exercise I'd recommend doing quarterly - or honestly, every time a major model release drops:

Pull up your roadmap and go through every item. For each one, ask: could someone build a decent version of this in an afternoon using the new model? If yes, cut it or dramatically deprioritize it. It's a commodity now. A competitor with the same API key can clone it by Friday.

Then look at what's left. Put your engineering time there. The ones that require integration work, distribution relationships, proprietary data, or platform-specific knowledge. Double down there. Put your best engineers on those, and make them so good that even when the next model drops, you're still ahead.

The winning teams over the next few years will have the shortest roadmaps. They're the ones who are ruthless about cutting the stuff that just got easy and focused on building the stuff that's still hard.

AI improves. Your roadmap should shrink. That's the game.

One More Thing: The Non-Technical Customer

Before I close this out, there was one piece of the conversation that I keep coming back to, because it's a targeting question that a lot of founders get wrong.

There was a discussion about whether to build Zapier or Make integrations, list the product on those platforms, go after the no-code audience. The argument was: these people already understand automation, they're builders, they'll get what you're doing.

I pushed back on this, and I think my pushback applies broadly.

The sophisticated no-code builder who's comfortable with Zapier workflows already knows how to wire up a GPT prompt. They can look at your product, recognize what's happening under the hood, and decide to just build it themselves. You're selling to people who can - and will - become your competitors the second they understand your product well enough.

The better customer is the person who has never heard the word API. Who doesn't know what a webhook is. Who just knows they have a business problem - leads aren't converting, support emails are piling up, abandoned carts are costing them money - and they need something that solves it without requiring them to learn anything technical.

Build for them. They pay, stay, and refer - and they can't replicate what you built on a weekend with a ChatGPT account.

Build for the person who needs the magic, not the person who understands the trick. The technical audience will always find a way to DIY. The non-technical audience just needs it to work.

That's your customer. Build your product for them, and build your roadmap around what they can't easily get anywhere else.

The Bottom Line

AI is moving fast enough that your competitive advantage can evaporate between product planning sessions. The landing page builder that looked like a smart bundled feature three months ago is a distraction today. The integration you deprioritized because it seemed too niche might be your moat.

The discipline is cutting your roadmap when AI improves. In asking what just got easy and refusing to build it. In staying focused on the layer of the stack - distribution, integration, channel depth - that the model can't do for you.

If you're building in this space and you want to pressure-test your roadmap against this framework, I do this kind of work inside Galadon Gold. Live calls, direct feedback, no theory. We go through your product, your positioning, and figure out what to cut and what to double down on.

And if you're on the outbound side of this - building lead lists, scraping contacts, setting up the data infrastructure that feeds your AI workflows - ScraperCity has the B2B database and scraping tools to handle that side. It's what I use for my own prospecting. The email finder alone saves hours every week.

If you're earlier stage and just trying to figure out the outbound fundamentals before you get into AI-powered anything, grab the top 5 cold email scripts - free download, just the sequences that work.

Your roadmap should be shorter than it was six months ago. If it's not, you haven't been paying attention to what just got easy.