How to Ruin Your Project (But Get Funding) by Adding AI to Your Product

1. Let me set the scene

Let me set the scene: I’m a 3-time founder and a CTO who’s deep in the trenches of startup life right now. I’m building my own company in a brutally competitive space. Honestly, odds are I won’t “win” this round—but I’m gaining unique hands-on experience with AI automation under relentless pressure.

When every dollar counts and the competition is fierce, I have to invent constantly. That means making things radically simple with AI, automating wherever possible, and avoiding hiring sprees. For example, I’ve built pipelines that parse Reddit to find and analyze relevant articles, then suggest threads for me to jump in and answer. If you want to survive, this is the reality: innovate with less, move faster, and use AI for leverage wherever you can.

So, why is everyone so eager to add AI? And what happens if you’re more focused on impressing investors than truly helping your users?

Here’s the thing—using AI internally for business ops, dev workflows, or automation is very different from building customer-facing AI features. My engineering instincts often work against me: I’m obsessed with creating smart AI-based tools and pipelines, but users rarely care about what’s under the hood. What people really want is simple—they want documentation search to work by meaning, not just keywords. But they don’t want that search to take over half the screen, and they definitely don’t want to hear it’s “powered by AI.” They just want their problem solved—nothing more, nothing less.

2. The AI Gold Rush: Hype vs. Reality

Investor interest in AI has exploded—I notice this firsthand when pitching my own startup. Every investor wants a sense of AI magic, even if they think that they think in a different way: there’s a clash between the subconscious and the conscious here.

Everyone’s eager to be part of the new era. Yet, many also worry everything is “easy to copy,” and the truth is, most AI features are quickly duplicated—often just a few prompts.

What truly matters isn’t unique features but real usability, UX, and solving tasks for users. We’re in the era of value and experience, not just building for the sake of “AI.”

3. The Temptation: Why Founders Add AI (Even When They Shouldn’t)

FOMO among investors is at an all-time high: if you’re fundraising, it feels like everyone expects to see the magic word “AI” on your slide deck. For many, AI is now a shortcut to being seen as innovative and fundable, even if the tech doesn’t genuinely serve the product or user needs.

As a result, founders often feel pressured to squeeze AI into products just to ride the wave and grab attention. But “AI” often ends up as a buzzword, layered on top of solutions that would work fine without it. The real challenge, and discipline, is staying focused on solving actual user problems rather than only chasing investor hype.

4. How Adding AI Can Backfire on Product Development

Adding AI just for the sake of hype often overcomplicates a product. Instead of solving the core user problem, teams end up building features designed to impress investors—a trap that can seriously hurt UI and UX.

A perfect example is our competitor, mem.ai. After receiving $23.5 million from OpenAI, They heavily pushed AI into their product, making it chat-centric—what sounds like a small tweak, but in reality, the chat and AI features distracted users from their main tasks. The fun part? This move nearly ruined their product. The internet is now full of former users saying: “We wanted a good PKMS, but they gave us a distracting chat.” Users don’t care about AI features for AI’s sake—they want tools that help them with less distraction, not more. Mem.ai’s mistake was optimizing their product for their investor (OpenAI) rather than for real user needs. I believe if they had received funding from a different source, the user experience—and the direction of the product—could have been entirely different.

5. Getting Funded: The AI Sales Pitch

Today’s pitch decks are overflowing with “AI-powered” diagrams, ensuring every slide checks the “future-proof” buzzword boxes investors are hunting for. Everyone’s trying to prove their project is riding the AI wave, whether or not AI actually adds value.

It works—at first. There’s no shortage of projects landing funding thanks to their shiny AI stories. But look closer: the graveyard is full of products that raised millions with flashy decks but ultimately failed because the real substance was missing. In many cases, “AI” was little more than a slide in the pitch—good enough (sometimes) to win applause or maybe a term sheet, but definitely not enough for any real traction or loyal users down the line.

6. The Hidden Costs and Risks

Adding AI can disappoint early adopters if features underdeliver or add bugs and complexity. The team may burn out racing to launch “AI magic,” derailing your roadmap as energy shifts to debugging and fixing AI mishaps. Costs like cloud fees, unpredictable support needs, and the risk of public AI errors quickly escalate.

We saw this with the rushed AI launch at mem.ai story, which pushed away loyal users and overwhelmed support. Before committing, check if AI truly solves user needs and if you can sustain its maintenance.

7. Warning Signs You’re Ruining Your Project (Even as the Money Rolls In)

When the roadmap grows to please the next demo instead of truly solving user issues, your users will feel it—and so will your numbers. That classic disconnect between what is built (AI! Magic! Demos!) and what is actually valuable (UX! VALUE!) is where even funded projects slowly start to slide off the rails.

Yet, on the other side, this chaos creates a goldmine for service-oriented startups. Most of the “hot” AI features today—think voice commands, smart navigation, auto-categorization—will, in 3-5-10 years, become as basic (and open-source 😈) as windows, a file system, trash bin, or search are today. The space is wide open for clever teams to productize and perfect what everyone will need soon.

In fact, it’s pretty clear even now which features the entire industry will soon crave. But let’s be honest (and yes, I know I keep repeating myself, deal with it)—building them right, with true UX and actual value, is very, very hard.

UX!!! VALUE!!

Don’t lose sight of this, no matter how much demo money rolls in.

8. How to Avoid the Trap (and Still Impress Investors)

• Use AI to solve real user pain—don’t just chase hype. Build small, practical features, and keep users at the center: test, iterate, and make sure each AI tool actually improves their experience. Be honest with investors about what your AI can and can’t do; long-term trust outlasts flashy promises. Bottom line: tie every AI feature to real UX and value.
• Only introduce AI if it brings clear user benefit.
• Regularly test with actual users – kill anything that doesn’t add value.
• UX/value first, hype second.

9. Conclusion

The rush to “AI-wash” every new product leaves behind a messy legacy—features hyped for investors, not for users. But lasting impact (and real, sustainable success) comes from focusing on what actually matters, with or without funding: building tools and features that genuinely help people and stand the test of time.