In my previous article on The AI Checkbox Fallacy, I mentioned Webflow's AI Site Builder as an example of grafting AI onto a product rather than thoughtfully integrating it. But there's a deeper concern here: as resources and attention shift toward these new AI initiatives, has the core product experience been neglected?
This question has been bugging me a lot lately. I've been watching products I use daily chase the sparkly allure of AI while their foundations show more and more cracks. It's like everyone's following the same playbook: rush to add AI features while letting the core experience deteriorate.
I always come back to the house analogy when thinking about product development. It just makes sense to me. Your product's core functionality is like the foundation and framing of a house - it's what everything else depends on.
What I'm seeing in this AI gold rush is homeowners obsessively installing smart home gadgets and fancy fixtures while ignoring that their foundation is cracking and their electrical wiring is shot. They're slapping on fresh paint while the plumbing leaks. They're installing voice-activated everything while the roof is about to cave in.
Sure, that sparkle is seductive - who doesn't want the newest, shiniest features? But man, that sparkle dims quickly when the basics stop working.
Coming back to Webflow, I've noticed increasing performance issues, longer load times, and growing reliability concerns. Whether these are directly connected to their AI push or not doesn't really matter - the perception is clear among many users (myself included): they're sacrificing fundamentals for flashy new features.
This shift is particularly frustrating for a design platform that built its reputation on reliability and performance. When I'm trying to design a site and the core functionality lags or glitches, no amount of AI-generated sites can make up for that terrible experience.
Apple might be the most glaring example of this problem. They're pouring massive resources into Apple Intelligence, hyping it as the next evolution of their ecosystem, while users like me face a daily reality of:
The disconnect is maddening. Apple wants me to trust them with sophisticated AI capabilities while basic functions I've relied on for years become less reliable. It's like they're installing a cutting-edge smart home system in a house where the toilets don't flush.
This AI obsession creates a bunch of cascading problems:
Engineering teams can only do so much. When significant resources shift to AI initiatives, maintenance and improvement of core systems inevitably suffer. This isn't just about money – it's about attention, expertise, and priority. I've seen this play out countless times.
As focus shifts to new shiny AI features, technical debt in core systems accumulates. Small issues go unfixed, optimizations are postponed, and the codebase becomes increasingly brittle. It's a slow decline that suddenly accelerates when it hits a tipping point.
When the basics become less reliable, I lose trust in the product as a whole. This erosion of trust contaminates even well-executed AI features. If your app crashes when I try to save my work, why would I trust your fancy AI assistant?
In the excitement over AI, longstanding user frustrations with core functionality get overlooked. These persistent pain points eventually drive users away, no matter how impressive the AI additions might be. I've abandoned products with amazing new features because the basics just didn't work reliably.
Here's a harsh truth that product teams need to understand: AI features have zero value if the product isn't reliably delivering on its core promise.
Users like me have an implicit reliability threshold – a minimum level of dependability we expect from core features. When a product falls below this threshold, no amount of innovation elsewhere can compensate. In fact, each new feature announcement in the face of deteriorating fundamentals only increases my frustration.
This creates a weird situation where the very AI features meant to delight users and drive growth instead accelerate dissatisfaction and churn. I've watched this happen with several products in my own workflow.
This drift from fundamentals doesn't usually come from bad intentions. Instead, it emerges from subtle shifts in organizational focus:
What makes it worse is that fixing fundamental reliability issues rarely generates headlines, while even minor AI features can get significant press coverage. The incentives are all messed up.
Not everyone is falling into this trap. Some companies have managed to pursue AI innovation while strengthening their foundations:
Notion: Their AI features are thoughtfully integrated into the core experience, enhancing existing workflows. Importantly, they've maintained and improved the reliability of the core platform alongside AI development. It feels like AI is making the product better, not competing with it.
GitHub: GitHub Copilot extends the core development experience without compromising the reliability of fundamental features like version control and issue tracking. They've invested heavily in infrastructure to ensure AI features don't degrade performance. It's a great example of foundation-first thinking.
Figma: After their initial AI misstep, Figma refocused on integrating AI in ways that enhance core design functions rather than replacing them, while simultaneously improving performance and reliability. They learned the hard way but course-corrected effectively.
What these companies understand is that AI should serve the product's core purpose, not become a distraction from it.
The companies that will truly thrive in the AI era aren't those racing to implement the flashiest features. They're the ones maintaining an unwavering commitment to product fundamentals while thoughtfully extending those fundamentals with AI.
Remember my house analogy: A smart home with a crumbling foundation is just an expensive problem. But a solid house thoughtfully enhanced with technology creates genuine, lasting value.
Before succumbing to the seduction of AI sparkle, ensure you've maintained the structural integrity of your product's foundation. Because no amount of technological magic can compensate for a product that fails at its core promise.
The true innovation isn't AI for its own sake – it's AI that strengthens foundations rather than concealing their cracks.