I've been thinking a lot about why so many AI products fail lately. Not the splashy failures that make headlines, but the quiet ones—the features that launch to fanfare and slowly fade into obscurity, the startups that raise millions only to become footnotes in TechCrunch archives.
The pattern is painfully consistent: teams with brilliant engineers, substantial budgets, and access to cutting-edge models somehow manage to build things nobody wants to keep using.
Here's what I've realized: they're solving the wrong problem.
The Model Obsession Trap
When most teams sit down to "build AI," the first question on the whiteboard is usually: "Which model should we use? GPT-4? Claude? Gemini? Should we fine-tune something open-source?"
It's the wrong starting point entirely.
Models are commodities now. They improve every few months. Your competitor can access the same API tomorrow. Building your strategy around model selection is like a restaurant building its identity around which brand of oven it uses.
The real question—the one that separates winners from failures—is: "What unique value can we create that compounds over time?"
Three Traps I Keep Seeing
The Red Ocean Trap
So many startups are rushing into markets that look exciting precisely because AI makes them newly accessible. The problem? Everyone else sees the same opportunity. You end up competing against Microsoft, Google, and Adobe, who can ship your entire product as a minor feature update.
Remember Kite, the AI code completion startup? They were early, they were good, and they went head-to-head with GitHub Copilot. Microsoft had better data, better distribution, and could afford to subsidize the product indefinitely. Kite didn't stand a chance.
The Cool Demo Trap
Generative AI makes it embarrassingly easy to build something that looks like magic in a demo. A few API calls, a slick interface, and you've got something that makes investors' eyes light up.
But demos aren't products. The gap between "80% accuracy that's impressive in a presentation" and "reliable enough for actual daily use" is where most AI products go to die. Users tolerate a lot from free tools they're experimenting with. They tolerate almost nothing from products they're paying for and depending on.
The Platform Trap
Here's the uncomfortable truth for anyone building a thin wrapper around foundation model APIs: the platforms powering your product are also your biggest competitors. When OpenAI releases a new feature or adjusts their pricing, your entire business model can evaporate overnight.
If all you've built is a nice UX layer over someone else's model and public data, you're not building a company. You're running an experiment on rented land.
What Actually Works: Building Moats
The AI products that succeed share a common trait: they create compounding advantages that get stronger with every user, every interaction, every day.
Data Moats
Spotify's moat isn't their music library—that's available to everyone. Their moat is your personal listening history. Every skip, every save, every playlist addition trains their recommendation AI in ways no competitor can replicate.
What proprietary data does your product generate through normal use? If the answer is "nothing unique," you have a problem.
Feedback Loops
The best AI products get smarter specifically because users use them. When a user corrects an AI output, that correction should feed back into the system. When users consistently ignore certain suggestions, that pattern should inform future behavior.
Grammarly does this beautifully. Every time you accept or reject a suggestion, every time you edit the AI's output, you're training a model that understands your specific writing style. That personalization becomes incredibly sticky.
Workflow Moats
When AI becomes embedded in how people actually work—not as an optional helper, but as the operating system for critical tasks—switching costs become enormous. The product knows your context, your patterns, your preferences. Starting over feels impossible.
The Questions That Actually Matter
Before building anything, sit with these:
- Where does AI unlock disproportionate value? Not "what can the model do," but "what business outcome does this enable that was previously impossible or prohibitively expensive?"
- What data will we own that nobody else can access? Your moat has to come from somewhere. If it's not proprietary data, it needs to be a proprietary workflow that generates that data.
- How does the product get better with use? If version one and version one million perform identically, you're not building an AI product. You're building a feature.
- What's our AI economics story? Every API call costs money. At scale, inference costs can destroy your margins. Do you have a plan for model mixing, caching, or value-based pricing that keeps the math working at 100x users?
- How do we build trust progressively? AI outputs are probabilistic. Users need to develop confidence gradually. What's your progression from "helpful suggestions" to "autonomous actions"?
The Uncomfortable Timeline
Here's what keeps me up at night: the window for establishing defensible positions in AI is closing fast.
Every day that passes, someone else is compounding their data advantage, refining their feedback loops, and embedding themselves deeper into user workflows. The gaps between leaders and followers are widening at an accelerating rate.
This isn't like previous technology waves where you could wait, observe, and enter later with a better mousetrap. In AI, the mousetrap literally gets better every time someone uses it. Late entrants face incumbents with years of compounding improvements.
The teams that figure out AI product strategy in the next year will likely dominate their categories for a decade. Everyone else will spend that decade trying to catch up to a moving target.
Moving Forward
I don't think this is about being smarter or having more resources. It's about asking better questions earlier and being honest about whether you're building something defensible or just something cool.
The difference between Chegg losing 90% of its value and a legal tech startup getting acquired for nine figures wasn't access to AI technology. Both had that. The difference was strategic clarity about where AI could create unique, compounding value versus where it was just a feature that could be copied.
That clarity doesn't come from frameworks or consultants. It comes from staring honestly at your competitive position and asking: "If a well-funded competitor launched tomorrow with the same API access, why would customers stay with us?"
If you don't have a compelling answer, that's the problem to solve. Everything else is decoration.
Building something in AI and want to think through these questions? I'd love to hear what challenges you're facing. The patterns I'm seeing across different industries are remarkably similar, and there's a lot we can learn from each other.