Beyond the Cool Demo: 10 Moves That Turn AI Products Into Market Leaders
Why most AI products fail (and what the winners do differently)
I realized six months until building shame.less (my mental health accountability app) that my founder dreams were going to have to wait.
Not because I was in a rigorous degree program, not because I was broke, not because I was bootstrapping everything, but because I realized that I cannot with any ethics stick .ai on my app and call it the ChatGPT of therapy.
I couldn’t with confidence have people use my application that weren’t going to stick with it for three months, yet alone three years.
I talk about the limitations of vibe coding in my book here. What I didn’t say was that even people that have been building apps for years have either overcame or collapsed under the same problem.
I hate AI slop, and hate apps that aren’t useful with (or without!) the AI.
And most people are the same way.
Some of these products are better, much better than mine. But into the graveyard they go.
The graveyard of AI products that had everything going for them: cutting edge models, sleek interfaces, impressive demos that wowed investors. They raised millions, generated buzz, landed on Product Hunt’s homepage.
And then they quietly disappeared.
Meanwhile, products like Perplexity grew from launch to serving over 10 million monthly active users, Claude reached $1 million in mobile app revenue within 16 weeks of launch, and ChatGPT doubled its weekly active users from 100 to 200 million in a year.
I wanted to be like them. The folly…
But!
What separates these winners from the graveyard residents isn’t better AI. It’s product-market fit: built on a fundamentally different understanding of what AI products need to succeed.
And after studying what actually drives AI product adoption (from Bessemer Venture Partners’ comprehensive PMF playbook for AI founders to Menlo Ventures’ survey of over 5,000 U.S. adults on AI usage), I’ve identified 10 moves that actually matter. Not the ones that make for good X threads. The ones that turn demos into defaults.
Let’s break them down.
1. Define What Your App Actually Does With AI
Here’s where most builders get it wrong: they frame their product as “adding AI to [popular app].” That’s thinking backward.
The fundamentals of product-market fit remain the same in the AI era: you need to define a clear ideal customer profile, solve an urgent pain for this market, and establish positioning that illustrates why your company is best to solve it.
But AI changes the question. Instead of “How can AI improve this workflow?” ask: “What high-frequency, high-advantage job can AI do that creates fertile PMF ground?”
Look at Perplexity. They didn’t build “Google with AI.” They built a conversational answer engine optimized to help users find knowledge rather than boost ads and keywords. That’s a fundamentally different function.
Traditional PMF playbooks are failing in 2025 because AI is changing what product-market fit really means. The winners understand this and build accordingly.
Your action: Write down the job your AI product does. If it includes the phrase “but with AI,” start over.
2. Target Acute Pain Points, Not Features
Mainstream AI usage comes through simple, everyday tasks like emails, lists, and quick research. Trust builds by addressing small, frequent needs first. But here’s the key: those small needs must be painful.
According to research on AI adoption trends, users with AI-powered features save 37% more time than those without. That’s not “nice to have.” That’s “I can’t go back.”
Brisk Chrome extension for educators addressed a critical insight: the average teacher juggles 9 different applications daily, and adding another platform only exacerbated their cognitive load. Teachers reported saving over 10 hours a week—time they described as giving them back their weekends with family.
That’s acute pain solving. Features can be copied. Deep pain alignment can’t.
Your move: Interview 10 users. Ask: “What would happen if this stopped working tomorrow?” If they shrug, you’re solving the wrong problem.
3. Narrow Your ICP to the Point of Discomfort
Because serving everyone = serving no one deeply.
Although more than half of U.S. adults report using AI, not one activity sees more than one in five relying on it. Even the most common use (writing emails) tops out at just 19%. AI adoption is broad but shallow.
The winners go narrow and deep. Perplexity targeted students by offering a free month of Perplexity Pro to anyone signing up with a valid .edu email address, with a follow-up rate of $4.99/month versus the standard $20. They didn’t try to be everything to everyone. They picked a segment and dominated it.
Pick a company type + pain point so specific it makes you uncomfortable. That discomfort is usually a sign you’re getting it right.
Your test: Can you name three specific people who fit your ICP? If not, it’s too broad.
4. Design for Edge Cases, Not Accuracy
High-performing AI organizations are more likely to say their organizations have defined processes to determine how and when model outputs need human validation to ensure accuracy. This is what distinguishes winners.
Most builders obsess over “90% accuracy!” They celebrate when their AI works for most cases. But users only write about the 10% where it fails. Those edge cases—the weird inputs, the unusual workflows, the corner cases—that’s where trust breaks or builds.
56% of those who never used generative AI in the past 12 months see it as a risk to society, compared to just 26% of those who use AI at least once a week in their work. The trust gap closes through consistent performance, even in edge cases.
Your priority: Document your 10 worst edge cases. Build explicit handling for each. Users will notice.
5. Build Trust Before Scale
AI doesn’t fail from inaccuracy. It fails from lack of trust.
Only 44% of people globally feel comfortable with businesses using AI, and in the U.S., that number is even lower. This presents the fundamental challenge: organizations that fail to address trust concerns will face resistance.
But here’s the data that matters: 43% of consumers would trust the information given to them by an AI chatbot or tool, up from 40% last year. Among consumers who currently use Gen AI tools, this figure increases to 68%. Trust grows through experience, but only if you design for it.
Add human-in-the-loop approvals. Build audit trails. Implement safe defaults. Companies leading in AI adoption focus on transparency first, explaining how AI works, what it does, and who benefits, and proactive governance to ensure AI is safe, fair, and accountable.
Perplexity’s integration with Claude 3 was chosen specifically because Claude provides information in concise, natural language so users can arrive at clear answers quickly. They prioritized clarity over cleverness.
Your implementation: Before adding features, add explanations. Show your work.
6. Measure PMF with Adoption Depth, Not Vanity Metrics
Sign-ups lie. Retention tells the truth.
Since many AI projects in 2025 are initially experimental, you need to track second-order engagement—measuring if users return to work on a new project, not just the first one.
The key metric? Second-bite usage rate, which serves as a powerful AI-native metric that distinguishes between fleeting experimentation and true product adoption.
Perplexity tracks cohort analysis and the number of queries people conduct within a specific timeframe, noting that “initially 80% of users that do one query do another. We wanted to focus on getting this to 100% so two queries can become five queries, and ultimately usage becomes a habit”.
Track:
Are users trusting your AI with real work?
How much time saved vs. old way?
Is it replacing old workflows?
Can they live without it?
Forget the vanity metrics. If users aren’t coming back, nothing else matters.
Your dashboard: Track weekly active users who complete your core action 3+ times. That’s your real number.
7. Build Moats During PMF, Not After
In AI, product-market fit without moats = temporary advantage.
AI is changing traditional PMF frameworks, requiring a new 4-phase approach specifically for AI startups that scale. The winners understand that technical differentiation alone isn’t enough.
While technical differentiation in AI is challenging to sustain, differentiation in distribution can create a lasting competitive advantage. Perplexity won not because their AI was better, but because their distribution was smarter.
Your moats in AI:
Data flywheels: Every user interaction improves your model
Network effects: User-generated content or connections
Switching costs: Integrated workflows users can’t easily replace
Brand trust: Established reliability in a skeptical market
Build these during PMF. After scale, it’s too late.
Your strategy: For every feature you build, ask: “Does this make us harder to replace?”
8. Price for Value, Not Features
Don’t charge for AI magic. Charge for outcomes.
ROI or nothing—adoption decisions hinge on numbers, not novelty. Research shows Superhuman customers who use AI save 37% more time than those who don’t.
That’s the pricing model: charge for the 37% time savings, not for “AI-powered email.”
Perplexity’s student strategy included a discounted rate of $4.99/month compared to the standard $20 fee, but they weren’t discounting features—they were investing in a market segment that would become evangelists.
Your pricing test: Can a user calculate their ROI within 5 minutes? If not, you’re charging for the wrong thing.
9. Treat Distribution as Part of PMF, Not Post-PMF
AI tools without distribution = forgotten demos.
Perplexity’s distribution successes were not accidental. In 2023, they hired Raman Malik, an early member of Lyft’s growth team, to lead growth strategy. This hire signaled Perplexity’s intention to win through distribution, not just technology.
Their growth team included:
Growth Marketing Manager for Paid Acquisition testing TikTok ads for 18-24 age group
Growth Manager for Lifecycle & Marketing Operations to improve onboarding
Dedicated “Growth Manager – Students” for the .edu strategy
Product Marketing Manager for launch campaigns
Through its distribution strategy, Perplexity successfully challenged established players, gained users in segments overlooked by OpenAI’s ChatGPT, and gained significantly more consumer recognition than Anthropic’s Claude.
Distribution isn’t what happens after you build. Distribution is how you build.
Your first question: Before building your next feature, ask: “How will users discover this?”
10. Institutionalize Feedback → Model → Product Loops
Every edit is training data. Every user interaction is a learning opportunity.
AI high performers are more likely to say their organizations have defined processes to determine how and when model outputs need human validation to ensure accuracy. They’ve turned feedback into system improvement.
All of the management practices tested correlate positively with value attributable to AI. These practices enable organizations to innovate and capture value from AI at scale.
The winners don’t just collect feedback—they build it into their development cycle:
User feedback → What’s broken or missing?
Model improvement → How do we fix the underlying AI?
Product iteration → How do we expose the improvement?
Repeat weekly, not quarterly
This is your compound interest. Each cycle makes you incrementally better than competitors who ship and forget.
Your system: Create a weekly ritual where engineering reviews user feedback and identifies model improvements.
The Bottom Line: Don’t Build a Wrapper, Build a Wedge
Here’s what all 10 of these moves add up to: AI product-market fit isn’t about building what users want. It’s about becoming the default in solving painful jobs, earning trust, and compounding feedback loops no one else has.
The global AI market is valued at $391 billion in 2025 and projected to reach $1.81 trillion by 2030. But over 73% of organizations worldwide are either using or piloting AI in core functions, which means the opportunity is massive—and so is the competition.
“Most businesses get zero distribution channels to work: poor sales rather than bad product is the most common cause of failure. If you can get just one distribution channel to work, you have a great business”.
The AI products that will dominate 2026 aren’t being built in stealth mode by technical geniuses. They’re being built by teams who understand that:
Distribution beats better AI
Trust beats features
Depth beats breadth
Outcomes beat novelty
The cool demo gets you funding. These 10 moves get you market leadership.
What’s your move?
Which of these 10 are you implementing first? And more importantly—which ones have you been ignoring because they’re harder than just making the AI “better”?
The winners in AI aren’t the ones with the best models. They’re the ones who understand that technology is necessary but insufficient. Product-market fit in AI demands a different playbook—one that prioritizes trust over hype, depth over breadth, and systems over features.
Don’t build a wrapper. Build a wedge. Then use these 10 moves to drive it deep into your market.