Everyone wants an AI product. But few know how to build one that actually works.
AI is the new gold rush, and founders are sprinting to stake their claim. But in that rush, many are skipping the fundamentals of good product development. The result? AI MVPs that overpromise, underdeliver, and crash before they even get a shot at product-market fit.
You’ve seen the headlines: “AI startup raises $5M pre-product.” “VCs doubling down on GenAI tools.” What don’t you see? The quiet shutdowns. The half-baked launches. The burned capital and bruised egos.
This article unpacks real AI MVP failures and the lessons founders can learn to build smarter, not just faster.
Contents
Humane AI Pin: Bold Vision, Bad Execution
Humane, founded by former Apple executives Imran Chaudhri and Bethany Bongiorno, launched the AI Pin with a bold vision: to replace smartphones with an AI-powered wearable that projected information onto your hand and responded to voice and gesture commands. The marketing was sleek, the mission ambitious, and the backers, who included OpenAI’s CEO Sam Altman, convinced.
But the reality was harsh. Overheating, battery issues, and slow response times plagued the device. Reviewers panned it for poor usability and unclear value. Internally, multiple engineers warned leadership about hardware problems and inadequate software readiness months before launch—but those concerns were reportedly dismissed in the push to ship.
Despite raising over $230M, Humane sold fewer than 10,000 units. Just 10 months after launch, it was acquired by HP at a discount, and the product was discontinued.
Where it Failed
- AI-first approach without solving a real user problem.
- Ignored critical internal feedback on launch readiness.
- UX and performance were fundamentally flawed.
Lesson: No amount of vision or capital can compensate for skipping product fundamentals. Flashy AI must be grounded in usability, trust, and clear value delivery.
Forward CarePods: AI in Healthcare Without Product-Market Fit
Forward’s “CarePods” were autonomous medical kiosks offering diagnostics via AI. Despite raising over $650M, the pods suffered from technical failures (failed blood draws, patient lock-ins) and were shut down by late 2024.
So why did investors pour hundreds of millions into a concept that eventually failed so publicly?
Much of it stemmed from confidence in the founder, Adrian Aoun, a former Google executive, and the bold pitch: AI-enabled, decentralised, affordable healthcare.
The promise of scalability, combined with sleek tech and a compelling mission, was incredibly attractive in a world hungry for healthcare disruption. But that optimism outpaced the product’s readiness.
Investors bet on vision and scale before validating usability, reliability, and real-world workflows.
Where it Failed
- High complexity, low reliability.
- No user trust in the experience.
- Skipped validating product-market fit.
Lesson: Fundraising success doesn’t equal product readiness. Particularly in regulated spaces, user trust, operational soundness, and evidence of demand must precede scale. Vision sells—but execution sustains.
Artifact: AI-Powered News Feed No One Needed
Created by Instagram’s co-founders, Artifact aimed to reinvent news with AI-powered recommendations. Despite a 160K+ strong waitlist and sleek design, it shut down in January 2024 due to low engagement and weak differentiation.
The core issue? It didn’t change how people already consumed news. Most users were, and still are, comfortable discovering content through platforms they already trust, like Twitter/X, Reddit, or Apple News. The incremental improvement Artifact offered wasn’t enough to pull users away from habits that were already working. Even with its personalisation and AI curation, it lacked a compelling use case to anchor new behaviour.
Where it Failed
- No real user need—felt redundant with existing apps.
- Failed to change ingrained user behaviour.
- Lack of compelling use case or unique advantage.
Lesson: In crowded categories, it’s not enough to be better. Rather, you must be meaningfully different. To change habits, you need a 10x better experience or solve a specific pain point, not just offer a shinier version of what already exists.
Vy by Vercept: Agentic AI That Fell Apart in Real Use
Vy set out to be your AI assistant for everything. A multi-agent system that could book meetings, send emails, and complete tasks across apps with minimal input. It rode the wave of “agentic AI” hype and secured $16M in funding, led by top-tier investors betting on the next Copilot-level breakout.
But when users got their hands on it, reality bit hard. Vy’s performance was inconsistent, flows broke midway, browser extensions failed silently, and integrations with core tools like Gmail and Notion felt brittle.
What was marketed as seamless became a source of frustration. The AI didn’t behave reliably, and the UX left users unsure what to expect or trust.
The onboarding experience also made assumptions about user comfort with delegation and automation. There was little hand-holding, no clarity on what the AI could or couldn’t do, and poor recovery from errors. As a result, usage dropped quickly and churn climbed. Beneath the agentic veneer, the product simply wasn’t useful often enough to become a habit.
Where it Failed
- Promised autonomy but delivered friction and fragility.
- Lacked clear task boundaries or transparency around AI behaviour.
- Didn’t build enough trust or reliability to change workflows.
Lesson: Agentic AI is only as good as its consistency. When users delegate, they expect clarity, reliability, and accountability. Break that trust once, and they won’t come back.
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Niki.ai: Chatbot E-Commerce Without Stickiness
Indian startup Niki.ai raised $2M+ to build a chatbot that helped users book movie tickets, pay bills, and shop through conversational AI. It launched during a period when WhatsApp was becoming India’s de facto messaging app, and chatbot-based commerce seemed like a natural evolution.
But despite the hype, Niki shut down by 2021. While the tech was functional, the broader ecosystem wasn’t ready for conversational commerce at scale. Indian consumers faced friction with inconsistent regional language support, unreliable internet in Tier 2 and Tier 3 cities, and discomfort with making financial transactions via bots. Add to that a highly price-sensitive market and entrenched user habits centred around trusted apps like Paytm and Flipkart, and adoption stalled.
Where it Failed
- Consumer behaviour didn’t align with conversational flows.
- Poor monetisation model in a value-conscious market.
- Lack of integration with dominant commerce ecosystems.
Lesson: Novelty isn’t enough—infrastructure, trust, and behavioural readiness must align. AI MVPs in emerging markets must deeply understand user context and friction points before scaling.
Peltarion: Enterprise AI Platform That Couldn’t Scale
Peltarion launched with the promise of democratising deep learning for the enterprise. Long before ChatGPT made AI accessible to the masses, Peltarion offered a full-stack platform designed to help non-technical teams deploy machine learning without writing code. It counted NASA, Tesla, and major Scandinavian institutions among its clients.
Yet despite technical sophistication and real-world traction, it never reached escape velocity. The enterprise sales cycle was slow and resource-intensive, while open-source alternatives like TensorFlow, PyTorch, and Hugging Face grew rapidly in capability and adoption—often for free. Peltarion’s paid platform struggled to compete in a market where developers increasingly preferred modular, open, and flexible tooling.
In 2022, it was acquired by King (maker of Candy Crush), with the intention of using its AI capabilities in gaming. But within months, the Peltarion brand, product, and team were folded. The platform was shut down.
Where it Failed
- A technically sound product with no defensible moat against open-source growth.
- Struggled to balance platform complexity with accessibility for non-technical users.
- Acquired for talent and IP, not for long-term platform adoption.
Lesson: Enterprise AI tools need more than great tech—they need staying power. Without clear defensibility, long-term user lock-in, or a thriving ecosystem, even the best-engineered platforms can be outpaced and orphaned.
CodeParrot: YC-Backed Dev Tool That Couldn’t Compete
CodeParrot, a Y Combinator Winter 2023 startup, set out to help developers move faster with an AI-powered coding assistant. Backed by early hype and a strong waitlist, it aimed to carve out a niche in the increasingly crowded space of AI dev tools.
But it entered a market already dominated by GitHub Copilot—and increasingly, by native GPT-4 integrations in IDEs. CodeParrot struggled to offer meaningful differentiation. Its AI suggestions weren’t significantly better or faster, and its interface didn’t introduce new workflows. Onboarding felt generic, and retention suffered as users reverted to familiar tools they trusted.
The team iterated quickly but couldn’t escape the gravity of incumbents with better training data, deeper IDE integrations, and broader user trust. Ultimately, it shut down within two years of launch.
By contrast, GitHub Copilot thrived by offering superior integrations, cleaner UX, and earlier access to developer feedback loops—three things CodeParrot never quite nailed.
Where it Failed
- Entered a hyper-competitive space without a clear, unique angle.
- Lacked integrations deep enough to replace existing dev habits.
- Couldn’t build a sticky user experience or compelling ROI story.
Lesson: In developer tools, utility trumps novelty. If you can’t 10x a core workflow—or wedge into it seamlessly—users won’t switch, no matter how cool your AI looks on demo day.
Common Patterns in These Failures
These companies didn’t fail because they lacked funding, ambition, or talent—they failed because they skipped fundamental steps.
When you zoom out across these failures, five repeatable mistakes emerge—mistakes that even well-funded, high-profile teams make.
| Mistake | Cause | Fix |
|---|---|---|
| AI-first, user-second | Building tech before validating demand | Start with pain points, not platforms |
| Poor UX & onboarding | Rushed or over-complex launches | Design a Minimum Viable Experience (MVE) |
| Weak differentiation | Clones with no strategic edge | Build deep, user-validated value props |
| Inconsistent results | Agents/tools fail in edge cases | Focus on robustness, not just features |
| Monetisation gap | No clear business model | Validate willingness to pay early |
How to Build AI the Right Way
Start With a Real Problem – Not a LLM Wrapper
At Altar.io, we work with founders building AI solutions across fintech, healthtech, SaaS, and more. And time after time, we see the same truth: the best AI products don’t start with “what can we build with GPT-4?” Instead, they start with “what’s broken?”
The founders who get traction are the ones who obsess over user problems before they even touch the tech. They know exactly who they’re building for, what behaviour they want to change, and why existing solutions fall short. AI, in that context, becomes a tool—not a strategy.
Ask yourself: Would anyone care about this product if it didn’t use AI? If the answer is no, you have a positioning problem.
Start by deeply validating the pain. Run founder-led user interviews. Do shadowing. Look for high-frequency, high-frustration workflows. When you find a pain that’s costing your users time, money, or sanity (and they’re already trying to solve it manually), you’re onto something.
Design a Minimum Viable Experience (Not Just a Prototype)
Most AI MVPs overbuild before they know what works. Instead, build a Minimum Viable Experience, a slice of the product that delivers real value in one, narrow but meaningful use case.
Don’t get stuck in demo-ware. A slick interface with brittle functionality won’t survive first contact with users.
Start small. Prove utility with:
- No-code prototypes + GPT plug-ins
- Human-in-the-loop simulations (Wizard of Oz)
- API-first backends with real user workflows
Then observe: Do users come back? Do they rely on it? Would they pay for it? That’s your bar.
Design for Chaos, Not Just the Demo
Most LLMs shine in rehearsed scenarios. But in the wild, user input is unpredictable—and failure is the default.
That’s why trust-first UX is essential. Show your users what the AI knows, how confident it is, and what it can’t do. Let them reset, override, or opt out. Build for edge cases early.
Predictable failure is better than flaky success.
This is especially true in high-stakes verticals like healthcare, legaltech, and fintech—where bad AI can mean legal risk, lost trust, or even physical harm.
Validate Monetisation Before You Scale
Too many teams push monetisation to “after traction.” But you can’t validate product-market fit without market willingness to pay.
It’s not just about revenue; it’s a test of how valuable your product truly is.
Try:
- Charging for priority access or premium features
- Running pricing experiments with mock paywalls
- Tracking what feature users would miss most if removed
Even if you’re pre-revenue, the goal is to gather evidence that your product isn’t just nice to have—it’s worth paying for.
Build With Intent, Not for Optics
AI hype is seductive. It’s easy to fall into the trap of building something that demos well and pitches even better—but doesn’t actually solve a user problem.
If your startup only makes sense because it’s “AI-powered,” it probably won’t survive the next wave of competition.
Great AI products often hide their AI. The user doesn’t need to know about models or embeddings—they just want something that works.
Tools like Notion AI and Runway succeed because the AI enhances existing workflows without getting in the way—proof that utility, not novelty, is what makes AI truly valuable.
Lead with clarity: This is the problem we solve. This is how users win. AI just happens to be one of the ways we do it better than anyone else.
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Final Thought: Build With Substance, Not Hype
“The best AI products won’t feel like AI. They’ll just feel indispensable.”
Founders who win in this space aren’t the ones who ship fastest—they’re the ones who validate early, iterate ruthlessly, and stay grounded in real user behaviour.
So if you’re building an AI MVP, pause for a moment. Zoom out. Ask yourself:
Are we chasing a trend, or solving a real problem in a way only AI can?
Because in the end, it’s not the tech that sets winning products apart. It’s the clarity, the conviction, and the craftsmanship behind them.
