Introduction
The artificial intelligence (AI) industry is undergoing a seismic transformation. Just a few years ago, the dominant narrative was that the real value in AI lay in building massive foundation models—sophisticated AI systems like OpenAI’s GPT, Google’s Gemini, and Anthropic’s Claude. Companies that developed these models were seen as the powerhouses of AI, while startups building applications on top of them were dismissed as mere “wrappers”—lightweight interfaces with little real innovation.
But the landscape is shifting. Today, the true value in AI is moving up the stack—from the underlying models to the applications solving real-world problems. Startups like Perplexity, Replit, Sesame, and Abridge, once derided as “AI wrappers,” are now proving that the most impactful AI innovations happen at the application layer, where user experience, domain expertise, and seamless integration matter more than raw model performance.
This blog explores:
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Why AI value is shifting from models to apps
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How commoditization is leveling the playing field
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The rise of “vibe-coding” and no-code AI development
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What this means for the future of AI startups and enterprises
The Early AI Race: Foundation Models Dominated
The Rise of the Model Titans
In the early days of the AI boom (2020-2023), the biggest players—OpenAI, Google DeepMind, Meta, and Anthropic—focused on training ever-larger models with billions (and later trillions) of parameters. These models, like GPT-4, Gemini, and Claude, were seen as the core differentiators in AI.
Tech giants like Microsoft, Amazon, and Nvidia poured billions into:
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AI infrastructure (cloud GPUs, TPUs)
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Massive training datasets
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Proprietary model architectures
The assumption was: Whoever builds the best model controls AI’s future.
The “Wrapper” Criticism
Startups that relied on these models to build applications were often dismissed as “AI wrappers”—thin interfaces with no real technical moat. Critics argued:
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“They’re just using OpenAI’s API—no real innovation.”
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“Anybody can build a ChatGPT plugin—where’s the defensibility?”
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“These companies will die once the big players launch competing apps.”
But this perspective underestimated the importance of execution, UX, and domain expertise.
The Great AI Shift: Why Value is Moving to Applications
1. Model Commoditization
As AI models improved, they became more accessible and interchangeable. Today:
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Open-source models (Llama 3, Mistral) rival proprietary ones
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APIs from OpenAI, Anthropic, and Google are becoming standardized
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Fine-tuning and RAG (Retrieval-Augmented Generation) reduce dependency on raw model strength
Shiv Rao, CEO of Abridge (healthcare AI), explains:
“There was an impression that the only way to compete in AI was to raise hundreds of millions to train massive models. But very quickly, people realized that value moves up the stack—to the applications solving real problems.”
2. The Rise of Vertical AI
Generic AI models aren’t enough—businesses need AI tailored to their industry. Examples:
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Abridge → AI for medical note-taking
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Sesame → AI-powered healthcare diagnostics
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Perplexity → AI-native search engine
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Replit → AI-assisted coding
Bryan Kim, Andreessen Horowitz partner, notes:
“Calling something a ‘wrapper’ makes it sound lazy. But the real question is: Are you solving a real customer problem? The best AI apps don’t just slap a UI on GPT—they deeply understand their users.”
3. The “Vibe-Coding” Revolution
AI is democratizing software development. Tools like Cursor (AI-first IDE) allow anyone to build apps without years of coding expertise.
Calvin Chin, E14 Fund Managing Partner, predicts:
“We’re entering an era of vibe-coding—where intuition and high-level direction matter more than low-level programming. Soon, we’ll see vibe-lawyering, vibe-accounting, and other AI-augmented professions.”
Case Studies: From “Wrappers” to Market Leaders
1. Perplexity AI – The Google Challenger
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Started as: An AI-powered search interface on top of GPT
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Evolved into: A full-stack search engine with its own indexing, RAG, and LLM fine-tuning
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Why it won: Better UX, real-time data, and no ads
2. Abridge – AI for Healthcare
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Started as: A voice-to-text wrapper for doctor’s notes
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Evolved into: A FDA-cleared AI medical scribe that integrates with EHRs
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Why it won: Deep healthcare domain expertise
3. Replit – AI-Powered Coding
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Started as: A cloud-based IDE with AI autocomplete
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Evolved into: A full software development platform with AI agents
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Why it won: Made coding accessible to beginners
The Future: Where Does AI Value Go Next?
1. The “Last Mile” Problem
The biggest opportunities lie in:
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Industry-specific fine-tuning (legal, healthcare, finance)
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Workflow integration (Slack, Notion, Salesforce AI plugins)
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Multimodal AI apps (voice + text + vision)
2. The Battle for Distribution
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Big Tech (Microsoft, Google) will bundle AI into existing products
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Startups must compete on speed, vertical expertise, and UX
3. The Next Wave: Autonomous AI Agents
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AI that doesn’t just answer questions but takes actions (e.g., Amazon’s Nova Act)
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Agentic workflows replacing manual tasks
Conclusion: The Age of AI Applications Has Arrived
The AI industry is maturing. Foundation models are becoming infrastructure—like cloud computing or databases. The real winners will be the companies that build indispensable applications on top.
Key Takeaways:
✅ Model superiority alone doesn’t guarantee success
✅ The best AI startups solve real problems, not just demo tech
✅ “Vibe-coding” and no-code AI will reshape software development
✅ Vertical AI (healthcare, legal, finance) is the next gold rush
The era of “AI wrappers” is over. Welcome to the era of AI-native applications.
What’s Next?
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Will OpenAI and Google dominate apps too?
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Which industries will AI disrupt next?
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Can startups compete with Big Tech’s distribution?
Let me know your thoughts in the comments!
