A developer reverse-engineered 200 AI startups and found that 146 were primarily wrappers for major APIs like OpenAI and Claude, despite marketing claims of "proprietary language models." This suggests a widespread disconnect between technical substance and marketing hype, a critical due diligence flag for investors and enterprise buyers in the AI space.
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The "AI wrapper" concern is mitigated by a multi-model strategy. A startup can integrate the best models from various providers for different tasks, creating a superior product. A platform like OpenAI is incentivized to only use its own models, creating a durable advantage for the startup.
The top 1% of AI companies making significant revenue don't rely on popular frameworks like Langchain. They gain more control and performance by using small, direct LLM calls for specific application parts. This avoids the black-box abstractions of frameworks, which are more common among the other 99% of builders.
Widespread anxiety from founders before OpenAI's Developer Day highlights a key challenge for AI startups. The fear is not a new competitor, but that the underlying platform (OpenAI) will launch a feature that completely absorbs their product's functionality, making their business obsolete overnight.
An analysis suggests most AI startups claiming proprietary tech are just wrappers around major LLMs. This can be verified by 'fingerprinting' their APIs; if a startup's service has the exact same unique, exponential rate-limiting pattern as OpenAI's, it's a clear sign they are just reselling the underlying service.
The assumption that startups can build on frontier model APIs is temporary. Emad Mostaque predicts that once models are sufficiently capable, labs like OpenAI will cease API access and use their superior internal models to outcompete businesses in every sector, fulfilling their AGI mission.
Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.
Counter to fears that foundation models will obsolete all apps, AI startups can build defensible businesses by embedding AI into unique workflows, owning the customer relationship, and creating network effects. This mirrors how top App Store apps succeeded despite Apple's platform dominance.
Startups are becoming wary of building on OpenAI's platform due to the significant risk of OpenAI launching competing applications (e.g., Sora for video), rendering their products obsolete. This "platform risk" is pushing developers toward neutral providers like Anthropic or open-source models to protect their businesses.
The choice between open and closed-source AI is not just technical but strategic. For startups, feeding proprietary data to a closed-source provider like OpenAI, which competes across many verticals, creates long-term risk. Open-source models offer "strategic autonomy" and prevent dependency on a potential future rival.
Despite billions in funding, large AI models face a difficult path to profitability. The immense training cost is undercut by competitors creating similar models for a fraction of the price and, more critically, the ability for others to reverse-engineer and extract the weights from existing models, eroding any competitive moat.
