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The generative AI boom has led to a surge in new startups, but not all are poised for success. Darren Mowry, who leads Google’s global startup organization across Cloud, DeepMind, and Alphabet, is sounding a warning about companies building their business models around “LLM wrappers” and “AI aggregators.” These types of AI startups, he says, are exhibiting early warning signs of distress, signaling a potential shakeout in the rapidly evolving AI landscape.

Mowry’s assessment, shared in a recent interview, highlights a shift in the industry’s expectations. While simply layering a user interface on top of existing large language models (LLMs) like GPT or Gemini was once enough to gain traction, that approach is becoming increasingly unsustainable. The focus is now shifting towards startups that can demonstrate genuine differentiation and build lasting value.

LLM wrappers, as Mowry describes them, are companies that essentially take existing LLMs – such as ChatGPT, Claude, or Gemini – and build a product or user experience layer on top to solve a specific problem. “If you’re really just counting on the back-conclude model to do all the work and you’re almost white-labeling that model, the industry doesn’t have a lot of patience for that anymore,” Mowry stated. He cautioned that building “very thin intellectual property around Gemini or GPT-5” isn’t enough to stand out in a crowded market. Success, he argues, requires establishing “deep, wide moats that are either horizontally differentiated or something really specific to a vertical market.”

Examples of companies that have successfully built such “moats” include Cursor, a GPT-powered coding assistant, and Harvey AI, a legal AI assistant. These startups aren’t simply repackaging existing models; they’re integrating AI into specialized workflows and offering unique value propositions. According to a report by Yahoo Finance, Mowry believes these deeper integrations are crucial for long-term growth.

The Challenges Facing AI Aggregators

The concerns extend to AI aggregators as well. These startups aggregate multiple LLMs into a single interface or API, allowing users to access a variety of models through one platform. Companies like Perplexity, an AI-powered search engine, and OpenRouter, a developer platform offering access to multiple AI models via a unified API, fall into this category. While these platforms have seen initial success, Mowry suggests their growth is slowing.

He argues that users are increasingly looking for more than just access to a range of models. They seek “some intellectual property built in” to guide them towards the most appropriate model for their specific needs. Simply providing a menu of options isn’t enough; the platform needs to offer intelligent routing and curation. This suggests a future where AI aggregators will need to add significant value beyond simply connecting users to different LLMs.

Agentic AI and the Evolution of B2B Commerce

The shift away from simple wrappers and aggregators coincides with a broader evolution in the AI landscape, particularly in the B2B space. Emerging “agentic AI” dynamics are reshaping product experiences, moving beyond simple aggregation towards more sophisticated data engineering challenges. As AI Chief reports, the digital shelf is becoming less about merchandising and more about managing complex data flows.

This agentic commerce collapses traditional separations between sourcing, contracting, and settlement, requiring AI-driven procurement decisions to consider financing, reconciliation, and real-time reporting. Payment terms, credit availability, and settlement speed are becoming critical inputs to purchasing algorithms, highlighting the importance of data structure and payment systems in the next generation of B2B marketplaces.

What’s Next for AI Startups?

Mowry’s warnings serve as a reality check for the AI startup community. The initial land grab, where simply leveraging existing LLMs was enough to attract attention, is over. The future belongs to companies that can build sustainable product value, establish strong competitive advantages, and deeply integrate AI into specific workflows. The emphasis is now on differentiation, specialization, and building “moats” that protect against competition.

As the AI landscape continues to mature, expect to see increased scrutiny of business models and a greater demand for demonstrable value. Startups that can adapt to this new reality will be best positioned for long-term success. What are your thoughts on the future of AI startups? Share your insights in the comments below.