The productive explosion of artificial intelligence created a startup one minute. But as the dust begins to settle, two once-hot business models are looking more like cautionary tales: LLM wrappers and AI concentrators.
Darren Mowry, who leads Google’s global cloud startup organization, DeepMind and Alphabet, says startups with these hooks have their “check engine light” on.
LLM wrappers are essentially startups that wrap existing models of big languages like Claude, GPT or Gemini with a product or UX layer to solve a specific problem. An example would be a startup that uses AI to help students study.
“If you’re really relying on the back end 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 said on this week’s episode of Equity.
Wrapping “very thin intellectual property wrapped around Gemini or GPT-5” signals that you’re not differentiating yourself, Mowry says.
“You have to have deep, wide moats that are either horizontally differentiated or something really specific to a vertical market” for “a startup to progress and grow,” he said. Examples of the deep-moat LLM wrapper type include Cursor, a GPT encoding assistant, or Harvey AI, a legal AI assistant.
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In other words, startups can no longer wait to put a UI on top of a GPT and gain traction in their product, as they could, perhaps, in mid-2024 when OpenAI launched its ChatGPT store. The challenge now is to build sustainable product value.
AI aggregators are a subset of wrappers — they are startups that aggregate multiple LLMs into an interface or API layer to route queries to models and provide users with access to multiple models. These companies typically provide a layer of orchestration that includes monitoring, governance, or assessment tools. Consider: AI search startup Perplexity or developer platform OpenRouter, which provides access to multiple AI models through a single API.
While many of these platforms have gained ground, Mowry’s words are clear for incoming startups: “Stay out of the aggregation business.”
In general, aggregators aren’t seeing much growth or progress these days because, he says, users want “some intellectual property built in” to ensure they’re routed to the right model at the right time based on their needs — not because of compute or access limitations behind the scenes.
Mowry has been in the cloud for decades, cutting his teeth at AWS and Microsoft before setting up shop at Google Cloud, and he’s seen how it’s done. He said the situation today mirrors the early days of cloud computing in the late 2000s/early 2010s, as Amazon’s cloud business began to take off.
At the time, a number of startups emerged to resell AWS infrastructure, marketing them as easier entry points that provided tools, pricing integration, and support. But when Amazon built its own enterprise tools and customers learned to manage cloud services directly, most of these startups were shut out. The only survivors were those who added real services such as security, migration or DevOps consulting.
AI aggregators are facing similar margin pressure today as model providers expand into business features, potentially crowding out middlemen.
For his part, Mowry is bullish on coding vibe and developer platforms, which had a record year in 2025 with startups like Replit, Lovable, and Cursor (all Google Cloud customers, per Mowry) attracting significant investment and landing customers.
Mowry also expects strong growth in direct-to-consumer technology, in companies putting some of these powerful AI tools in the hands of customers. He pointed to the opportunity for film and television students to use Google’s AI video creator, Veo, to bring stories to life.
Beyond AI, Mowry also believes biotech and climate tech are having a moment — both in terms of venture capital in the two industries and the “incredible amounts of data” startups can access to create real value “in ways we never could before.”
