The generative boom of artificial intelligence caused a launch in a minute. But as the dust begins to settle, two once-hot business models look more like guaranteed stories: LLM wrappers and AI aggregators.
Darren Mowry, who leads Google’s global startup organization across Cloud, DeepMind and Alphabet, says startups with these hooks have a red light on.
LLM wrappers are basically startups that wrap existing big language models 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 model to do all the work and you almost white label 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 around Gemini or GPT-5” signals that you’re not differentiating yourself, Mowry says.
“You have to have deep, broad margins that are either horizontally differentiated or something really specific to a vertical market” for a startup to “progress and grow,” he said. Examples of a deep-moat LLM envelope type include Cursor, a GPT-powered coding assistant, or Harvey AI, an AI legal assistant.
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In other words, startups can no longer expect to add a user interface to GPT and gain traction for their product, as they might 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 a single interface or API layer to route queries across models and give users access to multiple models. These companies typically provide an orchestration layer that includes monitoring, control, or eval tools. Think: 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 taken off, Mowry’s words are clear to budding startups: “Stay out of the aggregator 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 directed to the right model at the right time based on their needs — not because of behind-the-scenes limitations of computation or access.
Mowry has been in the cloud game for decades, dabbling in AWS and Microsoft before setting up shop in Google Cloud and seeing how things pan out. He said today’s situation mirrors the early days of cloud computing at the turn of the 20th century, when Amazon’s cloud business was starting to take off.
At the time, a number of startups sprang up that resold AWS infrastructure and promoted themselves as simpler entry points that provided tools, billing consolidation, and support. But as Amazon built its own enterprise tools and customers learned to manage cloud services directly, most of these startups were pushed out. Only those who added real services such as security, migration or DevOps consulting survived.
AI aggregators today face similar margin pressure as model providers themselves expand into enterprise functions, potentially displacing middlemen.
Mowry is on the rise in vibration coding and developer platforms, which had a record year in 2025 with startups like Replit, Lovable, and Cursor (all Google Cloud customers, per Mowry) attracting big investments and attracting customers.
Mowry also expects strong growth in direct-to-consumer technology from companies putting some of these powerful AI tools in the hands of customers. He pointed to the opportunity for film and TV students to use Google’s AI video generator Veo to bring stories to life.
Beyond AI, Mowry also thinks biotech and climate tech are having a moment — both in terms of venture capital going into the two industries and the “incredible amount of data” startups have access to to create real value “in a way we’ve never been able to before.”