The generative AI boom created a startup in a minute. But as the dust begins to settle, two once-hot business models are looking more like cautionary tales: LLM wrappers and AI aggregators.
Darren Mowry, who leads Google’s global startup organization for Cloud, DeepMind and Alphabet, says startups with these hooks have their “check light” on.
LLM wrappers are essentially startups that wrap existing large 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 just 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 the matter. this week’s episode of Equity.
Wrapping “very thin intellectual property around Gemini or GPT-5” means 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 LLM Deep Moat wrapper type include Cursor, a GPT-powered coding assistant, or Harvey AI, an AI legal assistant.
Techcrunch event
Boston, Massachusetts | June 9, 2026
In other words, startups can no longer expect to put a UI on top of a GPT and achieve success on their product, as they perhaps could in mid-2024 when OpenAI launched its ChatGPT store. The challenge now is to create lasting value for products.
AI aggregators are a subset of wrappers: they are startups that package multiple LLMs into a single interface or API layer to route requests between models and give users access to multiple models. These companies typically provide an orchestration layer that includes monitoring, governance, or assessment tools. Think: AI research startup Perplexity or development platform OpenRouter, which provides access to multiple AI models through a single API.
Even though many of these platforms have gained traction, Mowry’s words are clear for new startups: “Stay out of the aggregator business.”
Generally speaking, aggregators aren’t seeing much growth or progression these days because, he says, users want “some built-in intellectual property” to ensure they are directed to the right model at the right time based on their needs – not because of computational constraints or behind-the-scenes access.
Mowry has been in the cloud game for decades, cutting his teeth at AWS and Microsoft before moving to Google Cloud, and he’s seen how it’s done. He said the current situation reflects the beginnings of cloud computing in the late 2000s and early 2010s, as Amazon’s cloud business began to take off.
Around this time, many startups sprung up to resell AWS infrastructure, presenting themselves as easier entry points offering tools, billing consolidation, and support. But when Amazon created its own enterprise tools and customers learned to manage cloud services directly, most of these startups were squeezed out. The only survivors are those who added real services, like security, migration or DevOps consulting.
AI aggregators today face similar pressure on their margins as model providers expand into enterprise capabilities themselves, potentially marginalizing intermediaries.
For his part, Mowry is bullish on mood coding and development platforms, which had a banner year in 2025 with startups like Replit, Lovable and Cursor (all Google Cloud customers, according to Mowry) attracting major investment and customer traction.
Mowry also expects to see strong growth in direct-to-consumer technology, in companies that put some of these powerful AI tools in the hands of customers. He highlighted the ability for film and television students to use Google’s AI video generator, Veo, to bring stories to life.
Beyond AI, Mowry also believes biotech and climate tech are having a sweet spot — both in terms of venture capital investment in both sectors and in terms of the “incredible amounts of data” that startups can access to create real value “in ways we never could before.”
Rebecca Bellan is a senior reporter at TechCrunch where she covers the business, policy and emerging trends shaping artificial intelligence. His work has also appeared in Forbes, Bloomberg, The Atlantic, The Daily Beast and other publications.
You can contact or check Rebecca’s outreach by sending an email rebecca.bellan@techcrunch.com or via encrypted message to rebeccabellan.491 on Signal.
