The AI Startup Bubble Is Popping: Why LLM Wrappers and AI Aggregators Are Running on Empty

The generative AI gold rush minted startups faster than Silicon Valley could keep up. But as the hype cools and investors sharpen their pencils, two once-hot business models are starting to look more like cautionary tales than scalable unicorns: LLM wrappers and AI aggregators.

According to Darren Mowry, who leads Google’s global startup organization across Cloud, DeepMind, and Alphabet, startups relying on these models are flashing their “check engine light” — and it’s time to pull over before the whole thing breaks down.

The LLM Wrapper Problem: Pretty UI, No Substance

LLM wrappers are startups that take existing large language models — like OpenAI’s GPT, Anthropic’s Claude, or Google’s Gemini — and wrap them in a product or UX layer to solve a niche problem. Think of a startup that uses AI to help students study, or one that offers a sleek interface for summarizing documents.

But here’s the problem: “If you’re really just counting on the backend 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.

In other words, slapping a UI on top of GPT and calling it a product isn’t going to cut it anymore. Mowry warns that startups relying on “very thin intellectual property wrapped around Gemini or GPT-5” are not differentiating themselves in any meaningful way.

“You’ve got 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-powered coding assistant, or Harvey AI, a legal AI assistant. These startups aren’t just reselling AI — they’re building proprietary workflows, integrations, and domain expertise that make them indispensable to their users.

The AI Aggregator Trap: Too Many Models, Not Enough Value

AI aggregators are a subset of wrappers — they’re startups that aggregate multiple LLMs into one 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, governance, or eval tooling. Think: AI search startup Perplexity or developer platform OpenRouter, which provides access to multiple AI models via a single API.

While many of these platforms have gained ground, Mowry’s words are clear to incoming 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 intellectual property built in” to ensure they’re routed to the right model at the right time based on their needs — not because of behind-the-scenes compute or access constraints.

History Repeats: The Cloud Computing Parallel

Mowry has been in the cloud game for decades, cutting his teeth at AWS and Microsoft before setting up shop at Google Cloud, and he’s seen how this plays out. He said the situation today mirrors the early days of cloud computing in the late 2000s/early 2010s as Amazon’s cloud business started taking off.

At that time, a crop of startups sprang up to resell AWS infrastructure, marketing themselves as easier entry points that provided tooling, billing consolidation, and support. But when Amazon built its own enterprise tools and customers learned to manage cloud services directly, most of those startups were squeezed out. The only survivors were the ones who added real services, like security, migration, or DevOps consulting.

AI aggregators today face similar margin pressure as model providers expand into enterprise features themselves, potentially sidelining middlemen.

The Future Is Bright — If You Build Something Real

For his part, Mowry is bullish on vibe coding and developer platforms, which had a record-breaking year in 2025 with startups like Replit, Lovable, and Cursor (all Google Cloud customers, per Mowry) attracting major investment and customer traction.

Mowry also expects strong growth in direct-to-consumer tech, in companies that put some of these powerful AI tools into 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 investment going into the two industries and the “incredible amounts of data” startups can access to create real value “in ways we would never have been able to before.”

The Bottom Line

The AI startup landscape is shifting fast. The days of easy money for LLM wrappers and AI aggregators are over. Investors and customers alike are demanding real differentiation, deep moats, and tangible value.

If you’re building in AI, ask yourself: Are you just reselling someone else’s model with a pretty interface? Or are you creating something truly unique that solves a real problem in a way that can’t be easily replicated?

Because in the new AI economy, the only startups that will survive are the ones that build something real.

Tags: AI startup bubble, LLM wrappers, AI aggregators, generative AI, Darren Mowry, Google Cloud, venture capital, tech trends, AI infrastructure, direct-to-consumer tech, biotech, climate tech, vibe coding, developer platforms

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