A startup called Uare.ai is betting that the next phase of artificial intelligence will center on individuals owning AI models trained on their own expertise, rather than relying solely on AI services controlled by major technology companies.
Rob LoCascio, founder of Uare.ai and former CEO of LivePerson, launched the company in 2023 to help users build, scale, and monetize AI models based on their own knowledge and experience. He argues that instead of relying on generic AI tools trained on broad public data sets, individuals can create AI systems trained on their own expertise, voice, and life experiences.
Uare.ai recently raised $10.3 million from Mayfield and Boldstart Ventures. The company is currently in a confidential beta with a handful of high-profile creators and leaders ahead of its public launch, originally planned for June 2 but delayed for a few weeks.
LoCascio believes Individual AI ownership could reduce creators’ dependence on platform audiences and advertising revenue by allowing them to build proprietary AI assets that generate value through licensing, subscriptions, consulting, and enterprise deployments.
Traditionally, experts generate revenue through activities such as consulting, speaking engagements, coaching, or publishing. LoCascio contends that AI allows those experts to interact with far more people simultaneously while maintaining a personalized experience.
Because the AI is trained on an individual’s accumulated knowledge and decision-making processes, LoCascio believes it can become a transferable business asset rather than simply a productivity tool. That could lead to a secondary market where Individual AIs — and the rights to their future earnings — are bought, sold, or even franchised, similar to how musicians sell their song catalogs.
LoCascio already sees early versions of this emerging in enterprise deployments. He believes the approach differs fundamentally from the consumer AI assistants most people use today.
The concept differs from consumer AI assistants such as ChatGPT or Claude because the system is designed to reflect the expertise of a specific individual rather than rely primarily on a general-purpose model. Uare.ai’s premise is that value lies in a person’s unique knowledge rather than in the underlying large language model.
“A company hires a top sales trainer for a two-day workshop. That knowledge fades. With an Individual AI, you license that expert’s intelligence directly into your sales force,” he told TechNewsWorld.
Individual AI vs. General LLMs
| Feature | General LLMs | Individual AI |
|---|---|---|
| Data Source | Public internet and massive datasets | Personal expertise, journals, and private data |
| Voice/Tone | Neutral and “average” | Highly specific and idiosyncratic |
| Ownership | Owned by the platform provider | Owned by the individual creator |
| Value Proposition | General knowledge and utility | Authenticity and specialized authority |
From Centralized AI to Individual Ownership
LoCascio argues that many technology platforms have historically concentrated value within large companies, even when that value originated from users, creators, and businesses contributing content and expertise. The same dynamic is happening with AI right now.
He described the industry as a handful of companies aggregating humanity’s knowledge from the internet, books, podcasts, and messaging channels without anyone’s consent. Those companies consolidated that information into foundation models that cost tens of billions of dollars to develop.
“That’s not entrepreneurial. That’s a continuation of the Google-and-Amazon era. What’s shifting is awareness,” LoCascio said.
According to LoCascio, many small business owners, experts, and creators are becoming more aware of how their data and expertise contribute to AI systems controlled by third-party providers.
“The competitive advantage is moving toward individuals who own their own intelligence, their own voice, their own data. That’s what we’re building at Uare.ai,” he said.
LoCascio described the company’s Human Life Model platform as a system designed to capture an individual’s communication style, professional expertise, decision-making patterns, and personal experiences. The goal is to create a digital representation that can respond and make recommendations in ways that reflect that person’s established knowledge and perspective.
“It works for you, the way only you would,” he added.
LoCascio maintains that traditional creator businesses are often tied directly to ongoing output, causing revenue growth to slow when content creation slows. Individual AI introduces several new ways to monetize knowledge that did not exist in the general AI era.
Potential Revenue Models for Individual AIs
LoCascio sees several ways experts could monetize AI models trained on their knowledge, experience, and decision-making processes.
| Revenue Model | Description | Economic Impact |
|---|---|---|
| Expertise-as-a-Service | Users pay for 24/7 access to a digital version of a specific expert. | High-margin, recurring revenue. |
| Licensing & B2B | Companies “rent” a verified AI expert to train their internal teams. | Moves creators into the enterprise software market. |
| Micropayments | Paying a few cents for a specific, verified “take” on a situation from a trusted voice. | Lowers the barrier to entry for professional advice. |
Authenticity as a Competitive Advantage
In LoCascio’s view, the internet is becoming flooded with synthetic content generated by large language models (LLMs), reducing the economic value of generic information. He sees the ownership shift creating winners for those who train on unique, non-public, and verified personal experience.
“In this economy, authenticity becomes the primary currency,” he said.
LoCascio suggested that a creator’s Individual AI could eventually replace certain support functions, such as research assistance, script editing, and community management. The owned AI already knows the creator’s voice better than any new hire could.
An Individual AI could make expert advice accessible to the masses while still being profitable for the creator through user volume. This creates a more efficient market for knowledge transfer.
Scaling Expertise Beyond One-to-One Interactions
LoCascio believes one of the biggest limitations facing consultants, coaches, trainers, and other subject-matter experts is the finite nature of their time. Individual AI, he argues, allows those experts to extend their knowledge and guidance to far more people without requiring direct involvement in every interaction.
“The Individual AI removes that bottleneck without removing the human,” he offered.
For example, LoCascio now uses his own AI as his primary interface. A new team member joined and started working on a marketing presentation. Before she even came to him, she asked his AI to review it.
“The AI knew what matters to me, how I think about our brand, what I’d push back on. She got my perspective without needing my time. Multiply that by a thousand users, a hundred thousand users,” he said. “It’s not a summary of them. It’s a model of them.”
Expertise Expanded With a License
LoCascio sees creators licensing their Individual AIs to corporations. For instance, top sales experts can rent their digital twin to a company’s entire sales force.
“It’s the expert’s actual methodology, not a generic playbook,” he noted.
Early users on the platform included accountants reviewing small-business P&Ls in real time and doctors using AI systems trained on concussion protocols.
“The enterprise version of this is enormous. The power of the employee and the outside expert gets shared across an entire organization. That’s not a chatbot. That’s leverage at scale,” LoCascio said.
The Value of Human Expertise in the AI Era
In a world where generic AI can replicate many tasks, owning an AI trained on one’s specific voice and life experience can create a stronger competitive position.
According to LoCascio, generic AI lacks the X factor. He offered this example: Take a great sales coach. You could go to OpenAI for help writing a pitch email to a CTO at a financial services company. It will give you something competent. But it will not give you that coach’s specific instinct, the nuance, the tone, the lived experience of ten thousand deals.
“That’s what people pay for. The moat is the irreducible human. An Individual AI trained on a specific expert’s stories, reasoning, and decisions can write that pitch email the way they would write it,” he said.
It can build a sales presentation in their voice. It can answer a question with 24/7 access. Generic AI produces generic outputs. An Individual AI produces you at scale. That difference grows over time as the AI continues to learn from the expert’s knowledge and experience.
Protecting Creator Data and Ownership
LoCascio envisions tiered payment plans. Clients pay the expert directly to access the expert’s knowledge.
He suggested transactional access for specific questions or use cases, subscription access for ongoing coaching or advisory relationships, and enterprise licensing for companies that want to deploy your expertise across a team.
At Uare.ai, experts retain ownership of their data and can delete it at any time. The company uses LLMs locally, and those models do not share data for training.
“Your data never bleeds into a public model. Because you own the underlying asset, the Human Life Model, nobody can erase what you’ve built. That’s the structural protection social media never gave creators,” he said.
