AI twin examples: what a good one looks like

By Ankur Shrestha, founder of Twinsona – Updated July 2026

The best AI twin examples in the wild today are named after real experts: Tony Robbins runs a voice coach trained on four decades of his work, dating coach Matthew Hussey runs a chat version of himself, and Delphi hosts hundreds of creator "minds." What separates these from a party trick is not the voice or the face. It is whether the twin answers from the person's own content and can show its work.

The short version: Good AI twin examples share three traits: they answer from a specific person's real content, they stay in that person's voice, and they can be trusted with that person's name in front of an audience. Tony Robbins' AI (tonyrobbins.com), Matthew Hussey's Matthew AI (matthewhussey.com), and Delphi's creator minds (delphi.ai) are live, paid examples. A party-trick clone just sounds like you and makes things up. The difference is grounding: answering only from real source material instead of guessing.

Three real AI twin examples you can look at today

These are shipping products with public pages and paying users, not demos.

Tony Robbins AI

Tony Robbins AI is an app trained on four decades of Robbins' coaching methods, delivered in his own voice. It was built by Steno.ai with real-time voice from ElevenLabs. Per ElevenLabs' write-up, "decades of Tony Robbins' seminars, books, and interviews are distilled and delivered in his own voice while every answer is personalized to the individual." It runs 24/7 across 23 languages and costs $39 per month after a trial. This is the voice-first end of the spectrum: a real AI coach users talk to, not read.

Matthew AI (Matthew Hussey)

Matthew AI is a chat twin from dating coach Matthew Hussey. It answers dating questions across text, voice, SMS, and WhatsApp, with responses based on thousands of hours of his coaching materials. Users can even upload a text thread or dating profile for feedback. It also runs $39 per month after an intro rate. Matthew AI shows the pattern clearly: a narrow expert, a deep body of content, and a twin scoped to that expertise.

Delphi's creator minds

Delphi hosts hundreds of what it calls "Digital Minds" – AI twins for creators, coaches, and public figures. Its pricing page runs from a free tier through Builder ($79/mo) and Scaler ($299/mo) to a custom "Immortal" tier "for celebrities and public figures." Delphi is the clearest example of the twin-as-a-platform model, where many creators each stand up a mind from their own libraries rather than one celebrity commissioning a bespoke build.

What separates a good twin from a party trick

A convincing voice is the easy part. Any modern model can be prompted to sound like a confident coach. The hard part is being right, and being accountable when you aren't.

A party trick sounds like you and guesses. It borrows a tone, pulls from whatever the base model already "knows," and produces plausible answers that may have nothing to do with what you actually teach. Language models are documented to hallucinate – to state false things fluently and confidently. Put that behind your name and every wrong answer is your reputation.

A good twin answers from your content and shows its source. This is called grounding, or retrieval-augmented generation: the system retrieves passages from your real material and answers only from those. Retrieval is shown to reduce hallucination versus letting a model answer from memory. It reduces made-up answers; it does not eliminate them, and anyone who promises zero errors is overselling. The honest goal is fewer wrong answers, each traceable to a source.

Three traits show up in every good AI twin example:

Grounded. The twin answers from a specific person's own videos, posts, podcasts, and courses, not from the open internet. See what an AI twin is for how this differs from a generic chatbot.
Cited or attributable. A trustworthy answer can point to where it came from. Research on attribution and verifiability treats "can you check this claim against a source" as a core measure of a system you can trust.
In-voice and in-bounds. It sounds like the person and stays inside the topics they actually cover, declining cleanly when a question falls outside their expertise.

Where the named examples sit, and where the bar is going

Tony Robbins and Matthew Hussey are custom, celebrity-scale builds – impressive, expensive, and largely bespoke. Delphi opened the same idea to any creator on a subscription. The direction the whole category is moving is toward twins that don't just sound right but can prove where each answer came from.

That is the wedge I build at Twinsona: a twin grounded in your own content, that cites its sources, speaks in your own voice, and stays inside limits you set. Voice and consent controls are part of the product, and you can charge your audience for access to your twin and keep the revenue. If you're comparing platforms, my Delphi alternative breakdown covers where the models differ on grounding, ownership, and price.

The takeaway from the examples: pick your twin the way you'd pick a hire. Not "does it sound like me," but "would I trust it to answer in my name when I'm not in the room."

Frequently asked questions

What are the best examples of AI twins?

The clearest public, paid examples are Tony Robbins AI (a voice coach built by Steno.ai and ElevenLabs), Matthew Hussey's Matthew AI (a dating-advice chat twin), and the creator "Digital Minds" hosted on Delphi. Each is trained on that person's own content and is available to the public for a monthly subscription.

How much does an AI twin cost to use?

For the named consumer examples, both Tony Robbins AI and Matthew AI run about $39 per month after an intro offer. Platform pricing for creators who build their own twin varies – Delphi, for instance, lists tiers from free through $299 per month plus a custom celebrity tier.

What makes an AI twin trustworthy versus a gimmick?

Grounding and attribution. A trustworthy twin answers only from the creator's real content and can point to the source of each answer, which is shown to reduce hallucinated answers. A gimmick just imitates a voice and improvises, which risks putting confident, made-up claims under the creator's name.

Can an AI twin be wrong?

Yes. Language models can state false things confidently, and grounding reduces this without fully eliminating it. That is why the examples worth copying favor traceable, source-backed answers and clear limits over the illusion of perfect accuracy.

Are these AI twins the same as an AI avatar?

No. An AI avatar is a generated video face. The examples here are conversational twins grounded in a real person's content – what matters is the substance of the answers, not a synthetic face. See what an AI twin is for the full distinction.

About the author

Ankur Shrestha is the founder of Twinsona, where he builds the grounding-and-guardrail layer that keeps a creator's AI twin faithful – answering only from the creator's own content, citing its sources, and never drifting from what they actually said. Before Twinsona, he built agentic AI automating insurance-carrier portals – high-stakes work where being wrong carries real consequences, the same accountability problem he now solves for creators.