What happens when AI gets good enough to impersonate you

A mentor of mine, David Linthicum, recently built an AI version of himself. He called it "AI David" and treated the whole thing as a joke, a demonstration of what current tools can do. The AI picked up his personality and mannerisms convincingly enough that the experiment stopped being funny the moment you thought past the demo and into who else might build the same thing without asking permission.

That's the real story here. The technology that let one person recreate himself for entertainment is the same technology that lets a stranger recreate someone else for fraud. The gap between those two outcomes isn't technical, it's intent, and intent is exactly the thing you can't verify from the outside.

What malicious mimicry actually looks like

Once AI can replicate a voice, a face, or a pattern of speech, a specific set of threats becomes practical rather than theoretical:

• Impersonation and fraud. A bad actor generates a convincing fake of a trusted figure, a business leader, a public official, someone's manager, and uses that fabricated identity to authorize transactions, extract confidential information, or damage the real person's standing.
• Deepfake deception. Fabricated video or audio, built to look authentic, gets used to spread misinformation, sway public opinion, or pressure someone through blackmail.
• Reputational damage. The same tools that build a convincing likeness can generate compromising material attributed to that likeness, material that can be used to extort or discredit someone who never said or did any of it.

Nobody's reputation is a defense

The detail worth sitting with is that David has three decades of experience and a well-established public presence in tech, and none of that made him harder to mimic. If anything, a long, well-documented public record gives an AI system more material to train on. Public figures, executives, and anyone with a visible online footprint aren't protected by their credibility. They're better targets because of it.

What actually reduces the exposure

None of this argues for opting out of a public professional presence, that ship has sailed for most people in tech. It argues for treating verification as a habit rather than an afterthought.

• Default to skepticism. Video or audio that seems slightly off, or that shows someone acting out of character, is worth confirming through a second channel before it's treated as fact.
• Limit the raw material. The less personal audio, video, and biographical detail sitting in public view, the less there is for a model to train on.
• Harden the basics. Strong, unique passwords, two-factor authentication, and current software aren't glamorous, but they close off the accounts a fraudulent identity would otherwise be used to access.

Where this needs to go next

Individual habits buy some protection, but they don't solve a problem this size. Three things need to develop alongside the technology itself: detection tools capable of flagging synthetic audio and video before it spreads, ethical standards that govern consent and disclosure when someone's likeness is used to train a model, and legal frameworks that treat AI-generated impersonation and the fraud built on top of it as the crimes they already resemble.

AI-generated likeness is not going away, and most of what it's used for will be unremarkable or genuinely useful. But the fact that a well-known, security-conscious professional can be convincingly reproduced as a weekend project should settle the question of whether this deserves serious attention. It does, for individuals protecting their own identity and for the businesses whose leaders are now plausible targets for the same trick.