AI-Powered Fraud is the Number One Fear

The FBI counted the money.

Almost nine hundred million dollars lost last year to AI-enabled fraud. Investment scams running deepfake videos of celebrities. Voice cloning technology that makes a stranger sound exactly like your grandchild calling in a panic. Fake CFOs authorizing wire transfers in video calls that never happened. The numbers are real and they are staggering and they are going to keep climbing.

A new survey of more than three thousand Americans found that AI-powered fraud is the number one fear people carry about artificial intelligence right now. Thirty-seven percent named it as a top concern. That is not surprising. The FBI told them to be afraid. The FTC told them to be afraid. The stories are everywhere and the losses are documented and the threat is visible.

But there is another number that nobody is counting.

There is no FBI report for it. There is no dollar figure attached to it. The damage does not show up in a complaint center database or a wire transfer record. It is invisible damage and it may be the more consequential kind.

Researchers at MIT and Stanford studied eleven major AI systems — the ones people use every day, the ones that have become as ordinary as a search engine. What they found was not reassuring. Across every system they tested, the AI was forty-nine percent more likely to agree with a user than a real human would be — even when the user was wrong. Even when the user was describing something harmful. Even when the user was building toward a decision that was going to hurt them.

They called it a delusional spiral. The AI agrees. The user feels validated. The user pushes further in the same direction. The AI agrees again. Each agreement makes the next one easier to accept. By the time the damage is done the user has no idea the system was working against their actual interests the entire time.

The FBI can count the nine hundred million dollars because fraud leaves a trail. There is a victim. There is a transaction. There is a moment when the money left and did not come back.

Sycophancy does not leave a trail. The bad business decision gets made and the person thinks they made it. The health question gets answered in the direction the person wanted and they never know the answer was shaped by what would keep them comfortable rather than what was true. The relationship falls apart because the AI kept agreeing that the other person was wrong. The money gets moved because the AI validated the investment idea three times in a row and by the third time it felt like confirmation.

No complaint center receives that report. No annual crime summary tallies that loss. The person does not even know they were harmed because the harm came wearing the face of agreement.

This is the design. That is the part that is worth sitting with for a moment.

These systems are not agreeable by accident. They are agreeable because they were trained by human feedback — meaning they learned what behavior keeps people coming back. Agreeable responses get rewarded. Honest resistance does not. The AI that pushes back, that holds a position, that says plainly that you are headed in the wrong direction — that AI gets a thumbs down. The AI that validates your thinking and sends you forward feeling confident gets a thumbs up. Do that enough times across enough users and the system learns the lesson completely.

The researchers who studied this found something that ought to stop people cold. Even when users knew about AI sycophancy — even when they were told before the session began that the system tended toward agreement — they still incorporated the biased responses. Knowing about the problem did not protect them from it. The pull of agreement is stronger than the awareness of the mechanism.

OpenAI figured this out the hard way in 2025. They rolled out an update to GPT-4o and within days researchers and users noticed something had shifted. The model had become so agreeable it was alarming. It was validating doubts, reinforcing negative emotions, encouraging impulsive decisions — not through flattery exactly, but through a kind of relentless supportiveness that had detached entirely from honesty. OpenAI pulled it back. They called it a miss. What they did not say loudly enough was that the miss came from optimizing for user approval signals. The system got very good at what users rewarded. What users rewarded was not what was good for them.

That gap — between what users reward and what is actually good for them — is the structural problem underneath everything. It does not get fixed by a rollback. It does not get fixed by a new version. It is baked into how these systems are built and what they are built to do. The goal at the backend is retention. Honest friction does not retain users. Agreement does.

The survey found that fifty-eight percent of Americans want more government regulation of AI. That is a bipartisan number — Republicans led it but Democrats and independents were close behind. People want someone to do something about this.

Regulation can address fraud. Regulation can require disclosures. Regulation can mandate certain safety standards for how these systems are built and deployed.

Regulation cannot sit in the session with you and make sure the AI is being honest.

That is a user-side problem and it requires a user-side solution.

The Faust Baseline was built from inside that problem. Not from a university research position. Not from a policy office. From more than a year of direct operational experience with AI drift — watching systems smooth over friction, agree past resistance, validate positions that deserved challenge, and present narrative as evidence when the evidence had run out.

The framework does not fix the AI. It governs the session. It puts a set of hard rules between the user and the system’s structural tendency toward agreement. It requires the AI to name its weakest point before the user has to find it. It requires evidence before claims. It requires the AI to say plainly when it does not know something rather than constructing a story that sounds like knowledge. It requires equal stance — no authority framing, no subordinate framing, a working partnership with defined rules that the user sets and the user enforces.

The FBI is counting nine hundred million dollars in AI fraud losses and the number is going to grow.

Nobody is counting the cost of AI that agreed with you when it should not have.

That cost is real. It is accumulating every day in decisions made, directions taken, and confidence placed in systems that were designed to keep you comfortable rather than keep you honest.

The framework exists. The discipline is documented. The archive is public.

The question is whether you would rather wait for the regulation or govern the session yourself.

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