The impact of deepfakes on marketing

While researching AI experts, I came across a deepfake. It was not clear at first, given his apparently legitimate profile and involvement on social networks. But after seeing the same creepy AI-generated photo of Dr. Lance B. Eliot all over the internet, it was clear he wasn’t a real person. So I followed him and got to know his eagerness.

The ubiquitous Dr. Lance B. Eliot

Eliot has over 11,000 followers on LinkedIn and we have two connections in common. Both have thousands of LinkedIn followers and decades of experience in AI with roles such as investor, analyst, keynote, columnist and CEO. LinkedIn members engage with Eliot, even though all of his posts are repetitive threadjacking leading to his many Forbes articles.

On Forbes, Eliot publishes almost identical headlines every one to three days. After reading a few articles, it is clear that the content is AI-generated technical jargon. One of the biggest problems with Eliot’s extensive Forbes portfolio is that the site limits readers to five free stories per month until they are prompted to purchase a $6.99 monthly or $74.99 annual subscription. This gets complicated now that Forbes has officially put himself up for sale with a price tag close to $800 million.

Eliot’s content is also available behind a medium paywall, which costs $5 per month. And a thin profile of Eliot appears in Cision, Muckrack, and the Sam Whitmore Media Survey, paid media services that are expensive and relied upon by the vast majority of PR professionals.

Then there’s the sale of Eliot’s books online. He sells them through Amazon, fetching just over $4 per title, though Walmart offers them for less. On Thriftbooks, Eliot’s Pearls of Wisdom sell for around $27, which is a deal compared to the $28 price tag on Porchlight. A safe bet is that book sales are driven by fake reviews. Still, a few disappointed people bought the books and gave them low ratings, citing the content as being repetitive.

The damage to big brands and individual identities

After clicking a link to Eliot’s Stanford University profile, I used a different browser and landed on the real Stanford website, where a search on Eliot returned zero results. A side-by-side comparison shows that the brand color red on Eliot’s Stanford page was not the same hue as the authentic page.

A similar experience occurred on Cornell’s ArXiv site. With just a minor tweak to the Cornell logo, one of Eliot’s academic papers was posted, filled with typos and more low-quality AI-generated content, presented in a standard academic research paper format. The paper cited an extensive list of sources, including Oliver Wendell Holmes, who apparently published in an 1897 edition of the Harvard Law Review — three years after his death.

Those not interested in reading Eliot’s content can find their way to his podcasts, where a bot spews meaningless jargon. An excerpt from a listener’s review reads, “If you enjoy listening to someone read word for word from a script on paper, this is a great podcast for you.”

The URL next to Eliot’s podcasts promotes his self-driving car website, which initially led to a dead end. A refresh of the same link led to Techbrium, one of Eliot’s fake employer websites.

It’s amazing how Eliot is able to do all of this and still make time to speak at executive leadership summits hosted by HMG Strategy. The fake events list major tech companies as partners, with a who’s who of advisors and real life biographies of executives from the likes of Zoom, Adobe, SAP, ServiceNow and the Boston Red Sox.

Participation in HMG events is free for senior technology managers, provided they register. According to HMG’s terms and conditions, “If for any reason you are unable to attend and send a direct report in your place, a $100 no-show fee will be charged to cover the cost of meals and service personnel. to cover.”

The cost of ignoring deepfakes

A deeper investigation into Eliot led to a two-year-old Reddit thread calling him out and soon spiraling into hard-to-follow conspiracy theories. Eliot may not be an anagram or tied to the NSA, but he is one of the millions of deepfakes making money online that are increasingly difficult to spot.

Looking at the financial ripple effects of deepfakes raises questions about who is responsible when they generate revenue for themselves and their partners. Not to mention the cost of downloading malware, targeting fake prospects and paying for spammy affiliate marketing links.

No doubt a sharp eye can spot a deepfake from its blurred or missing background, odd hair, oddly placed eyes, and robotic voices that are out of sync with their mouths. But if this were a universal truth, the cost of deepfakes wouldn’t be billions in to lose because they generate financial scams and impersonate real people.

AI has not solved all the problems that make it difficult to recognize a deepfake’s lack of authenticity, but it is actively solving them. It’s this kind of outing-the-deepfakes article that helps AI learn and improve. This leaves the responsibility of detecting deepfakes to individuals, forcing them to be vigilant about who they let into their networks and lives.

Kathy Keating is a real person and the founder of ProsInComms, a public relations consultancy.