The 115-year-old luxury fashion house may be synonymous with heritage and exclusivity. But in her first-ever sit-down interview, Chanel’s CPO and COO Claire Isnard says the brand is far less interested in where candidates come from than who they are.

“When we look for talent, the first thing that we look for is personalities. You know, values,” Isnard exclusively tells Fortune. 

“The first thing that we look for is personality and the fit for the culture. Are they going to be a good fit with our high standards of excellence, integrity, collaboration, and long-term?”

“If people have big egos and want to work solo or are mercenaries doing things only for the short-term, they’re not going to fit,” Isnard says. 

The second thing she’s looking out for is a learning mindset. Skills, she says, come last. “But the other two are absolutely necessary.”

And unlike many of its competitors, Isnard stresses that Chanel doesn’t handpick talent from “one or two” elite schools. Instead, the company intentionally recruits from a broad range of backgrounds to ensure a diverse mix of perspectives and personalities at HQ.

How Chanel tests for personality

Isnard doesn’t rely on sneaky coffee cup tests or trick questions to assess character. Instead, she listens closely to how candidates tell their own story.

“I always ask, what is your story? What has shaped you, what has helped you to become the person that you are today?” she says.

From there, she’s looking for authenticity—especially around how you’ve dealt with any setbacks.

“You hear so much. You can already see if the person has learned from the failure, if people are vulnerable enough to tell you that they had a difficult moment or not.”

And if they give surface-level responses, she’s not afraid to probe deeper: “You can ask them also to describe who they are, what people think of them, and how the feedback they have received has been.”

Isnard says the way candidates tell their story reveals a lot about them: whether they can admit their faults, handle life’s inevitable ups and downs, and bounce back after.

Everybody wants to work at Chanel—Isnard’s words. So another big telltale sign that they’re a good egg (and not just wanting to add the glossy brand name to their LinkedIn profile) is whether they ask any questions. She says that’s a sure-tale sign that the candidate is actually interested in the job at hand, beyond the brand.

“There is almost an emotional attachment to this brand. That’s why you need to go deeper.”

The CEOs of Duolingo and Eventbrite are fans of personality tests too

Job-seekers already have to jump through flaming hoops to land a gig, navigating dinner tests and a mountain of ‘ghost’ postings. Now they’re increasingly being handed personality tests. 

As performance personality testing company Hogan Assessments told Fortune, personality tests aren’t new, but they’re currently trending as bosses double down on quality over quantity when it comes to talent. And it could actually be a good thing for young workers.

The CEO of Sweet Loren’s gives every new hire a personality test—and they don’t get the job if they’re too corporate, giving a perhaps unintended boost to Gen Z, who happen to be more entrepreneurial than previous generations. Meanwhile, Eventbrite’s CEO, Julia Hartz, told Fortune she is analyzing workers’ personalities to help reduce bias.

The shift comes as millions of Gen Zers find themselves unemployed. With more than 1.2 million applications submitted for fewer than 17,000 open graduate roles in the U.K. alone last year, personality tests could level the playing field in assessing workers, rather than it being about who went to the most prestigious school or has the snazziest experience under their belt. 

And some firms really are just hiring for vibes: “We’re looking for people who have fun working,” Luis von Ahn, CEO of Duolingo, said of the company’s hiring plans.

That’ll be music to Gen Z’s ears, many of whom are set on being the company’s “chief vibes officer” and bringing the joy back into the office amid gloomy RTO mandates, constant layoffs, and increased workloads. 

The world’s fifth wealthiest man started buying properties in the 400-resident town of Manalapan, Fla. in 2022, and since then has pumped $450 million into two landmark properties, Bloomberg reported. Ellison set a Florida record for the purchase of a $173 million estate spanning 16-acres that includes both beachfront and lakefront property. And in August 2024, he paid $277 million for the town’s biggest structure, a 300-plus room hotel, the Eau Palm Beach Resort & Spa, according to Bloomberg.

The hotel stands on the grounds of the original La Coquille Club, which in the 1950’s reportedly hosted the Duke and Duchess of Windsor as well as members of the Ford and Vanderbilt families. In the ’80s the original club was razed, but it still exists in name and is part of what is now the Eau Palm Beach hotel. Manalapan property owners can become members of the beach club without paying initiation fees or annual dues, according to the Town of Manalapan’s website.

For years, the ultra-rich have increasingly homed in on Florida properties. Amazon founder Jeff Bezos has bought three properties on the Florida island Indian Creek, near Miami. Ken Griffin, the founder and CEO of investment firm Citadel, has also spent an estimated $450 million over the years amassing a 25-acre spread in Palm Beach.

In Manalapan, several beachfront homes along the same road as Ellison’s property have sold for tens of millions of dollars, according to Zillow. Yet, Manalapan’s mayor John Deese told Fortune that the recent high-price purchases as well as Ellison’s investments in the town are more than welcome.

“Manalapan has for many years been one of the highest ranked communities in terms of home sales prices in the United States. The recent sales have just added to the overall success of the real estate market in south Florida. We feel very fortunate that Mr. Ellison and others chose Manalapan for their residential and commercial property investments,” Deese said in an email.

Ellison’s purchase of the Eau Palm Beach hotel now serves as a prime attraction for high-net-worth potential buyers. Stewart Satter, the local developer of a recently listed $285 million mansion adjacent to Ellison’s property in Manalapan, said Ellison’s plans for the hotel could become a focal point of the area.

“The hotel has the potential to be an extraordinary property in the town,” Satter told Bloomberg. “And Ellison certainly has a reputation for operating some beautiful resorts.”

Ellison purchased the majority of the Hawaiian island of Lanai in 2012 for an estimated $300 million, and through his company, Pulama Lanai, has remodeled the island’s two Four Seasons resort hotels, and received praise for new, ultra-luxe touches. Among the additions debuted in 2016 at the Four Seasons Resort Lanai were a $21,000 per night “Alii Royal Suite,” as well as grotto-style pools and iPad Air devices in every room to order room service and housekeeping, among other amenities, according to SFGate.

At the Eau Palm Beach Resort & Spa, Ellison has also promised renovations and has installed a pop-up Nobu restaurant on-site, Bloomberg reported. Nobu appears to be a favorite of Ellison’s. He and Tesla CEO Elon Musk brought Nvidia CEO Jensen Huang there last year to beg him for more GPUs. Ellison said he picked up the tab. 

Another draw of Manalapan and the surrounding areas is the proximity to Mar-a-Lago, President Trump’s “Winter White House,” where he often spends weekends, according to Palm Beach County Commissioner Maria Sachs. 

“Every place in that area is having a moment because of Donald Trump,” Sachs told Bloomberg. “You are so close to Mar-a-Lago, you can get a membership and everyone knows that he’s very public there.”

A version of this story originally published on Fortune.com on March 21, 2025.

More on real estate:

• In a frozen luxury housing market, buyers are asking to ‘try before they buy’ and having sleepovers in multimillion-dollar mansions
• Gen Z is defiantly ‘giving up’ on ever owning a home and is spending more than saving, working less, and making risky investments, study shows
• A ‘new era’ in the housing market is about to begin as affordability finally improves ‘for the first time in a bunch of years,’ economist says

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Writing rules to govern the use of AI-generated content is relatively easy. Enforcing them depends on something much harder: reliably detecting whether a piece of text was generated by artificial intelligence.

Some studies have investigated whether humans can detect AI-generated text. For example, people who themselves use AI writing tools heavily have been shown to accurately detect AI-written text. A panel of human evaluators can even outperform automated tools in a controlled setting. However, such expertise is not widespread, and individual judgment can be inconsistent. Institutions that need consistency at a large scale therefore turn to automated AI text detectors.

The problem of AI text detection

The basic workflow behind AI text detection is easy to describe. Start with a piece of text whose origin you want to determine. Then apply a detection tool, often an AI system itself, that analyzes the text and produces a score, usually expressed as a probability, indicating how likely the text is to have been AI-generated. Use the score to inform downstream decisions, such as whether to impose a penalty for violating a rule.

This simple description, however, hides a great deal of complexity. It glosses over a number of background assumptions that need to be made explicit. Do you know which AI tools might have plausibly been used to generate the text? What kind of access do you have to these tools? Can you run them yourself, or inspect their inner workings? How much text do you have? Do you have a single text or a collection of writings gathered over time? What AI detection tools can and cannot tell you depends critically on the answers to questions like these.

There is one additional detail that is especially important: Did the AI system that generated the text deliberately embed markers to make later detection easier?

These indicators are known as watermarks. Watermarked text looks like ordinary text, but the markers are embedded in subtle ways that do not reveal themselves to casual inspection. Someone with the right key can later check for the presence of these markers and verify that the text came from a watermarked AI-generated source. This approach, however, relies on cooperation from AI vendors and is not always available.

How AI text detection tools work

One obvious approach is to use AI itself to detect AI-written text. The idea is straightforward. Start by collecting a large corpus, meaning collection of writing, of examples labeled as human-written or AI-generated, then train a model to distinguish between the two. In effect, AI text detection is treated as a standard classification problem, similar in spirit to spam filtering. Once trained, the detector examines new text and predicts whether it more closely resembles the AI-generated examples or the human-written ones it has seen before.

The learned-detector approach can work even if you know little about which AI tools might have generated the text. The main requirement is that the training corpus be diverse enough to include outputs from a wide range of AI systems.

But if you do have access to the AI tools you are concerned about, a different approach becomes possible. This second strategy does not rely on collecting large labeled datasets or training a separate detector. Instead, it looks for statistical signals in the text, often in relation to how specific AI models generate language, to assess whether the text is likely to be AI-generated. For example, some methods examine the probability that an AI model assigns to a piece of text. If the model assigns an unusually high probability to the exact sequence of words, this can be a signal that the text was, in fact, generated by that model.

Finally, in the case of text that is generated by an AI system that embeds a watermark, the problem shifts from detection to verification. Using a secret key provided by the AI vendor, a verification tool can assess whether the text is consistent with having been generated by a watermarked system. This approach relies on information that is not available from the text alone, rather than on inferences drawn from the text itself. https://www.youtube.com/embed/oUgfQAaRL6Y?wmode=transparent&start=0 AI engineer Tom Dekan demonstrates how easily commercial AI text detectors can be defeated.

Limitations of detection tools

Each family of tools comes with its own limitations, making it difficult to declare a clear winner. Learning-based detectors, for example, are sensitive to how closely new text resembles the data they were trained on. Their accuracy drops when the text differs substantially from the training corpus, which can quickly become outdated as new AI models are released. Continually curating fresh data and retraining detectors is costly, and detectors inevitably lag behind the systems they are meant to identify.

Statistical tests face a different set of constraints. Many rely on assumptions about how specific AI models generate text, or on access to those models’ probability distributions. When models are proprietary, frequently updated or simply unknown, these assumptions break down. As a result, methods that work well in controlled settings can become unreliable or inapplicable in the real world.

Watermarking shifts the problem from detection to verification, but it introduces its own dependencies. It relies on cooperation from AI vendors and applies only to text generated with watermarking enabled.

More broadly, AI text detection is part of an escalating arms race. Detection tools must be publicly available to be useful, but that same transparency enables evasion. As AI text generators grow more capable and evasion techniques more sophisticated, detectors are unlikely to gain a lasting upper hand.

Hard reality

The problem of AI text detection is simple to state but hard to solve reliably. Institutions with rules governing the use of AI-written text cannot rely on detection tools alone for enforcement.

As society adapts to generative AI, we are likely to refine norms around acceptable use of AI-generated text and improve detection techniques. But ultimately, we’ll have to learn to live with the fact that such tools will never be perfect.

Ambuj Tewari, Professor of Statistics, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.