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The credibility concern

When questions began swirling about new (and now former) White House press secretary Katie Zacharia's legal credentials, a colleague asked the obvious question: Why should anyone care?

She was hired to be President Donald Trump's spokesperson, not his lawyer. Does it really matter if she overstated her qualifications?

It does.

She was let go on Friday, five days after starting the job. That's half the time Anthony Scaramucci infamously lasted as White House communications director during Trump's first term.

Zacharia was widely panned for her performance during her first (and final) press briefing on Monday, as well as for misrepresenting herself as a lawyer.

In workplaces, it turns out, bending the truth about one's background is not only problematic, but more common than you might think. And it can carry real consequences. BI's Sarah Needleman spoke with recruiters and found research suggesting that résumé inflation is very much a thing.

A recent survey found 37% of respondents had included inaccurate information on a résumé, according to Novorésumé, an online résumé-building service. Nearly 7% said they claimed a degree, certification, or credential they didn't actually have.

The trouble, of course, is that a small embellishment can raise much bigger questions.

Brian Fink, a managing partner at tech recruiting firm The ReWork Group, told Sarah he rescinded an offer to someone after a background check couldn't verify certain information on the candidate's résumé and LinkedIn profile.

"If you're willing to lie about your education," Fink said, "what else are you willing to lie about?"

Corporate America is full of cautionary tales. Over the years, executives at Samsonite, Walmart, and Yahoo, among others, misrepresented their backgrounds and ultimately lost their jobs as a result.

Credentials matter. They are a promise of basic trust. Once that trust is called into question, it's really hard to win it back.

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The future of AI might rely on getting your Slack DMs.

It's not just your DMs, to be clear. It's internal memos, transaction records, contracts, and even that onboarding guide you probably didn't read when you started your job.

Basically, if it's work-related and has been documented, AI companies are interested. The rationale is straightforward: If AI tools are going to automate work, they need a deep understanding of what the work is.

BI did a deep dive on AI companies' race to scoop up as much data as possible in its series "The AI Data Grab."

BI's Charles Rollet, Tom Carter, and Stephen Council first unpacked the wild market that's sprung up for this data. Things move quickly, including "exploding offers."

Others are figuring out how to leverage their databases. Law firms, which often have detailed archives, are a great example. BI's Melia Robinson has more on that.

And sometimes it's about actually creating the data yourself. That's the case in robotics, writes BI's Rya Jetha. Companies are shelling out big to have people record everyday tasks like cooking and cleaning.

AI's data push could renew interest in a space that's already navigated an info-hungry industry.

Alternative data, unstructured datasets such as satellite imagery and card transactions, became a hot commodity on Wall Street in the mid-2010s.

These non-traditional datasets are supposed to give investors an edge. Want to know if Home Depot will beat earnings estimates? Analyze satellite images of its parking lots to see whether foot traffic is rising or declining.

When I covered the space closely with my colleague Bradley Saacks, the data providers often highlighted that the real market opportunity was selling into big corporates.

Hedge funds might be rich, but there are only so many of them to go around. The market also has a ceiling. If too many people are trading off the same datasets, you lose your potential edge.

AI's data needs can fill the gap alt-data providers hoped corporates would. And unlike hedge funds interested in real-time, market-relevant data, these firms benefit from a wider, deeper catalog.

Some players already see an opportunity. When former Point72 data wizard Kirk McKeown launched a data startup last year, he told Bradley it aimed to package data in an easily accessible way for AI models to scoop up.

It's promising, but there are no guarantees. Alt data faced real growing pains as providers struggled to scale their offerings while maintaining data quality. AI companies' needs might differ, but their expectations won't be any easier. And in some cases, money managers might find the most useful data in their own backyard.

In the meantime, follow Bradley here for more coverage on how alt-data providers are navigating the AI era. And keep an eye out for a hedge-fund-focused newsletter from him later this year.

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