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Data and finance professionals are competing in Excel obstacle courses--amassing huge followings and keeping the Microsoft program relevant. For many people, Microsoft Excel is to be avoided at all costs. Maybe you lied about being "proficient" with the spreadsheet program to pad out your résumé and have lived in fear of being found out ever since. Or it could be that you do have a grasp on a few basic formulas but find the interface too labyrinthine to truly master. Still, like it or not, Excel remains an integral part of businesses, with Microsoft claiming "hundreds of millions" of users worldwide.

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Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Lauren Allain is a freelance journalist covering deals at Mashable. She graduated from Western Washington University with a B.A. in journalism and holds an M.B.A from Webster Leiden. You can find more of her work online from publications including Reader's Digest, U.S. News & World Report, Seattle Refined, and more. When she's not writing, Lauren prefers to be outside hiking, bouldering, swimming, or searching for the perfect location for all three. All products featured here are independently selected by our editors and writers.

How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual incentives for high-quality content creation? Existing approaches take polar positions: a "free-for-all" model based on fair use and a "strong intellectual property rights" model. We show that both fail: Free-for-all does not compensate creators, and -- by modeling as a static Stackelberg game -- strong intellectual property rights also underpower creative incentives. We find this especially true for more innovative creators, a phenomenon we term the "originality penalty." Extending this insight to a dynamic model, we find another market failure undermining AI model performance, even for an initially good model: Such a model induces greater reliance by humans on AI-assisted creation, resulting in homogenized content feeding back into training, which degrades the model performance -- a "curse of precision." We further propose a market design with a data intermediary internalizing cross-creator externalities and subsidizing innovative contributions, thereby restoring efficiency.