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In the race to deploy large language models and generative AI across global markets, many companies assume that English model translate it is sufficient. But if youre an American executive preparing for expansion into Asia, Europe, the Middle East, or Africa, that assumption could be your biggest blind spot. In those regions, language isnt just a packaging detail: its culture, norms, values, and business logic all wrapped into one. If your AI doesnt code-switch, it wont just underperform; it may misinterpret, misalign, or mis-serve your new market. The multilingual and cultural gap in LLMs Most of the major models are still trained predominantly on English-language corpora, and that creates a double disadvantage when deployed in other languages. For example, a study found that non-English and morphologically complex languages often incur 35X more tokens (and hence cost and compute) per unit of text compared to English. Another research paper places around 1.5 billion people speaking low-resource languages at higher cost and worse performance when using mainstream English-centric models. The result: a model that works well for American users may stumble in India, the Gulf, or Southeast Asia, not because the business problem is harder, but because the system lacks the cultural-linguistic infrastructure to handle it. A regional example worth noting Take Mistral Saba, launched by French company Mistral AI as a 24B-parameter model tailored for Arabic and South Asian languages (Tamil, Malayalam, etc.) Mistral touts that Saba provides more accurate and relevant responses than models five times its size when used in those regions. But it also underperforms in English benchmarks. Thats the point: context matters more than volume. A model may be smaller but far smarter for its locale. For a U.S. company entering the MENA region (Middle East & North Africa) or the South-Asia market, that means your global AI strategy isnt global unless it respects local languages, idioms, regulation, and context. Token costs, language bias, and global ROI From a business perspective, the technical detail of tokenization matters. A recent article points out that inference costs for Chinese may be 2X English, while for languages like Shan or Burmese, token inflation can be 15X. That means if your model uses English-based encoding and you deploy in non-English markets, your usage cost skyrockets, or your quality drops because you cut back tokens. And because your training corpus was heavily English-centric, your underlying model may lack semantic depth in other languages. Add culture and normative differences into the mix: tone, references, business practices, cultural assumptions, etc., and you arrive at a very different competitive set: not were we accurate but were we relevant. Why it matters for executives expanding abroad If youre leading a U.S. corporation or scaling startup into international markets, here are three implications: Model selection isnt one-size-fits-all: you may need a regional model or a specialized fine-tuning layer, not just the largest English model you can license. Cost structure will vary by language and region: token inflation and encoding inefficiencies mean your unit cost in non-English markets will likely be higher, unless you plan for it. Brand risk and user experience are cultural: A chatbot that misunderstands basic local context (e.g., religious calendar, locale idioms, regulatory norms) will erode trust faster than a slower response. How to build a culturally aware multilingual AI strategy For executives ready to sell, serve, and operate in global markets, here are practical steps: Map languages and markets as first-class features. Before you pick your largest model, list your markets, languages, local norms, and business priorities. If Arabic, Hindi, Malay, or Thai matter, treat them not as translations but as first-class us-cases. Consider regional models or joint-deployment. A model like Mistral Saba may handle Arabic content more cheaply, more accurately, and more natively than a generic English model fine-tuned. Plan for token-cost inflation. Use pricing comparison tools. A model may have a U.S. cost of $X per 1 M tokens, but if your deployment is Turkish or Thai, the effective cost may be 2X or more. Fine-tune not just for language, but for culture and business logic. Local datasets shouldnt just include language, they should capture regional context: regulations, business customs, idioms, risk frameworks. Design for active switching and evaluation. Dont assume your global model will behave locally. Deploy pilot tests, evaluate on local benchmarks, test user-acceptance, and include local governance in your rollout. The bigger ethical and strategic lens When AI models privilege English and Anglophone norms, we risk reinforcing cultural hegemony. The technical inefficiencies (token cost, performance gap) are symptoms of a deeper bias: which voices, languages, economies are considered core versus edge. As executives, its tempting to think well translate later. But translation alone fails to address token inflation, semantic mismatch, cultural irrelevance. The real challenge is making AI locally grounded and globally scaled. If youre betting on generative AI to power your expansion into new markets, dont treat language as a footnote. Language is infrastructure. Cultural fluency is a competitive advantage. Token costs and performance disparities are not just technical: they are strategic. In the AI world, English was the path of least resistance. But your next growth frontier? It might require language, culture, and cost structures that act more like differentiators than obstacles. Choose your model, languages, rollout strategy not on the size of the parameter count, but on how well it understands your market. If you dont, you wont just fall behind in performance: youll fall behind in credibility and relevance.
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The city of San Francisco filed a lawsuit against some of the nation’s top food manufacturers on Tuesday, arguing that ultraprocessed food from the likes of Coca-Cola and Nestle are responsible for a public health crisis.City Attorney David Chiu named 10 companies in the lawsuit, including the makers of such popular foods as Oreo cookies, Sour Patch Kids, Kit Kat, Cheerios and Lunchables. The lawsuit argues that ultraprocessed foods are linked to diseases such as Type 2 diabetes, fatty liver disease and cancer.“They took food and made it unrecognizable and harmful to the human body,” Chiu said in a news release. “These companies engineered a public health crisis, they profited handsomely, and now they need to take responsibility for the harm they have caused.”Ultraprocessed foods include candy, chips, processed meats, sodas, energy drinks, breakfast cereals and other foods that are designed to “stimulate cravings and encourage overconsumption,” Chiu’s office said in the release. Such foods are “formulations of often chemically manipulated cheap ingredients with little if any whole food added,” Chiu wrote in the lawsuit.The other companies named in the lawsuit are PepsiCo; Kraft Heinz Company; Post Holdings; Mondelez International; General Mills; Kellogg; Mars Incorporated; and ConAgra Brands.None of the companies named in the suit immediately responded to emailed requests for comment.U.S. Health Secretary Robert F. Kennedy Jr. has been vocal about the negative impact of ultraprocessed foods and their links to chronic disease and has targeted them in his Make America Healthy Again campaign. Kennedy has pushed to ban such foods from the Supplemental Nutrition Assistance Program for low-income families.An August report by the U.S. Centers for Disease Control and Prevention found that most Americans get more than half their calories from ultraprocessed foods.In October, California Gov. Gavin Newsom signed a first-in-the-nation law to phase out certain ultraprocessed foods from school meals over the next decade.San Francisco’s lawsuit cites several scientific studies on the negative impact of ultraprocessed foods on human health.“Mounting research now links these products to serious diseasesincluding Type 2 diabetes, fatty liver disease, heart disease, colorectal cancer, and even depression at younger ages,” University of California, San Francisco, professor Kim Newell-Green said in the news release.The lawsuit argues that by producing and promoting ultraprocessed foods, the companies violate California’s Unfair Competition Law and public nuisance statute. It seeks a court order preventing the companies from “deceptive marketing” and requiring them to take actions such as consumer education on the health risks of ultraprocessed foods and limiting advertising and marketing of ultraprocessed foods to children.It also asks for financial penalties to help local governments with health care costs caused by the consumption of ultraprocessed foods. Jaimie Ding, Associated Press
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E-Commerce
Spotify Wrapped 2025 is here, and its inspired by mixtapes, DIY aesthetics, and all things pre-internet. After plenty of anticipation, Wrapped has now debuted for the eleventh year in a row. As public interest in Wrapped has mounted exponentially each yearand other brands have flocked to dupe the formatSpotify has been compelled to continuously up the ante on its own design concept, and this year is no exception. Wrapped 2025 comes with 12 brand new features, each intended to make the experience more personalized than years past. In the music world (and everywhere else), 2025 has been a year dominated by conversation around the explosion of AI technology. In September, Spotify itself issued new policies around AI-generated music, explaining that while it wont ban AI-generated songs or AI tools, it is focused on removing what it calls AI slop from the platform. At the time, Spotify said it had already removed 75 million spammy AI tracks from the site in just 12 months. Now, it appears Spotify is going full anti-AI in the design of Wrapped. [Image: Spotify] If brands are looking to the future or to AI for inspiration, we did the opposite, Payman Kassaie, Spotifys director of brand and creative, said in a press conference ahead of the launch. This year, Wrapped is rooted in the world of mixtape cultureand its a refreshing change from last years Wrapped, which was widely critiqued for embracing AI. [Image: Spotify] How Spotify Wrapped became a marketing hit Since debuting in 2014, Wrapped has become a massive hit for Spotify. In 2023, the campaign drew in more than 225 million monthly active users and increased engagement by 40% year-over-year across 170 markets, according to an earnings report from the company. And thats not even counting the free marketing that Spotify rakes in annually through the thousands of user-generated, organic posts from Spotifys user base of 700 million, who share their Wrapped results with followers across socials. To meet the hype, Spotify has slowly turned Wrapped into a design-centric extravaganza, debuting an entirely fresh look and feel for the review each year. Spotify’s 2022 wrapped: “Listening Personality”. [Image: Spotify] In 2021, the brand introduced Audio Aura, a color analysis of users top musical moods. In 2022, it tried out a zodiac-esque feature called Listening Personality alongside a psychedelic design. And last year, it opted for a techy, glitchy aesthetic to complement a new add-on called Music Evolution, which tracked users musical eras over the course of the year, and an AI-generated podcast feature that narrated users’ listening history (but somehow did not include top album or genre stats). While typically an easy brand win, last year’s launch was broadly panned. [Image: Spotify] Spotify Wrapped 2025 embraces a retro aesthetic To appease those critiques, Spotify appears to be doing a full 180 with this years design. The techy aesthetic has been traded for a look that calls to mind an era when listening to music was a physical processfrom building a mixtape to burning your own CD or even putting together a scrapbook of your favorite artists. [Image: Spotify] We looked back at the way people used to share music before Wrapped existed, and that led us to rooting our visual identity this year in the world of mixtape culture, Kassaie said. I may be dating myself a bit here, but if you’ve ever burned a CD for a friend, you know that each one becomes its own little canvas for the creator. That’s kind of the feeling we wanted to captue with this year’s design.” [Images: Spotify] Every visual, he added, is made to feel handmade, with cutouts, images, doodles, and various textures lending the platform a DIY quality. The design is grounded in a palette of black and white, with pops of color reserved for key moments like artist images and album covers. [Image: Spotify] On the data side, Spotifys team went back to the drawing board to differentiate itself from competitors. This year, it will offer a top album list for the first time ever. In addition, its introducing 12 entirely new data-driven features, including Listening Age, which analyzes the five year span of music that users engaged with more than others in their age group; Wrapped Clubs, which sorts users into one of six clubs based on listening style; and Wrapped Party, which lets groups of friends compare their Wrapped data in a real-time, interactive setting. [Image: Spotify] Spotify hasnt entirely forgone AI in this process, either. Listening Archive is an AI-powered feature that spotlights certain days throughout the year, like a users biggest discovery day or most nostalgic day. Still, the overall vibe of Spotify Wrapped 2025 is less a celebration of AI, and more a return to the fundamentals that make sharing music fun. [Image: Spotify]
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