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Some mornings the blank page no longer feels intimidating but expectant: which part of me will meet the machine today? That question has become its own creative spark, my thoughts moving not only toward expression but toward an intimate exchange with a system that answers back. I often wonder: am I still writing, or are my sentences already a merger of my voice and a vast pre-trained model?

This isn’t just personal musing. In early September, Anthropic agreed to a $1.5 billion settlement after authors accused it of training on their books without permission (Fung, 2025). A few weeks later, a UK government adviser suggested that AI companies should never be required to compensate creators for training data, an idea that ignited fierce debate among artists and lawmakers (Hern, 2025). At the same time, Microsoft announced plans for an AI training marketplace, offering publishers a way to sell licensed content (Sullivan, 2025). A new open standard called Really Simple Licensing (RSL) is also emerging to let creators set explicit terms for AI use.

These headlines echo in my own experiments. I’ve used generative tools to restructure arguments, to test business concepts, even to chase metaphors I might not have found alone. The speed is intoxicating. Yet I feel the quiet tug of dependency: when a model anticipates my metaphors or organizes my thoughts before I’ve sat with them, part of the creative friction the hesitation that once made ideas distinct slips away.

That is the real frontier. With what people often call high abilities, I have always relied on an active, restless creativity. Now I find myself working beside another kind of intelligence; statistical, tireless, endlessly associative. The challenge is not choosing one over the other, but making sure they sharpen rather than dull each other. Staying deliberate deciding when to pause, when to let my own ideas lead, is how these two intelligences can coexist without either one quietly erasing the other.

Fung, B. (2025, September 5). Anthropic agrees to pay $1.5 billion to settle author class action. Reuters. https://www.reuters.com/sustainability/boards-policy-regulation/anthropic-agrees-pay-15-billion-settle-author-class-action-2025-09-05/

Hern, A. (2025, September 24). Adviser to UK minister claimed AI firms would never have to compensate creatives. The Guardian. https://www.theguardian.com/technology/2025/sep/24/adviser-uk-minister-claimed-ai-firms-never-compensate-creatives/

Sullivan, M. (2025, September 12). Microsoft to launch marketplace for licensed AI training data. eWeek. https://www.eweek.com/news/microsoft-ai-marketplace-publishers/

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I almost skipped the article the first time: LVMH deploys AI to reinvent luxury design. At first it sounded like a gimmick. Yet the more I read, the more it felt like a quiet master class in next-generation digital strategy.

Inside its private cloud, built with Google Cloud-LVMH has digitized over a century of couture sketches, fabrics and runway photography (Socha, 2024). A generative model trained solely on that archive now proposes silhouettes and textures in seconds. Unlike public models scraping the open web, this one learns only from proprietary data, so the “creative DNA” remains uniquely LVMH.

Around that core they’ve layered a full stack of emerging tech. Computer-vision algorithms authenticate a handbag faster than a seasoned craftsman, while predictive analytics forecast demand city by city, cutting overstock and waste (LVMH, 2024). And the group is edging into Web 3.0, issuing blockchain-backed digital twins and experimenting with immersive virtual showrooms where customers preview or even own a piece before a single seam exists in the physical world (LVMH, 2024).

What fascinates me is the strategic geometry. Cloud infrastructure and off-the-shelf AI are commodities; the scarcity lies in LVMH’s private data and the blockchain provenance layer. Together they create switching costs and a competitive moat that’s invisible but formidable, a kind of luxury IP that can’t be copied or reverse-engineered.

Still, the move raises subtler questions. If an algorithm trained on a century of design heritage suggests the line of a new Dior jacket, where does authorship reside? Is the creative act in the model’s probabilistic leap, in the artisan’s final cut, or in the decision to fuse both?

Perhaps the future of craftsmanship isn’t about rejecting technology but about curating it with taste, treating algorithms the way past designers treated rare fabrics: as raw material demanding judgment.

Would you value a garment differently knowing its first spark came from a private neural network and its proof of origin will live forever on a blockchain?

LVMH. (2024). Annual report 2023. https://www.lvmh.com/investors

Socha, M. (2024, March 12). LVMH taps AI to create a digital couture archive. Business of Fashion. https://www.businessoffashion.com