Drawing on experience building ML at Spotify, the author argues that machine learning is rarely a key differentiator in a product's first five years. Most ML is an 'enhancer' rather than an 'enabler' — it squeezes incremental gains on top of already-established products. For ML to be a sustainable competitive advantage, companies need to pair it with non-ML moats: unique proprietary data, sticky enterprise contracts, large user bases, or hard-to-replicate integrations. Pure ML techniques commoditize quickly as research gets published, but institutional knowledge from years of production iteration and human capital remain durable assets.
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