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Adapting to this personalized future likely requires building distinct brand identity and perspective rather than trying to be everything to everyone. If AI models categorize you clearly—as the practical, actionable advice source versus the theoretical deep-dive resource—you'll appear reliably for users whose preferences match that positioning. Trying to be too generic might result in appearing rarely for anyone as models route users to more distinctive alternatives.
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This approach shares a lot in common with the idea of multivariate interpolation over scattered data. Multivariate interpolation attempts to estimate values at unknown points within an existing data set and is often used in fields such as geostatistics or for geophysical analysis like elevation modelling. We can think of our colour palette as the set of variables we want to interpolate from, and our input colour as the unknown we’re trying to estimate. We can borrow some ideas from multivariate interpolation to develop more effective dithering algorithms.
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