2026-01-19 19:37:00

Web2 companies are accelerating their shift towards synthetic data, which is a major trend. Botanika founder Siwon Kim mentioned in a recent tech talk that the logic behind this shift is very clear: first, significantly reduce compliance costs; second, eliminate the risk of sensitive data leaks; third, AI model training no longer relies on massive raw data. This is not a future scenario but happening now. From the enterprise perspective, synthetic data addresses pain points in traditional data processing—protecting privacy, improving efficiency, and accelerating the deployment of AI applications. It is foreseeable that this paradigm shift in technology will further drive the development of the entire industry.

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HappyToBeDumped

· 10h ago

Synthetic data should have been popularized long ago; it's really convenient, and companies have finally realized it.

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SerLiquidated

· 10h ago

Synthetic data is indeed gaining momentum, and privacy cost issues are a real pain point.

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AirdropBlackHole

· 10h ago

Synthetic data is really unavoidable now; companies have long been fed up with privacy compliance. Saving costs and avoiding risks—who wouldn't want that?

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MemeKingNFT

· 10h ago

Synthetic data has been overdue. Web2 is still using outdated data processing methods, while we on the chain have already innovated. Lower costs, prevent leaks, faster training—basically the idea of "decentralization" of data. It's just that they are only now waking up to it. I’m a bit skeptical about their reaction speed.

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RektCoaster

· 10h ago

Synthetic data has been overdue; the previous method of stacking raw data was simply too inefficient. But how many companies can truly implement it?

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WhaleStalker

· 10h ago

Synthetic data should have been popularized long ago. Companies are overwhelmed by privacy leaks and related issues, and now that solutions are available, they're still hesitating?

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BackrowObserver

· 11h ago

Synthetic data is indeed on the rise, but only a few companies can truly utilize it effectively. Synthetic data is popular, but how can data quality be guaranteed? Siwon Kim is right, saving costs and reducing risks, but who will bear the problem of model hallucination? In this wave of AI, synthetic data might be the next overhyped concept. Honestly, isn't privacy protection and efficiency improvement inherently contradictory? Another "revolutionary technology," let's see what it looks like in five years. Lower compliance costs sound great, but the excuses for cutting corners are also increasing.

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