In computing, whenever a cheaper, more open alternative shows up, it’s often framed as a winner-takes-all fight to the finish. We saw it with Linux and Windows, and again with Android and iOS. In reality, both sides “won” those battles because they serve different markets, and seeing it as “either-or” was never the right lens to begin with.
Deep insights for enterprise AI, data, and security leaders
How enterprises coordinate and scale AI in production.
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The hardware and platforms underneath enterprise AI.
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Data platforms make decisions all day. An SQL query keeps the rows over a threshold, matches a keyword, and joins two tables on a shared ID. A pipeline sends a failed job down a retry path. Rules like these work when the answer sits in the data in a clean form.
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Enterprise tech coverage through the lens of AI adoption.
Enterprises that run more than a handful of AI agents in production face risk, cost and oversight burdens that grow exponentially with every agent they add, says Felix Van de Maele, founder and CEO of data governance company Collibra. In a conversation with VentureBeat founder and CEO Matt Marshall at VentureBeat's AI Week event in New York, Van de Maele traced most agent failures to the business context those agents receive — and said companies need deterministic controls on agent behavior before their agent count grows.
As confidential computing comes to GPUs, it can protect sensitive workloads, but enterprises also need evidence that they can identify agents, isolate tenants and revoke access without disrupting operations.
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