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Nvidia wants AI factories built in real factories

Nvidia wants new AI capacity to show up at the data center fully built. At its GTC Berlin conference on The post Nvidia wants AI factories built in real factories appeared first on The New Stack.

Nvidia wants new AI capacity to show up at the data center fully built. At its GTC Berlin conference on The post Nvidia wants AI factories built in real factories appeared first on The New Stack.

In context

  • Topic: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
  • Source: The New Stack
  • Published: 21/10/2026

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Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.

Why it matters

Every week there is an announcement that promises to change everything, and every week most organisations are still fighting the same battle: messy data, processes nobody documented, and expectations running faster than capability. This story reads better with that in the background.

I always separate two things that get mixed up: the demo and the operation. A demo needs to work once with someone watching. An operation needs to work a thousand times with nobody watching, with odd cases, dirty data, at two in the morning. The gap between the two is where the budget goes.

What usually goes wrong

What I see fail most is the expectation. Somebody saw a flawless demo and asked for the same thing in their area within a month, without considering the demo ran on clean data prepared for the occasion. When the real pilot shows uneven results, the unfair conclusion is that the technology does not work.

What to watch

  • Where the data ends up and under what contract, especially when customer information is involved.
  • Whether the organisation can change provider without rebuilding everything, which is the proof it did not get locked in.
  • Which concrete process is touched and how it is measured before and after; with no baseline there is no result, only opinion.

How I read this entry

My practical advice is to fix on day one what happens when the model is wrong: who reviews, how often, and at what point it gets switched off. It sounds defensive, but it is exactly what lets you be aggressive later, because the risk stopped being an unknown.

This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
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