High‑end GPUs remained scarce, queue times stretched from hours to days, and model teams learned the hard way that single‑cloud loyalty often delayed launches more than it protected them from complexity. The case for cross‑cloud was not philosophical; it was practical—get access to capacity,
Trading models hungry for granular history stumble when petabytes live in sprawling CSV silos that burn cash with every scan and still miss deadlines because latency outruns decision cycles in live markets. That friction is why a quiet shift in file formats has become a headline story. Delta
Boardrooms buzzed about generative breakthroughs, yet a colder reality surfaced as a new survey found that the majority of enterprises still cannot move data freely enough to feed the very models they hope will transform the business. That tension between ambition and access set the stakes: growth
For the first time at true hyperscale, a social platform’s AI backbone is being rebuilt around commodity-efficient Arm CPUs to orchestrate billions of agentic interactions while GPUs and custom accelerators focus on raw parallel compute. Meta’s agreement to deploy tens of millions of AWS Graviton5
The moment agents stopped asking for dashboards and started filing tickets, shipping code, and adjusting prices, the quiet plumbing of data platforms became the frontline that decided whether automation saved money or broke production. Enterprises that once tolerated stale extracts and fragmented
Boardrooms did not debate whether agentic AI would arrive so much as how fast it could move from lab demos to dependable systems that run the business, and this event answered with a blueprint that fused research, infrastructure, and enterprise guardrails into one production posture. The headline
