News Summary:
On May 27, 2026, the company highlighted a hidden inefficiency in modern AI infrastructure, explaining that fast GPUs often sit idle due to timing problems, rather than a lack of work or technical faults, an issue separate from model architecture or compute budgets. Previously, on April 10, 2026, it noted that cloud transformation, while boosting speed and scale, introduces hidden timing problems for organizations, contrasting with the more stable time synchronisation found in traditional infrastructure. This followed a discussion on April 3, 2026, asserting that time is no longer a commodity in modern infrastructure, where the assumption of its universal availability and reliability no longer holds, making traceable time essential. Earlier, on March 27, 2026, the company detailed how cloud migration can expose hidden timing risks, citing a global investment bank that faced challenges in maintaining regulatory control and auditability while moving to cloud environments. The company had previously stated on March 2, 2026, that AI performance is not exclusively limited by computing power, but often by the system waiting for itself due to the highly distributed nature of AI infrastructure, where requests traverse multiple sequential systems.
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