Between 2022 and 2025, artificial intelligence chips have been presented as advancing rapidly, but the core message of the article is that they have not really gotten better, they have just gotten bigger. The focus is on how vendors frame performance progress, suggesting that much of the perceived improvement comes from hardware aggregation strategies rather than architectural leaps in single dies. This framing questions how sustainable current growth claims are and whether the industry is reaching practical limits with general purpose graphics architectures for artificial intelligence workloads.
The article highlights that Nvidia’s B200, AMD’s MI300, Intel’s Gaudi 3, and Amazon’s Trainium2 count two chips as one card to “double” the output. By treating a dual chip configuration as a single accelerator card, these companies can market higher throughput without fundamentally increasing the per chip efficiency. The sentence All GPU performance improvements from 2022 to 2025 use this trick is used to underscore that this pattern is not an isolated tactic but a common industry practice across leading artificial intelligence hardware suppliers.
By pointing out that all GPU performance improvements from 2022 to 2025 use this trick, the article implicitly raises doubts about the long term viability of relying on general purpose GPUs for scaling artificial intelligence performance. The contrast between genuine architectural innovation and simple chip doubling sets the stage for the broader discussion in the full piece about whether more specialized approaches, such as application specific integrated circuits, could challenge Nvidia’s current dominance. In this context, the depicted comparison invites readers to reassess headline performance claims and consider what kind of hardware design will define the next phase of artificial intelligence computing.
