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Electronic Technology ApplicationUpdated Oct 26 16:35

Amazon CEO Jassy: AWS capacity sold out through 2027, with much of 2028 also locked up

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Amazon CEO Andy Jassy revealed in an interview on the Andreessen Horowitz podcast that AWS's large-scale compute capacity for 2026 has been fully booked, 2027 is essentially sold out, and a considerable portion of 2028 has also been locked in early by customers. Amazon disclosed earlier this year that its 2026 capital expenditure will reach about $220 billion, mainly directed at data centers, servers and networking equipment, but Jassy said data center, power and memory resources are tight, and the shortage may not end in 2027. He also said that 99% of AWS's top 1,000 EC2 customers already use the in-house Graviton processor, that Trainium chips offer about 30% to 40% better cost-performance than comparable processors, and that AI inference costs still need to fall by roughly ten times to drive enterprise-scale adoption.

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Jassy laid out the timeline for AWS capacity sellouts and cost-performance data for in-house chips, which can be used to gauge the compute supply-demand gap and the direction of inference cost reduction.

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据路透社报道,亚马逊首席执行官 Andy Jassy 近日在参加 Andreessen Horowitz 播客访谈时透露,受人工智能(AI)基础设施需求持续爆发推动,亚马逊云服务(AWS)的云计算产能已大部分售罄至2027年,并有相当大一部分2028年产能被客户提前锁定。

Jassy在采访中明确表示,AWS 2026年的大型运算产能已全部被预订。“2027年基本售罄。我们始终会保留一定产能用于按需业务和推理服务 Bedrock 或 EC2,但大批量产能基本都已售出。2028年也有相当大一块已经卖掉了。” 他补充称,AI实验室对算力的需求达到“前所未见”的规模,叠加企业云业务的持续增长,AWS 即使全力扩张,仍难以跟上客户需求的步伐。

这一供需缺口的规模,从亚马逊的资本支出计划中可见一斑。亚马逊今年早些时候已披露,2026年资本支出将达约2200亿美元,主要投向数据中心、服务器及网络设备。

然而 Jassy 也坦言,即便如此庞大的投入仍不足以填满需求。“公司完全可以投入更多资金,而且这不会是不明智的资本使用方式。”

当前,制约云端算力扩张的不仅在于资金。Jassy 指出,数据中心、电力及内存资源均处于紧张状态,且他不认为相关短缺会在2027年结束,部分零组件的供应限制甚至可能持续数年。在内存供应有限的情况下,AWS 需要在用于AI加速器的高带宽内存(HBM)与支撑传统云业务的动态随机存取内存(DRAM)之间进行权衡分配。

面对成本压力,亚马逊延续了其在通用CPU领域和AI芯片的自研路线。Jassy 透露,AWS 前1000大 EC2 客户中已有99%使用其自研处理器 Graviton,其性价比相较其他 x86 处理器高出约40%。如今,这一策略正被复制到AI加速器领域。他表示,Trainium 芯片相较同级处理器的性价比高出约30%至40%。

但 Jassy 同时承认,AI推理成本仍有大幅下降空间。“在我看来,要让AI被尽可能广泛地使用,目前的成本仍然高出一个数量级。”他认为,推理成本需要下降约十倍,才能真正推动企业级AI应用的规模化落地。这一判断构成了亚马逊持续押注自研芯片的核心逻辑:通过降低单位算力成本,打开更广阔的企业市场需求。

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Electronic Technology Application