We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity. This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.

We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity. This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.

Why did xAI hand over a 220,000-GPU cluster to Anthropic? The technical backdrop to xAI's decision to hand Colossus 1 over to Anthropic in its entirety is more interesting than it appears. xAI deployed more than 220,000 NVIDIA GPUs at its Colossus 1 data center in Memphis. Of these, roughly 150,000 are estimated to be H100s, 50,000 H200s, and 20,000 GB200s. In other words, three different generations of silicon are mixed together inside a single cluster — a "heterogeneous architecture." For distributed training, however, this configuration is close to a disaster, according to engineers familiar with the setup. In distributed training, 100,000 GPUs must finish a single step simultaneously before the cluster can advance to the next one. Even if the GB200s finish their computation first, the remaining 99,999 chips have to wait for the slower H100s — or for any GPU that has hit a stack-related snag — to catch up. This is known as the straggler effect. The 11% GPU utilization rate (MFU: the share of theoretical FLOPs actually realized) at xAI recently reported by The Information can be read as the numerical fallout of this problem. It stands in stark contrast to the 40%-plus MFU figures achieved by Meta and Google. The problem runs deeper still. As discussed earlier, NVIDIA's NCCL has traditionally been optimized for a ring topology. It works beautifully at the 1,000–10,000 GPU scale, but once you push into the 100,000-unit range, the latency of data traversing the ring once around becomes punishingly long. GPUs need to churn through computations rapidly to keep MFU high, but while they sit waiting endlessly for data to arrive over the network fabric, more than half of the silicon falls into idle. Google sidestepped this bottleneck with its own custom topology (Google's OCS: Apollo/Palomar), but xAI, by my read, has not yet reached that stage. Layer Blackwell's (GB200) "power smoothing" issue on top, and the picture comes into focus. According to Zeeshan Patel, formerly in charge of multimodal pre-training at xAI, Blackwell GPUs draw power so aggressively that the chip itself includes a hardware feature for smoothing power delivery. xAI's existing software stack, however, was optimized for Hopper and does not understand the characteristics of the new hardware; when it imposes irregular loads on the chip, the silicon physically destructs — literally melts. That means the modeling stack must be rewritten from scratch, which in turn means scaling is far harder than most of us imagine. Pulling all of this together points to a single conclusion. xAI judged that training frontier models on Colossus 1 simply was not efficient enough to be worthwhile. It therefore moved its own training workloads wholesale onto Colossus 2, built as a 100% Blackwell homogeneous cluster. Colossus 1, on the other hand — whose mixed architecture is far less crippling for inference, which parallelizes more forgivingly — was leased in its entirety to an Anthropic that desperately needed inference capacity. Many observers point to what looks like a contradiction: Elon Musk poured enormous capital into building Colossus, only to hand the core asset over to a direct competitor in Anthropic. Others read it as xAI capitulating because it is a "middling frontier lab." But these are surface-level reads. Look at the numbers and a different picture emerges. xAI today holds roughly 550,000+ GPUs in total (on an H100-equivalent performance basis), and Colossus 1 (220,000 units) accounts for only about 40% of the total available capacity. Colossus 2 — built entirely on Blackwell — is already operational and continuing to expand. Elon kept the all-Blackwell homogeneous cluster (Colossus 2) for himself and leased out the older, mixed-generation Colossus 1. In other words, he handed the pain of rewriting the stack — the MFU-11% debacle — to Anthropic, while keeping his own focus on training the next generation of models. The real point, then, is this. Elon's objective appears to be positioning ahead of the SpaceXAI IPO at a $1.75 trillion valuation, currently floated for as early as June. The narrative SpaceXAI now needs is that xAI — long the "sore finger" — is not merely a research lab burning cash, but a business with a "neo-cloud" model in the mold of AWS, capable of leasing surplus assets at high yields. From a cost-of-capital perspective, an "AGI cash incinerator" is far less attractive to investors than a "data-center landlord generating cash." As noted above, the most important detail of the Colossus 1 lease is that it is for inference, not training. Unlike training, inference requires far less tightly synchronized inter-GPU communication. Even when the chips are heterogeneous, the workload parcels out cleanly across them in parallel. The straggler effect — the chief weakness of a mixed cluster — is essentially neutralized for inference workloads. Furthermore, with Anthropic occupying all 220,000 GPUs as a single tenant, the network-switch jitter (unanticipated latency) that arises under multi-tenancy disappears. The two sides' technical weaknesses end up complementing each other almost exactly. One insight follows. As a training cluster mixing H100/H200/GB200, Colossus 1 was an asset that could only deliver an MFU of 11%. The moment it was handed over to a single inference customer, however, that asset transformed into a cash-flow asset rented out at roughly $2.60 per GPU-hour (a weighted average of the lease rates across GPU types). For xAI, what was a "cluster from hell" for training has become a "golden goose" minting $5–6 billion in annual revenue when redeployed for inference. Elon's genius, I would argue, lies not in the model but in this asset-rotation structure. The weight of that $6 billion becomes clearer when set against xAI's income statement. Annualizing xAI's 1Q26 net loss yields roughly $6 billion in losses per year. The $5–6 billion in annual revenue generated by leasing Colossus 1 to Anthropic, in other words, almost perfectly hedges xAI's loss figure. This single deal effectively pulls xAI to break-even. Heading into the SpaceXAI IPO, this functions as a core line of financial defense. From a cost-of-capital standpoint, if the image shifts from "research lab burning cash" to "infrastructure tollgate stably printing $6 billion a year," the entire tone of the offering can change. (May 8, 2026, Mirae Asset Securities)

SpaceXAI will provide @AnthropicAI with access to Colossus 1, one of the world’s largest and fastest-deployed AI supercomputers, to provide additional capacity for Claude →

SpaceX宣布收购xAI，合并后的公司预计将为每股股票定价约527美元，新公司估值将达到1.25万亿美元。新公司涵盖人工智能、火箭、太空互联网、移动设备直连通信以及全球领先的实时信息及言论平台，也应该是业务最庞杂的高科技企业。老马为什么要把xAI并入SpaceX？ 1. 解决xAI极端烧钱问题 这应该是最直接、最迫切的动机，xAI每月现金消耗约10亿美元（训练Grok、建造Colossus超级计算机集群等），估值虽已飙到2000–2300亿美元，但它本质上仍是一家纯烧钱阶段的公司。 SpaceX现金流健康（Starlink已开始大规模盈利 + 发射业务稳定）、估值更高（8000亿–1.5万亿美元级别），成为天然的“输血平台”。 2. 实现“太空超级计算中心”终极愿景 这是这次合并最具想象力的部分，老马之前多次公开讲过：地球上建超大AI数据中心会遇到电力、散热、土地、监管等极限，而太空（轨道）是更好的地方。 Starlink提供全球低延迟组网 SpaceX火箭/回收能力提供发射能力 未来太阳能 + 太空散热几乎无上限 收购xAI后，这不再是两家公司“合作”，而是同一实体内部的战略优先级，决策效率和资源调度完全不同级别。未来版本的Colossus很可能直接建在轨道上。 3. 为SpaceX IPO铺路 + 制造超级叙事 这是老马轻车熟路的资本市场操作，SpaceX原计划2026年IPO，估值目标1–1.5万亿美元。 单纯的“火箭+卫星公司”已经很贵，但加入xAI和Grok后，立刻变成“太空+AI+全球通信+宇宙理解”的超级故事。从叙事上更性感，更有想象力。 4. 进一步整合老马的业务“帝国”，强化控制权 这就是把老马现在分散的主力业务整合在一起，控制力更强，资源协调分配会更有效率。 5. 相对其他路径的最优解 1）让xAI独立继续烧，一级市场融资难度越拉越大（估值越高、市场能承载的资金体量跟不上），再加上大模型公司融资竞争很激烈； 2）让特斯拉收购xAI ，上市公司收购流程复杂，监管也复杂 2）SpaceX收xAI，两家都是老马私人控制、非上市，阻力最小，整合最顺畅。同时在太空数据中心等板块、也能深度合作。 总结一句话： SpaceX收购xAI本质上是“用最强的现金牛+发射能力，去保最烧钱但最有未来想象力的AI大脑”，同时为SpaceX IPO制造史诗级叙事，并为马斯克最终的“太空+AI+意识上传”宏大叙事铺路。 链上美股新玩法，从Bitget开始：