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xAI’s development of artificial intelligence is “a mess”, Co-Executive Editor @mvpeers says. “Elon has fired most of the people who he originally hired at xAI." "He has a tendency to set unrealistic deadlines. And when they're not met, he just fires people.”

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Elon Musk: xAI is the best at AI hardware. “The elements that define success for an AI company are going to be one, the talent, two, the hardware. How much AI hardware can you bring to bear? That's actually a very big deal. And we've shown that we're the best at doing that at xAI. And then third, unique access to data. And for that we've got the 𝕏 system, formerly the Twitter system, which is by far the best source of real time data in the world. Those are some pretty significant assets.” Source: Elon Musk Fireside Chat at Ron Baron's 32nd Baron Investment Conference 2025, November 14, 2025

*꙳:｡✧ エンディングテーマ決定 ✧｡:꙳* #ウタヒメドリーム# オールスターズ 「アンノウンミー」 ▼公式サイトではアーティストプロフィールも掲載！ 🌹TVアニメ #無自覚聖女# 2026年7月より、 TOKYO MX・BS11・AT-Xにて放送開始！

ALPHAZ! XG is so excited to be coming back to Hong Kong! 🤩 Tickets are on sale now, snag yours now before they're all gone. See you at 𝙓𝙂 𝙒𝙊𝙍𝙇𝘿 𝙏𝙊𝙐𝙍: 𝙏𝙃𝙀 𝘾𝙊𝙍𝙀 𝙞𝙣 𝙃𝙤𝙣𝙜 𝙆𝙤𝙣𝙜 - AsiaWorld-Expo, Hall 10 on 2 August (Sun).

Today is my last day at xAI. I joined xAI a year ago and had the pleasure of leading the search and factuality post-training team. Over time, we developed so many recipe and engineering co-optimizations, making Grok the best AI for search and real-time agent. I am also particularly proud of working with a small group of talented people delivering the recent iterations of the instant mode of Grok - the one I personally liked and used the most. My thanks to all the friends and teammates for their support and help over the past year. They are among the brightest minds I’ve met in my career. I am sure the team will continue the mission to make better Grok and understand the universe.

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)

【お知らせ📢】 AT-X『あにめすこ〜ぷ』に出演します! アニメ「その着せ替え人形は恋をする」Season 2特集回!!! #25（前編）2026年5月8日#(金)23:30~ #26（後編）2026年5月15日#(金)23:30~ ぜひ見てね✨ 番組HP▶︎