STH Q1 2026 Letter from the Editor AI Got Scary Good

ServeTheHome reported: That all may not seem exciting at first, but it is also a capability that was not really usable/ useful six months ago. If you take a derivative of this trendline, it is on a crazy slope. One of the strangest parts is seeing the STH user base fragment. We have folks who work at very large companies saying they use Claude or another AI coding tool daily. We have folks building clusters and running local LLMs. There are also folks who think that AI is just slop. To be fair, there is a lot of AI slop, and it is just going to get worse. It is just that longer-duration tasks are happening without intervention. That GB10 cluster task is not a trivial one. It is not officially supported by NVIDIA (NVIDIA currently supports up to four nodes.) Being frank, a few folks have 8x NVIDIA GB10 clusters, but it is still a relatively niche setup, and there are differences among them. This is not just a chatbot regurgitating. This is sending an agent on a task just like we would send someone on our team (or realistically, I would think it is fun and I would do it.) A great example of the evolution we saw this quarter. The GB10 video we did, using the Dell Pro Max with GB10 to Profit within 12 Months, which used n8n and gpt-oss-120b, went live in January, based on a great workflow in November/December 2025. By mid-February, we are using gpt-oss-120b very little, as Qwen3.5-122B tends to be a muc.

The story lands in a market where demand is already assumed. The more useful question is whether the supporting layer around cloud infrastructure is flexible enough to turn that demand into available capacity. The constraint is not just chip supply. Advanced compute depends on packaging, memory, networking, power delivery, and the ability to land systems inside facilities that can actually run them at high utilization.

The pressure point is timing. The underappreciated variable is deployment readiness across networking, power, and packaging, not just chip availability.

That matters for buyers because the useful capacity is the installed, cooled, powered cluster, not the purchase order. It also matters for suppliers because component shortages can shift bargaining power quickly across the stack.

The financial question is whether this development improves pricing power, locks in scarce capacity, or exposes execution risk that the market may still be discounting, the operating question is procurement timing, facility readiness, network design, and the likelihood that adjacent constraints will slow realized deployment, and the customer question is whether this changes build sequencing, partner dependence, or the economics of scaling regions and clusters over the next few quarters.

This is where AI infrastructure differs from ordinary software growth. Capacity has to be financed, permitted, powered, cooled, connected, staffed, and then sold into real workloads before the economics are visible.

The practical read is that infrastructure advantage is becoming more local and more operational. Two companies can chase the same AI demand and end up with very different outcomes if one has better access to power, more credible delivery dates, or a cleaner path through procurement and permitting.

The next signal to watch is the next disclosures on customer commitments, infrastructure readiness, and any evidence that power, cooling, silicon supply, or permitting becomes the real gating factor. The next test is whether delivery schedules, memory availability, and deployment readiness move together or start to diverge.