The Pattern I'm Watching
In 1999, every major enterprise was racing to build out internet infrastructure before the calendar turned. Colocation providers signed multi-year deals with tenants who had never run a data center. Power contracts were signed on the assumption that demand would keep doubling. Then the demand curve bent. Tenants went bankrupt, power contracts sat unused, and the landlords who had borrowed against committed revenue found that committed revenue was not the same thing as delivered revenue. The lesson the industry spent years absorbing was not that the internet was overhyped. It was that the physical layer, power, cooling, and interconnect, does not scale on the same curve as software. You can ship a new protocol in weeks. You cannot wire a substation in weeks.
That lesson is back, and it arrived this week with specific names attached.
Bloomberg reported on November 5 and 6 that Microsoft has been pulling back from a broad set of neocloud partnerships, citing delivery delays, regulatory hurdles, and costs. The company is souring on IREN, Nebius, and Nscale, and is winding down its AI computing rental arrangement with Oracle. Microsoft's total committed spend across its major neocloud partners, Nscale, Nebius, CoreWeave, and IREN, totals more than $80 billion, according to figures compiled by BigGo Finance. The company helped create the neocloud sector by signing those contracts. The contracts became the collateral those providers used to raise debt and build capacity. Now the anchor tenant is stepping back because the capacity is not arriving on schedule.
On the demand side, Amazon CEO Andy Jassy said on October 23 that AWS cloud capacity is largely sold out through 2027, with a significant portion of 2028 already committed, per Reuters. That is not a forecast. That is a statement about capacity that has already been contracted.
After 30 years of watching infrastructure cycles, the pattern I keep seeing is this: every major compute buildout eventually hits the physical layer, and when it does, the teams that locked in capacity early are insulated while the teams that assumed availability on demand are not. The 1999 internet buildout hit the demand wall. The 2010s data center buildout hit the power wall in specific geographies. The AI infrastructure buildout is hitting both simultaneously, with the added constraint of GPU supply chains that cannot be expedited the way a software license can.
The market is reading this clearly. Gartner's chief forecaster told CNBC that more than 80% of independent neocloud companies will stop operating independently by 2030, per CNBC's October 29 report. Shares of CoreWeave, Nebius, and IREN fell by roughly half over the past month. The companies that built their business models around being Microsoft's overflow compute are now discovering what happens when the overflow stops.
Two other signals from this week sharpen the picture. OpenAI secured $11 billion in commitments at a $1.25 trillion valuation, down from a $30 billion target at $1.4 trillion, per Reuters via Venture Atlas. The round got smaller and the valuation got trimmed. Anthropic filed its S-1 showing $16.3 billion in first-half 2026 revenue and $131.7 billion in cash and short-term investments, per CNBC, while targeting an IPO raise of $80 to $100 billion at a valuation in the $1.25 to $1.75 trillion range. Both labs are raising capital at scale. Both are doing it against a backdrop where the infrastructure required to run their models is the scarce resource, not the models themselves.
This is the shift that matters for teams making stack decisions right now. The question used to be which model is best. The question now is which vendor can actually deliver the compute to run it, at what cost, and on what timeline.
The Bottom Line (No Jargon Edition)
Microsoft has committed more than $80 billion to neocloud partners including Nscale, Nebius, CoreWeave, and IREN, per BigGo Finance, and is now pulling back from several of those relationships due to delivery delays and power connection failures. The Oracle AI compute rental deal is being wound down entirely. For teams that planned GPU capacity through neocloud providers, the delivery timeline assumptions you made six months ago are worth revisiting now.
AWS capacity is largely sold out through 2027, with substantial commitments already extending into 2028, per Reuters citing Andy Jassy. On-demand and reserved capacity still exists, but the window for securing preferred pricing and availability is narrowing. If your team has AI workloads planned for 2027, the time to lock in commitments is before the remaining capacity clears.
Gartner predicts more than 80% of independent neocloud companies will stop operating independently by 2030, per CNBC. The companies that survive will be the ones with the strongest anchor-tenant relationships and the cleanest power contracts. For teams that chose a neocloud provider for GPU access, the financial stability of that provider is now a vendor risk question, not just a pricing question.
OpenAI closed $11 billion in commitments at a $1.25 trillion valuation, trimmed from a $30 billion target at $1.4 trillion, per Venture Atlas. Anthropic filed its S-1 showing $16.3 billion in H1 2026 revenue and is targeting an $80 to $100 billion IPO raise, per CNBC. Both labs are well-capitalized. The valuation compression at OpenAI is a signal worth watching, not a crisis, but it reflects the same market reassessment that is hitting neocloud stocks.
Google Cloud added three OpenAI GPT-6 models, GPT-6 Astra, GPT-6.1 Sol, and GPT-6 Luna, to its Model Garden in private preview on October 21, per the Google Cloud blog. Teams with existing GCP spend commitments can apply those commitments to OpenAI model usage. This matters for the capacity question: GCP is now a distribution channel for OpenAI workloads, which gives teams another option if their primary model provider's infrastructure is constrained.
LiteLLM v1.108.0 adds Gemini 4 Argon and Microsoft 365 Copilot as supported endpoints, per the LiteLLM release notes. For teams running a unified LLM gateway, this release expands the routing options available without changing your application layer. More routing options matter more in a constrained capacity environment.
The practical audit for this week: map every AI compute dependency your team has against the vendor's financial stability, their delivery track record for committed capacity, and whether power and cooling constraints affect their roadmap. The teams that did this audit in 2000 for their colo providers were better positioned than the ones that assumed the capacity would be there when they needed it.
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The Question Worth Sitting With
The 1999 buildout produced a lot of stranded capacity, but it also produced the fiber and data center infrastructure that the modern internet runs on. The teams that survived that cycle were the ones who separated "this infrastructure will eventually be used" from "this infrastructure will be delivered on the timeline I need it." The AI infrastructure cycle is compressing that same lesson into a shorter timeframe. Microsoft signed $80 billion in neocloud contracts and is now discovering that signed contracts do not equal delivered capacity. AWS is sold out through 2027. The physical layer is the constraint, and it does not respond to urgency the way software does.
Here is the question I keep sitting with: when your team last reviewed its AI infrastructure dependencies, did you treat vendor delivery track record as a first-class criterion, or did you evaluate only on model capability and price? Leave your answer in a comment on the post, not as a reply to this email. Replies go to a private inbox where no other reader can see them, and this conversation is worth having in public.
Hit reply and tell me. I read every response.
— Darin