Original Title: An Interview with CoreWeave Executives: AI Demand Seems to 'Intensify' Every Day
Original Author: Tae Kim
Original Compiler: Peggy, BlockBeats
Editor's Note: This interview provides a window into the AI computing power cycle: demand has not cooled off due to the last round of GPU buying frenzy, but is instead being further driven by agents, inference, and enterprise-level AI applications.
This article interviews CoreWeave Co-founder and Chief Development Officer Brannin McBee, as well as Vice President of Corporate Development and Investor Relations Nick Robbins, discussing the current state of AI demand and the neocloud market. The core message from CoreWeave executives is straightforward—AI demand seems to intensify in new ways every day, and the real bottleneck is shifting from 'having GPUs' to more complex infrastructure issues: data center power shells, CPUs, storage, electricians, supply chain execution capabilities, and how much customers are willing to pay for the next generation of computing power.
CoreWeave's uniqueness lies in its position in the middle of the AI infrastructure chain: it serves top clients like OpenAI, Anthropic, Meta, Google, Microsoft, and Nvidia, while also directly sensing demand changes from research labs, enterprise clients, and hyperscale cloud providers. Therefore, what it sees is not just 'GPU shortages,' but the structural changes happening in AI workloads themselves. With the rise of agentic AI and reasoning models, computing power demand no longer revolves solely around GPUs; the importance of CPUs and storage is also increasing. The design of the next generation of data centers must reserve space for Vera CPUs, Vera Rubin servers, and more storage.
This also explains why the competition in AI infrastructure is shifting from mere chip procurement to more comprehensive engineering delivery capabilities. Whoever can acquire powered data centers, deploy servers, streamline supply chains, and optimize per-token costs faster will be closer to the core of this round of AI capital expenditure cycles. CoreWeave repeatedly emphasizes being 'customer-driven,' which actually reflects a bigger judgment: AI cloud providers are no longer just selling computing power, but are proactively reconstructing the next generation of AI factories based on the roadmaps of the most cutting-edge clients.
For investors and industry observers, the most noteworthy aspect of this interview is not any single data point, but the direction of change in AI infrastructure demand: GPUs are still important, but bottlenecks are spreading; Nvidia remains core, but CPUs, HBM, storage, and data center power supply capacity are becoming new variables; AI demand is still growing, but the future winner may depend on who can deliver complex infrastructure consistently, stably, and at scale.
Below is the original text:
CoreWeave is seen as an innovative early market leader in the neocloud (new cloud services) space.
It is the only cloud service provider to receive the highest-level 'Platinum Rating' from AI research firm SemiAnalysis. Founded in 2017, CoreWeave provides large-scale GPU computing power to startups and large enterprises.
Key Context recently interviewed CoreWeave Co-founder and Chief Development Officer Brannin McBee, and Vice President of Corporate Development and Investor Relations Nick Robbins, to discuss the current state of AI demand and the neocloud market.
Below are edited highlights from the conversation:
AI Demand Continues to Intensify
Tae: When did the demand wave for Agent AI begin to explode?
Brannin: We saw the real beginning in the fourth quarter of last year. At that time, we were having engineering-level conversations with clients about the products they expected to bring to market in the first quarter of this year.
This has always been a very important perspective for us when looking at customer demand. We have a deeply interconnected engineering relationship with our clients. It is this relationship that allows us to see trends early, rather than reacting passively after changes occur.
If we look from a product perspective in the AI market, I would say the first quarter was a moment of a huge inflection point for inference and AI consumption, and this acceleration is still ongoing.
Tae: What is the current state of AI demand? Compared to a few months ago, has there been absolutely no sign of slowing down in recent weeks?
Nick: It seems to intensify in new ways every day.
Tae: Talk about the rising trend in CPU demand relative to GPUs within the Agent AI wave. Will you deploy rows of Vera CPU racks next to Nvidia GPU servers?
Brannin: CoreWeave has been running CPUs since 2023. We've always had a full cloud product suite. So the question isn't whether we're just starting to add CPUs, but rather, what do customers actually need? Is that demand rising in relative terms? The answer is, very clearly, yes.
As agent and inference capabilities truly emerge within models, storage demand is also rising relative to previous generations. I believe this trend will continue.
Nick: To your question, the answer is yes. You will absolutely see a significant amount of Vera CPUs deployed alongside a significant amount of Vera Rubin servers. Last year, we fundamentally redesigned our base data center layout to allocate more space for storage and more CPUs to be deployed alongside GPUs.
We did this because we are in a very unique position within the entire ecosystem. We are the only independent cloud provider serving all the most advanced technology users. No other independent AI cloud provider can say that Anthropic, OpenAI, Meta, Google, Microsoft, Nvidia, etc., are all its customers.
This creates a beneficial flywheel, or positive feedback loop, for our business: we understand where customers are taking the technology and plan accordingly.
The Bottleneck Is No Longer Just GPUs
Tae: Will you primarily use Nvidia Vera CPUs in the future?
Nick: It depends on the specific workload. We operate in a customer-driven manner. We do expect to be an early and significant adopter of Vera CPUs, which we have disclosed. Currently, our fleet is actually predominantly AMD, but over time, this may change based on customer demand. Customer interest in Vera CPUs is very strong.
Brannin: This is also a good reminder to talk about how our contracts work. As you know, over 98% of our revenue is contract-driven. We are not guessing what kind of infrastructure our customers want. Customers tell us very explicitly what configurations they need. Everything is customer-driven. It is the customer defining what we build.
Tae: Talk about the competitive landscape. How are you entering the market and competing against neoclouds like SpaceX, Nebius, Oracle, and hyperscale cloud providers like Azure, AWS, Google?
Brannin: Regarding differentiation, I prefer to look at it from a third-party validation standpoint. Nine of the top ten AI labs globally, excluding China, use our platform. SemiAnalysis consistently rates us alone at the highest level for performance. I don't think we get the GPU allocations we do because of a personal friendship with Jensen.
It speaks to the deep confidence suppliers have in our execution track record and engineering capabilities, believing we can best demonstrate their product capabilities globally.
Nick: We are able to win hyperscale cloud provider customers because we are exceptionally good at execution. We can build these systems at incredible speed, and they run exceptionally well. We win research lab customers because we offer the strongest-performing versions of the technology and the best per-token efficiency.
We win enterprise customers because the infrastructure truly runs well, and we've built a superior, best-in-class orchestration layer, which is also recognized by the Platinum Rating and others.
But increasingly important, among AI cloud providers, we have built the most mature layer of capabilities covering inference and development tools, helping businesses actually put AI into production.
This means we are building and delivering products that ultimately help businesses with relatively lower technical maturity transform data into models, and then into agents that can run internally, while we can cross-sell CoreWeave cloud services in this process.
Tae: What are the current bottlenecks? Is it data center shells with power, GPUs, or electricians?
Brannin: It's powered shells, meaning data center shells with power availability. More precisely, the components inside these shells. You specifically mentioned electricians, and that's absolutely correct. This is a complex area.
But importantly, we already have 49 such sites live and operational. We are not pinning our hopes on one or two sites. We've done it 49 times.
This is a very deep execution track record.
It also means we have accumulated extensive knowledge on how to handle supply chain issues, which suppliers in this supply chain are suitable to partner with, and which are not.
Editor's Note: Powered shells refer to the data center building itself, excluding the actual computing server hardware.
Tae: What can you share about the cost and shortage of HBM memory? How are you coping? Do customers have to bear the cost increase?
Nick: The answer is yes. Our business model is designed to lock in the price we charge customers for GPUs—and more broadly, servers, whose price obviously includes HBM costs—at the same time we sign GPU purchase orders and determine our own costs.
This is how we isolate the impact of day-to-day price fluctuations.
If our component costs rise in the next transaction, we reflect that cost into the price we believe we can charge customers, thereby protecting our margins. We are well-protected in passing these costs on to customers. This is something we watch very closely.
Currently, acquiring components is not the biggest bottleneck. The biggest bottleneck is the powered shell. But at some point in the future, this answer may shift back and forth.
Tae: How do you expect the deployment ramp for Vera Rubin to unfold? What will the second half of this year look like?
Nick: We are clearly the first company globally to have powered on and fully validated VR, or Vera Rubin, cabinets. We did the same with GB200, GB300 last year. I expect VR to start appearing later this year.
I expect a truly large-scale, very strong deployment ramp throughout 2027. This rhythm is similar to GB: GB started appearing in 2025, but the truly massive ramp actually spanned the entirety of 2026. That is to say, a fair amount was deployed by the end of last year, but this year is the year of truly massive GB deployment.
I expect VR to follow a very similar rhythm over the next 12 to 18 months.
