{"slug": "the-future-of-compute-is-fungible", "title": "The Future of Compute Is Fungible", "summary": "Creative Strategies research concludes that the future of compute is fungible, with hyperscalers building data centers around workload fungibility that spans merchant compute, custom silicon, copper, optical, liquid and air cooling, and power delivery from 800 VDC to older generations. The firm, which based the view on a month of conversations with hyperscaler stakeholders, expects custom silicon to gain ground where steady demand justifies design and support costs while GPUs remain valuable for a changing mix of workloads. The report works through six questions on where greater customer choice improves economics and where the apparent benefit breaks down.", "body_md": "**Go deeper with this research in [CS Atlas](http://tlas.creativestrategies.com/):** We encourage you to take this research further in Atlas, where you can explore the company assessments, work through the model assumptions and connect this report with our broader infrastructure research. If you haven’t used Atlas yet, start with a question that takes the research further: “As hyperscalers design more of their own silicon and racks, which suppliers gain business and which lose pricing power?” “How much of NVIDIA’s opportunity remains when customers choose another company’s accelerator?” or “How does serving larger AI models across more racks change the opportunity for optical and networking suppliers?”\n\nAlso now in Atlas see our takeaways from meetings at AI Infra summit.\n\n- [CPO Summit Takeaways](https://atlas.creativestrategies.com/notes?note=cpo-summit-takeaways)\n- [800 VDC Transition and Timelines](https://atlas.creativestrategies.com/notes?note=800-vdc-transition-separate-clocks)\n- [Learnings from AWS Meetings on Compute and Model Trends](https://atlas.creativestrategies.com/notes?note=aws-executive-conversation-serving-larger-ai-models)\n\nWe have spent time over the last month in conversations with stakeholders at different hyperscalers and have landed on a conviction about how infrastructure decisions are likely to play out during this AI buildout. We believe the future of compute is fungible. While we appreciate NVIDIA’s framing that the GPU is fungible, and that is true, it is clear to us that hyperscalers have a vested interest in building their data centers with **workload fungibility** in mind.\n\nThis viewpoint is an important baseline observation on the diversity of solutions they will use, spanning merchant compute, custom compute, copper, optical, liquid and air cooling, and power delivery from 800 VDC to older generations. We expect these technologies to coexist across their infrastructure, with software coordinating the work across that diversity as one compute fabric. Note, this is a particular advantage unique the hyperscalers and we believe will be a key part of their continued competitive advantage in designing AI infrastructure at scale and competing for enterprise workloads. Having this perspective raises a number of implications and questions that our report explores.\n\nThe value of this viewing infrastructure choices this way is in how it changes our assessment of the companies supplying this buildout. More choice gives customers another way to buy and build capacity, but the savings and supplier profits depend on which workloads move, what it costs to support them and who continues to supply the rest of the system. We work through six questions to understand where those economics are likely to improve and where the apparent benefit of greater choice breaks down.\n\n**1. How does greater choice change the customer’s purchasing power?**\n\nWe expect hyperscalers to start by using a wider mix of systems for new workloads while keeping existing work on the hardware already running it. That includes buying from other suppliers and developing their own silicon and rack-scale designs, giving them more control over how the systems are built and which parts they buy. At first, these efforts may help them add capacity and better match hardware to their workloads. As the alternatives reach production at scale, customers gain more room to negotiate prices. We expect those benefits to develop before broad pricing pressure affects suppliers’ growth and margins.\n\n**2. Where does specialization offer better economics, and where does GPU flexibility remain more valuable?**\n\nWorkload fungibility gives operators more choice over where they run their work. We expect custom silicon to gain ground where steady demand justifies the cost of designing and supporting it, while GPUs remain valuable for a changing mix of workloads. The benefit comes from matching work to the systems that can run it most economically. That requires enough software support to make those choices practical. A cheaper chip offers limited savings when it cannot run enough of the customer’s work or takes too long to get into service.\n\n**3. How much influence moves to the software that decides where the work runs?**\n\nThe software deciding where work runs also influences which hardware the operator needs to buy or they design custom compute. We expect hyperscalers to keep considerable control over those decisions because better utilization and more purchasing options directly benefit their own businesses. Suppliers still have an opportunity where their software reduces the work required to deploy and operate a system. The distinction is who collects the economic benefit. More important orchestration software does not necessarily mean a larger revenue opportunity for an independent software company.\n\n**4. How does serving inference across larger compute domains change the network?**\n\nAs hyperscalers connect more systems to serve inference at scale, the network helps determine how much of that capacity they can use together. Workload fungibility depends on being able to send work to available compute without adding too much delay or cost. This raises questions about where copper remains sufficient, where optical connections become necessary, and how much flexibility operators can gain while keeping tightly connected systems running efficiently. We examine what those choices mean for networking and optical suppliers as operators build larger compute domains.\n\nThis builds on our ongoing optical and networking research and our [recent conversation with a senior AWS executive, covered in Atlas](https://atlas.creativestrategies.com/notes?note=aws-executive-conversation-serving-larger-ai-models). Larger, sparse models need fast connections between the accelerators holding their different parts. The race is on for the larger scale up domain where optical links are necessary extend those connections across more racks (bigger compute clusters), giving operators more room to spread power and cooling demands. The return depends on whether the larger system serves more work at the response times customers need, after accounting for the cost of connecting it.\n\n**5. What is infrastructure flexibility worth over the life of a data center?**\n\nPower and cooling decisions made today will affect what an operator can install several equipment generations from now. We see a stronger case for spending against a defined upgrade path than for preparing every part of a facility for the highest possible density. The extra investment has to earn its way back through lower modification costs, less downtime or getting paid for new capacity sooner. Our facility analysis shows why the return changes substantially when earlier installation does not produce earlier income, and why the lease matters as much as the engineering.\n\n**6. Which companies retain the most value as operators gain more options?**\n\nWe keep coming back to the work a supplier retains as the customer gains more options. Another company may supply the accelerator while NVIDIA continues to sell valuable infrastructure around it. A custom-silicon partner may win a design but retain less of the profitable work when it reaches production. This is why we assess implementation, proprietary IP, networking and support separately. It also explains how greater customer choice can pressure pricing and still support profit growth for suppliers that remain difficult to replace.\n\n## In the full report, we examine:\n\n- **When cheaper compute costs more.** We test how a lower-cost system’s apparent advantage holds up once utilization, software compatibility and deployment delays enter the calculation—and where paying more for GPU flexibility or integration produces a better return.\n- **What supports our company preferences.** Why Broadcom remains our largest modeled dollar opportunity in custom silicon, why MediaTek leads our incremental conviction ahead of Marvell, and what would change that order.\n- **How NVIDIA participates alongside custom compute.** Which parts of the system can remain valuable to NVIDIA even when a customer chooses another accelerator.\n- **Which developments would change our view.** The production orders, retained supplier work and completed facility upgrades we will assess over the coming quarters.\n\n**Take the analysis further in CS Atlas.** Explore seven company assessments and three illustrative models, then connect the findings with our broader industry research. Ask which suppliers benefit as hyperscalers design more of their own infrastructure, compare company opportunities, and test what would change our views. We built Atlas so subscribers and clients can work through our research on their own terms and apply it to the decisions they face.\n\n*Not yet a paid subscriber? Join The Diligence Stack to access the full report and go deeper with our research in CS Atlas.\nWant to engage more with our firm and our analysts, [reach out here](https://creativestrategies.com/contact-us/).*", "url": "https://wpnews.pro/news/the-future-of-compute-is-fungible", "canonical_source": "https://www.thediligencestack.com/p/the-future-of-compute-is-fungible", "published_at": "2026-09-17 18:47:13+00:00", "updated_at": "2026-09-17 18:53:45.856433+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-chips", "ai-research"], "entities": ["Creative Strategies", "NVIDIA", "AWS", "CS Atlas"], "alternates": {"html": "https://wpnews.pro/news/the-future-of-compute-is-fungible", "markdown": "https://wpnews.pro/news/the-future-of-compute-is-fungible.md", "text": "https://wpnews.pro/news/the-future-of-compute-is-fungible.txt", "jsonld": "https://wpnews.pro/news/the-future-of-compute-is-fungible.jsonld"}}