Gigawattonomics
The price of a watt, the price of a FLOP, and the return on an AI factory
Power Sets the Deployment Schedule
If you have followed our research, we have tried to maintain as close a view as possible on returns on invested watts. Power availability determines when capacity can be deployed, while revenue per watt determines whether that capacity earns an adequate return. We have consistently framed infrastructure decisions through tokens per dollar and tokens per watt because power is the binding budget. When the power envelope is fixed, the platform that turns each watt into the most useful monetizable output creates the better economic return. Revenue per watt is therefore the grounding metric for compute capex.
We built the Gigawattonomics model to maintain that lens on a common facility boundary. It begins with one facility GW, bridges that to IT and accelerator-rack power after cooling and other site load, and then asks how much useful compute reaches a customer or internal product. The financial question follows: what can each active watt earn on a given platform, and how much gross profit remains after the infrastructure cost is carried? Revenue per watt ties pricing to utilization. Intelligence per watt is the strategic output, but it becomes valuable only when published performance turns into useful work. Maintaining the model as a time series lets us track which infrastructure and product roadmaps improve that economic conversion.
The Competitive Read
Our Gigawattonomics model informs how we read NVIDIA’s competitive position in particular. NVIDIA carries higher modeled capex per GW than the leading custom-silicon cases. Its newest systems also carry a premium to the announced AMD alternative. Architecture closes part of that gap; software maturity and deployment certainty have to close the rest. For customers serving a changing workload mix, those advantages can justify the premium because they improve time to revenue and reduce the risk that expensive capacity sits idle. The premium becomes harder to defend when a customer has enough internal volume to tune the full stack around a stable workload. As we have argued in prior reports, vendor versus custom is ultimately a workload-specific decision.
Google’s TPU platform is the most demanding custom-silicon comparison in our current work. Its modeled cost and compute density create a real hurdle for NVIDIA, especially inside workloads Google can keep highly utilized. The evidence is less complete once the comparison moves beyond Google’s internal environment. Production rack power and useful goodput are still not disclosed on a fully comparable basis. We would take the direction seriously without treating the current ranking as settled.
AWS has a different strategic objective with Trainium. It does not need Trainium to replace NVIDIA everywhere for the program to create value. A credible internal accelerator gives AWS another cost curve for cloud inference and more control over service pricing. Against the current Trainium-class systems, NVIDIA’s density and software breadth appear capable of earning back much of the capex premium. Future generations could change that math, which is why this needs to be maintained as a time series rather than published once and left alone.
AMD remains the merchant alternative with the broadest opportunity to benefit from customers that want a second source without taking on a fully custom stack. The announced Helios system screens well in some scenarios, but the public dense-versus-sparse performance basis remains ambiguous. We are not ready to make a precise cost-per-compute ranking until production power and comparable workload data are visible.
Custom silicon is most compelling when the workload is stable enough to justify control of the architecture. The buyer can trade software flexibility for lower technical capital per unit of useful output and capture the benefit inside its own product. That trade is harder than a capex table makes it look. Compiler work, developer migration, and lower utilization during the ramp can absorb a meaningful part of the hardware advantage. A merchant platform retains value because it spreads those costs across a broader customer and workload base.
Memory and the Revenue Identity
The other underappreciated part of the analysis is memory. Capex per deployed peak dense FP8 exaFLOP falls quickly across the new platform roadmaps, while capex per deployed HBM bandwidth improves much more slowly. This is why the report carries a separate bandwidth curve. Inference can become limited by data movement before peak arithmetic is exhausted, giving HBM suppliers exposure across NVIDIA and custom designs. The earnings benefit still depends on supply discipline. More capacity can preserve the technical value while compressing the margin available to suppliers.
The final distinction is how the watt gets paid and who owns the shell. One-time rack revenue should not be compared with annual compute rental or the gross profit created inside an integrated product. Each path has a different cost base and duration. Our Gigawattonomics model keeps those revenue identities separate, then applies the selected ownership structure before testing the return. In the leased-shell case, the operator expenses occupancy and measures payback against technical capital. The owned-campus case removes that lease and asks the same operating revenue to repay the full site. That keeps a strong revenue-per-watt result from being mistaken for a complete return on the campus.
Our Current View
Our current view: NVIDIA remains highly competitive even with higher capex per GW because customers buy usable output and deployment certainty. Custom silicon becomes more compelling as workload stability and internal scale rise. For a given workload, the economic winner should be judged by gross profit per active MW, not the lowest hardware bill.
Inside the full report
A normalized facility-GW comparison of shipping and announced NVIDIA, Google TPU, AWS Trainium and AMD platforms.
The capex-per-compute curve, including the adjustment from facility power to accelerator-rack power.
A separate HBM bandwidth curve showing why the byte is not deflating at the same rate as the FLOP.
The operating premium NVIDIA must earn back against Trainium and TPU alternatives, with announced systems kept outside the base case.
Selected outputs from the v1.2 Gigawattonomics model across merchant rental, inference services and integrated custom silicon. The analysis shows how useful-goodput realization and ownership structure affect the return.
A quarterly monitoring framework for fleet repricing, useful-life policy and gross profit per active MW as new platforms enter production.
Institutional Gigawattonomics Model Access
The full report presents our analysis and selected model outputs. Access to the maintained interactive model is licensed separately for institutional clients.
Model clients can change the operating assumptions, compare ownership structures and run their own platform cases against our maintained cost basis. We update the model as production pricing, power requirements and realized-goodput evidence become available.
Contact us to discuss institutional model access.