The public conversation around artificial intelligence often starts with models and apps, but Niraj Yagnik is focused on the harder market underneath them. FPX AI’s research is focused on the harder market underneath AI models: the infrastructure that must be identified, verified, hosted, and deployed before AI systems can support real workloads.
Yagnik is Co-Founder and CTO of FPX AI, working alongside co-founders Dhyay Bhatt and Veronika Bhatt to build a company focused on the infrastructure layer behind artificial intelligence. FPX AI operates across hardware, colocation, and cloud rentals, combining research with advisory work and marketplace activity to help companies understand what compute resources are available, what they cost, and how quickly they can be deployed.
“People see AI through the models,” Yagnik says. “But a model is only useful if the infrastructure beneath it is real, accessible, and ready when builders need it.”
That view has become more important as the AI market moves from experimentation into deployment. Companies may know they need compute, but that does not mean they know what kind, where to find it, or whether an offer can actually support the workload. Two options can look similar in a spreadsheet and be very different once deployment begins.
FPX AI was built around that gap. Hardware providers often see GPU and server supply. Colocation providers are closer to questions of power, cooling, rack density, and site readiness. Cloud providers understand rental demand.
For FPX AI, the industry’s tendency to reduce AI infrastructure to a GPU shortage misses the more difficult reality. GPUs matter, but they are only one part of a working deployment. A company can secure accelerators and still run into site readiness issues. It can also find out too late that the economics do not work. The real issue is the availability of deployable compute.
This changes how companies should plan. A raw GPU count does not reveal whether a deployment can be brought online quickly. Pricing does not tell the full story if the surrounding infrastructure is not ready. A supplier relationship may be useful, but it still has to be verified.
“The real question is not only what compute exists,” Yagnik says. “The question is what compute is credible, usable, and ready in time.”
That is why FPX AI tracks the market beyond the obvious signals. The company works with compute buyers, neoclouds, hardware suppliers, colocation providers, high-density compute facilities operators, and infrastructure investors. Its research covers GPU supply, data center constraints, cloud pricing, power availability, hardware transitions, and emerging AI workloads.
FPX AI also studies compute diversification. NVIDIA remains central to the market, but companies are evaluating other options, including AMD accelerators, Google TPUs, Cerebras, SambaNova, Etched, and specialized inference chips. Those choices are not interchangeable. Each comes with tradeoffs around performance, cost, software support, energy use, and workload fit.
“The next phase of infrastructure planning will require more nuance,” Yagnik says. “The right answer depends on the workload, the timeline, and the economics around deployment.”
That kind of nuance is important because AI infrastructure increasingly depends on local context. FPX AI works with policy and community researchers to better understand grid readiness, permitting, local priorities, and responsible deployment so compute projects can move faster, earn trust, and align with real demand.
Yagnik and Dhyay Bhatt have helped shape FPX AI’s research around a central idea: AI infrastructure is no longer just a supply problem. It is an intelligence problem, requiring better visibility into hardware, colocation, cloud rentals, power, pricing, deployment timelines, and workload fit.
AI infrastructure sites are part of the physical economy. They touch power grids and local communities. That means infrastructure planning has to be both fast and responsible.
FPX AI’s work also reflects a concern about competition. If advanced compute becomes accessible only to the largest companies, AI innovation becomes more centralized. Smaller builders, researchers, open-source developers, enterprises, and emerging AI companies may have fewer realistic paths to deploy serious systems.
Access, in FPX AI’s view, is not just a procurement issue. It shapes who gets to build.
“If only a few companies can access advanced infrastructure, the future of AI becomes narrower,” he says. “Compute access is becoming a gatekeeper for innovation.”
That belief has influenced how FPX AI positions its work. The company is not only trying to help clients find capacity. It is trying to help them understand what tradeoffs matter. Sometimes that means identifying supply. Sometimes it means explaining why a seemingly available option may not be practical. In a market filled with urgency, discipline matters.
FPX AI focuses on time to compute. The metric is simple to state and difficult to deliver. It asks how quickly a company can move from needing infrastructure to deploying it in a way that works.
The founding team reflects the breadth of that problem. Niraj Yagnik and Dhyay Bhatt help lead the company’s research and infrastructure strategy, while Veronika Bhatt, COO of FPX AI, brings the commercial and operational layer through go-to-market, partnerships, customer relationships, and execution. Together, the team combines AI research, software engineering, infrastructure strategy, and commercial execution in a market that cannot be understood through one lens alone.
The company’s long-term goal is to become a core research, advisory, and execution layer for AI infrastructure, helping the market understand not just where compute exists, but what is credible, deployable, responsible, and aligned with real demand.
FPX AI’s research reaches readers across AI labs, venture funds, hedge funds, infrastructure operators, energy companies, policymakers, enterprises, and research organizations, giving the company a feedback loop between market activity and industry analysis.
Better intelligence can help the market move faster without losing discipline,. That is what AI infrastructure needs now. More clarity about what is real and a better understanding of what can actually be deployed.
As AI moves deeper into business, research, and public life, the infrastructure layer will only become more consequential. Models may continue to draw the attention. The harder question is whether the market beneath them can become transparent enough to support what comes next.
