If you look at Nvidia’s deals over the last three years, they seem scattered. A handful of small software buys. A couple of massive strategic investments. Some moves that look suspiciously like vendor financing dressed up in different clothes. But underneath the apparent chaos sits a remarkably clean thesis.
Picture an AI datacenter as a factory. GPUs are the machines on the factory floor. Schedulers and orchestration systems decide which jobs run, when, and where. Model tooling determines how efficiently those jobs actually use the machines. Financing and capacity contracts decide whether the factory gets built at all.
Nvidia is systematically buying and investing in the control points of that factory. At the same time, it’s using its towering market capitalization and cash generation to pre-sell the future by seeding the ecosystem that must buy Nvidia systems.
The pattern in the deals
Over the last three years, Nvidia has made two distinct types of moves. The first category consists of capability acquisitions targeting software that increases what you might call GPU utilization yield. These aren’t huge revenue generators today, but they fundamentally change how value gets created and captured by boosting throughput, reducing friction, and making Nvidia harder to replace.
Take Run:ai, which Nvidia announced in April 2024 and closed in December. This is Kubernetes-based GPU orchestration and scheduling for AI clusters. It’s not nice-to-have software. It’s the dispatch system for the whole factory.
Or consider Deci, acquired in 2024. This company builds model optimization and efficiency tooling. You get more inference or training per GPU-hour. That sounds incremental until you realize inference is becoming the dominant cost line item.
Brev.dev, acquired in July 2024, focuses on developer workflow and finding cost-effective GPU compute across clouds. It’s a funnel that makes it easier to start on Nvidia and stay on Nvidia.
OctoAI, picked up in September 2024, provides a platform for serving and running generative AI models efficiently. Again, more output per GPU-hour and simpler enterprise deployment.
Then came SchedMD in December 2025. This one matters strategically. Slurm is the de facto workload manager in high-performance computing and increasingly in AI. Nvidia bought the steward of a critical open standard and promised to keep it open source. This is the key move. Nvidia can now shape the job submission layer across heterogeneous clusters, not just Nvidia-only stacks.
These acquisitions share a common thread. They don’t introduce radically new business models. They amplify Nvidia’s existing approach of selling accelerated compute by lifting utilization, simplifying operations, and anchoring the software control plane. In business model terms, Nvidia is strengthening value delivery through ease and performance while tightening value capture through pricing power and ecosystem lock-in, all without changing the core engine of selling the machine. The notable callout is Slurm. It acts as a hedge. It increases Nvidia’s ability to earn influence and potentially services revenue even when the hardware mix includes competitors.
The second category gets more interesting. Nvidia is using its market cap and balance sheet to pull forward demand and reduce the risk of building AI factories.
Consider CoreWeave. Nvidia invested early, reportedly putting in one hundred million dollars in April 2023. After CoreWeave’s IPO, Nvidia ended up with roughly twenty-four million shares, about a seven percent stake. Separately, the companies expanded a long-term arrangement described as an initial six point three billion dollar order tied to capacity through 2032. Translation: Nvidia isn’t just selling GPUs. It’s helping ensure a GPU-native cloud exists at scale.
The OpenAI partnership announced in September 2025 takes this further. OpenAI and Nvidia announced plans to deploy at least ten gigawatts of Nvidia systems, with Nvidia intending to invest up to one hundred billion dollars progressively as capacity gets deployed. Nvidia is underwriting the biggest buyer and turning it into a semi-captive reference architecture.
The Intel stake completed in late December 2025 tells another story. Reuters reported Nvidia took a five billion dollar stake via private placement. This isn’t about Nvidia suddenly betting on x86 architecture. It’s a strategic stabilizer to keep a major ecosystem player aligned and cooperative while the compute stack gets reorganized.
The Nokia deal in October 2025 brought a one billion dollar investment alongside an AI-RAN and 6G partnership. Nvidia is extending the AI factory concept into telecom infrastructure. New territory, but the same fundamental play of accelerated compute plus networking plus software.
Even the Groq arrangement in December 2025, structured as a licensing deal plus key personnel moving to Nvidia while Groq continues independently, fits the pattern. Nvidia is buying optionality on inference technology and talent without absorbing the whole company.
This represents a business model expansion. Nvidia now shapes value creation by ensuring factories get built, influences value delivery through reference architectures and platforms, and captures value beyond GPU margins or at minimum defends existing profit pools. It’s transformational because it changes how Nvidia grows, moving from shipping chips to engineering the market structure that forces chip demand.
The core thesis
Jensen Huang’s strategy distills to a single sentence. AI is becoming a new industrial substrate, so Nvidia must own the factory blueprint, the dispatch system, and the financing rails that get factories built.
Think of it this way. If you sell the engines, you also want to own the air traffic control tower and help fund the airlines. Otherwise the planes never fly and someone else can swap your engine later.
The emerging end state
A plausible future looks like this. Nvidia becomes the operating system of AI factories through its software control plane covering orchestration, scheduling, inference serving, and observability. It becomes a market maker for compute capacity through deep ties to neoclouds and long-term capacity contracts. It extends the AI factory pattern into adjacent regulated infrastructure like telecom via Nokia, national AI programs, and sovereign clouds. It treats alternative inference silicon as a feature rather than a threat by licensing, partnering, or acquihiring to support heterogeneous backends while keeping the control plane Nvidia-shaped.
The uncomfortable question
What happens if the AI bubble crashes? These deals make Nvidia more resilient to demand shocks but potentially more exposed to credit-like risk.
The software acquisitions like Run:ai, Slurm, Deci, OctoAI, and Brev.dev carry relatively low risk. They mostly increase efficiency and stickiness. In a downturn, customers care even more about utilization and cost.
The ecosystem financing strategy presents different risks. CoreWeave-style entanglement and mega-partnerships can look like vendor financing. If end demand collapses, the weakest link becomes the leveraged capacity layer. Recent reporting has raised exactly these concerns about circularity and risk concentration in AI infrastructure financing.
Nvidia’s hedge becomes clear. Control more of the must-have software and standards so even a slower hardware cycle still runs through Nvidia-shaped infrastructure.
The next eighteen months
Treat each move as buying an option. Small premium today, big upside if the world moves that direction.
High probability next moves include more dispatch layer consolidation around observability, profiling, cluster telemetry, and cost governance for AI factories. This would complement Slurm and Run:ai. Expect inference-specific options through more licensing or acquihire plays like Groq, especially around low-latency serving, memory bandwidth optimization, and compiler toolchains. Networking and optics adjacency through partnerships or minority stakes securing the data movement bottleneck makes sense. More neocloud exposure via additional structured capacity deals or equity stakes with GPU-native clouds helps Nvidia defend volume if hyperscalers diversify silicon. Regulated edge expansion in telecom and industrial segments similar to Nokia could turn AI at the edge into another factory class.
One bolder but plausible move would be a larger acquisition in the control plane that makes Nvidia a first-class platform vendor even in heterogeneous clusters. Think scheduler plus serving plus governance as an integrated suite. SchedMD was the signal.
The predictive question
If you want to predict Nvidia’s next deal, ask one question. Does this asset increase the number of GPU-hours the world consumes, or does it increase Nvidia’s control over where those GPU-hours run and how they get scheduled?
If yes, it fits the thesis.
That’s the story. Nvidia is spending its market valuation windfall not on random diversification but on buying the knobs and levers that decide whether the AI factory runs, what it runs, and whose machines it runs on.
