Why Nvidia Buys Rather Than Builds: Inside Its Build vs Buy Strategy
Build vs Buy Series · The Executive Intelligence Series | September 19, 2026
Good Morning, Folks! It’s time for a new episode of our Build vs Buy series. This week, we’re looking at Nvidia. Why? Because even with enormous cash, world-class engineers, and massive market power, Nvidia still chooses to buy rather than build. And it does so deliberately and repeatedly. Why Nvidia? Because time matters. Talent matters. Risk matters. Control matters. And in AI, all four matter more than ever.
So what does Nvidia’s approach reveal about how a dominant technology company thinks about time, risk, talent, and control when AI becomes the terrain of competition?
Nvidia is, by most measures, the most important company in the AI economy. Its GPUs train the frontier models, its networking gear moves data between them, and its software stack, CUDA, is the language the entire industry writes in. It would be easy to assume that a company this dominant builds everything itself. It doesn't.
Since 2019, Nvidia has made more than thirty acquisitions, spanning networking, AI orchestration, model optimization, cloud tooling, and — in its single largest deal to date — a $20 billion purchase of assets from AI chip startup Groq in December 2025. It has also become one of the most active corporate investors in Silicon Valley, participating in dozens of venture rounds a year through its formal fund, NVentures.
The Paradox at the Center of Nvidia's Strategy
A company with virtually unlimited cash, engineering talent, and market power still chooses, deliberately and repeatedly, to buy rather than build. Understanding why reveals something more interesting than a simple shopping list of deals. It reveals how a dominant technology company thinks about time, risk, talent, and control — and how those calculations change once artificial intelligence, rather than any single product category, becomes the terrain of competition.
The CODEW Lens: The interesting question is not what Nvidia bought. It is what Nvidia refused to buy — and what it insists on building itself, generation after generation, even when writing a check would be faster.
The Build Case
Nvidia's instinct to build internally is strongest wherever a capability defines its core differentiation. GPU architecture itself is the clearest example. Nvidia does not buy chip designs for its flagship data-center GPUs; it designs them in-house, generation after generation, because this is the product the entire company is built around.
The same logic applies to CUDA. Nvidia has spent close to two decades building out compilers, debugging tools, optimized libraries like cuDNN, cuBLAS, and NCCL, and deep integrations with frameworks such as PyTorch and TensorFlow. This is not a capability Nvidia could acquire its way into, because its value comes from continuity: millions of developers, thousands of organizations, and an entire academic and industrial curriculum built up over time around a single, stable platform.
When Building Wins — Three Conditions:
1. Strategically central. It is the reason customers choose Nvidia over anyone else.
2. Compounds over long time horizons. CUDA's moat is not any one library but the accumulated weight of two decades of tooling, documentation, and developer habit.
3. Control matters more than speed. Nvidia cannot outsource the roadmap for its core silicon or its programming model without ceding the thing that makes it Nvidia.
The CODEW Lens: Nvidia builds what makes it Nvidia. Everything else is negotiable.
The Buy Case
"Buying" here means something narrower than acquiring a company — licensing technology, purchasing a product outright, or integrating an external tool without absorbing the organization behind it. This path fits capabilities that are useful but not identity-defining: components that can be evaluated, priced, and integrated relatively quickly without requiring Nvidia to inherit a team, a culture, or a long tail of unrelated intellectual property.
Nvidia's approach to Groq illustrates a hybrid version of this. Alongside its asset purchase, Groq structured a separate, non-exclusive licensing agreement giving Nvidia access to its low-latency inference technology — a way to absorb a specific technical capability without necessarily needing every part of the underlying company.
Buying suits situations where the technology itself is the prize, the market for that technology is reasonably competitive, and speed of integration matters more than owning the surrounding organization. It is lower-commitment than acquisition, and it lets Nvidia test a technology's fit before deciding whether deeper integration — or eventual acquisition — is warranted.
The CODEW Lens: Licensing is not a compromise. It is an option — and options are how a dominant company buys time.
The Acquisition Case
Acquisition is Nvidia's tool of choice when it needs not just a technology but the people, intellectual property, and operating platform behind it — and when time-to-market matters more than the premium paid for buying rather than building.
The pattern is visible across Nvidia's deal history. In 2019, Nvidia paid roughly $6.9 billion for Mellanox, a networking and interconnect company, because building comparable high-performance networking technology from scratch would have taken years Nvidia didn't have as AI training clusters were scaling into the thousands of GPUs. In 2024, it acquired Run: a i for AI workload orchestration and Deci AI, an Israeli startup specializing in model compression and deployment, reportedly for around $300 million. It also picked up Brev.dev and Shoreline.io, both aimed at strengthening Nvidia's DGX Cloud and infrastructure-management ambitions.
More recently, the pattern has extended into talent-driven "acquihire" deals. In September 2025, Nvidia reportedly spent more than $900 million to hire the CEO and staff of networking startup Enfabrica while licensing its technology — a structure that secures the people and the IP without necessarily absorbing the full corporate entity.
When Acquisition Wins — Three Simultaneous Conditions:
1. The technology is difficult to replicate quickly.
2. The talent behind it is scarce and hard to hire piecemeal.
3. The capability needs deep integration into Nvidia's existing stack, not a bolt-on.
The CODEW Lens: In AI infrastructure, the competitive window can close in a single product cycle. When it does, acquisition is not a shortcut. It is the only path that arrives on time.
Why AI Changes the Build-vs-Buy Equation
Traditional build-vs-buy analysis assumes relatively stable technology cycles: a company has years to evaluate whether to build a capability internally or acquire it. AI compresses that timeline dramatically. Model architectures, inference techniques, and infrastructure requirements can shift within a single year, and the specialized engineering talent capable of building at the frontier is in chronically short supply.
Under those conditions, the "cost" of building internally is not just capital — it is calendar time, and calendar time is often the scarcest resource of all.
This is also why Nvidia's strategy leans so heavily on venture investing rather than acquisition alone. By participating in dozens of funding rounds a year — reportedly 67 in 2025 alone across its various investment vehicles — Nvidia gets early visibility into promising technology and can decide later whether a deeper relationship, a licensing deal, or a full acquisition is warranted.
Its investments in OpenAI and, later, Anthropic function less like traditional portfolio bets and more like strategic options: Nvidia strengthens ties with the companies most likely to define how AI infrastructure gets consumed at scale, while preserving flexibility about how much control it eventually wants.
The CODEW Lens: In a market that reprices itself annually, patience is a strategy only if you can afford the option premium. Nvidia can.
Nvidia's Software and Ecosystem Advantage
CUDA is frequently described as Nvidia's real moat, more durable than any individual chip generation. Nvidia's own estimates put the CUDA developer base above four million registered developers and more than 40,000 organizations running CUDA-accelerated applications, backed by an ecosystem of optimized libraries, frameworks, and a decade-long head start in university curricula and technical documentation.
Rivals such as AMD's ROCm and Intel's oneAPI have made real progress, but switching away from CUDA means retraining engineers, rewriting optimized kernels, and revalidating performance — a cost that is organizational as much as technical.
Acquisitions extend this software advantage rather than replace it. Run: AI adds workload orchestration on top of CUDA-based infrastructure. Deci AI and Brev.dev make it easier for developers to deploy and provision models efficiently within Nvidia's ecosystem. Each deal is chosen, in part, for how well it reinforces the software layer that keeps developers inside Nvidia's platform rather than exploring alternatives.
The company is not simply buying isolated products; it is buying pieces that thicken the surrounding ecosystem, making the switching costs for any single customer even higher than before.
The CODEW Lens: CUDA is the moat. Acquisitions are the wall around it. Each deal raises the cost of leaving without ever mentioning lock-in.
Technology vs. Talent vs. IP
Not every acquisition is chasing the same prize, and Nvidia's deal structures make the distinction explicit. Three categories dominate:
Technology-first deals — A working product or platform Nvidia wants to integrate quickly. Example: Shoreline's incident-automation tooling.
IP-first deals — Patents, algorithms, or architectures that would take competitors years to reverse-engineer. Example: Mellanox's networking IP.
Talent-first deals — People. The Enfabrica transaction, structured as an acquihire with an accompanying technology license, is a transaction where the people mattered as much as, or more than, the underlying product.
This distinction matters because it shapes how a deal gets valued and integrated. Acquiring for technology alone can sometimes be handled through licensing or partnership instead. Acquiring for IP usually requires full ownership to avoid disputes over rights. Acquiring for talent requires retention strategies that have little to do with the target company's balance sheet and everything to do with keeping key engineers motivated once they join a much larger organization.
The CODEW Lens: The acquisition price is only the first number. The retention cost is the second — and it rarely appears on the term sheet.
The Economics of Building vs. Acquiring
Nvidia's scale gives it an unusual amount of room to buy rather than build. At the end of October 2025, the company held roughly $60.6 billion in cash and short-term investments, up dramatically from just over $13 billion in early 2023, and its market capitalization has fluctuated in the $2.5 trillion to $3.4 trillion range. Against that balance sheet, even a $20 billion deal for Groq's assets — Nvidia's largest acquisition ever — represents a fraction of available capital.
That scale changes the math in two ways. First, Nvidia can afford to pay a premium for speed. Building a comparable networking stack to Mellanox's from scratch might have cost less in absolute dollars but would have taken years Nvidia didn't have while the AI training market was accelerating. Second, Nvidia can absorb the failure of individual bets. Not every acquisition or investment will pay off, but a company generating tens of billions in free cash flow annually can tolerate a higher rate of strategic misses than a smaller competitor making a single, existential bet on internal development.
Balance Sheet Snapshot · October 2025
Cash & short-term investments: $60.6B
Cash position · early 2023: ~$13B
Market capitalization range: $2.5T – $3.4T
Largest acquisition ever: $20B (Groq assets)
The CODEW Lens: Each deal is a small, hedged bet against the much higher cost of being late to a critical capability. That is the entire economic logic.
Where Nvidia Should Keep Building
Despite its acquisition appetite, there are capabilities Nvidia has strong reasons to keep building internally:
Core GPU architecture and silicon design — The product the company's entire identity rests on. Outsourcing it would undermine the reason customers choose Nvidia in the first place.
CUDA's foundational layers — Compilers, low-level libraries, and the developer relationships that took two decades to cultivate cannot be purchased in a single transaction without losing what makes them valuable.
High-bandwidth networking and interconnect roadmaps — An area Nvidia deepened through Mellanox, now core enough to warrant continued internal investment rather than repeated outside sourcing.
Data-center system economics — Power efficiency, rack-scale design, and system-level performance sit close enough to the core product to justify building in-house.
Where Nvidia Should Buy or Acquire
Conversely, Nvidia has good reason to keep buying or acquiring in areas adjacent to its core stack, where speed and specialized expertise outweigh the benefits of ownership:
AI workload orchestration and MLOps tooling — The territory covered by Run: ai, a fast-moving software category where a strong existing product can be integrated faster than it can be rebuilt.
Model optimization and compression — Deci AI's specialty, a specialized niche where a small team of experts can outpace a much greater internal effort.
Developer-facing cloud tooling, incident automation, and infrastructure management — The Brev.dev and Shoreline pattern: useful complements to Nvidia's DGX Cloud ambitions, but not core enough to justify years of internal development.
Scarce engineering talent — Where hiring individually is nearly impossible, as with Enfabrica, an acquihire structure secures both people and technology in one move.
The Risks of Nvidia's Strategy
Nvidia's acquisition-heavy approach is not without real risk, and the company's own history offers the clearest cautionary tale. In 2020, Nvidia agreed to acquire Arm from SoftBank for $40 billion, a deal that would have given it control over the chip architecture licensed to hundreds of companies, including direct competitors and major customers such as Apple, Qualcomm, and Microsoft. Regulators in the United States, United Kingdom, European Union, and China all opened investigations, and rivals lobbied hard against the deal on competitive-neutrality grounds. In February 2022, Nvidia and SoftBank terminated the agreement, and Nvidia absorbed a $1.36 billion charge for the abandoned transaction.
The episode illustrates a structural risk: as Nvidia's market position grows, its acquisitions attract more antitrust scrutiny, and deals that would have sailed through a decade ago now face a genuine chance of collapse.
Regulatory risk — Antitrust scrutiny grows with market position. Even a dominant buyer cannot assume terms will hold.
Integration risk — Acquired teams do not always adapt to a much larger organization's processes and culture, and key talent retention is never guaranteed once earn-out periods end.
Strategic dependency — A company that gets used to buying its way into new capabilities can atrophy its own internal capacity to build from scratch — dangerous if the acquisition market tightens or antitrust pressure makes large deals harder to close.
Ecosystem friction — Rapid, wide-ranging acquisition activity can create conflict. Nvidia's reported interest in AI cloud startup Lepton AI drew attention precisely because it would have put Nvidia in partial competition with CoreWeave, one of its own major cloud customers and partners.
CODEW Lens: The Arm collapse is a reminder that even a company this dominant cannot assume every deal will close on its terms. Scrutiny scales with power.
Build vs Buy vs Acquire: The CODEW Verdict
| Decision | Why Nvidia Would Choose It |
|---|---|
| Build | Strategic technology, deep differentiation, long-term control — GPU architecture, CUDA's foundational layers, core networking and system design. |
| Buy | Commodity or specialized capability that can be integrated quickly — licensed technology like Groq's inference IP, discrete tools that plug into an existing stack. |
| Acquire | Critical technology, IP, or talent needed fast and fully — Mellanox's networking IP, Ru n: ai's orchestration platform, Enfabrica's engineering team. |
| Partner | Capability where ecosystem access matters more than ownership — strategic ties to OpenAI and Anthropic, cloud partnerships with providers like CoreWeave. |
CODEW Lens: Build what makes you you. Buy, acquire, or partner for everything else. The discipline is in knowing which is which.
The Build vs Buy Glossary
A strong understanding of Nvidia's strategy requires familiarity with its vocabulary:
Acquihire — An acquisition structured primarily to secure a target's engineering talent, often with a separate technology license.
CUDA — Nvidia's parallel computing platform and programming model, widely described as the company's true moat.
Calendar Time — The elapsed time required to build or acquire a capability. In AI, often the scarcest resource.
Ecosystem Thickening — Adding adjacent products and tools that raise switching costs and deepen platform lock-in.
Inference IP — Intellectual property covering how trained AI models run — latency, throughput, and efficiency.
NVentures — Nvidia's formal corporate venture fund, active across dozens of rounds annually.
Strategic Option — An investment that preserves the right, but not the obligation, to deepen a relationship later.
Switching Cost — The organizational and technical cost of moving away from a platform. Nvidia's most durable defense.
Time-to-Market — The speed at which a capability can be deployed. The variable Nvidia optimizes for above all else.
FAQ
Q: Why doesn't Nvidia just build everything internally?
Because building everything is slower than buying some things. Nvidia builds what defines its identity — GPU silicon and CUDA — and buys, acquires, or partners for capabilities that surround that core. The company treats acquisitions and venture investments as a portfolio of hedged bets against the risk of falling behind in a market moving faster than any single R&D roadmap can track.
Q: What is the difference between "buying" and "acquiring" in this context?
"Buying" means licensing technology, purchasing a product outright, or integrating an external tool without absorbing the organization behind it. "Acquiring" means taking ownership of the technology, the people, and the operating platform together. Buying is lower-commitment and reversible. Acquiring is faster and deeper, but harder to unwind.
Q: Why did the Arm acquisition fail, and what did it teach Nvidia?
The $40 billion deal collapsed in February 2022 after regulators in the US, UK, EU, and China opened investigations and rivals lobbied on competitive-neutrality grounds. Nvidia absorbed a $1.36 billion charge. The lesson: as market power grows, so does antitrust scrutiny. Deals that would have closed a decade ago now face a genuine chance of collapse.
Q: How does Nvidia decide between building, buying, and acquiring?
Three questions. First: does this capability define Nvidia's identity? If yes, build. Second: can it be licensed or integrated quickly without absorbing a team? If yes, buy. Third: is the technology difficult to replicate, the talent scarce, and the integration deep? If yes, acquire. Everything else is a partnership or an investment.
Q: What is the biggest risk to this strategy?
Regulatory risk. As Nvidia's market position grows, its acquisitions attract more scrutiny, and large deals become harder to close. Beyond that: integration risk (acquired teams don't always adapt), strategic dependency (relying on buying can atrophy internal build capacity), and ecosystem friction (buying into adjacent areas can put Nvidia in competition with its own customers and partners).
The CODEW Stat
30+ deals · $60.6B cash · 4M+ developers Nvidia has made more than thirty acquisitions since 2019, holds roughly $60.6 billion in cash and short-term investments, and counts more than four million registered CUDA developers inside its ecosystem. The company builds what makes it Nvidia — silicon and CUDA — and buys, acquires, or partners for everything else. Scale does not make acquisitions less necessary. It makes them more attractive.
Reviewed by Erwin Castro
on
Saturday, September 19, 2026
Rating:
