Nvidia's New Strategy: From Chips to AI Partnerships

Nvidia history continues to gain momentum. The company, once associated primarily with GPUs, now spans the entire artificial intelligence landscape, powering public administration, telecommunications networks, healthcare, retail, and many other sectors. It is becoming the backbone of the global AI economy.

Its latest partnerships span industries ranging from government supercomputers to telecommunications networks, pharmaceuticals, retail, and quantum computing. Together, they demonstrate that Nvidia is building the digital infrastructure for the AI-driven economy.

What areas is Nvidia expanding its operations into?

US Government (DOE × Nvidia × Oracle)

Nvidia announced a groundbreaking collaboration with Oracle Corporation and the U.S. Department of Energy (DOE) to build the largest AI supercomputer for DOE to support scientific discovery.

These projects are intended to accelerate research and strengthen national security, and estimated orders for AI systems reach approx. 500 billion USDThis strengthens Nvidia's position as a leader in AI infrastructure for public sector and private projects.

Telecommunications and 6G (Nokia × Nvidia)

In a surprising move, Nvidia invests 1 USD billion, taking up a 2,9% stake in Nokiato jointly develop AI-native 6G networks and next-generation radio access systems (AI-RAN). The goal is to combine Nvidia's AI computing stack with Nokia's telecommunications equipment. T-Mobile US and Dell Technologies will be pilot partners for the AI-RAN stack.

This agreement could help US operators modernize their networks, with Nvidia effectively building itself into the "nervous system" of the future of communications.

Enterprise AI (Palantir × Nvidia, CrowdStrike × Nvidia)

Nvidia deepens commitment to enterprise software:

In association with Palantir combines data analytics with AI models to facilitate the implementation and scaling of AI in key operations.
In association with CrowdStrike Nvidia powers real-time cybersecurity agents that learn and adapt on the fly.

With these agreements, the Nvidia ecosystem extends beyond hardware to include software layers and the security of enterprise AI deployment.

Robotaxi (Uber × Nvidia, Lucid × Nvidia)

Uber will partner with Nvidia to scale its network robotic axis and will deploy 100,000 autonomous taxis and delivery vehicles in the coming year. Regardless, the company Lucid will incorporate Nvidia Hyperion architecture into future models, including an upcoming SUV priced around $50,000, to enable driverless driving in private cars.

These initiatives combine Nvidia's expertise in automotive AI with real-world traffic data, accelerating the development of the next generation of autonomous driving systems and intelligent transportation networks.

Healthcare (Eli Lilly × Nvidia)

Nvidia and Eli Lilly are collaborating to accelerate drug discovery using generative AI. The Nvidia BioNeMo platform will help analyze molecular data and design potential molecules faster, potentially shortening R&D time across the pharmaceutical industry. This demonstrates how Nvidia's computing power is permeating life sciences, an area with significant societal impact and long-term business potential.

“Physical” Artificial Intelligence (Manufacturing and Robotics)

A wide range of industrial companies, including: Siemens, FANUC, Foxconn Fii, TSMC, Toyota, Amazon Robotics, Figure, Agility Robotics, will use Nvidia Omniverse, Isaac, Jetson and IGX Thor platforms.

These partnerships take AI from virtual training environments into the world of real-world robots and smart factory automation.

Retail (Lowe's × Nvidia)

Nvidia is already working with Lowe'sto introduce AI into brick-and-mortar store operations. Thanks omniverse and computer vision tools, Lowe's wants to automate inventory management and improve analytics in stores.

This is a foretaste of how AI is moving out of data centers and into everyday business processes.

Quantum computers (NVQLink × Nvidia)

Nvidia has launched NVQLink, a system that connects quantum processors with GPUs and CPUs. The collaboration includes 17 quantum technology companies and nine research laboratories, placing Nvidia at the center of the next frontier of high-performance computing.

Why does it matter?

Broader demand base: Nvidia is moving beyond Big Tech clouds and into government, telecom, healthcare, and retail, building more stable, diversified revenue streams.
Ecosystem advantage: Developed software and service platforms, including NeMo, Omniverse, and BioNeMo, keep customers in the Nvidia ecosystem and create space for recurring subscription revenue.
Implementation of artificial intelligence: as AI becomes part of the key infrastructure of countries and businesses around the world, Nvidia remains at the epicenter of a wave of investment in AI hardware and infrastructure.
Multiple Growth Engines: The company now has several long-term levers, from data centers to telecommunications and 6G networks to robotics and edge computing, supporting structural growth beyond short-cycle chip demand.
Strategic positioning: Nvidia is embedding itself into key infrastructure, from national laboratories to next-generation networks, becoming essential for global AI adoption.

Geopolitics remains the deciding factor

At the end of the APEC 2025 summit in South Korea, Donald Trump and Xi JinpingInvestors are closely monitoring potential changes to U.S. export policy for advanced AI chips. The question is whether the U.S. will relax, tighten, or redefine the rules for selling chips like the Nvidia Blackwell series to China. Even the emergence of a "China-safe" version of the chip could change Nvidia's Total Addressable Market (TAM) and its pricing power.

The significance, however, goes beyond the bilateral meeting itself. Nvidia executives are expected to hold talks with major Korean conglomerates, including Samsung Electronics, Hyundai Motor Group, and others, on the sidelines of the summit.

Such meetings could signal broader regional alignment on semiconductor supply chains and AI infrastructure investments. If Korean chip, memory, and automotive players support the US-led framework, Nvidia's role could strengthen; conversely, a shift toward China or a "decoupling" of supply chains would increase margin pressure and hinder market access.

Threats to watch out for

Pressure on margins: Rising costs and heavy investments in new technologies may limit profitability. With increasing competition and tougher negotiating terms from large customers, Nvidia's pricing power may weaken.
Regulatory uncertainty: The political outlook, especially between the US and China, is uncertain. Changes in export controls or sanctions could limit sales of advanced AI chips in key markets.
Challenges related to implementation: Many of Nvidia's new initiatives, from telecommunications and robotics to quantum computing, are long-term projects. Delays or slower adoption could dampen the momentum.
Cyclicality of artificial intelligence spending: AI infrastructure investments often come in waves. Large build-out phases are followed by periods of "digestion," which can cause short-term revenue fluctuations.
Increasing competition: Rivals like AMD, Qualcomm, and emerging AI chip startups are quickly catching up. Nvidia may have to balance market share with protecting margins.

Investment perspective

Nvidia's transformation from chipmaker to full-platform AI solutions provider puts it in a league of its own. Its partnerships span nearly every key growth area, including data centers, telecommunications, healthcare, robotics, and enterprise software. The company remains at the core of the global AI infrastructure expansion, and the potential for further growth remains significant.

At the same time, investors should remain realistic: Margin pressure, political risks and market cycles are realNvidia remains the undisputed leader of the AI infrastructure wave, but leadership means higher expectations and less margin for error.

About the Author

Charu Chanana, market strategist in the Singapore branch Saxo Bank. She has over 10 years of experience in financial markets, most recently as Lead Asia Economist in Continuum Economics, where she dealt with macroeconomic analysis of Asian emerging countries, with a focus on India and Southeast Asia. She is adept at analyzing and monitoring the impact of domestic and external macroeconomic shocks on the region. She is cited frequently in newspaper articles and appears regularly on CNBC, Bloomberg TV, Channel News Asia, and Singapore's business radio channels.