Innovation : Executing Growth Strategy at Nvidia
Strategy is simply a choice or a response to a future challenge or opportunity. This article explains how innovation fueled Nvidia’s super-growth story. We need to go a few years back to understand how the company’s valuation has exceeded USD 2.8 Trillion.
Understanding the Industry Context for Nvidia Corporation
Headquartered in California, USA, Nvidia Corporation designs graphics processing units (GPUs) and system-on-a-chip units (SOCs) used in gaming, professional visualization, data centers and automotive markets. In business model language, customers segments are gamers, researchers, data science analysts and equipment manufacturers. The company had been on a growth trajectory for consecutive years; its revenue increased by 20.6% to USD 11.7 billion in 2019, and its net margin increased by 4% for the same year. Nvidia’s rank on the Fortune 500 list was 292 four years back. For the fiscal year 2022, Nvidia revenue was a record $26.91 billion, up 61% from 2021. Fast forward, Nvidia revenue for the twelve months ending July 31, 2024, was USD 96.307 billion, a 194.69% increase year-over-year. By 2023, Nvidia’s rank on the Fortune 500 list was 139.
Nvidia’s CEO Jensen Huang announced the growth strategy in 2018 through three dimensions: New GPU architecture, new AGX family for autonomous machines and medical instrumentation. These represent three clear bets. Huang stressed strengthening the company’s leadership position in artificial intelligence (AI) and growing its technology into a broader range of segments.
This announcement indicated the importance of innovation to Nvidia through new products in its pipeline and through expanding on the relatively new technology applications of AI. Nvidia growth was propelled by the markets size growth of data centers, autonomous vehicles, software infrastructure and AI-based applications. To maintain its position as an innovation leader, 26% of its 2020 revenue funded R&D activities according to its corporate responsibility report.
So how did Nvidia use innovation to execute its growth strategy?
Components and Applications of Nvidia’s Innovation Strategy
1. Open Innovation & Collaboration Model
Open innovation is the exchange of valuable ideas between the inside and outside of an organization. The collaboration between Red Hat Inc., a global provider of open software platforms, and Nvidia is an application of open innovation strategy. When Red Hat and Nvidia collaboratively recognized the need to simplify the process for diverse developers to create AI-enabled applications, they worked on optimizing Nvidia EGX A100 processor across a range of Red Hat platforms. Following growth through AI, both companies focused on improving customer experience with data centers to accelerate the deployment of AI-based applications by their common customers.
In a step that puts both experimentation and collaboration into action, Nvidia availed, through open sourcing, the design of an AI chip used in deep learning. The motive is clear. The soaring demand expected on Nvidia chips needed for Internet of Things (IoT) devices, convinced the company to establish an ecosystem of devices and partners using its technology to support the growth of other product lines supporting its open-sourced design technology.
2. Culture Reinforcing Innovation
Availing the right creative climate is key to sharing and building ideas that create innovative outcomes. Examples? Freedom, openness, risk-taking, idea support and playfulness, all which impact innovation outcome. MIT Sloan Management Review and Glassdoor surveyed 1,101 Nvidia employees about nine corporate cultural values; innovation ranked as the highest positive value, innovation was the most frequently discussed value while, in general, employees confirmed that Nvidia is reflecting the right practice for the five corporate values of agility, collaboration, innovation, integrity, and performance.
3. Innovation Competencies
Association is a powerful driver for innovation in organizations; it happens when you properly connect unrelated ideas (at least seemingly unrelated). AI is an enabling technology that drives improvements and innovations across different domains. Nvidia focused its innovation competencies on connecting different AI needs with its chip capabilities. For example, radical advancements in computer science in the medical field through simulations can, in turn, accelerate the understanding of how proteins work and hence accelerate the drug formulation versus other classical slower processes. This is association in practice. In 2020, Nvidia, GE Healthcare, Nuance announced they will help AI-based medical technology startups innovate accelerate innovation in a connected ecosystem. This alliance demonstrated how Nvidia not only fostered innovation competencies within its borders but was promoting the competencies of association, networking through platforms, and experimenting through startups across its ecosystem as a market leader. To confirm the robust execution of its strategy, Nvidia created a robust ecosystem around its proprietary APIs and architecture over the last three years, and therefore, developers are more comfortable creating gaming and AI applications through interacting with its improved Compute Unified Device Architecture (CUDA) platform.
4. Sustainability-Driven Innovation
Nvidia’s 2020 corporate social responsibility report highlights innovation as one of the top economic priorities for the company. The report shows well defined numerical outcomes of environmental projects as greenhouse gas emissions, climate change, renewable energy, waste and energy efficiency. Further verification is needed to confirm how sustainability goals are imbedded in innovation projects not only as part of daily operations focused on achieving operational targets isolated from innovation behavior and culture. Recent global data show significant increase in computing power consumption required for AI calculations especially for deep learning functions; the environmental impact of machine learning and AI related technologies needs to be measured by Nvidia on global scale with special emphasis on data centers. The new generation of GPUs were designed to deliver higher performance per watt, and thus became more attractive for energy-efficient computing demands in data centers.
Final Reflections
In 2024, NVIDIA’s rank is 65 on the Fortune 500 list.
According to the 2024 Future Today Institute report, Meta utilized more than two thousand A100 GPUs to train its large language model (Llama) on 1.4 trillion pieces of text over 21 days – that is approximately 1 million hours of GPU time.
Nvidia stock grew by 123% in 2020, then by multiples of that till 2024. Three innovative gaming graphics cards are delivering up to double the performance of prior chips – this was a generational leap in technology. The company’s continuous innovation in the H100 GPUs has made it the preferred global supplier.
Success is not an overnight activity.
It’s a process fueled by clear vision, robust execution, leadership, culture and, sometimes, luck. Nvidia’s success story as an industry innovative leader followed the same path. To understand what happened over the last six months with Nvidia, we need to go years back and understand how the past informs the future. Nvidia had a clear strategy articulated by its leadership team through clear and specific business goals representing an innovation intent. Strategic foresight at Nvidia plays a significant role in planning for the future. The company continues to invest in technologies and platforms that are relevant to innovative business models – not all of them will be successful of course. Whether this strategic direction gains enough future traction or not is worth observing.
