TL;DR: Jensen Huang's declaration at Computex 2025 that NVIDIA will be an "AI infrastructure company" is more than a corporate pivot; it's a profound lesson in value sovereignty. In an AI-driven world, true sustainability and competitive advantage stem from either owning core technology or deeply integrating AI into unique, defensible domain expertise. Relying solely on leveraging others' platforms without adding substantial, differentiated value is an increasingly precarious strategy. The future lies in achieving symbiosis with AI, not a parasitic dependence.
Among the many innovations showcased, NVIDIA CEO Jensen Huang's bold proclamation about his company's future stood out: "We will no longer be just a technology company, but an AI infrastructure company!" This isn't just a shift in branding; it's a strategic masterstroke and a clear illustration of the 80/20 principle in hyper-drive. As leading companies excel and consolidate resources, they gain immense leverage, often leading to a scenario where the top 20% of players control 80% of the market share. NVIDIA's ambition, laid bare at Computex, is to become that indispensable 20% underpinning the global AI ecosystem.
NVIDIA's Grand Vision: The Foundational Layer of the AI World
What does this pivot to an "AI infrastructure company" truly entail? NVIDIA envisions a future where virtually any organization looking to build or scale its own data center and AI capabilities will turn to them. They aim to provide the entire stack – from every server rack and chip to sophisticated AI virtual dialogue platforms and development environments. If your business needs AI support at any level, NVIDIA intends to be your foundational partner. Their ambition is no less than to provide the world's underlying AI computational infrastructure. And to underscore this commitment right here in our region, they even announced plans to deepen their roots in Taiwan with a new headquarters, "Constellation," envisioned for the Beitou-Shilin Science Park, further dedicated to advancing Taiwan's AI infrastructure.
The Perils of "Borrowed Land": When Your Core Isn't Yours
NVIDIA's strategic clarity brings into sharp focus a conversation I had about two years ago, around mid-2023, with an enthusiastic AI consultant. He was excited about a new venture: helping companies deploy local, open-source AI interfaces like Anything LLM or Open WebUI, often bundling this service with hardware from a major manufacturer. Even then, my intuition raised a flag. This consultant, while well-intentioned, didn't possess any core, defensible technology. The computing power wasn't his, the server hardware wasn't his, and the AI models themselves were open-source. His role was primarily that of an installer and integrator of readily available components. I remember questioning how such a venture could genuinely lead or sustain itself in the long run against entities with deeper technological control.
This isn't an isolated thought experiment. If that technical example isn't immediately clear, consider a more personal reflection, one that perhaps even I, and others in similar advisory or educational roles, must constantly make: AI educators, those of us who teach how to use powerful tools like various GPT models or other AI platforms, risk becoming mere appendages to these tools if we don't deeply and continuously integrate this teaching with our own original, hard-won domain know-how. Without that unique value-add, we risk a kind of parasitic relationship, borrowing leverage from an AI host – a position that is inherently unstable.
Is there a market need for such services right now, in May 2025? Yes, undoubtedly, as businesses are still rapidly trying to understand and deploy AI. And this doesn't mean that those of us guiding others through this complexity offer no value. But we must be rigorously honest with ourselves: if we aren't bringing our deep industry knowledge, critical thinking, and unique strategic insights to the table, we are merely operating on borrowed leverage from the AI tools themselves. This isn't a sustainable long-term strategy.
This is precisely why NVIDIA's move into comprehensive AI infrastructure is so significant and, for some, so disruptive. What happens to those consultants or smaller businesses whose primary model was selling pre-packaged internal AI platforms or deploying open-source models? When a giant like NVIDIA can offer a complete, one-stop, highly optimized solution for enterprises – encompassing everything from building out data centers and deploying complex AI environments to providing training, ongoing application support, and strategic consultancy – the survival space for undifferentiated intermediaries shrinks dramatically.
If your core technology isn't your own, if you don't hold sovereignty over any critical technical components or truly unique intellectual property, your entire business model is essentially built on someone else's land. When that landowner decides to develop their property differently, change the terms of access, or simply build a more efficient direct path to the market, your footing can vanish overnight.
The Critical Question: What "Key Technology" or "Unique Value" Do You Truly Own?
This isn't meant to discourage entrepreneurship in the AI space or to deny the incredible potential that the democratization of AI tools brings. The accessibility of powerful models is a phenomenal catalyst for innovation. However, it compels us all – business owners, lecturers, consultants, entrepreneurs – to constantly ask ourselves a critical question: What is the "key technology," the unique intellectual property, the deep-seated domain expertise, or the inimitable process that I, or my organization, truly own and master?
Navigating the AI revolution requires far more than just surface-level understanding or proficiency with a few popular tools. We are in a dynamic, nascent AI generation, one that is constantly in flux. What's needed is a foundational understanding of AI applications within our specific domains, coupled with the "muscle memory" – the agility and deep expertise – to react, adapt, and collaborate effectively when new AI-driven challenges and opportunities arise. Simply teaching AI tool functionalities or demonstrating interfaces and then labeling ourselves "AI experts" falls dangerously short.
I’ve reflected deeply on my own path and our mission at Mercury Technology Solution. Unless we are profoundly embedding AI into our specific areas of expertise – whether that's enhancing business operations through intelligent automation, revolutionizing customer engagement with AI-powered CRM, or pioneering new frontiers in Generative AI Optimization for search – and making AI a true extension of our unique capabilities, then merely "teaching AI" or "using AI" as a generic offering for income would be extremely shortsighted.
It's perfectly fine, even wise, to stand on the shoulders of giants like NVIDIA and leverage their foundational power. But to imagine we can use the giant's own tools and energy to topple them, or to build a lasting business solely by reselling their core strength without adding our own distinct, defensible value, is an impossible fantasy.
The Path Forward: Symbiosis, Not Parasitism – Owning Your "Home Ground"
My perspective is this: our true, sustainable path lies in identifying and mastering our own unique domain, our "home ground." The goal isn't to be a parasite, drawing sustenance from a larger host without contributing unique value. The goal is to achieve symbiosis – a mutually beneficial relationship where we leverage foundational AI capabilities to amplify our distinct expertise, create novel solutions, and deliver unparalleled value within our chosen niche.
At Mercury Technology Solution, this philosophy is absolutely central to how we operate and how we guide our clients:
- While we certainly leverage the most powerful foundational AI models and platforms available, our primary focus is on creating Customized AI Integration Solutions. We architect and build intelligent systems that solve specific, often complex, business problems for our clients. This isn't about deploying a generic, off-the-shelf AI; it's about embedding sophisticated AI deeply into their unique operational contexts, data streams, and strategic value chains to create proprietary advantages.
- Our Mercury Muses AI assistant, for example, is designed as an adaptable intelligence layer. It works with your existing business systems and proprietary knowledge to augment your team's capabilities and streamline processes, rather than being a standalone AI that merely replaces tasks with generic outputs.
- When we engage with clients, a core part of our process is helping them identify their own "key technology," their unique data assets, or their inimitable market insights. We then explore how advanced AI can amplify these core strengths, ensuring they build sustainable, defensible market positions, rather than becoming overly reliant on external AI platforms alone. This includes guiding them on crucial aspects like data governance, building proprietary datasets where advantageous, and developing AI applications that truly differentiate them.
Conclusion: Your Unique Value is Your Anchor in the AI Storm
NVIDIA's strategic play to dominate AI infrastructure is a powerful and clarifying signal for every business in every industry. The future in this AI-driven age isn't about being a generalist AI user or a simple reseller of someone else's core technological breakthroughs without adding substantial, unique value.
It's about pinpointing your unique strengths, your deep domain knowledge, your proprietary processes or data, and then strategically and deeply integrating AI to enhance and scale those unique differentiators. It’s about creating a symbiotic relationship where AI amplifies your distinct value, ensuring enduring relevance, resilience, and success. Find your "home ground," own your unique contribution, and build from that position of strength.
