TLDR; It’s not about embarking on an AI journey that might delay your competitive edge; it’s about engaging in an AI race to quickly derive value and outpace your rivals. Don’t let vendors force you into proprietary architectures or dictate your operating model, as this can hinder your agility And reduce your options. Acting swiftly in the cloud to implement trials and experiments, rather than waiting for perfection, allows you to seize the initiative and force competitors to react to you, then scale-out either in the cloud or on-premises without delay.
‘Who Knows? Only Time’
Time is the one resource we can never replenish. In the realm of technology, this immutable truth takes on a profound significance. As the CEO of Qumulo and a lifelong student of network communications and computer science, I’ve observed how the relentless march of time intersects with the rapid evolution of technology, particularly artificial intelligence (AI), to reshape industries—including our own storage sector—in ways we could scarcely imagine a decade ago.
The Fixed Lifecycle of Technological Assets
In information technology, assets typically adhere to a fixed lifecycle. Most hardware components come with a five-year depreciation schedule, indicating their expected period of optimal usefulness. Occasionally, organizations attempt to “sweat the asset,” stretching its utility to seven or eight years. However, this extension often coincides with vendors phasing out support, discontinuing software updates, and ceasing to address vulnerabilities. The hardware becomes obsolete, and the risks of continued use outweigh the benefits. Ultimately, it’s time to invest in new infrastructure.
The Acceleration of Obsolescence
Moore’s Law has long dictated that the number of transistors on a microchip doubles approximately every two years, leading to exponential growth in computing power. This principle extends beyond CPUs to GPUs, network semiconductors, and solid-state drives (SSDs). Every couple of years, we witness the advent of new architectures that are faster, denser, and often more cost-effective. The equipment we deploy today becomes obsolete in a shorter span, supplanted by technologies that offer significantly enhanced performance.
The Time-Value Paradox in Infrastructure Deployment
This accelerated obsolescence presents a paradox when it comes to deploying AI infrastructure. The moment a new system is delivered—be it cutting-edge CPUs, GPUs, or advanced networking components—the clock starts ticking. You have roughly two years before this equipment is superseded by the next generation. If it takes months to integrate these components into a functional system—assembling servers, configuring storage arrays, setting up networks—you’ve lost a substantial portion of that high-value operational window.
Consider this: if it takes nine to twelve months to fully deploy an on-premises AI infrastructure, you’ve potentially expended 38% to 50% of the system’s peak value period before you even begin to reap its benefits. In a world where time equates to competitive advantage, this delay can be costly, even existential.
The Case for Accelerated Deployment
Given this reality, speed becomes paramount. The faster you can deploy infrastructure, the more value you extract from your investment before it becomes outdated. Rapid deployment maximizes the usable life of your assets, allowing you to leverage the latest technologies to gain a competitive edge.
However, building and deploying AI infrastructure is a complex endeavor that involves more than just procuring hardware. It requires meticulous integration of compute resources, storage solutions, and networking components, all optimized to work in harmony. The complexity and time investment can be daunting, prompting a critical question: “Is it more advantageous to build in-house or to leverage existing platforms?”
Cloud Providers: The Time Advantage
The largest consumers and purchasers of CPUs and GPUs are the hyperscale cloud providers. They receive the newest technologies earlier than the rest of the market and deploy them at a scale that is often unattainable for even the largest enterprises. By utilizing cloud services, organizations can tap into the latest AI capabilities almost immediately, without the lengthy lead times associated with building and deploying their own infrastructure.
This immediate availability allows businesses to begin training models, processing data, and extracting insights far sooner than if they waited for their own systems to come online. In essence, cloud providers offer a way to circumvent the time-value paradox inherent in AI infrastructure deployment.
Strategic Considerations: Time is Competitive Advantage
When assessing whether to build or buy AI infrastructure, time should be a central consideration. Delays in deployment not only reduce the operational life of your assets but also delay your entry into markets, slow down innovation, and potentially cede ground to competitors who have already embraced AI technologies.
Leveraging cloud services can offer immediate access to advanced AI capabilities, but it comes with trade-offs in control, customization, and potentially long-term costs. On the other hand, building your own infrastructure ensures tailored solutions but demands significant time and resource investments.
Freedom of Choice: Cloud-and-Data Center
Having the agility and flexibility to choose both cloud and on-premises data centers for your AI deployment is essential for avoiding proprietary vendor lock-in. This choice empowers businesses to select the infrastructure that best suits their specific needs, whether it’s the scalability of the cloud or the control offered by on-premises solutions. By not being tied to a single vendor’s ecosystem, companies can integrate diverse technologies, optimize costs, and adapt more quickly to changing market demands—all without sacrificing autonomy or performance.
In contrast, many vendors are jumping on the AI bandwagon by hastily rebranding their existing products as “critical” to AI initiatives. This rush to capitalize on the hype drives Artificially Inflated valuations (the real AI to many startups) and pressures businesses into adopting technology stacks that may not align with their best interests. Such approaches overlook the needs of IT operators, engineers, and executives by promoting solutions that serve the vendor’s agenda more than the client’s. By carefully selecting deployment options that prioritize flexibility and openness, businesses can sidestep these pitfalls and focus on strategies that genuinely advance their objectives.
Conclusion: Is Time on Your Side?
In the fast-paced world of AI and infrastructure technology, time is both an asset and a challenge. The key is to balance the need for speed with strategic considerations about control, cost, and long-term value. As we navigate this landscape, we must ask ourselves, as the Rolling Stones famously did, “Is time on my side?”
The answer lies in how effectively we can adapt to the temporal demands of technology deployment. By prioritizing rapid, efficient implementation—whether through streamlined internal processes or by leveraging external platforms—we can maximize the value of our investments and stay ahead in a competitive market. Then, after proving the value of the AI implementation we can make informed decisions, without deployment delay, about the best place to move from proving value to executing at scale.
At Qumulo, we’re committed to helping organizations navigate these challenges by offering storage solutions that integrate seamlessly, deploy quickly, and scale efficiently. Our goal is to ensure that time is, indeed, on your side, empowering you to harness the full potential of AI without delay whether in your data center or in the cloud.
