Success in technology was once determined by speed. Moving fast, breaking things, and learning along the way. Speed still matters and those slow to commit get counted out fast. However, the race to realize artificial intelligence’s potential might be the moment this approach comes unstuck.
The hype and pace of consumer adoption are unprecedented. In 2006, it took Twitter nearly two years to reach one million users. In 2010, it took Instagram two and a half months. For ChatGPT, it took just five days, with the service reaching 100 million users in a groundbreaking two months.
The AI frenzy is upending the quieter corners of the technology industry too. Over 40,000 companies across the world are now using NVIDIA chips for AI and accelerated computing, and the waiting list to purchase the chips is up to 52 weeks. With AMD and Intel set to launch rival hardware, and Microsoft and Google just some of those announcing multi-billion-dollar investments in infrastructure this year, the race to develop AI products is shifting gears. The focus has shifted from “what can be done” to “how we do it.”
This “buy AI now, figure it out if it works later” mode is the very mindset that could put sustainable AI innovation in jeopardy. We’ve got a supply and demand issue, and it is not just the bottleneck in the production of chips. It is the design and development of the infrastructure needed to support the hardware. We’re turning on the taps without investing in the plumbing. Without this, the AI industry risks a false start which could set it back years.
Data centers are the “how”–and have been for many of the technology hype cycles of the last decade. Put simply, there aren’t enough of them to meet the requirements of powering generative AI on the scale chips sales would indicate. We are entering an era of high-performance computing before the infrastructure is in place to house it. Here is the crux of our supply issue. What excites about the promise of AI is its limitless possibilities. Yet, even as the supply of hardware improves, the scarcity of the necessary infrastructure to house it will limit progress.
The development timeline to build new data centers is now between three to five years, or more in some cases. Those entering the AI marketplace thinking the infrastructure they need is readily available are in for a shock. Most of the data center supply in North America supposedly coming online in 2024 has been pre-released or is under exclusivity already.
Researchers at Epoch AI estimate that AI’s computational power is doubling every six to 10 months. Despite data center construction increasing, meeting this level of demand is becoming a global problem. In Europe alone, the pipeline of data centers needs to more than double by 2025 to meet the forecasted demand. The infrastructure layer of the AI economy is critical to its success. Only those able to secure access or invest accordingly will survive, let alone secure an advantage.
Data center operators have increased their prices by up to 30%–and this will only continue to climb. While a few have the funds to match this increase and are investing themselves, many smaller players will find it challenging to find space and power at prices they can afford. We are therefore facing market consolidation before most have even had a chance to experiment, iterate, learn, or make positive change.
AI can take inspiration from the digital asset sector in this regard. As demand for the asset has increased, capacity to meet demand has been impacted by a growing number of challenges, which weren’t necessarily evident at the outset. Crypto and its ecosystem have largely overcome these issues by retaining an agile approach–for example, by developing a great understanding of the importance of operational and energy efficiency and the optimization of hardware to ensure longevity and cost-effectiveness.
Big Tech is investing billions of dollars, seeking to seize control of the development of AI–and they are moving quickly. It’s a pattern we have all seen before. But should it be repeated? While the opportunity is real (PWC predicts a boost to the global economy of over $30 trillion by 2030), how it is realized will determine the health and longevity of the AI marketplace.
Despite the bold ambition, the potential of AI is only as great as the infrastructure capacity needed to support it. AI is in the balance, and we now face a choice to move fast and break things or build for the future.
Aroosh Thillainathan is the CEO and chairman of the management board at Northern Data Group.
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