The Demand For Data Centers

Do you know how much energy a ChatGPT query consumes?  If you use a traditional Google search to find the answer, that particular Google search would use about 0.0003 kWh of energy, which is enough power to light up a 60-watt light bulb for 17 seconds.  A ChatGPT query (or even Google’s own AI-powered search) consumes an estimated 2.9 Wh of energy, which is ten times the energy used by a traditional Google search.  Multiply the energy consumed by 200 million ChatGPT daily queries by a year and you have enough power for approximately 21,602 U.S. homes annually or to run an entire country like Finland or Belgium for a day.

No surprise then that AI’s growing mainstream popularity and usage have led to an increase in data center demand.  For years, data centers were able to maintain a stable amount of power consumption despite their workloads being tripled, thanks to efficient use of the power they consume.  However, that efficiency is now challenged by the AI revolution, with Goldman Sachs Research estimating data center power demand growing 160% by 2030.

McKinsey & Company noted in a September 2024 report that data centers consume 3% to 4% of total US power demand today, while Goldman Sachs puts worldwide use at 1% to 2% of overall power.  By the end of the decade, data centers could be seen accounting for 12% of total US power and 3% to 4% of overall global energy.  This level of demand spurred investors to push $39.6 billion into data center development and related assets in 2024, which is 12 times the amount spent in 2016.

Copyright: panumas nikhomkhai

How All That Power Is Used by AI

What were once warehouse facilities hosting servers mostly found in industrial parks or remote areas, data centers have now progressed into vital institutions in the digital world we’re living in today.  The “old” problem used to be that the traditional data centers had some space that was underutilized, while the “new” problem is that space is scarce and critically needed.  This has created a surging demand for more of these structures in order to address AI’s accelerating demand for more computing power.

As illustrated earlier, AI workloads consume more power than traditional counterparts like cloud service providers.  Out of the two primary AI operations, “training” requires more computing power to build and expand models over time.  However, the operation that derives responses from existing models called  “inferencing” is growing quickly in volume as AI-powered applications become more popular and widely adopted according to a Moody’s Rating report.  Their report further predicts inferencing growth over the next five years to make up a majority of AI workloads.

Amazon, Google, Microsoft, and Meta have stepped up to satisfy the demand by building, leasing and developing plans for hyperscale data centers.  Moreover, investors have demonstrated a pronounced prioritization of AI projects over traditional IT investments; in fact, an Axios article noted that “tech leaders are actually worrying about spending too little.”

Copyright: Brett Sayles

Chokepoints in the AI Boom

It’s not only power that tech companies and developers have to consider when addressing the demand for new data centers; their location and access to that power are challenging their progress and development.  Data centers may consume up to 4% of US power today, but because they are clustered in certain major markets they pose a substantial stress to local resources.  McKinsey & Company’s September 2024 report also noted that there is at least a three-year wait time for new data centers to tap into the power grid of a major market like Northern Virginia.  The search for available space, power and tax incentives have led developers to look into other markets like Dallas or Atlanta, according to a Pitchbook article.

Unlike investors, some local governments are not as keen on the development of data centers and may pause contracts or prioritize granting access to their power grid to other projects.  An October report by the Washington Post revealed that a number of small towns have strongly pushed back against the construction of data centers in their areas.

In addition to concerns from residents over the strain data centers would put on local resources, carbon dioxide emissions can’t be overlooked, as Goldman Sachs estimates that such emissions will more than double by 2030.  Areas with rising energy consumption from data centers might therefore find it challenging to meet climate targets.

Accelerated data center equipment demands are also straining the supply chain, as orders are taking years to fulfill.  Planned tariff increases could also stress the delivery of offshore-produced parts and components used by data centers.

Copyright: panumas nikhomkhai

Navigating Towards The Future

While it is exciting to witness AI’s mainstream adoption and technological advancements, the challenges brought forth by its explosive demand for data centers could be just as intimidating.  Perhaps Forbes expressed best  how we should navigate this period of growth and uncertainty: we need to plan with purpose, not panic.  We need to build with responsibility, not exuberance.

While the AI boom granted us the opportunity to correct the oversupply of demand centers resulting from the dot-com boom, we need to tread more carefully when addressing this new need for more of these infrastructures.  We need to understand what each data center is being planned and built for, its primary purpose, and how it connects to local resources. Yes, we were able to breathe new life into data centers that were underutilized after the Internet boom, but that took years and a new technological revolution to correct.  Who knows if we’re also able to enjoy the same chance to recover if we fumble today’s attempts to address the demand for data centers?  For all we know, the next technological wave might such be a swerve that it advances beyond the need for data centers.

We should also improve, innovate or discover new renewable and responsible energy solutions as well as increase the efficiency of our developing technologies’ use of power.  There’s also an opportunity for the tech industry to enter into dialogues with local audiences about what is happening in the digital world and what they can expect from AI, thus demonstrating transparency and social responsibility.

As investors and developers eagerly meet AI’s surging demand for data centers, perhaps we can pause and appreciate this opportunity to make decisions that not only fortify the connection between the digital space and the world we live in today, but also forge a more responsible and sustainable path towards the future.

Copyright: TheDigitalArtist

The Impact of DeepSeek

While the tech industry searches for solutions to the data center demand, a new player has emerged to shake things up: Chinese AI research lab DeepSeek has released their open-source large language model.  Quickly shooting to the top of Apple Store’s downloads, DeepSeek has challenged contemporary views on AI development with a platform that performs just as powerfully and efficiently as OpenAI while reportedly operating at a fraction of its Western counterparts’ cost.  While it feels like we’re on the brink of a paradigm shift, we believe its true impact is yet to be seen.  We expect to know and learn more in the coming weeks, and we’ll share our thoughts in a future blog.

Copyright: Yanu_jay

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