Commodities & AI—two asset classes that, while only in their current flirting stages, are destined to become the two hands that clap in the not-too-distant future.
Why do I think this?
Well, my go-to party piece is dropping the content that an AI-powered search, like using ChatGPT, gulps down a staggering amount of energy—significantly more than your everyday Google search.
While a typical Google query might sip just a few watts, AI-driven searches can chug up to 1000 times more computing power. This spike is due to the heavy lifting required to churn out those detailed, context-rich responses we’ve come to expect. As AI continues to scale, so does the demand for energy, making it crucial to weigh the environmental cost of our digital choices.
As someone with commodities in my DNA, and now a seasoned graduate of the WEB3 space for the past four years, I’m seeing the undeniable synergy between these two worlds.
My fellow commodity traders are also starting to tune in too. The bottom line? THE WORLD NEEDS MORE POWER.
GPUs and the Strain on National Grids
This latest piece from my site ( welcome by the way ) will talk about the impact of GPUs (Graphics Processing Units), the strain on National Grids, and how commodity traders will be heading down the lanes of profit from this very underrated revolution.
For those of you that use any kind of AI to generate any kind of graphics, like in any one of my articles as an example, you’re probably tapping into a number of GPUs (Graphics Processing Units). These are specialized processors crucial for AI, handling complex tasks through parallel processing. Originally designed for graphics, GPUs now drive AI technologies, requiring significant power.
For example, a cluster of eight GPUs can consume up to 15,000 watts, equivalent to a small power plant. As AI adoption grows, this increasing energy demand could challenge existing infrastructure, highlighting the need for advancements in energy efficiency and production. This is why companies like Microsoft, recognizing the strain that AI is placing on the grid, are investing heavily in backup power solutions, primarily using natural gas and nuclear energy.
This ensures that their data centers remain operational even when the grid is under stress, which is crucial given the increasing reliance on AI-driven operations.
The Strain on the Global Grid
The increasing power demand from AI technologies is likely to strain the existing power grid, which is already under massive pressure due to the rise of EVs—vehicles that, despite skepticism, are rolling off production lines every minute of every day.
This is particularly concerning in areas where the grid is already near capacity, which might correlate with why, in some regions of the globe, local state or countys taxes are rising due to increased demand on public utilities. The need to support AI workloads adds another layer of complexity to grid management, requiring upgrades and additional infrastructure to prevent overloads.
The Political and Environmental Landscape
But is the current global political drive through the landscape of energy consumption facing a critical juncture because of coal plant retirements and soaring energy demands?
While going green is best, the gap in electricity generation will need to be filled, but the current reliance on renewables may not be sufficient to meet the growing demand. This situation underscores the urgency of developing new energy solutions that can sustainably support the technological advancements driving modern economies.
The EPA and Coal Plant Retirements
One of those boring ( but very important ) agencies that nobody pays attention to, the Environmental Protection Agency (EPA), has introduced stringent new regulations that could force the retirement of coal plants by 2032.
With coal currently supplying 36% of the global electricity grid, these closures could significantly strain the power grid across the planet, especially as industries like AI continue to expand their energy requirements.
This strain becomes even more pronounced when considering the skyrocketing energy consumption of data centers, particularly those supporting AI operations. Companies like Google, for example, have seen their power demands double since 2019, leading to a substantial increase in CO2 emissions.
The rapid growth of AI and other high-energy-demand technologies is outpacing the capacity of renewable energy sources, which many had hoped would offset the environmental impact of these advances.
The Dual Crisis: Power and Carbon Emissions
Without a significant shift in energy strategy, Mother Earth could face a dual crisis: an overburdened global power grid and an increase in carbon emissions, both of which could hinder progress in AI and other critical technologies.
The Limits of Renewable Energy
The energy demands of data centers are exposing the limitations of renewable energy sources like wind and solar. While critical for a greener future, these sources are intermittent, depending on weather conditions and daylight, making them unreliable for the continuous, high-demand operations required by AI. Additionally, wind and solar installations require vast land areas, which can be impractical in densely populated regions, further complicating their ability to serve as primary energy sources.
To address these challenges, tech companies are increasingly turning to backup power solutions to ensure reliability. Natural gas is emerging as a key player, offering a flexible and reliable power source that can quickly ramp up to meet peak demand.
This makes it essential for supporting AI technologies, where uninterrupted power is critical. In parallel, nuclear power is being explored as a long-term solution, providing a stable, low-carbon energy supply capable of meeting the continuous demands of data centers. Some companies are even partnering to develop nuclear solutions tailored for their AI operations.
The energy sector must also engage in long-term strategic planning to accommodate the growing energy needs of AI. This involves not just investing in new infrastructure but also rethinking energy policies to create a diversified, resilient energy mix that can support both reliability and sustainability.
As AI continues to evolve, the ability of the energy sector to adapt and innovate will be crucial in sustaining growth while advancing toward a low-carbon future. Balancing immediate energy needs with long-term sustainability goals is the ongoing challenge that will define the future of AI and energy.
The Return of Nuclear and the Reign of Coal
As AI continues to demand more power, it’s becoming clear that the energy sources of yesterday are far from obsolete. Nuclear energy, once sidelined, is making a comeback as a stable, low-carbon solution capable of meeting AI’s relentless hunger for power. But while nuclear is on the rise, let’s not forget that coal, despite its environmental drawbacks, remains the backbone of global electricity generation. It’s ironic, perhaps even tragic, that the same AI technologies driving us into the future are forcing us to rely on energy sources from the past.
Coal may be bad for the environment, but in the world of energy, it’s still KING—keeping Mother Earth from buckling under the strain of AI. This creates a paradox where our push for technological advancement is bound to the very resources we thought we’d outgrown.
The Winners in This Revolution
And who stands to gain the most from this energy conundrum? The commodity traders. As the world grapples with the balancing act between meeting energy demands and reducing environmental impact, traders will be the ones navigating these turbulent waters.
They’ll be the ones who understand that in the dance between AI and energy, it’s the commodities—whether it’s uranium for nuclear or coal for those still clinging to the old ways—that will drive the profits of tomorrow.
In the end, just as AI and commodities are destined to become the two hands that clap, the Commodity traders will be the ones making sure that sound of profit echoes across the globe.
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