When AI Needs Nuclear Power

There's something deeply paradoxical about our digital age. We've built technologies that can recognize faces across billions of images, translate languages in real-time, and generate human-like text - all running on invisible infrastructure we rarely think about. Until, that is, the power goes out.

Last week, Meta announced deals to secure up to 6.6 gigawatts of nuclear power by 2035. To put that in perspective, that's roughly equivalent to the entire electricity generation capacity of a country like Austria. This isn't just about keeping the lights on at Facebook. It's about ensuring that the next generation of AI models - the ones that might transform everything from drug discovery to climate modeling - can train without interruption.

And it got me thinking about a question we're wrestling with at DOMS as we explore AI applications in tax administration: What happens when the infrastructure requirements for transformative technology become so massive that they fundamentally reshape industries we thought were settled?

The Hidden Cost of Intelligence

Working on AI implementation in taxation, I've become acutely aware of something most people outside the tech world don't fully appreciate: artificial intelligence is hungry. Not metaphorically hungry for data - though that's true too - but literally hungry for electricity.

Training a single large language model can consume as much energy as hundreds of homes use in a year. When you're Meta, running the Prometheus AI supercluster, you're not talking about hundreds of homes. You're talking about powering a small city, continuously, for months at a time.

Here's what makes this particularly challenging: these training runs can't be interrupted. Imagine you're ninety days into a hundred-day training cycle for a frontier AI model - an investment potentially worth hundreds of millions of dollars in compute time and researcher expertise. A power fluctuation doesn't just pause your work. It can corrupt the entire run, forcing you to start over.

This is why Meta's nuclear bet matters. It's not about being green (though that's a welcome benefit). It's about reliability. Nuclear power plants run at 90%+ capacity factors, compared to 35-40% for solar and 25-35% for wind. When you're making a hundred-million-dollar bet on uninterrupted computation, that difference between 90% and 40% isn't academic - it's existential.

What This Means Beyond Silicon Valley

Now, you might be wondering: what does Meta's energy strategy have to do with public service or tax administration?

More than you'd think.

At DOMS, as we explore AI applications - from pattern recognition in transfer pricing to compliance automation - we're confronting a scaled-down version of the same question: What infrastructure do transformative applications actually require?

It's not just about computing power (though that matters). It's about the entire ecosystem that makes sustained innovation possible. Reliable data pipelines. Uninterrupted processing capacity. The ability to run complex analyses without worrying about system failures mid-stream.

But here's where it gets interesting for public service: while Meta can sign multi-gigawatt nuclear deals, government agencies need to think more creatively. We can't just throw money at the problem. We need to be smarter about architecture, partnerships, and what we're actually trying to achieve.

This brings me back to something I emphasize when speaking to students about AI in finance: The constraint isn't the limitation - it's the clarifying force. Meta's constraint is power availability. Ours in government might be budget or legacy systems. Both constraints force us to think more carefully about what problems we're actually solving and whether AI is genuinely the right tool.

The Deeper Question About Sustainability

There's an elephant in the room that Meta's announcement highlights: if artificial intelligence is going to transform healthcare, accelerate scientific discovery, and help solve climate change, it's going to need a lot of power. The estimates vary, but data centers could consume 3-4% of global electricity by 2030, up from about 1% today.

This raises an uncomfortable question: Are we willing to make the infrastructure investments required for the future we say we want?

I find myself thinking about this in the context of India's development trajectory. We're simultaneously trying to:

• Expand electricity access to everyone
• Reduce carbon emissions
• Build digital infrastructure
• Deploy AI for public benefit

These aren't contradictory goals, but they're certainly in tension. Meta's solution - nuclear power - might work for a company with virtually unlimited capital. But what's the pathway for developing economies? For government agencies? For small startups with transformative ideas but limited resources?

What I'm Taking Away

Meta's nuclear deals won't be the last of their kind. We're going to see more announcements like this - tech giants securing dedicated energy sources, building their own infrastructure, effectively becoming their own utilities.

But what's calling to me isn't just the scale of these investments. It's the reminder that transformative technology requires transformed infrastructure. You can't bolt revolutionary capabilities onto legacy systems and expect them to just work.

This applies whether you're training AI models or modernizing tax administration. The question isn't "Can we use AI?" but "Have we built the foundation that makes AI sustainable, reliable, and equitable?"

As we work on the new Income Tax Act 2025 and revise the Taxpayers' Charter, I keep coming back to this: the most important innovations aren't always the flashiest ones. Sometimes they're the unglamorous infrastructure decisions - the data architecture, the processing pipelines, the reliability standards - that make everything else possible.

Meta is betting billions on nuclear power because they understand something fundamental: the future they're trying to build requires infrastructure decisions made today.

In public service, we're making similar bets, even if they're denominated in different currencies. The question is whether we're being as intentional about our infrastructure choices as Meta is being about theirs.

What infrastructure investments - technical, institutional, or human - do you think are missing from conversations about AI in government? I'm genuinely curious, especially as we navigate these questions in real-time at DOMS.

 

AI’s Energy Disruption: A Game Changer?

For years, the rise of artificial intelligence (AI) has been linked to skyrocketing energy demands. Data centers worldwide have been consuming massive amounts of electricity to power AI models, leading to concerns about energy shortages and higher infrastructure costs. But what if this assumption is no longer valid?

DeepSeek, a Chinese AI startup, has just introduced the R1 model, an energy-efficient AI system that could disrupt the entire energy industry. This breakthrough challenges the traditional narrative that AI development will require an ever-expanding power grid. So, what does this mean for the future of energy? Let’s break it down.

AI’s Energy Revolution: What’s Different?

Traditional AI models, particularly large-scale ones, require enormous computing resources. Think of AI training as an elite athlete preparing for the Olympics, it takes time, effort, and most importantly, a lot of energy. Nvidia GPUs, specialized hardware, and cooling systems all contribute to AI’s heavy electricity consumption.

But DeepSeek’s R1 model operates 10 to 40 times more efficiently than comparable U.S. AI systems. It requires only 2,000 Nvidia chips and less than $6 million worth of computing power far less than its competitors. This means significantly lower energy consumption, shaking up predictions about the future of AI-related electricity demand.

How This Affects the Energy Industry

DeepSeek’s innovation could have ripple effects across multiple sectors, from data centers to power grids and even natural gas markets.

1. Data Centers: No Need for Massive Expansion?

Tech giants like Google, Microsoft, and Amazon have been investing billions in expanding their data center infrastructure, assuming AI’s growth would require more energy. But if AI models become drastically more efficient, these expansion plans might not be necessary. Companies could achieve the same results with far less energy, reducing infrastructure costs.

2. Power Grids: Less Pressure, Fewer Upgrades

With AI models consuming less electricity, power grids might not need extensive upgrades to handle expected demand. This could delay or even eliminate costly grid expansion projects, saving billions for utility companies and governments.

3. Natural Gas Demand: A Sudden Drop?

Many analysts predicted that natural gas would play a key role in meeting AI’s energy needs. But if AI’s energy consumption is much lower than expected, natural gas demand might not rise as forecasted. This uncertainty has already triggered a sell-off in energy stocks, as investors reassess the future of power generation.

The Bigger Economic Picture

From an economics perspective, this is a classic case of technological innovation disrupting market expectations. When firms and governments plan infrastructure projects, they rely on forecasts demand projections, energy consumption trends, and investment cycles. If those forecasts suddenly change, it creates inefficiencies and financial losses.

For example, consider the concept of sunk costs. Many energy companies have already invested heavily in power generation projects assuming high AI-related demand. If that demand doesn’t materialize, those investments become less valuable, leading to potential financial instability.

Similarly, opportunity costs come into play. If companies no longer need to spend billions on expanding energy infrastructure, they can allocate resources elsewhere perhaps into developing better AI software, improving renewable energy, or advancing semiconductor technology.

What’s Next?

DeepSeek’s R1 model is just the beginning. If other AI companies follow suit, the energy industry will need to rethink its long-term strategies. Instead of building more power plants and data centers, firms might shift toward optimizing existing resources.

However, there are still questions to answer:

 Will other AI models achieve similar energy efficiency?

How will energy companies adjust their investment strategies?

 Could this innovation lead to lower electricity prices for consumers?

One thing is certain, DeepSeek has upended the assumptions driving energy policy and infrastructure planning. As AI becomes smarter and more energy-efficient, we might be witnessing a major shift in the global energy economy.