The Hidden Cost of AI: Why Rising Energy Prices Could Reshape Automation ROI
Six months ago, AI automation was a software problem. Now, it's a power problem.
While recent headlines focus on ChatGPT's new group chat features or Cisco's internal AI assistant, few are paying attention to the economic infrastructure underneath it all: electricity. According to Forbes, data centers—especially those powering AI and crypto—are about to push U.S. electricity costs higher starting in 2026. That's not a distant problem for big tech. It's an imminent cost curve shift for every professional betting on automation.
And yet, in the same news cycle, we saw CoreWeave—a data center company few outside of enterprise AI have heard of—raised billions in debt to feed the GPU hunger of LLMs. If you're running a service business doing $2M in revenue, this may feel like someone else's problem. It's not.
This is the new reality: AI isn't just about algorithms anymore. It's about energy economics. And that changes how you should evaluate every automation decision from here forward.
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The Invisible Bottleneck: AI's Growing Appetite for Power
CoreWeave's story is a cautionary tale. Originally a crypto mining operation, it pivoted to serve the AI boom by offering GPU-based compute infrastructure. Now, they're one of OpenAI's biggest suppliers. But that pivot comes with a cost: their electricity consumption—and debt profile—has ballooned.
Meanwhile, Cisco is rolling out internal AI assistants that are secure, integrated, and built in-house. It's a model that works for enterprises with deep pockets and private infrastructure. But when OpenAI or Microsoft offers similar tools to the public—through ChatGPT or Copilot—they're relying on shared public data centers like those CoreWeave builds.
Why does this matter? Because unlike software, power doesn't scale for free. As demand from AI workloads surges, grid capacity becomes a constraint. And that eventually gets passed down in the form of cost—whether you're running a GPU model yourself or subscribing to a SaaS product that does.
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Jevons Paradox: When AI Efficiency Increases, So Does Demand
Here's the twist: better AI leads to more usage, not less. This is classic Jevons Paradox—an economic principle where increased efficiency leads to increased overall consumption. We've already seen this play out with generative AI: faster models encourage broader adoption, which inflates compute needs, which drives up energy demand.
As the A16Z piece noted, the Baumol Effect also plays a role. While the cost of compute may fall, the cost of human services (like AI consulting or system integration) rises. The economic pressure doesn't disappear—it just shifts.
So if you're a CPA automating client onboarding or a law firm streamlining document review, the question isn't just "how much time can AI save me?" but "how sustainable is the infrastructure behind my automation stack?"
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Strategic Implication: Evaluate AI Through the Lens of Marginal Cost & Control
This brings us to the real framework:
Control + Cost = Strategic Leverage.
1. If you own the infrastructure (like Cisco), you control cost.2. If you rent it (via SaaS tools or APIs), you inherit their cost structure.3. If that cost structure is tied to energy prices, your ROI is no longer fixed—it's variable.
For small businesses, the leverage comes in choosing the right layer of abstraction. You don't need to own a data center. But you do need to:- Avoid tools with opaque pricing models that spike with usage- Prioritize automation platforms that optimize compute efficiency at the agent level- Focus on workflows where AI reduces net compute by eliminating redundant steps
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Action Steps for Smart Operators This Week
1. Audit Your Automation Stack for Hidden Usage Fees - Many AI tools charge per interaction, not per user. As usage grows, so do your costs.
2. Ask Your Vendor How They Optimize for Energy Efficiency - If they can't answer, they're likely reselling from a high-cost provider.
3. Shift Toward Task-Specific AI Agents, Not General Chatbots - Agents built for precision workflows are more compute-efficient than general LLMs doing open-ended tasks.
4. Plan for ROI Volatility in Your Forecast Models - Don't assume flat monthly fees. Build in variable cost projections based on usage growth.
5. Look for Providers With Transparent Infrastructure Partnerships - If your automation vendor is tied to CoreWeave or similar, their pricing may spike post-2026.
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Closing Thought: AI as Infrastructure, Not Just Interface
What we're seeing now is a shift in how small businesses must relate to AI—not just as an interface (like ChatGPT) but as infrastructure. You wouldn't run your firm on a power grid you don't understand. The same should go for your automation stack.
The real winners in this next phase of AI adoption will be those who treat automation not as a magic wand, but as an operational utility—budgeted, monitored, and continuously optimized.
And that starts now, before infrastructure costs reshape your margins.
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This Week's Resource
This week, we're sharing "The 8th Disruption: AI Strategies for the Employeeless Enterprise", a free eBook that breaks down how to profit from AI agents without spiraling infrastructure costs.
Inside, you'll learn:- How to structure automation for predictable ROI even as energy costs rise- What Fortune 500s are doing that small firms can model (without the budget)- The 5 hidden costs in most "AI tools" and how to avoid them