At Verse, we imagine a world where artificial intelligence and energy evolve in tandem, reinforcing one another in a virtuous cycle. AI will undoubtedly increase electricity demand — but it also has the potential to dramatically accelerate the transition to carbon-free energy if applied responsibly and strategically.
Industry fervor around artificial intelligence (AI) reached a new crescendo last year as transformer architectures for generative applications became mainstream. In terms of energy consumption, these applications are not cheap. Some researchers project that by 2027, AI workloads may consume anywhere between 85 and 134 terawatt hours (TWh) of electricity each year – roughly 0.5% of global electricity usage (about as much consumed by the entire country of Argentina).
Meanwhile, electricity infrastructure in many nations is undergoing a significant transformation as wind and solar power resources become cheaper and more abundant, reaching over 1 terawatt of installed capacity worldwide. However, with global annual energy use over 1 exawatt hours (1 million TWh), we would need a 116x growth in renewable installed capacity just to reach parity across all energy consumers today.
This is a daunting challenge — but we believe AI and energy are not inherently at odds. Two trends are converging that can help close this gap:
Most AI software runs on Graphics Processing Units (GPUs) that execute model training and inference computations orders of magnitude faster than typical CPUs. As much as 95% of the market for GPU chips is supplied by one company: Nvidia. Their most hotly demanded chip, the A100, is powerful but energy-hungry, performing up to 312 trillion floating point operations per second (TOPS) while consuming 400 watts. Other application-specific hardware solutions, like Google’s Tensor Processing Unit (TPU) chips, consume 38% less power but with 2.24x more TOPS.
Google’s TPUs are thus 3.6x more energy efficient, and more energy efficiency means less carbon emissions than Nvidia’s GPUs. Furthermore, Google’s recently announced Gemini family of generative transformer products (which run on Google TPU supercomputers), combined with the company’s transparency around energy use and carbon emissions in its data centers speaks to a commitment we hope other technology companies will adopt to reduce the energy intensity of all AI workloads and data centers.
Legacy chip companies like AMD and Intel as well as nearly a dozen (and growing) other semiconductor startups will compete against Nvidia’s incumbency to build more energy efficient substrates for artificial neural network computations. To unlock the benefits of these more efficient chips, software teams will need to build new drivers for these next-generation hardware solutions (it took one startup two years just to switch from Nvidia’s CUDA toolkit to their own drivers). Innovations in AI hardware and software that reduce our carbon footprint will be essential to our global sustainable energy transition, as many of the companies building these technologies are also driving substantial progress in procuring sustainable energy sources for their internet businesses.
Reducing AI’s carbon footprint is not just a hardware challenge — it is a system-level design problem.
Software choices determine how efficiently models are trained, when workloads run, and whether computing resources are aligned with grid conditions. As AI systems become more sophisticated, grid-aware computing, dynamic scheduling, and real-time optimization will play an increasing role in reducing emissions.
This is where AI becomes more than a consumer of energy — it becomes an enabler of decarbonization.
Innovation cycles in AI should also accelerate clean energy project development and procurement. That is why we believe AI and carbon-free energy will co-evolve in a virtuous cycle.
Today, buying clean power remains complex. In fact, only four megacap companies — Amazon, Microsoft, Meta, and Google — accounted for more than 50% of all corporate clean energy PPAs in 2021. Smaller organizations face high barriers to entry due to analytical complexity, transaction costs, and operational risk.
AI can change this by improving every stage of the clean energy lifecycle:
Aligning incremental energy procurement with sustainability goals requires advanced linear and integer programming, scenario analysis, and long-term risk modeling. AI helps manage uncertainty across PPA pricing, market volatility, regulatory shifts, and basis risk — ensuring clean energy portfolios remain financially viable over time.
Carbon-free energy valuations rely on predictive grid models across multiple probability windows. AI accelerates these calculations, enabling faster, more accurate assessments of cost, risk, and emissions outcomes.
Request-for-Offer (RFO) processes are still largely manual. AI can help standardize solicitations, evaluate bids, and parse hundreds of pages of utility tariffs — clarifying the true costs of interconnection, delivery, and operation.
As grids diversify, AI-powered model-predictive control will enable precise dispatch of intermittent resources, paired with batteries and flexible demand, to maximize value and reliability.
The domain-adapted AI assistants and agents Verse is building are designed to lower the barrier to clean energy access — especially for organizations that lack the scale or expertise of hyperscalers.
This vision is not without risk. The same AI technologies that enable decarbonization could also be misused — to accelerate fossil-fuel extraction, support deforestation, attack grid infrastructure, or spread disinformation about clean energy policies.
We must remain vigilant. Combining AI and energy responsibly means actively safeguarding against misuse while directing innovation toward outcomes that benefit society.
Verse is committed to building world-class software that applies AI thoughtfully — empowering organizations to plan, procure, and manage carbon-free energy with confidence.
Our survival and economic prosperity depend on a future where AI and clean energy advance together, forming the foundation of a resilient, sustainable global energy system.
