Green by Design: Yuma's Commitment to Responsible AI Transformation
Why Must AI Transformation Be Green?
Artificial intelligence is no longer a distant technological frontier, but a transformative force reshaping industries, economies, and daily life at unprecedented speed. The rapid advancement of AI models, from large language models to multimodal agentic systems, has ushered in remarkable capabilities. But this progress carries a hidden cost: an exponentially growing demand on the world's physical infrastructure, energy, rare earth metals, and water.
As AI models grow more sophisticated, so does the computational power required to train and operate them. Data centers that support this infrastructure are among the fastest-growing consumers of natural resources worldwide.
The scale of this impact becomes clear when examining three concrete indicators:
- Energy Consumption: Global data center electricity consumption is projected to more than double from 415 TWh in 2024 to approximately 945 TWh by 2030, roughly equivalent to Japan's entire annual electricity consumption [1]. Combined with 5G and other digital technologies, ICT could consume a whopping 20-30% of the world’s energy production between 2030 and 2060.
- Carbon Footprint: AI-driven data centers in the United States alone could emit between 24 and 44 million metric tons of CO₂ annually by 2030, the equivalent of putting 5 to 10 million additional combustion-engine vehicles on the road [2]. A single ChatGPT query produces an estimated 2.2 grams of CO₂ equivalent, which may seem negligible in isolation, but with users sending 2.5 billion prompts to ChatGPT every single day, the cumulative weight of that footprint becomes impossible to ignore [3].
- Water Scarcity: AI models require massive amounts of water for cooling the hardware that runs them. By 2027, global AI demand is projected to account for approximately 5 billion cubic meters of water annually for cooling systems. This volume is more than the total annual withdrawal of 4 – 6 Denmarks or half of the United Kingdom [4].
Beyond the environmental toll, there is a stark economic reality: AI uses electricity as "food." Carbon emissions are therefore directly related to the costs of doing business. In the current landscape, more carbon is not only bad for the environment, but also for your pocket. Much like the global rise in human obesity, AI models are becoming increasingly "obese", bloated by inefficient architectures that are difficult to trim once established.
To combat this, it is vital to implement "cardio training" for AI systems, ensuring they stay lean, efficient, and economically fit.
Right now, large companies are hemorrhaging money on serving monolithic LLMs in hopes of capturing market share. We can look at OpenAI’s ballooning costs as a primary example of this "growth at any cost" mentality. The company reportedly faces potential annual losses of $5 billion and may require another massive funding round just to stay afloat.
This financial strain is already manifesting in aggressive price tiering, such as the April 2026 release of GPT-5.5 model, whose standard API pricing doubled overnight from its GPT-5.4 predecessor to $5.00 per million input tokens. This marks a staggering 10-fold increase in input costs compared to the $0.50 per million tokens established during the 2023 GPT-3.5 era. [5]
But herein lies a trap: at the current rate, the time will likely come soon when these companies will be forced to raise prices just to afford their power bills. With the cost of training a frontier model projected to exceed $1 billion by 2027, and electricity demand for data centers expected to double by 2030, the low-cost era of AI is hitting a physical ceiling. [1, 6]
This means that as a consumer or a business partner, you will not be spared. The costs accumulating at the top of the AI industry will cascade down into your contracts, subscriptions, and infrastructure bills. Transitioning to a “Green” framework is no longer just a moral choice or an abstract existential question; it will soon be a financial necessity. Being green means preserving resilience against such shocks, ensuring that your operations remain viable even as energy prices and infrastructure demands climb. Efficiency is not just an ethical stance, but also the only viable path to affordability.
We define Green AI from a utilitarian perspective: the practical, controllable actions an organization takes to maximize the intelligence output of every joule consumed. It is not a quest for a perfect zero-footprint as that remains impossible so long as chip manufacturing, cloud infrastructure, and hardware supply chains lie beyond any single organization's control.
What is within reach is how intelligently we build on top of them: deploying “right-sized” models instead of unnecessarily massive ones, utilizing quantization to lower the energy cost of every inference, scheduling heavy training workloads during hours when the local energy grid draws from a higher proportion of renewables – these are the levers of algorithmic efficiency that any organization can pull today.
The utilitarian definition of Green AI extends beyond how AI systems are built to what they are built for, a concept known as "Green-by-AI" where the environmental cost of computation is weighed against the massive resource savings it enables. This approach transforms AI from an environmental liability into a strategic asset by ensuring its net impact is a substantial reduction in global waste.
From precision agriculture systems that reduce pesticide and water usage by up to 90%, to logistics platforms that calculate the most fuel-efficient routes in real time, to smart grids that stabilize the fluctuating flow of renewable energy, AI’s net impact on the planet can far exceed its footprint. Under this framework, AI is classified as "Green", and it shifts its role from a carbon burden to a vital tool for conservation.
Towards Sustainable AI Innovation
Yuma is dedicated to embedding Green AI principles at the heart of its AI transformation strategy. We believe that building powerful AI solutions and building responsible ones are not competing objectives; they are deeply complementary. As AI becomes more deeply integrated into business operations, Yuma holds that every architectural decision carries an environmental dimension, and that sustainable design must be considered from the ground up.
This commitment is not simply aspirational. It is operationalized through deliberate choices in how Yuma designs its AI systems, the models it selects for specific tasks, and the frameworks it develops to manage agent behavior. Yuma's stand on Green AI reflects a broader conviction: that the companies shaping the future of AI bear a responsibility to align their innovation with global sustainability goals and that doing so is not a constraint, but a competitive advantage.
Architecture for Structural Sustainability
Enter Akgents, an open-source multi-agent framework developed by Yuma. By reimagining how AI agents interact and exist, Akgents shift the focus from monolithic, one-size-fits-all AI to organizational efficiency, making it the bedrock of Yuma's sustainable AI strategy.
- Akgents maximize efficiency through role-specific model assignments. Instead of deploying a massive, energy-hungry monolithic LLM for every interaction, the framework enables a heterogeneous intelligence architecture. A "Researcher" agent can utilize a high-parameter model for complex reasoning, while a "Formatter" or "Summary" agent operates on a Small Language Model (SLM).
This precision avoids the "one-size-fits-all" waste, reducing total energy consumption by ensuring computational weight perfectly matches the task's complexity. You are not bound to use any particular model, you get to choose what fits your needs and your vision of sustainability. - The framework eliminates idle waste by harnessing a message-driven Actor Model. In many standard agentic frameworks, each agent is mapped to a dedicated Operating System (OS) thread that consumes fixed memory and CPU cycles even when inactive. Akgents ensure that computational resource consumption happens only during active processing. By effectively hibernating until a specific trigger, like receiving a message, occurs, the system maintains a state of zero-idle waste.
- Human-AI collaboration prevents resource-draining hallucination loops. Akgents uniquely support human-in-the-loop integration, treating experts as agents within the same squad. By routing high-level edge cases and nuances to a human and leaving routine tasks to AI, you can prevent the energy-intensive circular reasoning that leads to autonomous failures. This ensures compute power is not squandered on tasks the AI is ill-equipped to handle.
- Akgents leverage dynamic team composition to align computational overhead with active functional needs. A "Manager" agent creates specialized expert agents on-the-fly to handle discrete sub-tasks, rather than maintaining a persistent "standing army" of active processes. By spawning these roles, and their associated LLM configurations, only for the duration of their utility, the framework ensures the system’s resource footprint scales and shrinks in perfect synchrony with the workflow’s live demands.
Transparency Through Data
Sustainable AI is not only about reducing resource consumption, but it is also about making that consumption visible. Akgents come equipped with a carbon emission dashboard, along with performance metrics dashboard, that brings full transparency to the environmental footprint of every workflow it powers. The dashboard provides users with a real-time CO₂ emission estimate for every session involving a team of Akgents. As illustrated in Figure 1, this includes a granular breakdown that distinguishes between the specific agent roles and the underlying models that power them, giving users precise visibility into the origin of their computational footprint.
For business organizations, such data-driven insight transforms sustainability from a vague corporate goal into a tangible operational strategy. Rather than relying on abstract declarations, organizations can produce Environmental, Social, and Governance (ESG) reporting with genuine authenticity. By backing sustainability claims with hard data, companies move beyond "greenwashing" narratives toward the auditable transparency increasingly required by regulators and investors.
Figure 1. Sample carbon emission dashboard for one session with a team of Akgents
Conclusion
The story of AI's future cannot be written without accounting for its environmental footprint and its economic burden. As models grow more capable and deployments more widespread, the mounting costs of energy, infrastructure, and compute will cascade downstream to every organization that relies on AI, making the imperative to build efficiently, not just powerfully, both an ethical and a financial necessity. Yuma's mission is to build AI solutions that are deliberately aligned with this reality. Akgents are already a concrete expression of that commitment.
By embedding sustainability into the very architecture of multi-agent AI, Akgents demonstrate that the most intelligent systems are not necessarily the largest ones, but the most deliberate. In orchestrating how AI agents think, collaborate, and consume resources, Akgents point toward a future where AI transformation and environmental stewardship are not in tension but in concert.
Sources:
[1] IEA (2025), Energy and AI, IEA, Paris
[2] Xiao, T., Nerini, F. F., Matthews, H. D., Tavoni, M., & You, F. (2025). Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA. Nature Sustainability, 8, 1681.
[3] Tomlinson, B., Black, R. W., Patterson, D. J., & Torrance, A. W. (2024). The carbon emissions of writing and illustrating are lower for AI than for humans. Scientific Reports, 14, 3732.
[4] Li, P., Yang, J., Islam, M. A., & Ren, S. (2025). Making AI Less 'Thirsty'. Communications of the ACM, 68(7), 54–61.
[5] OpenAI Pricing Guide as of April 2026
Join the webinar: Green AI in Practice
Reducing Cost, Carbon & Complexity
Friday 15 May 2026 | 9h00 - 10h00
If your organization is investing in AI but starting to feel the pressure of rising costs, energy consumption, and complexity, this session is for you.
Most companies don’t struggle with adopting AI. They struggle with sustaining it. What begins as promising innovation often evolves into expensive, resource-heavy systems that are difficult to scale and even harder to justify long-term.
In this webinar, we’ll show you there is another way.
You’ll discover how to rethink AI through the lens of Green AI, where performance and sustainability reinforce each other instead of competing. We’ll explore how to reduce energy consumption, lower operational costs, and design AI systems that are both efficient and future-proof. Using approaches like multi-agent architectures such as Akgents, you’ll learn how to move beyond monolithic models toward smarter, leaner systems.
This is not a theoretical discussion about sustainability. It’s a practical, experience-driven perspective on how to make AI more efficient, scalable, and responsible—without compromising performance.
Join us to understand how AI efficiency can become a real competitive advantage for your organization, while reducing both cost and environmental impact.
