The rise of artificial intelligence, particularly in areas like translation, brings undeniable benefits in terms of efficiency, accessibility, and global communication. However, it’s crucial to acknowledge the environmental footprint associated with these powerful technologies, and more importantly, to actively work towards mitigating it.
The environmental footprint of language AI: A growing concern
The immense computational power required for AI, especially for training and running large language models (LLMs) used in advanced machine translation, comes with significant environmental costs:
Energy consumption
AI models demand staggering amounts of energy. Data centers, the backbone of AI operations, consume vast quantities of electricity to power servers and maintain optimal cooling. This energy often comes from fossil fuels, contributing to carbon emissions. Studies indicate that AI computing is far more power-intensive than traditional computing, with AI server racks consuming significantly more electricity. The energy footprint extends beyond training, with a substantial portion dedicated to “inference” – the continuous running of AI models in real-time.
Water usage
To prevent overheating, AI data centers require extensive cooling systems, which can consume billions of liters of water annually. This places increasing pressure on local water supplies, particularly in regions already facing water scarcity.
Electronic waste
The rapid advancement of AI technology drives demand for specialized hardware, leading to a greater volume of discarded processors and server parts, contributing to the growing problem of e-waste.
Embodied emissions
Beyond operational energy use, the environmental impact also includes “embodied emissions” – the carbon generated across the entire supply chain, from raw material extraction for components to manufacturing and transportation.
The exponential growth of AI means these environmental concerns are escalating. Estimates suggest AI could account for 3-4% of global electricity consumption by the end of the decade, and carbon emissions linked to AI are projected to double between 2022 and 2030. It’s a challenge we cannot ignore.
Translated’s proactive approach to green AI translation
At Translated, we believe that innovation and sustainability must go hand-in-hand. We are committed to minimizing our environmental impact while continuing to deliver world-class language solutions. Our strategy focuses on several key areas:
Optimizing model efficiency Lara
Our latest breakthrough AI, Lara, is designed with efficiency at its core. Instead of relying on brute force computation, our models are trained on meticulously curated, high-quality linguistic datasets. This focused approach reduces the energy required for training while simultaneously improving accuracy and reducing “hallucinations” – which, in turn, minimizes the need for iterative corrections and associated computational cycles. Lara, in particular, represents a significant leap forward, achieving sub-second P99 latency across 50 widely spoken languages, making it 10 to 40 times faster than leading LLMs for translation tasks, with higher quality. This speed directly translates to reduced computational time and, therefore, lower energy consumption per translation.
Strategic hardware collaboration and infrastructure design
We understand that hardware is a critical component of the environmental equation. We are actively collaborating with partners like Lenovo to co-design purpose-built hardware solutions optimized specifically for translation workloads. This includes leveraging NVIDIA’s advanced GPUs for AI workloads, but crucially, it’s about making smart choices that enhance efficiency. A significant part of this partnership involves implementing liquid-cooling systems across Translated’s infrastructure. This dramatically reduces energy consumption compared to traditional air-cooling methods and allows for greater machine density, supporting more sustainable and scalable AI operations.
Human-in-the-Loop for precision and efficiency
Our philosophy of “human-in-the-loop” is not just about quality, but also sustainability. By integrating the expertise of our 500,000 professional translators with our AI, we ensure that the AI focuses on what it does best – speed and consistency – while human translators refine for nuance, cultural relevance, and critical accuracy. Our adaptive quality estimator allows us to direct human attention only where it’s truly needed, avoiding unnecessary computation for already high-quality machine output. This synergistic approach reduces redundant processing and ensures that computational resources are used effectively.
Continuous research and development for greener AI
Through our research center, Imminent, we are constantly exploring new frontiers in AI development, with a strong emphasis on efficiency. Our ongoing two-year research initiative, highlighted in the 2025 Annual Report “Evolution in Words: Beyond AI,” delves into the shift from large language models to multimodal AI systems. This research aims to develop a new class of AI models that can gain understanding through real-world interaction, potentially leading to more resource-efficient learning paradigms and minimizing the need for massive, energy-intensive pre-training on generic datasets. We are also collaborating with leading European research centers as part of projects funded by the European Commission, fostering a collective effort towards greener AI.
Data center selection and renewable energy commitment
While we don’t own all our data centers, we prioritize partnerships with providers who demonstrate a clear commitment to renewable energy sources and sustainable practices. We advocate for and select data center partners that are actively working towards net-zero operations and transparently report on their environmental footprint, including water usage and energy mix.
The path forward: A collective responsibility
The environmental impact of AI is a complex issue that requires a multi-faceted approach. At Translated, we’re dedicated to being part of the solution. By focusing on model efficiency, intelligent hardware design, human-AI collaboration, and ongoing research into sustainable AI architectures, we aim to lead the way in making language AI a powerful tool for global understanding, without compromising the health of our planet.
A cornerstone of this commitment is our acquisition and reactivation of Einstein’s Watermill. This hydroelectric power plant, originally designed by Albert Einstein’s father in 1895, now generates clean electricity to power Translated’s operations, particularly our energy-intensive AI tasks. This initiative moves us significantly closer to carbon-free translations, producing more than enough renewable energy to cover our current and near-future needs. Any surplus energy is resold, demonstrating a sustainable and financially efficient approach to mitigating the environmental impact of AI.
We believe that a truly intelligent future is a sustainable one. As we continue to innovate and expand the capabilities of language AI, our commitment to environmental stewardship will remain a core pillar of our mission.