The AI rollercoaster of expectations and concerns continues to move at a breakneck pace as companies move closer to understanding the rapidly changing technology and its potential functions within their business. Recently, advanced artificial intelligence platforms such as generative AI and large language models (LLMs) have come under scrutiny for their voracious energy consumption and resulting ecological impact, with some researchers hypothesizing that LLMs consume hundreds of liters of fresh water and produce annual emissions equivalent to that of a small country.
As global warming exceeds 1.5 degrees for the first time in a full year, global stakeholders are asking where the bulk of the responsibility should lie to prevent the climate crisis from worsening. Climate change remains an issue of critical importance to both consumers and businesses amid these global efforts to reduce carbon emissions, which bodes ill for the public image of any company that uses consumptive AI tools without considering its carbon footprint to keep control. More importantly, rampant unchecked AI use could have disastrous consequences for the environment. Research from MIT shows that training actually has the potential to significantly slow global progress against climate change.
Despite the apparent environmental apathy of recent legislation such as the EU AI Act and President Biden’s executive order, which largely focus on other facets of AI responsibility, some major AI players have begun proactive self-regulation and are working towards sustainable AI -usage. Here are ways the leaders in artificial intelligence are approaching AI with environmental consciousness, while retaining the technology’s deep business value.
Senior Director of AI Strategy, ABBYY.
Purpose-built AI
Many disadvantages of generative AI and LLMs stem from the sheer volume of data that must be navigated to deliver value. This not only poses risks in terms of ethics, accuracy and privacy, but also significantly increases the amount of energy required to operate the tools.
Instead of very general AI tools, companies have turned to narrower, purpose-built AI, specialized for specific tasks and goals. For example, ABBYY has adopted this approach by training its machine learning and natural language processing models to specifically read and understand documents running through enterprise systems, just like a human. With pre-trained AI skills to process highly specific document types with 95% accuracy, organizations can save trees by eliminating paper use while reducing the amount of carbon emitted by cumbersome document management processes.
Empowering developers
AI companies don’t have to bear the burden of sustainable AI all alone; some proactively put the proverbial ball in developers’ court.
OpenAI, the artificial intelligence pioneer responsible for the widely popular ChatGPT, recently announced that developers can create their own “GPT” platforms for specialized purposes. This allows developers and organizations to limit their AI use with a high degree of customizability, cutting out excessive features and data that increases ecological damage. For example, developers can design GPTs for purposes limited to creative writing advice, cooking information, technical support, or any other niche purpose.
Given the increased risks of inaccuracy and privacy invasion associated with very general AI models, developers will likely be motivated to take advantage of these narrower, more specialized GPT platforms, not only for environmental responsibility, but also for improved business outcomes .
Sustainable business operations
Companies must also take a step back from the technology itself and look for more ways to leverage AI sustainably within their organizations. For example, Microsoft revealed that their AI-enabled hardware runs exclusively on clean energy, eliminating the need to create so-called “operational emissions.”
Additionally, companies can use AI as a tool to explore other facets of their business where sustainability can be prioritized. Forrester highlights the measurement, reporting and data visualization capabilities of artificial intelligence to suggest it could power its own climate revolution.
Although objectively important, emissions are not the only metric used to measure ecological impact; studies have shown that a combination of robotics and AI has reduced herbicide use by 90% in some contexts. As companies continue to grapple with the usefulness and implications of AI, they must explore the full breadth of its ability to improve and contribute to sustainability.
Companies are catching up
To date, early AI legislation has largely failed to manage the ecological implications of artificial intelligence, focusing instead on privacy and other ethical areas. While these areas are also critical to responsible AI use, companies must hold themselves accountable for how they use AI to drive business value.
2023 may have been a year of hype, noise, expectations and misconceptions around artificial intelligence, but the maturity that companies have built over the past year has given them the tools needed to make informed and responsible decisions about using AI. Yet it is wise to critically examine, question and hold large organizations accountable for their carbon footprint and other environmental consequences. Those who prioritize environmental responsibility should have nothing to hide.
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