Will Generative AI Help Us Solve The Climate Crisis (Or Will It Make It Worse)?
Thank you for reading my latest article Will Generative AI Help Us Solve The Climate Crisis (Or Will It Make It Worse)? Here at LinkedIn and at Forbes I regularly write about management and technology trends.
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You might be surprised to learn that AI is already proving to be a powerful weapon in the fight against climate change. The UN, for example, has incorporated AI into its UNOSAT satellite imagery center – using algorithms to analyze images of flooded areas and assess where disaster response teams are needed.
AI can also help to preempt future disasters and help vulnerable communities anticipate what may be coming their way. Indeed, the UN works with vulnerable communities in Burundi, Chad, and Sudan – using AI to analyze past environmental change in displacement hotspots and build future projections.
But isn’t AI, with its heavy energy usage, adding to the climate problem, you ask? Well, yes, that’s also true. (I never said there was a simple answer to the title question!) One of the biggest concerns is that with the rise of generative AI tools like ChatGPT, the energy cost of AI is only going to increase.
In this article, we’ll explore the estimated energy cost of generative AI. Plus, we’ll look at how the new technology may actually help us solve or mitigate the climate crisis.
ChatGPT (And Friends) Are Energy Guzzlers
Back in 2019, researchers at the University of Massachusetts Amherst decided to look into the carbon footprint of large AI models – of the sort that power today’s tools like ChatGPT. They found that training a single AI model can emit five times the lifetime emissions of the average American car (including the manufacture of the car).
That in itself isn’t ideal. It's not a disastrous number, but it’s not ideal. However, things have moved on drastically since then. At the time of the study, OpenAI’s GPT large language model was GPT-2. Nowadays, we’re up to GPT-4 – meaning the large language model has grown a whole lot, well, larger and more advanced. GPT-2 was trained on 1.5 billion parameters (the number of variables fed into the model), but GPT-4 reportedly has a whopping 1.8 trillion parameters . That’s an enormous leap. Naturally, creating such a vastly bigger model uses more energy than the 2019 study found.
And, let’s not forget, that’s just the energy needed to train the AI. There’s also the energy associated with people actually using these tools. Asking ChatGPT a question uses more energy than a standard internet search, and this, across the approximately 180 million users that ChatGPT has amassed so far, really adds up. Working out the energy cost of these conversations is tricky, but some estimates suggest that ChatGPT searches could use as much power as 33,000 American households each day. Each day. That’s a staggering estimate. Each conversation with ChatGPT also consumes roughly 50cl of water (a small bottle).
If tools like ChatGPT, Google Bard and Microsoft’s Bing AI chatbot continue to amass users, future numbers will undoubtedly be even more alarming.
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So Generative AI Is Going To Worsen The Climate Crisis, Right?
Well, it’s not quite that simple. Because, while generative AI tools are well on their way to being major sources of carbon emissions, they can also help us find the solutions needed to solve our biggest climate problems.
There are multiple ways generative AI can boost efforts to tackle climate change. It can be used to better forecast and manage energy demand, for example. It can be used to generate more accurate forecasts about future energy production, allowing providers to allocate resources more effectively. It can be used to design better renewable systems – systems that are more efficient and scalable. And it can be used to design better carbon capture and storage solutions – where emissions from factories and other industrial processes are captured and stored in the ground instead of being released into the atmosphere.
Of course, this doesn’t offset the enormous energy cost associated with generative AI (and AI in general). We absolutely need AI companies to commit to reducing their carbon footprints and invest in sustainable AI. But we can’t look at the climate cost of AI without also recognizing its potential benefits.
The bottom line is that the climate crisis is one of the most pressing problems that humanity has ever faced. And generative AI is one of the most powerful technologies we’ve ever had access to. Bring the two together, and we can hopefully arrive at some innovative solutions and, ultimately, aid the fight against climate change.
Read more about generative AI and its impact in my new book, Generative AI in Practice: 100+ Amazing Ways Generative Artificial Intelligence is Changing Business and Society.
About Bernard Marr
Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of over 20 books , writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations.
He has a combined following of 4 million people across his social media channels and newsletters and was ranked by LinkedIn as one of the top 5 business influencers in the world. Bernard’s latest book is ‘Generative AI in Practice ’.
This is very interesting and thought-provoking! The point that even technologies that are completely digital - and therefore seem emissions-free - are actually generating a meaningful amount of emissions shows the challenge we also face in the supply chain. The challenge on the one hand is looking at everything that feeds into a product holistically and how to reduce all end-to-end emissions. But then we also, as Bernard Marr points out, need to understand and somehow account for the potential of the end product in reducing emissions in the future.
Senior Digital Marketing Specialist- Data Dynamics
9moGreat insights, Bernard! You clearly outline the high energy costs of generative AI while recognizing its potential climate benefits through forecasting and system design. A balanced view on this complex issue. AI companies must commit to reducing emissions even as they advance powerful new applications. Thanks for the thoughtful analysis on the intersection of AI and climate change - look forward to more!
Top LinkedIn Community AI, Research, Data Science Voice| AI Innovation Leader/Mentor| Board Director| MITXPRO AI Learning Facilitator| PhD Tech/AI Candidate Space Situational Awareness🚀Harvard Business Analytics Program
9moOne thought here is companies like Microsoft looking at nuclear tech and small modular reactors (SMRs) to power their data centers. I did two small polls on LinkedIn recently, one was regarding this use of SMR’s of which majority of respondents was all for it. A second poll was regarding biggest trends in AI in the next few years. Sustainability had only 1 vote out of the entire population and ironically it was after I called out on a repost no one finds this a large use case for AI in the near future? Healthcare and personalized education beat sustainability out. It will be interesting to see how this pans out.
Gen(AI) Business Development Manager for South Europe @AWS | Driving business transformation through Data & Artificial Intelligence | Strategy + Technology + People | Speaker | Advisor
9moGood point! I would also talk about how AWS has innovated with their chips optimized for training (Trainium) and running (Inferentia) these models. They are not also offering 4 times higher throughput and up to 10 times lower latency, AWS Inferentia enables sustainable GenAI. For instance, for common task of document understanding AWS obtained 82% fewer instances, 92% less energy or 90% lower cost.
The dual role of Generative AI in the climate crisis presents both challenges and opportunities.