when the artificial machine arrives in this future–present that is always inevitably imminent, it will manifest as a superior intelligence, capable of solving the problems that humans have created – and climate change in primis.
As citizens, we are almost left with the sense that this artificial entity that will come to rescue humanity, the world, and all living things is a divine, magic hand, a deus ex machina.
This portrayal of AI as a benevolent deity has a crucial effect: it obfuscates the materiality of the infrastructures and devices that are central to AI’s functioning. In all its variety of forms, AI relies on large swathes of land and sea, vast arrays of technology, and greenhouse gas-emitting machines and infrastructures that deplete scarce resources through their production, consumption and disposal. AI requires increasing amounts of energy, water and finite resources.
Why are we not talking about the negative impact of AI on the climate crisis? This is precisely what I want to discuss in this book. And more: I want to bring the climate crisis to the centre of debates around AI developments.
Clearly there are other important concerns about AI developments, from moral and ethical appeals for caution in the use of AI in military operations to mounting fears in areas where human expertise is crucial to safeguarding human rights (such as public health and the judiciary). There are huge ethical issues concerning documented algorithmic racial and gender biases, and fears that AI will make human labour redundant, producing a class of supereducated employees and another of less educated, unemployable workers.
These concerns are beyond the scope of the present book. If we lose our environment, we lose our planet and our lives. So we must understand and debate the environmental costs of AI.
The COVID-19 global pandemic caused the worst economic contraction since the Great Depression. It underscored the need to rethink the type of economy and society we want to build as we face the worsening climate crisis. Bold green recovery plans have been announced all around the globe (European Commission 2020b), and we have been told that technological innovations and AI are at the centre of recovery, as they have the potential to create millions of jobs and to boost economies devastated by the pandemic. But what would happen if we discovered that the environmental impact of AI is so massive that it compromises the plans to decarbonize the economy proposed by Green New Deals around the world?
The climate crisis is here to stay
In October 2018, the world’s leading climate scientists who comprise the Intergovernmental Panel on Climate Change (IPCC) warned the world that there were only twelve years for global warming to be kept to a maximum of 1.5-degree C (see IPPC 2018). Beyond that level, the risks of life-threatening weather events such as drought, wild fires, hurricanes, extreme heat and poverty for millions of people will be significantly worse. One need only look at the devastation of Australia’s ecosystem by the bushfires in the summer of 2019 to understand the gravity of the situation.
In November 2019 we received even clearer warnings from the UN Emissions Gap Report 2019, which assessed the gap between anticipated emissions in 2030 and levels consistent with the 1.5°C increase in temperature outlined in the Paris Agreement (UN Environment Programme 2019). It is estimated by the IPCC that an increase in average global temperature beyond this limit will lead to loss of habitable land for many humans and other life forms, as well as to catastrophic water shortages and crop shortages; this will cause massive migration and possible conflicts, as populations seek safer ground. The UN report also explains that the national commitments made in Paris must increase at least fivefold if we are to prevent a temperature increase greater than 1.5°C. Unless emissions fall by 7.6 per cent each year in the period between 2020 and 2030, the world will miss the opportunity to limit the damage. We are currently on a trajectory for a temperature rise of over 3°C (UN Environment Programme 2019).
Unfortunately, even despite the lockdowns of 2020, greenhouse gas emissions have remained stubbornly high. Daily global carbon dioxide emissions fell by as much as 17 per cent in early April 2020. But, as the world’s economy started to recover, emissions rebounded; and the UN showed that 2020 only saw a 4–7 per cent decline in carbon dioxide relatively to 2019 (United Nations News 2020). While transportation and industrial activity declined from January 2020, electricity consumption remained constant, which partly explains the minimal drop in emissions. How, you may ask? According to the World Energy Outlook 2019, globally 64 per cent of the global electricity energy mix comes from fossil fuels (coal 38 per cent, gas 23 per cent, oil 3 per cent: IEA 2019). Since fossil fuels are the largest source of greenhouse gas emissions, without fundamental shifts to renewable resources in the global energy production we shall not be able to prevent incalculable loss of life, as the planet becomes uninhabitable.
What is the connection, then, between the climate crisis and the energy used by AI?
The chapters of this book help readers to answer this question through a discussion of the following themes:
1 a definition of AI and its promises for the world and the environment;
2 why data capitalism is crucial to an understanding of AI and who controls and develops AI;
3 why AI worsens the climate crisis; and
4 what we can do about it.
In chapter 1 you will learn about the hype and awe of AI. From the European Union to the United States and China, governments and global consultancies are urgently signing declarations that promise that the effects of AI are comparable to those of previous scientific revolutions, such as steam and electricity. This belief that AI will rescue humanity, solve the climate crisis and reduce the inequalities of capitalism has a crucial effect: it obfuscates the materiality of the infrastructures and devices that are central to AI’s functioning. Setting aside the mythical discourse on AI, chapter 1 aims instead to shed light on definitions of AI and asks you to think about AI in a different, more material way than most of us have done in the past.
In chapter 2 you’ll discover why data capitalism is crucial to the development of AI. You will understand the reasons for AI’s rapid adoption, since 2010, as a result of vast computing resources and oceans of data. From that time, pushed by digital lords of the West such as Google, Facebook, Amazon, Microsoft and Apple (Brevini 2020), AI has been adopted virtually by all businesses and already extends through almost every sector of the economy and society. This chapter also explores the gatekeepers of AI power and imperialism, from the United States to China, and concludes with an examination of lobbying efforts by the most powerful tech giants to set the terms of public debates on AI and to determine policy outcomes (Benkler 2019, p. 161).
In chapter 3 you will discover the environmental costs of AI and its relationship to the climate crisis. The converged communication systems upon which AI relies generate a plethora of environmental problems, starting with energy consumption and emissions, material toxicity, electronic waste and disposal (Brevini and Murdock 2017). As you will realize, AI relies on large amounts of data, since it works with unsustainable energy demands imposed by algorithm training and cloud computing. Finally, while promising to solve the climate crisis, AI companies are marketing their services to coal, oil and gas companies, thus compromising efforts to reduce greenhouse gas emissions and to divest from fossil fuels.
The Conclusion argues that without challenging the current myths of limitless economic growth and boundless consumerism, without reconsidering the way in which the structures, the violence and the inequality of capitalism work, we won’t be able to achieve the radical change
