How Bitcoin mining devastated this New York town
Economist Matteo Benetton, a coauthor of the paper and a professor at the Hass School of Business at the University of California, Berkeley, says that crypto mining can depress local economies. In places with fixed electricity supplies, operations suck up grid capacity, potentially leading to supply shortages, rationing, and blackouts. Even in places with ample access to power, like upstate New York, mining can crowd out other potential industries that might have employed more people. “While there are private benefits, through the electricity market, there are social costs,” Benetton says.
These impacts are now being felt across the country. Benetton says there are strong profit incentives to keep as many servers running as possible, and he is now calling for greater transparency in these companies’ energy usage. That’s not a popular opinion within the industry. But, says Benetton, “if you’re really doing good, you shouldn’t be afraid to disclose the data.”
The federal government does not currently monitor cryptocurrency’s energy consumption, but Securities and Exchange Commission chair Gary Gensler recognizes that there are gaps in regulation. In a 2021 speech at the Aspen Security Forum, he referred to the industry as “the Wild West.”
As long as mining is so profitable, Read warns, crypto bans just shift the harm to new locations. When China banned crypto mining in 2021 to achieve its carbon reduction goals, operations surged in places like Kazakhstan, where electricity comes primarily from coal. As a result, a recent study found, Bitcoin’s use of renewable energy dropped by about half between 2020 and 2021, down to 25%.
Even when the industry invests in renewable energy, its sheer consumption makes it a significant contributor of carbon emissions.
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Read dismisses the promises that green investments or greater efficiencies can solve this problem. In a recent working paper, he found that cryptocurrency’s energy usage will rise another 30% by the end of the decade—producing an additional 32.5 million metric tons of carbon dioxide a year. As long as the price of Bitcoin goes up, the rewards of mining increase, which spurs energy use, he says. He refers to this situation as “the Bitcoin Dilemma.”
Those 32 million metric tons of carbon dioxide will make the climate crisis even worse, whether the emissions are coming from upstate New York or Kazakhstan. “We all suffer as a consequence,” says Read.
Lois Parshley is an investigative science journalist.
Technology and industry convergence: A historic opportunity
And it’s that combination of technology and human ingenuity, as we say, and as Danielle just alluded to in her medical example on cancer treatment, that is really where the greatest value and the greatest impact is going to come. We believe the companies which are going to be leaders in the next decade are going to need to harness five forces, and all of these forces are going to require technology and ingenuity to come together. They’re going to require organizations to work across all elements of their organization, to work with new partners, to expand into new areas and ecosystems, to learn and collaborate with innovators across industry, as well as across industry and academia and beyond to really push the boundaries of science and impact.
The five forces that we see right now, the trends that we’re seeing that are impacting our clients the most really start with what we believe underpins everything right now, and that is something we’re calling total enterprise reinvention. And we really started to see this come to the fore as we moved through covid. And what we’re seeing now is that as companies are looking to enter these new waves of change and opportunity, that they’re needing to execute strategies to change and transform all parts of their business through technology, data, and AI, as Daniela just talked about, to enable new ways of growth, new ways of engaging customers, new business models, new opportunities, but they’re doing it in a very different way. They’re doing it in a way where they’re looking at every part of their organization and the technology and digital core that underpins it at the same time, so we believe we’re in the early stages of this profound change, but we believe it’s going to be the biggest change since the industrial revolution.
And embracing total enterprise reinvention often requires something that we call compressed transformation, which are bold transformational programs that, as I said, span the entire organization with different groups working together in ways that they never did before in parallel, but in very accelerated timeframes. And underpinning all this is leading edge technology, data, and AI. At the same time, the second trend we’re seeing with our clients, and we certainly are all reading about it and of hearing about it for the past few years, is the power of talent and the importance of the human side of this equation. And we think that one of the forces that’s going to shape the next decade with talent at front and center is not just the ability to access talent, but really for organizations to learn to be creators of talent, not just consumers. To unlock the potential of the humans in their workforce. And that’s going to require technology to unlock that potential. And again, as Daniela just gave in some of her examples, to compliment the talent that they have in the organization.
The third is sustainability. That trend is … I would say personally, I’m very pleased to see this trend underpinning everything that we’re doing and everything that our clients are thinking about right now. We believe that every business needs to be a sustainable business. And every industry is looking at this in a way that is unique to their industries. But whether it’s consumers, employees, business partners, regulators, or investors, we know that we’re moving in a direction where companies are being required to act. To make a change, not just around climate and energy, but areas like food insecurity and equality. All of those issues are coming to the fore, and underpinning this, again, is the ability to leverage new bleeding technologies to accelerate the pace of change and find solutions to the issues that we’re facing as a planet and across society.
The fourth force that we’re seeing is the metaverse. Now, there’s been a lot of confusion, and a lot of talk about the metaverse, but our view is that the metaverse is a continuum, and we’re seeing this come to the fore in the marketplace right now. As we look at the metaverse and how that’s going to impact, just if you think all the way back to when the internet was in its early stages, we believe that the impact is going to be that great. And while it’s early stages and not everybody can see exactly how the impact is going to be there, we believe that this is going to impact not just consumers, and of course interesting areas like virtual reality and using AI to bring new experiences to life, but also to look at extended reality, to look at digital twins, smart objects. So how do cars and factories run? What’s happening with edge computing? Looking at blockchain and new ways of payment. All of those things are going to change the way businesses operate and really the way society operates, and we believe that this is going to underpin change as we move forward over the next five to 10 years.
And then lastly, the fifth force is what we’re calling ongoing tech revolution. And the ongoing tech revolution is a pretty broad expansive category, often pushed by our friends in the academia world around science, but we believe in the coming decade, the pace of technological innovation is not just going to continue but accelerate, which we believe is going to create positive change. New technology, whether it’s in quantum computing or it’s in areas, as I said, like blockchain or material science or biology, or even space, we believe this is going to open brand new areas of opportunity. And all of these things are allowing companies, our clients to find new ways to not just serve their customers, but to monetize their investments, to impact society, to impact their employees, and to drive positive change for their business as well as for the world around them.
Laurel: Yeah. Kathleen, I feel like some of that acceleration happened in these last few pandemic years so that businesses and consumers are operating differently from remote healthcare solutions to digital payments, greater expectations of those immersive virtual experiences. But how can organizations and technologists alike then continue to innovate to anticipate the future, or as Accenture likes to say, learn from the future? You have some good examples there, but the five different areas all kind of also lead to this acceptance of change.
Kathleen: Yeah, they do. And they also lead to embedding data in everything, in new ways into every change that organizations are putting forward. When we think of learning through the future, we think about organizations and leaders who are constantly seeking new data and insights, not just from inside their organization, but from outside their organizations’ four walls. So we like to use the phrase intentional futurists. These are people and leaders and organizations who use AI-based analysis to find patterns, anticipate trends, detect new sources of growth opportunities, understand their consumers, their customers, other enterprises, the markets and their employees better.
Delivering insights at scale by modernizing data
This data is often siloed in enterprise resource planning (ERP) systems. However, with ERP data modernization, businesses can integrate data from multiple sources, which will ensure data accessibility and create the framework for digital transformation. Migrating legacy databases to the cloud also gives companies access to AI and ML capabilities that can reinvent their organization. According to Anil Nagaraj, principal in Analytic Insights, Cloud & Digital at PwC, companies that modernize their ERP data see increased efficiencies, costs savings, and greater customer engagement, especially when it’s built on a cloud platform like Microsoft Azure.
Cloud transformation—along with ERP data modernization—democratizes data, empowering employees to make decisions that directly impact their segment of business. And in an increasingly competitive marketplace, becoming data-driven means organizations can make faster, timelier, and smarter decisions.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
The Download: the threat of microplastics, and mitigating AI bias
The news: While we know that tiny pieces of plastic are everywhere, we don’t fully understand what they’re doing to us or other animals. Now, new research in seabirds hints that it might affect gut microbiomes—the trillions of microbes that make a home in the intestines and play an important role in animals’ health.
The findings: Seabirds ingest plastic from the ocean, which can accumulate in their stomachs. The research shows it leaves the birds with more potentially harmful microbes in the gut, including some that are known to be resistant to antibiotics, and others with the potential to cause disease.
Why it matters: The report expands our view on what plastic pollution is doing to wildlife, and shines a light on the wide spectrum of adverse effects brought about by current plastic levels in the environment. The next step is to work out what this might mean for their health and the health of other animals, including humans. Read the full story.
What if we could just ask AI to be less biased?
Think of a teacher. Close your eyes. What does that person look like? If you ask Stable Diffusion or DALL-E 2, two of the most popular AI image generators, it’s a white man with glasses.
But what if you could simply ask AI models to give you less biased answers? A new tool called Fair Diffusion makes it easier to tweak AI models to generate the types of images you want, such as swapping out the white men in the images for women or people of different ethnicities. A similar technique also seems to work for language models.
These methods of combating AI bias are welcome—and raise the obvious question of whether they should be baked into the models from the start. Read the full story.