Artificial intelligence is powered by data centers which require enormous amounts of energy to operate. Credit: Photo by QUANTIC69/ISTOCK
Are you one of those loudly demanding that companies create artificial intelligence-powered systems to amuse you on Facebook, provide answers to your search questions, write the news or enhance management surveillance of worker activity? I would guess not. And yet, everywhere you look, AI is promoted as the ticket to a more productive and fulfilling life.
Martin Hart-Landsberg is a professor emeritus of economics at Lewis and Clark College.
The fact is that tech companies, not our needs, are driving the AI craze. These companies are working nonstop to sell us on how much we need AI in our lives. They have a lot at stake; if they succeed, they stand to make a fortune. Of course, they couldn’t care less about the social consequences of their effort.
However, the AI craze has gone on long enough for us to start drawing plausible conclusions about where it leads. There are good reasons to believe that big tech will never deliver the transformative AI it promises. One is that AI’s ongoing development is seriously constrained by data limitations and unexplained hallucinations, making its output unreliable. Another is that the financial costs of developing and operating ever more sophisticated systems are staggering and likely to prove prohibitive.
But we cannot afford to stand on the sidelines and let the AI craze continue unchecked. It comes at great public cost. Governments at all levels are subsidizing it, robbing our cities and states of needed tax revenue. Even more importantly, it is driving us ever faster to a future of climate chaos.
False promises
When people talk about AI, they normally have generative artificial intelligence (or machine learning AI) in mind. OpenAI started the AI craze with its November 2022 release of ChatGPT, where the GPT stands for generative pre-trained transformer. This chatbot, and later versions, including by competitor companies, requires both large amounts of data, mostly taken from the web, and an algorithm called a transformer, which enables it to draw on that data to determine, based on probability, a response to prompts.
No matter how conversational and intelligent a chatbot might sound, it is important to remember that it doesn’t know what it is saying. It identifies material in its database related to the pattern of words in the prompt it is given and then, guided by algorithms, assembles a set of words or images that best satisfies the inquiry.
Generative AI is just the beginning, according to tech companies, who see a future of rapid improvements, with more data and computing power enabling them to develop systems that are ever closer to human performance. Interactive artificial intelligence (IAI), capable of deciding on and taking different actions to complete assigned tasks without step-by-step prompts, comes next. And then, in the not-too-distant future, we can expect artificial general intelligence or AGI systems with the ability to think, learn and solve problems independently. According to the cheerleaders, these systems will enable us to develop new vaccines, lower greenhouse gas emissions, boost productivity and income, eliminate uninteresting and low-paid work, and the list goes on.
But despite substantial spending on AI development, which has led to ever faster and more capable generative AI systems, AI companies find the returns disappointing.
As the tech writer Ed Zitron notes, “Bloomberg reported that OpenAI, Google, and Anthropic are struggling to build more advanced AI, and that OpenAI’s ‘Orion’ model — otherwise known as GPT-5 — ‘did not hit the company’s desired performance,’ and that ‘Orion is so far not considered to be as big a step up’ as it was from GPT-3.5 to GPT-4, its current model.”
The primary reason for the lack of progress is a shortage of new data. Simply put, AI companies have essentially picked the Internet clean of human-generated data, and without extensive new data sets, their systems cannot develop new capabilities. Their hoped-for workaround is to prompt their current systems with questions and requests for information to generate the new data they need.
However, there are serious problems with this strategy. One is that the existing data, mainly scraped from the web, includes all sorts of racist, sexist and ill-informed posts and articles. Those are part of the database the system draws on when generating new material for its training. As a result, these harmful notions and misinformation get more deeply embedded.
But there is an even more serious problem. Feeding the system with its own responses creates a feedback loop that yields an ever-narrowing range of possible responses. While human-generated output varies considerably, AI models are structured to provide responses based on likely probabilities. If their training data is primarily self-generated, this means their responses will soon converge on the model’s determined “conventional wisdom.” This limits the reliability and usefulness of the system.
As The New York Times points out in an article titled “When AI’s Output Is a Threat to AI Itself”: “Just as a copy of a copy can drift away from the original, when generative AI is trained on its own content, its output can also drift away from reality, growing further apart from the original data that it was intended to imitate.”
Then, there is the potentially more serious problem of hallucinations, which refers to AI output that has no basis in reality — dates, times, places, events can be entirely made up. As Zitron describes, “The hallucination problem is one that is nowhere closer to being solved — and, at least with the current technology — may never go away, and it makes it a non-starter for a great many business tasks, where you need a high level of reliability.”
These technological challenges have their financial consequences. To this point, AI companies are shelling out a lot of money without much to show for it. For example, the tech publication The Byte reports that “Microsoft has spent a staggering amount of money on AI — and serious profits likely remain many years out, if they’re ever realized.” Not surprisingly, some investment analysts are now warning people about investing in the AI industry.
At great public cost
It is tempting to stand on the sidelines and let big tech pursue its dreams. If they come to fruition, great, and if they don’t, they are the ones to lose. But that is not the way things work. We are all paying a high cost for their efforts.
One example is that states and cities have been competing to attract data centers with enormous tax breaks. Oregon is near the top of the list. According to an Oregonian investigation, one of the main reasons so many tech firms are establishing data centers in Oregon is that they receive “some of the most generous tax breaks anywhere in the world. Data centers don’t employ many people, but the wealthy tech companies that run them enjoy Oregon tax giveaways worth more than $225 million annually.”
These tax breaks mean less money for things we do need — like schools, libraries and parks.The data centers themselves occupy land that could be used for more productive purposes.
An ever-greater concern is that these data centers place enormous demands on our energy sector — demands that pose critical challenges for our communities. Goldman Sachs estimates that AI will drive a 160% increase in data center power demand by 2030. This exploding demand for electricity translates directly into a dramatic growth in fossil fuel use, including coal, driving up U.S. greenhouse emissions and increasing the likelihood of climate chaos.
“Microsoft said its emissions had soared 30 percent since 2020 because of its expansion of data centers. Google’s emissions are up nearly 50 percent over the past five years because of AI,” according to The New York Times.
But, as The New York Times reports, tech leaders are willing to gamble with our lives: “Eric Schmidt, the former chief executive of Google, recently said that the artificial intelligence boom was too powerful, and had too much potential, to let concerns about climate change get in the way.”
What we have here is a prime example of capitalism’s destructive logic.
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