By SHANNON O. WELLS

When it comes to the rapid rise of artificial intelligence in society and academia, the more that many Pitt students, staff and faculty learn about how it functions — and its implications on knowledge, career fields and energy usage — the more concerns, apprehensions and questions they seem to have.

That was among the takeaways from “What Should I Do About AI: A Pitt Community Workshop on AI and Energy” forum on Sept. 2 in the William Penn Union Assembly Room.

Moderated by David Sanchez, associate professor in the Swanson School of Engineering’s Civil & Environmental Engineering Department and associate director for the Mascaro Center for Sustainable Innovation, event speakers, all from the Dietrich School of Arts & Sciences, included:

• Michael Blackhurst, teaching assistant professor and environmental studies program coordinator in the Department of Geology and Environmental Sciences

• Justin Kitzes, associate professor in the Department of Biological Sciences

• Sungwan Hong, assistant professor in the Department of Economics

The interactive event also invited audience members to share their thoughts through question-and-answer and workshop sessions.

After hearing detailed overviews from the speakers on the definition of AI, how it’s being used and why it requires considerable energy to maintain, audience members were invited to engage with those at their tables and share their topics and ideas with the room.  

“Even though a lot of us came up with environmental concerns, we can see some clear solutions to those,” a spokesperson from one table said. “This cultural piece kind of comes into it where we’re like, ‘What is the next generation of our world going to look like if they don’t have to depend on critical thinking and other non-AI skills or pre-AI skills …

“If we create renewable energy solutions that help cut back on the energy concerns with data centers, would that just expand the amount of AI, and I guess strip people from even more of their human qualities?”

Another table of participants questioned how data centers that fuel AI systems could be built sustainably. “Can we lessen impacts? Can we build them in different places that perhaps could reduce the need for (water-based) cooling, or build them in a modular fashion as well?”

Other questions addressed AI’s effect on cognitive development and creativity, and the possibility of representatives from different countries coming together in a summit to share concerns and possible solutions “about how could we handle this.”

“Is this something that we could have a Montreal Protocol or a Paris Accords?” the table’s spokesperson pondered. “Could we have a global solution arise, which of course sounds pretty challenging, given the geopolitics today.”

Deeper philosophical questions also arose. “Are we even asking for this?” the spokesperson said of AI. “Why are we doing this? Who is it if we are not asking for this? Who is imposing this upon us?”

The retraction of personal freedoms and personal liberty also was mentioned, along with the potential for the explosion in AI-related investment to become a dangerous economic “bubble.”

“There’s a lot of fear about how AI’s been overhyped, and you can’t even control money in your own pockets, especially if any of you are interested in retirement or 401(k)’s … with stocks being involved in the Nasdaq and things like that,” one table’s spokesperson said. “And AI being used to steal data, not only from things online but also from (surveillance cameras).”

While the afternoon gathering did not produce hard and fast conclusions or simple solutions, the forum was clearly effective in stimulating thought and discussion based on insights from some of Pitt’s foremost authorities on AI.

Predicting words

Justin Kitzes, one of the event’s three featured speakers, reminded the audience that “AI is old.”

“The concept of artificial intelligence is at least 70 years old in academia. It’s not a new thing, and it has a fairly broad definition,” he said. “AI is widely understood as a system, usually a computer system, that can perform a task that otherwise would require human intelligence.”

The evolution of AI in the past decade or so has been spurred by a subset of “all possible AI methods that are possibly out there, which is the subset of AI that is machine learning.”

This is AI that can learn to perform a task directly from data, rather than being explicitly programmed.

“Think of machine learning as learning to tell images of a cat from images of not a cat by being given images of cats and not cats and figuring it out on its own,” he explained. “That would be in contrast to making a model where you specify that a cat has two ears, a cute face, an indifferent personality, and anything else that you might decide to tell it.

“Within machine learning, you have (the subset of) deep learning that is based around a particular type of model called a neural network. Within deep learning, there is a particular type of neural network called a transformer, and just one application of transformers are large language models.”

These, he explained, are models behind the popular chatbots that “many of you are using today: ChatGPT, Gemini, Claude, etc.”

Kitzes elaborated that AI is used as shorthand for large language models (LLMs), which have what he called a “dictionary of tokens” or word fragments … maybe 100,000 of them that it can read or say. (The LLM) reads a string of these tokens … and then generates a probability distribution for what word it thinks should come next in that string.”

“That’s not the only type of AI out there, but it is the type leading to a lot of the impacts and development and discussion going on today.”

Kitzes emphasized that possibly negative effects of AI should always be considered within other impacts and demands placed on the environment through “our other everyday actions.”

“The cost or impact of an activity, what we would consider acceptable or desirable, always needs to also be contextualized against the potential benefits that that cost may be bringing you,” he noted. “I imagine many of us could be potentially supportive of a large data center that was built dedicated to cancer research, versus a large data center that was dedicated solely to generating images of rabbits on skateboards.”

Efficiency vs. demand

In his presentation, Mike Blackhurst outlined an analysis of the software and hardware LLMs require and the energy required to support them.

“I’m sure you’ve all heard stories about how data centers may be influencing the electric power system in terms of price and emissions, so we want to help you understand those connections as well,” he said, noting that data centers, like other energy converting processes, generate waste heat. “They lose heat as they operate on your data.”

Much like a heating boiler or water heater, Blackhurst said data centers also generate waste heat in converting “chemical energy and natural gas or electricity into heat. … There’s also waste heat in the sum of the generators that are used to supply electricity.”

“If we burn coal or natural gas or nuclear power, that’s burning something to create electricity, and it’s also generating heat.

“On the other hand, renewable energy doesn’t generate waste heat,” he noted. “So, if we can use that, we have less waste heat to deal with in the power system.”

The significant need for cooling water at data centers is another source of controversy.

Of what he called the “two choices for coolant in treating your waste heat,” water requires less energy than air-based cooling systems.

Blackhurst cited ways to make the process more efficient and less consumption heavy, including more efficient hardware to convert an “inference” — asking a model to respond to a prompt as opposed to training it — into a service.

“The chips (would) use less energy to do that,” he noted. “We can also have more efficient algorithms. … It’d be nice if, as we get more efficient, we use less energy.”

However, some of those efficiency gains are “basically being cannibalized by AI itself into new services. So, as we make the process more efficient, people are using it more aggressively and finding new ways to use AI.

“This is a very dynamic and uncertain space, but one that I think we should be keeping track of as an institution doing research and teaching about these things,” he said.

Noting that the Grand Coulee Dam on the Columbia River in Washington state is the biggest power generator in the U.S., Blackhurst said AI-based “hyperscale facilities are starting to get as big as our biggest power generators.

“We have the power draw from about 800,000 homes.”

New generation

In his presentation, Sungwan Hong highlighted the rising electricity demands from data centers. Based on statistics he shared from 2024, AI and non-AI data centers accounted for 5% of total U.S. electricity use, with AI data centers consuming only a 1% share.

By 2030, however, many projections suggest the share of consumption from data centers will rise to around 12%, he said, with more than half of that coming from AI data centers.

“This 6% of electricity demand is not a small number. It’s the total amount of California’s electricity consumption per year. So, once we have this huge demand shock in electricity, now the question is how would the electricity supply respond?”

If a data center opens tomorrow, the grid operator cannot build a new supporting generator, as they typically take more than a year to build, he noted.

“Then we (must) use the current generators more intensively, burn more natural gas or burn more coals, pour on more nuclear, and so on. On the other hand, if we have two years, we already know that in four years we will have a lot of data there … In the long run, we may start to think about building more generators.”

Another question surrounds the source of fuel, whether we are “expecting to have more fossil fuel burning in the short run, or going to have more renewables? … In the long-run period, are we going to have more solar panels or wind turbine fuels in the economy, or (will we) have more fossil power plants that will drive (emissions-related) data?”

With generators able to run on nuclear, renewables, natural gas or “peakers” — plants that run only in high-demand situations — once they are already built, renewables “tend to be the cheapest one to run because there’s no marginal cost” of running them.

While there are variables in natural gas plant efficiencies that affect the cost, “they will be more expensive compared to nuclears,” Hong said.

Responding to the increasing demand response varies by area.

“Some regions respond by more renewables, and some respond by more fossil fuels.” In the Pittsburgh region, “Ohio is going to respond with a lot by coal, and Pennsylvania responds more by natural gas. Virginia responds by natural gas, solar and wind.”

Despite the regional differences, fossil fuel, Hong explained, is “going to work a lot to meet” data-center demands. And where data centers are planned, new fossil-fuel generators will follow, particularly in Pennsylvania, Ohio and West Virginia. “Except Texas, where the companies are proposing building their own gas turbines.”

With more data centers, therefore, “we would expect to have more gas turbines rather than renewables.”

“Hyperscale” users like Microsoft, Amazon and Google have a net-zero consumption commitment in which they “try to cancel out the environmental impact they make … by purchasing clean renewable electricity generation from other places to meet the electricity use in their production process,” Hong noted, with one caveat. The region from which the clean electricity was purchased can be very different from where Google data centers are actually running.

In 2024, Google used 30 kilowatts per hour and reported that it matched 100% of its electricity use with clean power contracts.

“They also reported that only 66% was carbon-free on the grids they serve in its data centers. … They can have a data center in Pennsylvania, which is not 100% carbon-free in their grid, but they are buying electricity from Texas.”

Ameliorating fears

During the forum’s question-and-answer segment, Blackhurst responded to an audience question about young people being told their jobs will be replaced by artificial intelligence while they’re footing bills for water and electricity use of the new data centers, he said he hoped “you’re not afraid of it.

“And I would hope that (the University) would help you prepare to be successful financially, professionally beyond your time here. I think there’s a lot of people here, myself included, who really want to prepare you accordingly.

“I think abstaining from it, even if you’re fearful of it, is probably not going to be helpful. … This is a very safe space for you to dabble,” he noted. “You have a lot of people with life experience that can help you adjust to that reality here. But I don’t know that I would encourage anybody to be afraid per se, and I would hope we could help ameliorate those fears here in this institution.”

Noting that he and other panelists have growing children, Justin Kitzes acknowledged that the influence and “disruption” from AI on the job market is “on the mind of everybody.”

“I mentioned the Industrial Revolution. If you look back at other major technological changes that have brought about large social change — electrification, steam engines, cars, the internet. Some of us remember when the internet was brand new and it was disrupting everything and changing the way that we did everything.”

With the once-strong job-training appeal of digital coding now being usurped by AI, Kitzes admitted the difficulty in predicting future trends.

“Remember ‘Just learn to code’? That probably wasn’t the right answer, it turns out. … In a time of disruption, one of the best things that you can do for yourself is to be flexible and to put yourself in a position where you can go in many different directions.

“Because quite frankly, we don’t know exactly what’s going to happen, and anyone who says they do is full of it, right?” he added. “Probably the best thing you can do for yourself is consider, ‘Well, if things went all sorts of different directions, what could I do to give myself a place, no matter what happened’?

“Rather than trying to find the one option that you think is going to optimize for some future that may or may not come to pass. That’s my personal answer.”

Shannon O. Wells is a writer for the University Times. Reach him at shannonw@pitt.edu.

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