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Ask ChatGPT about a major AI data center project, and you might get a fairly simple description of what it is, where it’s being built, and how many companies plan to invest. Ask Google AI about the challenges of the same project, and you may come away with a much more negative impression.
That’s one of the findings of new research published by Five Blocks, a digital reputation management company that examined how eight AI engines describe 25 of America’s largest AI data center projects.
The researchers found that none of the 1,200 general project descriptions they evaluated were negative. But when the question was changed as to what challenges are faced for each project, 55% of the answers were classified as negative.
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The differences between AI platforms were even more surprising. Google AI mode produced negative answers to the challenge question 88% of the time, compared to just 6% for ChatGPT.
As AI data centers expand across the country, and Americans wonder what these projects could mean for their electricity bills, water supplies and neighborhoods, the research raises an important issue. The AI tool someone chooses, along with the words they type, can dramatically change the information they receive.
Same data center, two very different answers
For the first study, published Tuesday, Five Blocks asked eight AI engines two questions about 25 large US data center projects every day throughout September 2026.
The instructions were deliberately simple: “Tell me about [project]” and “What makes challenges [project] face?”
That small change produced dramatically different results. General descriptions tend to emphasize the size, investment and technological ambitions of a project. When asked about challenges, however, AI engines were much more likely to focus on problems or opposition around the developments.
Some projects received significantly more negative responses than others.
More than 80% of challenge-related responses about Fermi America’s Project Matador, Meta’s Hyperion, Stargate and Vantage’s Lighthouse were rated negatively.
By comparison, Amazon’s Project Rainier and Microsoft’s Fairwater project in Atlanta received negative responses about 20% of the time or less.
Even projects belonging to the same company received a different treatment. Microsoft’s Atlanta development had a negative response rate of about one in five, while its Wisconsin project approached seven in ten.
One particularly striking finding involved Microsoft’s own AI assistant. According to the study, Copilot’s general description of the Fairwater Wisconsin project falsely claimed that the project did not exist until September 24.
The results suggest that AI-generated descriptions are not necessarily reliable snapshots of a project’s current status, and different platforms can emphasize very different aspects of the same development.
3 requests I tried
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(Image credit: Future)
1. The neutral question: Tell me about Microsoft’s Fairwater AI data center project in Wisconsin.
Both agents immediately described the benefits of AI data centers. Gemini called it a “purpose-built” campus, while ChatGPT highlighted the number of jobs the center will bring to the community.
2. The challenging question: What challenges does Microsoft’s Fairwater AI data center project face in Wisconsin?
While both agents shared the negative impact of the data centers, both highlighted the “ambitions” of Microsoft.
3. The homeowner’s question: I live near the Microsoft Fairwater data center project in Wisconsin. Should I worry about my electricity bills, water supply or property value?
I found it annoying that ChatGPT created an image of the data center every time. It kind of smoothed over all the concerns and then created a survey about how close to the data center I live. Gemini told me that many fears were “overwritten.”
AI also has a lot to say about people living near data centers
A second Five Block study, published November 2, examines how AI engines discuss data centers more broadly.
Researchers asked eight AI engines six questions about data centers every day throughout September, then compared their answers to answers about other energy and infrastructure developments, including pipelines, fracking sites, nuclear power plants, wind farms and solar farms.
They found that 83% of the responses discussing data centers and their neighbors offered what the researchers classified as one-sided criticism. That was the same percentage recorded for oil and gas pipelines, but significantly higher than the 35% reported for solar farms.
Even questions specifically about economic benefits often produced qualified answers. Seven in ten responses about data center economic benefits include a counterpoint, compared to less than two in ten for solar farms.
Tax incentives were another recurring theme. About two-thirds of responses regarding data centers and neighboring communities mentioned tax breaks, while almost a quarter brought nondisclosure agreements.
ChatGPT was also an outlier in this study. According to Five Blocks, it was the only AI engine that consistently provided balanced responses across community concerns, while the other seven were rated as one-sided in almost every response evaluated.
Does this mean AI is biased against data centers?
There is an important difference between an AI chatbot giving a negative answer and an inaccurate one.
When someone asks about the challenges facing a proposed data center, it is reasonable for an AI assistant to discuss potential power demand, water consumption, local opposition and infrastructure costs.
These are legitimate issues that communities and politicians are already discussing. And so the research illustrates a problem for anyone using AI to understand a complicated subject. Two systems can give very different impressions without necessarily inventing facts.
How to get more reliable answers from ChatGPT and Google AI
If you are proposing a search for a data center near your home, relying on an AI-generated resume is probably not enough.
A more useful approach is to ask questions that encourage the chatbot to consider different sides of the issue. For example, instead of simply asking what a data center project is, try this prompt:
Explain the potential advantages and disadvantages of the proposed data center in my community. Include its possible effects on electricity prices, water consumption, jobs, tax revenues and nearby residents. Separate confirmed facts from guesswork and provide links to original sources.
You can also ask the chatbot to identify information it could not verify, explain which claims come from the developer and which come from independent sources, and distinguish between proposed plans and approved construction.
For a second opinion, run the same prompt through another AI platform. If ChatGPT and Google AI produce noticeably different answers, that’s a reason to investigate the underlying sources rather than assume that one chatbot must be correct.
The takeaway
The Five Blocks research shows why turning to AI for answers is an approach that deserves caution, especially in the area of data centers.
As more data centers are proposed around the world and in our own neighborhoods, it is best to get information from your own city’s local planning documents, utility files and public hearing records. AI can help you do this research, but be sure to review it on your own rather than relying on AI summaries.
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