AI’s Biggest Challenge Isn’t Computing Power—It’s Public Trust

0 Share Newsweek is a Trust Project member See more of our trusted coverage when you search. Prefer Newsweek on Google to see more of our trusted coverage when you search. Artificial intelligence is reshaping nearly every sector of the economy, but the infrastructure supporting that transformation is receiving unprecedented scrutiny. It is projected that by 2030, global data center electricity consumption will more than double to around 945 terawatt-hours per year, with AI identified as the primary driver of that growth. In the United States, data centers are expected to account for nearly half of the country’s electricity demand growth during the same period, placing increasing attention on how these facilities are designed, powered and integrated into local communities.

As investment in AI infrastructure accelerates, conversations have expanded well beyond computing performance. Water availability, electrical capacity, permitting, environmental stewardship and community acceptance have become central considerations for developers, policymakers and residents alike. Public confidence is increasingly becoming as important as technical capability because infrastructure succeeds only when communities believe it has been planned responsibly.

That changing landscape reflects a broader reality. The long-term success of AI will depend not only on advances in software and computing power but also on whether the physical infrastructure behind it demonstrates careful engineering, thoughtful planning and transparent decision-making.

That shift is also changing what leadership looks like within the AI infrastructure sector. Engineering decisions that once remained largely behind the scenes are becoming matters of public interest as communities, regulators and investors increasingly evaluate how projects will affect local resources over the long term. As a result, engineers are playing a more visible role in shaping conversations that were once driven primarily by technology companies and policymakers.

According to Eric Sonner, founder and CEO of Data Airflow , those conversations deserve greater attention. The company works with organizations designing and deploying the mechanical infrastructure that enables modern AI data centers to operate safely and efficiently. Data Airflow specializes in engineering cooling systems and critical infrastructure that help facilities manage heat, optimize performance and support long-term operational reliability.

Sonner believes many public concerns surrounding AI infrastructure are both understandable and necessary. From his perspective, the industry’s responsibility is not to dismiss those concerns but to address them through engineering decisions that are measurable, practical and accountable.

“Communities deserve to feel confident in infrastructure that is being designed with the future in mind,” Sonner says. “Trust is earned through thoughtful engineering, careful planning and demonstrating that facilities can operate responsibly for decades, not simply by explaining why they are needed.”

He explains that cooling technology illustrates how rapidly the industry continues to evolve. While public discussions often focus on water consumption, many newer facilities increasingly incorporate closed-loop cooling approaches that continuously circulate the same water rather than requiring constant replacement. Sonner notes that engineering decisions made during the earliest stages of facility planning frequently determine long-term operational efficiency, resource management and environmental performance.

From his perspective, engineering also requires looking beyond today’s challenges. Although water remains an important consideration, Sonner believes the industry’s next major infrastructure discussion will increasingly center on electricity generation, transmission capacity and grid resilience as AI workloads continue expanding. That outlook has also shaped his investment strategy, including support for companies such as Deployable Energy , which is advancing compact nuclear technology for localized power generation. He believes solving tomorrow’s energy challenges requires investing in technologies that can strengthen the infrastructure supporting AI long before demand begins to outpace supply.

While technology companies can develop data centers within two to three years, expanding the broader energy system requires substantially longer planning and construction timelines. That difference highlights why infrastructure planning must extend well beyond individual facilities and consider long-term regional capacity.

Sonner explains that responsible infrastructure also depends on collaboration among engineers, utilities, local governments, developers and technology companies throughout the planning process. Addressing community questions early, incorporating efficient cooling technologies and designing facilities that anticipate future demand can help reduce uncertainty while strengthening confidence in new projects.

“Engineering has always been about solving problems before they become failures,” Sonner says. “AI infrastructure should follow the same principle. Every project is an opportunity to demonstrate that innovation and responsible planning can move forward together.”

As artificial intelligence becomes increasingly integrated into everyday life, public conversations surrounding infrastructure are unlikely to diminish. If anything, they will become more consequential as additional facilities are proposed across the country. In that environment, engineering may prove to be more than a technical discipline. It may become one of the most important foundations for maintaining public confidence in the infrastructure powering AI’s next chapter.

Sonner notes that every generation inherits an infrastructure challenge that shapes its future. From steel manufacturing to modern semiconductor production, he explains that industrial growth has always required s...

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