"Really can apply"— Shark Tank investor Mark Cuban explores AI's potential to tackle government inefficiencies

In a recent interview on The Weekly Show with Jon Stewart podcast, posted on Instagram on December 19, 2024, Mark Cuban— entrepreneur, and Shark Tank investor— discussed the transformative potential of artificial intelligence (AI) in addressing inefficiencies within government processes.

Cuban highlighted AI's ability to streamline and expedite bureaucratic operations, particularly in areas like environmental protection.

The Shark Tank investor emphasized that AI could play a key role in reducing the friction in regulatory frameworks, such as the National Environmental Policy Act (NEPA), and improving decision-making efficiency across government agencies.

“In my opinion, and the conversations I had with some of the Harris folks is that's where AI really, really can apply... Artificial intelligence is great for that,” Cuban noted.

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Shark Tank investor Mark Cuban explores AI's potential to facilitate government methods and reduce bureaucracy

The role of AI in streamlining government regulations

Government agencies face challenges when processing large amounts of data, making regulatory decisions, and adhering to complex guidelines. Cuban highlighted one area where AI could make a meaningful impact: environmental assessments. He explained that the NEPA process involves extensive review procedures to evaluate the environmental impact of projects.

Cuban described how AI could be used to automate these processes by training large language models on the data and rules that govern such decisions. The Shark Tank investor emphasized that AI could help reduce bureaucratic friction and expedite decision-making, especially when dealing with large volumes of regulatory data.

“There are rules that the individuals on those councils and boards make those determinations…Yeah, it’s onerous. There’s tons of bureaucracy, but tons of data there. You put that into artificial intelligence into a large language model and you let, you know, you use that to train the large language model,” he stated.

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Reducing bureaucracy in infrastructure projects

Cuban referred to recent occurrences in showing how government procedures may speed up when required, such as citing a highway collapse on I-95 in Pennsylvania. Cuban talked about how this event ended rapidly, even with standard regulatory procedures that typically lengthened the process over many years.

Cuban added that while the decisions might seem obvious when urgency calls, problems arise where decisions are less obvious. The Shark Tank investor cited the possible use of AI in infrastructural decision-making that could remove bureaucratic red tape and reduce delays.

"The challenge is who makes that decision? When it's obvious, it's easy. When it's not so obvious, it's far more difficult. And so that's where the AI comes in in large language models because across the breadth, however many instances of evaluations that need to take place across the country, you don't want individuals having to make those decisions,” Cuban said.

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Balancing AI with human decision-making

While Cuban admitted that AI can improve government operations, he also emphasized the need for human judgment in certain decision-making processes. He said that while AI can handle data-driven and routine decisions, there are situations where human judgment is required.

This is where the Shark Tank investor pointed out accountability with decisions made by AI. An elected official has voters held accountable for the action performed. This kind of accountability isn't extended to an AI system.

"I'm nervous about abdicating that at least with people, there is a certain regime of accountability that we can bring through. I can't vote out a large language model,” Stewart commented.

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