Congressman Nick Begich (R-AK) sat down with the Bitcoin Policy Institute at PubKey in New York for a wide-ranging conversation that touched on his path from startup founder to Capitol Hill, his landmark American Reserve Modernization Act, and the dual promise and danger of artificial intelligence.
The interview offered a window into one of the more technologically fluent members of Congress — a distinction Begich traces not to his political career but to the decades before it.
Begich’s resume sounds unlike most of his colleagues. After undergraduate studies in entrepreneurship at Baylor University and an MBA from Indiana University focusing on information technology and decision science, he spent time at Ford Motor Company before returning to Alaska to found a software development company.
Starting with a credit card and a laptop, he built the company to about 150 employees in three countries, with a practice centered on early-stage startups—helping founders transform PowerPoint pitch decks into fundable products, often in exchange for equity stakes.
That background, he said, shapes how he operates in Washington. “Congress can be a frustrating place,” Begich said. “You’re not a CEO. You can’t say, ‘We’re doing this.'”
He drew a parallel between the consensus-building required in Parliament and the kind of obstacle navigation that defines startup life — facing capital constraints, entrenched competitors and perpetual investor skepticism. The difference, he noted, is that in Congress, runway is measured in election cycles, not funding rounds.
The case for a strategic bitcoin reserve
Begich entered Bitcoin in early 2013, working on the thesis that it could serve as a hedge against dollar depreciation for his business.
He lost around 440 Bitcoin in Mt. Gox collapse — “I was Goxed,” he said — but emerged from the bankruptcy process with what he described as a positive outcome and his convictions about the asset intact.
That belief is now law in proposal form. The US Reserve Modernization Act, or ARMA, which attracted significant co-sponsorship, would create a mechanism for the federal government to keep Bitcoin seized through law enforcement rather than auctioning it off.
The idea, Begich said, stems from a simple question: If Bitcoin can act as a reserve asset for a private business, what can it do for a government?
His argument rests on two properties that he considers non-tradable for reserve assets: scarcity and diffusion. Gold, he said, satisfies both—it’s hard to produce, and broad ownership has created consensus about its value over centuries.
Bitcoin, he argued, is approaching that same status within the digital asset ecosystem, representing close to 60 percent of the total cryptocurrency market capitalization.
“When these network effects are at play,” Begich said, “the earlier you are in that cycle, the more advantageous you will be.”
He also framed ARMA as insurance — not a bet on Bitcoin’s dominance, but a hedge against the possibility that the dollar will not remain the world’s reserve currency.
“Every 93 years on average, the reserve currency changes hands,” he noted, pointing to historic transitions through Portugal, Spain, France and Britain. Holding gold is an acknowledgment of that reality, he argued. Bitcoin must be seen in the same light.
AI: Promise and Peril
The conversation shifted to artificial intelligence, where Begich was measured, but direct about the effort. He described two competing visions of an AI future: one defined by abundance—cheaper health care, higher productivity, wider access to economic opportunity—and one defined by displacement, where the large-scale removal of human roles creates what he called “a disintermediation of purpose.”
On the issue of open source AI models, Begich pushed back against the idea that openness is an unqualified good at advanced capability levels. He mentioned the logic behind keeping nuclear and certain biotech research limited – some asymmetric risks, once released, cannot be contained.
“The spirit is out of the box,” he said broadly of AI, but argued that the full open-sourcing of frontier models, especially post-AGI systems, gives negative actors a tool with no practical upper limit to the damage they can cause.
He was pointed in his characterization of China’s open source model strategy, suggesting it is less a gesture of openness than an economic tool — a way to undermine the investment base for American AI development and collapse the domestic ecosystem from the outside.