The board includes academics from Stanford, Harvard, and the University of California with specialties in fields like computer science, cryptography and fintech. Formally known as the Coinbase Independent Advisory Board on Quantum Computing and Blockchain, it is also composed of experts in blockchain and security from the Ethereum Foundation, the DeFi platform EigenLayer and from Coinbase itself.

In an interview with Fortune, Coinbase Chief Information Security Officer Jeff Lunglhofer explained how the arrival of quantum computing could defeat current encryption mechanisms, including the ones employed to protect the wallets and private keys held by Bitcoin owners.

“In simple terms, modern cryptography relies on hard math problems that would take thousands of years for a modern computer to solve,” he said. “But when we have a million times the horsepower [with quantum computing], that will provide the computation power to solve them.”

While the security threat of quantum computing is real, it is unlikely to be an urgent issue for at least a decade, according to Lunglhofer. His view is consistent with other experts who note that, while companies like Google and IBM have been building quantum computers for years, the current generation of these machines can only operate at a small scale and are not close to being able to crack the algorithms that protect Bitcoin and other networks.

The purpose of the new Advisory Board, says Lunglhofer, is to explore the coming impact of quantum computing in a “non-hype based way.” This will include promoting efforts by the blockchain industry, which are already underway, to update Bitcoin and other networks so that they are resistant to quantum-based attacks.

Currently, the Bitcoin network secures wallets by means of private keys, which are long strings of random numbers and letters that are visible to their owners, but that can only be guessed by means of an impossibly long series of trial-and-error attempts. When the quantum computing era arrives, it will be possible to guess a private key using trial-and-error. In response, Lunglhofer says, blockchain experts anticipate that Bitcoin and other networks will respond by creating larger keys and, at the same time, introducing “noise” to make the location of the key harder to detect in the first place.

All of this will require blockchain networks to introduce and deploy these defensive upgrades, a process that is likely to take years. In the interim, the new Advisory Board will begin publishing research papers and issuing position statements to help the crypto industry prepare for the arrival of quantum computing. The group plans to publish its first paper, which will focus on quantum’s impact on the consensus and transaction layers of blockchain, in the next month or two.

“Quantum computing is both a technological opportunity and a security challenge. By bringing together the foremost experts in the world, Coinbase is ensuring that the blockchain ecosystem is prepared, not just reactive,” said Yehuda Lindell, Head of Cryptography at Coinbase, in a statement.

Rather than defaulting to full-time executive roles that command lofty salaries and long-term overhead, companies are increasingly turning to experienced consultants, strategists, and advisors to provide leadership on a limited and targeted basis.

This is not a dilution of leadership, but a recalibration of where experience delivers the most value.

According to LinkedIn’s latest Jobs on the Rise report, the fastest-growing roles in the U.S. economy sit at the intersection of AI and strategy. AI engineers claimed the top spot, while AI consultants and strategists ranked No. 2 overall. Strategic advisors and consultants also placed in the top 10. Together, the data show that as execution becomes cheaper, human judgment becomes more valuable.

The underlying driver is the implementation gap. After years of AI experimentation, organizations are struggling to convert tools into returns. While they do not lack models or software, many lack orchestration. Companies are increasingly turning to AI consultants and strategists to align technology with business realities, governance, and incentives, work that requires credibility, cross-functional fluency, and the kind of judgment typically associated with senior leadership roles.

The labor market now reflects a clear division of labor. Demand is rising simultaneously for full-time technical AI talent and for senior professionals who can translate those capabilities into business outcomes. As companies scale internal AI teams, they are increasingly relying on external advisors and consultants to provide the judgment required to direct that work at critical moments.

The supply side of this shift is shaped by organizational reality. Executives continue to make daily decisions, but AI has concentrated risk into fewer, more complex, and higher-impact choices around operating models, capital allocation, and governance. Rather than expanding permanent headcount, companies are bringing in experienced external leaders to guide those decisions when the stakes are highest.

The economics reinforce the model. Although senior advisors and consultants often command higher hourly rates, their total annual cost is typically a fraction of a comparable full-time executive role because they are engaged for a limited scope and time. Just as important, this approach allows organizations to draw on multiple forms of expertise rather than binding themselves to a single permanent hire.

The talent profile filling these roles is equally telling. Many of these advisors are former founders, CEOs, and COOs. Experience functions as a filter. LinkedIn’s data shows that many of the fastest-growing strategic roles carry a median of eight or more years of experience. These are not entry-level positions, but mid- or second-act careers for professionals with deep industry context.

The rise of founders and independent consultants on the Jobs on the Rise list also signals that this shift is driven by talent behavior, not just employer demand. Senior professionals are increasingly opting for career paths that offer autonomy, variety, and the opportunity to leverage their skills rather than committing to a single organization in an uncertain environment.

As AI automates and cheapens execution, the market value of human judgment, strategy, and accountability rises. As a result, pricing power shifts from doing the work to deciding what work should be done and how it should scale.

In this environment, experience is the moat. What is often described as “fractional leadership” is better understood as the unbundling of executive judgment from full-time roles. Over time, this model is likely to become not a stopgap but a structural response to the redistribution of value, risk, and expertise in the AI economy.

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