Animoca co-founder Yat Siu says one AI-era skill will dominate hiring

Animoca Brands co-founder Yat Siu says AI is going to change what skills are most valuable in the workforce. In other words, the next hiring cycle will not just reward people who can do their current job faster. It will reward people whose skill set maps cleanly to how work gets done when AI tools become standard.

That is the core message, and it matters because it reframes “talent” from a static checklist into something more dynamic. If AI changes the value of skills, then job seekers and employers will both be forced to update what they believe is worth paying for. Employers will not only compete for candidates, they will compete for the right kinds of capabilities, and those capabilities can evolve quickly when AI adoption accelerates.

To understand why this is such a live-wire topic, zoom out to what the AI era usually does to work. When a technology can automate, augment, or speed up parts of a task, the bottleneck moves. The work that used to be hardest might become less differentiating, while the work that requires judgment, problem framing, integration, and supervision becomes more differentiating. Siu’s claim is essentially that AI will shift the center of gravity in hiring, so the “most valuable” skills today may be less valuable tomorrow.

This is not just a career issue. It becomes an operational issue for companies trying to scale. If AI changes the in-demand skills, leaders have to decide how they will respond when their current teams are not the right match for the new reality. That creates real board-level tension: do you hire new talent, retrain existing employees, or redesign workflows so fewer specialized skills are required? The budget math is brutal either way, because workforce changes tend to be expensive, slow, and politically sensitive.

There is also a policy and compliance angle that often gets missed in discussions about AI and employment. Even when the technology is moving faster than regulation, governments are still working through frameworks for AI use, data handling, transparency, and accountability. Those constraints can affect how companies deploy AI in real roles. So when leaders try to anticipate which skills will be most valuable, they also have to think about which AI tasks can be done safely and legally inside their operating environment. That means “AI skill” is not only technical. It can include the ability to operate within governance requirements, understand data boundaries, and implement controls.

For job seekers, the second-order effect is equally consequential. AI-era hiring is likely to reward people who can demonstrate they understand how to work with AI systems as part of an end-to-end process, not just those who can produce output. For employers, that can change screening criteria, interview formats, and onboarding. You can imagine a shift away from purely credential-based signals toward evidence of applied capability and adaptability. That would align with Siu’s premise that AI is changing skill value, because adaptability is what keeps performance relevant when tools and workflows evolve.

If you are an executive, the most strategic takeaway is not to chase buzzwords. It is to treat skill planning as a continuous process. The “one quality” framing matters because it suggests prioritization. In practical terms, companies should ask: which competencies are likely to remain useful as AI tools become embedded in the job itself? The companies that get this right can move faster, reduce rework, and keep teams effective as the role landscape shifts. The companies that get it wrong may find themselves with talent that can execute today, but not tomorrow, and that gap becomes more expensive the longer it persists.

Bottom line: Siu’s point about AI reshaping what skills are most valuable is a workforce forecast with immediate operational consequences. Hiring, training, and governance all have to line up with the new reality that skill value is not fixed, it is changing. For boards and leaders managing growth, the risk is not just missing hires. It is building an organization whose capabilities drift out of alignment with how work will be done in an AI era.