AI Will Commoditize Coding, Not Creativity: Labor Market Outlook from Animoca’s Yat Siu

Coding Is Becoming a Commodity

Siu’s central argument is direct:

The superpower of an AI is it can code everything.

He goes further, predicting that AI’s coding capabilities will eventually surpass that of humans.

This is not a distant forecast. It is already visible in how developers use tools like GitHub Copilot, Cursor, and Claude to generate, debug, and refactor code at speeds no individual engineer can match. The implication is structural — if coding becomes a commodity, it can no longer serve as a durable competitive advantage for workers or organizations.

What fills that vacuum matters enormously.

The Skill That Survives: Creativity and Coordination

Siu’s answer is unambiguous.

We have a real commoditization on capability and intelligence, which means that the skill has to be about creativity and coordination.

This framing is precise in a way that generic soft skills discourse rarely is. Creativity here is not decorative — it is the capacity to define problems, generate novel directions, and make judgment calls that machines cannot derive from training data alone. Coordination refers to the human ability to align people, incentives, and context across complex systems.

Together, these two qualities represent what AI cannot yet replicate at scale: the ability to decide what to build, not just how to build it.

A Structural Argument for More Jobs

Siu does not dismiss disruption. He acknowledges it plainly. But his net assessment is growth, not contraction:

AI is going to be creating a lot more jobs.

The historical parallel holds some weight. Automation in manufacturing displaced certain roles while generating entirely new categories of work — logistics, quality systems, product design. AI is likely to follow a similar pattern, compressing execution costs while expanding the surface area of what is economically feasible to attempt.

The critical variable is transition speed. Displacement and creation rarely happen in sync, and the workers caught in between carry the real cost.

Reclaiming Creativity From the Machine Age

One of Siu’s more striking observations is not about AI at all — it is about what industrialization already took from us.

We’re born creative, and we’re losing our creativity to fit into a system because we’re trying to be turned into machines and do actions that are sort of regular,

he said.

This is a pointed inversion of the standard AI-threat narrative. The argument is that humans have already been partially mechanized by the demands of industrial and knowledge-work systems. AI, paradoxically, could reverse that process — freeing people to operate at the level of genuine human capability rather than optimized task execution.

Machines can ultimately deliver what we need to do on that side of things, while we can be truly human,

Siu said.

On Risk: Measured Optimism, Not Dismissal

When pressed on Anthropic’s warnings about AI dangers, Siu was clear about where he stands — without being dismissive of the concern.

Most people are going to be using AI in a way that would be beneficial, he said. There’ll be a few people that will do bad things, they would have to be stopped, but… this, to me, doesn’t feel like it’s a nuclear arms race.

This is a calibrated position. It acknowledges that misuse is real and requires active response, while rejecting the framing that AI risk is categorically existential in the way nuclear proliferation is. For a founder who has overseen more than 200 investments across gaming, DeFi, and real-world assets, this is not naivety — it is a risk-weighted read from someone operating deep inside the ecosystem.

What This Means for AI Tool Adopters

For founders, marketers, and operators choosing how to integrate AI into their workflows, Siu’s framework offers a useful filter.

If a task is primarily about execution — writing boilerplate, generating code, processing data, formatting outputs — AI will handle it with increasing competence and decreasing cost. Investing heavily in human capacity for those tasks is a diminishing return.

If a task requires judgment, originality, or the ability to navigate ambiguous human systems, that is where human investment compounds. The tools that will matter most are those that extend creative and coordinative capacity, not those that simply automate what was already routine.

The Takeaway

Yat Siu’s labor market outlook is neither reassuring nor alarming — it is structural. Coding fluency, long treated as a proxy for technical intelligence, is being absorbed into the machine layer. What emerges on the other side is a premium on the qualities that industrialization spent decades suppressing: creativity, judgment, and the ability to coordinate across complexity.

The question for anyone building a career or a company in the AI era is not whether to use these tools. It is whether the work you are protecting is the kind that machines cannot yet do — and whether you are actively developing the capacity to do it well.