One quality will be most in-demand from job-seekers in the AI era, Animoca co-founder Siu says

Yat Siu said AI will disrupt labor markets but ultimately create more jobs by commoditizing coding and shifting value toward creativity and coordination. He struck an optimistic tone, arguing humans can focus on creative work while machines handle repetitive tasks, and said most AI usage will be beneficial. The comments are broadly positive for the AI and venture ecosystem, but they are opinion-based and unlikely to move markets materially.

Analysis

The investable implication is not simply “AI adds jobs,” but that labor value migrates from execution to orchestration. That favors platforms that sit on top of fragmented workflows—workflow software, low-code automation, and creator tools—while compressing pricing power for commodity implementation, outsourced coding, and routine back-office services. The second-order effect is a widening gap between firms that can redeploy labor into higher-margin judgment work versus those whose revenue model depends on billable repetitive labor.

This also reinforces a multi-year CapEx cycle in tooling, model integration, and security layers even if headline AI enthusiasm cools. The near-term winners are the picks-and-shovels vendors that reduce friction between models and enterprise systems; the longer-term losers are agencies, IT consultancies, and BPO-heavy businesses that are slow to reprice. A key nuance is that increased “creativity demand” can expand the market for premium human brands and bespoke content, but only after a painful transition where most intermediate creative labor gets unbundled.

The contrarian risk is that optimism about job creation may be true in aggregate but wrong at the margin for public-market earnings: AI can raise GDP while still flattening wage growth in white-collar segments, which pressures consumer discretionary spend and elevates political scrutiny. If enterprises discover they can substitute AI for entry-level knowledge workers faster than they can create new coordination roles, hiring freezes can show up first in 1-2 quarters, with profit sensitivity most visible in 12-18 months. That would likely force a rotation from “AI beneficiaries” into “AI cost cutters,” favoring companies that monetize automation more directly than those depending on speculative usage growth.

Key Decisions for Investors

• Long MSFT / short EPAM as a 6-12 month pair: MSFT captures platform and workflow monetization, while EPAM is exposed to AI-driven pressure on billable implementation hours; target 15-20% relative outperformance if enterprise automation adoption accelerates.
• Buy GOOGL or MSFT call spreads 9-15 months out: upside comes from embedded copilots and cloud attach, with defined risk if AI monetization remains usage-light; structure for 2:1 to 3:1 payoff on incremental enterprise adoption.
• Short a basket of labor-arbitrage services names (e.g., ACN, CTSH, WIT) on rallies over 3-6 months: the thesis is margin compression as clients internalize more coding and process work; cut if bookings hold and pricing power proves stickier than expected.
• Long CRM / NOW on 6-12 month horizon: workflow ownership becomes more valuable as AI shifts the interface from task execution to coordination; these names can reprice on seat expansion and higher ARPU if copilots lift productivity rather than cannibalize licenses.
• Avoid chasing pure-play AI application names with no distribution moat; prefer incumbents with embedded enterprise data and workflow rights, since the first monetization wave is likely to accrue to sellers of integration, not standalone model wrappers.