My work places me simultaneously in three fields being reshaped by technology: AI, semiconductor supply chains, and circular economy.

This position has one advantage: I can see both technological acceleration and touch physical limits daily. When you’re running debate engines with four AI models on one hand while processing water and electricity bills for metal recycling production lines on the other, your feelings about “technology will change everything” become rather complex.

So my view of 2030 doesn’t start from technological potential, but from constraints. Behind every beautiful-looking technological curve lies a physical, economic, or ethical ceiling. Only by seeing the ceilings clearly can you know where the real opportunities lie.

AGI: Deepening Will Come, General Won’t Yet

AGI is the hottest narrative of recent years. But if you’re actually using AI to get things done—not playing around, but making daily decisions with it—you know we’re still far from general intelligence.

In “US AI Three-Year Countdown,” I analyzed how current AI bottlenecks aren’t just algorithmic, but also include physical limits of computing resources (energy consumption, heat dissipation) and the ceiling of high-quality training data. AI in 2030 is more likely to present “deepening in specialized fields”—medical diagnosis, materials science, manufacturing optimization—rather than comprehensive AGI breakthroughs.

But the impact of “deepening” itself will be substantial enough. When AI reaches superhuman expert levels in specific fields—as I discussed in “AI Is More Socially Intelligent Than Humans,” this is already happening in social intelligence—the cost structures and talent requirements of industries will be completely rewritten. Human-AI collaboration isn’t an option; it’s inevitable.

Quantum Computing: Threats Arrive Before Applications

Quantum computing has enormous potential in cryptography and materials science, and is also a key geopolitical variable. But it faces challenges with quantum coherence instability and extremely high error correction costs. By 2030, it will still be dominated by hybrid systems, with general-purpose quantum computers remaining in experimental stages.

Interestingly, quantum computing’s threats are advancing faster than its applications. Countries are already preparing for how “quantum supremacy” might break existing encryption systems, directly affecting strategic positioning in the semiconductor industry. In “Taiwan Semiconductor’s Tenfold Leap,” I discussed how Taiwan’s next round of competition isn’t just in manufacturing processes, but in systems integration capabilities. The quantum computing timeline will accelerate this transformation.

Climate Resilience: Not a Slogan, But Cost Structure

Doing metal recycling at my company, climate resilience isn’t an abstract concept for me—it’s the monthly water and electricity bills.

Renewable energy development is constrained by materials science bottlenecks and the long cycles of infrastructure updates. But the more fundamental issue is: climate risk isn’t a single event, but a trigger for compound crises—when extreme weather simultaneously impacts supply chains, food systems, and population movement, no single-point solution is sufficient.

This is why I keep saying “circular economy isn’t idealism.” When water, electricity, and chemical costs all rise, resource reallocation efficiency directly determines your competitiveness. This is especially true for the semiconductor industry—if recycling efficiency improves by 5%, the impact on profit structure far exceeds cutting 1% in personnel costs.

Silent Restructuring of Society

Technological change attracts all attention, but social structural changes are often more profound.

Aging populations, gig economy, and digital transformation are happening simultaneously. Education systems are still teaching skills from twenty years ago, while the job market already demands completely different capabilities. In “Canary in the Coal Mine: AI Employment,” I discussed how what’s being eliminated isn’t specific positions, but entire division-of-labor logics. UBI (Universal Basic Income) has evolved from academic concept to policy experiment, not because of idealism, but because the old social contract can’t hold up.

Don’t Predict, Build Resilience

My most honest attitude toward 2030 is: I don’t know. No one knows.

Every seemingly certain trend has variables behind it that could be overturned by black swan events. AGI might arrive five years early due to an algorithmic breakthrough, or stagnate for ten years due to exploding energy costs.

So my chosen strategy isn’t precise prediction, but building resilience that can survive across multiple scenarios. Deepening human-AI collaboration capabilities in the AI field. Strengthening systems integration beyond just manufacturing processes in semiconductors. Making resource reallocation a core competitive advantage rather than just an ESG label in circular economy.

True foresight isn’t about predicting the future. It’s understanding which variables you can influence, then concentrating resources there.