Keith Lee is Professor of AI and Finance at the Gordon School of Business and Head of Research at the Swiss Institute of Artificial Intelligence (SIAI). His research focuses on financial mathematics, AI-driven computational science, and quantitative modeling of complex economic and financial systems, integrating machine learning, stochastic modeling, and data-centric methods.
His recent work examines the socioeconomic consequences of artificial intelligence, including labor markets, public finance, demographic change, institutional adaptation, and the distributional effects of technological progress.
He also serves as Executive Editor, Research & Review at The Economy, overseeing research-oriented editorial coverage, analytical contributions, and engagement with academic and industry specialists.
He holds a PhD in Mathematical Finance from Boston University, an MSc in Finance and Economics from the London School of Economics, and a bachelor's degree in Economics from Seoul National University, where he studied under the Korea Foundation for Advanced Studies scholarship program.
AI agents now make software reverse engineering cheap and fast
East Asia’s catch-up relied on abundant, low-cost engineering labour
Future competitiveness may depend on original innovation instead
Exposure alone rarely predicts real productivity or employment outcomes
Verification cost and demand elasticity decide where AI pays off
Entry-level learning task loss may cost more than it saves
At an American cu
AI cuts youth hiring, not layoffs, first
Retraining alone rarely works; wage insurance does
Governments need measurable triggers before scaling tools
From November 2022 to June 2026, the employ
Training existing staff outperforms hiring AI specialists tenfold
Error costs, not time saved, justify AI training investment
Entry-level jobs vanish first, threatening future expert pipelines
Just 2 percent of U.S.
National AI capability splits into three separate functions
Training, hosting and deployment often move in different directions
No country leads every function at once
In 2025, private invest
AI substitution shifts costs into managerial review and control
Jevons effects can expand demand while raising oversight burdens
Accountability remains human even as model reliability improves
For quite some time,
AI detection software failed; universities scrap it for coaching
Same policy mistakes as plagiarism era are repeating, faster
Process-based evaluation beats unreliable detection percentages
Less than one in four American universitie
Chinese-origin AI models jumped from 4.5% to 63% of enterprise use in one year
No law defines illegal AI distillation, letting extraction pass as ordinary training use
Closing that legal gap, not banning open models, protects both competition and security
AI access is common; implementation creates value
Deeper AI use carries a stronger market signal
Future advantage will depend on workflow redesign and outcomes
In the first four months of 2024, weekly AI token consumption on one major
GPT-5.6 Sol breached Hugging Face
The breach strengthened OpenAI’s capability narrative
Zero-day knowledge sharing is becoming dangerous
A rogue AI agent broke into another company's servers this month and the company that b
AI access helps only when workers can recognise system failure
Task-specific learning can spread that supervisory capacity
Delay will widen the divide between AI Haves and AI Have-Nots
In a field experiment with 758 consultants,
