AI talent is unique for its finite property, mid-mobility, and high dependence on education and immigration policies. It’s renewable, but only through long-term investment.
Unlike infrastructure or energy, which require years of heavy investment, talent can be imported. By hiring skilled professionals from abroad, you’re reaping the benefits of 20+ years of education funded by another country’s taxpayers. Mid-level and senior talent, in particular, can deliver measurable impact in less than three years.
And unlike data, endlessly replicable with a flick of a license agreement, talent is semi-liquid and finite.
Think of AI talents as rare Pokémon, which makes the AI war nearly a zero-sum game, especially in the short run. Every researcher, engineer, or scientist gained by one country is a loss for another.
My inspirations for this article:
I was an expat once, now a Brit. I thought it’d be romantic, until reality hits. I am now ready to explore yet another continent that I could call home.
Reports like those from OECD.ai and Tortoise Media look impressive—eye-catching headlines and sleek dashboards. But if you take their numbers at face value, you risk misleading your business—or worse, your country’s policy.
What happened in our world today?
In the UK, we feel the economy is stuck in reverse since Brexit. In Germany, the decline of manufacturing casts a shadow over its future. In the U.S., millions are bracing for what another Trump term might mean:
American interest in moving abroad is about to ‘go into overdrive.’ — Fortune
For some of you who live in Ukraine, Israel, or Taiwan, uncertainty is your daily life (link below).
When you can’t fix the system, you do the next best thing: you move to another. A better life for yourself, your career, and your family.
I am slowly building up an AI knowledge database. I aim to share it with you hopefully before Christmas, as a holiday gift 🎁 for you. This article is about understanding—where nations stand, what’s overlooked by the AI data companies, and how this AI arms race could change your opportunities.
The questions I aim to answer:
Some history: what’s the cost of losing talent?
Are the big-name AI talent data trustworthy?
What must go wrong for the US to lose its attraction to talent?
How likely and how long would it take for other countries to catch up?
The Cost of Losing Talents.
The Talent Exodus to Taiwan and the Cultural Revolution (1940s)
In 1949, as the Chinese Civil War reached its climax, Chiang Kai-shek and the Nationalist government retreated to Taiwan. The exodus included the most brilliant minds like scholars, scientists, and administrators, they joined the journey, driven by fears of persecution under Communist rule.
On the mainland, the Communist Party focused on mobilizing workers and peasants, sidelining intellectuals during its early years of governance. The Cultural Revolution created a significant intellectual gap. This gap led to the further loss of thousands of educated individuals, and many of them chose not to flee to Taiwan. As a result, education and innovation came to a standstill. The process of rebuilding took decades.
Meanwhile, Taiwan flourished.
Those intellectuals who relocated laid the groundwork for a tech-driven future. Today, beyond TSMC, Taiwan is home to other giants like UMC, a pioneer in foundry services, and ASE Group, the largest provider of semiconductor packaging and testing services globally. China is 10 years behind Taiwan on chips.
Operation Paperclip a Post-WWII Rescue Mission.
In the rubble of post-WWII Germany, the U.S. and the Soviet Union weren’t just fighting over territory—they were fighting over brains. Operation Paperclip, a covert U.S. program, brought more than 1,600 German scientists, engineers, and technicians to America, including Wernher von Braun, the man who would later take the U.S. to the moon. The Soviets weren’t far behind, scooping up their own share of rocket experts.
These scientists had been the backbone of technological advances in Germany during the war. The departure slowed the nation’s technological recovery for decades.
In the U.S., these scientists became heroes of the Space Race. Von Braun’s team didn’t just build rockets—they built national pride, culminating in the Apollo 11 moon landing. The Soviets also leveraged their talent, putting Sputnik into orbit and scaring the U.S. into ramping up its own space program.
India’s Brain Drain (1950s–Present)
India is a paradox when it comes to talent.
It produces engineers and scientists by the millions, yet for decades, the country has struggled to retain them. The story begins in the 1950s, just after independence. India was brimming with ambition but hamstrung by red tape, limited infrastructure, and caste-based inequalities…
For many of India’s brightest, the dream wasn’t at home—it was abroad. An exodus of engineers and doctors to the West was underway.
The loss was profound, even until today, and the trend continues. By 2024, it is estimated that around 2 million Indian students will be studying abroad. Among them the top scorers of India’s prestigious Indian Institutes of Technology (IITs) revealed that 36% of the top 1,000 scorers in 2010 migrated abroad, with this figure rising to 62% among the top 100 scorers left.
By 2024, 2 million Indian students study abroad annually, while India’s IT sector misses out on $15-20 billion each year due to talent migration.
Storm Clouds Over the U.S.
The U.S. didn’t stumble into AI dominance—it built it brick by brick over 200 years. Geography, history, and culture all played a part.
English as the internet’s default language gave U.S.-trained models a treasure trove of data. Their policy is tech-friendly, venture capitalists fund bold, moonshot ideas, and their entrepreneurs thrive on risk-taking and learning from failure.
Europe? The moneymen are more cautious, and failure feels more like a career-ender than a lesson learned.
As long as the “US innovative, China replicates, and the EU regulate” pattern stays as is, the US is nearly unbeatable.
The chances of a dramatic fall are slim but gradual erosion?
What Must Go Wrong for the US to Lose Its Attraction to Talent?
Immigration Blockades: During Trump's first term, there were significant immigration restrictions, including the temporary suspension of H-1B visas. If similar policies return, talent could choose other countries like Canada or Europe.
Cost of Living Crises: Tech hubs like San Francisco are absurdly expensive. Talented professionals might opt for affordable, thriving alternatives like Berlin or Toronto.
Supply Chain Disruption: Trade wars and tariffs could choke the flow of critical hardware—think GPUs and chips from Mexico or Asia—slowing AI research to a crawl.
Worsen Fundamental Education: Only 16% of Americans are “AI literate,” and with the U.S. ranking 36th in general literacy, it means most citizens can’t effectively communicate with AI and include AI in their workflow, let alone develop one. This leaves America reliant on foreign talent and exposed to immigration shifts.
Losing focus on either one of the factors would hand over the lead to nations willing to outwork and outsmart the U.S.
Other developed nations are, of course, building their own AI ecosystems. The U.S. is notoriously hard to enter, much less friendly to stay, and lacks work-life balance; hubs like the UK, Canada, and Germany have become the obvious choice.
Are the Big-Name AI Talent Data Trustworthy?
Education and Salary as a Rough AI Talent Measurement.
Here's the Stack Overflow developer survey data that I got from OECD.ai.
Combined it with Tortoise Media’s AI talent rankings
You would get the following conclusions if you didn’t look at how they got the data.
Countries like India and Russia show a concentration in lower salary ranges.
Russia has limited high-paying roles, suggesting an underdeveloped AI market.
China and Korea have only single digits AI talents.
CAUTION!! Critical points these data failed to consider:
1 Salary ≠ True Competitiveness: Focusing on salary alone ignores cost-of-living differences. A $40K salary in India offers a vastly higher standard of living than $100K in Germany, skewing global comparisons.
2 Platform Bias Misleads Rankings: Data from English-speaking platforms like LinkedIn or GitHub excludes talent in countries like China, where professionals operate in isolated ecosystems. This massively underrepresents China’s AI capabilities. China has Zhihu and CSDN instead of Stack Overflow and uses Gitee instead of GitHub.
3 Quantity Over Quality: India’s #2 ranking in AI talent reflects a large number of engineers, but high output doesn’t guarantee expertise in cutting-edge AI fields like research or product innovation.
4 Duplicate Data Inflates Scores: Rankings often double-count metrics (e.g., LinkedIn activity, Coursera enrollments), overestimating regions with high platform adoption while undervaluing talent in countries with alternative systems.
That said, let’s give credit where it’s due—it’s not easy to compress a complex, 20-dimensional concept into a simple two-dimensional dashboard. The process inevitably risks oversimplifying reality or relying on biased weights to explain a nuanced idea.
Global Competitors Are Eating from the U.S.’s Plate
So, let’s triage other information sources to see if they provide a more direct lens into AI competitiveness.
The OECD and Tortoise Media datasets focus heavily on proxy indicators like salary ranges, certifications, and survey responses. These are indirect measurements that rely on self-reported or institutional data to infer talent capacity. While valuable, they don’t capture the tangible outcomes of AI activity.
In contrast, focusing on research output (via OpenAlex), foundational technology development (via Epoch AI), and investment activity (via Github) shifts the narrative to measurable outputs.
This shift from input-based to output-based metrics enables a more nuanced understanding of AI ecosystems.
China
China’s AI strategy is like a game of Go—quiet, deliberate, and deeply strategic. They produce many research papers, rank second in the large-scale language models, and are not stingy throwing money into the problem. Oh, of course, the support of a state that views AI as a geopolitical weapon.
They focus on industrial automation, robotics, and the military and spend less talent, finance, and energy on commercial products.
China might lack the creative freedom and entrepreneurial chaos of the U.S., but it operates like a sniper rifle in the world of AI: precise, focused, and unwavering in its aim. The totalitarian drive channels immense resources and talent into specific niches like robotics and industrial AI, ensuring they hit their targets with unerring accuracy.
U.S. as a benchmark, what China is like in AI models, research, and ranking.
United Kingdom
The UK is like an aged Duke—polite, ethical, and always striving to do the right thing.
It wants to lead explainable AI and governance, leading the global conversation on ethics, but here’s the problem: noble intentions don’t win races.
A lack of infrastructure holds back the UK’s AI ecosystem, particularly data centers and energy resources, which are critical for developing large-scale models. These sheer disadvantages have already stopped the ambitious AI talents because there's no chance to develop a high-energy consumption model in the UK.
Add to that a salary gap that drives top talent across the Atlantic, and it’s clear the UK is fighting an uphill battle. While it excels in theory and thought leadership, it struggles to execute at scale.
U.S. as a benchmark, what the UK is like in AI models, research, and ranking.
India
India is a paradox in the AI talent landscape: a country with vast numbers but struggling to deliver on quality. Its talent pool is immense, producing an army of engineers and coders every year, but the pipeline for high-end research and innovation is barely trickling. The brain drain isn’t just a symptom—it’s the outcome of deeper issues.
The focus often leans toward quantity over quality. The output of research papers is low, and the culture prioritizes execution over exploration. This leaves India excelling in repetitive coding tasks but lagging in producing groundbreaking ideas or foundational AI models.
A large portion of its workforce operates at low salary ranges, feeding global tech supply chains rather than driving them forward.
France
France is like a master craftsman in a world of mass production. Its “AI for Humanity” initiative underscores its intellectual roots, prioritizing ethics, governance, and societal impact over brute-scale innovation.
While the U.S. and China race ahead with massive budgets and foundational models, France focuses on niche applications like healthcare AI, where precision and intention matter more than volume.
But strict regulations, underfunded startups, and a brain drain to higher-paying markets threaten its ability to compete globally. France risks becoming the world’s ethical advisor rather than a heavyweight in AI innovation.
U.S. as a benchmark, what France is like in AI models and research.
Germany
Germany’s AI strategy is like a beautifully engineered BMW stuck in traffic—it’s efficient and reliable, but it’s not going anywhere fast.
While the country shines in industrial applications like robotics and autonomous vehicles, it struggles with scaling foundational models, thanks to a lack of computing infrastructure and heavy regulatory baggage.
The EU’s AI Act, designed to ensure ethical AI, often feels more like a speed limiter than a safety feature. Germany has the precision and expertise to lead in industrial AI, but unless it invests in foundational technologies and clears bureaucratic roadblocks, it risks falling behind in the race for digital sovereignty.
U.S. as a benchmark, and what Germany is like in AI models, research, and ranking.
Canada
With world-class research hubs like Mila and the Vector Institute, as well as a multicultural talent pool, Canada punches above its weight in AI research. But here’s the reality: its infrastructure and computing power are woefully inadequate to support large-scale model development, and its limited funding trails far behind the likes of the US and China.
Add to that a growing brain drain, as top talent is lured away by higher salaries and bigger ecosystems of its neighbor. Canada risks becoming a training ground for AI experts who drive innovation elsewhere. Canada’s role in the AI world may remain more supportive than central.
Singapore
Singapore’s AI strategy is like a precision-engineered watch—efficient, reliable, and designed for applied AI solutions like smart cities and urban planning. However, its small size limits its ability to compete in foundational model development, and a persistent talent shortage challenges its growth.
While its supportive government policies keep innovation ticking, Singapore’s AI influence will likely remain focused on niche applications rather than global leadership.
Until Next Time…
Writing this article felt like running a marathon while juggling ideas. I threw half of them off a cliff—if I’d written this 50 years ago, you’d see piles of crumpled paper around my bin.
Over the last two weeks, I’ve sifted through research, questioned every flashy headline, and discarded more ideas than I kept. The hardest part is always deciding what not to write.
Every country’s story comes with its own twists—some inspiring, some frustrating, and others downright contradictory.
There were moments I stared at data for hours, only to realize it didn’t mean what I thought. Rabbit holes became my second home, often leading to dead ends and a headache for the company.
As I mentioned earlier, I’m working on organizing the information I’ve uncovered. Though it isn’t ready to share just yet, I’ll be cleaning it up and creating a knowledge base for you soon.
If you think someone you know could benefit from this article, don’t keep it to yourself—just hit the share button. Let’s keep the conversation going.
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