The AI-Productivity Gap: Talent is the biggest factor limiting global GDP Growth
Why AI’s True Limitation Isn’t Technology—It’s People
Hi, I’m Guillermo Flor — a venture capital investor and entrepreneur. I write two newsletters:
Product Market Fit, focused on business, startups, and growth
The AI Opportunity, which explores the biggest business opportunities in AI before they happen.
I invest in AI companies and spend most of my time speaking with founders, operators, and other investors who are building the future.
Lately, I’ve been noticing a strange paradox in the market — and it inspired this piece.
AI Is Accelerating. Humans Aren’t.
Why people—not technology—are now the biggest bottleneck to global growth.
Over the past few years, we’ve witnessed one of the most explosive technology shifts in history. AI tools can now write essays, generate videos, discover new drugs, optimize supply chains, and even debug code.
Companies are being built in weeks. Industries are being reshaped in months. The scale and speed of innovation are breathtaking.
And yet—when you zoom out and look at global GDP growth, something doesn’t add up.
Despite this technological leap, global economic growth remains stubbornly linear. Productivity has ticked up in some sectors, but not at the scale or speed you’d expect from a revolution of this magnitude.
Why?
Because the bottleneck is no longer technology.
It’s people.
We’ve Entered the Age of the Human Bottleneck
AI is moving exponentially. But most of the world isn’t.
Every week, new tools come out that could automate work, speed up research, or unlock entire new businesses. But to make them work, you still need someone who knows how to use them.
Someone who understands the context, can adapt quickly, and can integrate these tools into real-world systems.
That person is increasingly rare.
There’s now a widening gap between:
Those who can use AI effectively,
And everyone else.
This gap is growing daily. It’s not just about knowing how to prompt ChatGPT. It’s about being adaptable enough to learn a new paradigm every 3–6 months. Most people (and companies, and institutions) simply aren’t wired for that pace.
Talent Is the Scarce Resource
For decades, the world has optimized around capital as the limiting factor for innovation. If you had money, you could build things. But in the AI era, capital is abundant.
What's scarce is talent that can turn this capital into leverage through technology.
Try hiring a world-class AI engineer. Or even someone who can combine off-the-shelf tools into a scalable workflow. The best people are snapped up immediately. The rest are learning — slowly.
Startups with a breakthrough product can’t scale fast enough because they can’t find enough AI-native operators.
Enterprises are sitting on mountains of inefficiency they know AI could fix — but they don’t have the people who can implement the fix.
Governments could radically improve education, healthcare, and infrastructure — but they don’t even know where to start.
The AI-Productivity Gap
Here’s the real problem:
We’re creating technology faster than we’re creating people who know how to use it.
Let’s call this the AI-Productivity Gap — the growing distance between what AI enables and what the current workforce can deliver. This gap is already slowing down innovation, despite the raw potential being higher than ever.
The longer this gap persists, the more we’ll see:
Capital misallocated.
Projects delayed.
Innovation trapped in labs or demos instead of deployed in the real world.
The opportunity cost is massive.
The Future Will Belong to Those Who Learn Fast
This is not just a hiring problem. It’s a global capacity crisis.
The organizations and countries that figure out how to massively upskill their people, adapt their institutions, and reduce friction between invention and implementation — they’ll win.
Everyone else? They’ll fall behind. Not because they lack the tools, but because they can’t make use of them.
AI can write code, write emails, optimize logistics, and generate marketing plans — but it still needs humans to:
Know what to build.
Validate it.
Deploy it.
And most importantly: adapt to what comes next.
Hope this was valuable!
Talk soon
- Guillermo
📌 Frequently Asked Questions (FAQs)
What is the AI-productivity paradox?
The AI-productivity paradox refers to the disconnect between rapid advancements in artificial intelligence and the relatively slow growth in global economic productivity. Despite AI tools transforming how we work, many organizations and individuals haven’t adapted fast enough to realize the full economic benefits.
Why isn’t AI improving productivity faster?
AI isn’t improving productivity at the expected pace because the real bottleneck is human adaptation. While AI capabilities are growing exponentially, most people and institutions aren’t adopting or integrating these tools fast enough. This lag creates what Guillermo Flor calls the AI-Productivity Gap.
What is the AI-Productivity Gap?
The AI-Productivity Gap is the growing divide between what AI makes possible and what people are actually able to implement. It highlights how a lack of talent, training, and institutional adaptability is slowing down the impact of AI on the real world.
Why is talent more important than capital in the AI era?
In the age of AI, capital is abundant — but talent is scarce. The limiting factor is no longer funding, but people who can understand, implement, and adapt to AI tools. The best AI-native operators are in high demand and are critical for turning AI potential into real-world results.
How do I become AI-native?
To become AI-native, you must develop the ability to quickly learn and integrate new tools, think in systems, and adapt to constant change. It’s not just about prompting ChatGPT — it’s about building workflows, validating ideas, and iterating fast in an ever-evolving landscape.
What skills are needed to stay competitive in an AI-driven economy?
Key skills include prompt engineering, automation design, systems thinking, data literacy, fast learning, and the ability to translate problems into AI-augmented solutions. The ability to combine off-the-shelf AI tools into scalable processes is becoming a superpower.
Why are companies struggling to hire AI talent?
There’s a global shortage of AI-native talent. Many companies lack people who can bridge the gap between cutting-edge technology and practical implementation. As a result, projects stall, innovation slows, and productivity gains remain unrealized.
How can organizations close the AI-talent gap?
Companies must invest aggressively in upskilling, internal training, and creating environments that reward adaptability. Building internal capability is now as critical as buying software or hiring consultants. Those who build fast-learning teams will lead.
How can governments use AI to improve public services?
Governments can deploy AI to optimize education, healthcare, and infrastructure—but most lack the in-house expertise to make it happen. Bridging this talent gap through training, partnerships, and forward-thinking policy is key to unlocking transformative results.
Where can I learn more about the intersection of AI, startups, and global innovation?
Subscribe to Product Market Fit, a newsletter by venture capital investor Guillermo Flor. It breaks down the biggest ideas in startups, growth, and technology—before they go mainstream. Join thousands of founders and operators who read it every week.