The AI Revolution's Hidden Divide: Who Gets Left Behind?

The Real AI Threat Isn't What You Think

Anthony Tan's warning cuts straight to the bone: "AI itself will not replace humans, but humans who know how to use AI will replace those who do not."

As CEO of Grab, Southeast Asia's super-app serving 200 million users, Tan isn't speaking in hypotheticals. His company uses AI daily to optimize ride-hailing, food delivery, and payments across eight countries. He's witnessing the transformation firsthand—and it's not pretty for everyone.

The Invisible Casualties

Tan's biggest concern? The "invisible and underprivileged people" who risk being left behind. These aren't just abstract statistics—they're the small business owners still using paper ledgers while competitors deploy AI chatbots, the factory workers unaware that machine learning algorithms are already reshaping their industry.

Consider this: Studies show AI-enabled employees can be 3-5 times more productive than their non-AI counterparts. Within the same company, this creates a new kind of hierarchy—not based on traditional skills or experience, but on technological fluency.

Microsoft reported that 70% of workers would delegate as much work as possible to AI to lessen their workloads. But what about the 30% who don't even have access to these tools?

Two Paths Diverge

Companies face a crucial choice: pursue efficiency at all costs, or invest in inclusive AI adoption.

Grab chose inclusion. They're training drivers to use AI-powered route optimization and teaching restaurant partners to leverage data analytics. It's expensive and time-consuming, but Tan argues it's essential for social stability.

Most companies, however, take the efficiency route. Why retrain a struggling employee when you can hire someone already AI-literate? Goldman Sachs estimates that AI could automate 300 million jobs globally, but doesn't specify how many workers will successfully transition versus how many will simply be displaced.

The Corporate Calculation

The math is brutal but simple. Training existing employees costs money upfront with uncertain returns. Hiring new talent who already understand AI tools delivers immediate productivity gains.

Amazon spent $700 million retraining 100,000 employees for higher-skilled roles. Noble, but how many smaller companies can afford such investments? The result: AI adoption becomes a privilege of large corporations, widening the gap between big and small businesses.

Meanwhile, OpenAI reports that 80% of workers could see at least 10% of their tasks affected by AI. The question isn't whether AI will impact jobs—it's who will thrive and who will struggle in the transition.

The Policy Puzzle

Governments face their own dilemma: accelerate AI adoption to remain competitive, or slow down to ensure no one gets left behind?

The EU's AI Act emphasizes safety and ethics but might slow innovation. China's approach prioritizes rapid deployment with less concern for individual impacts. The US tries to balance both, with mixed results.

Tan advocates for a third way: "We must reach out to invisible and underprivileged people" through targeted inclusion programs. But who pays for this outreach? And what happens to countries or companies that can't afford to be inclusive?