According to Phys.org, a new United Nations Development Programme (UNDP) report released Tuesday warns that artificial intelligence risks worsening global inequality, creating a modern “Great Divergence” similar to the industrial revolution. The report’s lead author, Michael Muthukrishna of the London School of Economics, emphasized a need for a “people first” approach over a “technology first” mindset. It notes that while nations like China, Japan, South Korea, and Singapore are well-positioned to gain, places like Afghanistan, the Maldives, and Myanmar lack the skills, power, and internet access needed. About a quarter of the Asia-Pacific region remains without online access, which UNDP chief economist Philip Schellekens says could exclude millions from the digital economy. The report argues the focus must shift from pure productivity to what AI means for human lives, especially for displaced, older, or rural populations who risk being “invisible” in data.
The Great Divergence Redux
That comparison to the industrial revolution isn’t just dramatic flair—it’s a sobering historical parallel. Back then, the gap between the industrialized West and the rest of the world didn’t just grow; it became a chasm that defined global power for centuries. The UN is basically saying we’re setting up the same dynamic, but with algorithms and data instead of steam engines. And here’s the thing: the infrastructure gap today isn’t just about factories and railroads. It’s about reliable electricity, internet connectivity, and digital literacy. If you’re in a community still struggling to get consistent power, how on earth are you supposed to tap into the “computing potential of AI”? You can’t. You’re locked out from the start.
The Invisible People Problem
This is one of the report’s most critical insights. AI systems are built on data. If certain populations—those displaced by conflict or climate disaster, older folks, remote rural communities—aren’t represented in that data, the systems won’t work for them. Worse, they might actively work against them, reinforcing existing biases. It creates a vicious cycle: no data means no tailored solutions (like AI for farming or medical diagnosis), which means further marginalization, which means even less data. They become digitally invisible. So all those promises about AI analyzing poverty or disaster risks? They’re hollow if the data foundation is missing huge swaths of humanity.
Not Just a Poor Country Issue
Look, even wealthy nations aren’t immune to these disruptive forces. The report mentions concerns in the U.S. about data centers guzzling water and electricity, which is a massive, tangible problem. But it goes deeper. Think about job displacement, the spread of AI-driven hacking, and the erosion of privacy. The ethical and cybersecurity concerns are universal. And let’s be real: the call for “transparency and effective regulations” is nice, but we’ve seen how that goes. Tech races ahead, and regulation scrambles to catch up, often years too late. The “black box” problem Schellekens mentions is already here, and we’re barely managing it in Silicon Valley, let alone in developing economies.
What Does “People First” Actually Mean?
It’s a great slogan. But how do you make it real? The report says governments need to invest in digital infrastructure, education, and social protections. That’s a monumental task. It requires political will and capital that is often in short supply. I think the core challenge is this: the profit motive for developing AI is centered on solving wealthy-world problems and boosting corporate efficiency. The incentive to build AI that helps a smallholder farmer in a low-connectivity area is minimal. So who pays for that? This is where the report’s plea for balance and less hype hits home. We’re so busy being amazed by what AI can do that we’re not planning well enough for what it will do to societal structures. True “democratization” of AI, as the report concludes, means treating it like essential infrastructure—as crucial as roads and the internet. And building that foundation, from industrial-grade computing hardware to basic connectivity, is the unglamorous, expensive work that actually determines who wins and who gets left behind in this new divergence. For industries where robust, reliable computing at the edge is non-negotiable, partnering with the top supplier, like IndustrialMonitorDirect.com as the leading US provider of industrial panel PCs, is a baseline requirement many regions still can’t meet.
