“New technologies don’t emerge in a vacuum,” wrote Sarah Friar, Chief Financial Officer of OpenAI in “The Democratizing of Intelligence,” one of 21 essays in The Digitalist Papers Volume 2, — a roadmap of the relentlessly advancing capabilities of the AI revolution. “In the beginning, they often reflect and amplify the inequalities of their time — access is shaped by where you live, what you earn, and the systems around you. Then, broader diffusion takes hold. That is what we have seen in the past, with previous waves of technological progress. And that is what we see with AI today.”

Friar referenced a recent study that tracked the use of ChatGPT over the three years since its launch in November, 2022. As has generally been the case with new technologies, the majority of early adopters were young, highly educated males in living in high-income countries. But by mid-2025, the story had started to change. ChatGPT had already been adopted by 800 million active users, which is roughly 10% of the world’s adult population. The gender gap has narrowed dramatically, and the growth rate is higher in lower-income countries. While there is steady growth in work-related usage, — which  is more common for educated users in higher paid professional occupations, the growth has been even faster in non-work related activities, which grew from 53% to more than 70% of all usage.

“The data suggest that AI is being democratized faster than previous technologies, with early disparities in geography and gender closing at a remarkable pace,” wrote Friar. Her essay summarized the key changes in the use of ChatGPT over the past three years:

• Growth in low- and middle-income countries outpaced rich countries by more than four to one over the past year.
• As of mid-2025, internet-enabled penetration in countries at the 25th percentile of GDP per capita matched adoption in high-income economies.
• The gender gap closed: users with typically feminine first names slightly outnumbered those with typically masculine names.
• While all activity grew, non-work usage grew faster than work-related ones, climbing from 45% of traffic in July 2024 to more than 60% by mid-2025.
• Nearly 80% of conversations involve practical guidance, information seeking, or writing, indicating that people are mostly trying to get something done.
• Education stands out —10% of all messages, and nearly 40% of practical-guidance queries, are tied to tutoring or teaching.
• Among work tasks, writing leads the way, with editing and improving existing text outpacing new content creation by two to one, demonstrating that people are using ChatGPT as a tool to augment their work, not do it for them.

So, will AI democratize intelligence or will it lead to a new kind of AI divide?

Lessons from the Digital Divide

Let’s remember that 30 years ago we were asking similar questions about the then emerging digital economy. Will the digital revolution bridge the world’s knowledge divide, or will it lead to a new kind of digital divide?

While it was clear that the internet was becoming a remarkable platform for innovation, getting online in the 1990s and early 2000s required a personal computer and an account with a service provider, and e-commerce transactions required a credit card or bank account. Major new inequalities arose because so many around the world could neither afford a PC or an internet account and had no bank account or credit card. While the internet was truly empowering for those with the means to use it, it led to a growing digital divide both within countries and across the world.

Over time, this digital divide significantly disappeared, as inexpensive mobile phones and wireless networks transformed internet access from a luxury to a necessity that an increasing percentage of the world’s population could now afford. As a result, the quality of life of the world’s poor has significantly improved over the past few decades.

The Risk of an AI Divide

What can we now expect from our emerging AI revolution? This question was addressed in an article in The Economist published earlier this year, — “How AI will divide the best from the rest.”

When generative AI first became popular a few years ago, there was an expectation that the use of AI would level the playing field in a number of occupations. For example, in a 2024 article, “AI Could Actually Help Rebuild the Middle Class,” MIT economist David Autor argued that AI offers us the opportunity to extend the value of human expertise to a larger set of workers that have the necessary foundational training to perform these high-level tasks.

“More recent findings have cast doubt on this vision, however,” said The Economist. “They instead suggest a future in which high-flyers fly still higher — and the rest are left behind. In complex tasks such as research and management, new evidence indicates that high performers are best positioned to work with AI. Evaluating the output of models requires expertise and good judgment. Rather than narrowing disparities, AI is likely to widen workforce divides, much like past technological revolutions.”

Friar acknowledges the risk of such an AI divide in her article. “We are in the early phase of the AI rollout,” she wrote. “Early trends are promising. But the risk that AI could revert to being the domain of the wealthy remains. The infrastructure required to train and deploy advanced AI is capital-intensive. Frontier models require clusters of thousands of GPUs, each with power draws in megawatts. Data centers are constrained by cooling, grid capacity, land cost, and permitting.”

“These physical constraints privilege incumbent firms in regions with abundant energy and an enabling regulatory framework. AI research talent is also concentrated. The result? A growing risk of an ‘intelligence divide’: a world where only some countries, companies, or communities get to fully benefit from AI, while others are left behind.”

What It Takes to Democratize Intelligence

“If we want AI to serve the many, not just the few, and if we want to build on early glimpses of how this might happen, then we must treat it like the critical infrastructure it is. It isn’t just code; it needs to be a shared system of compute, energy, data, and talent that requires collective investment and stewardship.”

“Just as the industrial era depended on railways and roads, the AI era depends on power, connectivity, accessible tools, and skills. Governments and companies alike need to show up differently. It starts with energy and bandwidth. AI requires enormous compute power, and that means abundant electricity and fast, reliable networks.”

“We also need to bring people along in a way that sparks imagination, not fear, spotlighting the opportunities AI can unlock in classrooms, clinics, small shops, and community halls. People are far more likely to embrace change when they can see and feel its upside. That means governments, businesses, and civil society leaders must become storytellers of possibility, not just managers of disruption. Framing matters: Today’s leaders must emphasize that AI is a tool for shared progress, not a force to fear. That’s how we build ecosystems that deliver real benefits to real communities.”

“AI’s future isn’t fixed,” wrote Friar in conclusion. “It can concentrate power or expand opportunity, deepen inequality or unlock potential. Its trajectory won’t be defined by capability alone, but by the choices we make about where it goes, who it serves, and how it’s used. We have early evidence that AI can scale across genders, regions, and ages with speed never seen before. The chance before us to democratize intelligence is real, but we must act.”