As always seems inevitable, new digital technologies raise concerns about “digital divides.” And so it already is with artificial intelligence.
To be sure, we might argue the natural outcome of competition is concentration, especially in industries where capital investment is a requirement. That arguably means AI frontier models, built on a foundation of expensive high-performance computing hardware, will favor contestants with lots of capital access.
And that might apply not only to firms but to countries. But maybe we should not be surprised. In the internet era, we saw the development of “winner take all or most” market structures in most application areas. And there are clear reasons to believe this precedent will continue in the AI era as well.
And that has implications for the “AI divide” thesis.
AI Divide Concerns |
Source | Publication Date | Key Findings | Implications |
UNCTAD Technology and Innovation Report 2025 | April 3, 2025 | AI market projected to reach $4.8 trillion by 2033, but benefits may concentrate in wealthy nations. 118 countries, mostly from the Global South, are absent from AI governance. | Highlights risk of global inequality and need for equitable access to AI tools and infrastructure. |
The Emerging AI Divide in the United States | April 17, 2024 | High ChatGPT search volumes in U.S. West Coast; low in Appalachia and Gulf states. Education is the strongest predictor of AI awareness. | Indicates spatial and socioeconomic divides in AI adoption, reinforcing existing digital disparities. |
AI Is Deepening the Digital Divide | November 30, 2023 | AI exacerbates digital divide, excluding billions. Global North-South gap widens due to lack of access, skills, and participation. | Calls for inclusive education, stakeholder engagement, and ethical AI strategies to mitigate divide. |
How to Bridge the AI Divide | August 3, 2023 | AI amplifies economic inequality, with high-skill workers gaining while low-skill workers face displacement. Geographic disparities worsen. | Emphasizes need for equitable policies to distribute AI benefits and prevent economic downturns in automation-prone regions. |
The Current State of the AI Market: The AI Divide | April 15, 2025 | AI market to reach $1.81 trillion by 2030, driven by U.S. and China. Developing nations struggle with infrastructure and talent shortages. | Urges localized AI development and global cooperation to prevent concentration of AI benefits. |
AI for the Global Majority | February 21, 2025 | 2.6 billion people lack internet access, limiting AI benefits. Biased datasets exclude Global Majority communities. | Stresses need for inclusive AI design and digital literacy to prevent deepening socioeconomic divides. |
PwC’s Global Artificial Intelligence Study | June 25, 2017 | AI to boost global GDP by 14% by 2030, with 70% of gains in China and North America, <6% in developing regions. | Warns of unequal distribution of AI’s economic benefits, necessitating strategic investments. |
The Rising Costs of AI | December 7, 2024 | AI development costs create a “new rentierism,” locking advanced AI behind paywalls, deepening inequality. | Advocates for EU-led efforts to democratize AI access and foster innovation ecosystems. |
Network effects, near-zero costs of content reproduction, global reach, data flywheels and platform roles explain the “winner take all or most” structures.
In digital platforms, the value of the network increases with each additional user, creating positive feedback loops that favor a few firms with the largest networks.
Software and digital services can be replicated at low to virtually “no” cost, so a firm deemed to have the “best” product can ride user preferences to leadership.
The internet dissolves geographic barriers, so leading firms can scale globally without proportional increases in cost.
Products which benefit from network effects (especially social and user-generated-content networks) also find that having more users that generate more data leads to better products which leads to more users.
Finally, some companies became platforms, enabling third parties to conduct transactions, spurring their growth (Apple's App Store, Amazon's Marketplace).
AI might follow a similar path as well. AI models improve with more and better data, so entities owning or controlling massive datasets can build better-performing models, attracting more users, yielding even more data. It’s a flywheel effect.
Also, AI model development is capital intensive in a way that internet apps have tended not to be.
Training state-of-the-art models requires massive compute power and engineering talent. Only a few firms can afford this.
Also, AI frontier models are likely to eventually emerge as something like operating systems, creating whole ecosystems built around them. That, of course, will lead to a “brand preference” as well.
All of which will inevitably create an unequal market structure and concern about an “AI divide” that could affect individuals, firms, industries and countries.
On the other hand, we are early in the process, so there typically also is a lag between the time an important new technology, such as general-purpose technology begins to be introduced, and the time when we can clearly see the impact. (GPTs are technologies with pervasive applications, high productivity potential, and continuous improvement that affect most to all of an economy).
And we are likely to see uneven benefits and outcomes, early on, reinforcing concerns about “divides.”
Economist Robert Solow’s 1987 quip that “you can see the computer age everywhere but in the productivity statistics,” reflects the fact that entities need time to redesign their business processes to take advantage of new GPTs. It wasn’t until the late 1990s when PC-influenced productivity surged, for example.
Likewise, the steam engine took decades to significantly boost industrial output, and electricity’s full economic impact wasn’t realized until the early 20th century, nearly 50 years after its invention.
AI, often considered a GPT due to its broad applicability across sectors like healthcare, finance, and manufacturing, is in its early stages. Despite rapid advancements, its economic contributions presently might be called modest compared to projections.
The point is, we will be hearing lots about AI divides. It’s sort of inevitable.
On the other hand, there is room to question whether an “AI divide” actually has serious consequences.
Even when a few firms dominate a technology category, widespread benefits can still accrue to individuals, firms, industries, and nations. The dominance of providers does not inherently preclude others from capturing significant value—as long as access to the technology is available and usable.
Value creation by people, firms, industries and nations is different from “value capture” within a technology segment. In other words, Microsoft productivity tools might be dominant within global enterprises. But that never prevents user firms from wringing value out of the tools they do not create or own.
The same arguably goes for platforms that many third parties can take advantage of. Taken all together, productivity increases can happen broadly even when dominant suppliers of tools exist. The analogy might be electricity, which often is a monopoly in a given area, but all people, businesses and entities can use it to create value.
The further point is that “AI divides” might not exist too long, and that supplier leadership, in any case, does not imply general societal or economic inability to wring value out of the tools.
