Since the launch of ChatGPT at the end of 2022, AI has quickly changed how we work, how we learn, how we love, and how we heal. It has also made one thing painfully obvious: the AI race is not just about models. It is about who gets to set the rules, who captures the economics, and who can ship capabilities fast enough to become the default.

At a Rest of World event during New York Tech Week, the discussion centered on “the Great AI Divide,” a split in dominance between U.S. and Chinese AI companies. The event looked at the challenges created by this geopolitical and industrial bifurcation, and it also explored possible solutions. If you are an executive, the stake is simple: your company is likely building in an environment where the competitive baseline and the regulatory baseline do not match.

Here is why the divide matters. When one geography holds an edge in frontier model development, tooling, deployment infrastructure, or distribution, the entire market starts to organize around that advantage. Buyers prefer what works, integration gets easier, talent migrates toward faster feedback loops, and venture capital follows momentum. But that pull effect does not happen evenly across borders. Instead, it can produce two AI ecosystems with different incentives, different compliance expectations, and different risk tolerances.

Meanwhile, the regulatory backdrop is moving at a similar pace, just with different emphases. In the U.S. and China, regulators and policymakers have been reacting to AI with an eye toward security, competition, and governance. That means leaders in product, legal, and risk cannot treat “AI regulation” as one global checklist. It is more like multiple overlapping scorecards that change as governments respond to new capabilities and new threats.

This is where board dynamics and capital strategy get tricky. A company can feel “ahead” in one dimension, but behind in another. You can have strong AI applications, yet still face uncertainty about data access, model provenance, export or cross-border compliance, or how your system is expected to behave under different legal regimes. The Great AI Divide turns those uncertainties into strategic costs. It can slow partnerships, complicate procurement, and make enterprise customers ask for assurances they did not need before.

Second-order effects are also the quiet danger. When two ecosystems dominate, they can set different technical norms. One side may push interoperability and rapid iteration through its commercial ecosystem. The other side may emphasize state priorities, different deployment pathways, and distinct approaches to governance and oversight. Over time, that divergence can create integration friction for multinational businesses. It can also affect how talent evaluates opportunity, how developers learn new tooling, and how quickly teams can operationalize models in regulated industries like finance, health, and infrastructure.

That is why the solutions discussed in the Rest of World session matter for leaders who are not in the U.S. or China. If your strategy assumes a single global AI market, the divide can force you to re-plan. You may need redundancy in vendor choices, a clearer plan for compliance across jurisdictions, and more disciplined evaluation of risk and performance. In practice, “possible solutions” often means investing in governance, building safer deployment processes, and designing products that can adapt as rules change.

Ultimately, the event framed the Great AI Divide as a leadership problem, not only a technology problem. ChatGPT’s launch at the end of 2022 accelerated AI adoption worldwide, but adoption does not automatically equal alignment. For executives and boards, the strategic question is: can your company operate in a world where U.S. and Chinese dominance shape both competition and compliance, and where second-order consequences show up in procurement, partnerships, and product timelines? If you can answer that, you can move faster without stepping on landmines.