Michael Glenwood Gibbs/theispot.com
Digital platforms and generative AI have lowered the barriers to accessing global talent, capital, and knowledge for companies everywhere while making it possible to reach customers across languages and cultures. Research my colleagues and I have conducted suggests that such tools make it easier for entrepreneurs to serve global markets and for investors to evaluate startups from afar.
Access to those technologies should result in a leveling of the global playing field that allows new ventures to thrive anywhere. Promising early-stage startups are emerging in areas like Jakarta, Nairobi, Kyiv, and São Paulo. But when it comes to scaling, the old pattern remains: Companies that scale and become category leaders are disproportionately concentrated in a handful of traditional hubs, such as Silicon Valley, while early-stage companies outside of them struggle to scale into larger businesses. Technology, it appears, is not enough to overcome the barriers to scaling.
When Technology Reduces Some Gaps — but Creates Others
Even as digital technologies reduce structural differences across locations, some companies respond to the new opportunities they create in ways that undermine the ability to scale. When entering new markets or adopting new technologies becomes as simple as a click, businesses can fall into one of two traps: either chasing every available opportunity or defaulting to what is closest and most convenient. Both responses can systematically disadvantage companies outside major hubs.
The first trap stems from the urge to go global before the company is ready. Today, companies can attract users from around the world with a single post on a digital product platform. As a result, many organizations — especially those in smaller markets under pressure to show global traction — try to pursue multiple global markets at once. But in doing so, they often overlook a critical resource: early users whose feedback they could more easily interpret because they share a common background or geography. My research shows that business leaders can more easily recognize the demand signals of local users and, as a result, learn more effectively about their company’s nascent product and refine it before expanding further.
Generative AI is making market expansion more complex, particularly for companies based outside English-speaking hubs. Research I conducted with colleagues shows that while GenAI helps high-quality ventures in these contexts stand out by improving the pitches of expert entrepreneurs more than those of non-experts, it disproportionately enhances pitch quality in English-speaking environments. As long as investors and customers rely on pitches as an input, ventures in non-English-speaking hubs may continue to face disadvantages when entering hub markets. This challenge is particularly acute for non-hub firms because they rely more heavily on text-based signals to reach global audiences.
Yet overcorrecting to avoid this trap can lead to the second trap: defaulting to what is close and familiar. This is particularly evident in startups’ technology choices. The global boom in tech entrepreneurship has dramatically expanded the set of available tools, many of which have the potential to accelerate adopters’ growth. At the same time, tool vendors are increasingly relying on GenAI to craft persuasive product descriptions, meaning that a well-written pitch is no longer a reliable signal of a tool’s value. This makes it harder for potential buyers to distinguish tools that will accelerate growth from those that are merely well packaged. When managers face too many persuasive options, they often fall back on simple rules — like choosing to adopt tools that were locally developed or are already familiar to them. Indeed, in research I coauthored, judges evaluating many polished pitches in a startup accelerator competition favored ventures from their own regions — even though the judges were no better at assessing local ventures’ quality — and passed over 1 in 20 promising startups as a result. The same bias shapes how companies choose their technologies.
Defaulting to what is local as a heuristic can particularly penalize companies in remote markets. These companies often encounter fewer locally developed or locally targeted tools — partly because tool providers themselves face pressure to cater to hub-based customers. Ongoing research by my colleagues and me shows that, as a result, genuinely useful technologies often go unseen, are misunderstood, or are deprioritized by the companies that could benefit most from them.
In this way, AI can unintentionally reinforce geographic disparities rather than eliminate them unless companies bring something that technology alone cannot provide: strategic focus.
Strategy as the Missing Equalizer
Prioritizing what is nearby can narrow the opportunity set for companies in remote locations because frontier innovations are often concentrated in hub markets. At the same time, pursuing every distant opportunity — which is often encouraged via external investor pressure — can diffuse scarce resources and weaken execution.
The solution is strategic clarity: a clear articulation of how a company intends to combine local and distant opportunities to create a differentiated position in the market. This begins with a single question: What is your competitive advantage? Are you serving a market that competitors have largely ignored? In this case, your advantage may lie in access — bringing a technology or use case to customers who have been overlooked. Or are you competing in an established market by offering a superior solution? Here, the advantage may be quality — delivering better performance than existing alternatives.
This distinction has direct implications for subsequent technology and market choices that can either widen or narrow the global scaling gap. When a company’s strategy centers on serving an underserved local market that it knows exceptionally well, the key may be to adapt an existing technology to the needs of that market.
For example, Grab and GoJek, founded in Malaysia and Indonesia, respectively, adapted the ride-sharing model pioneered by Uber to conditions that a hub-based competitor wouldn’t have easily been able to read or replicate: cash payments in largely unbanked markets, motorbike taxis suited to dense urban traffic, and an eventual expansion into food delivery and financial services that matched how people in the region lived and spent. Lack of local knowledge became a barrier to entry for global competitors.
When the advantage is quality, the strategic imperative is different: Identify distant markets where demand for a superior solution is strongest, and draw on differentiated local assets that hub-based competitors cannot easily access — such as exceptional software engineers or exclusive access to local university research labs — to deliver it.
Grammarly, which has its roots in Ukraine, illustrates this logic well. Its founders used the country’s deep pool of local developers to build a technically superior writing-assistance tool — harnessing a local talent advantage that hub-based co
