Can AI help countries leapfrog?

Technological advantage has usually followed wealth. Countries with capital could build infrastructure, develop expertise and absorb the cost of experimentation. Organisations with established technology could invest again. Advantage accumulated over time. AI introduces the possibility of a different development path.

In Episode 19 of The AI Values Podcast, hosts Edosa Odaro and Lindley Gooden examine what they call the opportunity for AI leapfrogging: countries, organisations and communities using AI to advance more quickly because they carry fewer legacy systems and can learn faster.

The opportunity is significant. So is the risk. AI could help emerging economies bypass some of the infrastructure and processes that slowed previous waves of development. It could also allow already powerful economies and companies to extend their lead.

For leaders, the central issue is how to create the conditions that turn access to AI into lasting economic and social value.

Agility is becoming an economic asset

Capital remains powerful. Computing infrastructure, specialist expertise, investment and access to technology all shape what countries and organisations can do with AI.

Episode 19 introduces another factor: agility.

As Edosa says:

“Agility is what's actually the fuel right now for AI.”

This matters because established organisations often carry years of accumulated systems, processes and dependencies.

Every new deployment has to interact with what already exists. Decisions may travel through multiple layers. Technology built for an earlier period can become expensive and difficult to replace.

Smaller organisations can sometimes move differently.

The episode discusses a health technology organisation that developed a diagnostic system, gained the confidence of doctors and moved quickly towards adoption.

Its experience illustrates the potential advantage of focus. A smaller organisation may lack the resources of a global company while possessing the ability to concentrate on a specific problem, execute quickly and learn from what happens.

Countries can face a similar dynamic. The ability to learn quickly could become an increasingly important source of advantage as AI capabilities become more widely accessible. Edosa captures the idea directly:

“The advantages of AI aren't only going to go to actually the richest countries, it's actually going to go to, in some ways, the fastest learners.”

That possibility should change how leaders think about AI readiness. While infrastructure matters, so does lLearning capacity. 

Leapfrogging has happened before

The idea of technological leapfrogging has precedents. The episode points to mobile telecommunications in Africa. Mobile technology allowed many communities to adopt connectivity without following the same fixed-line development path taken elsewhere.

Edosa also references solar energy in Southeast Asia and fintech in Latin America as examples of newer technologies creating alternative routes to development.

AI could create another such opportunity. Lindley captures an important condition:

“To leapfrog, you need a solid foundation to jump over.”

Leapfrogging still requires foundations.Education matters. Skills matter. Digital access matters. Institutions matter. The ability to understand and apply technology matters.

The episode highlights Finland's Elements of AI initiative as an example of broadening public understanding of AI.

For Edosa and Lindley, education becomes part of the infrastructure required for participation in an AI economy.

This has consequences for governments and organisations. AI literacy determines who can participate in decisions about the technology. It influences whether people can recognise useful applications, question inappropriate ones and develop ideas around problems that matter locally.

A society with broad AI understanding may therefore possess an advantage that cannot be measured purely through computing capacity.

The global divide could become wider

Leapfrogging is an opportunity. Its outcome depends on what happens next. Episode 19 also raises the prospect of greater divergence between economies.

Large economies already possess significant capital, markets, expertise and technology infrastructure. Those resources allow them to invest heavily in AI and develop capabilities at scale.

Edosa describes the possibility of two paths emerging.

One consists of established economies using their existing resources to continue expanding.

The other involves what he calls "leapfrog economies":

“But the opportunity also sits on the other side with what I'm calling the leapfrog economies, ones where they have the opportunity because they haven't got all the legacy built out.”

Fewer legacy systems can create room to move quickly. That advantage needs supporting capabilities around it.

Countries require people who can use AI effectively. They need organisations capable of applying it to real problems. They need institutional capacity to make decisions about consequences, access and accountability.

Without those capabilities, access to AI alone may have limited value.

The episode therefore raises an important question for policymakers: can countries build enough capability around AI to shape how the technology is used within their own economies?

The answer will influence how widely AI value is distributed.

Clarity can become a competitive advantage

One of the strongest ideas in Episode 19 concerns clarity. Edosa says:

“One thing different about AI is that it doesn't actually reward power. What it rewards is clarity.”

He describes clarity across several dimensions: purpose, data, processes and values.

This provides a useful framework for leaders.

AI creates an enormous range of possible applications. Organisations can automate processes, generate content, analyse information, support decisions and redesign services.

Possibility can easily become a distraction. 

Clarity helps leaders decide where AI deserves attention and where it does not. That means establishing the problem the organisation is trying to solve, identifying who should benefit, understanding what information is required and defining what a valuable outcome would look like. Leaders also need to consider the consequences and trade-offs that may accompany that outcome.

This gives organisations a clearer basis for concentrating resources around a defined purpose rather than pursuing AI simply because the capability exists.

The same discipline matters at national level.

Countries have different economic priorities, public services, populations, resources and social needs. The AI applications capable of creating the greatest value will therefore differ between them.

Healthcare may be a priority in one setting. Agriculture, financial inclusion, education or public services may matter more somewhere else.

A clear understanding of those priorities can help leaders decide where AI investment deserves attention.

Values determine where the leap leads

Speed can accelerate whatever direction has already been chosen. That makes purpose particularly important.

Edosa asks:

“Why are you doing what you're doing? What is it actually going to, how is it going to benefit yourself, your community, your country, your region?”

This is where AI strategy meets governance. Leaders need to understand who benefits from a deployment and which consequences accompany that benefit.

An AI application can improve access to a service while creating new dependencies. Automation can improve productivity while affecting employment and skills.

Greater use of externally developed AI can expand capability while changing how much control an organisation or country retains over important technology.

These are trade-offs and they need to be visible when decisions are made.

For countries seeking to leapfrog, the same principle applies at a larger scale. Progress needs a locally meaningful definition.

AI strategies built around domestic priorities give governments and organisations a clearer basis for deciding where to invest, what capabilities to develop and which outcomes deserve protection.

Values therefore have a practical role in AI strategy.

They help determine where technology should take an organisation or society.

The fastest learners could gain ground

Episode 19 presents learning as a form of competitive capability.

Countries with significant resources retain substantial advantages. Organisations with established technology and expertise also begin from a strong position.

Those advantages still require adaptation.

Legacy infrastructure can slow change. Established processes can create friction. Skills gaps can prevent people from making effective use of increasingly accessible tools.

Learning capacity becomes critical under those conditions. Institutions need to recognise useful AI applications quickly and carry lessons from one project into the next. Employees need opportunities to develop the skills required to use AI effectively, while leaders need enough understanding to distinguish genuine value from technological enthusiasm. Governance must also be able to evolve as applications, capabilities and associated risks change.

These capabilities matter across companies, governments and economies.

AI may therefore change the meaning of readiness.

Access to technology provides one part of the foundation. The capacity to learn, apply, govern and improve determines what happens after access is achieved.

Listen to Episode 19

Going deeper: AI readiness includes the ability to learn

Episode 19 has implications for how leaders measure AI readiness. Investment is relatively easy to count through technology spending, licences, infrastructure and the number of AI projects underway. Governments can track similar indicators through investment, research activity and adoption.

Learning capacity is harder to see, yet it could become one of the factors determining who creates lasting value from AI.

For boards, this means looking beyond individual projects and asking whether the organisation is becoming more capable with each deployment. Knowledge gained from one initiative should inform the next, while teams need ways to share what worked, what failed and what should change.

AI literacy is part of that capability. Employees need enough understanding to challenge inappropriate applications, and leaders need enough knowledge to make informed decisions about where AI belongs. Governance must also evolve as the organisation learns more about the technology and its consequences.

Legacy systems deserve attention too. Established processes and infrastructure can provide stability, but they can also slow experimentation and make it harder to apply lessons quickly.

At national level, education, institutional capability, digital infrastructure and access to technology influence how quickly an economy can move from AI consumption towards meaningful application. Countries need the capacity to make decisions around their own priorities, data, risks and desired outcomes.

This is where the possibility of leapfrogging becomes significant. Access to AI creates an opportunity, while the ability to learn and apply it determines how much of that opportunity can be converted into lasting value.

Established economies face their own pressures. Infrastructure, capital and expertise provide strong foundations, yet previous success can create assumptions about future advantage. Rapid technological change can expose slow decision-making, outdated systems and institutions that struggle to adapt.

Leadership therefore needs to consider learning speed alongside technological capability when assessing readiness.

The opportunity described in Episode 19 comes from combining agility with strong foundations, clarity and purpose. Turning that opportunity into durable value requires deliberate choices about skills, institutions, access, accountability and the outcomes societies want AI to produce.