How nations can build sovereign AI and homegrown talent for economic competitiveness

Artificial intelligence is intellectually driven, not policy driven. It’s about the people, and these are the people that build the foundation of what makes an AI ecosystem.

The tech and algorithms in AI have been commoditized, but AI ecosystems become fully functioning when every stakeholder is aligned towards building the next generation of AI talent to populate their local ecosystems.

Examples can already be seen across the United States. In December 2023, the state of New Jersey, NJEDA and Princeton University launched the NJ AI Hub for AI innovation and, a month later, the state of New York launched Empire AI, a state-of-the-art computing centre for ethical artificial intelligence (AI) research in collaboration with New York University, Columbia University and five other institutions.

Then in March, the state of Massachusetts launched an AI task force to, among other factors, advise the state on its role in AI innovation, along with a $100 million fund to create an applied AI hub.

The year 2023 was the year that AI burst onto the global public consciousness, the inflection point. According to the 2023 Global AI Rankings, the top five countries in AI were the United States, China, Singapore, the UK and Canada. The countries are ranked according to seven pillars and levers: talent, infrastructure, operating environment, research, development, government strategy and commercial.

However, to enhance any of these pillars, you need people. Infrastructure, operation environment, R&D, government strategy and the commercial application of AI can’t function without people.

Sovereign AI is the ability for sub-national, state entities and federal level authorities to build AI with homegrown talent based on its local policy or national AI strategy.

That is having the generation of talent that will support state and national interest in aerospace, defence, education, housing, transportation, public safety, supply chain, manufacturing and many other industries critical to their safekeeping.

Building it is important simply because we can’t all rely on Silicon Valley to solve localized problems we have with AI. Aside from having the hardware infrastructure often associated with sovereign AI, regions must build the intellectual infrastructure in AI. The hardware infrastructure is useless without the trained AI talent, but the inverse of the AI talent is useless without the hardware is not true.

Because AI is not policy driven, it is intellectually driven. Accordingly, a state or nation must be able to adapt AI to their local needs so that they can preserve their values and regulatory oversight. All with local talent.

An exercise that every government must perform is a willingness to participate (WTP) AI ecosystem assessment. For an AI talent to participate in an AI ecosystem, their decision is driven by their WTP.

Therefore, each region must rank the top six attributes that drives the WTP of each stakeholder in their ecosystem. Once plotted, the government deploys resources to promote the top attributes or strengthen the weaker attributes.

The attributes that drive the WTP are directly correlated to the number of AI resources on an AI ecosystem map of that region. For example, by referring to the Massachusetts AI ecosystem map, we are able to derive the top six attributes that drive the WTP in Massachusetts AI ecosystem:

1. Affiliation to Massachusetts Institute of Technology (MIT) or Harvard University

2. High number of AI centres of excellence/AI labs

3. Proximity to AI research (AI faculty/thought leadership)

4. AI educational opportunities

5. AI student groups and a steady influx of students

6. Thriving tech/investor/AI entrepreneurial opportunities

Capital is not the barrier to building an AI ecosystem anchored in sovereign and homegrown AI, because a government increasing spending plans to boost infrastructure and computing capacity does not create a competitive advantage. Instead, it is the AI talent in the local ecosystem that creates an advantage.

Leaders must realize that there is a shift towards sovereign AI that is homegrown AI, that is built in a localized ecosystem because each region has local talent, local problems to solve, local capital, local research, local small businesses, local universities, community colleges, and technical schools, local ethical and regulatory considerations – all of which impact the future of work.

While emerging tech hubs can learn from Silicon Valley and other established hubs how they built their AI centres of excellence, they must localize their AI building capabilities around their people.

The private sector, small and medium enterprises and industries are the flywheel for AI talent and researchers, and market driven research and development challenges. With more projects and opportunities offered by them to the local talent, it builds capabilities overall in the community.

For countries that have not yet launched their national AI strategy, they must do so immediately. Because there is a generation of learners being left out of the AI economy. Advanced AI hubs are quickly shifting from the “knowledge-economy” to the “generative AI economy” and the longer they wait, the more they will be left behind.

Despite the US being ranked as the number one global powerhouse, even it is shifting towards building state-wide capabilities instead of relying on national rankings. And emerging nations can learn from the success of Saudi Arabia when it comes to AI.

Saudi Arabia ranked number one in government AI strategy in the 2023 Global AI Rankings, and the testament to this success is highlighted in the February 2024 Saudi Arabia AI ecosystem map in which there are more AI centres of excellence than any other AI resource.

Why are AI centres of excellence important to a nation? Because they create global bridges in AI and create pathways for all who want to participate.

AI talent is attracted to an ecosystem where they can learn from leaders in the field and have a pathway into AI. Because AI affects every part of society and work, learners from all backgrounds want to have a pathway.

It is your responsibility as a government stakeholder to create a pathway into AI by redesigning the entry margin not removing the barrier, to maintain the educational merit.

State and federal governments launch local AI ecosystems to advance leadership in AI to promote workforce development, including AI skills training for government employees, and guide governments and public entities on AI implementation.

It’s only when we have an educated government employee, public servant and elected politician learn about AI that we can have an impact at the grassroots.

And having an impact at the grassroots, which involves populating an ecosystem with AI talent in which the government supports their WTP, is what drives economic competitiveness.