Development and the DNA of AI

An AI Nation demands a capabilities approach to development. The DNA of AI must be spliced into the institutions, the economy, and the civic systems of the state. We are at the start of the AI diffusion climb. However, the slope is not the same for all nations. The climb requires a rethink of every layer of national capability, including digital architectures, the moribund colonial ecological frames of the Lord Harris ward school system, and outmoded export models.

The shift necessitates the development of public sovereign algorithms, GPU clusters, decentralised IDs or globally unique identifiers independent of any organisation, real-time data streams, expanding beyond STEAM degrees, and governance sandboxes. Sovereign algorithms will safeguard the nation’s ability to control, create, and deploy AI models using its infrastructure, unique cultural métissage, data, indigenous workforce, and networks. It involves robust governance policies and technical diligence to help protect corporate assets, safeguard citizen privacy, and harden civic computing infrastructure.

The GPU clusters will provide accelerated computing power. More than just STEAM degrees, the AI Nation must educate data ethnographers, hybrid policy-tech builders, prompt engineers, and data visualisation specialists. Legal frameworks must shift to proactive agile governance sandboxes. A seminal step is a robust data and digital infrastructure spine built using a digital transformation agenda. This infrastructure lifeblood will form the substratum upon which AI assemblages will run without friction.

To accelerate AI diffusion, primacy must be given to tasks that add value to digital spaces which support the use of data collection toolkits, well-organized data sharing practices, digitisation of public records, open data guidelines, the seamless integration of high-quality data across civic systems that meet global semantic and syntactic interoperability benchmarks, and policies to safeguard data integrity.

Policy frameworks must remove those unfreedoms that barricade the entry of new players in the data centre and connectivity markets. These policies should also optimise data localisation requirements, provide certainty on data transfer principles, and establish a rulebook for cloud service providers. The AI Nation must provide access to AI infrastructure and deploy AI applications in a manner that is geographically and socially equitable.

The AI Nation must promote a robust data management practice, and scaffold domestic data-sharing ecosystems. The ambition of AI is not to imitate humans. The aim of AI is to offer humans the models to better realise human objectives. The most earnest ambition for national development cannot be the reduction of income inequality. In his Wealth of Nations , Adam Smith prioritised the benefits of a growing economy over making a more serious effort to stem inequality. The inequality arising from capitalism is a source of existential worthlessness for the poor. The principal aim of development must be freedom; that is, to enlarge the capability of all citizens and to allow them to lead the lives which they have reason to value. Poverty is fundamentally epistemic – it is a deprivation of capability.

The risk of becoming a digital colony is real. AI is a sovereign strategy. The AI shift is not technological; it is civilisational. Nations that will succeed must invest in compute infrastructure, National Data Libraries, and data embassies as national assets. These countries will democratize access to automation and cultivate new knowledge, skills, and dispositions needed to participate effectively in a democratic society using AI systems that can act autonomously, within a specific environment, to achieve goals. What will propel these countries into that future is not imitation or a new digital economy policy, but imagination, grit, and boldness. To build an AI Nation, countries will have to reimagine their startup ecosystem.

Being an AI-Native Nation does not require mere adoption; it requires digital infrastructure and architectures, interoperability, telecommunication governance, cultural rewiring, and network readiness. It necessitates rethinking every layer of sovereign capability, including infrastructure, regulation, talent attraction and retention, and AI diplomacy. It also demands the development of a robust AI ecology encompassing investment in strong AI talent, AI infrastructure, an enabling AI regulatory framework, and a focus on AI ethics.

AI is not a new vertical. It is already the operating system (OS) of global competitiveness. Fair Trading Commissions and innovative regulatory bodies in Australia, France, Germany, and the United Kingdom have been developing procompetion standards, rules, and codes of conduct, including approaches to addressing new competition issues that surface as algorithms advance. These agencies would also serve as focal points for international coordination on the regulation of digital markets. Competition policy has not adapted well to the shifts in digital market structures and the challenges of keeping markets competitive as AI permeates every aspect of our lifeworlds.

AI assemblages bring economic benefits and potential risks. Technical risks include those related to the development and maintenance stages of AI systems, including those related to data privacy and biases. Use case risks include those related to the context in which AI systems are used and include those that shine a light on the lack of transparency, the de-skilling of humans, system failures, and the unethical use of AI systems. Control risks are those related to the potential for malicious actors to exploit AI technology, to manipulate public views, and influence behaviour using Deepfakes, targeted advertising, and social engineering.