From AI Buzzwords to Real Diversification

Context

Over the past decade, your country has experienced:

• Rising macro volatility: supply chain shocks, geopolitical tensions, climate-related disruptions.
• A surge of interest in "AI strategies" and "digital economies."
• Persistent structural challenges: slow diversification, concentration in a few export products or services, and uneven labor market outcomes.

Globally, trade and technology data are painting a more nuanced picture:

• UN Comtrade and related trade datasets show that most countries still derive a large share of export revenues from a relatively narrow basket of goods and services.
• New research on Progression Networks uses revealed comparative advantage (RCA) and statistical validation to map out which products, services, and AI subsectors tend to "co-occur" and follow one another in countries' development trajectories (e.g., s41598-023-45723-x – you won't read the paper in full, but we will use its logic).
• At the same time, AI investment and capabilities are highly uneven across countries and sectors. Different sub-types of AI (e.g., computer vision, NLP, robotics, time series forecasting) are differentially useful for different industries.

Many national AI plans have responded with ambitious but vague targets: "become a global AI hub," "build national compute," or "train 1 million AI engineers." The problem, as recent work argues (e.g. Brookings, What national AI plans get wrong and how to fix them), is that:

• Most national strategies treat AI as a sector, not as a cross-cutting cognitive infrastructure.
• They chase "full-stack sovereignty" rather than focusing on specialization in parts of the AI–industry stack where the country has real comparative advantage.
• They focus on capital-intensive demonstrations instead of the "boring" work of data interoperability, human capital, and institutional learning.

Your country is now at a decision point.

Your Task

You are part of a cross-sector strategy group tasked by your country's leadership to draft a 10-year AI-enabled diversification strategy grounded in:

• What your country already does well – current export specializations in goods and services.
• How AI can make existing industries more competitive – which AI sub-types augment current strengths.
• Where to diversify next – adjacent high-value goods, services, and AI specializations suggested by empirical relatedness (Progression Network logic, density).
• What this implies for labor markets and institutions – training, regulation, and governance so that AI deepens Cognitive Capital rather than hollowing it out.

You will use ConstructChat working over:

• Public trade statistics (UN Comtrade-like data, services trade, where available).
• Descriptions of AI capability taxonomies and benchmarks.
• The logic of progression/relatedness (RCA, assist matrices, density).
• Relevant policy and research reports (including critiques of national AI plans).