Artificial intelligence has moved from being a discrete “tech vertical” to a capability that seeps into every corner of the economy. It shapes how products are built, how operations run, and how decisions are made. For startup ecosystem developers, the key question is no longer whether to use AI, but how to use it. Two paths are emerging: places that produce AI technologies and places that adopt them widely across sectors. Our CEO Eli David Rokah dives deep into this framework in our podcast episode: The Adoption Path For most ecosystems, the highest-return first step is widespread adoption. Rather than rushing to build new AI technologies, the initial priority is ensuring that people and organizations can actually use existing tools. This includes founders, SMEs, universities, and public agencies, all of whom benefit when AI becomes part of everyday work.Some governments are already taking proactive measures, even underwriting access to professional AI accounts to accelerate usage and close skill gaps. Time-to-access is crucial: if new capabilities arrive months later due to regulatory caution, local teams lose valuable learning cycles that compound into long-term disadvantages.Accelerating adoption means removing these barriers early. Governments and ecosystem leaders should focus on expanding access, offering training, and streamlining regulations so experimentation happens quickly and safely.Once adoption is underway, ecosystem leaders face a pivotal question: should the focus remain on using AI effectively, or shift toward creating it? This decision marks the beginning of the second possible route the Producer Path. Is Adoption a Stepping Stone or a Permanent Role? Adoption comes first, and it’s indispensable because it lays the foundation for everything that follows. Without widespread use of AI tools, ecosystems can’t build the skills, data, or confidence needed to compete globally. Ensuring broad access and removing regulatory delays helps people integrate AI into real work which quickly boosts efficiency and builds hands-on experience.From there, two outcomes are realistic. In many places, adoption will remain a permanent and valuable role, creating stronger, more productive industries. In others, steady adoption in a specific niche can become a bridge to production, as local knowledge and data evolve into specialized AI products. The Producer Path Production means creating core AI technologies or defensible, AI-centric products. Doing this broadly requires rare ingredients: dense talent networks, abundant capital, compute access, and geopolitical room to maneuver. Only a handful of places can push the frontier across the board. How can smaller ecosystems stand out in AI production? Production means creating core AI technologies or defensible, AI-centric products. Doing this broadly requires rare ingredients: dense talent networks, abundant capital, compute access, and geopolitical room to maneuver. Only a handful of ecosystems can push the frontier across the board.Yet, smaller ecosystems can still carve out meaningful producer roles. Competing across the entire AI landscape is unrealistic, but focusing on a well-defined niche can yield global visibility. The key is to build around real leverage, existing anchor customers, specialized talent, proprietary or hard-to-obtain data, and local testbeds.For instance, port cities might lead in AI for logistics; manufacturing hubs could specialize in industrial vision and quality control; agricultural regions may advance precision farming. In these cases, production becomes a targeted bet where a region can credibly aim to be world-class, rather than a diffuse ambition to build a general-purpose AI model. Can Anyone Close the Gap With San Francisco? The current global leader is San Francisco. Its concentration of model labs, chip companies, and dense talent networks creates a flywheel that is hard to replicate. That doesn’t leave everyone else out of the story. Other ecosystems can still matter by mastering specific niches, building applied-AI startups that become acquisition targets, or moving decisively on national programs that accelerate adoption at scale. The realistic goal isn’t to beat San Francisco everywhere, but to become unmistakably strong somewhere that aligns with your advantages. What Should Policymakers Do First? Whether you choose the adoption path or the producer path, the following action points will be important milestones for catching up with AI. 1. Start with an honest analysisRun an AI readiness scan: know your real position, not your aspirations.Map strengths, unfair advantages, and hard limits.Choose your role: broad adopter now, niche prod