{"id":77592,"date":"2026-03-04T17:24:28","date_gmt":"2026-03-04T17:24:28","guid":{"rendered":"https:\/\/creativecommons.org\/?p=77592"},"modified":"2026-03-04T17:25:34","modified_gmt":"2026-03-04T17:25:34","slug":"ais-infrastructure-era","status":"publish","type":"post","link":"https:\/\/creativecommons.org\/2026\/03\/04\/ais-infrastructure-era\/","title":{"rendered":"AI’s Infrastructure Era: Reflections from the AI Impact Summit in Delhi"},"content":{"rendered":"

Last month, we <\/span>published a preview<\/span><\/a> of what we intended to bring to the<\/span> AI Impact Summit in Delhi<\/span><\/a>: a focus on data governance, shared infrastructure, and democratic approaches to AI that genuinely advance the public interest rather than replicate existing power imbalances. That piece outlined our core interventions and the principles that have guided our thinking as we grapple with how to ensure openness, agency, and equity in the age of AI.\u00a0<\/span><\/p>\n

Since then, the Summit\u2014a major global gathering of policymakers, technologists, civil society leaders, and researchers\u2014unfolded against the backdrop of widespread calls for cooperative frameworks and measurable outcomes. For an excellent summary of the highs and lows of the Summit, take a look at this article<\/a> by CC Board Member Jeni Tennison.<\/span><\/p>\n

From CC\u2019s perspective, what became clear in Delhi is that AI governance is shifting. The conversation is moving beyond high-level principles and into harder, more structural questions about infrastructure, stewardship, and power.<\/span><\/p>\n

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Photo by Rebecca Ross\/Creative Commons, 2026, CC BY 4.0<\/a>.<\/figcaption><\/figure>\n
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Data as a Leverage Point<\/b><\/h2>\n

Concerns about data capture and extraction abounded at the Summit. But alongside those concerns, a persistent theme emerged: data scarcity.<\/span><\/p>\n

Participants repeatedly pointed to the lack of high-quality, localized, representative datasets as a fundamental constraint on public interest AI. The call for \u201creally good data\u201d came from startups, researchers, governments, and civil society actors alike\u2014many working to build contextually grounded systems. Without accessible datasets, cultural representation is limited, competition falters, open-source development slows, and meaningful innovation remains concentrated in the hands of those with the most resources.<\/span><\/p>\n

The gaps are especially pronounced across Global South languages and cultural contexts. Researchers are working to supplement large models with local norms and knowledge to address bias and misrepresentation. This is particularly urgent in sectors such as health, agriculture, climate, and development, where high-quality open datasets could unlock substantial public benefit.<\/span><\/p>\n

There is a real tension here<\/span><\/a>. High-quality open data is required to power public interest AI. At the same time, without guardrails, open data can be exposed to extraction and misuse. Communities are often presented with a false choice: open their data and risk exploitation, or close their data and risk exclusion from shaping AI systems that affect them. Addressing this tension is essential if governance frameworks are to support both individual agency and shared stewardship. In essence, we need to:<\/span><\/p>\n