To take full advantage of all that cutting-edge AI makes possible we will need access to frontier AI. Since these powerful models will likely only be made available to us through an API, this means that we will need to ensure we always have access to these APIs.

This is a link-enhanced version of an article that first appeared in the Mint. You can read the original here. For the entire archive of my Ex Machina articles please visit my website.

Last week, I wrote about how frontier artificial intelligence (AI) has begun to improve exponentially—to the point where it is not just introducing linear improvements in functionality, but literally augmenting capabilities. Personally, this became evident when I realized I could code anything I could imagine, allowing me to create various applications and programs to eliminate micro-frustrations in my workflow. I am sure that those in other domains are experiencing similar capability uplifts. 

According to scaling laws, a model’s performance will improve so long as increasingly large amounts of compute power and data are used to train it. For many years now, leading AI labs in the US have been proving that thesis at an eye-watering scale. It feels like those investments have begun to pay off, particularly over the past six months. Across the board, frontier models have begun to demonstrate dramatic improvements—both in the quality and accuracy of their outputs and in what they can do. 

For countries like India, advanced AI that not only reduces friction but also unlocks brand new capabilities presents exponential opportunities for advancement. By blurring the boundaries between domains and professions, it can expand the scope of what is possible, allowing those who use it to do far more than before. This makes brand-new pathways available through which the potential of AI can be harnessed across all domains—from healthcare and education to governance.

But just as these exponential capabilities of AI open up new opportunities to transform our industries and markets, they also expose us to new vulnerabilities. At present, the transformative capabilities of AI are only available in frontier models offered by top American AI companies. Open-source AI, which countries like India have relied on so far, seems to be slowly falling behind the cutting edge of what is possible, and while we have made tremendous strides with our own foundation models, they are still behind the state of the art on several key parameters.

Today, access to leading AI models is only available through application programming interfaces (APIs), which would not have been a problem had it not been for the geopolitical uncertainty of the current moment. Any country whose AI future depends on API access to solutions from leading AI labs risks having the core infrastructure on which it depends pulled out from under it should geopolitical winds shift—much like access to the SWIFT financial network was weaponised during the conflict in Ukraine. When core national capabilities depend on access to technology under foreign control, our sovereignty will only last as long as remote servers remain accessible.

So, how does India navigate its way ahead?

History has shown that strategic concessions can be extracted from technology providers seeking market access. When the Brazilian Air Force acquired advanced military aircraft in the 1d960s and 70s, the government, through state-owned enterprise Embraer, required that foreign suppliers transfer manufacturing know-how to Brazilian engineers and facilities. As a result, Embraer grew from a military supplier into one of the world’s most successful commercial aircraft makers, competing globally with Boeing and Airbus in the regional jet category. When Samsung licensed early DRAM technology from Micron, it invested aggressively in manufacturing scale and process improvements to the point that it eventually outcompeted its licensor. The massive electronics supply chain that grew out of this is now one of the world’s most sophisticated. Throughout the 1990s and 2000s, China made market access explicitly conditional on foreign companies forging joint ventures with Chinese partners and enforcing technology transfer under the terms of those agreements. This is how China developed the process expertise it currently has across a range of sectors, allowing it to dominate the global industrial value chain.

The question of the moment is this: What leverage does India have to achieve its goals in the AI race? 

As the largest user base for AI technology outside the US, India generates enormous volumes of behavioural, transactional and social data. This is exactly the kind of real-world interaction data that AI companies need to improve their models. India also possesses vast repositories of traditional knowledge, cultural information and community practices that could provide AI models with much-needed context.

In a paper that I presented as chair of the Expert Engagement Group on the New Deal for Data under the India AI Summit, I argued that one option available to a country like India is to make access to this data conditional on AI companies depositing the weights of all AI models enriched by Indian data in Indian facilities under a model escrow agreement. This would ensure that, if for any reason API access is cut off, Indian companies can continue to access the AI models they need locally. This would let India safeguard its AI sovereignty by strategically deploying one of its most valuable assets—its data. If successful, India could become the first country to leverage data as a strategic currency in the AI era. 

If we are to do this, we must act swiftly. Once AI models approach human-level performance across domains, any data advantage we currently have will disappear. And with it, the leverage we need to secure our technological sovereignty.