OPINION | Will AI open the Nobel door for the global south or shut it further?

As Nobel season unfolds, the deeper question is whether AI will help build more inclusive global scientific commons.

Every October, the Nobel Prize announcements capture the world’s imagination. The moment when laureates’ names are read out in Stockholm and Oslo is not just a celebration of discovery but a ritual of recognition as to who counts as a genius, who embodies science’s legitimacy, and which institutions are seen as sites of knowledge production.

For countries in the Global South, especially India Brazil or South Africa, the Nobel season brings mixed emotions. Pride in past rare science laureates (like C.V. Raman in India) collides with a stark reality: recognition remains disproportionately concentrated in Western institutions. Even when Global South scientists contribute, their breakthroughs often only achieve Nobel recognition when anchored in Western labs.

Now, just as debates on equity in science recognition intensify, Artificial Intelligence has entered the laboratory. From DeepMind’s AlphaFold to generative models that draft papers and reviews, AI is disrupting not just how science is done, but who gets credit. Could AI finally level the playing field for scientists in India, Africa, and Latin America? Or might it entrench inequities and make Nobel recognition for the Global South even rarer?

AI as an Equaliser: Lowering barriers, expanding reach

There are reasons for optimism. AI lowers technical barriers. Tools like Elicit, Litmaps, and coding copilots allow researchers in resource-constrained settings to rapidly synthesise literature, automate data analysis, and design experiments at speeds once reserved for elite labs. For an Indian PhD student outside the IIT or IISc ecosystem, such tools could be transformative. They could enable globally competitive outputs without access to high-end infrastructure.

AI also democratises serendipity. Sussex University academic Ohid Yaqub’s typology of serendipity - Walpolian (accident plus sagacity), Bushian (problem-driven), Stephanian (solution-driven), and Mertonian (rediscovery) - shows how discoveries emerge through unexpected pathways. AI, with its capacity to trawl neglected literature and vast datasets, could multiply “Mertonian” rediscoveries in contexts where overlooked local knowledge has global value. For instance, underexplored biodiversity in India or Africa might yield solutions that AI helps uncover and reframe as Nobel-worthy science.

Finally, AI can amplify visibility. Citation gaps between Western and Global South publications remain stubborn. But AI-driven literature curation, if equitably deployed, might surface high-quality work from beyond the Euro-American core. In theory, this could make Nobel committees more likely to notice breakthroughs originating in the Global South.

AI as a Divider: Compute gaps and credit capture

But the risks are equally stark. First, the compute divide. Elite institutions in the US, UK, EU, UAE and China are already monopolising access to large-scale AI infrastructure. For labs in India, compute costs are prohibitive. Without state investment in shared cloud platforms, AI could widen disparities, making it easier for Western labs to dominate discovery while others remain dependent on crumbs of access.

Second, AI risks amplifying misconduct. Paper mills have already flourished in India and China, where pressures to publish are intense. With generative AI, fraudulent science could multiply at scale, polluting the scientific record. This risks stigmatising entire regions in the eyes of Nobel committees, which already err on the side of conservatism when considering prize-worthy contributions.

Third, AI may reinforce cognitive biases. Nobel recognition is often tied to institutions with reputational weight. If AI systems are trained disproportionately on Western literature and citation networks, they will reflect and reinforce this bias. Discoveries from the Global South may be overlooked unless already “validated” by Western collaborators.

Finally, there is the question of interpretive genius, a topic covered recently by The Economist. With his coauthors, Toronto economist Joshua Gans has examined the Demand for Genius reminding us that supply of ideas alone does not guarantee breakthroughs; demand-side structures - institutions and societies that reward deep, risky science, matter. AI may flood the system with hypotheses and correlations, but unless Global South institutions evolve to reward sagacity, patience, and interpretive leaps, our scientists may still struggle to produce the kind of Nobel-worthy work that survives the test of time.

The Indian Context: Opportunity and threat

India in specific sits at a crossroads. On one hand, it has immense pools of talent, rising investments in digital infrastructure, and strong traditions of mathematical and computational science. On the other, it faces structural weaknesses: underfunded universities, bureaucratic research systems, and limited access to high-performance computing. Many Western Universities now see this as a gap setting up India campuses.

But if India fails to invest in shared AI infrastructure, open data platforms, and rigorous governance, creating its own sovereign AI architecture, AI in general may become another mechanism of exclusion. The risk is not just that Nobel recognition remains elusive, but that India becomes associated with the “low-trust” side of AI-enabled science - fraudulent outputs, retractions, and skepticism from global committees.

But if India seizes the moment, it could turn AI into a lever for scientific legitimacy. National cloud platforms for research compute, AI literacy programmes across universities, and ethical frameworks for responsible AI in science could transform the country into a serious contender for Nobel-worthy contributions in the coming decades.

Global science governance and the Nobel question

The Nobel Prize itself may not reform easily, its institutional conservatism is part of its brand. But global science governance in this season of AI and science of science policy - can help shape the conditions that make Global South recognition more likely. Simple steps may help like:

* AI Disclosure Mandates: Requiring scientists to declare AI use in research and writing would reinforce transparency.

* AI Audits: Independent checks on AI-assisted causal claims, especially in sensitive areas like medicine, could safeguard credibility.

* Democratised Compute: Shared infrastructure, modelled on CERN or the Human Genome Project, could ensure that Indian and African labs compete on equal footing.

* Global South Science Observatory: A monitoring body for AI in science, akin to the IPCC, could track disparities and propose corrective policies.

These measures will not guarantee Nobel Prizes for the Global South. But they can ensure that when breakthroughs emerge from India, Brazil, South Africa or elsewhere, they are recognised as legitimate, credible, and prize-worthy.

Conclusion: Genius in the Age of AI

AI is not a Nobel-maker or Nobel-breaker in itself. It is an amplifier. If the Global South invests in equity, infrastructure, and governance, AI could open doors long closed by structural inequality. But if it fails to do so, AI will only magnify exclusion, entrenching a world where Nobel recognition remains the preserve of Western institutions.

For countries like India, the Nobel question is not about chasing prizes for their own sake. It is about ensuring that when Indian science produces breakthroughs of genuine global value - as it has in physics, economics, medicine, and beyond - they are not dismissed or overlooked.

As Nobel season unfolds, the deeper question is thus whether AI will help build more inclusive global scientific commons, or whether it will harden the hierarchies that the prizes already reflect. The answer will depend not just on algorithms, but on the choices countries in the Global South make in the coming decade. India could play the pied piper here but does it want to? We shall have to wait for the answer.