In his conversation with The Indian Express last week, Bill Gates offered a sweeping — if carefully hedged — vision of the future, touching on artificial intelligence, climate change, and philanthropy in a time of global flux.
When asked about the future of employment in an AI-dominated world, Gates offered one of his more thought-provoking responses. Rather than leaning on familiar arguments around job reskilling or economic transition, he suggested that we might be heading toward a post-labour world altogether. He floated the idea that AI could eventually enable a form of economic support that releases people from the “drudgery” of traditional jobs, freeing them to explore other pursuits. The market-driven model of work and income we know today, he implied, might become obsolete. “We cannot conceive,” he said, “of what that world will look like.”
Gates did not use the term “universal basic income”, nor did he offer a detailed policy vision. But the remarks raise a deeper question: If people are no longer employed in the conventional sense, how will they access the wealth generated by AI-driven productivity?
The AI revolution is first sweeping through the software stack, not the hardware layer. Language models, coding agents, financial assistants, document processors — all of these are already reshaping white-collar workflows. Unlike robotics, which faces constraints in materials, safety, and physical dexterity, AI software can scale frictionlessly. So lawyers, teachers, journalists, and even programmers are likely to feel the first wave of transformation.
Robotics still has a longer arc. There’s enormous promise, but the maturity curve is steeper and slower than pure software. So paradoxically, many physical labour jobs may survive longer than white-collar ones, at least in the short to medium term.
If AI radically increases efficiency across sectors, particularly in services (education, healthcare, legal advice), one plausible scenario is that the cost of living plummets. If you can get a personalised tutor, a lawyer, a doctor, or even a companion for near-zero cost (thanks to AI), then the amount of money needed to live a dignified life also declines.
In this world, material needs don’t vanish, but the price of fulfilling them drops drastically. Think of AI not as making us richer in cash, but richer in access. This kind of “deflationary abundance” means fewer people may need to work simply to survive. But that doesn’t answer how they get what they need. That’s where the next layer comes in.
A universal basic income is a regular payment from the state (or some entity) to individuals, irrespective of their employment. If AI and automation do most of the wealth-creating, there’s a philosophical and practical case for redistributing that wealth.
The money might come from taxing AI-driven enterprises: If a few companies own the models that run the economy, taxing their super-profits could be a mechanism. Other ideas include sovereign AI models, where countries could develop their own foundational models as public infrastructure and lease them to businesses. Or, ownership redistribution. Some radical proposals even suggest making the public partial owners of AI-based productivity tools so citizens earn dividends.
In a post-labour economy, income can’t be tied to traditional “jobs”. Something else has to fill the gap. If people no longer need to work to live, we must ask: What do they live for? Here’s where Gates’ optimism becomes more existential.
Work is not just economic — it structures identity, time, purpose. If AI releases people from work, society will need new structures of meaning, community, and contribution. Some may turn to art, care, science, or spiritual pursuits. Others may struggle without the discipline and validation of employment.
In some ways, this brings us full circle to the ancient philosophical question: What is a good life? AI may force us to answer it, collectively.
To underscore his optimism, Gates compared the current moment in AI to the personal computing revolution. Just as the PC brought computing power into the hands of individuals by drastically lowering cost and broadening access, AI, he argued, could bring intelligence itself — diagnostic, instructional, analytical — into the hands of every person on Earth. The result, he said, should be seen as a democratisation of intelligence — a rebalancing of expertise and opportunity across class, geography, and circumstance.
It was a powerful metaphor. Whether this vision can be realised depends not just on innovation, but on policy, regulation, and the collective choices societies make.
Another point that Gates did not address, but one that hovers over the entire AI discourse, is the growing concern around wealth concentration. If AI becomes the principal driver of economic productivity, and only a handful of entities own the models and infrastructure, we may face an unprecedented concentration of power and resources. The worry is that the benefits of AI will flow primarily to those who build and control it, leaving the rest of society increasingly dependent on systems they have no stake in.
Gates, in contrast, emphasised a more hopeful trajectory. His view seemed to be that if access to powerful services becomes universal and nearly free, the result will be a net gain in the quality of life for everyone.
Whether that vision is achievable depends on many factors beyond technological development: Political will, institutional reform, ethical design, and a rethinking of what it means to live a meaningful life in a post-labour world.
Gates was also asked about India’s role in the unfolding global AI revolution — a question that often prompts concern about being left behind in a race dominated by a few large Western or Chinese players. But his response was strikingly optimistic. He argued that India doesn’t need to build its own foundational models from scratch to participate meaningfully in the AI ecosystem. Thanks to the growing number of open-source models and the wide availability of research papers, datasets, and implementation guides, the tools of frontier AI are now more accessible than ever.
In Gates’s view, this dramatically narrows the gap between countries like India and those leading in model development. While India may not yet be creating foundational models at scale, it can download, adapt, and fine-tune existing ones for Indian contexts. The lag, he suggested, is no longer structural or insurmountable. At worst, India is a few months behind; at best, just a few days.
This marks a subtle but important reframing of the global AI landscape. Early narratives around AI emphasised domination by a small set of players. The current wave is increasingly shaped by openness and diffusion. Model architectures, training techniques, and weights are now frequently shared, allowing smaller players to leapfrog infrastructural constraints.
In this sense, Gates’ comments also serve as a quiet counterpoint to concerns about AI-driven inequality. While the risk of wealth and power concentrating among the AI elite remains real, the trend toward democratisation is also accelerating. The open-source movement, in particular, plays a crucial role in this dynamic. Tools like Hugging Face, PyTorch, and a wave of community-led initiatives have made it possible for researchers, developers, and startups across the world — including in India — to meaningfully participate in the AI economy.
The path forward, Gates implied, is less about catching up and more about tuning in: Taking the global infrastructure and adapting it creatively and locally. For a country with as many languages, sectors, and societal challenges as India, this is not just a workaround — it may be a strategic advantage.
The writer is professor at the Department of Astronomy and Astrophysics at the Tata Institute of Fundamental Research
