AI agents are quickly becoming a fixture of modern business, but the gap between those who can deploy them effectively and those who cannot is emerging as a new form of economic inequality.
Companies race to deploy AI agents, but not everyone benefits equally
Since the emergence of AI agents last year — first popularized by tools like Clawdbot — businesses and individuals have used them to write code, send emails, run shops, and more. Big tech firms including Google, Amazon, Anthropic, and Perplexity are launching agents that can handle increasingly complex tasks autonomously. Still, many agents fail at basic tasks or perform unauthorized actions.
McKinsey estimated in a report last year that AI-powered agents and robots could generate about $2.9 trillion in economic value per year in the U.S. by 2030. The report described the future of work as “a partnership between people, agents, and robots — all powered by AI.”
That future is not arriving evenly. Nick Srnicek, a senior lecturer in digital economy at King’s College London, told reporters that companies and entities deploying agents will benefit disproportionately compared to those that cannot. “We will see new inequalities of access, scale, quality and trust,” he said. “Agentic inequality can harden into systems of dominance.”
Access to better agents means “the outcomes of negotiations and transactions will be structurally biased towards those with greater access,” Srnicek added.
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For Indian startups, agents already change the math
Raman Choudhary began using a Claude Code agent at his startup DentNode, a platform for dental clinics and labs in Bengaluru, last year. The agent reviews code, conducts market research, drafts publicity material, builds financial models, and prepares him for partner calls. “Without it, I’d have needed at least one additional engineer, a part-time researcher, and a content or marketing hand,” he said.
In salary terms, that’s 1.5 million to 2.5 million Indian rupees ($15,700–$26,000) per year. His agentic workflow costs just a few hundred dollars a month. Yet Choudhary noted the same Claude model isn’t built for Indian workflows — regional payments, tax systems, and local languages. Still, he said, “Those who figure this out early are going to build disproportionately large companies.”
Matthew Sharp, a research affiliate at the Oxford Martin AI Governance Initiative, said adoption of generative AI is growing worldwide but the gap between wealthy and poorer nations is widening. “Every layer above the model — including scaffolding, tool integration, security, workflow design, and supervision — reintroduces skill and capital barriers,” he said.
These divides appear not just between countries but within wealthy nations, between firms, and between individuals. A well-resourced firm can integrate agents into proprietary data, procurement, customer operations, and decision-making workflows. A wealthy person can use premium agents to navigate legal systems, improve financial decisions, or negotiate contracts. “Better agents may help already-advantaged people and organizations move faster, bargain better, avoid costly mistakes, and accumulate further advantage,” Sharp said.
India experiments with citizen agents — but privacy risks remain
India is taking a different approach. The government aims to provide personal AI agents to some 50 million Hindu pilgrims at the months-long Kumbh Mela festival next year, and eventually deploy agents to each of its 1.4 billion citizens. The Kumbh Doot — doot means messenger in Hindi — will be a voice-first agent operating in more than 20 Indian languages. It will coordinate with civic, transport, health, and commercial agents.
A separate system, Digi Doot, will interact with public and private service providers. According to a white paper, it is “designed for inclusion, optimized for users who face the highest friction: rural populations, the elderly, migrants, multilingual users, and those with low literacy.” Both systems tap India’s digital public infrastructure: Aadhaar citizen ID, UPI payment gateway, DigiLocker verification, and ONDC commerce platform. This infrastructure connects to a broader trend where China tech fuels new tourism and service booms globally.
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Ramesh Raskar, an associate professor at MIT who leads Project Nanda, a foundational infrastructure for AI agents, said, “It’s about decentralization and democratization of AI.” He is involved in India’s programs. “Right now, we have no agency, no control over how AI is delivered to us. When every one of us has our own AI agent that can talk to each other, transact with each other, and create economic opportunities for us, then we can participate as first-class citizens in the agentic economy.”
Yet citizen agents raise concerns. “The same infrastructure can become a surveillance layer if the data flows, defaults, and oversight are wrong,” Sharp said. “The safeguards around consent, purpose limitation, auditability, and political independence would need to be real, not merely architectural.” Governments and companies such as Anthropic and OpenAI can also cancel access anytime, leaving agent-dependent nations vulnerable.
The Kumbh Doot white paper says the framework is “committed to privacy, dignity, and inclusion,” adding that no pilgrim will be surveilled beyond what they consent to, and they can revoke consent anytime. The Digi Doot proposal promises “end-to-end privacy with agent actions that are attributable, bounded, and safe.”
Small differences in agent quality can grow into large gaps
Most Indians don’t need a sophisticated research agent, Sharp said. They need a “reliable, safe, low-cost agent that can handle high-friction tasks” — like filling a form or navigating benefits. For individuals, small businesses, and nations, that remains a challenge.
“Without deliberate design, premium agents will help already-advantaged users move faster, while everyone else gets weaker, riskier or less-integrated systems,” he said.