(co-authored with Luke Jordan, originally posted on Reboot Democracy Blog)
Inequality in accessing public services is prevalent worldwide. In the UK, “priority fees” for services like passport issuance or Schengen visas allow the affluent to expedite the process. In Brazil, the middle-class hires “despachantes” – intermediaries who navigate bureaucratic hurdles on their behalf. Add technology to the mix, and you get businesses like South Africa’s WeQ4U, which help the privileged sidestep the vehicle licensing queues that others endure daily. An African exception? Hardly. In the U.S., landlords use paid online services to expedite rental property licensing, while travelers pay annual fees for faster airport security screening.
If AI development continues and public sector services fail to evolve, inequalities in access will only grow. AI agents – capable of handling tasks like forms filling and queries – have the potential to transform access to public services. But rather than embracing this potential, the public sector risks turning a blind eye – or worse, banning these tools outright – leaving those without resources even further behind.
The result? The private sector will have to navigate the gaps, finding ways to make AI agents work with rigid public systems. Often, this will mean operating in a legal grey zone, where the agents neither confirm nor deny they are software, masquerading as applicants themselves. Accountants routinely log into government tax portals using their clients’ credentials, acting as digital proxies without any formal delegation system. If human intermediaries are already “impersonating” their clients in government systems, it’s easy to envision AI agents seamlessly stepping into this role, automatically handling documentation and responses while operating under the same informal arrangements.
The high costs of developing reliable AI agents and the legal risks of operating in regulatory grey zones will require them to earn high returns, keep these tools firmly in the hands of the wealthier – replicating the same inequalities that define access to today’s analogue services.
For those who can afford AI agents, life will become far more convenient. Their agents will handle everything from tax filings to medical appointments and permit applications. Meanwhile, the majority will remain stuck in endless queues, their time undervalued and wasted by outdated bureaucratic processes. Both groups, however, will lose faith in the public sector: the affluent will see it as archaic, while the underserved will face worsening service as the system fails to adapt.
The question is no longer whether AI agents will transform public services. They will. The partners of Y Combinator recently advised startup founders to “find the most boring, repetitive administrative work you can and automate it”. There is little work more boring and repetitive than public service management. The real question is whether this transformation will widen the existing divide or help bridge it.
Banning AI agents outright is a mistake. Such an approach would amount to an admission of defeat, and entrenching inequalities by design. Instead, policymakers must take bold steps to ensure equitable access to AI agents in public services. Three measures could lay the groundwork:
- Establish an “AI Opportunities Agency”: This agency would focus on equitable uses of AI agents to alleviate bureaucratic burdens. Its mandate would be to harness AI’s potential to improve services while reducing inequality, rather than exacerbating it. This would be the analogue of the “AI Safety Agency”, itself also a necessary body.
- Develop an “Agent Power of Attorney” framework: This framework would allow users to explicitly agree that agents on an approved list could sign digitally for them for a specified list of services. Such a digital power of attorney could improve on existing forms of legal representation by being more widely accessible, and having clearer and simpler means of delegating for specific scopes.
- Create a competitive ecosystem for AI agents: Governments could enable an open competition in which the state provides an option but holds no monopoly. Companies that provided agents which qualified for an approved list could be compensated by a publicly paid fixed fee tied to successful completions of service applications. That would create strong incentives for companies to compete to deliver higher and higher success rates for a wider and wider audience.
A public option for such agents should also be available from the beginning. If not, capture will likely result and be very difficult to reverse later. For example, the IRS’s Direct File, launched in 2024 to provide free tax filing for lower-income taxpayers, only emerged after years of resistance from tax preparation firms that had long blocked such efforts – and it continues to face strong pushback from these same firms.
One significant risk with our approach is that the approval process for AI agents could become outdated and inefficient, resulting in a roster of poorly functioning tools – a common fate in government, where approval processes often turn into bureaucratic roadblocks that stifle innovation rather than enable it.
In such a scenario, the affluent would inevitably turn to off-list agents provided by more agile startups, while ordinary citizens would view the initiative as yet another example of government mismanaging new technology. Conversely, an overly open approval process could allow bad actors to infiltrate the system, compromising digital signatures and eroding public trust in the framework.
These risks are real, but the status quo does nothing to address them. If anything, it leaves the door wide open for unregulated, exploitative actors to flood the market with potentially harmful solutions. Bad actors are already on the horizon, and their services will emerge whether governments act or not.
However, we are not starting from scratch when it comes to regulating such systems. The experience of open banking provides valuable lessons. In many countries, it is now standard practice for a curated list of authorized companies to request and receive permission to manage users’ financial accounts. This model of governance, which balances security and innovation, could serve as a blueprint for managing digital agents in public services. After all, granting permission for an agent to apply for a driver’s license or file a tax return involves similar risks to those we’ve already learned to manage in the financial sector.
The path ahead requires careful balance. We must embrace the efficiency gains of AI agents while ensuring these gains are democratically distributed. This means moving beyond the simple dichotomy of adoption versus rejection, toward a nuanced approach that considers how these tools can serve all citizens.
The alternative – a world of agents for the few, and queues for the many – would represent not just a failure of policy, but a betrayal of the fundamental promise of public services in a democratic society.
In light of this epochal shift, he declares, the critical question is: “Will [this shift] allow embedding economic production in the same kind of social solidarity trust models that we saw with the emergence of Wikipedia? Or will the externalization of risk onto the people formerly known as employees create severe disruption?” 