The AI-Native State: What It Is and Where the Boundary Lies

Evgeny opens with his experience introducing electronic prescriptions in Belarus. Digitizing individual agencies does not necessarily create a coherent citizen journey: a single entry point can still conceal many forms and handoffs. Alexander connects this to organizational culture. Competition encourages companies to focus on outcomes, whereas consistent application of rules is an important public-sector objective in its own right. Citizens may still have to connect the work of several agencies themselves.

Aleksey proposes viewing a state as a particularly complex system seeking to preserve its existence. Alexander challenges the assumption that survival necessarily entails citizens’ wellbeing. Switching a commercial provider is usually easier than moving countries and obtaining rights elsewhere. Transferring the AI-native company model therefore starts with questions about goals: whose outcome improves, and who has the authority to define improvement?

Aleksey’s most immediate use case is assistance with everyday documents, preparation, and communication. Natural language could make tools accessible to people comfortable with conversation but reluctant to learn complicated software. Evgeny points to another obstacle: parts of government workflows still rely on paper. A connected automated process is difficult to build when its inputs are not digitally available.

Alexander separates providing subscriptions from delegating work to agents. An assistant beside a person can leave the underlying process largely intact. Autonomous execution requires an operating environment, knowledge, procedures, result checks, and security controls. An individual employee’s initiative raises questions about hosting, authority, and accountability. Lengthy procurement cycles sit awkwardly alongside the ability to automate a personal task quickly.

The hosts revisit earlier digital systems while discussing agents that execute rules. Alexander cites examples involving automated penalties, debts, and social assistance where correction took a long time. In his comparison, the controversy over school grades illustrates how public pressure can sometimes accelerate reversal. The examples underline a practical distinction: launching a system is easier than restoring the position of someone harmed by it.

Alexander’s criteria for autonomy include verifiable results, reversibility, and bounded consequences. An agent must operate within granted authority, and saying that it made a decision does not settle accountability. Naming a responsible manager is insufficient if that person has no incentive to correct failures. Automated support can compound the problem when citizens receive repeated refusals from bots without reaching someone empowered to reconsider their case.

Evgeny describes an acquaintance repeatedly denied a bank account after moving to the UK. According to his account, an agent called support, reached staff, and helped escalate the request to a manager. The person monitored the conversation and adjusted the assistant’s actions. The bank subsequently removed an outdated flag and allowed the account to be opened. This is a participant’s anecdote about overcoming a bureaucratic impasse, rather than a general claim about banking procedures.

A second example is a small website Evgeny built to compare housing areas using information about neighbourhoods, schools, and other conditions from separate sources. Alexander relates it to his own exploration of London. Such a tool addresses a specific life task on top of existing services. It can be useful before every organization redesigns its systems, although this household example does not resolve restrictions on government data.

Aleksey invokes Hyrum’s Law as an analogy: users come to depend even on incidental interface behaviour. Institutional routines can develop similar dependencies. Someone who alone knows how to complete a difficult form gains influence, and simplifying it changes that position. Introducing AI therefore involves identifying both technical inefficiencies and the people who benefit from the current arrangement.

Evgeny observes that an agency often lacks a competitor forcing it to improve the user experience. An infrequently needed service may remain inconvenient for years because people endure it once and move on. The hosts discuss combining employee initiative with leadership support. A teacher preparing materials with AI and showing colleagues provides a concrete example: useful practice spreads, then a group requests shared tools and support.

Aleksey suspects that public employees may already use external assistants without an agreed organizational approach. He treats this as a reason to provide safer access, rather than evidence of a measured adoption rate. The context of government work makes data handling especially sensitive. The conversation raises both proprietary national models and open models without establishing a universally suitable choice.

Evgeny suggests that major AI labs may want to supply infrastructure to governments. That possibility also introduces dependence on an outside company. Alexander highlights the risk of connecting collected information to automated restrictions on individuals. Scenarios of pervasive control are discussed as risks and thought experiments; uncertain accounts of particular surveillance systems are not established descriptions of their operation.

Aleksey emphasizes leadership conviction, finding people capable of driving change, and giving them resources. He uses historical examples as management analogies. Alexander stresses the different timescale of states: established institutions evolve slowly, and experience from a small country does not automatically transfer to a large one. Their disagreement remains open; the conversation offers no agreed blueprint for transforming an entire state.

Alexander expects wider assistance for employees and incremental automation of public services. Relatively simple decisions could be separated from complex cases that remain with people. A digital property transaction illustrates the value of a predictable, verifiable process without proving that agents are necessary. Aleksey cautions against underestimating the potential gain: even a small country, in his view, could secure a significant advantage through successful AI adoption.

Near the end, Aleksey hypothesizes that subtle communication skills could help manage large agent systems. He also describes a personal experience reading difficult books with a model: the assistant appears to introduce its own interpretations instead of explaining the author’s thinking. He finds it necessary to distinguish understanding the source from accepting the model’s proposed view.

Alexander questions why political techniques should be imported into an engineered agent system. He returns to clear goals, limited resources, and accessible, consistent information. Skill at influencing people is not a substitute for designing a process. The discussion ultimately points toward evaluating an AI-native state through the public task it serves, verifiable execution, and people’s ability to obtain a correction when the outcome is wrong. Alexander and Evgeny close with a scenario of continued digitization in which AI helps work happen faster and potentially at lower cost.