In today’s hyperconnected world, the notion of economic efficiency is being recast. The friction and delays in information flows, once considered an inevitable cost, are now up for disruption. At the forefront of this change stands ChatGPT and other conversational AI tools—technologies that promise to compress the time, cost, and uncertainty of information retrieval and processing. For British citizens, businesses, policymakers, and general readers alike, understanding how this transformation is unfolding is not just intellectually interesting; it may well shape the next decade of economic growth, inequality, innovation, and public life.
In this commentary, I explore how ChatGPT influences economic efficiency in the digital age. I begin by revisiting the classical framework of efficiency in economics, then examine how information frictions matter in markets, and thereafter dive into concrete ways ChatGPT and related AI systems are reshaping information flows, decision-making, and institutional architectures. Along the way, I address the risks and limitations, and finally reflect on what this means for regulation, public policy, and the role of human expertise.
Economists typically divide efficiency into two broad types: allocative (or Pareto) efficiency and dynamic (or productive) efficiency.
Allocative efficiency means that resources are allocated in a way where no one can be made better off without making someone else worse off; marginal costs equal marginal benefits.
Productive (or technical) efficiency means that goods and services are produced at the lowest possible cost given the technology.
Over time, dynamic efficiency considers how innovation and growth optimize welfare across multiple periods.
In classical models, information is often taken as given or costless. But in reality, information frictions—delays, search costs, uncertainty, miscommunication, bounded rationality—are endemic. Economic actors frequently expend resources gathering, verifying, and interpreting data before making decisions. Market outcomes deviate from textbook optimality when information is asymmetric or costly to acquire.
Joseph Stiglitz, George Akerlof, and Michael Spence brought these frictions to the centre of economic theory in the late 20th century, showing how markets fail when information is imperfect. Imperfect information gives rise to adverse selection, moral hazard, and principal–agent problems. In addition, decision-making under uncertainty often demands predictive models, experience, heuristics, and iterative feedback.
In sectors like finance, healthcare, education, and public administration, quality, timeliness, and accessibility of information are paramount. Traditionally, improvements in information flow—faster networks, better databases, improved matching platforms—have nudged efficiency upward. But we may now be entering a new phase: intelligent conversational agents as infrastructure.
ChatGPT, built on large language models (LLMs), sits at a crossroads between search engines, expert systems, and personal assistants. It is not just a faster Google; it can parse, summarise, reason, and interact using contextual prompts. This gives rise to a new kind of information infrastructure. Below are several key pathways through which ChatGPT-like systems influence economic efficiency.
One of the most immediate benefits is that ChatGPT can condense volumes of text—policy papers, academic articles, news reports, technical specifications—into coherent summaries, highlighting key points, caveats, and contradictions. This does not eliminate the need for domain experts, but it lowers the marginal cost of access to knowledge, especially for non-specialist readers.
Thus, policy makers, business executives, journalists, and citizens can more easily access expert debates, compare views, and form better-anchored opinions. This accelerates the diffusion of ideas, reducing informational bottlenecks.
Unlike traditional keyword search, ChatGPT supports follow-up questions, clarifications, rebuttals, and conversational refinement. Instead of reformulating search terms, a user can say “explain why that assumption matters,” or “give an alternative view,” or “what’s the implication for UK regulation.” This iterative interactivity allows deeper probing on the fly, yielding better quality responses.
This can reduce the time for diagnosis, policy exploration, or competitive intelligence.
Beyond summarisation, ChatGPT can assist in scenario planning: “If interest rates rise by 1 %, how might inflation and consumption respond?” Or “What tradeoffs should a UK local authority consider if it wants to invest in AI literacy?” Though not flawless, the model can help non-expert users structure complex tradeoffs and surface counterintuitive consequences.
In business or government settings, ChatGPT may become a front-line decision-support tool—a co-pilot in strategic deliberation.
One recurring critique of the digital economy is that large incumbents dominate because they can afford expensive analytical teams, proprietary data, or in-house technology. ChatGPT shrinks that gap. Entrepreneurs, NGOs, or local authorities might now access high-quality insights at low cost.
This leveling effect can help democratise efficiency gains. In principle, better-informed small actors can respond more nimbly, mount innovations, and contest monopoly power.
In many sectors, there is cognitive drudgery: drafting memos, summarising reports, writing first drafts, producing boilerplate legal or regulatory texts, preparing background briefs. ChatGPT can perform or accelerate these tasks, freeing human experts to concentrate on the highest-value reasoning, oversight, and judgment.
From the standpoint of productive efficiency, this is significant: more output (reports, research, policy memos) can be generated with fewer human hours wasted on repetitive tasks.
As ChatGPT tools are deployed in real-world settings, usage data and user corrections can feed back into better models. Over time, domain-specific fine-tuning, retrieval augmentation, and specialization may reduce errors and improve reliability. The evolving architecture thus becomes adaptive, improving performance in critical domains (e.g. medicine, engineering, public policy) over time.
The channels above are promising, but what do they imply for macro- and micro-level outcomes? Below are several domains in which ChatGPT (and related AI) may reshape economic performance.
By lowering the cost of learning about new technologies, designs, and best practices, ChatGPT can narrow the lag between invention and adoption. Firms can more rapidly scan research frontiers, reverse-engineer insights, or assess competitive landscapes. The net effect: faster technological diffusion, which supports aggregate productivity growth.
In competitive markets, firms that harness ChatGPT for decision support, market analysis, and process efficiency may gain advantage. Over time, competition may intensify, pushing laggards to adopt similar tools just to survive. This dynamic can create a “race to intelligence,” where failure to leverage AI becomes a competitive handicap.
In some industries, ChatGPT may reduce information asymmetry: for example, prospective buyers can use it to get sharper comparisons among product attributes, warranties, performance reviews, and regulations. This reduces the scope for exploitation or misinformation by sellers.
Governments and regulators face a chronic information disadvantage: they must monitor sectors, understand technical research, anticipate innovation trajectories, and deploy policy interventions. ChatGPT offers a way to scale expert knowledge across departments, enabling quicker evidence review, regulatory drafting, scenario assessment, and stakeholder consultation.
In principle, this could improve regulatory efficiency — the ratio of social benefit to administrative cost. For instance, local planning departments might process applications more intelligently, or public health agencies might better model outbreak responses.
One risk of automation is that it displaces certain skilled labor. If ChatGPT becomes a first-pass analyst, fewer junior analysts or research assistants may be needed. The value chain shifts: instead of writing drafts and summarising papers, human professionals may be asked to oversee, critique, audit, and validate AI outputs.
This shift may increase the “returns to oversight skills” while reducing demand for routine cognitive tasks. Over time, this could reshape wage structures and employment patterns in knowledge-intensive sectors.
Much economic inefficiency arises from misinformation, poor coordination, and errors. ChatGPT can play a role in fact-checking, error detection, and alignment of understanding across actors. In collaborative settings—multinationals, intergovernmental bodies, NGOs—ChatGPT may act as a common interpretive tool, reducing misunderstandings across languages or technical domains.
Moreover, in journalism, education, and public discourse, ChatGPT can accelerate fact verification, summarise conflicting evidence, or flag contradictions, helping slow the spread of misinformation.
A caveat: ChatGPT and its derivatives are likely to be platform-driven. The more users rely on a particular AI, the better its models become, attracting even more users—a positive feedback loop. This raises concentration risks: control over the dominant model can confer disproportionate influence over access to knowledge, framing of discourse, and even economic direction.
In short, while ChatGPT may lower barriers more broadly, it may also reify new monopolies or gatekeepers in the digital economy of ideas.
Despite its transformative potential, we must be realistic about what ChatGPT can and cannot do—and guard against missteps. Below are several cautionary points.
Large language models are prone to hallucinate—that is, generate plausible but incorrect statements. In high-stakes settings (public policy, medicine, engineering), unverified AI outputs can introduce errors, biases, or misleading conclusions. Blind reliance would be irresponsible.
Hence, human validation, auditing, and domain oversight remain essential. Economic efficiency gains cannot come at the cost of systemic errors or misinformed decisions.
The training data of models like ChatGPT reflect historical distributions, power asymmetries, and cultural biases. These biases can be replicated or amplified in outputs. In economic or social domains, biased models can distort analysis, marginalise minority perspectives, or perpetuate structural inequality.
Ensuring fairness, transparency, and accountability in AI design and deployment is critical.
Humans interacting with AI may develop automation bias—a tendency to overtrust machine-generated results even when they are flawed. Decision makers might abdicate judgement, accepting AI outputs uncritically. This undermines the beneficial interplay of human and machine.
Organizations must cultivate AI literacy, scepticism, and robust validation cultures so that humans engage critically with AI suggestions rather than passively accepting them.
ChatGPT is powerful in general-purpose tasks, but it lacks deep domain-specific grounding, especially in dynamic or cutting-edge fields. In science, law, finance, engineering, or philosophy, model outputs may lack essential nuance, omit critical caveats, or ignore context-specific constraints.
Hence, blending ChatGPT support with human subject-matter experts remains necessary.
If actors use ChatGPT strategically to spin narratives, generate persuasive messaging, or automate propaganda, information flows can be gamed. The same infrastructure that accelerates good information can also accelerate disinformation or tactical deception.
Governance of AI and norms around responsible use must anticipate misuse threats.
While ChatGPT can lower barriers, its benefits may disproportionately accrue to those with reliable internet access, computational resources, or ability to pay for premium AI services. Regions, firms, or individuals in the periphery risk being left behind, exacerbating digital divides.
Ensuring equitable access to AI must be a priority for public policy.
Given the opportunities and risks, what should the response be in the UK? Below I outline several priority areas.
Generalisable gains depend on broad public familiarity with AI: how it works, what it can and cannot do, and how to use it critically. The UK education system should integrate AI literacy not just in computer science but across disciplines—history, economics, politics, ethics. Public media, libraries, citizen workshops must also provide accessible resources.
A well-informed populace will more reliably benefit from AI and resist misuse or overconfidence.
The government, in partnership with industry and academia, should develop open standards for AI evaluation, audits, transparency, explainability, and bias detection. Certification regimes—akin to safety testing in pharmaceuticals, or stress-testing in banking—could instil trust while managing risk.
For instance, models used in sensitive domains (health, criminal justice, planning) might be required to undergo third-party audits.
To avoid overdependence on a handful of private providers, the UK could support public-interest AI models or data platforms—open-source, non-profit, mission-driven systems designed for public good (education, science, public policy). This acts as a counterbalance to private monopoly AI infrastructure.
Public institutions (e.g., the Office for Artificial Intelligence, the Alan Turing Institute, regulatory agencies) should maintain both capacity and expertise to engage critically with dominant models.
Targeted subsidies, grants, or technical assistance could help small and medium-sized enterprises (SMEs) adopt AI tools effectively. Many public good or local-level organisations (charities, community groups, local councils) lack capacity to deploy AI, but stand to benefit from decision-support, summarisation, and analysis tools.
Bridging this adoption gap is crucial to equitable diffusion of efficiency gains.
Regulators must guard against undue concentration of influence in AI platforms. Potential measures include:
Data portability and interoperability mandates (allowing users and institutions to switch providers).
Open access to critical models or APIs under fair terms.
Transparency obligations: public reporting of model updates, biases, performance metrics.
Antitrust or platform regulation when dominant AI providers distort competition or lock in users.
While heavy-handed regulation could stifle innovation, a balanced approach is vital to preserve contestability and public interest.
Especially in domains affecting rights, livelihoods, or safety, decision systems should require human oversight, not full automation. Clear liability frameworks are needed: if AI-assisted decisions cause harm, who is responsible? The designer, the deployer, the user?
UK law should evolve to clarify AI liability and risk-sharing, encouraging safety-conscious deployments.
To illustrate concretely, imagine how ChatGPT might change how a UK local authority handles planning applications.
Currently, planning officers must parse architectural designs, traffic studies, environmental impact assessments, public comments, legal constraints, and national/local policies. They write reports, draft conditions, respond to appeal arguments, and explain decisions.
With ChatGPT support:
Summaries & Alignment: The tool could summarise hundreds of pages of technical input, distilling key tradeoffs, inconsistencies, and external precedents.
Scenario Exploration: Officers might ask: “What alternative conditions could yield similar environmental impact but lower cost to the applicant?”
Drafting & Communication: The system could generate draft decision notices, explanatory texts, or citizen-friendly summaries in plain English.
Consistency Checking: It could flag inconsistencies across appeals or past decisions, helping ensure coherence.
Public Interface: A simplified citizen-facing chatbot could answer applicant questions, reducing routine inquiries and freeing staff for complex issues.
The result: faster decisions, fewer errors, better transparency, and lower administrative burden. But only if oversight, validation, and expert review remain integral.
A healthy economy does not aim for maximum mechanical efficiency alone; it values deliberation, diversity, accountability, serendipity, and resilience. In the digital era, our challenge is not just automating, but designing human–AI systems that combine machine scale with human wisdom.
As ChatGPT and its successors permeate decision-making, the core task for societies becomes:
Ensuring plurality of voices (so that AI does not ossify dominant narratives).
Maintaining critical spaces where human creativity and dissent remain unconstrained.
Cultivating institutional modes of oversight so that AI supports democratic legitimacy rather than usurps it.
Efficiency is necessary but not sufficient. Systems that over-optimize for narrow metrics may neglect justice, dignity, or the unforeseeable value of minority insights.
We are at an inflection point. ChatGPT is more than a flashy tool; it is part of an emerging digital infrastructure of intelligence. By lowering the cost of accessing, interpreting, and acting on information, it has the potential to reshape economic efficiency in profound ways.
Yet with opportunity comes responsibility. The gains of speed, diffusion, and automation must be balanced by rigorous oversight, fairness, and human accountability. The United Kingdom, with its strengths in research, regulation, and public discourse, is well positioned to shape a path that captures the upside of AI without succumbing to its hazards.
For British readers—from business leaders to concerned citizens—the question is not whether ChatGPT will matter, but how we will choose to integrate it. The shape of that integration may define the next chapter of prosperity, justice, and public trust.
