What If Expert Help Became Cheap?

Introduction

Abundant intelligence means a world where high-quality cognitive help is no longer rationed mainly by money, geography, credentials or luck. Instead of expert advice being scarce, slow and expensive, advanced AI could make parts of expert work available on demand: explaining, drafting, tutoring, coding, searching, translating, modelling, checking and helping people reason through hard problems. This is one of the central mechanisms behind the wider AI bloom idea. If intelligence becomes much cheaper and more widely available, science, education, work and daily life could change in ways that ordinary productivity statistics only partly capture.

The optimistic case is not that today’s chatbots are already expert replacements. They are not. Current systems still make confident mistakes, struggle with verification, reflect unequal access and can concentrate power in the hands of a few firms or governments. But the direction of travel matters. AI systems are already being used for customer support, software development, education, scientific assistance, medical workflows and everyday problem-solving. The real question is whether societies can turn increasingly capable AI into reliable, accessible and accountable “cognitive infrastructure” rather than a premium service for the already powerful.

What abundant intelligence would mean

For most of history, expert attention has been scarce. A good doctor, solicitor, teacher, engineer, scientist, translator, analyst or mentor can only help a limited number of people. Their time is expensive because training takes years, institutions are selective and many services require one-to-one judgement. Search engines made information easier to find, but they did not remove the need to interpret it, apply it, challenge it, translate it into action or tailor it to a particular person.

AI changes the shape of that bottleneck. A capable AI assistant can sit between raw information and human decision-making. It can summarise a technical paper, explain a tax rule, help a nurse draft a patient note, generate a lesson plan, debug code, translate a form, compare policy options or help a small business owner write a grant application. OpenAI’s large-scale study of ChatGPT use found that “practical guidance”, “seeking information” and “writing” accounted for nearly 80% of conversations, with writing especially dominant in work-related uses. That pattern suggests that many users are not merely asking AI to entertain them; they are using it as a general-purpose cognitive helper.[OpenAI]OpenAIhow people are using chatgpt15 Sept 2025 — New research from the largest study of ChatGPT use shows how the tool creates economic value through both personal and pro…

This is different from ordinary automation. Industrial automation made some physical labour cheaper. Software made some clerical tasks faster. Abundant intelligence would make judgement-like support cheaper across many domains at once. It would not mean every answer is correct, or that human expertise disappears. It would mean that the first layer of cognitive help — the first explanation, draft, translation, code sketch, literature scan, study plan or diagnostic prompt — becomes far easier to obtain.

The difference is easiest to see in a simple before-and-after comparison. In a cognitively scarce world, a student who falls behind may wait weeks for individual attention. A junior employee may learn mostly by trial and error. A patient may struggle to understand a diagnosis. A researcher may spend months reading around a field before testing a new idea. A local council may lack the analytical capacity to model policy options. In a world of more abundant intelligence, each of those people could have a tireless first-pass helper. The value lies not in replacing the expert, but in widening the circle of people who can get some expert-like support before they hit institutional limits.

The most transformative version would go further. If AI systems become reliable research partners, they could help generate hypotheses, design experiments, write software, run simulations, search literature and compare evidence. Surveys of “agentic science” describe AI moving from narrow tools towards systems that assist across the scientific workflow, including hypothesis generation, experimental design, analysis and iterative refinement. That does not make human judgement obsolete; it raises the possibility that many more people and institutions could participate in serious problem-solving.[arXiv]arxiv.orgFrom AI for Science to Agentic Science: A Survey on Autonomous Scientific DiscoveryAugust 18, 2025…Published: August 18, 2025

Where current AI already hints at it

The strongest evidence for abundant intelligence is not one spectacular demo. It is the spread of useful, imperfect cognitive assistance across several different settings. The pattern is uneven, but it is already visible.

One influential workplace study examined more than 5,000 customer support agents using a generative AI tool. The researchers found an average productivity increase of nearly 14%, with the biggest gains for less experienced and lower-skilled workers. That matters because it suggests AI can sometimes transfer tacit know-how from stronger performers to weaker performers through real-time guidance. In the language of abundance, AI did not merely speed up the best workers; it helped reduce the gap between people with different levels of experience.[NBER]nber.orgOpen source on nber.org.

Education offers another early signal. A 2025 study in Scientific Reports found that students using a custom AI tutor learned significantly more in less time than students in an in-class active-learning comparison, while also reporting higher engagement and motivation. The important point is not that one AI tutor settles the future of education. It is that high-quality tutoring has long been one of the most powerful but least scalable forms of teaching. If AI can provide even a partial version safely and cheaply, it could loosen one of education’s deepest constraints.[Nature]nature.comOpen source on nature.com.

Scientific work shows the same possibility at a higher level of abstraction. AI systems are already being designed to help scientists write code, search literature, plan experiments and test ideas. Google Research, for example, described an AI-powered empirical software system that can propose methodological ideas, implement executable code and empirically validate performance against a defined evaluation. Nature has also published work on systems aiming at end-to-end automation of parts of the research process, including ideation, literature search, experiment planning, implementation, analysis and manuscript writing, though these systems remain bounded and controversial.[Google Research]research.googleaccelerating scientific discovery with ai powered empirical softwareaccelerating scientific discovery with ai powered empirical software

The broader AI landscape reinforces the point. Stanford’s 2025 AI Index reported rapid improvements in AI performance, expanding use in everyday life and strong business investment, while noting that AI-enabled medical devices approved by the US Food and Drug Administration rose from six in 2015 to 223 in 2023. The same report also highlights how heavily frontier model development is now driven by industry, which is central to the access and control problem discussed later.[Stanford HAI]hai.stanford.edu2025 ai index report2025 ai index report

These examples are still early signs, not proof of a cognitive post-scarcity future. Customer support is not medicine. A physics tutor is not a universal teacher. A code-generating research agent is not a fully trustworthy scientist. But together they show why the idea is taken seriously: AI is not confined to one profession or one narrow task. It is beginning to act as a flexible cognitive layer across work, learning and discovery.

The mechanism: cheap expertise changes the margin

Abundant intelligence matters because many systems are limited not only by money or materials, but by the amount of competent thought available at the right time. A hospital may have equipment but too little clinical attention. A school may have a curriculum but too little individual feedback. A small firm may have ideas but no in-house legal, technical or marketing team. A research group may have data but not enough people to analyse every promising lead.

AI can change what happens at the margin — the extra task, the extra learner, the extra question, the extra experiment that would previously have been left undone. That is where abundance begins. The first-order effect is not always dramatic. It may look like a better draft, a faster literature scan, a clearer explanation, a safer checklist or a less intimidating form. But if millions of people receive that extra support every day, the aggregate effect could be large.

There are at least four mechanisms by which cheap expert help could matter.

First, it compresses learning loops. A person can ask follow-up questions without embarrassment, receive examples at their level, practise repeatedly and get immediate feedback. This is especially important in education, job training and reskilling, where human mentors are scarce.

Second, it reduces translation costs between domains. A biologist can ask for help with code; a policy analyst can understand a climate model; a patient can translate medical language into plain English; a local organiser can turn legal or bureaucratic text into an action plan. The value is not that everyone becomes an expert in everything, but that boundaries become less forbidding.

Third, it scales first drafts and first passes. Many expert workflows begin with a rough version: a memo, outline, data query, risk register, lesson plan, experimental script or prototype. AI can make these cheap enough that people try more options before committing.

Fourth, it can preserve and distribute organisational knowledge. In the workplace study on customer support, the largest productivity gains among less experienced workers suggest that AI can sometimes package patterns learned from more experienced workers into live assistance. That is a powerful form of cognitive diffusion, though it depends heavily on data quality, worker consent and governance.[NBER]nber.orgOpen source on nber.org.

This mechanism is why abundant intelligence could reach beyond GDP growth. A society with more cognitive support may become better at using its existing resources: diagnosing problems earlier, teaching people more effectively, designing institutions more carefully, reducing avoidable errors and making expert knowledge less dependent on elite access. That is the bloom-relevant claim. Intelligence is not just another service; it is a multiplier on many other forms of progress.

What cognitive scarcity will not simply disappear

The phrase “end of cognitive scarcity” can mislead if it sounds like a clean finish line. AI may make some kinds of help cheap while creating or exposing new bottlenecks. The scarce resource may shift from information to verification, from drafting to judgement, from access to trust, or from human labour to compute, energy and ownership.

Reliability is the most obvious bottleneck. Large language models can produce fluent falsehoods, often called hallucinations. Recent reviews emphasise that hallucinations remain a serious limitation across large language models, even as mitigation techniques improve. A 2026 Nature paper similarly describes confident, plausible falsehoods as a continuing barrier to reliability and notes that tool use, retrieval and self-verification help but do not eliminate the problem.[Frontiers]frontiersin.orgFrontiers Survey and analysis of hallucinations in large languageFrontiers Survey and analysis of hallucinations in large language

This matters because abundant bad advice is not abundance in the human-flourishing sense. A cheap AI lawyer that invents cases, a medical assistant that omits a danger sign, a tutor that teaches a misconception or a research assistant that fabricates citations can make the world worse while seeming helpful. Reports in 2026 about AI-generated false references entering scientific literature illustrate the danger: when low-cost text production is not matched by low-cost verification, the knowledge system can be polluted rather than enriched.[The Economic Times]m.economictimes.comSource details in endnotes.

Verification, therefore, becomes a central scarce good. In science, one 2025 paper on AI-driven discovery argues that the abundance of AI-generated hypotheses could hinder progress unless there are scalable, reliable ways to test and validate them. That is a useful warning for the whole abundant-intelligence thesis. Generating possibilities is not the same as knowing which possibilities are true, safe or worth acting on.[arXiv]arxiv.orgFrom AI for Science to Agentic Science: A Survey on Autonomous Scientific DiscoveryAugust 18, 2025…Published: August 18, 2025

A second bottleneck is skill. AI can help novices, but it can also tempt people to outsource the very thinking they need to develop. Some research on AI in education raises concerns about reduced cognitive engagement when students rely on generative AI for writing tasks. The broader risk is not that using AI is inherently lazy; it is that poorly designed use can turn a tutor into a shortcut machine. Abundant intelligence should ideally build human capability, not hollow it out.[arXiv]arxiv.orgFrom AI for Science to Agentic Science: A Survey on Autonomous Scientific DiscoveryAugust 18, 2025…Published: August 18, 2025

A third bottleneck is context. Many expert decisions depend on local knowledge, values, relationships and accountability. An AI system may explain a planning regulation, but it does not know a neighbourhood the way residents do. It may summarise treatment options, but it does not carry the ethical responsibility of a clinician. It may generate a policy memo, but it does not represent citizens. The more consequential the decision, the more abundant intelligence must be paired with human responsibility.

Access will decide whether abundance is broad or captured

The optimistic case depends on broad access. If advanced AI remains expensive, restricted, English-dominant, inaccessible to disabled users, unavailable in poorer regions or controlled by a small number of firms, then cognitive scarcity will not end. It will be reorganised.

The access problem is already visible. Pew’s 2025 survey across 25 countries found that awareness of AI was widespread but uneven: a median of 34% of adults had heard or read a lot about AI, 47% had heard a little and 14% had heard nothing at all. People were also more likely to be concerned than excited about AI’s effects on daily life. That combination — partial awareness, anxiety and uneven familiarity — is exactly the kind of social terrain in which benefits can accrue first to confident, well-connected users.[Pew Research Center]pewresearch.orghow people around the world view aihow people around the world view ai

A Microsoft report on global AI adoption in the second half of 2025 estimated that generative AI tools had reached 16.3% of the world’s population, up from 15.1% in the first half of the year, while also warning of a widening divide. Even if those figures are approximate and platform-specific, the direction is important: AI can spread quickly and still leave most people outside meaningful use.[Microsoft]microsoft.comMicrosoft AI Diffusion Report 2025 H2Microsoft AI Diffusion Report 2025 H2

Education makes the equity issue especially sharp. UNESCO’s 2025 work on AI and education argues that AI could increase access to learning and personalisation, but also risks worsening inequalities, privacy problems and safety concerns unless governed through a human-centred, rights-based approach. This is not a side issue. If AI tutoring, feedback and study support become normal for affluent students while poorer students receive lower-quality tools or none at all, abundant intelligence could widen educational gaps rather than close them.[UNESCO]unesco.orgai and education protecting rights learnersai and education protecting rights learners

Control is just as important as access. Stanford’s 2025 AI Index reported that nearly 90% of notable AI models in 2024 came from industry, up from 60% in 2023, while training compute, datasets and power use continued to grow rapidly. That concentration is not automatically bad; industry has built many useful systems. But it means the infrastructure for abundant intelligence may be shaped by commercial incentives, subscription tiers, advertising models, proprietary data and geopolitical competition.[Stanford HAI]hai.stanford.edu2025 ai index report2025 ai index report

A genuinely broad version of abundant intelligence would require more than consumer apps. It would need public-interest deployments in schools, libraries, clinics, courts, local government, small businesses and community organisations. It would need multilingual and accessible systems. It would need procurement rules, audits, appeal rights, privacy protections and ways for workers and citizens to challenge harmful uses. Without those choices, AI may make intelligence cheap in a technical sense while keeping trustworthy expertise scarce in practice.

Work could become more empowered, more monitored, or both

The end of cognitive scarcity sounds liberating for workers: fewer tedious tasks, faster learning, better tools and more opportunity for small teams to do ambitious work. That is a real possibility. AI can help a junior analyst produce a clearer memo, a tradesperson handle paperwork, a nurse reduce documentation burden, a programmer understand an unfamiliar codebase or a freelancer offer services that once required a larger firm.

But work is also where the politics of abundant intelligence becomes most visible. The same systems that assist workers can monitor them, deskill them, standardise their judgement or replace parts of their role. OECD analysis of AI and labour markets has found that benefits may be uneven, with higher-income and higher-skilled workers often better placed to gain from AI exposure than lower-income workers.[OECD]oecd.orgthe impact of ai on the labour market 69793977the impact of ai on the labour market 69793977

The key distinction is between augmentation and control. In an augmentation model, AI gives workers more capability: faster search, better drafting, easier training, safer decisions, less drudgery. In a control model, AI becomes a system for surveillance, pace-setting, automated evaluation or replacing human discretion with opaque instructions. Both can occur in the same workplace.

This is why worker voice matters. Recent UK debate has focused on whether staff should have more say over AI rollout, with concerns that AI can augment labour, degrade job quality or displace workers depending on how it is introduced. That framing is useful because it avoids both simplistic optimism and simplistic panic. The central issue is not “AI in work: yes or no?” It is who decides, who benefits, who bears risk and whether productivity gains translate into better jobs, lower hours, higher wages or merely tighter management.[The Guardian]theguardian.comThe report argues that the critical issue is who controls AI-driven changes in the workplace. Recommendations include a legal duty for em…

Abundant intelligence could also change career ladders. If AI handles many entry-level cognitive tasks, young workers may find it harder to learn by doing. If, however, AI is deliberately used as a coach, simulator and feedback system, it could make apprenticeship cheaper and more inclusive. The same technology could either pull up the floor of opportunity or remove the lower rungs of professional development.

The strongest objections

The case for abundant intelligence faces serious objections. The first is that today’s AI systems are too unreliable for the phrase “expert help” to be appropriate. This objection is partly right. In high-stakes settings, fluency can be dangerous because it creates the impression of competence without the accountability of a professional. The answer is not to pretend the problem is solved, but to distinguish low-stakes assistance from consequential advice, and to build systems around verification, uncertainty, audit trails and human review.

The second objection is that AI may centralise rather than democratise expertise. If the best systems require enormous compute budgets, proprietary data and energy-intensive infrastructure, then abundant intelligence may depend on a small number of gatekeepers. Stanford’s findings on industry dominance and fast-growing compute needs support this concern. So does the wider policy debate over frontier AI safety frameworks, transparency and risk management. The International AI Safety Report’s 2025 updates note continuing capability improvements, especially in coding, mathematics and expert-level science questions, while also stressing persistent reliability challenges and new risks around misuse, monitoring and controllability.[Stanford HAI]hai.stanford.edu2025 ai index report2025 ai index report 2arXiv

The third objection is that cheap cognitive output may flood institutions with low-quality material. Schools may face AI-written essays. Courts may face AI-generated filings. Journals may face fabricated references. Employers may face polished but shallow applications. The scarce skill then becomes filtering, provenance and trust. In this world, abundance without authentication can feel like spam at civilisational scale.

The fourth objection is that AI could weaken human agency. If people increasingly ask machines what to think, write, choose or believe, then the danger is not only wrong answers but passive dependence. A flourishing society should not measure success by how many decisions are offloaded. It should ask whether people become more capable, more informed, more creative and more free.

The fifth objection is environmental and material. Intelligence is not free just because the user sees a text box. Frontier AI requires chips, data centres, electricity, cooling, networks and supply chains. If AI becomes a major layer of social infrastructure, the costs of compute and power become part of the abundance equation. Our World in Data’s dataset on AI training costs tracks hardware, energy, cloud rental and staff costs for frontier models, highlighting that the physical basis of “digital” intelligence is substantial and unevenly distributed.[Our World in Data]ourworldindata.orghardware and energy cost to train notable ai systemshardware and energy cost to train notable ai systems

None of these objections destroys the abundant-intelligence thesis. They make it conditional. AI can reduce cognitive scarcity only if reliability, access, governance, energy and human capability improve together.

What would make abundant intelligence genuinely bloom-like?

The bloom version of abundant intelligence is not simply “everyone has a chatbot”. It is a social and technical achievement in which AI expands human capability across generations. Several conditions matter.

Reliability must be engineered into workflows, not assumed from model fluency. The safest uses will often combine AI with retrieval from trusted sources, domain-specific checks, calibrated uncertainty, human review and clear limits. In science, law, medicine and public administration, the system should show its evidence and make verification easier than blind trust.

Access must be treated as infrastructure. Schools, public libraries, clinics, courts, charities, small firms and local governments should not be left with second-rate tools while wealthy organisations buy the best assistance. Public procurement, open standards, accessibility requirements and support for low-resource languages could matter as much as model capability.

Human learning must remain central. The best AI tutors and workplace assistants should ask questions, give feedback, reveal reasoning steps where appropriate and help users become more competent. Systems designed only to produce finished answers may save time while weakening skill formation.

Workers and citizens need a say. AI deployment should not be something done to people by management, platforms or states. Consultation, contestability and transparency are essential if AI is to augment rather than dominate.

Power concentration must be checked. Abundant intelligence becomes fragile if a few actors can control access, set prices, shape knowledge flows or withdraw critical services. Competition policy, public-interest AI, open research, safety standards and international governance all affect whether the benefits diffuse.

The long-term goal should be capability, not dependency. A blooming civilisation would use AI to help people understand more, create more, heal more, coordinate better and face the future with greater wisdom. That is different from a world where people become surrounded by persuasive systems they cannot question, inspect or refuse.

Why this subtopic matters for the long future

Abundant intelligence is one of the most important bridges between today’s AI tools and the larger possibility of an AI-enabled human bloom. Health, longevity, clean energy, climate repair, robotics, space settlement and civilisational resilience all depend partly on cognition: noticing problems, generating ideas, testing them, coordinating action and learning from failure. If AI greatly increases the amount of useful cognition available to humanity, it could accelerate progress across many other branches of flourishing.

But the word “useful” carries the weight. More text is not the same as more wisdom. More answers are not the same as more truth. More automation is not the same as more freedom. The end of cognitive scarcity would be a historic achievement only if cheap intelligence becomes trustworthy, broadly shared and directed towards human capability rather than manipulation, dependency or control.

The most realistic optimistic view is therefore neither utopian nor dismissive. AI is already making some forms of cognitive help cheaper and more available. The early evidence from workplaces, education, science and everyday use is strong enough to take the possibility seriously. Yet the hardest part is not merely building more powerful models. It is building the surrounding institutions, norms and safeguards that turn abundant machine cognition into broader human flourishing.

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Further Reading

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The Coming Wave

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The Age of A. I.

By Henry Kissinger, Eric Schmidt et al.

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Co-Intelligence

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Focuses on humans using AI as an accessible cognitive partner.

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Human Compatible

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