The Brain Economy Thesis
Brain Capital as Productive Infrastructure in the Age of Artificial Intelligence
Rui de Oliveira e Silva
Working Paper — September 2026
Abstract
The accelerated diffusion of Artificial Intelligence is changing not only the distribution of tasks between people and machines, but also the nature of economic scarcity. As certain cognitive processes — research, synthesis, classification, translation, programming, text production, comparison or the generation of alternatives — can be performed by artificial systems at progressively lower cost, it becomes necessary to reconsider where economic differentiation will reside in an economy characterised by a growing abundance of computational and artificial cognitive capacity.
This paper argues that this transformation increases the economic relevance of an asset that conventional productivity models capture only partially: Brain Capital. The World Economic Forum and the McKinsey Health Institute define Brain Capital as the combination of Brain Health and Brain Skills. Brain Health corresponds to the conditions that support healthy brain functioning; Brain Skills include the cognitive, interpersonal, self-leadership and technological-literacy capabilities that allow people to learn, adapt, relate and contribute in a meaningful way. [1]
The thesis proposed here does not seek to redefine Brain Capital, nor to claim authorship of the concept. It seeks instead to develop the economic consequences that follow from its combination with Artificial Intelligence. It argues that Brain Capital should be understood as productive infrastructure: not only as a direct source of health, participation and productivity, but as a capacity that shapes the way other assets — technology, knowledge, information and financial capital — are selected, combined and governed.
The central hypothesis is that, as Artificial Intelligence makes certain forms of cognitive execution more abundant, a growing share of economic differentiation shifts towards complementary, higher-order capabilities, including problem formulation, learning, interpretation, adaptation, verification, judgement and decision. The strategic question therefore ceases to be only who owns the best technology. It also becomes who possesses the human and institutional capacity required to convert available intelligence into realised economic value.
Keywords: Brain Capital; Brain Economy; Artificial Intelligence; productivity; human capital; judgement; decision-making; innovation; absorptive capacity; competitive advantage.
1. From the knowledge economy to the Brain Economy
Modern economies have developed progressively more sophisticated systems for identifying and valuing the assets regarded as decisive for production. Land, natural resources, machinery, infrastructure, financial capital, technology, intellectual property and data have acquired explicit status in economic analysis. Human Capital theory represented a particularly important advance by demonstrating that education, knowledge, skills and experience were not merely individual attributes, but also forms of investment with consequences for productivity, income and growth.
Human Capital, however, tends to presume the functional availability of the system that mobilises those resources. It is possible to know a person's schooling, qualifications or professional experience without knowing the cognitive condition in which that knowledge will have to be used. Two people with comparable formal training can differ substantially in their capacity to learn new information, sustain attention, adapt mental models, integrate contradictory perspectives or exercise judgement under conditions of uncertainty. Accumulated competence and the functional capacity to mobilise it are therefore not equivalent concepts.
It is precisely in this space that Brain Capital introduces an additional dimension. The World Economic Forum and the McKinsey Health Institute define it through the association between Brain Health and Brain Skills, and stress that the two dimensions are interlinked. The 2026 report includes within Brain Skills capabilities of a cognitive, interpersonal, self-leadership and technological-literacy nature, while Brain Health covers the promotion of healthy brain development and the prevention or treatment of mental, neurological and substance-use conditions. [1]
The concept becomes particularly interesting when it ceases to be interpreted exclusively through individual health. The brain participates directly in the processes through which information is turned into meaning, knowledge is updated, problems are formulated, alternatives are compared and resources are allocated. For that reason, the functional condition of the brain is not only relevant to the quality of life of the person who has it. It has economic consequences whenever it affects learning, participation, productivity, innovation, coordination and decision-making.
The formulation was not born with the 2026 report. Eyre and colleagues proposed in 2021, in the journal Neuron, a Brain Capital agenda linking brain health, skills and economic development. [2] Nail-Beatty and colleagues subsequently deepened the relationship between Brain Health and the major contemporary economic transitions, arguing that the quality of brain capital becomes particularly relevant in the face of digital, demographic, ecological and social transformations. [3] The OECD has likewise developed work on neuroscience-informed policy and on Brain Capital as part of a systemic approach to development. [4]
What the current wave of Artificial Intelligence adds is a substantive change in the structure of demand for human cognitive capacity.
2. Artificial Intelligence changes the location of scarcity
Much of technological history can be read as a succession of reductions in the cost of particular capabilities. Mechanisation reduced dependence on human physical strength; electrification multiplied the energy available; computing reduced the cost of calculation and storage; the internet radically altered the cost of reproducing and distributing information. Contemporary Artificial Intelligence is now reducing the cost of producing many forms of cognitive output.
The economic consequence is not necessarily a proportional decline in the value of the human brain. In certain domains, value may shift along the cognitive chain. The Human Advantage in an AI Economy, from the McKinsey Health Institute, observes that the AI-induced transformation of work may move activity from execution towards interpretation, prioritisation and decision. The automation of routine tasks removes one class of demand, but what remains may be cognitively more complex. [5]
This observation matters economically. When producing a first analysis takes hours of work, the production of that analysis is a scarce resource. If an artificial system can produce it in seconds, scarcity shifts partly to other stages: determining whether the question asked was the right one, assessing the assumptions used, checking the quality of the evidence, distinguishing a correlation from a causal mechanism, understanding contextual factors absent from the model and deciding whether or not the recommendation should be acted upon.
The same logic applies to the generation of alternatives. In an environment where formulating ten scenarios costs significantly less, the relative value of the capacity to select among them rises. Abundance does not eliminate the need for choice; it frequently increases it.
The McKinsey Global Institute's research on agents, robots and the reorganisation of work points in a compatible direction. The technical potential for automating activities is high, but automating activities is not mechanically equivalent to eliminating occupations. In many contexts, the content of the role changes and the boundary between human and artificial work comes to be organised around new complementarities. [6] Human work may shift towards problem framing, handling exceptions, integrating context, social interaction, supervision and decision-making.
From this follows a proposition that deserves systematic investigation: when one layer of productive capacity becomes more abundant and cheaper, relative value may shift towards the complementary capabilities needed to govern it. In the case of AI, those capabilities include a substantial part of what the contemporary framework calls Brain Skills.
This proposition does not depend on the claim that creativity, judgement or other capabilities will remain forever impossible for machines. It would be imprudent to define an economic theory on a prediction of that kind. The argument is institutional. As long as human beings retain legal, fiduciary, political or moral responsibility for decisions, there will continue to be a relevant difference between the capacity to produce an output and the authority to determine what to do with it.
3. Brain Capital as productive infrastructure
Using the word capital demands more rigour than a generic language of well-being. Health has intrinsic value, regardless of a person's economic contribution. But a concept of Brain Capital becomes economically relevant when we can identify mechanisms through which its formation or deterioration alters present and future productive capacity.
The available evidence already allows part of those mechanisms to be identified. The WEF-McKinsey report estimates, on the basis of the projections used by its authors, that Brain Health conditions — including the primary and associated burden of mental, neurological and substance-use disorders, stroke and self-harm — account for approximately 24% of the global burden of disease. [1] The same report estimates that scaling proven interventions could reduce the global burden of disease by more than 260 million DALYs and generate up to US$6.2 trillion in cumulative GDP gains. [1]
These figures need to be interpreted with care. They do not mean that "Brain Capital is worth 6.2 trillion dollars", nor that any brain-related intervention automatically carries an economic return. They are modelled estimates of the potential impact of scaling specific Brain Health interventions. Their relevance for the present thesis lies elsewhere: they show that brain-related conditions produce consequences that cut across health, economic participation, income, productivity and growth.
A broader analysis of the health of the working population, also used by the WEF-McKinsey report, estimates that proactive investment in workers' health — a wider concept than Brain Health — could generate up to US$11.7 trillion in economic value and correspond to a potential increase of up to 12% of global GDP. It would not be methodologically correct to attribute the whole of this estimate to Brain Capital; it is, however, further evidence that human conditions long classified mainly as well-being have material economic consequences.
The formation of this asset begins long before entry into the workforce. The WEF-McKinsey report draws on the literature on child development and on investment in capabilities across the life cycle, including the work of Heckman and colleagues. [7] The underlying economic principle is important: part of the productivity observed in an adult results from investments and conditions accumulated over decades. A productivity policy that begins only when a worker is hired ignores a substantial part of the formation of the asset it intends to use.
Brain Capital thus makes it possible to connect policies that are usually kept apart. Health, education, lifelong learning, working conditions and social participation can act on different dimensions of the same human productive system. This does not mean they are equivalent or interchangeable, but that they have convergent effects on future capacity.
4. The channels through which Brain Capital can produce economic value
The economic value of Brain Capital becomes clearer when the mechanisms through which it can affect outcomes are distinguished. A first mechanism is economic participation. Mental and neurological conditions can reduce labour participation through disability, absenteeism, premature mortality or caring responsibilities. The McKinsey Health Institute refers, for example, to the global loss of approximately 12 billion working days a year associated with mental health problems. [5]
A second mechanism is productivity during participation itself. Presence is not equivalent to full productive capacity, and factors affecting concentration, learning, collaboration or flexibility can influence the quality of work even where there is no formal absenteeism.
The third mechanism is the formation and updating of skills. The Future of Jobs Report 2025, cited by WEF-McKinsey, estimates that 59% of workers will need additional training by 2030. [8] The list of skills whose importance tends to grow includes analytical thinking, creativity, resilience, flexibility, curiosity and technological literacy. The Brain Capital report itself frames many of these capabilities as Brain Skills.
A fourth mechanism is adaptation. In an environment where technology, markets and business models change rapidly, possessing knowledge is no longer enough if that knowledge cannot be revised. The capacity to abandon a rule when it stops working, to recognise a change of regime or to incorporate a new tool without surrendering judgement about that tool's purpose is an economic asset.
A fifth mechanism is innovation. Creativity does not happen in isolation from the capacity to sustain curiosity, combine knowledge, tolerate ambiguity and persist with problems that have no immediate answer. These characteristics are not independent of the conditions in which the cognitive system operates either.
There is, finally, a mechanism less often included in discussions of Brain Capital, but potentially of enormous value: the quality of allocation. Some people do not merely produce an additional unit of work; they decide on the distribution of the organisation's remaining assets. They determine where financial capital is invested, which people occupy critical positions, which technology is acquired, which markets receive resources and which risks are accepted. The economic consequence of a marginal change in the quality of decision of someone with high leverage over resources can be far greater than that of a marginal change in a low-consequence operational task.
For this reason, the value of Brain Capital does not depend only on the quantity of cognition produced. It also depends on the place that cognition occupies in the architecture of decision.
5. Artificial Intelligence and the problem of absorptive capacity
Access to a technology does not automatically determine the economic value extracted from it. This is well known in the history of general-purpose technologies: there is frequently a lag between technological investment and productivity gains because organisations need to change processes, skills, structures and business models.
AI adds a particularity. It can extraordinarily increase the quantity of cognitive output available. An organisation can come to generate more analyses, forecasts, alternatives, documents, simulations and recommendations without a proportional increase in the number of people responsible for interpreting or validating that material.
This is precisely where McKinsey identifies a potential value leakage: the greater the gap between what AI can produce and what people can absorb, the smaller the share of the technological potential that is actually converted into value.
This observation allows Brain Capital to be introduced as part of the problem of absorptive capacity. An organisation may possess technological capacity that exceeds its human and institutional capacity to integrate that technology into the decision process. In that case, more available intelligence does not proportionally guarantee more organisational intelligence.
The paradox matters. A fall in the marginal cost of producing information can raise the marginal value of the attention able to select it. The proliferation of alternatives can raise the premium on prioritisation. The automation of analysis can raise the importance of verification. The multiplication of recommendations can raise the need for judgement.
A company can thus become technologically very sophisticated while remaining cognitively weak. It can own advanced models, agents, automation and large volumes of data, yet keep a low capacity to formulate problems, detect errors, understand second-order effects, challenge premises or decide under uncertainty. Technology and judgement should therefore not be treated as perfect substitutes.
6. The Brain Capital Multiplier hypothesis
From this relationship a hypothesis is proposed which, for convenience, we shall call the Brain Capital Multiplier. The expression is used as a conceptual label, not as a claim of lexical originality or as the name of a coefficient already demonstrated empirically.
The hypothesis holds that higher levels of Brain Capital may not only produce direct effects on participation, learning and productivity, but also increase the realised return on complementary assets, particularly technology, knowledge and financial capital.
In economic terms, the strong proposition is not simply that more Brain Capital is associated with better performance. It is that the marginal return on certain technological investments may vary according to the human capacity available to understand, adopt, supervise and integrate them into decisions.
Consider two organisations with access to comparable AI systems. If one of them has greater capacity for learning, adaptation, critical thinking, collaboration and judgement, it is plausible that it will redesign processes faster, recognise the technology's errors or limitations better and choose higher-value applications. The difference in performance would not, in that case, result only from the volume of technological capital; it would result from the complementarity between technological capital and the existing Brain Capital.
This hypothesis is compatible with the WEF-McKinsey framework, which recommends combining AI-enabled changes with deliberate investment in Brain Skills and states that integrating training and workflows can make AI adoption more effective. However, the Brain Capital Multiplier, as formulated here, remains a theoretical extension. Demonstrating it would require empirical research comparing organisations, sectors and functions with different levels of technological exposure, human capacity and results.
Prudence is central. There is at present no quantifiable "Brain Capital Multiplier" that can be applied to a company. Turning the hypothesis prematurely into a commercial formula would weaken, rather than strengthen, the concept.
7. Formation, use and depreciation of Brain Capital
If Brain Capital is treated as capital, the analysis cannot be confined to its formation. Assets can also depreciate.
Nail-Beatty and colleagues use the distinction between brain-positive and brain-negative economic environments to underline that certain conditions can strengthen Brain Capital while others contribute to its erosion. [3] The implication is particularly relevant for contemporary organisations, which may simultaneously invest in training and create operating environments that fragment attention, hinder reflection or make continuous learning structurally improbable.
An economy can raise average years of schooling while deteriorating other factors that condition functional cognitive capacity. A company can hire highly qualified people and organise work so that those qualifications are used predominantly in reaction, fragmented coordination and administrative processing. In both cases there is a difference between the asset theoretically accumulated and the asset that can actually be mobilised.
The field is still far from having a satisfactory accounting for this question. WEF-McKinsey itself acknowledges that there is no widely adopted framework for defining success, comparing results or measuring Brain Capital. The Brain Capital Dashboard represents an attempt to build indicators for more than one hundred countries, crossing Brain Health, Brain Skills, environments and factors of policy and innovation. The report even suggests that, in future, Brain Capital satellite accounts could complement traditional economic measures and make the costs of inaction and the returns on investment more visible.
A mature research agenda should therefore seek to distinguish the formation, use, depreciation and return of Brain Capital. It is not necessary to presume that each of these dimensions can be summarised by a single index. On the contrary, the interdisciplinary nature of the phenomenon suggests that different measures will be needed for different scales of analysis.
8. Brain Capital as a matter of strategy and governance
As long as Brain Capital remains classified essentially as a matter of People & Culture or wellness, a substantial part of the economic argument will be lost. The WEF-McKinsey report explicitly identifies this fragmentation as a barrier and argues that Brain Capital should become a priority for the CEO and the board, integrated with strategy, organisational design, leadership and technological transformation.
This change of level does not imply that boards should monitor brains, collect biometric data or medicalise leadership. Such an interpretation would confuse an economic thesis with clinical intervention and could create serious ethical problems.
The implication is institutionally simpler. An organisation that depends on critical decisions must recognise that the quality of those decisions is not independent of the conditions under which they are taken. Traditional governance concerns itself with the distribution of authority, independence, quality of information, conflicts of interest, compliance, succession, technology and concentration of risk. A Brain Capital perspective adds a complementary question: does the organisational architecture create conditions compatible with the level of cognition it demands at its main points of decision?
That question includes the quantity and quality of information made available, the existence of dissent, the distribution of decisions, the concentration of approvals, the time available for irreversible questions and the way outputs from artificial systems reach the humans responsible. It does not replace existing governance; it makes explicit a human condition that governance frequently presumes.
The cognitive quality of decision thus becomes a legitimate part of institutional design.
9. A new dimension of competitive advantage
The Brain Economy Thesis does not claim that Brain Capital will replace financial capital, technology, intellectual property, scale or data. It claims something different: Brain Capital shapes the quality with which those assets are chosen, combined and governed.
Two companies can buy the same AI model and formulate different problems. They can receive access to the same information and recognise different signals. They can have comparable financial resources and allocate them differently. They can hire professionals with equivalent qualifications and build environments in which those skills combine in radically different ways.
The faster a fundamental technology spreads, the less sustainable an advantage based exclusively on access to the tool tends to be. Part of the differentiation shifts towards organisational, human and institutional complementarities.
It is in this sense that Brain Capital can become a new variable of competitive advantage. Not because the human brain is presented as superior to Artificial Intelligence, but because artificial systems do not enter the economy autonomously. They are chosen, trained, configured, supervised, integrated and used within human institutions.
The fundamental question of the Brain Economy then ceases to be only how much intelligence is available. It becomes how much of that intelligence can be turned into value without the organisation losing the capacity to understand and govern it.
10. Propositions for research
The scientific usefulness of this thesis will depend on its capacity to produce propositions that can be rejected by the evidence. The first hypothesis is that Brain Capital will relate positively to certain indicators of technological absorptive capacity, even after traditional Human Capital variables are controlled for. The second is that the return on investment in Artificial Intelligence will show complementarity with human skills of adaptation, critical thinking and judgement, rather than depending only on the technical quality of the system. The third is that roles with high decision leverage will show a stronger relationship between certain dimensions of Brain Capital and economic results than roles in which decisions carry more limited consequences. The fourth is that organisational environments characterised by greater fragmentation of attention, low opportunity for learning and a heavy supervisory load will show a lower capacity to convert AI output into AI value, even with similar technological investments.
These propositions are deliberately more modest than a "total theory" of Brain Capital. The aim is not to produce a narrative impossible to falsify, but to identify a zone of intersection between economics, neuroscience, organisational psychology, strategy and technology that justifies more precise research.
Conclusion
Brain Capital adds a necessary dimension to the economic analysis of the age of Artificial Intelligence. By combining Brain Health with Brain Skills, it makes explicit that human productive capacity cannot be reduced to formal qualifications or accumulated knowledge. The system that uses that knowledge has functional conditions, limits, learning potential and vulnerabilities of its own.
Artificial Intelligence raises the importance of this problem because it reduces the cost of certain forms of cognitive execution and shifts part of the value to other stages: formulation, interpretation, verification, judgement and decision. As a result, technology can increase an organisation's total available capacity without proportionally increasing its capacity to understand and govern what the technology produces.
The Brain Economy Thesis therefore proposes that Brain Capital be understood as productive infrastructure, complementary to other forms of capital. The Brain Capital Multiplier hypothesis takes this idea one step further: the return on technology, knowledge and financial capital may depend in part on the existing human cognitive capacity to steer those assets. This hypothesis needs to be tested, not celebrated.
If the proposition is confirmed, the economic transformation induced by AI will require an important change in how companies and states think about investment. Building greater artificial capacity and building greater human capacity will not be competing strategies. They will be components of the same productive architecture.
The great economic question of the Brain Economy may therefore not be who can produce more intelligence. It will be who can convert more intelligence into better decisions and greater value.
References
[1] World Economic Forum; McKinsey Health Institute. The Human Advantage: Stronger Brains in the Age of AI. Geneva: World Economic Forum; 2026.
[2] Eyre HA, Ayadi R, Ellsworth W, et al. Building brain capital. Neuron. 2021;109(9):1430-1432. doi:10.1016/j.neuron.2021.04.007.
[3] Nail-Beatty O, Ibanez A, Ayadi R, et al. Brain health is essential for smooth economic transitions: towards socio-economic sustainability, productivity and well-being. Brain Commun. 2024;6(6):fcae360. doi:10.1093/braincomms/fcae360.
[4] Hynes W, Linkov I, Love P, editors. A Systemic Recovery. Paris: OECD Publishing; 2022. Chapter 6, Build Back Brainier: Base Policies on Brain Science. doi:10.1787/62830370-en.
[5] McKinsey Health Institute. The Human Advantage in an AI Economy. McKinsey & Company; July 2026.
[6] Yee L, Madgavkar A, Smit S, et al. Agents, robots, and us: Skill partnerships in the age of AI. McKinsey Global Institute; 2025.
[7] Garcia JL, Heckman JJ, Leaf DE, Prados MJ. Quantifying the life-cycle benefits of an influential early-childhood program. J Polit Econ. 2020;128(7):2502-2541. doi:10.1086/705718.
[8] World Economic Forum. The Future of Jobs Report 2025. Geneva: World Economic Forum; 2025.
[9] McKinsey Health Institute. The new case for brain health: Scaling interventions for health and economic growth. McKinsey & Company; 2025.
[10] Jeffery B, Weddle B, Brassey J, Thaker S. Thriving workplaces: How employers can improve productivity and change lives. McKinsey Health Institute; 2025.
[11] World Health Organization. Optimizing brain health across the life course: WHO position paper. Geneva: World Health Organization; 2022. ISBN:9789240054561.