Editorial
For over a century, the intelligence quotient (IQ) has been treated as a definitive measure of human potential. Anchored to a statistical average of 100 with a standard deviation of 15, IQ scores have been used to determine entry into elite academic institutions, as a screening tool for coveted jobs and as a proxy for overall human capability.
In the UK, according to the Times Higher Education Supplement, the most prestigious universities tend to admit students with IQs of at least 130, placing those students in the top two percent of the population. At the same time, research suggests that senior managerial workers, including professors, doctor and bank managers, have an average IQ of around 115, while those in unskilled roles average closer to 90.
Given that roughly two-thirds of the variation in IQ is thought to be genetically inherited, and assuming the selection processes are entirely meritocratic, then the inevitable outcome would be that high-IQ children would predominantly come from the upper social classes and dominate admissions to elite universities.
Why IQ Alone Fails to Predict Real-World Success
This narrative has, for some, appeared to legitimize the lack of social mobility. If intelligence is largely inherited and we allocate opportunity in proportion to measurable ability, then perhaps the stratification we see today is simply the natural order of things — or so the thinking goes. But scratch beneath the surface and the flaws of this reasoning quickly become apparent. IQ does reliably predict high school exam performance, but its ability to forecast anything more meaningful, like sustained career success, wealth creation or economic contribution, is far weaker than most people assume.
Consider income. Research has found that each point of IQ above the average is associated with an increase in annual earnings of around $400. At first glance, that suggests a clear link between intelligence and financial reward, but the correlation quickly plateaus at roughly $60,000 per year. In fact, many ultra-high earners, including entrepreneurs and creative leaders, score slightly lower on IQ tests than those who earn a little less. When it comes to wealth, which is a measure not just of earning power but of the ability to accumulate and grow resources over time, the correlation with IQ all but disappears.
The inference is stark: if capitalism rewards those who create the greatest economic value, and if IQ is only weakly related to those rewards, then other factors must be doing most of the heavy lifting. Emotional intelligence, social agility, entrepreneurial flair, resilience and risk tolerance appear to play a much larger role in driving real-world outcomes than exam scores or abstract reasoning tests.
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The Economic Cost of Misallocated Talent
Yet our institutions continue to allocate opportunities largely around IQ and its proxies. To apply for most top managerial positions, a degree is still an essential prerequisite, effectively making university admission the gatekeeper to senior roles. By overemphasizing academic credentials, we systematically filter for certain cognitive strengths while ignoring others.
The consequence of this is a misallocation of human capital. We funnel positions of responsibility towards those with the right educational badges, regardless of whether their other capabilities match the demands of the role. Over time, this damages our businesses and economies, because the people best placed to deliver results may never get the opportunity to do so.
The economic cost of this misallocation has been quantified. The Sutton Trust, in research published in 2017 and later cited by McKinsey in 2022, examined the relationship between social mobility and GDP across Western Europe. They found a clear pattern: countries with higher social mobility tend to enjoy higher GDP per capita.
How Low Social Mobility Drags Down GDP
The UK for instance sits near the bottom of the social mobility rankings. On the basis of this corelation, if the UK were to raise its social mobility to match the EU average, the effect would be an increase in GDP equivalent to around £2,800 per person. That is not a marginal gain; it represents a massive, recurring uplift in national prosperity. In other words, our failure to identify and promote talent from across the social spectrum is not just unjust, it is economically disasterious.
One of the structural causes of low mobility lies in the unequal distribution of social capital. People from affluent families inherit more than just money; they inherit social networks. These networks provide a steady flow of opportunities; introductions to potential employers, tips about upcoming ventures, early access to resources. Someone born into a working-class family on a rundown estate will, on average, know fewer people with influence, investment capital or specialist knowledge. Role models demonstrating the behavioral traits that lead to success may also be rarer.
Social scientists have long noted that societies are composed of clusters of people linked by relatively weak ties, and it is through these weak ties that individuals can cross between clusters. When there are too few links between the top and bottom of the social ladder, mobility stalls.
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How AI Can Unlock Hidden Talent and Boost Mobility
This is where AI and data mobility could offer something genuinely new.
At the heart of this approach are three interconnected concepts:
- Personal Data Stores (PDS)
- Digital Identity
- Personal AI.
PDS’s act as secure personal data vaults, storing a rich data about an individual’s experiences and behavioral traits. This data is aggregated into the PDS from across the individual’s life by leveraging API’s offered by big tech platforms such as Google and Meta. Digital Identity ensures the right data is mapped to the right person. Personal AI sits on top of these, continuously inferring traits and skills from the full breadth of data; not just formal qualifications or job history, but also quantifying attributes such as creativity, adaptability, social skills and resilience.
Over time, this data forms a multidimensional profile that is far more predictive of real-world potential than IQ alone. Critically, these profiles are calculated locally within the individual’s personal data space, where the underlying data remains private. The profiles can then be used by AI to match people to the roles, projects and collaborators where they are most likely to thrive.
Rethinking Workforce Strategy With Personal AI
For enterprises, the value of this technology is twofold.
First, it allows them to compete for talent more effectively, reaching candidates who would otherwise remain invisible. Second, it enables them to measure and manage their internal talent pool with a precision that has not previously been possible. By understanding the true capabilities of their staff, organizations can deploy people more effectively and improve engagement by aligning work with an employee’s true strengths. The macroeconomic effect of this, if adopted widely, would be to boost productivity of existing staff by ensuring that people with high potential are nurtured rather than left to drift.
Beyond the corporate boundary, AI-powered profiling and matching can be used by schools, employment agencies and charities to increase social mobility, by creating a way for individuals to move between clusters in the wider social graph.
Suppose a promising young person from a disadvantaged background shows exceptional aptitude for product design, coupled with a track record of self-motivated learning. An AI system could match this individual with a hiring manager in an innovation team who is actively seeking such a skillset. The system could then facilitate an introduction, not as an unsolicited cold call, but as a targeted, high-value connection between two people whose profiles suggest mutual benefit. Over time, millions of such micro-connections could start to erode the barriers that keep talent trapped in local clusters.
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From Job Titles to True Talent: A New Model for Matching
This approach is fundamentally different from today’s professional networking platforms. LinkedIn, for example, is built on self-reported data and job titles, and its search tools operate on a relatively shallow dataset. This makes the matching challenging for individuals without an impressive job title or elite alma mater, and so breaking into new circles becomes difficult.
By contrast, AI-driven matching draws on deep data in the individual’s personal data store, to reveal verified behaviors and traits which enable the individual to be matched to the right opportunities and people. This reduces reliance on serendipity and counteracts the bias against people who do not look like conventional high-flyers.
The potential prize is too great to ignore. IQ may have been a useful measure in an industrial-era economy, that valued uniformity and rule-following. In today’s economy, which thrives on adaptability, innovation and complex problem-solving, it is an increasingly poor predictor of who will create value. AI, combined with rich, secure personal data, offers a way to see people as they are, not just as their test scores or job titles suggest.
In the end, the case for moving beyond IQ is not just a moral one; it is a hard-headed economic calculation. When we limit opportunity to those who happen to excel at a narrow set of cognitive tasks, we waste an ocean of potential. We depress growth. We make our organizations less resilient and less innovative. The tools to fix this are now within reach. The question is whether we have the will to use them, to redesign recruitment and workforce development for the reality of human capability, rather than the convenient fiction of a single number.
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