It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Tuesday, June 23, 2026
Looking at AI start-ups to predict which jobs AI will affect
Bar plot of average sectoral AI Startup Exposure (AISE) for industries in the US economy, where blue and green indicate lower to intermediate exposure, yellow to red indicate high exposure.
A study of funded AI startups provides a glimpse of which jobs may be most affected by AI. As AI tools are embraced by industry after industry, the impacts of these tools on jobs remain unclear. Previous analyses have focused on the theoretical capabilities of LLMs, but social factors are also likely play a role in shaping what aspects of work see AI integration—or full automation. Enrico Maria Fenoaltea and colleagues validated a version of Meta’s Llama3 LLM, which they used to cross reference products developed by AI startups backed by the venture capital firm Y Combinator with descriptions of essential tasks for various jobs drawn from the O*NET occupational database. Because AI products that have attracted significant funding are seen by investors as economically viable and socially appealing, these products are more likely to become marketplace realities than other theoretical uses for AI.
The resulting Occupational AI Startup Exposure (AISE) index seeks to capture the potential near-future AI exposure of occupations. “Exposure” could include AI complementing or substituting for human labor in performing a job. Occupations with high AI exposure include office clerks, data scientists, computer and information systems managers, and market research analysts and marketing specialists. Occupations with low AI exposure include those primarily composed of manual tasks, such as athletes, chefs, and construction workers. Compared with indices based on the theoretical abilities of AI, the AISE predicts lower exposure for occupations requiring high levels of responsibility and ethical decision-making and occupations requiring a master’s degree or higher and significant experience.
While LLMs could theoretically perform many of the tasks completed by high school teachers, judges, or marriage counselors, people may be reluctant to trust AI with roles that require social skills, judgment, or ethically charged decision-making. According to the authors, rather than hitting the entire economy as an indiscriminate technological wave, AI will gradually spread into the economy, its path shaped by social factors as much as by the technical feasibility of AI applications.
Journal
PNAS Nexus
Article Title
Follow the money: A startup-based measure of AI exposure across occupations, industries, and regions
Article Publication Date
23-Jun-2026
Tool predicting NHS staff resignations scoops top AI prize
Following a landmark collaboration between the University of Reading and the Royal Berkshire NHS Foundation Trust
The University of Reading team, including Professor Shixuan Wang and Associate Professor Rita Fontinha from Henley Business School, and Dr Son-Kien Nguyen
A landmark collaboration between the University of Reading and the Royal Berkshire NHS Foundation Trust (RBFT) has been crowned The Alconics AI Enterprise Business of the Year at the National AI Awards 2026, one of the highest accolades in UK artificial intelligence.
The award recognises "Improving Staff Retention at the RBFT", a project that harnesses AI to tackle one of the NHS's most pressing challenges: keeping skilled staff in post.
The team built an AI forecasting tool that predicts the likelihood of staff resigning, giving managers an early warning system to intervene before someone leaves. The tool also highlights the specific factors driving an individual's risk of leaving, so HR teams can see why a prediction has been made rather than treating the AI as a black box.
Professor Shixuan Wang, University of Reading, said: "This award reflects what's possible when academic expertise in AI and forecasting is applied directly to a real problem facing the NHS. Our model doesn't just predict who might leave, it shows managers why, so they can act early and make a genuine difference to people's working lives."
RBFT employs around 7,500 staff and provides acute and specialist care across Berkshire, serving a population of around a million people. Like much of the healthcare sector, the Trust has faced high staff turnover, which disrupts patient care and drives up recruitment and temporary staffing costs. Its existing HR processes relied on reactive reporting, meaning managers often only learned about retention problems after staff had already decided to leave.
The project supports the goals of the NHS Long Term Workforce Plan, which aims to stabilise the healthcare workforce, reduce reliance on temporary staff, and protect continuity of care for patients.
Paul Da Gama, Chief People Officer at Royal Berkshire NHS Foundation Trust, said: “We’re proud to see this innovative work recognised nationally. Retaining our staff is a key challenge, and this project is helping us to better understand our workforce and supports the NHS long term workforce plan.”
Winners were announced online on 9 June, with a celebratory reception held the following day at the AI Summit London, the UK's largest gathering for AI professionals and innovators.
The University of Reading team, including Professor Shixuan Wang and Associate Professor Rita Fontinha from Henley Business School, and Dr Son-Kien Nguyen, attended the ceremony. Professor Shixuan Wang brought world-leading experience in data analytics and developing AI solutions. Associate Professor Rita Fontinha provided vital Strategic HRM insights, grounded in her extensive published research on the quality of working life. Dr Son-Kien Nguyen was employed as the Research Assistant for the project, and he has been instrumental for the success of the project by data analysis, model development, and model deployment. This expertise was perfectly matched by RBFT’s operational leadership, including Peter Sandham in the staff experience, Arran Rogers in nursing informatics, along with Faraz Rasihi and Donna Kellman in workforce data.
Fergus Bruce, CEO of The National AI Awards, said: “Entries for the 2026 National AI Awards were hugely impressive with companies spanning a huge range of industries and innovations. As organisations increasingly look to AI to solve real-world challenges, it is more important than ever to demonstrate measurable value, responsible innovation and tangible business results. Winners this year really did demonstrate the tangible value and outcomes from AI innovation. Attending the AI Summit highlighted how far the UK has progressed in just the last 12 months alone regarding innovation and expertise and we’re so excited to see this continue into 2027.”
Psychologists shouldn't replace thinking with AI
‘Research based on artificial intelligence models will never be an adequate substitute for understanding and replicating human thought’
For some psychologists, it's becoming more common to use AI systems to replace human thinking in research. That's a very risky choice based on misconceptions, warn Iris van Rooij and Olivia Guest in a new paper appearing today in Current Directions in Psychological Science. ‘Research based on artificial intelligence models will never be an adequate substitute for understanding and replicating human thought.’
The technology companies behind the latest popular artificial intelligence models often make big promises, such as claiming to have developed artificial minds that can rival human brains. ‘That's a promise that can be very alluring to psychologists’, explains Iris van Rooij, professor of computational cognitive science at Radboud University. ‘It's easy to think you can do psychological experiments with artificial participants, but you simply can’t automate science.’
‘Following the replication crisis [where it was found that many results in peer-reviewed studies in psychology could not be reproduced], some researchers are pushing for a more methodological, statistical approach to psychology research, something that can be proceduralised and maybe even automated’, says Olivia Guest, associate professor of computational cognitive science at Radboud University. ‘But the whole point of doing science is to produce knowledge. That’s why it’s so important to emphasise AI can never meaningfully replicate human cognition.’
Identifying traps
In their paper, Van Rooij and Guest point out three traps to avoid for fellow researchers in their field. ‘First, AI systems are not minds. As we’ve discussed in our previous research, AI systems can never be sufficiently trained to reach human-level cognition. Even if tech companies continue to use astronomical levels of resources to train them, they won’t even be able to get close’, says van Rooij. ‘At best, you’ll be able to produce a decoy: something that may look impressive and trick you into thinking it can act like a human, but in no way a replacement for the real thing.’
Guest adds: ‘Furthermore, these systems are based on predicting, that’s not a basis for actual new theories. Just because an AI system can predict what a human would say or do, doesn’t mean it can explain what humans do. Compare it for example to the tides. Long before humans understood what caused the tides, we created tide tables to predict when ebb and flood would happen. But no one would argue that those tables explain the tides – yet that’s what some people are claiming AI models do.’
Science is slow
And finally, the researchers warn that it’s a fallacy to think that cognitive science can be automated. Guest: ‘Doing theoretical work is very difficult, and sometimes researchers might fall for instant gratification, or maybe they haven’t been taught how to do it. But if you ask AI to take over, you run into many risks, from getting stuck in existing theories to deskilling future scientists. It creates inconsistencies, which runs the risk of introducing pseudoscientific ideas into the field.’
If we truly want to advance the study of cognition, the authors argue, we can’t rely on AI models to take shortcuts. Van Rooij: ‘Part of the problem is inherent to the system: we ask researchers to write as many papers as possible, while good science is slow. But we can’t rely on AI as if it’s a cheat code, and the best way to avoid the traps of AI is to be aware of them. Only once we’ve acknowledged that can we expect to move cognitive science forward.’
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