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Problem RadarCoordination

Coordination: problems worth solving

Nobody is missing. Nothing is broken. There is simply no place where it all comes together.

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6 problems · all 28 on the Index

Framed 27 Sep Ed. 1

FinanceProblem 0028

Europe already has a way to move money between bank accounts in seconds, at any hour: instant payments. Merchants can rarely take them, and almost never from a customer in another country. Each country's card scheme works at home and stops at the border, and the software that would let a till or an online checkout accept an instant payment has been slow to arrive. So money can move, but a shop cannot be paid that way. Meanwhile the international card networks keep their hold because every shopper and every merchant already uses them, which makes any alternative hard to adopt even when the plumbing beneath it is ready.

Who lives with itEuropean merchants · shoppers · payment service providers

Why nowWriting for CEPR on 25 September 2026, Agnès Bénassy-Quéré of the Banque de France reports that 13 of the euro area's 21 countries still rely entirely on non-EU networks for card payments, and that even the domestic schemes stop at national borders, although the euro area has had its own instant-payment system since 2018. She argues the private sector has been too slow to build the software at tills and online checkouts that would let people use it.

Why it mattersA way to move money is not yet a way to be paid. The gap stays open because the card networks already connect most shoppers and merchants, while the newer schemes stop at national borders, so a merchant gains little by adding one. A small team could start narrow: let merchants who trade across one border accept instant payments at checkout, instead of trying to replace Europe's payment networks.

Inspired by reporting from Centre for Economic Policy Research

A funded startup to make a dent in High (Ainna's editorial grade)
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Framed 10 Oct Ed. 1

EducationProblem 0034

Students choosing what to study need a fair picture of the work their degree leads to. The evidence they are given describes an earlier market. Official long-run earnings figures, and the rankings that use them, follow graduates who started work before generative AI. Government job forecasts are built on historical patterns, and their authors warn against reading them as predictions. School advisers are stretched, and the people building AI contradict one another in public on what to study. Meanwhile employers are redesigning entry-level roles in AI-exposed occupations, and some students are already switching subjects because of AI, before the evidence settles. The choice comes early, costs years and often debt, and is hard to undo. It stays unsolved because the current evidence is unsettled, the trusted evidence is old, and the two reach students separately.

Who lives with itStudents choosing what to study · parents helping to pay for a degree · school counsellors and university careers services · governments, schools and universities that fund guidance

Why nowIn September 2026 a Federal Reserve governor said many recent graduates find first jobs harder to get, “possibly partly related” to AI taking on entry-level tasks. Stanford’s Digital Economy Lab finds that employment of 22-to-25-year-olds in AI-exposed occupations has fallen 19% behind less-exposed peers, a descriptive finding, while Yale’s Budget Lab finds no significant effect so far. In a Lumina–Gallup panel, 16% of US students say they have already changed major because of AI.

Why it mattersOfficial graduate earnings statistics provide valuable evidence about past outcomes, but they cannot by themselves establish which degrees will offer the strongest prospects in a changing labour market. The US Bureau of Labor Statistics explains that its projections assume technological progress broadly in line with historical experience, while acknowledging that unusually rapid technological change could make historical relationships unreliable. Students and families must therefore make consequential choices by interpreting evidence that varies in age, certainty and relevance to their circumstances.

Framed by Ainna editorial

A funded startup to make a dent in Critical (Ainna's editorial grade)12 sources
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Framed 5 Oct Ed. 1

WorkProblem 0032

AI is changing work at several levels at once. Inside organisations, tasks are being automated, roles redesigned and entry routes narrowed; across markets, demand is moving between kinds of work and between sectors; across countries, exposure is uneven and highest in the richest economies. Some jobs will go, but far more will change. For the people doing the work, the transition opens three gaps. Awareness: many cannot clearly see how their role, employer or industry will change, or which of their skills will stay valuable. Readiness: the capabilities their work needs are shifting, and for many the training to match is unlikely to arrive. Defined alternatives: routes from changing roles into viable new work, much of it still taking shape, are not yet mapped at scale. Left unmanaged, the transition risks economic and social disruption.

Who lives with itWorkers in roles AI is changing, from clerical and customer service to professional work · young people entering the workforce · employers restructuring around AI · governments, educators and employment services

Why nowThe ILO finds 25% of global employment in occupations potentially exposed to generative AI. The World Economic Forum expects disruption equal to 22% of jobs by 2030, driven by technology alongside demographic, geoeconomic and economic forces, and says that if the global workforce were 100 people, 59 would need reskilling and 11 of them are unlikely to get it. In the first half of 2026, US employers cited AI in 101,743 announced job cuts, Challenger, Gray & Christmas reports.

Why it mattersThe cost of an unmanaged transition is in the forecasts: the World Economic Forum counts over 120 million workers at medium-term risk of redundancy, and the IMF expects AI to worsen overall inequality in most scenarios, warning of social tensions. Employers are already acting: 77% plan to upskill staff and almost half expect to move people out of roles exposed to AI, the WEF found. Governments, employers and educators would pay for ways to do this well and at scale.

Framed by Ainna editorial

A moonshot to make a dent in Critical (Ainna's editorial grade)6 sources
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Framed 5 Oct Ed. 1

CitiesProblem 0033

Dublin has many homes approved for development, but too many never make it from planning permission to construction. Projects can get stuck because of funding, costs, infrastructure, planning or construction capacity, while people continue to struggle to find homes they can afford. The result is a gap between homes that exist on paper and homes that people can actually live in.

Who lives with itPeople looking to rent or buy in Dublin · young people and families trying to find a home · builders and developers with approved projects · employers trying to attract people who can afford to live in Dublin · public bodies responsible for housing and infrastructure

Why nowDublin has a substantial number of homes already approved, but too many are still stuck before construction. The Department of Housing reported in August 2026 that only 41.9% of the 80,285 homes with planning permission across Dublin's four councils had started by the end of June. Unstarted homes rose 10% in a year to 46,662, more than 41,000 of them apartments, and an estimated 6,664 have less than a year left on their permission. In July, the CSO reported that new-home completions in Dublin fell 16.4% year on year in Q2 2026.

Why it mattersThe city does not just need more planning permissions. It needs to turn approved projects into homes people can actually live in. Dublin's median residential property price was €500,000 in the 12 months to July 2026, and BPFI reports that the share of newly completed homes bought by households in Dublin fell from 55% in 2019 to 25% in Q2 2026. The opportunity is to find where projects get stuck, understand what is preventing them from moving forward, and help the right people act before another approved development sits unused. Developers and lenders would pay to know which stalled schemes can still work; councils and state agencies, to aim funding and infrastructure where they unlock the most homes.

Framed by Ainna editorial

A funded startup to make a dent in Critical (Ainna's editorial grade)3 sources
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Framed 21 Sep Ed. 1

HealthProblem 0019

Caring for an ageing parent means medications, appointments, home visits, bills and forms, shared between siblings in different cities and often different countries. It runs on a group chat, a shared folder and whoever remembers. Things fall through, one person quietly carries most of it, and the professionals involved each see a fragment of a picture nobody holds whole.

Who lives with itAdult children caring for parents · home-care workers · family doctors seeing only their slice

Why nowPopulations are ageing across high-income countries and many middle-income ones while the paid care workforce is not growing with them, so more of this coordination falls to families who were never set up to do it.

Why it mattersThis is coordination, exactly what software is good at, and the tools built for it have yet to become the way families actually run it. The work is already being done; it is simply being done badly, by people with no tools and no training, at one of the hardest times of their lives.

Framed by Ainna editorial

A funded startup to make a dent in Critical (Ainna's editorial grade)
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Framed 21 Sep Ed. 1

WorkProblem 0026

A small business with roles to fill competes on the same job boards as employers with recruitment teams, brand recognition and budget, and loses. Meanwhile capable people a few streets away never see the role, or see it and do not recognise themselves in the wording. Both sides are present, willing and unable to locate one another.

Who lives with itOwners of trades, care and hospitality businesses · local jobseekers and career changers · the boards that serve neither well

Why nowHiring has concentrated onto national platforms optimised for volume and for employers who can pay to be seen, which is the opposite of what a local employer with two vacancies needs.

Why it mattersThis is a matching failure rather than a shortage, and matching failures are solvable. Local, skills-first matching remains underbuilt because the large platforms are built for a different customer.

Framed by Ainna editorial

A side project to make a dent in High (Ainna's editorial grade)
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