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The IndexA side project

A side project

Months rather than a weekend, patience rather than capital. Big enough to need a plan, small enough that nobody has to quit their job to start.

6 problems · all 20 on the Index

Ed. 1

Enterprise AIProblem 0009

The tooling that would protect a small firm assumes somebody to run it: a console to watch, alerts to triage, a policy to maintain. A twelve-person company has none of those, and a serious incident can threaten the business itself. What is missing is not awareness (owners know) but anything that works without an operator.

Who lives with itOwner-managed firms and their one technical person · the larger customers whose supply chain runs through them · insurers pricing cyber cover

Why nowAn ENISA survey of small and medium enterprises, carried out during the pandemic, found that around nine in ten expected serious harm to their business within a week of a cyber incident and that over half believed it would likely end the business, against a backdrop of low security budgets and a lack of in-house cyber skills.

Why it matters · the JudgeAn existential risk to the firms that make up the overwhelming majority of businesses, where the binding constraint is operator time rather than money or intent. Security that needs nobody to watch it is a different product from the one being sold.

Inspired by reporting from ENISA (European Union Agency for Cybersecurity)

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Ed. 1

WorkProblem 0010

We can now say with some precision which occupations generative AI reaches first, how that varies by country, and that the exposure falls hardest on clerical and administrative work done mostly by women. What does not exist is the other half: a route from an exposed role into an adjacent one that uses the same accumulated judgment. Retraining is too often offered as courses unrelated to what the person already knows how to do.

Who lives with itClerical, administrative and business support workers · employers holding the institutional knowledge in those roles · public employment services

Why nowThe ILO finds 29% of female-dominated occupations carry exposure to generative AI against 16% of male-dominated ones, with exposure reaching around 41% of jobs in high-income countries versus 11% in low-income ones, and women more exposed than men in 88% of the countries analysed.

Why it matters · the JudgeThe measurement is done and the response is missing, which is an unusually clear place to build. Mapping what an exposed role actually contains onto roles needing those same capabilities is a matching problem, and matching problems are tractable.

Inspired by reporting from International Labour Organization

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Ed. 1

EducationProblem 0011

Most institutions are writing or planning a policy about AI; few have changed what they actually measure. The essay, the problem set and the report were proxies for thinking that worked only while producing them was expensive. That assumption is gone. Detection tools are unreliable enough to risk accusing the wrong students, and much of what is graded no longer shows reliably what a student understands.

Who lives with itTeachers and lecturers redesigning assessment alone · students wrongly suspected · institutions whose credentials depend on the answer

Why nowA UNESCO survey found 19% of higher education institutions had formal AI guidance with a further 42% developing it, while more than half reported uncertainty about how to apply AI pedagogically and one in four had already encountered ethical problems including authorship disputes.

Why it matters · the JudgeThe question has moved from catching misuse to evidencing learning, and nothing is built for the new question. Making a student's reasoning visible, cheaply and without treating them as a suspect, solves what detection never could.

Inspired by reporting from UNESCO

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Ed. 1

FoodProblem 0015

Loss happens at the handovers: field to packer, packer to distributor, distributor to shelf. Every party measures only its own side of each one. So each set of numbers looks defensible while the total is enormous, and the interventions that would work are precisely the ones nobody can see, because they sit between two businesses rather than inside one.

Who lives with itGrowers and packers · distributors and independent retailers · redistribution charities working the same corridors

Why nowFAO estimates that 13.3% of the world's food was lost in 2023 after harvest and before retail, on farms and in transport, storage, wholesale and processing, up slightly from 13.0% in 2015 when global monitoring began.

Why it matters · the JudgeMeasurement at the handover is the wedge, and it is cheap: the produce is already photographed, weighed and scanned on both sides of every transfer. Nobody is joining those records, because each side owns only its own.

Inspired by reporting from Food and Agriculture Organization of the United Nations

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Ed. 1

Enterprise AIProblem 0020

Support has been largely automated, and for routine questions that is an improvement. The unusual case is where it breaks: a bereavement, a billing error, a locked account, a situation the script has no branch for. The customer loops, rephrases, tries again, and eventually gives up, and the company never learns it happened, because a failed escape is rarely a metric anyone collects.

Who lives with itCustomers in genuinely unusual situations · the few remaining human agents · support leaders flying blind

Why nowAutomated support is now the default first line almost everywhere, while the escalation path behind it has often been thinned.

Why it matters · the JudgeThe savings are measured precisely and the losses are invisible entirely, which is why the balance keeps tipping the wrong way. Detecting the moment a conversation has stopped working is a narrow, concrete capability, and whoever builds it is selling the missing half of every deployment.

Framed by Ainna editorial

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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 matters · the JudgeThis 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

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