The constraint on the energy transition moved from manufacturing to administration, and no tooling followed it. Projects sit in interconnection queues longer than they took to design, studies are redone by hand each time the queue ahead reshuffles, and developers cannot see where they stand or what would move them. Most of the capacity that enters the queue is eventually withdrawn, often after years of waiting.
Who lives with itIndependent power producers · utility interconnection teams · the financiers whose models assume a connection date
Why nowLawrence Berkeley National Laboratory's latest queue analysis counts roughly 8,200 active projects representing over two terawatts of generation and storage, a median wait from request to operation of more than five years, and about three quarters of the capacity that requested connection between 2000 and 2020 later withdrawn.
Why it mattersA queueing and document problem wearing an engineering costume, exactly the kind that goes unsolved because no single party owns both ends of it. Faster paperwork is the cheapest capacity available anywhere on the grid.
AI has made cyber attacks cheap to run and easy to scale. Large firms can answer in kind: they have security teams, budgets and tools, and they can grow each as the threat grows. Small businesses cannot. Their systems are often set up and looked after by outside suppliers, their defences are seldom tested, and a break-in is often found by someone else. So the same change raises the risk for big and small firms alike but the defence only for the large, and the gap between big and small firms widens. What is missing is defence that scales the way attacks now do: priced for a small firm, and able to run without a security team.
Who lives with itOwners of small businesses without a security team · the IT and web suppliers that set up and look after their systems · customers whose data is taken in a break-in · the banks and insurers that carry the losses
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. BleepingComputer reported on 23 September 2026 that the security firm Gambit traced one operator who used open-source AI agents to breach at least 27 companies in five days and take more than 600,000 card records, at an average of $25.46 per completed scan. The UK government's Cyber Security Breaches Survey, published in April 2026, shows the defending side split by size: 76% of large businesses had a formal plan for handling an attack, against 21% of micro businesses
Why it mattersAn 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. Security products assume someone to run them: alerts to sort, tests to commission, fixes to make. Small firms buy what their IT or web supplier offers, and many already pay one for security. A small team could start with one case where the stakes are clear, such as small online shops whose checkouts draw card thieves: constant, automated checks of the site, priced for a small firm and sold through the suppliers that already look after it.
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 mattersThe 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.
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