- New Zealand as a Haven: Silicon Valley figures, including Peter Thiel, have purchased land in New Zealand, viewing it as a geographically isolated, politically stable, and safe place to wait out global catastrophes.
- Bunker Features: Modern "doomsday" bunkers are high-tech, featuring luxury amenities like gyms, swimming pools, greenhouses, and, in some cases, advanced air filtration systems and,, defensive,, measures.
- The "Wink, Wink" Mentality: Purchasing property or building shelters in New Zealand is often seen as a coded signal among elite circles for preparing for worst-case scenarios.
- Loyalty Risk: A former bodyguard for a billionaire noted that, in a true crisis, security teams might prioritize their own safety over that of their employers, potentially taking over the bunker.
- Cost: While billionaire-level bunkers cost up to $100 million or more, smaller, more accessible,,,,,,, panic rooms are becoming available to others, with the market booming.
- Motivations: Beyond AI, these preparations are fueled by fears of nuclear war, cyberattacks,, and climate change.
- Rather than just building a physical bunker, many focus on building community,, self-sufficiency, and,, diversifying assets, to manage future uncertainties.
“Solving fiendish maths problems, making complex medical diagnoses, conjuring up new software in moments: the feats of generative AI get more impressive by the day. But anxiety about its social consequences is mounting, too. Kristalina Georgieva, the head of the imf, has warned of a job-crushing “tsunami”. Sir Demis Hassabis, boss of Google DeepMind, a leading ai lab, says he would support a slowing of innovation to allow society to adapt. Jamie Dimon, high priest of American finance, says governments should ban lay-offs if it “saves society”. The scene seems set for wrenching upheaval.
The course of AI is uncertain, obviously. Yet the latest series of Boss Class, our podcast on management, shows that there are good reasons to think society has more scope to adapt than these luminaries suggest. It takes time for a new technology to diffuse from the cutting-edge to the office cubicle. Firms and governments should use the breathing space to help those most at risk of being displaced.
So far labour markets seem unruffled. Service jobs are most exposed to generative ai, yet in America the number of white-collar jobs has gone up by 3m since ChatGPT was launched, while blue-collar jobs have stayed flat. Employment has risen even in areas that have been keen adopters, such as coding.
One reason for the slow economic impact is the technology’s “jagged frontier”: it excels at some tasks but then confidently spouts nonsense, or struggles to count the number of “r”s in “strawberry”. This unpredictability means companies and workers need to spend time working out where to apply ai.
Moreover, business processes don’t change overnight. Electricity was first harnessed commercially in the 1880s, but took 40-50 years to generate productivity gains on factory floors. Plants had to be redesigned and workflows rethought. This time, too, companies must think about how to encourage workers to use ai, how to mitigate the problems it poses, and how to apply it successfully.
This friction should be good news for those worrying about the speed of technological change. Asking developers around the world to down tools, when a winning lead could confer gigantic commercial and geopolitical rewards, would be a fantasy. But precious time elapses between invention and diffusion, and this can be used to identify who is most exposed to the technology and to work out how to help them.
Many jobs require skills that are hard to automate, such as judgment or empathy. AI tools could make these roles more productive, lucrative and even more enjoyable: think of a doctor liberated from paperwork. And new technology tends to create jobs; already there is a rise in white-collar jobs that are so new they have no label in the statistics. Yet some roles also look dangerously exposed to automation. Much back-office work involves simple tasks and following a script. Young people in entry-level positions are often asked to crunch data, or summarise reports—precisely the sort of things ais excel at.
Helping these groups find new work is crucial, and not just because of the impact on the people themselves.
The loss of factory jobs through globalisation and automation in the West helped spur the rise of populism.
No government wants a youth revolt on its hands. A backlash would be a sure way to stymie the economic gains of ai.
What to do? This time at least the disrupted are likely to be geographically dispersed: unlike factory work or mining, back-office and entry-level jobs are not concentrated in company towns. New opportunities should therefore be easier to find. But governments must also encourage movement by keeping labour markets flexible, rather than barring lay-offs as Mr Dimon suggests. Education will need an overhaul, to teach AI and skills that complement it.
Companies, too, must prepare. To thrive they need not only to make the best use of ai, but also to find and nurture the best people to work with it. Some back-office workers will lose their jobs. But others with tacit knowledge of the business may be trained for new roles.
The biggest mistake would be to stop hiring young people altogether. That would not only choke off the pipeline for future talent, it would rob businesses of AI natives. Instead, companies should rethink the type of work they offer young people—less grunt labour, more judgment and analysis; speedier rotations across the business so they gain insight that ai cannot have; piloting new roles and trying new approaches.
Disruption and job losses will be unavoidable. Such is the nature of technological progress. But, despite AI’s feats, there is still time to cushion the blow. It should not be wasted.” [1]
Regret to mention, that some blows are so strong, it is impossible to cushion them. Remember the dinosaurs with their asteroid?
1. Stop panicking about AI. Start preparing. The Economist; London Vol. 458, Iss. 9484, (Jan 31, 2026): 11.
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