Last week Elon Musk told Joe Rogan that AI is a ‘supersonic tsunami’ that will rip through digital jobs ‘like lightning’. Cooking, plumbing, electrical work, those might last a little longer, but eventually, he says, everything goes.
His forecast? A post-work world where ‘UBI’ becomes ‘UHI’ - Universal High Income. Sounds utopian. Until you remember he also predicts ‘a lot of trauma’ along the way.
But is this jobs apocalypse actually happening or is this just another Silicon Valley prophecy in search of an economic data point?
According to a recent study reported by the Financial Times, generative AI hasn’t yet done anything the Industrial Revolution wouldn’t recognise. Productivity? Up in pockets. Job losses? Not especially. The researchers found no major employment shock so far, just the usual slow churn as some roles shrink and others mutate.
In other words, the tsunami’s still out at sea.
In a recent conversation with Joe Rogan Elon Musk described AI as AI is a ‘supersonic tsunami’ that will rip through digital jobs ‘like lightning’.
The hype cycle vs. the hiring cycle
This is what we’re seeing everywhere: Executives say they’re ‘restructuring for AI’. Tech influencers say ‘learn to prompt or perish’. Yet labour-market data tells a quieter story. The same sort of quiet that followed spreadsheets, email, and early internet automation before the real shifts took hold years later.
Digital work is easier to automate than physical work, but implementation is hard. Re-engineering workflows takes time, regulation, and, yes, humans.
The Yale/Brookings team behind the study found something interesting: most companies talk about replacing people; most end up augmenting them instead. AI doesn’t so much “eat” jobs as nibble at the boring bits.
A graph from the Financial Times article ‘AI is not killing jobs, finds new US study’.
What’s really changing (and what isn’t)
If you work with words, numbers, or pixels, you’ve already felt it. The pace, the pressure, the sense that the machines are slowly learning your shortcuts. But so far, AI hasn’t deleted whole professions, it’s rearranged them.
Tasks are changing faster than titles. Entry-level roles are thinning out, mid-career ones are stretching wider, and everyone’s being asked to “partner with the model.” That’s disruption but not destruction.
As we argued in our video Will AI Take My Job? The Future of Work Explained, the real story (in the immediate future at least) isn’t replacement, it’s redistribution.. Work won’t vanish any time soon; it’ll migrate. Some towards machine-management, some towards meaning-making.
Machine-management means the growing ecosystem of human jobs built around supervising, training, and fine-tuning AI systems. Think of prompt engineers, data curators, compliance auditors, AI ethicists, and product managers who act as translators between code and company strategy.
These roles sit on the fault line between human judgment and algorithmic scale. The people in them aren’t coding models, they’re shaping how models behave inside messy human institutions.
Meaning-making, on the other hand, is everything the machines still struggle with: narrative, context, persuasion, taste, trust. It’s the creative, interpersonal, and interpretive side of the economy - the work of storytellers, teachers, strategists, psychologists, designers, and leaders who make sense of the outputs and turn them into something people actually value.
As routine production becomes automated, meaning becomes the premium layer that differentiates human work.
Perhaps the future labour market splits along two new axes: those who manage the machines, and those who make sense of what the machines produce. Everyone else sits somewhere between.
UBI or U-hype-I?
Before we get carried away with the acronyms, a quick refresher. Universal Basic Income (UBI) is the idea that every citizen receives a regular payment from the state. No strings attached - enough to cover the basics of living. It’s not new: economists from Milton Friedman to Martin Luther King Jr. have backed versions of it. But in the age of automation, it’s taken on new urgency as a potential safety net for a world where full-time employment isn’t guaranteed.
Musk’s twist on the idea - Universal High Income - imagines a future so productive that society can afford to pay everyone well, regardless of whether they work. In theory, AI handles the labour; humans enjoy the leisure. It’s a seductive vision, and an easy soundbite. But it skips a few steps, like how you’d fund it, govern it, and stop it from detonating social contracts built around the idea of earning your keep.
Realistically, some kind of income support may become necessary if automation starts outpacing job creation. We’ve seen early experiments - Finland’s trial in 2017, and smaller pilots in California and Wales - that show UBI can reduce stress and improve wellbeing, though not always increase employment. The bigger question isn’t whether people need money; it’s whether societies are ready to decouple income from work without collapsing the sense of purpose that comes with it.
Musk’s Universal High Income idea is classic tech-messiah stuff: skip straight to the endgame. But we’re still in the messy middle - uneven adoption, fragile trust, lots of PowerPoints about “AI transformation” and not a lot of measurable output yet.
The new research suggests we’re experiencing what economists call the diffusion lag - the long, boring stretch between invention and widespread productivity. Steam engines took decades to reshape factories. Generative AI might do it faster, but it’s still running on human bureaucracy.
The AI jobs narrative can be split into two timelines:
The Musk timeline - fast, dramatic, headline-friendly.
The labour-economist timeline - slow, data-driven, kind of dull.
The reality’s probably somewhere in between. Change is coming, but not necessarily in the form of a devastating lightning strike.
