Let me be straight with you from the jump, because that’s what we do here. What is happening right now — in the labor market, in the boardrooms, in the quiet desperation of a generation raised on the promise of credentials — is unlike anything I’ve witnessed in my career. And I’ve watched a lot of disruptions come and go. This one is different. This one isn’t a wave. It’s a tide shift. The ocean floor is moving.
So let’s start where this has to start: with a single corporate decision that should be setting off alarm bells in every office building in America.
The Number That Tells You Everything
Jack Dorsey — founder of Twitter, now running his payments company Block — just laid off 4,000 people. Not a restructuring. Not a strategic pivot with a promise to hire in other areas. A flat 50% reduction of his entire company’s headcount. One in two people, shown the door. The stated reason? Artificial intelligence can now do the work.
Now here’s the part that should make you set down your coffee and stare at the wall for a minute.
50%
Largest percentage layoff in S&P 500 history
When Block cut half its workforce citing AI capabilities, Wall Street didn’t punish the decision. The stock went up. The market rewarded the elimination of humans — not for profitability gains, not for growth, simply for the act of replacing people with machines.
That moment deserves to be understood for what it is. Financial markets — which are at their core a collective bet on the future — just sent a clear signal. The signal says: companies with fewer humans and more AI are worth more. That is the new incentive structure baked into our economic system. Every CEO reading those stock reports absorbed that lesson. Many of them are acting on it right now.
For the 4,000 people who lost their jobs at Block, the consequences are concrete and immediate. These aren’t statistics. These are mortgages, daycare payments, health insurance continuations, and the particular psychological weight of being told your skills have been automated away. For the broader economy, this event is a data point in a pattern that is becoming impossible to ignore.
The Numbers Underneath the Headlines
Here’s what the broader picture looks like right now, on the ground, in the actual economy that actual people are living in:
1 in 4
Unemployed Americans out of work for 27+ weeks
27%
Of new hires this month accepted a pay cut to get the job
44%
More home sellers than buyers — unprecedented in modern data
66%
Of Americans report running out of cash — scarier to them than death
That housing number should stop you cold. Forty-four percent more sellers than buyers. That doesn’t happen in a healthy economy where people are building equity, growing their families into bigger homes, and feeling secure enough to put down roots. That happens when people need liquidity. When they’re nervous. When the math of staying put no longer works.
And here is the technological reality underlying all of it: at current capability levels, AI agents can already handle tasks that account for 44% of all U.S. work hours. Add robotics into the equation — another 13% on top — and you’re looking at well over half of the American labor economy at some level of displacement risk. Right now. With technology that, by every measure, is only going to get more capable.
“At current capability, AI agents could handle tasks making up 44% of all U.S. work hours. Robots add another 13% on top. And this is the worst this technology will ever be.”
— The economic reality we need to reckon with, 2026
Think about that last line. The worst this technology will ever be. The disruption we are experiencing today — the layoffs, the wage compression, the credential deflation — is happening at the floor of what AI can do. The ceiling isn’t visible from here.
The Credential Collapse
There’s a generational story buried inside all of this that doesn’t get told with enough honesty. We told an entire generation — particularly Millennials and Gen Z — that the path was clear: get the degree, pay the tuition, collect the credential, get the job, build the life. It was always more of a promise than a guarantee, but at least the map was legible.
That map is gone.
Fifty-one percent of Gen Z now says their college degree was a waste of money. Sixty percent of Gen Z workers are in jobs that have nothing to do with their field of study. And the brutal irony is that the very analytical and administrative roles that a university education was supposed to unlock — the white-collar knowledge work — are precisely the jobs most exposed to AI displacement. The barbell economy, as some economists are calling it, is here: high-skill, high-output builders on one end; skilled trades on the other; and a collapsing middle ground in between.
Only 1.6 jobs currently exist for every 100 white-collar service workers. That number alone should reframe how we think about what it means to be educated and employable in 2026.
What Jack Dorsey’s Layoff Actually Tells Us About the Future
Let’s return to Block, because there’s more to this story than the headline number. When a company cuts 50% of its workforce and the stock rises, the effect ripples outward in ways that go beyond that company’s four walls.
First, it establishes a template. Other CEOs — already under constant pressure from boards and institutional investors — now have a case study. A real-world data point that says: the market tolerates, even rewards, aggressive AI substitution. The social compact that once existed between corporations and their employees — loyalty exchanged for stability — has been quietly renegotiated without anyone signing off on the new terms.
Second, it accelerates the timeline. What might have taken a decade of gradual adoption has been compressed. When bold moves get rewarded publicly, the pressure on competitors to follow intensifies. The CEO who doesn’t move fast enough now looks like a liability, not a humanist.
Third — and this is where it gets existential — it exposes the absence of a social safety net designed for structural, technology-driven unemployment. The unemployment system was built for cyclical job loss: people lose jobs in a downturn, they find new ones in the recovery. What happens when the recovery doesn’t produce the same kinds of jobs in the same quantities? That is not a hypothetical. Credible economists are raising the prospect of Great Depression-level structural unemployment. Not as a worst-case fringe scenario. As a serious forecast.
For the 4,000 people at Block, the immediate reality involves navigating a job market where AI competency is increasingly a baseline requirement, where mid-level roles are shrinking, and where the skills that made them valuable in the previous chapter may or may not translate to the next one. That transition — individual, unguided, and largely unsupported — is happening simultaneously to workers across industries, across geographies, across age groups. The human cost is real. It is happening now. And it is, largely, invisible in the stock charts.
The Mirror Problem: AI Learns From Us
Here’s the conversation that almost nobody is having, and it might be the most important one.
We are not building AI in a vacuum. We are building it in a society — our society, with our values, our incentive structures, our biases, our history, and our habits. Every interaction, every dataset, every decision about what to optimize for, is a form of teaching. And what we teach, the machines will learn.
The Ethics We Cannot OutsourceThe way humans treat other humans right now is not separate from the AI ethics conversation. It is the AI ethics conversation. The algorithms being trained today are being trained on the world we have built — a world where a company eliminating half its people is celebrated in financial markets, where people who have been unemployed for six months are treated as statistically inconvenient, where the economic security of millions is weighed against quarterly earnings reports and found lighter.
If we normalize the dehumanization of labor — if we treat displacement as an acceptable externality, if we build systems that optimize for efficiency with no weight given to dignity, stability, or human flourishing — we should not be surprised when the machines reflect those values back to us at scale. You cannot outsource your ethics to a model that was trained on your behavior.
The machines are watching how we treat each other. They are learning what we value. They are being trained on our cruelty as much as our creativity, on our indifference as much as our generosity. This isn’t a future problem. The training data is being generated right now, in every boardroom decision, every layoff notice, every policy choice about who deserves support and who gets to be a rounding error in someone else’s efficiency calculation.
This is not a call to slow down AI development. That ship has sailed, and frankly, the technology itself is not the villain here. But it is a call to be deliberately, intentionally, stubbornly ethical in how we build, deploy, and govern these systems. Because the values we bake in now — through training data, through objective functions, through the incentive structures we build around these tools — will shape how AI interacts with humans for decades.
Kindness is not weakness. Dignity is not inefficiency. And a system that treats humans as disposable inputs will, if left unchecked, eventually produce AI that treats humans the same way. We don’t get to be brutal in practice and then demand that our machines be gentle. Ethics isn’t a feature you add at the end. It’s the architecture.
The Opportunity in the Wreckage
None of this means the future is only dark. But I refuse to skip past the wreckage to get to the silver lining, because too many people are doing exactly that, and it is leaving real people behind.
The opportunity is real. The ability to build useful things — software, systems, services — has never been more democratized. Non-coders are building working products. Small businesses are sitting on enormous inefficiencies that a person with AI tools and genuine curiosity can address and monetize. The barrier to entry for starting something, building something, helping someone, has never been lower.
But the opportunity does not erase the obligation. The people who will build well in this moment are not just the ones who learn to use the tools. They are the ones who bring genuine values to what they build. The builders who ask not just “what can this automate?” but “who does this serve, and how, and at what cost to whom?” Those people will build things that last. And they will build a version of the future that’s worth living in.
The trades are undervalued and about to become more so. The electrician billing $180 an hour doesn’t have to worry about her work being automated away. Physical presence, embodied skill, the judgment that comes from working with your hands in a specific place at a specific time — these are not things that current AI can replace, and they won’t be for a long time. The cultural stigma around skilled trades has always been more about class signaling than actual value. That’s worth reconsidering, urgently.
AI literacy is not optional anymore — but it’s also not sufficient on its own. Knowing how to use these tools is table stakes. The real differentiator is having something worth building toward: a genuine understanding of a problem, a relationship with the people who have that problem, the judgment to know what matters. Curiosity plus craft plus ethics. That combination is still rare enough to be worth something.
What We Owe Each Other Right Now
I want to end where I started, because I think it matters more than the strategic advice.
The people who are most exposed to this disruption — the mid-level white-collar workers, the recent graduates who followed the rules and are now watching the rules change, the 4,000 people at Block and the hundreds of thousands in similar situations elsewhere — they did not create this moment. They navigated the world they were handed, made reasonable decisions with the information they had, and are now caught in a structural shift they didn’t cause and cannot individually stop.
What we owe them is honesty. Not the soft-pedaled reassurance that everything will be fine, that the jobs will come back, that disruption always creates more than it destroys. Maybe it will. But maybe this time is different, and pretending otherwise doesn’t help anyone prepare.
What we also owe them — what we owe each other — is humanity. In how we design these transitions. In whether we build systems that catch people or just let them fall. In how we talk about the displaced: as people with dignity and history and specific skills, not as inefficiencies that have been optimized away.
The machines are watching. They’re learning. The humans we choose to be right now — in this exact moment, at this exact inflection point — will echo through the systems we build and the future we create. That’s not a metaphor. That is the mechanism. The ethics we live, the machines will learn.
Build accordingly.
— Ryan “Dickie” Thompson
Founder, Disruptarian.com
Submit a Comment