Workers Deserve Ownership, Not Rhetoric, in AI Wealth

He says workers can build wealth in the AI era. Look — that’s not nonsense. But the column skips the hard parts and sells a romantic version of individual uplift that won’t land for most people.

Ramaswamy’s core claim in his Wall Street Journal op-ed is tidy: AI will rearrange economic value; workers who adapt can grab a bigger slice. That logic has history behind it. New tools do create new winners. People who learn faster, who can manage systems and not just tasks, usually end up with better roles and better pay.

As a former operations manager in a Fortune 500 shop, I watched this play out every budget cycle. The people who understood workflows, incentives, and where value actually flowed — not just their own job description — got tapped for promotions and special projects. They didn’t just “work harder”; they positioned themselves closer to the money.

But here’s what nobody tells you: tech transitions don’t automatically translate into wealth for workers. They translate into higher margins for whoever owns the assets and sets the rules.

If your company rolls out AI, cuts staff, and keeps wages flat, the productivity gain doesn’t show up in your bank account. It shows up in earnings reports. If you’re in a region where capital is thin and training options are junk or nonexistent, “use AI to upgrade your skills” is a slogan, not a path. And if your job is automated away and your severance barely buys you a laptop, your “chance to reinvent yourself” is really a forced reset under pressure.

The op-ed reads like a playbook for people who already have several rungs under their feet: savings, networks, access to good training, maybe an equity plan. It treats things like capital, mentorship, and market access as if they’re waiting for anyone who shows enough initiative. Give me a break. Anyone who has worked outside the top-tier metro bubble knows that’s fantasy.

There’s a quiet assumption running through the piece: once workers become more productive with AI, employers will naturally share more of the upside. Reality check. Companies are very good at measuring output and very bad at voluntarily handing out power. When generative tools make one analyst as productive as three, the default response is to keep the best one, maybe give them a small raise, and release the other two.

Look at Amazon’s warehouses or Uber’s driver model. When software made granular tracking and optimization possible, the gains didn’t flow proportionally to frontline workers. They flowed to the corporate P&L, with workers fighting for incremental improvements through organizing, public pressure, or regulation. The technology was new. The bargaining pattern was old.

Ramaswamy’s optimism about individuals “using AI” also flattens geography and infrastructure. It assumes you have reliable broadband, quiet space, and a local or online network willing to pay for higher-value work. Plenty of workers sit in places where the main employers are logistics centers, nursing homes, or hospitality. Telling them to spin up AI-powered consulting on the side is like telling a line cook to become a hedge-fund quant.

Wealth is not just income. Wealth is ownership plus insulation: equity, profit-sharing, retirement accounts, claims on productive assets that compound while you sleep. An op-ed that focuses on skill uplift without asking who gets access to those ownership channels is giving motivational speeches to people standing outside the building where the money is made.

Here’s where the column misses the real levers. If you want workers to build durable wealth in an AI transition, you don’t start with software tutorials. You start by rewiring who participates when productivity jumps.

That can mean portable equity or long-term incentive plans that follow workers across roles when automation shrinks headcount. It can mean profit-sharing formulas that automatically allocate a slice of efficiency gains to the teams whose work was redesigned. It can mean tax incentives for firms that build internal ladders — structured roles, training, and promotion paths — instead of treating constant churn and contracting as a default setting.

History already ran this experiment. When mechanization hit manufacturing, some firms paired new machines with real apprenticeships, seniority systems, and pensions. Those workers didn’t just learn new tools; they got a stake. Others pushed skill demands up, kept bargaining power down, and told people to be grateful for any job. Guess which model produced a middle class and which one produced resentment and constant turnover.

Training absolutely matters, but not the 48-hour “AI ninja” bootcamps cluttering LinkedIn. Training that matters is embedded in real jobs, evaluated on outcomes, and funded so workers aren’t eating all the risk. That means apprenticeships that include AI systems, credentials that don’t get trapped inside one company’s HR system, and employer co-investment where firms actually put money and time into reskilling instead of just issuing press releases about “upskilling initiatives.”

And yes, some people will absolutely get rich in this era. Founders, power users, smart consultants — the ones who see a wedge and build products or services around it. That’s good. We want people building things. The mistake is treating their edge-case careers as if they’re a universal manual.

The likely pushback is predictable: grit and initiative have always separated outcomes, and policy can’t hand people adaptability. True enough. I’ve seen workers squeeze out promotions and career pivots through sheer stubbornness and smart risk-taking, even inside rigid organizations.

But initiative doesn’t write equity plans. Hustle doesn’t decide who controls the AI stack that replaced three departments. Personal effort matters — a lot — inside the boundaries of a system. It doesn’t magically redraw those boundaries so more people share in the capital side of the equation.

Bottom line: Ramaswamy is right that AI opens real wealth-building possibilities for workers who can ride the wave. He just treats the ocean like a swimming pool — same water, no undertow.

If policymakers and firms follow his narrative without the institutional realism, we’ll get a familiar pattern: a handful of “AI success story” workers on stage, and a much larger group wondering why doing everything right still didn’t buy them a real stake.