Ramaswamy's AI wealth pitch misses the factory floor

If you read Vivek Ramaswamy’s Wall Street Journal column and walked away thinking AI is a worker-side gravy train, look: that’s optimistic in a way that dodges how value actually flows in capitalism. The piece argues workers can build wealth in the AI era. Fine. The harder question is who actually captures the gains when machines make labor more productive — the person writing the code, the platform owner, or the person whose job just got “augmented”?

Let’s give Ramaswamy his due first. He’s right that workers shouldn’t just wait for rescue. Seeking ownership, equity, and new income streams tied to the tech you use is the right instinct. Waiting for some benevolent employer to “share the upside” has never been a winning strategy.

But here’s what nobody tells you: owning a tool and owning the revenue the tool generates are different things. That new AI subscription you pay for? You own access, not the asset. A platform provider gets the recurring revenue; the onboarded worker usually gets the marginal wage. Big tech firms own the models, the distribution channels, and the customer relationships. Workers, unless they hold equity or intellectual property, are suppliers to a system that centrally prices and packages AI services.

That’s not theory. Look at how software businesses turned small engineering teams into massive economic moats — not because developers were showered in cash, but because firms retained rights and recurring revenue. Microsoft didn’t get huge because individual Excel power users were getting rich. It got huge because the company locked in licensing, distribution, and default status. AI platforms are building the same kind of moat: own the infrastructure, set the terms.

So when arguments pop up that “workers will just start businesses” around AI, give me a break if we pretend that’s equally available to everyone. You run into real barriers: access to capital, networks that send you customers, regulatory compliance overhead, and platforms that optimize for scale, which favors larger, incumbent players. Hustle is not a substitute for bargaining power.

If you actually want workers to build wealth, you don’t just cheer on more individual initiative; you change the plumbing. Policy and contract design need to force or reward revenue-sharing — employee equity, profit-sharing, portable ownership models, shared IP rights — not just slogans about “upskilling.”

Ramaswamy is also right that skills and access to AI tools matter. Workers who can wield these tools effectively will be more valuable than those who can’t. But skills and access are necessary, not sufficient.

Not every nurse, machinist, or retail associate has the time, equipment, or mentorship to retool into an AI-enabled micro-entrepreneur. Training programs exist, and some are good, but a lot of what’s on offer is controlled by for-profit providers with mixed incentives and uneven outcomes. Geography plays a role too — a laid-off worker in a manufacturing town is not swimming in the same opportunity pool as someone sitting inside a coastal tech hub with a dense network of startups and clients.

From an operations perspective, this isn’t just about “learning AI.” It’s an operational design problem. In a Fortune 500 ops role you learn the ugly truth: processes scale when incentives and logistics align. You can train ten thousand workers on a new tool, but if the company buys the AI output and centralizes revenue, the training is a cost center — not a path to wealth. Real change means redesigning incentive systems so productivity gains translate into worker equity or higher marginal pay, and aligning distribution channels so independent workers can reach buyers without handing most of their margin to a gatekeeper.

History backs this up. When mechanical looms and railroads arrived, productivity exploded — but most of the early gains went to factory owners and financiers, not the people running the machines. Only when unions, regulation, and new ownership structures came into play did more of that wealth get redistributed via wages, safety standards, and benefits. AI is just the latest round of the same contest over who owns the productive assets and who negotiates from strength.

That’s where Ramaswamy’s column feels light: it nods at the promise of agency without really wrestling with the levers that determine who gets what. Labor law, corporate tax treatment, and securities rules quietly shape whether worker-owners can hold meaningful stakes or just token slivers. Public policy could make employee ownership less of a legal and administrative maze by simplifying equity grants for small businesses or creating tax-favored vehicles for worker-held shares in AI-heavy firms.

Cities and states could stop funding training pipelines that end in pretty certificates and no bargaining power. Instead, they could back apprenticeships and partnerships that end in guaranteed placement plus options for ownership or revenue share tied to AI-enabled workflows. Don’t just teach someone to prompt a model; give them a stake in the product that model helps deliver.

Platform design is another big blind spot. When marketplaces like Upwork, Amazon, or app stores set their take rates and ranking algorithms, they’re deciding how much value stays with the worker and how much gets skimmed off. If large platforms had to publish their take rates clearly and were pushed or rewarded to offer worker-facing revenue-split products — say, shared ownership of high-traffic AI tools or royalty-like structures for user-generated training data — that would shift the economics faster than another motivational speech about grit.

Some companies have hinted at what this could look like. Co-op platforms where workers own the marketplace itself. Stock grants that reach frontline employees, not just management. Royalty shares for user-generated AI assets that keep paying as long as those assets earn. These ideas sound political, but they’re really operational: change incentives, change outcomes.

Now, you could argue that markets are dynamic and will create new layers of service where workers can own value — that the best prompt engineers, productizers, and niche experts will command serious money. Fair point. Markets do create winners. Skilled freelancers already use AI to 3x their throughput and charge premium rates in some niches.

But winners are not the same thing as a broad middle. Left to markets alone, we tend to get superstar outcomes concentrated among a small slice of workers and capital holders. That’s a distribution problem, not a technological destiny. If policy and business responses stay ad hoc, the new wealth from AI will rhyme with past waves where capital, not labor, took the lion’s share and only a thin layer of workers broke through.

If you actually want Ramaswamy’s vision to land in reality, three moves would make it less rhetorical and more operational:

• Tie worker equity to AI-generated IP or customer relationships, not just badges of skill that look good on LinkedIn and pay nothing.
• Build standardized, portable apprenticeships with clear pathways from training into roles that include ownership or defined revenue share.
• Force transparency in platform take-rates so workers can choose where to sell their output with eyes open instead of guessing who’s quietly taxing their effort.

Bottom line: rhetoric about empowerment won’t bridge the gulf between owning skills and owning wealth while AI monetization stays locked up by a few platforms. If the AI era plays out the way past tech waves have, the people who actually follow Ramaswamy’s advice will still find that the biggest prizes track who controls the code, the customers, and the contracts.