Analyzing Ramaswamy's wealth plan: workers deserve equity

He says workers can build wealth in the AI era — yet most of his prescription lands on individual hustle. Here's the thing: telling people to "adapt" and "seize opportunity" is motivational copy; it isn't a plan for reversing decades of capital concentration. Vivek Ramaswamy's Wall Street Journal column is energetic and sincere — and it mostly skips the mechanics of how those gains would actually flow to workers rather than the owners of the very tools workers are told to adopt.

Start with what he gets right: the AI productivity story is real, sometimes magical.

Look, tools do amplify people. Software engineers, designers, marketers and small-business owners who adopt better toolchains will probably produce more value per hour; writers and coders already use AI systems as accelerants. That’s not speculative; it’s the same basic pattern we saw with spreadsheets, desktop publishing, and cloud software. Think of the worker-plus-AI as a skilled archer with a better bow. When the bow’s widely available, the archer can compete more effectively.

But bows aren’t distributed equally.

And not everyone is an archer.

Ramaswamy’s core move is to lean hard on worker agency — take the tools, upskill, own the upside. I’ll be honest: agency matters, but ownership matters more. If the returns from AI-driven productivity show up as higher margins for the platforms that sell the AI, or as dividends to the investors who own those platforms, then workers gain only insofar as their wage bargaining power improves. That bargaining power hasn’t suddenly strengthened just because we added a chatbot to the workflow.

In fact, the same tech dynamics that make AI tools powerful also make it easier for a handful of firms to capture the surplus. Microsoft, Google, Amazon — pick your acronym of choice — build platforms that sit in the middle of everything. When everyone else gets more efficient, those platforms are designed to hoover up a significant share of the gains through pricing power, distribution control, and data lock-in. You can be the most productive contractor on the platform and still watch the platform rewrite the terms.

This is why industrial-era analogies still matter. In the 19th century, better machinery boosted output; but without changes in property rights or labor institutions, factory owners reaped most of the rewards. You don’t need Asimov to see the sequel: unless ownership structures change, capital wins again, now with nicer UX and better autocomplete. Ramaswamy gestures toward entrepreneurship and side income as the escape hatch, but entrepreneurship is risky, unevenly distributed, and contingent on access to capital and networks that many workers simply don’t have.

Look at how stock-based compensation and broad-based employee ownership turned rank-and-file workers at places like Microsoft and Costco into actual asset holders, not just higher-paid labor. Contrast that with many gig platforms, where workers bring their own tools (and often their own car), absorb the risk, and still have no claim on the asset they’re helping to build. The choice between those models is political and institutional, not just a matter of “try harder.”

That’s why training — which Ramaswamy rightly emphasizes — can’t just be a vibes-based solution.

Sure, people need new skills. But which institutions will actually train workers at scale, and on whose terms? Community colleges? Employers? Tech firms running their own academies? There’s a familiar chicken-and-egg here: companies have an incentive to train workers for tasks that make the company more profitable, yet they also have an incentive to automate away whole roles or to swap full-time employees for contractors who absorb entrepreneurial risk without sharing entrepreneurial upside.

Public policy is the invisible character in Ramaswamy’s column. Funding for reskilling, portable benefits, tax incentives for employee ownership, support for worker co-ops — those are the boring levers that decide whether “own your tools” is a slogan or a reality. If you actually want workers to build wealth in an AI-saturated economy, democratizing ownership of the tools, the data, and sometimes the firms themselves has to sit at the center of the story, not in the footnotes.

There’s another blind spot in his framing: who even has a shot at this AI-boosted future. Not everyone sits at a keyboard in a well-connected city. Rural workers, service-sector employees, and contingent labor deal with lousy broadband, unstable hours, and app platforms tuned to externalize risk. Saying “learn new skills” to a barista juggling childcare and unpredictable shifts isn’t a strategy; it’s a slogan you print on a conference backdrop.

And if you look closely, a lot of the early AI deployment is aimed precisely at reducing headcount in those kinds of roles — customer service, basic support, back-office processing. That doesn’t mean those workers are doomed, but it does mean their timeline for “own your AI tools” is very different from that of an already-remote software engineer.

Now, the counterpoint I have to concede — and then push back on.

Yeah, no, some will say: markets will create new classes of winners; entrepreneurship has always been an engine of mobility. That’s partially true. New industries and startups do create outsized gains for some, and AI will be no exception. But pointing to the handful of breakout stories and calling it a worker-wealth strategy is like pointing to early Bitcoin millionaires and calling it a retirement plan.

Markets, left to themselves, tend to produce a few big winners and a long tail of smaller beneficiaries. You only get broad-based upside when institutional design spreads it: shared ownership models, stronger collective bargaining, labor standards that don’t evaporate the second you open an app and log on as an “independent contractor,” and tax policy that doesn’t treat capital gains and wage income as different species from different planets.

There’s a historical parallel worth dragging in here: the way postwar governments handled automation in manufacturing. In some countries, unions negotiated clauses around retraining and job guarantees; in others, the same machines arrived, but the gains went straight to profits while communities hollowed out. The technology was similar. The difference was who had a claim on the new wealth.

If the goal is genuinely to let workers build wealth, not just cope gracefully with disruption, then both policy and private practice have to be rearranged: real access to high-quality training; incentives and infrastructure for employee ownership and profit-sharing; scrutiny of platforms that concentrate value; contracts and portable credentials that give workers some continuity even as specific tasks get automated away.

Ramaswamy’s optimism about AI and agency isn’t wrong; it’s just incomplete. Without changing who owns the bows — not just who’s told to draw them — we’ll get ever-faster tools, a few new stars, and a familiar ending to the story about who actually gets rich.