In San Francisco’s AI era, even a $180,000 tech salary is no longer enough to compete with the new AI elite that is forming as OpenAI and Anthropic get ready to go public. The source frames the reality bluntly: tech workers making six figures are grousing that they cannot match the compensation and positioning available to AI insiders, and some doubt they can afford to stay in high-cost hubs.

That is the pressure point, and it matters because the IPO runway is not just a corporate milestone. It is a force multiplier for compensation, status, and network effects. When OpenAI and Anthropic move toward going public, they bring attention, capital, and prestige that can pull both top talent and the incentives that keep them anchored. For employees on the outside looking in, the gap is not abstract. It shows up in who can buy into equity-rich, future-upside roles and who feels stuck earning “enough” by old standards.

To understand why $180,000 stops feeling like safety, you have to zoom out to how AI companies tend to structure compensation. In tech hubs, compensation is usually a mix of base salary and equity. Base pay can look solid on paper, but equity (and the momentum around it) is where the biggest upside tends to cluster when a company is scaling fast and preparing for liquidity events like an initial public offering. As OpenAI and Anthropic approach the public markets, workers who are closer to the AI core tend to have more direct paths to that future upside.

Meanwhile, workers in more traditional tech roles are often comparing themselves to the wrong reference point, and not because they are irrational. The “AI elite” is getting rewarded in ways that can dwarf the six-figure baseline. That creates a market narrative in San Francisco where “salary” alone is the wrong metric. A six-figure check might still be normal, but if the people around you are capturing equity upside that could materially change their long-term wealth, the comparison becomes immediate and personal.

The source also signals something important about employee sentiment: the grousing is happening now, not just after an IPO closes. In other words, the emotional and practical effects begin during the lead-up period. Even before trading starts, looming public-market readiness can change internal dynamics. Employees hear more about who is getting invited into the most impactful projects, and leaders can feel pressured to justify offers relative to competitors that are gaining momentum and signaling liquidity and growth.

This is where boards and leadership teams get their second-order problem. Retention is not only about paying at the top of the salary band. It is about aligning incentives so key employees believe their path to upside remains credible. If workers conclude that their compensation cannot compete with AI-adjacent roles that might be captured through OpenAI and Anthropic-linked ecosystems, they may start making plans that their employer cannot easily reverse. Some doubt they can afford to stay, which means the issue is not only workplace morale. It can become migration, career churn, and knowledge leakage.

Second, the impending IPOs can shift bargaining power. In talent markets, leverage often moves toward the side with more optionality. Workers compare multiple offers, and companies compete not just for candidates, but for the stories candidates believe: the story of where growth is headed, where equity upside lies, and which teams are closest to the value creation engine. As OpenAI and Anthropic get ready to go public, the story becomes harder to ignore, and companies outside that gravitational pull can find their internal salary benchmarks out of date.

For peers managing similar roles, the strategic stakes are straightforward. San Francisco’s AI era is turning compensation into a sorting mechanism, not a paycheck issue. Executives who treat this as a routine market adjustment risk missing the real threat: workers may not leave because they earn too little on day one. They may leave because they see too little upside relative to the new AI elite, and they start calculating what life in the city costs versus what their career can realistically deliver.

In short, the source’s warning is practical. When an AI wave builds around companies preparing to go public, “six figures” can stop being a ceiling and become a floor that other roles can outgrow. The $180,000 number is less a punchline than a signal that talent expectations and retention math have changed.