What AI Lab Employees Are Actually Doing With Their Equity

S has $3.2M in unvested equity and no idea what to do with it. Not because she's naive. Because nobody around her is being honest.

We met at a coffee shop three blocks from Mission Street. She works at a major AI lab. Won't say which one. Doesn't matter — the numbers she described are consistent with what several labs are paying senior researchers and engineers right now.

$580K base and bonus. $3.2M in unvested equity at current valuation. She's been there two and a half years. The equity is mostly RSUs in a private company, with a small tranche of ISOs from an earlier grant.

"I don't know what to do," she said. Not because she's unsophisticated — she has a PhD in machine learning and has shipped two of the products that made the lab famous. But because the financial situation she's in has no obvious peer context.

Her friends from grad school are at academic salaries. Her colleagues at the lab are in the same boat but not talking about it. Her parents think she's doing well but have no frame for "pre-IPO equity." Her financial advisor manages ETF portfolios for retirees and hasn't worked with pre-liquidity paper wealth at this scale.

She has life-changing paper wealth and no honest peer to benchmark against. This is not an unusual problem at AI labs right now. It's the defining financial moment for a specific cohort.

How AI Lab Compensation Actually Works

The AI lab compensation structure is unusual and worth understanding precisely, because the standard RSU playbook doesn't map cleanly onto it.

At the major private AI labs — OpenAI, Anthropic, xAI, and a few others — total compensation packages for senior engineers and researchers run $400K to $1.5M+. The base salary component is typically $200K-$350K. Cash bonus adds another $50K-$150K. The rest is equity.

For many employees, equity is 50-70% of total compensation. At current valuations, four years of equity grants can represent $5-15M in paper wealth for senior technical staff. For research leads and VP-level employees, the numbers are larger.

But the equity structure at private labs is not the same as public company RSUs. There are several important differences.

The Problem Nobody Talks About Honestly

The AI lab employee with $2-5M in paper wealth faces a specific version of a problem that shows up in r/fatFIRE regularly: life-changing wealth that you can't access, in an asset class with no liquid comparable, surrounded by peers who are either in the same boat or have no frame of reference.

A r/fatFIRE thread from late 2025 asked AI lab employees to share their NW and TC. The post got 274 upvotes and 258 comments. Reading through it, the dominant emotion isn't smugness. It's uncertainty. Comments like "I have no idea if I'm making good decisions." And "Does anyone actually know what to do with pre-IPO RSUs at this valuation?" And "My financial advisor looked at my equity and said he'd never seen anything like it."

The uncertainty is rational. The situation is genuinely novel. AI lab valuations have expanded faster than financial planning frameworks have adapted. The people who would normally give advice — financial planners, accountants, estate attorneys — are working from frameworks built for public equity. Pre-liquidity private equity at $3-10M scale for people in their 30s is not a problem those frameworks handle well.

What Reddit Says People Are Actually Doing

Synthesizing across the fatFIRE thread and similar discussions on r/HENRYfinance and r/cscareerquestions, the approaches break into three camps. These are honest descriptions, not recommendations.

Camp 1: The Sellers

These employees sell equity at every opportunity. Tender offer opens? They sell the maximum allowed. Secondary market access? They take it. The logic is straightforward: paper wealth is not real wealth. Diversification is the only way to convert optionality into certainty. A $3M equity grant that goes to $0 in a lab shutdown or valuation reset is worth less than $500K in index funds.

The sellers are often people who've watched paper wealth evaporate before — at startups that didn't make it, or in the 2022 tech correction. They've internalized that the paper price is not the real price.

Camp 2: The Holders

These employees hold almost everything. The conviction is that the AI lab they work at is one of the most important companies in human history, the equity represents real ownership in that, and selling now is selling the future short. Some of them have done the math and genuinely believe the upside case is $20-50M. They're making a concentrated bet deliberately.

The holders are often people with strong lab conviction and low personal financial pressure. They have enough base salary to live well. The equity is a long-term bet, not a current need.

Camp 3: The Hedgers

The hedgers are using structured products to capture upside while protecting against catastrophic downside. This includes collars (when equity is public), variable prepaid forwards, exchange funds (for diversification without an immediate taxable event), and charitable vehicles like donor-advised funds for pre-IPO shares. This is the most sophisticated approach and the least common — because the products require significant minimum thresholds and financial advisors who specialize in concentrated pre-liquidity equity.

The Tax Layer for AI Lab Equity

The tax treatment of AI lab equity depends heavily on the equity type, the timing, and whether the lab is public or private at the time of disposition. This gets complex fast.

For RSUs: income tax at vest. Full ordinary income treatment on the fair market value at vest date. No choice about this. The tax happens when the shares vest, whether you sell or hold.

For ISOs: no income tax at grant or at exercise (regular tax). But the spread at exercise is an AMT preference item. If the lab's valuation is high at exercise and then drops, you owe AMT on a gain you haven't realized and may never realize. This is the ISO trap that burned people in 2001 and again in 2022.

For pre-IPO shares that qualify as QSBS: up to $10M in gains excluded from federal tax if you hold for 5 years and the company qualifies as a C-corp with gross assets under $50M at issuance. This is the most valuable tax treatment possible. Most AI labs at current scale don't qualify anymore — the $50M gross assets threshold rules them out for current grants. But some earlier grants may qualify. Check the details carefully.

The QSBS strategy in 2026 is worth understanding even if you don't qualify — because knowing why you don't qualify is as important as knowing why you do.

What the Patterns Actually Show

This isn't financial advice. It's pattern observation from people who've navigated concentrated pre-liquidity equity before — at earlier tech companies, at startups that IPO'd, at firms that were acquired.

The pattern that shows up most consistently among people who came out well from a concentrated pre-liquidity equity position:

1. They treated vested equity as current income and diversified it. They did not hold vested RSUs waiting for a better price.
2. They sized their liquid savings to be completely independent of their equity outcome. They didn't need the equity to be okay. It was upside, not baseline.
3. They thought about the AMT trap for ISOs before exercising, not after. They modeled the downside scenario: what happens if the valuation drops 70% after I exercise?
4. They found one financial advisor or tax attorney who had genuine experience with pre-liquidity concentrated equity. Not an advisor who said "this is interesting, let me look into it." One who had done it before.
5. They made decisions from a plan, not from FOMO or fear. The sellers who panic-sold at the worst moment and the holders who refused to sell at all both made emotion-driven decisions. The people who came out well had a written plan and followed it.

What Peer Benchmarks Actually Solve Here

The concentrated tech equity problem has a specific peer context gap: the people who most need comparison data are the ones with the rarest financial situations.

S doesn't need a financial advisor telling her the general principles of diversification. She knows them. She needs to know: of the people who had $3M+ in pre-IPO equity at a major AI lab, at her income level, at her career stage — what did they actually do? What did the sellers do with the proceeds? What did the holders do to protect against downside? What do the people who've been through a lab tender offer wish they'd known beforehand?

That's not general advice. That's peer data. And peer data is what NettWorth's corpus is built to provide — not synthetic benchmarks constructed from general population data, but actual patterns from actual people who actually look like you financially.

The equity compensation decision stack is complex at the best of times. For AI lab employees with pre-liquidity private equity, it's a situation where the stakes are high, the decisions are irreversible, and the peer context is nearly nonexistent. That combination is exactly what NettWorth was built for.

S finished her coffee. She asked me what I thought she should do.

I told her I thought she should find out what people who actually looked like her financially had done. Not what the theory says. What the people did.

She said that was the first useful thing anyone had said to her about it.