Is OpenAI equity worth it at a $1.25T valuation?

Does OpenAI's $1.25T valuation make the equity worth it?

It can still pay very well, but it is no longer the asymmetric bet it was two years ago. OpenAI is reportedly nearing a raise of up to $30 billion at a $1.25 trillion pre-money valuation, a bridge round led by Coatue alongside UAE investors, ahead of a potential public listing. The detail candidates should actually fixate on is this: that price sits roughly $150 billion below the $1.4 trillion the company had pitched to prospective investors only weeks before, and reporting described demand as muted rather than frenzied. You would be joining after most of the easy multiple has already been taken.

Run the arithmetic before the recruiter does it for you. A grant priced at $1.25 trillion needs the company to reach around $2.5 trillion for your shares to double, and roughly $3.8 trillion to triple — a figure larger than all but a tiny handful of public companies have ever reached. That is not impossible, and the revenue trajectory is genuinely extraordinary. But it means your equity behaves less like a startup option and more like a concentrated bet on a mega-cap stock you cannot sell for years. Price it accordingly: as deferred, taxable, illiquid compensation, not as a retirement plan.

There is a second reason the mark deserves a discount in your head. The company filed confidential IPO paperwork in June 2026, and its finance leadership has told staff a listing could land in 2027 — which means your first realistic liquidity window may sit two to three years out, after a lockup. A lot can move the number in between. If you want the longer version of that calculation, the mechanics of pricing an offer when the listing keeps sliding apply almost perfectly here, because the fuse on this grant is long whichever way the IPO goes.

Key valuation marks reported for OpenAI in 2026

How do you value a pre-IPO grant before you sign?

Value it against the most recent price real money actually paid, not the valuation in the press release. For OpenAI, that benchmark exists and it is unusually concrete: the company completed a roughly $7 billion employee share sale in August 2026 priced at an $852 billion valuation. That is the number at which colleagues converted paper into cash. A grant quoted to you at $1.25 trillion is therefore quoted at a premium to the last price employees could transact at. Ask which valuation your grant was struck against, how many units you are getting, and what the current total share count is. Without the denominator, the dollar figure is theatre.

Understand the instrument too. OpenAI's employee equity began life as profit participation units rather than conventional stock, and the 2025 restructuring converted staff holdings into shares in the public benefit corporation. That shift matters because it changes how the shares are taxed, how they vest and how they can be sold. Confirm in writing whether you are receiving restricted stock units, options with a strike price, or something structured differently — and if there is a strike, whether it was set against the $852 billion mark or something closer to the new round. The gap between those two strikes is real money.

Then map the liquidity rules, because they bind harder than the valuation. New hires have historically been barred from selling for their first two years, and sales have happened through periodic company-run tenders with per-person caps — the October 2025 programme raised its cap from $10 million to $30 million and saw more than 600 current and former employees sell about $6.6 billion. That is a functioning market, which is better than most pre-IPO companies offer. If you want to compare routes, there's a useful breakdown of selling into a tender versus holding for the listing.

What should you ask on the offer call?

Ask four questions, and ask them before you discuss base salary. First: how many units, out of how many total shares outstanding, and at what valuation was the grant struck? Second: what is the vesting schedule, the cliff, and does anything accelerate on a change of control or a listing? Third: what are the sale rules — the blackout period for new hires, expected tender cadence, lockup length after an IPO? Fourth: what happens to vested equity if you leave before liquidity? Recruiters at well-run labs answer all four without blinking. Hesitation on any of them is your signal.

Keep the tone matter-of-fact. Something like: 'I'm excited about the role and I want to model the package properly. Can you tell me the unit count and the total share count the grant was priced against?' You are not being difficult; you are doing what any sane person does before accepting several hundred thousand dollars of illiquid paper. Get the answers in writing — email is fine. Verbal assurances about tender frequency and acceleration have a habit of evaporating when the person who gave them moves teams, and in a company hiring at OpenAI's current pace, that happens constantly.

One more question worth slipping in: what does a refresher grant look like, and when does it land? A four-year grant that stops producing in year three is a retention problem, and at a company this far along, refreshers are often where the real long-run value sits. Ask what a strong performer at your level received last cycle. If the answer is vague, assume nothing and weight your base salary negotiation harder — that is the part of the package that cannot be repriced by a future round or a soft market.

How do you negotiate when the upside is already priced in?

Trade paper for certainty. When a company is already valued at $1.25 trillion, the marginal unit of equity is worth far less to you than the equivalent cash, so push the negotiation toward base salary, a sign-on bonus paid in the first cheque, and a shorter cliff. A reasonable framing: 'The equity is attractive and I'd like to hold it long term. Given the stage, I'd rather move some of that value into base so the package works regardless of listing timing.' Many labs will flex on cash precisely because cash is cheap relative to the shares they are protecting.

Where flexibility usually exists: sign-on bonuses, relocation, a six-month rather than twelve-month cliff, annual refreshers written into the offer, and a commitment to include you in the next tender. Where it usually doesn't: the level you were slotted into, and the blanket lockup after a listing. Spend your leverage on the first list. And remember that an IPO is only one of the exits a pre-IPO grant can meet — the reasons equity can return nothing in an acquisition are worth reading before you convince yourself the outcome is binary between good and great.

Here is my contrarian position: the most useful number in this whole news cycle is not $1.25 trillion, it is the $150 billion the company reportedly shaved off its own asking price in a matter of weeks. That tells you private marks are soft, negotiable and sentiment-driven — which is exactly the argument for discounting your grant and holding firm on cash. If a recruiter leans on the valuation as a reason you should accept less salary, you can politely note that the mark itself moved 11% in a month. Valuations are opinions. Base salary is a fact.

How should you position your CV for an AI lab hiring on IPO money?

Write for the problem the lab is solving this quarter, not the one it solved in 2023. Research and infrastructure roles at the frontier labs now screen hard for evidence you have shipped at scale: training or serving models in production, inference cost reduction, evaluation pipelines, safety and alignment work, distributed systems at thousands of GPUs, or applied product work with measurable retention and revenue impact. Name the stack, name the scale, name the outcome. A bullet that reads 'cut inference latency 38% by rewriting the batching layer, serving 400M weekly requests' beats a paragraph of adjectives every single time.

Expect automated screening first. A company raising tens of billions and hiring aggressively runs applicant tracking software like everyone else, which means a two-column template with icons and text boxes can mangle your experience before a human reads it. Keep it single-column, use standard headings, put your tooling in plain text rather than skill-rating graphics, and mirror the exact phrasing from the job ad — if they say 'distributed training', don't write 'multi-node model parallelism' and hope the parser infers it. The first-screen rules for engineering CVs are unglamorous and they decide a lot of outcomes.

Then verify the file rather than trusting it. Upload your CV for a free HRLens analysis and you'll get a score out of 100, five category scores covering experience, tech stack, impact and ownership, clarity and structure, and ATS compatibility, plus a visual layout check that shows how the document reads to a machine. Paste the job description in and the analysis lists the skills you are missing against that specific ad and generates ATS-friendly rewrites in six templates. Fifteen minutes of that is worth more than another week of guessing why nobody replied.

Frequently asked questions