A wave of new millionaires is being created without waiting for a company to go public. A significant data point reveals that in October of last year, over 600 current and former OpenAI employees collectively cashed out approximately $6.6 billion in stock. Among them, around 75 individuals each realized gains of $30 million. This means that even before OpenAI's potential initial public offering, a group of executives and regular employees have already reaped substantial financial rewards from the AI boom. This represents one of the most noteworthy shifts in the current AI industry.
Traditionally, startup employees typically had to await an IPO to liquidate their equity holdings. However, leading AI companies are now accelerating the timeline for wealth realization. OpenAI serves as the most prominent example. Other firms like DeepSeek are catching up by establishing external valuations and equity incentive programs. Companies such as Anthropic, Cerebras, and Character.AI demonstrate that avenues for wealth creation in AI are diversifying, including financing rounds, tender offers, secondary market transactions, technology licensing, and team transfers.
For AI companies, this trend serves as a new tool to attract top-tier talent. For AI professionals, technical expertise no longer merely translates to high salaries and stock options; it increasingly offers the possibility of tangible financial returns well before a company's public listing.
Let's first examine the "wealth creation myth" at OpenAI. The substantial earnings of OpenAI executives have been publicly disclosed through recent legal proceedings. During a court case involving Elon Musk versus OpenAI and Sam Altman, President Greg Brockman testified that his equity stake is valued at approximately $30 billion. Concurrently, former Chief Scientist Ilya Sutskever revealed in the same trial that his OpenAI equity is worth about $7 billion. Chief Executive Sam Altman stated he does not hold company shares, citing its non-profit origins, though some investors anticipate he may receive an equity arrangement if OpenAI's for-profit restructuring proceeds successfully.
Many rank-and-file employees have also realized significant wealth. It was reported that in October, OpenAI facilitated a large-scale stock sale where over 600 current and former employees liquidated their shares on the same day, totaling roughly $6.6 billion. Among this group, about 75 employees reached the company's maximum allowable sale limit, cashing out $30 million each. Some employees donated remaining shares to donor-advised funds to support charitable causes and receive tax benefits for the year.
This sale stands as one of the largest employee equity liquidity events in the AI industry to date. The transaction also marked the first time OpenAI allowed newly hired employees to sell shares since the launch of ChatGPT, signaling a significant policy shift towards greater generosity in equity liquidity. Previously, the company required employees to be with the firm for at least two years before selling shares, preventing many technical experts from cashing out earlier.
Compared to the initial equity grants seven years ago, the value for early employees has surged over 100-fold, vastly outperforming the roughly threefold increase in the Nasdaq index over the same period and exceeding the wealth growth seen in traditional tech companies.
OpenAI's equity incentive structure has itself undergone adjustments. The previous per-employee sale cap was $10 million, which was raised to $30 million in the fall of 2025 in response to investor and employee demand. This system addresses external investors' desire to purchase shares while providing employees with a pathway to monetize their paper wealth. Historical data suggests that if early employees could only sell post-IPO, wealth appreciation could be impacted by market volatility; OpenAI's pre-IPO liquidity mechanism effectively mitigates this risk.
Compensation and equity incentives are crucial tools for OpenAI to attract and retain top talent. Some technical roles at OpenAI offer annual salaries up to $500,000, supplemented by stock awards and one-time bonuses, with some bonuses valued in the millions of dollars. This compensation package provides significant financial returns for employees, enhances stability in key positions, and supports the company's rapid pace in technology development and product iteration.
Meta reportedly offered compensation packages worth up to $300 million to its top AI talent last year. The intense competition for high-end AI talent and the corresponding compensation levels within the industry generally surpass those of traditional tech companies.
The AI boom is creating a new cohort of wealthy individuals in San Francisco, even revitalizing the city's long-sluggish housing market. Some properties have sold for well above asking price due to multiple competing offers—for instance, a house listed at $1.6 million sold for $2 million. Data from Apartment List shows San Francisco rents rose 14% year-over-year in February, the highest increase in the nation.
These developments—whether the vast wealth held by executives, the high salaries and bonuses for regular employees, or the increasingly generous equity plans—confer an obvious benefit for OpenAI: they are certain to enhance its appeal to talent. This appeal isn't merely about "higher pay." More importantly, it shows employees a clear and viable path to liquidity. Joining a top-tier AI company, receiving options or stock, benefiting from rising valuations, and then realizing wealth through tender offers, secondary market trades, or a future IPO.
This context makes recent funding rumors about DeepSeek particularly noteworthy. According to reports, DeepSeek is advancing its first external funding round, targeting a potential valuation of $50 billion, with a fundraising goal of $3 to $4 billion. In rumors less than a month old, DeepSeek's valuation was pegged at only $10 billion.
On the surface, this represents a Chinese AI star company gaining capital recognition. However, viewed through the lens of the OpenAI case, it carries another implication: DeepSeek needs not just capital, but an externally validated market price.
DeepSeek has not been a typical venture capital-driven company. Its funding has primarily come from founder Liang Wenfeng and his quantitative trading firm, 幻方量化. Consequently, it long maintained a "research team" image: low-profile, technology-focused, and emphasizing model efficiency. But when a company enters the global AI competitive arena, it becomes difficult to sustain an organization solely on technical reputation. Models require computing power, products require commercialization, and teams need long-term incentives.
The primary function of a funding round is to establish a company valuation. Once a valuation is set, the options and equity held by employees gain a discussable price. Otherwise, equity incentives resemble a forward promise: theoretically valuable, but employees remain uncertain of its actual worth or liquidity timeline.
The prerequisite for OpenAI's pre-IPO, large-scale employee cash-out was the establishment of a pricing system, accepted by investors, through multiple funding rounds and tender offers. For DeepSeek to retain its core team in China's competitive AI talent market over the long term, it must also establish this mechanism. This is especially critical for DeepSeek.
Reports indicate the intended use of the new funding includes strengthening computing infrastructure and improving employee benefits. The report also notes that DeepSeek faces talent and capital competition from Chinese AI firms like ByteDance, Alibaba, MiniMax, and 月之暗面, and has already experienced talent attrition, such as the case of 罗福莉 joining Xiaomi. A new challenge for DeepSeek is whether it can offer sufficiently compelling long-term rewards to core talent in an industry where compensation benchmarks have been elevated by companies like OpenAI, Anthropic, and Meta.
OpenAI's employee cash-out demonstrates that leading AI companies can generate substantial wealth before going public. DeepSeek's pursuit of external funding shows that newer players are also catching up on valuation, equity incentives, and computing power investment.
Wealth creation in this AI cycle is not limited to "waiting for an IPO." While the most standard exit for startups historically was an IPO, capital is now flowing through more complex pathways. Employees can cash out early via secondary markets, startups can exit through acquisitions, and chip and infrastructure companies can enter public markets riding the AI wave.
The most direct exit remains the IPO. Beyond OpenAI, Anthropic serves as another model company example, with expectations it could go public as early as 2026. Its uniqueness lies in not being at the first external funding stage like DeepSeek, nor entangled in complex non-profit to for-profit transition debates like OpenAI. It has Claude, enterprise clients, and support from cloud providers like Google and Amazon.
Another IPO example is chip startup Cerebras. Reports state that due to strong investor demand, Cerebras plans to raise its IPO price range from $115-$125 per share to $150-$160, while also increasing the number of shares offered from 28 million to 30 million. At the higher price, the fundraising amount would be approximately $4.8 billion. The deal reportedly received over 20 times subscription and is planned to list on the Nasdaq under the ticker CBRS. This could potentially become the world's largest IPO in 2026. The AI boom is not only increasing the value of model teams but also creating new wealth exits in chips, computing power, and data centers.
Mergers and acquisitions represent another path. In June 2023, Databricks announced the acquisition of generative AI platform MosaicML for approximately $1.3 billion, including retention incentives. MosaicML, which focused on helping enterprises train and deploy their own generative AI models, essentially provided Databricks with a model training platform, team, and enterprise AI capabilities. MosaicML had only about 62 employees at the time, leading media to describe the deal's cost as approximately "$21 million per employee."
Acquisitions are no longer just about "a company being bought outright." Character.AI presents a more contemporary model. In 2024, Google entered into a roughly $2.7 billion technology licensing deal with Character.AI, gaining access to its model technology while hiring co-founders Noam Shazeer and Daniel De Freitas, along with other core researchers, to join Google DeepMind. Subsequent reports indicated that after this deal, Character.AI shifted focus from training cutting-edge large models to enhancing its consumer-grade chatbot product. Furthermore, the company used the funds to buy out investor shares, transferred company ownership to employees, and provided them with a one-time cash compensation. Approximately 30 employees joined Google, while about 100 remained at Character.AI.
In this case, Google did not fully acquire Character.AI but secured the technology and most稀缺的人才 through a substantial licensing fee. The original company continues to operate, and both investors and employees gained liquidity ahead of schedule. A company doesn't necessarily have to be bought outright; its technology, team, and future收益权 can be repriced by major tech firms.
This is a key difference between the current AI boom and many past technology cycles: wealth is no longer concentrated and released solely at the IPO moment, nor does it belong exclusively to founders and investors. The people behind the models, data, computing power, products, and infrastructure are gaining earlier and more complex liquidity opportunities through secondary markets, technology licensing, team transfers, acquisitions, and public listings.
For AI companies, this is a new weapon to attract talent. For AI professionals, it means they don't necessarily have to wait for an IPO to translate their technical capabilities into real-world financial gains.
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