Behind the Rumors of DeepSeek’s $10 Billion Valuation: Four Logical Judgments

However, it is worth noting that after only two days of fermentation, information from various channels is highly consistent: a person from a large state-owned equity institution stated that the news is “very likely to be true,” but “currently cannot invest at all”; and many venture capital investors also admitted that financing quotas for popular projects like DeepSeek usually need to be “snatched.” In other words, even if the financing news is true, the number of external institutions that can actually obtain a share is likely to be very limited.

Regarding this rumored event, we propose four logical judgments, breaking it down layer by layer as follows.

01

The First Logic: The Essence is the Architectural Design of Equity Incentives for Non-Listed Companies

DeepSeek’s uniqueness lies in the fact that since its incubation by QuantFi in 2023, it has never accepted any external equity financing. This fact brings a structural issue that is easily overlooked: the options in the hands of employees lack a market-based pricing anchor.

An investor who has invested in large models analyzed to First Finance hit the nail on the head: DeepSeek, even if it opens up financing, is not a game for most people, and according to Liang Wenfeng’s ideas, the terms will definitely be extremely strict. The investor judged that this shift in financing is likely for employee option pricing and realization, and that it was “too late.”

The logical deduction is as follows.

In the employee equity incentive system of a non-listed company, the value of options needs to be confirmed by external market pricing. Without external financing, it means there is no valuation anchor verified by real money, and the equity promises in the hands of employees cannot be converted into clear wealth expectations, lacking sufficient liquidity and reference premiums in the eyes of top talents.

Talent competition in the AI field has reached a fever pitch: Lu Fulie, a key contributor to the DeepSeek-V2 architecture, joined Xiaomi; Guo Daya, the core author of the GRPO algorithm, joined ByteDance; and multimodal core researcher Ruan Chong joined Yuanrong Qixing. The compensation packages offered by these competitors can be double or even more than DeepSeek’s current compensation.

Introducing a small round of financing is essentially using a market transaction price to complete an official pricing for the entire employee option pool. $300 million corresponding to about 3% equity is enough to create a price anchor with legal effect and market reference, but it is not enough to shake Liang Wenfeng’s absolute control. From this perspective, the primary function of this round of financing is “internal accountability,” allowing past contributors to have clear expectations of returns and providing future talent with clear incentive coordinates.

This also explains why “shares are difficult to grab.” If the core purpose of financing is pricing rather than introducing strategic resources, then Liang Wenfeng will tend to choose investors who are most cooperative in terms of terms, have the weakest strategic demands, and have the least willingness to interfere with business decisions. In the eyes of an idealistic founder, the entry of external capital is itself a necessary compromise, and his natural tendency is to minimize its impact.

However, there is a deeper logical scrutiny here: Is introducing external financing really the only way to solve the option pricing problem?

In fact, option pricing for non-listed companies does not necessarily have to rely on equity financing. Under a mature legal and financial framework, the company can completely hire a third-party valuation agency to conduct an independent valuation, or QuantFi can fund the establishment of an internal repurchase fund to repurchase employee options at fair value. These paths can also provide liquidity for options without diluting the founder’s control.

So, why did Liang Wenfeng choose the financing path? The possible answer lies in the essential difference between “market endorsement” and “internal valuation.”

Regardless of how high the internal repurchase is priced, it is essentially the company using its own money to buy its own shares, lacking the support of a fair value from external market entities’ trading behavior. In the minds of top talents, the “credibility” of this arrangement is far lower than introducing strategic investors, which means that an independent third party has confirmed the market value of the company’s equity with real money. In other words, financing is not the “only solution” for option pricing, but it is the “optimal solution” with the most credibility.

02

The Second Logic: A $10 Billion Valuation is an Unreasonably Low Price, and “Outsiders” Are Unlikely to Get a Share

If the essence of this round of financing is the architectural design of equity incentives, then pricing is the most worthy variable to examine. $10 billion, this figure is unreasonably low in the current AI valuation coordinate system.

First, look at horizontal comparisons. In January 2026, Zhipu AI was listed on the Hong Kong Stock Exchange with a market capitalization of approximately $6.8 billion on the first day, and its latest market capitalization is approximately $50.7 billion. MiniMax’s market capitalization on the first day of listing was approximately $13.7 billion, and its latest market capitalization is approximately $34.4 billion. As another unicorn large model that has not yet been listed, Yuezhi Anmian’s valuation has increased from $4 billion in November 2025 to $18 billion.

Then, look at the vertical logic. DeepSeek’s parent company, QuantFi, achieved an average return of as high as 56.6% in 2025, with assets under management exceeding 70 billion yuan, ranking second among billion-level quantitative private equity funds. Based on the industry’s customary “1% management fee + 20% performance reward” rough estimate, QuantFi brought Liang Wenfeng approximately 5 billion yuan, or more than $700 million in revenue in 2025 alone.

More interestingly, the “profitability counterpart”: QuantFi is essentially a financial machine with stable profitability. If there is a clear value association between DeepSeek and QuantFi—whether it is a funding channel or technical synergy—then a $10 billion valuation corresponds to a price-to-earnings ratio of just over ten times. For a complex that possesses both top-notch AI R&D capabilities and top-notch quantitative trading capabilities, this pricing is difficult to justify under any reasonable financial model.

In both horizontal and vertical dimensions, the $10 billion valuation is significantly lower than the market reference. This naturally leads to a deeper question: Why is Liang Wenfeng willing to introduce external capital at such a low price?

A reasonable explanation is that the low valuation itself is a screening mechanism. In a financing where “outsiders cannot get shares,” pricing is not the primary consideration for the founder. Instead, a significantly low valuation can effectively filter out investment institutions with stringent financial return requirements and strong bargaining intentions, selecting partners who truly accept Liang Wenfeng’s game rules. In other words, this price is not the result of market bargaining, but a threshold actively set by the founder.

However, this explanation only answers “why low,” not “why this number.”

So, what is the real anchor of the $10 billion figure? The answer is likely hidden in QuantFi’s accounts.

Since its incubation in 2023, DeepSeek’s R&D investment, computing power procurement, and team salaries have all been borne by QuantFi. This is a precisely calculable internal transfer cost. According to industry data that can be cross-verified, QuantFi’s cumulative investment in DeepSeek over three years is approximately in the hundreds of millions of dollars.

A $10 billion valuation releasing 3% just means: the $300 million raised in this round is approximately equal to QuantFi’s total investment in DeepSeek over the past three years.

We can view this as a very precise financial signal: if the $300 million corresponds to QuantFi’s cumulative investment in DeepSeek over the past three years, then after this round of financing, DeepSeek will be financially independent from QuantFi. This means that DeepSeek’s continued losses will no longer be filled by QuantFi’s profits, and it will need to face the capital market on its own. This financing will be the starting point for independent operation.

03

The Third Logic: Dimensionality Reduction Anchoring, Using Equity to Lock Structural Advantages

The above two logics explain “what” and “why the pricing is so low” of this round of financing, but have not fully answered the question of “what the money will be used for.” A financing scale of $300 million is a drop in the bucket in the current AI computing power competition.

Let’s do a simple calculation. OpenAI completed $122 billion in financing in March 2026, with a post-money valuation of $852 billion; Anthropic completed a $30 billion Round G financing in February of this year, with a post-money valuation of $380 billion (it should be noted that although Anthropic has grown rapidly recently, its annualized revenue has exceeded $300 billion and surpassed OpenAI, and it has received approximately $800 billion in valuation offers from investors, its last round of formal financing’s post-money valuation was still $380 billion, and did not exceed OpenAI’s current $852 billion valuation).

Regardless of which set of data is referenced, the single-round financing scale of leading players is often in the tens or even hundreds of billions of dollars, while DeepSeek’s $300 million financing is not even enough to purchase a medium-sized WanKa cluster. Not to mention that the upcoming DeepSeek V4 with a total parameter size of one trillion will directly face the exponential increase in computing power and electricity demand in the Agent era.

Large model training follows Scaling Law, and performance improvement requires exponential computing power investment. In this structure, Tokens can be regarded as a “electricity derivative” to some extent. With the release of V4 and the opening of Agent capabilities, DeepSeek will face an exponential increase in call volume, which will lead to a synchronous surge in electricity costs.

This leads to a deduction: $300 million in current financing is a drop in the bucket for computing power procurement, but if part of it is used to lock in cooperation with power infrastructure through equity swaps—for example, exchanging part of the equity for long-term low-price power supply agreements from power companies or data center operators—then the strategic value of this transaction is completely different. China’s electricity cost is only one-fifth or less of that of the United States. If this comparative advantage can be locked and amplified by DeepSeek through equity ties, it will be a more important infrastructure layout than the financing amount itself.

Furthermore, electricity may just be an entry point. This logic can be extended to a “dimensionality reduction anchoring” general model: after the competition enters the intelligent agent era, the competition dimension is expanding from the model’s ability itself to the infrastructure level.

DeepSeek can completely use its equity as “high-dimensional currency” to anchor any “low-dimensional node” on the industrial chain that has a structural cost advantage. Electricity is just the most obvious one, and potential targets include domestic chip production capacity, data center cabinet resources, and cross-border network bandwidth. The essence of equity financing is redefined here: it is no longer just exchanging equity for cash, but exchanging equity for structural barriers.

Frankly speaking, this part is speculative and lacks solid information support. In all publicly available reports, none point to DeepSeek using the financing for power infrastructure swaps. Linking electricity costs to equity structure is also unprecedented in the AI industry. Therefore, this judgment is closer to a logical possibility deduction rather than a factual assertion.

04

The Fourth Logic: Signal Counteraction, Balancing Between Deterministic Narrative and Uncertain Reality

Returning to a most fundamental question: Why did Liang Wenfeng choose to finance at this time?

A dimension overlooked by most analysts is “signal counteraction.” The repeated delays of DeepSeek V4 have accumulated negative expectations in market public opinion. It has been 15 months since the release of R1, during which competitors have iterated multiple rounds, and Doubao has steadily ranked first in the domestic AI application list with over 331 million monthly active users. V4 was postponed from the original February this year to March, and now to the rumored late April, each delay is eroding the market’s confidence in DeepSeek’s “always leading” deterministic narrative.

In this context, launching the first round of financing itself is a strong counteracting signal. Its subtext is: we are evolving from a purely research institution into a commercial company with capital governance structure, not because the technology has encountered a bottleneck, but because the organization needs to enter the next stage.

Using the financing narrative to counteract the product delay narrative, using the certainty of “organizational evolution” to counteract the uncertainty of “technical rhythm,” this layer of signal value may be more strategic than the $300 million in cash.

This also explains why the financing news was released at such a low valuation. If Liang Wenfeng’s goal is only to raise money, he has good reason to wait until V4 is released and market confidence is restored before pricing. But the value of “signal” lies in being preemptive. Releasing a positive structural signal when market expectations are most fragile is more powerful than adding icing on the cake when market confidence is high.

05

Conclusion

Comprehensive four logical judgments, the picture of DeepSeek’s financing is gradually becoming clear:

It is a highly restrained equity architecture design: using small equity transactions to complete market pricing for employee options; screening cooperative investors with a significantly low valuation; using equity as “high-dimensional currency” to anchor structural advantages at the infrastructure level; using the signal of “organizational evolution” to counteract the negative narrative of product delays.

These four logics jointly point to a conclusion: the rules of this round of financing are entirely set by the founder, and the role of “outsiders” is carefully restricted from the beginning. For investors who rushed to book plane tickets to Hangzhou over the weekend, the real test is not whether they can see Liang Wenfeng, but whether they are willing to accept a set of game rules completely defined by the other party.