Original | Odaily Planet Daily (@OdailyChina)
Author | Golem (@web3_golem)
When LPs learn to use AI, the days for small private equity fund managers are becoming even tougher.
Ergou (@ryansoon777) was an ordinary partner (GP) at a small offshore private US dollar fund in China that primarily focused on US stocks until before the Chinese New Year, but after the holiday, he resigned and joined an AI startup.
"Raising capital for small private equity funds is already difficult nowadays, and with the proliferation of AI, many investors (LPs) would rather use Doubao to assist in stock trading than entrust their money to us."
Ergou stated that his decision to switch careers was largely due to observing the subtle impact of AI on the relationship between LPs and GPs. Information and analytical capabilities appear to be leveled by AI, making it easier for LPs to question the professional judgment of GPs. Friction between the two parties may also increase, sometimes even leading to withdrawals or liquidations.
The Already Tough Days for Small US Dollar Private Equity Funds
The private US dollar fund where Ergou previously worked was actually performing quite well, with assets under management reaching tens of millions of dollars. It mainly invested in highly liquid US stocks and was also involved in a small amount of crypto asset management. Its annualized returns over the past three years far exceeded those of the Nasdaq.
In theory, with solid performance and the increased demand for overseas wealth management among investors in recent years, raising capital shouldn't be too difficult. However, Ergou revealed that for small US dollar funds like theirs, it's nearly impossible to attract institutional LPs.
Currently, top domestic billion-dollar private equity funds (such as Hillhouse, Gao Lin, and Bo Yu, etc.) generally adopt a "offshore + onshore" combined structure. That is, the fund entity remains in the Cayman Islands, often registered as a Cayman Islands Exempted Company or a Cayman SPC, while the management entity is based in Hong Kong or Singapore.
In recent years, however, due to changes in regulation and the fundraising environment, there has been an increase in private US dollar funds adopting purely onshore structures like Hong Kong LPF or Singapore VCC.
Yet, the small US dollar private equity fund that Ergou joined still uses the most "primitive" US dollar fund structure: the Cayman SPC + BVI (British Virgin Islands) fund manager structure.
A common saying in the fund industry is that LPs determine the structure. One reason why top domestic US dollar private equity funds still cling to "Cayman" is that their overseas LPs include US university endowments, Middle Eastern sovereign wealth funds, and large European family offices. These international top-tier "old money" have been familiar with the Cayman structure for decades. Continuing to adhere to this convention helps top-tier US dollar private equity funds reduce communication and trust costs with these LPs.
However, small domestic private US dollar funds with entities also in the Cayman Islands cannot attract the favor of these international top-tier funds. Their LP sources remain primarily in Asia, placing them in an awkward position.
From an Asian perspective, the primary backers behind US dollar private equity funds come from private banks, mainland China (offshore capital), Hong Kong local family offices, and Southeast Asian tycoons, among others.
Even for small US dollar private equity funds of similar scale, these circles naturally feel closer to and safer with Hong Kong or Singapore. Therefore, they are more willing to invest in Hong Kong LPF or Singapore VCC rather than Cayman SPC.
In addition to fund structure and scale limiting the fundraising channels for such small US dollar private equity funds, differences in investment strategies also make it difficult for Ergou and his colleagues to raise capital.
Among the investment strategies employed by private equity funds, they are mainly divided into discretionary strategies and quantitative strategies. Discretionary strategies involve GPs deciding what to buy and sell based on their own research, experience, and judgment; the core of profitability lies in the fund manager's understanding of the market. Quantitative strategies involve writing investment logic into mathematical models and programs, which are then executed automatically or semi-automatically at high frequency; the core of profitability lies in the statistical patterns the model exploits.
"Currently, funds employing quantitative strategies find it easier to raise capital than those using discretionary strategies, especially with the empowerment of AI. LPs now trust quantitative strategies even more", Ergou said, especially after DeepSeek (Odaily note: incubated by the quantitative fund team Huan Fang Quantitative) became popular last year, market enthusiasm for quantitative strategies has grown even higher.
Furthermore, the difference between quantitative funds and discretionary strategy funds lies in that quantitative strategies can build trust with LPs by showcasing data and algorithms. Whether the fund is profitable or experiences drawdowns, it's within a controllable range; excellent quantitative strategies can even serve as fixed-income products. Discretionary strategies are more abstract; GPs need to invest significantly more communication effort to gain the complete trust of LPs, especially when encountering substantial drawdowns, as LPs can easily question the GP's investment capability.
Therefore, in summary, the survival space for small US dollar private equity funds like the one Ergou previously worked for has been compressed by the macro environment, and fundraising has become increasingly difficult. Even the few remaining major LPs within the fund are questioning whether AI's "investment capability" far surpasses that of GPs?
The "Complex" LPs
"In the past, LPs, considering we were professionally trained, basically listened to us. But now, they throw our reports into AI to translate them into plain language, then come back and 'teach' us how to do it," Ergou said. After AI became widespread, the "concern" LPs, who previously only looked at the final results, showed towards his investment operations clearly increased.
Ergou even had to liquidate an LP because of this once. This was a 50-year-old owner of a physical enterprise with a strong sense of self-importance. He had invested about $1 million in the fund where Ergou worked, but he didn't simply hand over the reins. Instead, he often clashed with Ergou using fragmented information he saw in the market and conclusions drawn from AI. "His attitude was poor, and he believed that a young guy like me knew nothing. Trust couldn't be established, so ultimately, after coordination, we liquidated him."
"To be honest, our LPs are all very accomplished individuals in their respective fields. They are authorities in their own domains, but now, with AI as an assistant, they also believe they have become authorities in investing," Ergou lamented.
For small US dollar private equity funds, fundraising channels are inherently narrow, so LPs mostly come from the boss's friends or referrals from acquaintances, hence the "complex composition." According to Ergou, LPs in their fund included domestic high-net-worth individuals, owners of physical enterprises, and FOFs (Fund of Funds). "Among our LPs, there are Shanxi coal bosses, also individuals ranked around three or four hundred on the Forbes list, and some LPs were even introduced by their second-generation children who are friends with us."
Their relationship with LPs is also quite delicate. For some LPs, they might not even charge a 2% management fee, only taking a 20% performance fee. The main characteristic of this LP structure is that they have an enthusiasm for participating in financial markets and "moving capital offshore," but they themselves lack the time and energy to quickly learn and research market trends.
Therefore, in a sense, the core value of GPs lies in undertaking tasks such as information gathering, market research, opportunity screening, and investment judgment for LPs, using their professional capabilities to compensate for the latter's lack of time, energy, and cognition, thereby completing the transformation from information to decision-making.
However, with the proliferation of AI tools, this past heavy reliance on professional institutions' information processing and research capabilities is being rapidly leveled. Apart from the final capital allocation and trade execution stages, a large portion of the traditional functions of GPs has begun to be replaced by AI in a lower-cost and more efficient manner.
"It's not difficult for our LPs to open an IBKR brokerage account. With AI assistance, they can basically buy whatever industry or stock they like themselves." Ergou believes the impact of AI on funds employing discretionary strategies is particularly significant because investment is ultimately result-oriented. If an LP hits a trend and their personal investment returns surpass the fund's, they will naturally start questioning the fund's competence.
In contrast, the "information leveling" brought by AI has a smaller impact on quantitative private equity funds and might even widen the gap between funds.
The parameters and algorithms within quantitative fund strategies themselves are constantly iterated. The addition of AI makes the iteration speed of quantitative strategies even faster. This is a field competing on efficiency and intelligence. Quantitative strategies constructed by ordinary people using AI, without specialized knowledge in mathematics, finance, etc., absolutely cannot compete with those of large quantitative funds.
"Quantitative strategies essentially need to constantly stay ahead of market peers to achieve excess returns. If you think your ordinary AI has constructed a good strategy, perhaps it has already been discovered and iterated upon by most smart people," Ergou stated, highlighting the advantage of top quantitative funds.
Will AI Replace GPs?
However, Ergou isn't anxious that AI will completely replace GPs or analysts because AI is always neutral and accessible to everyone. It's a lever. GPs can use AI to improve their own knowledge systems and investment strategies, creating more returns for LPs. What truly annoys Ergou is that AI increases friction between GPs and LPs.
"Some LPs even question you, asking why you didn't invest in currently hot stocks, and they analyze it with apparent logic. They don't understand that GPs don't just invest in whatever is popular at the moment," Ergou finds this phenomenon somewhat absurd, especially after US AI and semiconductor stocks became a trend this year, allowing retail investors to achieve excess returns by betting on sector leaders.
In a bull market, retail investors' investment returns can indeed easily surpass those of funds. First, personal investment is more flexible, allows for more error tolerance, and capital is more focused. Second, with AI-assisted research, the research efficiency of retail investors is indeed greatly enhanced, equivalent to having an all-around expert available 24/7.
Especially in this year's US stock market, if retail investors bet correctly on hot memory stocks like SanDisk, Micron, Hynix, etc., their return on investment might exceed that of most funds. "At this point, LPs might consider allocating more to their own accounts and less to the fund, or potentially withdrawing from discretionary private equity funds altogether," Ergou said, as in bull markets, people often think they are possessed by the 'stock god.'
But all of this hinges on retail investors knowing how to use AI correctly. If poor-quality AI is used, it can lead to half the result with twice the effort. Ergou stated this is also the biggest reason for friction between him and LPs. "These domestic high-net-worth individuals primarily use conversational AI like Doubao for companionship, while more analytical AIs like ChatGPT, Claude, etc., are not yet widespread. Such companion AI, in order to provide emotional value to users, is highly prone to machine hallucinations in professional fields."
Essentially, the issue isn't about the high or low capability of AI, but rather that most people don't truly understand how to use it. AI can integrate vast amounts of information within seconds, constructing a logically self-consistent analytical framework, but logical self-consistency does not equate to alignment with facts. For LPs lacking a professional background, they often find it difficult to distinguish which conclusions are based on real data and which are merely probabilistic inferences generated by the model.
Therefore, most investors are not so much seeking analysis from AI as they are seeking validation from it. AI's ultimate goal is not to help investors "separate truth from falsehood" but to complete the conversation.
So, will AI replace GPs? AI can generate ten thousand logically coherent investment research reports at low cost, but the underlying nature of asset management is actually an "ancient service industry" based on trust and entrustment of judgment. The relationship between GPs and LPs is also a process of mutual selection.
It's just that in a future where any "task" will ultimately be handed over to AI for execution to maximize "results," "human private equity" should also learn from AI and further cultivate the provision of emotional value.
