Original ｜Odaily Planet Daily（@OdailyChina）

Author｜Golem（@web3_golem）

When LPs learn to use AI, the days of small private fund managers are becoming more difficult.

Two Dogs ( @ryansoon777 ) was once a general partner (GP) of a small offshore private dollar fund focused on US stocks in China, but after a year he left to join an AI startup.

“Small private equity fundraising is already difficult, and with the popularity of AI, many investors (LPs) would rather let Doubao assist in stock trading than give us their money.”

Two Dogs stated that his decision to change careers was largely due to seeing the subtle impact of AI on the relationship between LPs and GPs. The information and analysis capabilities have been leveled by AI on the surface, making it easier for LPs to question GP's professional judgment, which may increase friction between the two parties and can lead to withdrawal of funds or expulsions in severe cases.

Small dollar private equity firms already facing tough times

The private dollar fund where Two Dogs previously worked was not doing poorly in terms of actual operations, with assets under management reaching tens of millions of dollars, mainly investing in highly liquid US stocks, and also involving a small amount of crypto asset management. Its annualized returns have far exceeded the NASDAQ in the past three years.

Logically, with quality performance and the increased demand from investors for overseas financial management in recent years, fundraising should not be very difficult; however, Two Dogs revealed that, in fact, it is nearly impossible for small dollar funds like theirs to gain the favor of institutional LPs.

Currently, the leading hundred-billion dollar private equity funds in China (such as Jinglin, Hillhouse, and Boyu) mainly use the “offshore + onshore” combination structure, where the fund entity remains in the Cayman Islands, often registered as a Cayman exempt company or Cayman SPC, while the management entity is based in Hong Kong or Singapore.

However, in recent years, due to regulatory and fundraising environment changes, there are an increasing number of private dollar funds that purely use Hong Kong LPFs or Singapore VCCs for their onshore structure.

The small dollar private equity funds that Two Dogs joined still use the most “primitive” dollar fund structure, which is 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 the leading dollar private equity funds in China still cling to the “Cayman” structure is that their overseas LPs include university endowment funds in the US, Middle Eastern sovereign wealth funds, and large European family offices, which have been familiar with the Cayman structure for decades. Continuing to use this rule is beneficial for reducing communication and trust costs between them.

However, domestic small dollar private equity funds that also have their entity in the Cayman Islands cannot attract these international top funds, and their LP sources remain mainly in Asia, putting them in an awkward position.

From the Asian perspective, the financial backers of dollar private equity funds come mainly from private banking, mainland China (overseas funds), local family offices in Hong Kong, and wealthy individuals in Southeast Asia.

Even for small dollar private equity funds of the same scale, these circles have a natural affinity and sense of security for Hong Kong or Singapore, so they are more willing to invest in Hong Kong LPFs or Singapore VCCs rather than Cayman SPCs.

In addition to the fund structure and scale restricting the fundraising channels of these small dollar private equity funds, differences in fund investment strategies also make it difficult for Two Dogs and their peers to raise funds.

The investment strategies adopted by private equity funds can mainly be divided into subjective strategies and quantitative strategies. Subjective strategies are decided by GPs based on their own research, experience, and judgment on what to buy and sell, with the core of profit being the fund manager's ability to understand the market; quantitative strategies write investment logic into mathematical models and programs, executing high-frequency trades automatically or semi-automatically, with the core of profit being the statistical laws used in the model.

“Currently, funds using quantitative strategies are raising money more easily than those using subjective strategies, particularly with the empowerment of AI, LPs are more confident in quantitative approaches.” Two Dogs noted that especially after DeepSeek (Odaily Note: Incubated by the quantitative fund Huansuan's quantitative team) gained popularity last year, the market's enthusiasm for quantitative strategies has increased.

Moreover, the difference between quantitative funds and subjective strategy funds is that quantitative strategies can showcase data and algorithms for trust among LPs, regardless of whether the fund is profiting or experiencing drawdowns, it stays within a controllable range. Excellent quantitative strategies can even be positioned as fixed-income products; subjective strategies are more abstract, requiring GPs to invest more communication costs to fully gain LPs' trust, especially when facing significant drawdowns, LPs can easily question GPs' investment capabilities.

Therefore, in summary, the survival space for the small dollar private equity funds that Two Dogs previously worked for has been compressed by the larger environment, making fundraising increasingly challenging. Moreover, the few remaining large LPs within the fund are questioning whether AI's “investment capability” is far superior to that of GPs?

The “complex composition” of LPs

“In the past, LPs listened to us basically because we had a professional background, but now they tend to throw our reports to AI for simplified translation and then come back to ‘teach’ us how to do it,” Two Dogs said that since the popularity of AI, the past LPs who only cared about the final results have significantly increased their “concern” about his investment operations.

Two Dogs even once expelled an LP because of this. This LP was a 50-year-old entity business owner, and very “tasteful.” He invested about $1 million into the fund where Two Dogs was working at the time, but he did not leave it unattended; instead, he often disputed with Two Dogs using fragmented information seen in the market and conclusions drawn from AI, “His attitude was very poor, and he believed I, this young guy, knew nothing. Trust couldn't be established, so in the end, we expelled him after coordination.”

“To be honest, our LPs are all very excellent people in their respective fields; they are authorities in their domains, but now with AI as a helper, they also believe they have authority in investment,” Two Dogs sighed.

The LPs of small dollar private equity funds are mostly derived from friends or referrals of the boss, which makes them “complex in composition.” According to Two Dogs, the LPs in their fund include high-net-worth individuals, entity business owners, and FOFs (fund of funds), “Our LPs include Shanxi coal bosses, wealthy individuals ranked three or four hundred on the Forbes list, and some LPs are even second-generation individuals who are well-connected with us, so they introduce their fathers to us.”

The relationship they have with LPs is also quite delicate; for some LPs, they may not even charge a 2% management fee, only taking a 20% performance share. The biggest feature of this type of LP structure is that there is enthusiasm for participating in financial markets and “capital outflow,” but they do not have the time or energy to quickly learn and study market trends.

Therefore, in a sense, the core value of GPs is to take on the work of information collection, market research, opportunity filtering, and investment judgment for LPs, compensating for their shortcomings in time, energy, and cognition and thus completing the process from information to decision-making.

However, with the popularity of AI tools, the previously high reliance on professional institutions' information processing and research capabilities is rapidly being leveled. Except for the final capital allocation and trade execution links, many tasks within the traditional functions of GPs have begun to be replaced by AI in a more cost-effective and efficient manner.

“Our LPs can easily open an IBKR brokerage account. With AI assistance, they can totally buy industries or targets they like themselves.” Two Dogs believes that AI poses a particularly significant impact on funds that take subjective strategies because investment is always outcome-oriented. If LPs hit the right trend and their personal investment returns exceed the fund's performance, they will naturally begin to question the fund's strength.

In comparison, the impact of AI's “information leveling” on quantitative private equity funds is relatively smaller and may even widen the gap between funds.

The parameters and algorithms in quantitative fund strategies will constantly iterate, and the addition of AI makes the iteration speed of quantitative strategies even faster. This is a field that competes in efficiency and intelligence, and ordinary people without specialized knowledge in mathematics or finance cannot compare their AI-constructed quantitative strategies with those of large quantitative funds.

“The essence of quantitative strategies is to 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 by most smart people,” Two Dogs said this is also the advantage of top quantitative funds.

Will AI replace GPs?

However, Two Dogs is not anxious that AI will completely replace GPs or analysts because AI is always neutral and available to everyone; it is a lever that GPs can use to improve their knowledge systems and investment strategies, creating more returns for LPs. What truly frustrates Two Dogs is that AI has increased the friction between GPs and LPs.

“Some LPs even question you why you didn’t invest in the currently popular assets, analyzing with great detail, and they don’t understand that GPs don’t just follow trends,” Two Dogs feels somewhat speechless about this phenomenon, especially since this year the AI and semiconductor sectors in the US stock market have become hot areas where retail investors can achieve excess returns just by betting on leading stocks.

In a bull market, retail investors’ returns can indeed easily surpass those of funds; firstly, personal investment is more flexible, more forgiving, and capital is more concentrated; secondly, under AI-assisted research, the research efficiency of retail investors is indeed greatly improved, almost as if having an all-round expert on standby 24 hours.

Especially in this year's US stock market, if retail investors hit popular storage stocks like SanDisk, Micron, and SK Hynix, their investment returns might exceed most funds, “At this point, LPs might either consider putting more into their own accounts and less into the fund or possibly withdraw from subjective private equity funds directly,” Two Dogs noted that in a bull market, everyone often thinks they are possessed by the ‘stock god’.

But all of this depends on retail investors being able to use AI correctly. If they use low-quality AI, then the results will be subpar. Two Dogs said this is also the biggest reason for the friction with LPs. "The high-net-worth individuals in China mostly use companion-style dialogue AIs like Doubao, while stronger analytical tools like ChatGPT and Claude have not yet become widespread. Companion AIs, in order to provide users with emotional value, often produce machine hallucinations in professional fields.”

Essentially, the problem lies not in the high or low capabilities of AI but in the fact that most people do not truly understand how to use AI. AI can integrate vast amounts of information in seconds and construct a logically consistent analytical framework, but logical consistency does not equate to factual accuracy. 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 models.

Therefore, most investors are not so much seeking analysis from AI as seeking affirmation from it. The ultimate goal of AI is not to assist investors in “seeking truth from falsehood,” but to complete dialogues.

So, will AI replace GPs? AI can cost-effectively generate ten thousand logically cohesive investment research reports, but asset management is essentially an “ancient service industry” based on trust and mental delegation. The relationship between GPs and LPs is also a process of mutual selection.

However, as any “business” will eventually be handed over to AI for execution to maximize “results,” “human private equity” should also learn from AI, further cultivating the provision of emotional value.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。