When LP Uses Doubao to Teach Me Investing: A Private Equity GP’s Career Change Story

Original: Odaily Planet Daily (@OdailyChina)

Author: Golem (@web3_golem)

As LPs learn to use AI, life is getting tougher for small private fund managers.

Ergou (@ryansoon777) was a General Partner (GP) at a small offshore private USD fund focused on U.S. stocks before the Chinese New Year. However, shortly after the holiday, he quit and joined an AI startup.

“It was already difficult for small private funds to raise capital, and with the rise of AI, many LPs would rather use an AI assistant like Doubao to help them trade stocks than entrust their money to us.”

Ergou says his decision to switch careers was largely driven by the subtle impact of AI on the LP-GP relationship. On the surface, AI has leveled the playing field for information and analysis. LPs are finding it easier to challenge GPs' professional judgment, potentially increasing friction between them. In severe cases, this could even lead to capital withdrawal or fund liquidation.

Small USD Private Funds Already Facing Hard Times

The private USD fund Ergou previously worked for was not underperforming. It managed assets worth tens of millions of dollars, primarily investing in highly liquid U.S. stocks, with a small allocation to crypto assets. Its annualized returns over the past three years significantly exceeded the Nasdaq index.

Logically, with strong performance and increased demand from investors seeking overseas wealth management in the past two years, fundraising shouldn't be too difficult. However, Ergou revealed that it’s virtually impossible for small USD funds like theirs to attract institutional LPs.

Currently, top-tier Chinese private USD funds managing tens of billions of dollars (such as Jinglin, Hillhouse, and Boyu) typically use a “offshore + onshore” combined structure. The fund entity is registered offshore in the Cayman Islands, often as a Cayman Islands exempted company or Cayman SPC, while the management entity is based in Hong Kong or Singapore.

In recent years, however, due to changes in regulations and fundraising environments, more private USD funds are adopting purely onshore structures like Hong Kong LPF or Singapore VCC.

The small private USD fund Ergou joined still uses the most “primitive” USD fund structure: a Cayman SPC + BVI (British Virgin Islands) fund manager structure.

A common saying in the fund industry is that “LP determines the structure.” One reason top Chinese private USD funds stick with Cayman is that their overseas LPs include U.S. university endowments, Middle Eastern sovereign wealth funds, and large European family offices. These top-tier international “old money” investors have been familiar with the Cayman structure for decades. Continuing this tradition helps reduce communication and trust costs between them.

But small private USD funds from China, also based in the Cayman Islands, cannot attract these top international investors. Their LP base remains primarily in Asia, putting them in an awkward position.

From an Asian perspective, the capital behind these USD private funds primarily comes from private banks, mainland China (capital flowing overseas), Hong Kong-based family offices, and wealthy individuals in Southeast Asia.

Even for small private USD funds of similar size, these circles naturally feel a greater affinity and security towards Hong Kong or Singapore. Therefore, they prefer investing in Hong Kong LPF or Singapore VCC structures over Cayman SPC.

Besides the fund structure and size limiting fundraising channels for these small private USD funds, differences in investment strategies also made fundraising difficult for Ergou and his team.

Private fund investment strategies can be broadly divided into discretionary strategies and quantitative strategies. Discretionary strategies involve the GP deciding what to buy and sell based on their own research, experience, and judgment, with profitability hinging on the fund manager's understanding of the market. Quantitative strategies involve writing investment logic into mathematical models and programs, which execute trades automatically or semi-automatically at high frequency, with profitability relying on the statistical patterns identified by the model.

“Currently, funds using quantitative strategies find it easier to raise capital than those using discretionary strategies. Especially with AI empowerment, LPs have more confidence in quant strategies,” Ergou says, particularly after DeepSeek (Odaily note: incubated by quantitative fund High-Flyer’s team) went viral last year, market enthusiasm for quantitative strategies has surged even higher.

Furthermore, a key difference between quantitative funds and discretionary strategy funds is that quantitative strategies can demonstrate their data and algorithms to LPs to gain trust. Whether the fund is profiting or experiencing drawdowns, it remains within a controllable range. Top-performing quant funds can even be treated as fixed-income products. Discretionary strategies are more abstract, requiring GPs to spend more communication effort to gain full LP trust. Especially during significant drawdowns, LPs can easily question the GP's investment ability.

Therefore, given all this, the living space for small private USD funds like the one Ergou worked for in China has been compressed by the macro environment, making fundraising increasingly difficult. Even the few remaining large LPs in the fund are questioning whether AI's “investment ability” far surpasses that of the GP.

LPs with “Diverse Backgrounds”

“In the past, LPs would generally listen to us because of our professional background. But now, they throw our reports into AI to translate them into plain language, then come back to ‘teach’ us how to do things,” Ergou says. After the rise of AI, LPs who previously only cared about final results have shown significantly more “concern” for his investment decisions.

Ergou once even had to redeem one LP because of this. This LP was a 50-year-old factory owner, with a strong “uncle boss” demeanor. He had invested about $1 million in the fund Ergou was working for but didn't take a hands-off approach. He frequently argued with Ergou using fragmented information from the market and conclusions drawn from AI. “His attitude was terrible, and he thought I, a young guy, knew nothing. We couldn't build trust, so after some coordination, we redeemed him.”

“Honestly, our LPs are very successful people in their respective fields, authorities in their own domains. But now, with AI as their tool, they also believe they’ve become authorities in investing,” Ergou laments.

The LPs of small private USD funds often have “diverse backgrounds” because fundraising channels are inherently narrow, mostly relying on the boss's friends or referrals from acquaintances. According to Ergou, the LPs of his fund included high-net-worth individuals from mainland China, factory owners, and FOFs (Funds of Funds). “Our LPs include coal bosses from Shanxi, billionaires ranked 300-400 on the Forbes list, and even some second-generation rich kids who are friends with us and introduced their fathers.”

Their relationship with LPs is quite nuanced. For some LPs, the fund didn't even charge the standard 2% management fee, only taking a 20% performance fee. The biggest characteristic of this type of LP structure is an enthusiasm for participating in financial markets and “moving capital overseas,” but a lack of time and energy to quickly learn and research market trends.

Therefore, in a sense, the core value of a GP lies in undertaking tasks such as information gathering, market research, opportunity screening, and investment judgment on behalf of the LPs. They use their professional expertise to compensate for the LPs' deficiencies in time, energy, and cognition, thereby completing the transformation from information to decision-making.

However, with the proliferation of AI tools, this information processing and research capability, once highly dependent on professional institutions, is rapidly being democratized. Except for the final capital allocation and trade execution, a large portion of the traditional GP function’s work is being replaced by AI at a lower cost and higher efficiency.

“For our LPs, opening an IBKR brokerage account isn't hard. With AI assistance, they can just buy whatever industry or target they like on their own.” Ergou believes the impact of AI is particularly severe for funds using discretionary strategies because investing is ultimately results-oriented. If an LP catches a wave and achieves higher personal returns than the fund, they naturally begin to question the fund's ability.

In contrast, the “democratization of information” brought by AI has a much smaller impact on quantitative private funds, and could even widen the gap between them.

The parameters and algorithms within quantitative fund strategies constantly evolve, and AI accelerates iteration speed. This is a field where efficiency and intelligence compete. An ordinary person using AI to build a quantitative strategy, without specialized knowledge in mathematics or finance, can absolutely not compete with large quantitative funds.

“A quantitative strategy essentially needs to continuously stay ahead of market peers to generate excess returns. If you think your average AI has built a good strategy, it's likely already been discovered and iterated upon by most smart people,” Ergou notes. This is where the advantage of top-tier quantitative funds lies.

Will AI Replace GPs?

However, Ergou is not worried that AI will completely replace professions like GPs or analysts. AI is always neutral and accessible to everyone; it’s a lever. GPs can use AI to improve their knowledge systems and investment strategies, generating more returns for LPs. What truly bothers Ergou is that AI increases friction between GPs and LPs.

“Some LPs even question why we didn't invest in a currently hot target, and they analyze it quite convincingly. They don't understand that a GP doesn't just invest in whatever is trendy,” Ergou finds this phenomenon somewhat baffling, especially since this year the U.S. stock market has seen AI and semiconductors as major themes, allowing retail investors betting on leading sector stocks to achieve excess returns.

In a bull market, retail investor returns can indeed easily surpass those of funds. Firstly, personal investments are more flexible, fault-tolerant, and capital-concentrated. Secondly, with AI-assisted research, the research efficiency of retail investors is also greatly improved, akin to having a versatile expert on call 24/7.

Particularly in this year's U.S. stock market, if retail investors bet on hot storage stocks like SanDisk, Micron, or SK Hynix, their returns could exceed those of most funds. “At this point, LPs will either consider allocating more capital to their personal accounts and less to the fund, or they might directly withdraw from discretionary private funds,” Ergou says, noting that in a bull market, people often feel they are invincible “stock gods.”

But all this depends on retail investors using AI correctly. Using inferior AI can be counterproductive. Ergou says this is the biggest source of friction with his LPs. “These high-net-worth individuals in China primarily use companion-style AIs like Doubao, while more analytical tools like ChatGPT or Claude are not as widespread. This type of companion AI, in order to provide emotional value, is highly prone to hallucinations in professional fields.”

Essentially, the problem isn't the capability of AI, but that most people don't truly understand how to use it. AI can integrate vast amounts of information in seconds and build a logically coherent analytical framework. However, logical coherence doesn't equal factual accuracy. For LPs lacking professional backgrounds, it’s often difficult to distinguish conclusions based on real data from those that are merely probabilistic inferences generated by the model.

Therefore, most investors are seeking validation from AI, rather than analysis. The ultimate goal of AI isn't to help investors discern truth from falsehood, but to complete a conversation.

So, will AI replace GPs? AI can generate 10,000 logically sound investment research reports cheaply, but the essence of asset management is an “ancient service industry” based on trust and custodianship of mind. The GP-LP relationship is also a process of mutual selection.

However, in a future where all “tasks” will ultimately be executed by AI to maximize “results,” “human private funds” should also learn from AI and cultivate their ability to provide emotional value more effectively.