Private Placement "Detroit Becomes Human" Moment: When AI Takes Over Alpha, What's Left for Human Fund Managers?

Author: Yuan Chuan Investment Commentary

Anthropic’s latest unemployment report sends chills down the spine of financial professionals.

The report states that the replacement rate for financial jobs is as high as 94%, ranking second among all professions, but the current actual replacement rate is only 28%, leaving huge room for growth. Fortunately, 30% of jobs are almost unaffected, so financial practitioners can consider re-employment opportunities like dishwashers or plumbers.

After working in the industry for a long time, there’s always a sense of anxiety—financial professionals live in a world of constant comparison, with sales evaluations and performance rankings pressing down daily. As long as they don’t keep learning, they feel uneasy.

It’s like after the Spring Festival holiday, returning to the office and still chatting with Chatbot, while the colleague next to you has already raised 8 lobsters and is passionately arguing about crude oil price fluctuations.

The financial industry has never rejected efficiency—shifting from manual order entry to algorithmic trading, from offline bank sales to online distribution—all these are driven by the same goal. But this time, AI isn’t replacing inefficient financial tools; it’s replacing the inefficient people behind those tools. After all, the highest cost in finance is human resources. Behind asset management firms’ profits, the key is how to manage more money with fewer people.

Thus, private equity firms are beginning to embrace advanced productivity: Diewei Asset offers online courses teaching how to tame 24/7 self-service “digital researchers”; Mingxi Capital uses Manus to automatically generate promotional materials for dividend increases, with layouts rivaling high-end magazine aesthetics. Even clients have become more cautious—after a financial advisor just recommended a popular private fund, they immediately ask whether they should buy it through Doubao.

The private equity industry is gradually entering a “Detroit: Become Human” moment, where every link in the mature chain—research, operations, sales—is beginning to be replaced.

Salary VS Token Cost

In a competitive environment with high operating costs and increasing difficulty in alpha generation, efficiency per person is a key metric private equity bosses obsess over before sleep.

In the private equity industry chain, researchers’ salaries are generally high. According to MuliCube data, quantitative stock researchers earn between 800,000 and 1.5 million yuan annually. Subjective research analysts earn slightly less, but sometimes with shocking incentives—early this year, a top subjective researcher managing hundreds of billions of yuan received over 20 million yuan in year-end bonuses for recommending Nvidia.

If private equity can rely on AI for research and investment, saving millions in costs, and if AI can work 24 hours, reducing hourly wages while increasing output, then travel, overtime, transportation, and meal allowances—money that would normally come out of the boss’s pocket as carry—are all unnecessary for AI.

In asset management, all technological progress boils down to two words: increasing efficiency and reducing costs. Private equity bosses don’t care whether AI can truly think like humans; they only care if the work gets done.

Howard Marks calculated that if an analysis can be produced by an assistant earning $200,000 a year, then for the payer, whether it’s real thinking or pattern matching doesn’t matter—what matters is whether the work output is reliable enough to be useful.

After the Spring Festival, eight securities firms’ quantitative teams collectively released tutorials on “Lobster Farming,” accelerating the process of replacing human researchers. They personally tested OpenClaw, which can proactively produce research results like humans.

On the app, a presentation titled “OpenClaw: From Beginner to Master” was played 4,839 times; Northeast-based Xu Jianhua promoted 20 skills that can boost research efficiency tenfold; Fangzheng Cao Chunxiao used lobsters to reproduce PB-ROE strategies, cup-and-handle stock selection strategies, and fully automated factor mining and backtesting.

Thinking about it, this is equivalent to OTA (over-the-air update) of Buffett, O’Neil, and Simmons’ skill sets simultaneously.

Eager Learners in Trading

Sell-side firms are actively popularizing, and buy-side firms are also learning enthusiastically. A private equity firm in Beijing, concerned about contamination of their mainframe, provided each researcher with a new computer and a 50,000 yuan token subsidy specifically for lobster farming.

Yang Xinbin from Snowball Asset Management has trained two lobster researchers. He says that daily conversations with AI are much more frequent than with humans. The AI agents they develop can do in two days what a mature quant researcher might take half a year to accomplish, with even greater potential.

Paul Wu from Qinyuan Investment is gradually integrating AI into various departments. He feels that AI can complete closed-loop tasks and operate independently. He foresees that soon, the company’s expenses will be spent on purchasing and maintaining an Apple analyst AI, and later perhaps a portfolio advisor named Paul.

In the past, many private equity firms experienced friction in research transformation—researchers think fund managers are ineffective, and fund managers think researchers are useless. The emergence of OpenClaw offers private equity bosses a new possibility—no more endless friction with mediocre researchers, and no worries about core researchers being poached by competitors with high salaries.

Lobsters meet all the idealized qualities fund managers imagine in researchers: working around the clock, no vacations or loafing; long-term memory retention, key data at their fingertips; absolute loyalty and obedience, no side strategies; continuous self-iteration, unlike veteran researchers who get stuck in path dependence and are eventually phased out.

If future silicon-based tokens cost far less than carbon-based salaries, how can private equity bosses refuse a well-behaved, trainable, and developable AI researcher?

More Than Just Lobsters

Private equity firms are still weighing whether token costs are worthwhile. Large quant firms, with their own infrastructure, have already driven token costs down to very low levels. But they remain surprisingly calm about this trend.

“OpenClaw is just a semi-finished toy for the quant tech community,” a top quant expert in Shanghai told me. Its significance lies in lowering technical barriers for subjective institutions and retail investors, and providing a clear cost recovery path for large model companies’ initial infrastructure investments. But for serious production environments like quant investing, it’s of little practical significance.

Another leading quant expert bluntly said that lobsters in finance are like a pyramid scheme. OpenClaw has randomness, non-systematic features, and low security, which could introduce huge uncertainties into the entire quant system.

In the quant community, OpenClaw isn’t considered advanced productivity. Cui Yuchun from Xuntu Technology believes there’s no need to worry:

Lobsters’ capabilities in agent optimization and tool invocation (including research browsers, writing, data analysis tools) are even weaker than Manus or Kimi. For a researcher without programming background, deploying and launching takes 5-10 hours, and most tasks can’t achieve more than 60 points.

When retail investors use China Stock Analysis Skill to select stocks, it feels like opening a new world. Quant firms have built multi-agent platforms, with richer agent libraries, crushing lobsters. Yet, this powerful system may not require more human input.

Traditional quant research systems usually follow a pipeline: data cleaning → factor calculation → model prediction → portfolio optimization. In the AI era, some institutions, like top overseas quant firms such as Man Group, are simplifying roles into: role division → tool invocation → workflow design. Standardized, repetitive tasks are increasingly replaced by AI agents, reducing the need for many researchers in the factor sweatshop.

For example, Xiyue Investment’s Apollo AI multi-agent system embeds AI agents into research, data, trading, and operations. Founder Zhou Xin describes it as having the equivalent of 700-800 AI employees.

With the initial wave of “factory-like” quant automation and retail investors leveraging OpenClaw to reduce information gaps, the situation of traditional fund managers caught in the middle—watching research outputs from their teams being diminished by quant models and pressured by retail investors—becomes awkward. They face AI FOMO and anxiety.

During the Spring Festival, I reviewed an annual report from a leading private fund manager in Shenzhen. He lamented that fund managers have overly high expectations for researchers:

They want researchers to be sensitive to the market, timely identify opportunities, provide research and judgments ahead of peers, and even stay constantly in the “core circle.” If a researcher can do all that, why do they still need a fund manager? They could just trade on their own and get rich, so why serve a fund manager?

So, he lowered expectations—researchers only need to focus on specific targets and issues, without needing to discover opportunities or give investment advice. Those are the fund manager’s responsibilities.

Conversely, if a subjective fund manager only needs someone who doesn’t enter the core industry circle and relies solely on desk analysis to track targets, then such a researcher might soon be replaced by an AI agent.

Epilogue

Living in the A-share market these past two years feels like being on an acceleration button.

Especially in the first half of this year, so many things happened. Last year’s Spring Festival, DeepSeek’s release; Qingming holiday’s tax hikes; this year’s Spring Festival’s lobster farming craze; and before the holiday even ended, Middle East conflicts erupted. Financial minds have been overloaded, and I can’t remember the last holiday without learning.

At least as an editor, my brain’s computing power is no longer enough.

I recall that two years ago, when communicating with fund managers, they often happily described their work as “dancing tap shoes to work.” But in the past two years, they talk about “iteration” of team organization, investment philosophy, and industry understanding—without smiles.

AI develops so fast, industry progress is rapid, and it seems that only through iteration can one avoid being eliminated.

The industry remains too anxious.

AI doesn’t understand human nature; it can’t predict whether the current trading in the crowded A-share market is driven by third-order or fifth-order derivatives; it can’t empathize, unable to grasp why someone has held onto two oil giants for so many years just waiting for a breakout; it can’t take responsibility—won’t be blamed by investors for a 30% loss, nor does it need to write apology letters or reflect on its soul.

If in the future AI replaces all fund managers and researchers, then the efficient market hypothesis holds—there will be no Alpha, and perhaps no next Buffett.

So the real question is: in the future asset management industry, when AI takes over data scraping, modeling, and report writing, what is left for humans? What remains is precisely the love for investing, intuition about uncertainty, and the reasons for staying—despite being scolded that research is no better than AI.

We can’t stop the trend of AI’s increasing role, but we can change our mindset from exhaustion and frantic chasing to acceptance.

Like in the game “Detroit: Become Human,” the ultimate choice isn’t to eliminate AI or submit to it, but to decide what roles humans and AI should each play.