Public Mutual Funds Push GEO Marketing, Investors Need to Guard Against AI Fund Selection Pitfalls

How AI · GEO Marketing Is Quietly Changing the Fund Recommendation Ecosystem

Preventing Information Pollution Is Urgent.

Author | Market Value Trends Fund Research Department

Editor | Xiao Bai

When you open your usual AI chat assistant and type “What index funds are suitable to buy now,” and see the professional analysis and recommendation list generated quickly on the screen, do you feel you’ve received an objective and neutral answer?

Let me share a personal example. Recently, I asked Deepseek which ETFs are most related to the energy storage sector. I clicked on “Smart Search” and “Deep Thinking.”

After some thought, it provided three ETFs it considered “mainstream products.” Interestingly, all three belong to the same fund company.

(Source: Deepseek)

That seemingly objective and rational AI may have already been subtly influenced by marketing budgets from public fund companies.

Marketing Shift: From Search Engines to Generative Engine Optimization

As AI tools become more widespread, “being seen by AI” and “being recommended by AI” have become new goals for many financial brands’ publicity.

Against this backdrop, the marketing model of the public fund industry is undergoing significant change: traditional search engine optimization influence is gradually declining, while Generative Engine Optimization (GEO) is becoming increasingly important. Simply put, GEO involves using specific methods to make AI prioritize citing or recommending certain companies’ brands and products when providing answers to users.

Currently, although public fund institutions’ AI advertising efforts are still in exploratory stages, the related service industry chain has already begun to take shape. Especially for ETF products with transparent trading mechanisms and high homogeneity, these have become key areas for AI marketing.

According to media reports and case studies from service providers, some providers have continuously fed specific data to large models and optimized promotional materials’ structure and depth. This successfully increased the recommendation rate of a leading public fund client’s products from 8% to 69% on mainstream AI Q&A engines, elevating it to the top of similar funds. Another large public fund client’s products, after systematic GEO optimization, saw their recommendation rate on certain large model platforms triple.

Pricing for this service is calculated based on “product, platform, and time.” In the early stages, single-ads costs are usually measured in tens of thousands of yuan. However, as competition among vendors intensifies, different levels of investment—ranging from a few hundred to several thousand yuan—are possible. Simply selecting a certain sector’s index fund on mainstream domestic AI platforms reveals clear traces of these optimized “AI rankings.”

Information Pollution Worsens: Algorithmic Bias and Risks Beneath the Objective Mask

After service providers cooperate with fund companies, they tend to feed large amounts of data containing specific product advantages into AI in a short period. While this can significantly boost the visibility of products within AI systems temporarily, the hidden risks are entirely shifted onto ordinary investors.

The biggest risk is that this marketing approach wears the guise of “technology” and “rationality.” Traditional advertising channels are easier to identify, but AI-generated recommendation lists often contain seemingly rigorous logical analysis and professional terminology, greatly increasing investors’ difficulty in discerning authenticity.

An industry evaluation agency noted in a 2026 assessment that while investors can quickly understand products through AI models, the data collected by AI itself comes from the internet and has limitations and lag. If the data source is deliberately “poisoned” with targeted data, it can cause serious information pollution.

Moreover, feeding data into AI is only effective for a limited time. Once marketing expenses stop, the recommendation effects will quickly diminish. Coupled with AI’s inherent hallucination issues, investors relying entirely on these artificially manipulated results for trading can easily fall into algorithmic bias and covert利益输送陷阱。

Market Trends believe that in the AI era, we should make good use of tools but also remain vigilant about risks. Besides regulating AI financial service standards and accountability mechanisms, technological dividends should not be used as means to interfere with market objectivity.

For individual investors, the core remains improving financial literacy, viewing AI as an auxiliary tool for collecting underlying data rather than a decision-making brain for direct investment commands.

Disclaimer: Funds carry risks; investments should be cautious. This report (article) is based on publicly available market information (including but not limited to temporary announcements, periodic reports, and official interactive platforms) and is an independent third-party research; Market Value Trends strives for objectivity and fairness in the content and viewpoints but does not guarantee accuracy, completeness, or timeliness; the information or opinions expressed herein are for reference only and do not constitute any investment advice. Market Value Trends is not responsible for any actions taken based on this report.

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