Meitu's AI integration supports paying revenue and ARPU improvement

Overview

This report is Goldman Sachs' Meitu conference summary released after the Asia Communacopia + Technology conference in Hong Kong on May 18, 2026. The core discussion centers on ARPU growth in productivity and leisure businesses, opportunities and threats brought by AI large model integration, and the potential improvement in the company's cost structure from self-developed vertical models.

Core views

The report's core view is positive: AI large models are supporting Meitu's productivity tools to offer more advanced features and, through subscription growth and token consumption, increase ARPU; while ToC leisure applications are constrained by long-term consumer habits and paid conversion is unlikely to jump sharply, the company can gradually improve monetization and usage efficiency through more practical features and an AI recommendation engine; in the long run, in-house vertical small models may reduce reliance on third-party AI APIs and lower costs.

Analysis framework

The analysis is primarily based on management feedback from the conference, combined with Goldman Sachs' valuation framework and rating system for Meitu. The report breaks down business drivers into productivity tool ARPU, ToC paid conversion, AI recommendation features, lower third-party API costs, and in-house vertical model development, and uses a two-stage DCF to derive the 12-month target price.

Methodology notes

• Valuation methodsTwo-stage DCF

  12-month target price

  Goldman Sachs uses a two-stage DCF to estimate Meitu's 12-month target price of HK$14.3, assuming a WACC of 11.5% and a terminal growth rate of 2%.

• Stock ratingGoldman Sachs Buy/Neutral/Sell rating system

  Total return potential relative to the coverage universe

  Goldman Sachs' Buy rating indicates that the stock has higher total return potential relative to its coverage universe; the price target typically covers the target price and potential dividends over the relevant time horizon.

• Factor analysisGS Factor Profile

  Growth, financial return, valuation multiples, and composite percentile

  GS Factor Profile compares stocks with the market and peers through growth, financial return, valuation multiples, and composite indicators, but this report does not disclose Meitu's specific factor percentiles.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• Meitu (1357.HK)

  Covered name in the report; Hong Kong-listed company

  Strengths

  AI features can improve ARPU for productivity tools; vertical scenario demand is clear; management emphasized its understanding advantages in user needs and application scenarios; in-house vertical models may lower long-term API costs.

  Weaknesses

  ToC paid conversion is constrained by long-term consumer habits, and management does not expect a step-change increase in the near term; monetization of AI features and cost reduction from self-developed models still depend on execution.

  Comparison

  Management believes Meitu has differentiated strength in understanding vertical use cases and is closer than general large AI models to specific image, video, and leisure application needs.

  Risks

  Slower-than-expected AI adoption and monetization, lower-than-expected paid conversion, and stronger-than-expected competition.

Key data

• Report date2026-05-18The report was published at 2:58 p.m. Hong Kong time.
• Target priceHK$14.312-month target price derived from a two-stage DCF.
• RatingBuyGoldman Sachs indicates a Buy rating on Meitu.
• WACC11.5%DCF valuation assumption.
• Terminal growth rate2% YoYDCF valuation assumption.
• Disclosure priceHK$4.72Meitu's price listed in company-specific regulatory disclosure.
• Conference locationHong KongAsia Communacopia + Technology conference.

Impact & implications

If management's views on AI feature monetization and cost reduction are realized, Meitu's revenue quality could shift from being driven purely by user scale to being driven by feature depth, subscription conversion, and token usage. If in-house vertical models can replace part of third-party APIs, gross margin and product controllability should improve; however, if AI adoption, paid conversion, or the competitive environment falls short of expectations, the positive logic behind the target price and Buy rating would be challenged.

Risks

• Slower-than-expected AI adoption and commercialization.
• Lower-than-expected paid conversion, especially as ToC leisure applications are influenced by long-term consumer habits.
• Stronger-than-expected competition, including competition from general large models and other image and video tools.
• Progress in training in-house vertical AI models and replacing third-party APIs may fall short of expectations, potentially limiting cost declines.

What to watch

• Subscriber growth and ARPU changes in productivity tools.
• Whether token consumption growth truly translates into higher revenue.
• Paid conversion after the launch of new features and the AI recommendation engine in ToC leisure applications.
• Progress in training in-house vertical AI small models and the extent to which they replace third-party API costs.
• Competitive intensity in the AI image and video content tools market.