Analysis of the Impact of AI Giants' 'Burn to Grow' Strategy in India on Long-Term Profitability

AI giants are fiercely competing in the Indian market, using large-scale free strategies to quickly acquire a user base. Google’s partnership with Reliance Jio is particularly notable, offering its 500 million users an 18-month free Gemini AI Pro subscription worth approximately $399 [1]. OpenAI and Perplexity have also launched similar free plans, forming a comprehensive AI user battle.

Based on public data and industry analysis, the investment scale of major AI companies is staggering:

As can be seen from the chart, Google has the highest per-user investment at $399, targeting the largest user group (500 million), with an estimated total investment cost of approximately $20 billion. Microsoft, OpenAI and Perplexity although single-user input is lower, but the total investment also reached $7.2 billion, $2.4 billion and $1.2 billion respectively.

Google has strong financial strength to support this aggressive strategy [0]:

• Market Capitalization
  : 3.58 trillion USD
• Net Profit Margin
  : 32.23%
• Operating Profit Margin
  : 32.19%
• Cash Flow
  : Healthy, market cap/operating cash flow ratio is 23.71x
• Shareholder Return
  : 35%

In addition, Google has announced a $15 billion investment in building AI data centers in Andhra Pradesh, India, as part of a five-year investment plan [2], demonstrating its long-term commitment to the Indian market.

The conversion rate from free users to paid users is key to the success of this model. Historical data shows that most free users may not convert to paid users after the trial period ends, leading to lower-than-expected return on investment.

Microsoft announced a $17.5 billion investment in India during the same period, along with Google’s $15 billion investment [2], plus the entry of OpenAI and Perplexity, making market competition extremely fierce and potentially leading to further escalation of price wars.

The operating costs of AI services are extremely high, including:

• Computing resource costs
• Model training and update fees
• Data storage and transmission costs
• Localization adaptation costs

According to estimates, Google may need 24-30 months to recover its investment costs in the Indian market, not including ongoing operating expenses.

1. Market Lock-in Effect
   : Once users get used to a company’s AI services, the switching cost will be high
2. Data Advantage
   : A large amount of user data can help improve model quality
3. Ecosystem Integration
   : AI services can form synergies with other products
4. Brand Effect
   : Market leadership helps enhance overall brand value

1. Short-Term Profit Pressure
   : Large-scale free strategies will directly affect short-term profitability
2. Capital Consumption
   : Continuous capital investment is needed to maintain competitive advantages
3. Shareholder Return Pressure
   : Investors may be dissatisfied with long-term low returns
4. Opportunity Cost
   : Investing funds in the Indian market may affect investments in other high-return projects

From the history of internet development, the ‘free to grow’ model has been successfully applied in search, social media, e-commerce, and other fields. However, the特殊性 of AI services lies in:

• Higher marginal costs
  : The computing cost of AI services increases with usage
• Technology iteration speed
  : Rapid evolution of AI technology may devalue early investments
• Regulatory uncertainty
  : AI regulatory policies in various countries are still being formed

For companies like Google, investment strategies in the Indian AI market need to balance the following factors:

1. Gradual investment
   : Avoid one-time large-scale investment and adopt a phased investment strategy
2. Diversified monetization
   : Explore multiple monetization methods such as advertising and enterprise services in addition to subscription fees
3. Cost control
   : Reduce AI service costs through technological innovation
4. Regulatory compliance
   : Closely monitor changes in India’s AI regulatory policies

Although the ‘burn to grow’ strategy of AI giants in the Indian market has long-term value, it poses significant challenges to short-term profitability. Google, with its strong financial foundation [0] and market position, is better able to withstand the financial pressure of this strategy than companies like OpenAI and Perplexity. However, the ultimate key to success lies in the improvement of user conversion rates and operational efficiency.

If these companies can achieve a high paid conversion rate (target 15-20%) within 18-24 months and reduce operating costs through technological progress, this strategy may eventually bring considerable long-term returns. But if conversion rates are lower than expected or competition intensifies further, this may lead to severe financial pressure and loss of shareholder value.

[0] Gilin API Data - Alphabet Inc. (GOOGL) Financial Data and Real-Time Quotes
[1] Yahoo Finance - “Alphabet Expands India AI Strategy With Accel Partnership to Back Startups” (https://finance.yahoo.com/news/alphabet-expands-india-ai-strategy-114021444.html)
[2] Yahoo Finance - “Microsoft unveils $23 billion in new AI investments with big focus on India” (https://finance.yahoo.com/news/microsoft-invest-more-5-billion-111811705.html)