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Goldman Sachs Identifies Key Indicator for AI Trade's Durability

Goldman Sachs Identifies Key Indicator for AI Trade's Durability 

According to a recent report from Goldman Sachs, sales revisions are a crucial metric for gauging the long-term viability of the artificial intelligence (AI) trade. While the AI sector has experienced significant growth, particularly in the infrastructure phase, skepticism lingers regarding its long-term profitability. 

AI Trade: A Multi-Phase Phenomenon

  • Goldman Sachs outlines four phases of the AI trade:
    • Phase 1: Research and Development (R&D) - Focused on foundational AI technologies.
    • Phase 2: AI Infrastructure Buildout - Encompasses companies like semiconductor firms and cloud service providers that enable the development and deployment of AI applications.
    • Phase 3: Specialized AI Companies - Includes businesses developing and offering industry-specific AI solutions.
    • Phase 4: Broader AI Adoption - Represents the widespread integration of AI across various industries.

Sales Revisions: A Critical Factor

  • Strong Growth, Uncertain Profitability: The AI sector, particularly Phase 2 (infrastructure), has witnessed impressive growth. However, Goldman Sachs emphasizes that such growth doesn't necessarily translate to long-term profitability.
  • Focus on Sales Revisions: Investors should prioritize companies demonstrating that their AI investments are leading to tangible sales growth. Upward revisions to sales forecasts by analysts signify investor confidence in a company's ability to monetize its AI efforts. Conversely, downward revisions could indicate challenges in converting investments into revenue.

The Upcoming Earnings Season: A Test for AI Companies

  • The approaching second-quarter earnings season presents a critical test for the AI trade. Companies must demonstrate that their AI investments are translating into actual sales and earnings growth to sustain their valuations.
  • Failure to meet sales expectations could lead to a decline in valuations for AI stocks. Investors may become more cautious if companies struggle to translate their AI investments into tangible financial results.

AI vs. Tech Bubble: A Different Landscape

  • Goldman Sachs highlights that the AI capex cycle, or investment cycle, is still significantly smaller compared to the Tech Bubble of the late 1990s. This suggests that the AI trade might have more room for growth, but it also underscores the need for concrete results to justify current valuations.

Overall, Goldman Sachs' emphasis on sales revisions as a key indicator for the AI trade's durability injects a dose of caution into the current optimism. While the potential of AI remains undeniable, companies must demonstrate their ability to convert that potential into financial performance to sustain investor confidence and high valuations in the long run.

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  • Analyze the impact of potential future sales growth on the valuation of AI companies.
  • Factor in the costs associated with AI development and deployment into your financial models for AI companies.

Make smarter investment choices. Get started with FMP Advanced DCF API today!

Link: https://site.financialmodelingprep.com/developer/docs#advanced-dcf-discounted-cash-flow 

What are your thoughts on the importance of sales revisions for the AI trade? Are there other key factors to consider when evaluating the long-term prospects of AI companies? Share your comments below!