AI Data Stocks Set to Explode. Here's Why.

Education

Introduction

You're probably seeing all this talk about AI, and you're worried that you're not invested in the appropriate AI stocks. Take advantage of what some are calling the biggest investment opportunity ever. In the second part of our three-part series on investing in AI, we're going to talk about data, the undervalued aspect of this investment niche that is being overlooked by many. Data, often dubbed the "new oil," is crucial because AI algorithms are only as good as the data fed into them.

The Crucial Role of Data

AI's value is increasingly seen in proprietary data sets that companies can leverage to feed their AI algorithms. Spear Invest suggests that data might be the most underappreciated niche in AI investing. This view is highlighted in a report by Ivana Delva, the founder and CIO of Spear Invest. According to her, "AI is a data problem, and we are just in the early innings of data companies seeing an inflection."

The Cloud Service Providers

At the core of the AI value chain are cloud service providers, particularly the big three hyperscalers: Amazon, Microsoft, and Google. Amazon's cloud computing Ventures accounted for 16% of revenues but a staggering 67% of operating income in 2023. This could arguably make Amazon a pure-play cloud computing stock.

Hyperscalers like Amazon, Microsoft, and Google are in a strong position to benefit, irrespective of who wins the so-called AI battle because AI startups usually lack the immense computational power that these giants can provide.

Foundation Models and Data Infrastructure

Foundation models are pre-trained, generalized, and adaptable; they serve as the backbone for creating large language models like ChatGPT. Examples include closed-source models like OpenAI and open-source models like Meta's LLaMA 2.

Spear Invest believes larger companies will gravitate towards open-source foundational models, while small to medium-sized businesses will lean towards ready-to-use closed-source models.

Platforms for Storing Data

Natural Language Processing (NLP) is a rapidly growing field that allows businesses to make sense of unstructured textual data. Enterprises appear to be preferring solutions that let them customize, train, and deploy their transformer models rather than use pre-built APIs.

Snowflake and Databricks are two names that stand out in this area. Snowflake holds and analyzes data, capturing market share from traditional database providers like Oracle. Databricks, a private company, enables startups to build their own generative AI models without being tied to big tech.

Emerging Database Technologies

MongoDB offers a document-based database model that is highly relevant for handling unstructured data, which forms a significant part of generative AI. Though MongoDB's consumption-based model is appealing, it's missing key investor metrics.

Vector databases are another emerging technology aimed at dealing with unstructured data sets. Pinecone and Weaviate are leaders in this field, although both are private companies.

AI Developer Tools

Operationalizing generative AI models requires AI developer tools, which firms like Confluent and Hugging Face provide. Confluent focuses on real-time data, while Hugging Face specializes in fine-tuning models.

Cybersecurity Concerns

Two main approaches exist: AI for cybersecurity and security for AI. CrowdStrike and SentinelOne have incorporated AI into their cybersecurity products for years, while companies like Zscaler and Palo Alto are deploying AI models specifically for AI security implementations.

Conclusion

This article has covered foundational elements of AI-related investments, focusing specifically on the data infrastructure layer. While it isn't exhaustive, it provides a comprehensive overview of key stocks like Snowflake, MongoDB, and Amazon, among others. Remember, proprietary data is poised to offer the most significant competitive advantage, and revenue growth will remain the ground truth for any AI stock.

Stay tuned for Part 3 of our series, where we will cover AI applications and discuss the buzz around these niches in even greater detail.

Keywords

  • AI investments
  • Data infrastructure
  • Cloud service providers
  • Hyperscalers
  • Foundation models
  • Natural Language Processing (NLP)
  • Snowflake
  • Databricks
  • MongoDB
  • Vector databases
  • AI developer tools
  • Cybersecurity

FAQ

  1. Why is data considered the new oil in AI?

    • Data is vital for AI algorithms because they rely on the data they are fed to function efficiently. Proprietary data sets can offer a competitive advantage.

  2. What are hyperscalers, and why are they important?

    • Hyperscalers are large cloud service providers like Amazon, Microsoft, and Google. They are crucial because they provide the computational power needed for AI development.

  3. What is a foundation model in AI?

    • Foundation models are pre-trained, generalized, and adaptable models. They serve as the groundwork for specialized models like large language models (LLMs).

  4. How are companies like Snowflake and Databricks positioned in the AI landscape?

    • Snowflake specializes in data storage and analysis, capturing market share from traditional database providers. Databricks enables startups to build their AI models without depending on big tech.

  5. What role does MongoDB play in AI?

    • MongoDB offers a document-based database model essential for handling unstructured data, which is crucial for generative AI applications.

  6. What are vector databases?

    • Vector databases help manage and retrieve large sets of unstructured data. Companies like Pinecone and Weaviate are leading in this technology.

  7. Why is cybersecurity important in AI?

    • AI for cybersecurity helps to detect and mitigate threats, while security for AI is necessary to protect AI implementations from sophisticated attacks, especially those leveraging tools like ChatGPT.