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Generative AI (All you need to know)

The world of artificial intelligence (AI) continues to evolve rapidly, and OpenAI's latest surprise release, GPT-4o, has once again caught the attention of AI enthusiasts and experts. This article delves into the concept of Gen AI, or generative AI, its investment opportunities and risks.

What is Gen AI?

“Fundamentally, generative AI reduces the money and time needed for content creation — across text, code, audio, images, video and combinations thereof,” said Gokul Hariharan, Co-Head of Asia Pacific Technology, Media and Telecom Research at J.P. Morgan.

Source: Sequoia

Trends and Investment Opportunity

JP Morgan:

Generative AI has the potential to contribute to a substantial increase in global GDP, estimated at $7–10 trillion or up to 10%. Other than software companies, hardware companies can also expect to benefit from the adoption of generative AI. It is projected to drive an expansion in the global semiconductor market, with a forecasted revenue growth of 16% in 2024 and continued demand in 2025.

Investments in AI infrastructure will continue to grow, specifically in areas such as servers, switches, and optics. This increased leverage of AI in certain sectors of the broader hardware industry will create a significant opportunity for growth.

Furthermore, there is an expectation of increased product launches for AI end-point devices, including smartphones and PCs, in the latter half of 2024. This trend is anticipated to gain further traction in 2025, with a higher level of adoption and utilization of AI technologies in these devices.

Morgan Stanley:

  1. Semiconductor Investing

Semiconductors are crucial in enabling generative AI across various industries. As the demand for accelerated computing continues to grow, companies are actively competing to develop and introduce next-generation semiconductor products and comprehensive solutions to meet the increasing needs of their customers. This race to bring advanced offerings to the market reflects the recognition of the significance of semiconductors in supporting the widespread adoption and advancement of generative AI technologies.

Growth Factors:

  • Demand from Data Centers: Data centers are the backbone of modern computing infrastructure, handling massive amounts of data storage and processing for businesses. The increasing demand for data center services drives the need for high-performing chips designed by semiconductor companies.

  • Partnerships with Hyperscalers: Hyperscalers are large cloud service providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Semiconductor companies forming partnerships with hyperscalers gain valuable insights into the specific requirements and use cases of generative AI technology within enterprises. These insights allow semiconductor companies to develop tailored solutions that meet the demands of generative AI workloads. By aligning their products with the needs of hyperscalers, semiconductor companies can drive revenue growth and gain a competitive advantage in the market.

  • Growing Interest from Sovereigns: National governments are increasingly recognizing the importance of accelerated computing and generative AI for various applications. This growing interest from governments presents a significant growth opportunity for semiconductor companies. As governments invest in computational infrastructure, they will require advanced chips to support their AI initiatives, leading to increased demand and revenue for semiconductor companies.

2. Software Investing: Trends in Demand and Uses

With efficiency and productivity gains from AI a major area of investment interest, investors are also monitoring the landscape of software as a service (SaaS) companies that are offering traditional AI and generative AI products that automate workflows.

3. Big Data Investing: Consolidated Offerings and AI Products

The increasing significance of big data in generative AI gives big data companies a considerable influence over how enterprises manage, store, and deploy their vast data resources. These companies possess the expertise and technologies to handle and extract value from large datasets. As the demand for generative AI solutions continues to grow, big data companies are in a position to shape the strategies and offerings related to data management and utilization.

Trends in Big Data Companies:

  • Consolidated vs. best-of-breed offerings: The "best-of-breed" approach refers to using specialized, siloed data tools. In contrast, the "consolidated" or platform-based approach involves building more unified data management solutions. Investors are trying to determine which big data companies will gain and lose market share, as some customers may want to pare down their software and double down on a few data partners because of potential cost and security benefits. While best-of-breeds could move faster and be more specialized, customers are also considering how unified platforms can help them reduce cloud usage and heighten visibility and control of security protocols.

  • Labeling Data: Before data can be useful to AI models, it must be labeled to add context or meaning. Companies cited opportunities resulting from large amounts of raw, unlabeled data held by corporates.

  • Real-time Data Streaming: Big data companies are helping process customer data from various sources together in real time with large language models (LLMs) to improve customer service and drive employee productivity.

Risk and Limitations


Biased Output: Generative AI models often produce outputs that sound highly convincing, which is intentionally designed. However, it is important to acknowledge that these outputs can be incorrect or biased. The biases present in the training data and the broader societal biases can influence the generated information. Organizations that rely on generative AI models must carefully consider the reputational and legal risks associated with unintentionally publishing biased, offensive, or copyrighted content. It is crucial to prioritize responsible usage of generative AI technology to mitigate these risks and ensure ethical and legal compliance.

JP Morgan:

Ethical and Legal Concerns: There is a risk of unethical or criminal activities enabled by generative AI, as the technology can be manipulated to generate harmful content. Organizations using generative AI models should be aware of reputational and legal risks associated with unintentionally publishing biased, offensive, or copyrighted content.


There are many investing opportunities around Gen AI, including hardware, software, bigdata. Be aware of the legal and ethical risks in stock picking process.


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