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    GSAM Connect

    ARTIFICIAL INTELLIGENCE: AN UNPRECEDENTED DISRUPTION

    May 11, 2023  |  8 Minute Read

    Brook Dane

    Portfolio Manager, Fundamental Equity

    Brook Dane

    Key Takeaways

    • Breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to disrupt entire industries as we currently know them.
     
    • We believe these technologies can generate huge business value across multiple areas, from digital advertising and healthcare to cybersecurity and semiconductors.
     
    • As winners and losers quickly emerge, we believe this as an exciting time for investors to evaluate opportunities and believe active management will be critical for long-term success.

    Artificial Intelligence (“AI”) and Machine Learning (“ML”) have been utilized in tech for over a decade; however, recent headlines and actions taken by ChatGPT, Microsoft, and Google have shown how accessible and more advanced this generational innovation is. While many users’ first experience of AI involved using prompts to generate grocery lists or vacation ideas, the implications for businesses and employees will be far more impactful.

     

    In his recently published seven-page letter, “The Age of AI has Begun”, Bill Gates outlined the areas AI could transform, including education, healthcare, and the workforce. The proliferation of AI should enable industries to become much more efficient, allowing for businesses to further enhance their processes.

     

    We are currently in the very early stages of a technology that will undoubtedly change our lives. In the nearer term, the integration of generative AI and predictive analytics, and the optimization of semiconductor production are areas of tech we believe will enable the proliferation of AI and, as a result, benefit from this rollout.

     

     

    Key Terms1

    • Artificial Intelligence: the development of computer systems able to perform tasks that normally require human intelligence
     
    • Generative AI: a type of AI system capable of generating text, images, or other media in response to prompts using a database
     
    • Machine Learning: the use and development of computer systems that can learn and adapt using algorithms and statistical models to analyze and draw inferences from patterns in data

     

    • Deep Learning: a subset of machine learning based on artificial neural networks with multiple layers of processing to imitate the human brain

     

    • Predictive AI: the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data

    Generative AI and Business

     

    In the evolution of artificial intelligence, the latest innovation is generative AI. Generative AI falls under the category of machine learning and has the potential to transform entire industries. While previous iterations of AI have required human direction, machine learning is able to observe data patterns with little to no human intervention to make decisions. After forming a database, generative AI then utilizes deep learning, which uses complex algorithms modeled on the human brain to create content.

     

     

    ChatGPT

    OpenAI’s generative pre-trained transformer (“GPT”) is the preeminent example of generative AI and its self-learning capabilities. The company’s chatbot, ChatGPT, has been popularized by its ability to provide users with original text outputs when given text inputs.

     

    ChatGPT amassed 100 million users in January, marking the first time a platform has reached that milestone within one month of its launch.2 In the same month, Microsoft invested $10bn in OpenAI, with the aim of accelerating breakthroughs in AI and commercializing future cutting-edge technology.3 The tech giant will incorporate GPT functions in its Bing search engine and Edge web browser, while companies such as Google have launched similar initiatives.

     

    There are several other platforms emerging that generate images as well as written content. We believe more companies will follow suit and attempt to take advantage of the rapidly changing technological landscape, as first movers will likely be rewarded for their early investment in this space.4

     

    Generative AI in Businesses

    The practical uses of generative AI span across many industries and markets, as companies see an evolving opportunity set across various business segments. Some examples include:

     

     

    In the near-term, we expect businesses to adopt this technology to reduce costs and increase efficiencies. Companies can leverage chatbot functions to enhance customer experiences by reducing wait times and by providing more accurate solutions. Generative AI can be used as a tool for critical thinking and conceptualizing new business plans. The improvements made since the first iterations of generative AI are staggering and are an encouraging sign for the future of artificial intelligence.

     

     

    Predictive AI in Cyber and Ads

     

    Whereas generative models produce new data points based on what they learn from databases, predictive AI infers information about different data points to make decisions. Predictive AI has primarily been used to automate human processes, performing them at very high levels of accuracy and with minimal human oversight. In the near to medium term, there are several applications for predictive AI that have the potential to significantly impact existing enterprises.

     

    Cybersecurity

    Over the past decade, the world has observed an uptick in cybercrime, cyber-attacks, and malware infections, partially due to AI enabling less experienced hackers with tools to write harmful code or emails for cyberattacks. For example, attackers can use AI tools to create more sophisticated and targeted attacks, such as using AI-powered chatbots to create authentic-looking emails that deceive users into revealing sensitive information. AI algorithms themselves can also be manipulated or deceived which could lead to false alarms or missed threats. Additionally, using automated processes lowers the costs associated with launching ransomware and phishing attacks, increasing the rate at which these attacks may occur.

     

    While AI is democratizing cyberattacks, it also has the potential to significantly improve security by automating the identification of potential threats, the detection of anomalies and patterns that may indicate a breach, as well as responding to security incidents in real-time. Predictive analytics can also be used to help detect outlier patterns or potential threats, mitigate risk, and optimize workflows.

     

     

    As the costs of cyberattacks and data breaches continue to increase, the need for more robust, effective and cost-efficient solutions continues to be top of mind for large enterprises, especially those with sensitive customer data.

     

    Digital Advertising

    Predictive AI is already being leveraged by several marketers and sales teams in business-to-business and business-to-consumer platforms. We believe AI will supplement and enhance salesforces’ ability to market their products.

     

    Business-to-Business

    Predictive AI can transform enterprise software companies and how customers utilize them. We believe there is a tremendous opportunity for existing enterprise software companies to gain market share as first movers in adding AI capabilities to their existing framework, particularly in marketing.

     

    Currently, marketers can obtain leads through data from client relationship management (“CRM") platforms, surveys, and websites. However, AI would be able to sift through a much larger set of data faster, enabling marketers to operate much more efficiently. We believe these enhancements through AI will make enterprise software companies even more necessary, as companies work to cut costs, particularly in marketing.

     

     

    Business-to-Consumer

    Advertisers can now segment populations into targeted groups to get the most out of their ads, making advertising dollars far more efficient. These groups are selected by machine learning algorithms that recognize patterns in consumer behavior and determine which messages will resonate best with each consumer. Several platforms have leveraged some form of AI to create targeted ads for users.

     

     

    AI’s Impact on Semiconductors and Semi-Cap Equipment

     

    With AI increasing in relevance for businesses and consumers, the infrastructure to support this new volume and overall buildout of capabilities will need a substantial upgrade. AI servers will require 10x the memory content of a traditional server, requiring innovative new chips and processes to develop them en masse.8

     

    The size of transistors or nodes in semiconductors has come down dramatically from nearly 100 nanometers (nm) at the beginning of the 21st century to just 5nm, with plans for production of 2nm semiconductors in 2024.6 The smaller transistors enable more powerful and efficient chips, which are fundamental in powering advancements in artificial intelligence. These chips and the foundries to produce them require significantly more capital investment due to their sophistication.

     

     

    Exhibit 1: Chip Design and Semiconductor Fabrication Plant by Node Size

     

    Source: IBS, McKinsey Analysis. As of October 20, 2022.

     

     

    Investment in machine learning and artificial intelligence have the potential to generate huge business value for semiconductor companies at every step of their operations, from research and chip design through to production and sales. It is estimated that this could generate $85bn for semiconductor companies over the next 5 years.9

     

    • AI currently contributes between $5 billion and $8 billion annually to earnings, reflecting only ~10% of full potential contributions.10
     
    • Artificial intelligence / machine learning may eventually reduce the current R&D cost base by ~30%.

     

    Another important innovation is the Application Specific Integrated Circuit (ASIC) chip. This chip allows multiple algorithms to operate simultaneously without negatively affecting the computational power, by ensuring that instructions are properly programmed to accelerate different algorithms used at the same time. ASIC chips incorporate many more and smaller transistors that run faster and are less energy-consuming than other semiconductors produced today. ASIC chips also have AI-optimized design features accelerating the predictable, independent, and identical calculations AI algorithms require. We believe the semiconductor companies working on this technology will disproportionally benefit from the buildout of AI and AI servers.

     

     

    Conclusion

     

    Companies across sectors will need a clear strategy to incorporate AI if they want to remain a market leader in five years’ time. The need for Chief Artificial Intelligence Officers (CAIOs), staff, and additional resources dedicated to this innovation is increasing as companies look to harness this innovation effectively.

     

    The integration of AI within enterprises and wide scale adoption by consumers will have a huge impact in the coming months and years. Active management is critical when investing in AI as winners and losers are quickly emerging around the globe. This is a particularly exciting time to be evaluating opportunities in AI as long-term investors.

     

    We believe AI is a growth driver with a wide variety of applications and is on a path to disrupting entire industries as we currently know them. As long-term investors, we are constantly evaluating the opportunities across the tech landscape and investing in companies aligned with long-term secular growth areas. In our view, companies that are harnessing generative AI to enhance their business, leveraging predictive AI to make their business smarter, and those manufacturing the hardware to enable the proliferation of AI are set to disproportionately benefit.

     

     

     

     

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    1IBM, Microsoft, Stanford University, 2023.

    2Bank of America Research, 2023.

    3Bloomberg, January 23rd, 2023.

    4Demand Sage, March 2023

    5QuantumBlack AI by McKinsey, December 2022

    6McKinsey & Company, November 2022

    7Cyber Management Alliance, July 2022.

    8Micron Technologies, 2023.

    9McKinsey, IBS, 2022.

    10McKinsey & Company, April 2021

     

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    Date of First Use: May 11, 2023.  317158-OTU-1792940

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