Marvell Technology Inc.: 10-K Risk Factor Changes

Rapid advances in artificial intelligence (“AI”) and machine learning (“ML”) technologies, including generative AI, could fundamentally alter the semiconductor industry and disrupt our business model and operations. AI-driven tools and platforms are increasingly being deployed across the integrated circuit (“IC”) development lifecycle, including in chip architecture design, electronic design automation (“EDA”), layout optimization, verification, testing, and process node development. If AI-enabled efficiencies substantially reduce the complexity, cost, or time required to design, develop, and manufacture semiconductor products, our competitive position could be materially and adversely affected. AI-driven design tools may lower traditional barriers to entry in the semiconductor industry by enabling new market participants, including technology companies that have not historically engaged in chip design, to develop high-performance, custom semiconductor solutions in-house with reduced reliance on third-party chip suppliers. This trend toward internal chip development, sometimes referred to as "insourcing" or "vertical integration," could reduce demand for our products and erode our market share. In particular, large cloud computing providers, automotive original equipment manufacturers, and other technology-focused enterprises have already begun investing in proprietary chip design capabilities, and advancements in AI may accelerate this trend. 18 18 18 Table of Contents Table of Contents AI and ML technologies may enable our existing competitors to achieve design and manufacturing efficiencies that we are unable to match, thereby diminishing or eliminating our current technological or cost advantages. Competitors that more effectively integrate AI into their IC development workflows may be able to bring products to market faster, at lower cost, or with superior performance characteristics compared to our offerings. If we fail to adopt and integrate AI technologies into our own design and development processes at a pace consistent with or faster than our competitors, our products could become less competitive, which would have a material adverse effect on our revenue and profitability. AI-generated efficiencies may compress product development cycles across the industry, which could shorten the useful commercial life of our existing products and reduce the return on our research and development investments. As AI tools enable more rapid iteration and optimization of chip designs, customers may expect faster product refresh cycles, placing additional pressure on our research and development resources and potentially leading to accelerated inventory obsolescence. Our investment in AI-related capabilities may not yield the anticipated benefits. Developing, acquiring, or integrating AI-driven tools and talent into our operations will require significant capital expenditures and operational resources, and there is no assurance that these investments will generate a return sufficient to justify their cost. Additionally, the deployment of AI technologies in our design and manufacturing processes may introduce new and unforeseen risks, including design errors, security vulnerabilities, intellectual property concerns, and regulatory compliance challenges that could increase our costs, expose us to liability, or delay product launches. See also, “Costs related to defective products could have a material adverse effect on us” and “Cybersecurity risks could adversely affect our business and disrupt our operations” for additional information. AI technologies may disrupt the broader semiconductor supply chain and ecosystem in ways that are difficult to predict. For example, AI-driven advances in chiplet-based architectures, advanced packaging, or novel materials science could render certain of our existing product lines, manufacturing processes, or intellectual property less valuable or obsolete. Furthermore, the increasing use of AI in semiconductor design raises complex and evolving questions around intellectual property ownership, patentability, and trade secret protection, and the legal frameworks governing these issues remain uncertain and may develop in ways that are unfavorable to our business. See also, “We may be unable to protect our intellectual property, which would negatively affect our ability to compete” for additional information. We cannot predict the pace or trajectory of AI development or the extent to which AI-driven disruption will affect the semiconductor industry. If we are unable to anticipate and adapt to these changes in a timely and effective manner, our business, financial condition, results of operations, and competitive position could be materially and adversely affected.