Swiftly integrating AI into its creative and document management tools, successfully monetizing AI features. The company has been continuously improving its Firefly image generation model and acquiring proprietary data to train its AI models. Adobe has demonstrated agility in showcasing how AI complements and enhances its existing offerings.

End-to-end cloud analytics platform that empowers organizations with automated data preparation, AI-powered analytics, and approachable machine learning. Key capabilities include generative and conversational AI, ETL/ELT, data prep and enrichment, analysis, geospatial, AutoML, reporting, and analytics apps. The AI-driven insights and self-service capabilities help users make data-driven decisions faster and more effectively. The company was delisted on March 19th, 2024, as a result of its acquisition by Clearlake and Insight Partners.

AMD is actively enhancing its AI capabilities to compete with industry leaders like NVIDIA. Their latest Ryzen PRO 8040 mobile processors and 8000 desktop processors now feature integrated AI neural processing units (NPUs) designed to boost AI performance directly on devices. Additionally, AMD's Instinct MI300X accelerators are tailored for high-performance AI applications, providing advanced support for training generative AI models and large language models (LLMs), as well as for AI inferencing tasks. These innovations signify AMD's strategic expansion into AI-driven markets, emphasizing both integrated and dedicated AI processing solutions.

Designs energy-efficient processors that power AI across various devices. The company licenses its IP to chip manufacturers and companies developing AI-enabled products, positioning itself as a critical player in the AI hardware ecosystem.

People had been wondering and spreading rumors for months about what Apple's role in the new Gen AI wave was going to be. In June-24, Apple finally unveiled their vision during their WWDC event. They shared their new intelligence focused on deeply personalizing user interactions with their devices while enhancing functionality through the use of advanced generative models. This new AI framework is integrated into iOS, iPadOS, and macOS to improve device capabilities in understanding and generating language and images, tailored to individual users.

Offers essential connectivity solutions, such as PCIe, CXL, and Ethernet products, to tackle data, network, and memory bottlenecks in large-scale AI infrastructure deployments. Their products are designed specifically to optimize the performance of GPUs and AI accelerators in cloud environments. As AI models become increasingly complex and widely adopted, Astera Labs is well-positioned to benefit from the growing demand for efficient and high-performance connectivity solutions in AI infrastructure.

Heavily invested in AI, with AI-related revenue growing from less than 5% of their Semiconductor Solutions revenue to around 15% in 2023, and targeted to reach 35% by FY2024. Broadcom is developing custom AI accelerators (XPUs) optimized for lower power, cost, and maximum performance. They are also focusing on AI-optimized networking hardware, such as Jericho3-AI.

Provides enterprise AI software solutions across various industries like utilities, and financial services among many others. However, the company may face competition from emerging AI-native, vertically-focused solutions.

As a key supplier to major semiconductor companies like AMD, NVIDIA, and ARM, Entegris is well-positioned to benefit from the AI boom. While the company's exposure to AI is more indirect, the growth in AI applications is expected to drive significant demand for advanced semiconductors, which in turn will benefit Entegris. According to their latest investor presentation, the AI processor market is projected to grow from $45 billion in 2023 to $400 billion by 2027. Entegris identifies AI as one of the key new demand drivers contributing to the semiconductor industry's growth over the next decade.

Provides essential infrastructure that supports AI operations, particularly for distributed AI applications. Their edge data centers facilitate AI processing closer to data sources, adhering to data compliance and reducing latency issues. Equinix collaborates with NVIDIA to offer robust AI infrastructure solutions, providing environments suitable for both AI training and inferencing across the continuum from edge data centers to core clouds.

Their research team played a key role in the Generative AI wave with the release of the "Attention Is All You Need" paper. They have been pioneers in AI with their Google Brain and DeepMind teams. They released one of the most performant models, Gemini, and offer a suite of Generative AI-related services through the Vertex AI platform. They have started to embed AI into their existing products, such as G Suite. It is worth watching their search business closely, as it could be disrupted by players like Perplexity.ai and You.com

Has been embedding AI capabilities in its CRM core product, which can help their customers leverage and extract more value from their existing data. As an established player, HubSpot may be more resilient against displacement by disruptors. Their current focus is to become the AI-powered system of record.

Pioneer in the field of AI, with a long history dating back to the development of the Watson. The company's focus is on bringing AI capabilities to its large enterprise customer base, helping them improve productivity, boost efficiency, and lower costs. IBM recently announced the launch of its new AI platform called watsonx, designed to provide enterprises with self-service access to high-quality, trustworthy data and generative AI capabilities. IBM Research has been actively working on developing generative AI models and exploring their applications, such as using them to write software code faster, discover new molecules, and train trustworthy conversational chatbots.

Intel is concentrating on enhancing AI capabilities in cloud data centers with high-performance processors like the new Xeons, which offer increased cores and efficiency for AI-heavy workloads. Intel developed accelerators like Gaudi2 and upcoming Gaudi3 chips which focus on optimizing AI workflows, providing an alternative to traditional GPUs for AI tasks.

Despite the fact that so far people consider Intel a loser in the Gen AI wave, especially in comparison to NVIDIA, their announcement at Computex showed the priority they are giving to AI use cases, which fits criteria for inclusion in the Index. It will be interesting to see if Intel is able to catch up and start capturing some of the high market share NVIDIA holds for Gen AI applications.

Actively investing in AI, with AI-related revenue projected to increase from approximately $200 million in FY23 to $2.5 billion by FY26. The company offers custom compute solutions optimized for AI applications, such as AI accelerators and AI training accelerators, which are crucial for machine learning and data processing at scale. Marvell is also investing in Ethernet switching tailored for AI applications and data center switching architecture optimized for AI, supporting higher data transmission speeds and efficiency.

Has established itself as one of the leading open-source players in the AI ecosystem with their LLAMA models. The company has been actively embedding AI capabilities within its products, such as recommendation engines, and recently released chatbot features across all of its applications, powered by their LLAMA models. In a strong display of commitment to advancing their Generative AI capabilities and model training, Meta announced its intention to acquire 350,000 GPUs.

Primarily manufactures memory and storage solutions, which are critical components for various computing systems, including those used in AI applications. Micron is developing advanced server DRAM modules and high-capacity DRAM products to support AI workloads.

Has emerged as one of the early big winners in the AI race, quickly integrating AI capabilities into their offerings, such as Microsoft 365 Copilot and GitHub Copilot. The company has strategically invested in and partnered with leading AI model providers, including OpenAI and Anthropic, whose models are now accessible through Microsoft's Azure cloud computing platform.

Database company that stores large volumes of customer data, which can be utilized for Retrieval-Augmented Generation (RAG) applications. The company recently launched its Atlas Vector search offering, aiming to cater to the growing demand for AI-driven solutions. As more MongoDB customers deploy their projects in production environments, Generative AI is anticipated to become a significant driver of the company's long-term growth. (Disclaimer: Flybridge General Partner Chip Hazard is a member of MongoDB’s Board of Directors).

They have developed proprietary AI solutions like "Enlighten AI" for customer experience, which fuses people with processes to optimize customer interactions. NICE offers various AI-driven products such as "Autopilot" and "Copilot" for virtual agents and employee productivity in customer experience, as well as AI-powered analytics and decision-making processes for financial crime compliance. The company has over 3,000 R&D professionals dedicated to developing and enhancing their AI and cloud capabilities. The customer experience space is one that is seeing a high volume of AI-native startups, so their speed of execution will be important to maintain their growth as more competitors emerge.

A central player in AI development due to its GPUs, which are essential for AI processing and applications. The company has emerged as the biggest early winner in the generative AI wave, as its advanced graphics processing units provide the computational power needed to train and run large AI models efficiently.

Renowned for its AI-powered data analytics and decision-making tools, catering to both government and commercial clients. The company harnesses AI to provide predictive analytics, anomaly detection, and optimization, empowering organizations to make data-driven decisions.

Has surpassed $100M in AI-First ARR with its offerings including Cortex XSIAM, AI Ops, ADEM, and various AI-embedded platforms. The company is targeting significant growth in AI-related revenues, aiming for a TAM of $13-17 billion by 2030, focusing on different AI sectors such as AI Supply Chain, AI App Runtime, and Employee Access. Palo Alto Networks has deployed enterprise AI co-pilots and AI-driven malware identification tools, suggesting deep integration of AI in its product offerings.

Qualcomm's AI platforms are designed to enhance on-device AI capabilities, providing efficient and powerful processing for machine learning and inference tasks. They recently released the ARM-based processor - Snapdragon X Elite, capable of running generative AI LLM models with over 13B parameters on-device at blazing-fast speeds. As on-device AI grows, Qualcomm will play a central role.

Has been actively integrating AI capabilities into their products, with Einstein co-pilot being a prime example. The company's access to vast amounts of customer data positions them well to power novel use cases for their clients. Demonstrating their commitment to AI, Salesforce appointed Clara Shih as their head of AI 14 months ago, signaling a strategic focus on developing and deploying AI-driven solutions across their platform.

Giving AI a high priority. They announced in their 2023 Investor Day that all their workflows will leverage Generative AI. They mentioned their desire to develop domain-specific models. Depending on the speed of execution, ServiceNow could benefit from already owning customer relationships, data access, and distribution. However, many new AI-native businesses could disrupt the market and gain market share, making ServiceNow a player to watch closely.

Offers validated AI, machine learning, modeling, and simulation software for drug development. Their AIDD (Artificial Intelligence-Driven Drug Discovery) module can generate and evaluate up to 10 million molecules, allowing users to control molecule optimization while considering synthetic feasibility. The company has repurposed its advanced machine learning technology to enable rapid development of custom applications. While not a pure play AI company, Simulations Plus is also representative of the role that AI can play in the Life Sciences vertical and worthy of inclusion as a result.

Stores a significant amount of customer data, which is key to pre-training and fine-tuning models on proprietary data. They recently appointed Sridhar Ramaswamy as the CEO to guide their new focus on Generative AI capabilities. Snowflake has launched their state-of-the-art (SOTA) Generative AI model called Snowflake Arctic.

Providing advanced infrastructure solutions that cater to the demands of AI workloads, their extensive portfolio includes high-performance servers, cooling and storage solutions, and integrated systems designed for AI training, inference, and deployment across various industries. Supermicro collaborates with leading companies in the space, having been the first to bring optimized GPU platforms with NVIDIA in 2014.ion

Increasingly leveraging AI to enhance its products and expand its market opportunities. The company is heavily invested in edge AI technologies, developing AI-enabled processors that offer advanced capabilities for IoT devices. These processors integrate AI to facilitate lower latency, increased privacy, and enhanced intelligence, making them suitable for a wide range of applications from smart home devices to industrial IoT. The company projects a considerable increase in its serviceable addressable market due to AI innovations, with the AI-enhanced processors segment anticipated to reach a SAM of $24 billion by 2028.

Plays a pivotal role in the global semiconductor supply chain, which is essential for AI hardware development. Numerous companies that design AI chips, including NVIDIA, AMD, and Apple, depend on TSMC's advanced technology nodes to manufacture high-performance, energy-efficient chips capable of handling demanding AI workloads.

Investing heavily in AI, offering AI capabilities integrated into its platform like AI Computer Vision, Document Understanding, Communications Mining, Semantic Automation, and more. It also provides both Specialized AI models tailored for enterprise use cases as well as Generative AI experiences powered by OpenAI and other models. UiPath enables organizations to combine the power of UI+API+AI to build robust automations. Based on feedback in various review platforms, UIPath is not loved by its users and could lose market share to AI native companies.