Introduction

The AI Stack represents a shift in how businesses and individuals approach productivity, innovation, and problem-solving. It includes a comprehensive ecosystem of tools, platforms, and platforms designed to apply the power of artificial intelligence across various layers of functionality. From foundational AI models that enable machine learning to applications that redefine specific workflows, the AI stack is paving the way for the next wave of digital innovation.

This insight aims to provide a deep dive into each layer of the AI Stack by highlighting its components, applications, and potential. 

What is the AI Stack?

The AI Stack is a conceptual framework that represents the layers of technology, tools, and infrastructure powering artificial intelligence across various domains. It provides a structured view of how AI integrates into workflows and systems, from the foundational models that drive learning to the applications delivering end-user functionality. The AI Stack we propose is built on four core layers:

1. AI Operating System (AI OS)

At the top of the stack are AI Operating Systems which serve as platforms for managing and unifying AI-driven tasks. These systems streamline interaction by integrating intelligent agents, automating workflows, and enabling communication between applications. AI OS solutions like Steve are transforming how users interact with technology by replacing manual, fragmented processes with intelligent, conversational interfaces.

2. Applications

Below the AI OS layer is the applications layer, which delivers specialized tools for industries like healthcare, education, productivity, and design. Examples include mental health support through Woebot Health, AI-assisted learning with Kira learning, and workflow governance using Credo AI. These tools leverage AI to redefine specific workflows and drive innovation.

3. Foundation Models

Foundation models are the underlying engines of the application layer by providing capabilities like natural language understanding, image recognition, and decision-making. These models are trained on large datasets to allow them to perform a wide range of tasks and adapt to specific use cases. Notable examples include models developed by OpenAI, Anthropic, and Meta.

4. Cloud Infrastructure and Semiconductors

The base of the stack includes cloud infrastructure and semiconductors. Cloud infrastructure serves as the backbone for hosting and processing AI workloads, providing resources for training, deployment, and inference. Providers like AWS, Google Cloud, and Azure offer platforms that allow organizations to take advantage of AI capabilities without owning physical hardware.

On the other hand, semiconductors represent the physical hardware that powers AI computations. Companies like NVIDIA, AMD, and Intel produce high-performing chips that perform the complex mathematical operations required for training and running AI models. While cloud infrastructures provide the scalability and accessibility for AI to operate, semiconductors deliver the raw computations power that drives the performance of these systems. 

Together, these layers form a cohesive ecosystem, with each component playing an essential role in advancing AI’s potential. 

AI Operating Systems

AI Operating Systems represent the next frontier in the evolution of technology. Designed to unify AI-driven workflows, these platforms integrate intelligent agents, automate complex processes, and enhance productivity through seamless and contextually-aware interfaces. By centralizing AI capabilities, AI OS offers an opportunity to move beyond the manual, fragmented interactions that define legacy operating systems.

1. The Need for AI OS in the AI Stack

The introduction of AI OS as the top layer of the AI Stack highlights the growing demand for systems capable of managing the complexity of AI-driven tools. While foundational models, applications, and infrastructure layers provide the technical backbone for AI, legacy operating systems limit their full potential. Current architectures were not designed to accommodate intelligent agents or automate workflows at scale. Instead, they rely on outdated paradigms like clicking, typing, and copy-pasting, which constrain the efficiency of modern AI tools.

AI OS aims to address these limitations by providing;

Unified AI Integration: Seamlessly connecting diverse AI agents to collaborate on tasks and deliver cohesive solutions.

Enhanced User Interaction: Enabling natural language and conversational interfaces to reduce reliance on manual actions.

Centralized Productivity: Consolidating workflows, tools, and insights into a single platform that reduces inefficiencies.

By acting as a control center for AI capabilities, AI OS has the potential to reshape how individuals and organizations interact with technology.

2. Steve: The First AI OS for Product Engineering

Among emerging AI OS platforms, Steve stands out for its focus on product engineering. Although not yet released, Steve is being developed to address the unique challenges of product development workflows such as ideation, design, task management, and engineering. 

Steve’s goal is to redefine how product teams work by creating an AI-first platform optimized for collaboration and task automation. Key features of Steve include:

Conversational Interface: Allowing users to interact with the system through natural language, eliminating the need for complex manual inputs.

Collaborative AI Agents: Enabling multiple AI tools to interact with each other to execute tasks such as generating design assets, modifying documents, and deploying code - all through simple instructions.

Centralized AI Platform: Offering integrated tools for design, engineering, and analytics within one cohesive environment.

Developer Ecosystem: Providing a platform for developers to build, launch, and monetize AI applications.

Steve’s focus on product engineering distinguishes it from general-purpose AI OS platforms, showcasing how AI can be tailored to specific domains to solve complex problems.

3. Emerging Players in AI OS

While Steve targets product engineering, other major players are taking broader approaches to AI OS, integrating AI into their ecosystems for general productivity and user assistance. These include:

Apple: Apple has integrated Apple Intelligence capabilities across its ecosystem covering features like natural language processing, on-device machine learning for predictive text and content generation, and AI tools for photo editing and health monitoring. While not explicitly positioned as an AI OS, these features signal Apple’s direction towards an intelligent platform that enhances user productivity and personalization. 

Google: Google’s Gemini project represents a significant step toward a conversational AI OS. By embedding AI deeply into products like Google Workspace, Android, and Chrome, Gemini enables users to automate tasks, draft content, and manage workflows more efficiently. Its focus on broad consumer and enterprise productivity positions it as a strong presence in the AI OS space. 

4. Comparison and Differentiation

While Apple and Google emphasize broad consumer and enterprise productivity, Steve’s focus on product engineering fills a gap in the market. By tailoring its features to the specific needs of product teams, Steve aims to offer a level of functionality and integration that general-purpose AI OS platforms cannot match. 

5. The Future of AI OS

AI Operating Systems have the potential to redefine how users interact with technology by offering intelligent and adaptive platforms that go beyond traditional operating systems. As the ecosystem matures, competition between general-purpose platforms and domain-specific solutions will drive innovation to ensure that AI OS continues to evolve to meet diverse user needs.

Applications

The application layer, which positions itself below the AI OS layer, focuses on delivering specialized tools and solutions across various industries. This layer leverages the capabilities of foundation models and cloud infrastructure to address specific challenges in fields such as healthcare, education, productivity, and governance. By integrating domain expertise with AI, these applications encourage more efficiency and innovation.

1. The Role of Applications in the AI Stack

Applications are the most visible layer of the AI Stack which directly impact end-users by providing targeting functionality. They serve as the operational tools powered by the underlying layers of foundation models, cloud infrastructure, and semiconductors. Each application addresses a specific need such as automating processes, analyzing data, and improving decision-making. 

Applications in the AI Stack are characterized by the following factors: 

Domain-Specific Focus: Designed to address challenges in specific industries like healthcare, education, design, and compliance.

Scalability: Powered by foundation models and cloud platforms, applications scale to meet the demands of diverse users and industries. 

Enhanced Accessibility: Many applications use conversational interfaces and simplified workflows to make advanced AI accessible to all users.

2. Examples of Applications

The applications layer has seen significant growth in recent years, with numerous tools emerging across industries. The table below covers 8 notable applications, along with their descriptions. 

Foundation Models

Foundation Models, powerful engines that enable applications, serve as the core of the AI Stack. These models are trained on massive datasets, allowing them to generate text, recognize images, understand speech, and more. Their adaptability makes them an essential element in advancing AI across industries.

1. The Role of Foundation Models

Foundation models form the backbone of the AI Stack by providing the computational intelligence that powers higher-level applications. They are not task-specific but instead provide generalized capabilities that can be fine-tuned or adapted for specific needs.

Key attributes of foundation models include:

Generalization: Trained on diverse datasets, foundation models can perform tasks across multiple domains such as language processing and image recognition.

Scalability: Their architecture allows them to handle increasing complexity through larger datasets, more parameters, or fine-tuning.

Adaptability: Organizations can customize foundation models for specific use cases.

2. Notable Foundation Models

Several organizations have distinguished themselves through the development of advanced foundation models. 

OpenAI has been at the forefront with its GPT series. Their recent models, including GPT-4o and o1, extend beyond text, incorporating image and audio processing capabilities. Open AI’s models power widely used applications like ChatGPT and GitHub Copilot facilitating natural language understanding, code generation, and creative AI solutions.

Anthropic has also made significant contributions with its Claude series which emphasizes safety and ethical considerations in AI development. Their models are designed to exhibit explainable behavior and advanced natural language capabilities which make them suitable for enterprise solutions where ethical AI implementation is important.

Meta has contributed to open research and innovation through its LLaMa series. These models are open source and are trained on large-scale, multilingual datasets to support a wide range of applications from chatbots to multilingual understanding. 

Foundation models are continually evolving with organizations increasing their size, complexity, and efficiency. Innovations such as reinforcement learning, multimodal capabilities, and parameter-efficient fine-tuning are extending the range and depth of these models. Looking ahead, foundation models will continue to drive innovation in AI by serving as the engines that power applications and platforms across a wide array of industries. 

Cloud Infrastructure and Semiconductors

Cloud infrastructure and semiconductors form the base of the AI Stack. These layers provide the computational power and scalability needed to fuel AI innovation across industries. While the upper layers of the stack including applications and AI OS platforms focus on end-user interactions and specialized solutions, the base layer serves as the enablers for these advancements by addressing the technical requirements of AI workloads.

1. Cloud Infrastructure

Cloud infrastructure provides the storage, computational capacity, and distributed networks which are essential for training and deploying AI models. Modern AI workflows require significant resources to handle large datasets, process complex algorithms, and ensure scalability for real-world applications. Cloud platforms address these needs by offering pay-as-you-go access to powerful infrastructure. 

Amazon Web Services (AWS) stands as the largest cloud provider which offers a comprehensive suite of AI and machine learning services. Amazon SageMaker allows organizations to build, train, and deploy ML models at scale. AWS’ extensive global network of data centers, combined with its marketplace of tools and services, makes it particularly attractive for enterprises requiring robust infrastructure.

Google Cloud Platform (GCP) also excels in machine learning and AI capabilities, making it particularly suitable for LLM deployments. Its flagship service, Vertex AI, provides an end-to-end platform for machine learning operations while Cloud TPUs (Tensor Processing Units) offer specialized hardware acceleration for AI workloads. The platform leverages Google’s leadership in TensorFlow and machine learning research to provide users with cutting-edge AI capabilities. 

Microsoft Azure combines enterprise-grade cloud infrastructure with comprehensive AI services. Azure Machine Learning stands out for its seamless integration with Microsoft’s ecosystem. The platform offers support for both code-first and low-code approaches to AI development, making it accessible to users with varying levels of technical expertise. 

2. Semiconductors

At the physical level, semiconductors serve as the computational engines that execute AI algorithms. These chips are specifically designed to handle the demanding requirements of AI workloads such as processing large amounts of data and performing complex mathematical computations at high speeds.

GPUs have evolved from their origins in graphics rendering to become essential tools for AI computation. Their parallel architecture excels at the matrix operations required for deep learning to allow for faster training and inference of complex models. The industry’s shift toward GPU computing has driven innovations in memory architecture and interconnect technologies specifically optimized for AI workloads.

NVIDIA has established itself as the dominant force in AI computing through its GPU technology. The company’s CUDA framework has become the de facto standard for AI development while its specialized hardware like A100 and H100 GPUs deliver unprecedented performance for training and inference. NVIDIA’s ecosystem extends beyond hardware to include software tools and libraries that accelerate AI development. 

Google’s Tensor Processing Units represent an effort in custom AI accelerators. These Application Specific Integrated Circuits (ASICs) are designed to accelerate deep learning workloads particularly those built with TensorFlow. TPUs demonstrate the potential of purpose-built hardware to achieve superior performance and energy efficiency for specific AI tasks.

3. The Symbiosis of Cloud Infrastructure and Semiconductors

Cloud infrastructure and semiconductors work together to create the foundation for the AI Stack:

Training Models in the Cloud: Semiconductors power the computational backend of cloud platforms to enable the training of large-scale foundational models.

Inference at Scale: AI applications leverage the cloud for real-time inference while relying on high-performance chips to ensure quick response times.

Hybrid Solutions: Organizations can adopt hybrid models, combining on-premises hardware with cloud services to optimize costs and performance.

By serving as the backbone of the AI Stack, cloud infrastructure and semiconductors enable the transformative potential of AI across industries, ensuring that the upper layers of the stack - AI OS, applications, and foundation models - can deliver on their promise of innovation and productivity. 

Conclusion

In conclusion, the AI Stack defines how technology powers innovation by integrating foundational models, infrastructure, specialized applications, and emerging AI Operating Systems like Steve. This layered architecture allows organizations to build AI solutions by combining components according to their needs, while maintaining interoperability between layers. As AI continues to evolve, the effective use of these layers will remain essential for translating AI’s potential into practical solutions.