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AWS AI takeover: 5 cloud-winning plays they’re using to dominate the market

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The video call connected with a burst of static, like the sudden death of a thousand startups. Here is Matt Wood, VP of AI products at AWS, crammed into what might be a janitor’s closet at the Collision conference in Toronto. I imagine the scene outside Wood’s video prison, as thousands of glassy-eyed developers are probably shuffling past like extras from a Kubrick film, blissfully unaware of the leviathan growing beneath their feet. Wood’s eyes gleam with secrets.

“Machine learning and AI at AWS is a multi-billion dollar business for us by ARR at the moment,” says Wood, casually dropping a figure that would send most unicorn startups into the valuation stratosphere. “We’re very bullish about generative AI in general. It’s probably the single largest shift in how we’re going to interact with data and information and each other, probably since the early internet.”

Their recent moves underscore this commitment:

  • A $4 billion investment in Anthropic, securing access to cutting-edge AI models and talent.
  • The launch of Amazon Bedrock, a managed service offering easy access to foundation models from Anthropic, AI21 Labs, and others.
  • Continued development of custom AI chips like Trainium and Inferentia, optimizing performance and cost for AI workloads.

As Wood speaks, methodically painting a picture of AWS’s grand strategy with broad, confident strokes, I couldn’t help but think of the poor bastards out in Silicon Valley, prancing about with their shiny models and chatbots, bullshitting each other about AGI and the superintelligence. The peacocks admire their own plumage, seemingly oblivious to the enormous constrictor, even as it slowly coils around them. 

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The leviathan

While the flashy AI demos and chip CEOs in their leather jackets capture the public’s attention, AWS is focused on the less glamorous but absolutely essential task of actually building and operating AI infrastructure.

Amid all the noise in the AI market, it’s easy to forget for a moment just how massive AWS is, how brutally efficient they are at converting customer needs into cloud services, and how decisively they won The Great Cloud Wars. Now, they’re applying that same playbook to AI.

In its quest to conquer the AI market, AWS is deploying five proven strategies from its win-the-cloud playbook:

  1. Massive infrastructure investment: Pouring billions into AI-optimized hardware, data centers, and networking.
  2. Ecosystem building: Fostering partnerships and acquisitions to create a comprehensive AI platform.
  3. Componentization and service integration: Breaking AI into modular, easily combined services within the AWS ecosystem.
  4. Laser focus on enterprise needs: Tailoring AI solutions to the specific requirements of large, regulation-bound industries.
  5. Leveraging its security and privacy expertise: Applying AWS’s established cloud security practices to address AI-specific data protection concerns.

While everyone is playing with chatbots and video generators, AWS builds. Always building. Chips. Servers. Networks. Data centers. An empire of silicon, metal, and code. AWS’s $4 billion investment in Anthropic is just one example of how the company is building a comprehensive AI ecosystem, absorbing innovations and startups with terrifying efficiency. 

Make no mistake, fellow nerds. AWS is playing a long game here. They’re not interested in winning the next AI benchmark or topping the leaderboard in the latest Kaggle competition. They’re building the platform that will power the AI applications of tomorrow, and they plan to power all of them. AWS isn’t just building the infrastructure, they’re becoming the operating system for AI itself. 

And the suits? Oh, they’re coming alright. Banks, hospitals, factories – those boring, regulation-bound giants that make the world go ’round. They’re diving into the AI pool with all the grace of a three-legged elephant, and AWS is there, ready with a towel and a chloroform-soaked rag.

Wood noted these industries are adopting generative AI faster than average. “They’ve already figured out data governance, they’ve got the right data quality controls, right data privacy controls around all of their data,” he explained. This existing infrastructure makes adopting generative AI a relatively small step.

These customers often have vast amounts of private text data – market reports, R&D documents, clinical trials – that are perfect fodder for generative AI applications. “Generative AI is just really good at filtering, understanding, organizing, summarizing, finding differences, gray areas, and interesting parts across very, very large amounts of documents,” Wood said.

Wood emphasized AWS’s holistic view of generative AI, investing in three major buckets across the entire stack:

  1. Infrastructure: “At the very lowest level, we make sure that we’ve got the right infrastructure for customers to be able to train and tune foundation and specialized models, using their own data and using large data sets,” Wood explained. This includes custom-designed chips like Trainium for training and Inferentia for inference, as well as high-performance networking capabilities.
  2. Model Access: Through their Bedrock service, AWS offers a broad set of AI models from various providers. “We have by far the broadest number of generative AI models,” Wood stated. This includes models from Anthropic, AI21, Meta, Cohere, Stability AI, and AWS’s own Titan models.
  3. Application Development: AWS provides tools and services to help developers build AI applications quickly and easily. This includes SageMaker for machine learning workflows and various AI services for specific tasks like text analysis, image recognition, and forecasting.

To gain an appreciation for how AWS already stacks up and how it’s maneuvering versus Microsoft Azure and Google Cloud, it’s helpful to understand where each of the AI services across clouds are pitted against each other. 

Table 1: AI Features and Clouds 

Category Feature AWS Azure GCP
Machine Learning Platforms ML Platforms Amazon Bedrock, Amazon SageMaker Azure Machine Learning, Azure OpenAI Service Vertex AI
Model Training & Deployment Trn1n Instances, SageMaker Azure Machine Learning, Azure OpenAI Service Vertex AI
AutoML SageMaker AutoPilot Azure Machine Learning AutoML AutoML
Generative AI Generative Text Amazon Q, Amazon Bedrock GPT-4 Turbo, Azure OpenAI Service Vertex AI
Text-to-Speech Amazon Polly Azure Speech Service, Azure OpenAI Service Cloud Text-to-Speech
Speech-to-Text Amazon Transcribe Azure Speech Service Cloud Speech-to-Text
Image Generation & Analysis Amazon Rekognition Azure AI Vision, DALL-E AutoML Vision, Cloud Vision API
Conversational AI Chatbots Amazon Lex Azure Bot Service Dialogflow
AI Assistants Amazon Q GPT-4 Turbo with Vision, GitHub Copilot for Azure Gemini
Natural Language Processing NLP APIs Amazon Comprehend Azure Cognitive Services for Language Cloud Natural Language
Text Summarization Amazon Connect Contact Lens Azure OpenAI Service Gemini
Language Translation Amazon Translate Azure Cognitive Services for Language Cloud Translation API
AI Infrastructure AI Chips Inferentia2, Trainium N/A TPU (Tensor Processing Units)
Custom Silicon Inferentia2, Trainium N/A TPU
Compute Instances EC2 Inf2 N/A Compute Engine with GPUs and TPUs
AI for Business Applications AI for Customer Service Amazon Connect with AI capabilities Azure OpenAI Service, GPT-4 Turbo with Vision Contact Center AI 
Document Processing Amazon Textract Azure Form Recognizer Document AI
Recommendation Engines Amazon Personalize Azure Personalizer Recommendations AI
AI Content Safety Content Safety Features N/A Azure AI Content Safety, configurable content filters for DALL-E and GPT models Vertex AI safety filters
Coding Assistants Coding Assistants Amazon CodeWhisperer GitHub Copilot for Azure Gemini Code Assist

Similarly, let’s try to understand how the chess pieces are moving by looking at the major AI announcements at each of the cloud’s recent annual conferences: 

Table 2: Recent AI Announcements

Category AWS (reInvent 2023) Azure (Microsoft Build 2024) GCP (Google I/O 2024)
Generative AI Amazon Q: Generative AI-powered assistant for various business applications (Amazon Connect, Amazon Redshift) GPT-4 Turbo with Vision: Multimodal model capable of processing text and images Bard Enterprise: Enhanced capabilities for integrating generative AI in enterprise applications
Amazon Bedrock: Expanded choice of foundation models from leading AI companies and enhanced capabilities Azure OpenAI Service: Updates including new fine-tuning capabilities, regional support, and enhanced security features Vertex AI: Enhanced support for generative AI and integration with other GCP services
Machine Learning Platforms Amazon SageMaker: New capabilities including a web-based interface, code editor, flexible workspaces, and streamlined user onboarding Azure Machine Learning: Enhanced capabilities for training and deploying models with integrated support for Azure OpenAI Service Vertex AI Workbench: New tools and integrations for improved model training and deployment
AI Infrastructure AWS Graviton4 and AWS Trainium2: New instances for high-performance AI and ML training Azure AI Infrastructure: Enhanced support for AI workloads with new VM instances and AI-optimized storage solutions TPU v5: New generation of Tensor Processing Units for accelerated AI and ML workloads
Data and Analytics Zero-ETL Integrations: New integrations for Amazon Aurora, Amazon RDS, Amazon DynamoDB with Amazon Redshift and OpenSearch Service Azure Synapse Analytics: New features for data integration, management, and analysis using AI BigQuery ML: New AI and ML capabilities integrated into BigQuery for advanced data analytics
AI for Business Applications Amazon Connect: Enhanced generative AI features for improved contact center services Microsoft Dynamics 365 Copilot: AI-powered capabilities for business process automation AI for Google Workspace: New generative AI features integrated into Google Workspace for productivity and collaboration
Document Processing Amazon Textract: Enhanced capabilities for text, handwriting, and data extraction from documents Azure Form Recognizer: Improved accuracy and new features for document processing Document AI: New tools and integrations for automated document processing
AI Content Safety Guardrails for Bedrock Azure AI Content Safety: Configurable content filters for DALL-E and GPT models AI Security and Governance: New features for ensuring responsible and secure use of AI across applications
Conversational AI Amazon Lex: Enhanced natural language understanding capabilities Azure Bot Service: Improved integration with Azure OpenAI Service for advanced conversational AI Dialogflow CX: New features and integrations for building advanced chatbots and virtual assistants
Coding Assistants Amazon CodeWhisperer: Enhanced AI-powered coding suggestions and integrations with developer tools GitHub Copilot for Azure: New extensions and capabilities for managing Azure resources and troubleshooting within GitHub AI-Driven DevOps: New AI tools and features for improving software development and operations workflows

When we analyze the AI cloud services together with the recent announcements across all three major cloud shows – AWS re:Invent, Microsoft Build, and Google Cloud Next – it becomes a little clearer how the subtleties in these moves are playing to their respective strengths:


  • Generative AI and Enterprise Applications: AWS has a strong emphasis on enabling developers to create enterprise-grade applications with AI, using tools like Amazon Q and Amazon Bedrock to enhance productivity, customer service, and data management within organizations. This focus on practical, enterprise-ready AI solutions positions AWS as a leader in addressing real-world business needs.
  • Robust AI Infrastructure: AWS offers high-performance infrastructure like Graviton4 and Trainium2 specifically optimized for AI and ML workloads, catering to the demands of enterprise-scale operations. This infrastructure advantage allows AWS to support extensive AI training and inference at scale, which is critical for large enterprises and developers who need reliable, scalable performance.
  • Integrated AI Services: Services such as Amazon SageMaker, which streamline model building and deployment, and zero-ETL integrations, which simplify data workflows, are clearly geared towards developers and enterprise users looking for efficiency and scalability. These comprehensive solutions make it easier for businesses to implement and scale AI quickly and effectively.

Microsoft Azure

  • Enterprise Integration: Azure’s AI services are deeply integrated with Microsoft’s broader enterprise ecosystem, including products like Dynamics 365, Office 365, and GitHub. This integration provides a seamless experience for developers and business users, making Azure a strong contender for enterprises already invested in the Microsoft ecosystem.
  • Partnership with OpenAI: Azure leverages its partnership with OpenAI to offer cutting-edge generative AI models like GPT-4 Turbo with Vision, which serve both enterprise and consumer applications. This partnership enhances Azure’s AI capabilities, making it a versatile choice for developers and various applications.
  • Comprehensive AI Suite: Azure offers a wide range of AI and ML services through Azure Machine Learning and Azure Cognitive Services, addressing diverse needs from vision to language understanding. This broad suite of tools provides flexibility and capability for developers and enterprises of all sizes.

Google Cloud Platform (GCP)

  • Advanced Analytics Integration: GCP excels in integrating AI with data analytics, making it a strong choice for developers focused on data-driven AI applications. Tools like BigQuery ML and Vertex AI highlight this focus, which is particularly beneficial for enterprises that rely heavily on data analytics.
  • Consumer AI: Google’s AI efforts often span both enterprise and consumer domains. Google’s AI models and capabilities, such as those used in Google Search and Google Assistant, have strong consumer applications but also offer significant enterprise benefits. This dual focus allows GCP to serve a wide range of developers and users.
  • Innovative AI Research: GCP benefits from Google’s leadership in AI research, translating into advanced AI tools and capabilities available to developers. This research excellence positions GCP as a leader in cutting-edge AI technologies.


  • AWS: Predominantly focused on enabling developers to build enterprise-grade applications with robust, scalable AI solutions designed to integrate seamlessly with business operations. AWS’s strategic partnerships and infrastructure investments make it a formidable leader in enterprise AI.
  • Azure: Balances between enterprise and consumer applications, leveraging deep integrations with Microsoft’s ecosystem and advanced AI models through its OpenAI partnership. Azure provides a versatile and integrated solution for developers and businesses.
  • GCP: Strong in data analytics and AI research, with a noticeable focus on both consumer and enterprise applications, driven by Google’s broader AI initiatives. GCP’s dual focus allows it to cater to a diverse set of developers and needs

Stacking the stack

What does it mean when a technology truly succeeds? It fades into the background, becoming as ubiquitous and invisible as electricity or cellular data. This looming dynamic aligns with researcher Simon Wardley’s model of how technologies evolve from genesis to commodity and utility models.

For example, in the early “Genesis” stage, generative AI required novel, custom-built models created by skilled researchers. But in just a short time, the underlying methods – transformer architectures, diffusion models, reinforcement learning, etc. – have become increasingly well-understood, reproducible and accessible. 

Wardley’s idea of componentization suggests that as technologies mature, they are broken down into distinct, modular components. This process allows for greater standardization, interoperability, and efficiency. In the context of AI, we’re seeing this play out as various elements of the AI stack – from data preprocessing to model architectures to deployment frameworks – become more modular and reusable. 

This componentization enables faster innovation, as developers can mix and match standardized parts rather than building everything from scratch. It also paves the way for the technology to become more of a utility, as these components can be easily packaged and offered as a service.

AWS has always been the master of componentization, and it’s this very approach that led to its dominance in the cloud computing market. By breaking down complex cloud technologies into distinct, modular services that cater to specific customer needs, AWS made cloud computing more accessible, flexible, and cost-effective.

Now, AWS is repeating this winning playbook in the AI domain. Services like Bedrock, which offers a smorgasbord of pre-trained models, and SageMaker, which streamlines the machine learning workflow, are perfect examples of how AWS is componentizing the AI stack. By providing a suite of purpose-built AI services that can be mixed and matched to suit specific requirements, AWS is democratizing AI and making it easier for businesses to adopt and integrate into their operations.

Bedrock is not just a product, it’s an ecosystem. Bedrock is AWS’s play to become the app store of AI models, a honeypot luring them in with promises of scale and efficiency. Anthropic, AI21, Meta, Cohere – all there, all feeding the beast – neatly packaged and ready for deployment with a few lines of code. AWS aims to position Bedrock as a critical component in the AI/ML value chain, reducing complexity and driving adoption across industries.

Think about Bedrock in the context of Amazon’s starting position, its competitive advantage in cloud computing. It’s a trap so beautiful, so efficient, that to resist is not just futile, it’s almost unthinkable:

  1. A massive customer base: AWS is the leading cloud provider, with millions of customers already using its services.
  2. Vast amounts of data: That customer data is already stored on AWS servers, making it easier to use for AI training and inference.
  3. Trained workforce: Most developers and data scientists are already familiar with AWS tools and services.
  4. Economies of scale: AWS’s massive infrastructure allows it to offer AI services at competitive (unbeatable) prices. 
  5. Operational expertise: AWS has years of experience managing complex, large-scale computing environments.

Another of AWS’s key strategies is providing customers with flexibility and future-proofing. “We do not believe that there’s going to be one model to rule them all,” Wood says, channeling his inner Gandalf. This approach allows customers to choose the best model for each specific use case, mixing and matching as needed. Wood noted that many customers are already using several models in combination, creating a “multiplier in terms of intelligence.”

Security is another area where AWS’s years of experience in cloud computing give it a significant edge. AWS has invested heavily in Nitro, which provides hardware-level security for cloud instances. Wood emphasized: “We’ve architected all the way down onto the accelerators to ensure that customers can meet their own, and exceed their own, privacy and confidentiality requirements. We can’t see the data. Put it in an enclave internally so their own employees can’t see the data or the weights.”  This level of security is essential for enterprises dealing with sensitive data, particularly in regulated industries. 

AWS’s financial resources allow it to play the long game. For example, it can afford to wait and acquire struggling AI startups at bargain prices, further consolidating its position. This strategy is reminiscent of AWS’s approach during the early days of cloud computing when it actively acquired from its own partner ecosystem.

By offering a wide range of services and continually lowering prices, AWS made it difficult for smaller cloud providers to compete. Most would-be competitors eventually exited the market or were acquired. I think history is about to repeat itself. . 

The sound of inevitability

Imagine the year 2030. You wake up, mumble to your AI assistant, and your day unfolds like a well-oiled machine. That helpful assistant? Running on AWS, of course. The autonomous vehicle that glides you to the office? Powered by AWS. The AI that diagnoses illnesses, manages investments, or engineers products? All purring contentedly in the AWS ecosystem.

Wood is wrapping up now, I can tell he needs to go. He hasn’t told me his secrets, but he’s polished, confident and at ease with this. He layers on the final brushstroke, like one of Bob Ross’ happy little clouds: “AWS, through the use of chips, SageMaker, Bedrock, really has everything that you need in order to be successful, whether you’re using big models, small models, and everything in between.”

This confidence in AWS’s existing infrastructure extends beyond Wood. At the upcoming VB Transform event, Paul Roberts, Director of Strategic Accounts at AWS, will make the case that we don’t need any other technology breakthroughs right now to accommodate infrastructure scaling needs for Generative AI. Roberts asserts that software improvements are sufficient, reflecting AWS’s belief that their cloud infrastructure can handle everything AI throws at it.

As the AI hype crescendos, then fades, AWS continues its relentless march, quiet and inexorable. The AI revolution comes then goes. Not with a bang, but with a server fan’s whir. You run your AI model. It’s faster now. Cheaper. Easier. You don’t ask why. The AWS cloud hums. Always humming. Louder now. A victory song. Can you hear it?

From a strategic perspective, I think AWS’s dominance in the AI space seems all but inevitable. Their established position in the cloud landscape, coupled with their vast ecosystem and customer base, creates formidable barriers to entry for potential competitors. As AI services evolve from custom-built solutions to standardized products and utilities, AWS is perfectly positioned to leverage its economies of scale, offering these services at unbeatable prices while continuously innovating.

AWS’s doctrine of focusing on user needs, operational excellence, and innovation at scale ensures they remain at the forefront of AI development and deployment. Their comprehensive suite of AI services, from foundational models to high-level APIs, makes them a one-stop shop for businesses looking to adopt AI technologies. This breadth of services, combined with enterprise-grade features and seamless integration with existing AWS products, creates a value proposition that’s hard for competitors to match.

Their strategic partnerships and collaborations with leading AI startups and research institutions allow them to incorporate new models and technologies into their platform, future-proofing their customers and further cementing their position as the go-to provider for AI services.

As we move towards 2030, the switching costs for businesses already deeply integrated into the AWS ecosystem will continue to rise, making it increasingly difficult for new entrants to gain a foothold in the market. The trust and brand recognition AWS has built over the years will serve as an additional moat, particularly for enterprise customers who prioritize reliability and performance.

As AI becomes more ubiquitous and fades into the background of our daily lives, it’s likely that AWS will be the invisible force powering much of the transformation. The question isn’t whether AWS will dominate the AI space, but rather how complete that domination will be. The cloud’s hum isn’t just a victory song – it’s the soundtrack.

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