The Microsoft's AI Stack: From Copilot, Azure to the Last Mile

Microsoft is positioning itself as the cornerstone of the AI economy through a sophisticated three-layer strategy encompassing infrastructure (Azure), interface (Copilot), and development (GitHub). With $281.7 billion in FY25 revenue and $102 billion in net income, Microsoft leverages its financial strength, global distribution network, and technological expertise to create an AI ecosystem that mirrors its Windows-era dominance. This post provides a methodical analysis of Microsoft’s strategy, starting with the current AI landscape, followed by strategic analyses using established frameworks, a deep dive into its AI and distribution play, financial backing, and concluding with future prospects and risks.

Source: Generative AI - The New Reality - 2023

1. The Current AI Landscape

1.1 The Rise of AI in Business and Technology

The artificial intelligence (AI) market is experiencing unprecedented growth, driven by advancements in machine learning, natural language processing (NLP), and generative AI. According to industry estimates, the global AI market is projected to reach $1.8 trillion by 2030, growing at a CAGR of 37.3% from 2023. Enterprises across industries—healthcare, finance, retail, and manufacturing—are adopting AI to enhance productivity, automate processes, and deliver personalised customer experiences.

Key trends shaping the AI landscape include:

• Generative AI: Tools like large language models (LLMs) are transforming content creation, customer service, and decision-making.

• Multi-Agent Systems: Collaborative AI agents are enabling complex workflows, such as supply chain optimisation and fraud detection.

• Cloud-Based AI: Cloud platforms are democratising access to AI, allowing organisations of all sizes to leverage scalable infrastructure.

• Developer Ecosystems: Platforms like GitHub are empowering developers to build AI-native applications, driving innovation.

1.2 Key Players and Competitive Dynamics

The AI market is highly competitive, with several players vying for dominance:

• Amazon Web Services (AWS): Leads in cloud infrastructure with AWS SageMaker for machine learning and Bedrock for generative AI.

• Google Cloud Platform (GCP): Excels in AI research with models like Gemini and a strong focus on data analytics.

• Microsoft: Combines cloud (Azure), AI interfaces (Copilot), and developer tools (GitHub) for an integrated ecosystem.

• Emerging Players: Companies like Anthropic, Databricks, and Snowflake target niche AI and data analytics markets.

• Open-Source Communities: Frameworks like Hugging Face and PyTorch provide accessible AI tools, challenging proprietary platforms.

Despite this competition, Microsoft’s unique integration of infrastructure, interface, and development layers gives it a distinct advantage, as explored later.

1.3 Microsoft’s Position in the AI Ecosystem

Microsoft has emerged as a leader in the AI economy, leveraging its cloud infrastructure (Azure), AI interface (Copilot), and developer platform (GitHub) to capture value across the AI stack. Its strategic partnership with OpenAI, the creator of ChatGPT, has bolstered its AI capabilities, while its $281.7 billion FY25 revenue and $102 billion net income provide the financial muscle to sustain massive investments. Microsoft’s 1 million+ partner ecosystem further amplifies its reach, positioning it as the foundational platform for AI adoption.

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2. Strategic Analysis of Microsoft’s AI Approach

To understand Microsoft’s dominance, we apply three strategic frameworks: Porter’s Five Forces, Mintzberg’s Five Ps of Strategy, and Blue Ocean Strategy. These analyses provide insights into the competitive dynamics, strategic intent, and market creation efforts driving Microsoft’s AI strategy.

2.1 Porter’s Five Forces Analysis

Porter’s Five Forces evaluates the competitive environment in the AI and cloud computing markets.

1. Threat of New Entrants (Low to Moderate)

   • Barriers to Entry: The AI market requires significant capital, technical expertise, and scale. Microsoft’s $75 billion annual capital expenditure (capex) on data centres and AI hardware creates high barriers. Its partnerships with NVIDIA for GPUs and development of custom silicon (e.g., Azure Maia) further deter new entrants.

   • Brand and Ecosystem: Microsoft’s established brand and integrated ecosystem (Azure, Copilot, GitHub) make it challenging for startups to compete.

   • Challenges: Niche players like Anthropic or Databricks could disrupt specific segments, but they lack Microsoft’s breadth and distribution.

2. Bargaining Power of Suppliers (Moderate)

   • Key Suppliers: Microsoft relies on hardware providers like NVIDIA for GPUs and chip manufacturers for custom AI accelerators. NVIDIA’s dominance in AI chips gives it leverage.

   • Mitigation: Microsoft is reducing dependency by developing in-house silicon (e.g., Azure Maia) and diversifying suppliers.

   • Impact: Supplier power is moderated by Microsoft’s scale and long-term contracts.

3. Bargaining Power of Buyers (Moderate)

   • Enterprise Customers: Large enterprises can negotiate contracts, but Microsoft’s ecosystem lock-in (e.g., Office 365 integration) limits their ability to switch.

   • Smaller Customers: SMBs and developers have less power due to Microsoft’s market dominance.

   • Impact: Switching costs and ecosystem benefits reduce buyer power.

4. Threat of Substitutes (Moderate)

   • Substitutes: AWS, Google Cloud, and open-source frameworks like Hugging Face offer alternatives. However, Microsoft’s integrated ecosystem and developer tools create differentiation.

   • Differentiation: Copilot’s seamless integration and Azure’s multi-agent capabilities set Microsoft apart.

   • Impact: Substitutes exist, but high switching costs protect Microsoft’s position.

5. Industry Rivalry (High)

   • Competitors: AWS, Google Cloud, and emerging players compete intensely in cloud and AI markets.

   • Microsoft’s Advantage: Its three-layer strategy, partner ecosystem, and financial strength provide a competitive edge.

   • Impact: Rivalry drives innovation, but Microsoft’s integration gives it a defensible position.

Conclusion: Microsoft operates in a competitive but favourable environment, with high barriers to entry and ecosystem lock-in mitigating threats.

2.2 Mintzberg’s Five Ps of Strategy

Mintzberg’s Five Ps—Plan, Ploy, Pattern, Position, and Perspective—dissect Microsoft’s strategic intent.

1. Plan: A Deliberate AI Roadmap

   • Microsoft’s AI strategy is a deliberate plan to dominate through infrastructure (Azure), interface (Copilot), and development (GitHub). Investments in AI research, partnerships (e.g., OpenAI), and $75 billion capex reflect a clear roadmap.

2. Ploy: Ecosystem Lock-In

   • Microsoft’s integration of Copilot across its productivity suite and partnerships with enterprise software providers is a ploy to create lock-in, increasing switching costs for customers.

3. Pattern: Consistent Innovation

   • Microsoft’s history of adapting to technological shifts—Windows, cloud computing, AI—shows a pattern of innovation. Acquisitions like GitHub (2018) and investments in Azure since 2010 demonstrate strategic foresight.

4. Position: Platform Leader

   • Microsoft positions itself as the AI economy’s foundational platform, controlling infrastructure, interface, and development layers.

5. Perspective: AI as a Universal Enabler

   • Microsoft views AI as a tool for all—enterprises, developers, and consumers. Copilot’s accessibility, Azure’s scalability, and GitHub’s developer tools reflect this perspective.

Conclusion: Microsoft’s strategy combines deliberate planning, competitive tactics, and a vision to democratize AI.

2.3 Blue Ocean Strategy

Blue Ocean Strategy focuses on creating uncontested market space through value innovation.

Value Innovation Microsoft’s three-layer strategy delivers:

• Scalable Infrastructure: Azure’s multi-agent systems and Cosmos DB enable complex AI workloads.

• Accessible Interface: Copilot simplifies AI for non-technical users.

• Developer Empowerment: GitHub Copilot accelerates AI-native application development.

ERRC Grid (Eliminate, Reduce, Raise, Create)

• Eliminate: Complexity of AI adoption through Copilot’s intuitive interface.

• Reduce: Infrastructure costs via Azure’s serverless architecture.

• Raise: Developer productivity with GitHub Copilot’s AI-driven tools.

• Create: A multi-agent AI ecosystem with Azure’s collaborative capabilities.

Uncontested Market Space Microsoft’s integrated ecosystem creates a blue ocean where competitors struggle to match its breadth. AWS and Google Cloud offer robust AI tools, but lack Microsoft’s seamless integration across productivity tools, developer platforms, and infrastructure.

Conclusion: Microsoft’s Blue Ocean Strategy creates a unique AI ecosystem, positioning it as the default platform.

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3. Microsoft’s AI and Distribution Strategy

The core of Microsoft’s AI dominance lies in its three-layer strategy—infrastructure (Azure), interface (Copilot), and development (GitHub)—amplified by its global distribution network. This section explores each layer and the distribution advantage, highlighting how they create a self-reinforcing ecosystem.

3.1 Infrastructure Layer: Azure’s Multi-Agent Revolution

Azure is the backbone of Microsoft’s AI strategy, designed to support multi-agent systems that enable
collaborative AI workflows.

Azure Functions: Scalability for AI Workloads Azure’s serverless architecture, particularly Azure Functions, is optimised for the bursty nature of AI workloads. For example, an AI agent processing insurance claims may remain idle for hours before handling thousands of claims. Azure Functions scales from zero to thousands of concurrent executions in seconds, reducing costs and ensuring performance. In FY25, Azure processed 1.5 trillion transactions daily, showcasing its scalability.

Cosmos DB: The Distributed Brain Azure Cosmos DB is a globally distributed, multi-model database that acts as the “brain” for multi-agent systems. It supports:

• Document-based agents: Storing conversation context as JSON for NLP applications.

• Graph-based agents: Using the Gremlin API for relationship networks (e.g., fraud detection).

• Time-series agents: Leveraging the Cassandra API for temporal data (e.g., IoT analytics).

Cosmos DB’s 99.999% uptime and global distribution ensure low-latency access, enabling seamless collaboration across regions. For example, a European customer service agent can access conversation history initiated in North America, enhancing global operations.

Competitive Edge Azure’s 200+ data centres and partnerships with NVIDIA for GPU clusters give it unmatched computational power. Microsoft’s $13 billion investment in OpenAI has also enhanced Azure’s AI capabilities, integrating models like GPT-4 into its infrastructure.

3.2 Interface Layer: Copilot as the New Windows

Copilot is Microsoft’s AI interface, abstracting the complexities of LLMs for non-technical users, much like Windows simplified computing.

Adoption Metrics Copilot’s adoption is staggering:

• 100 million monthly active users.

• 800 million monthly users across Microsoft’s AI features.

• 1.8 billion messages summarised in Teams.

• 60% of Fortune 500 companies are using GitHub Copilot.

Simplifying AI Copilot integrates with Microsoft’s productivity suite:

• Excel: Automates data analysis and visualisation.

• Teams: Summarises meetings and extracts action items.

• Word: Assists with drafting and summarising documents.

This integration reduces the learning curve, making AI indispensable to daily workflows.

3.3 Development Layer: GitHub’s Developer Ecosystem Control

GitHub, with 100 million developers and 500 million open-source projects, is Microsoft’s platform for controlling the AI development ecosystem.

GitHub Copilot’s Impact GitHub Copilot, powered by AI, has transformed coding:

• 75% sequential growth in enterprise adoption in Q4 FY25.

• Code autocompletion: Suggests entire functions based on context.

• Code review: Identifies bugs and suggests optimisations.

Strategic Importance: Developers define how users interact with technology. By empowering developers with AI tools, Microsoft ensures that the next generation of AI applications is built within its ecosystem, reinforcing its platform dominance.

3.4 The Distribution Advantage: Microsoft’s Partner Ecosystem

Microsoft’s 1 million+ partner ecosystem—including system integrators, ISVs, MSPs, and VARs—amplifies its AI reach.

Partner Roles

• Localised Expertise: Partners tailor AI solutions to regional needs.

• Industry Solutions: Develop AI applications for verticals like healthcare and finance.

• Compliance: Ensure adherence to regulations like GDPR and CCPA.

• Human Touch: Provide training and support for AI adoption.

Force Multiplier Partners enable Microsoft to scale globally. For example, Accenture and Deloitte deploy Copilot for multinational clients, while local MSPs serve SMBs in emerging markets.

3.5 Creating Ecosystem 

Microsoft’s strategy creates an ecosystem across the following

• Product Integration: Office 365, SharePoint, Teams, and Windows are tightly integrated, increasing switching costs.

• Third-Party Partnerships: Companies such as Adobe, SAP, ServiceNow, and Workday integrate AI capabilities within Microsoft’s ecosystem, reinforcing its platform status.

Product integration ensures that customers experience the same CX  across Microsoft’s ecosystem and ensures there is no disruption to workflows and productivity. 

Source: Generative AI - The New Reality - 2023

4. Data and Financial Backing

Microsoft’s financial strength and data-driven approach underpin its AI strategy.

4.1 Financial Performance Metrics

In FY25, Microsoft reported:

• Revenue: $281.7 billion, up 14.93% year-over-year.

• Cloud Revenue: $98.4 billion (35% of total revenue).

• EBITDA Margin: 55.6%, compared to a sector median of 10.6%.

• Operating Profit: $128.5 billion, up 17.5%.

• Net Income: $102 billion, providing ample capital for AI investments.

4.2 Investment in AI Infrastructure

Microsoft’s $75 billion capex in FY25 focused on:

• Expanding Azure’s 200+ data centres.

• Developing custom silicon (e.g., Azure Maia).

• Partnering with NVIDIA for GPU clusters.

4.3 Revenue Streams and Market Penetration

The Server products and Cloud services segment, including Azure and GitHub, generates $98.4 billion in recurring revenue, ensuring financial stability. Copilot’s adoption by 60% of Fortune 500 companies and GitHub’s 100 million developers highlight Microsoft’s market penetration.

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5. Future Outlook and Risks

5.1 Future Opportunities in the AI Economy

Microsoft is poised to capitalise on:

• Generative AI Growth: Expanding Copilot’s capabilities for new use cases.

• Multi-Agent Systems: Leading in collaborative AI for complex workflows.

• Global Expansion: Leveraging partners to penetrate emerging markets.

5.2 Potential Risks and Challenges

• Competition: AWS, Google Cloud, and niche players like Databricks.

• Regulation: Antitrust scrutiny, particularly around the OpenAI partnership.

• Ethical Concerns: Bias in AI models and data privacy issues.

• Execution Risks: Challenges in Scaling AI Across Diverse Markets.

5.3 Mitigation Strategies

Microsoft mitigates risks through:

• Diversification: Broad product portfolio reduces reliance on any single revenue stream.

• Compliance: Partnerships ensure adherence to regional regulations.

• Innovation: Continuous R&D to stay ahead of competitors.

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6. Conclusion: Platform-Level AI Dominance

Microsoft’s three-layer AI strategy—Azure, Copilot, and GitHub—positions it to dominate the AI economy. Backed by a 1 million+ partner ecosystem, $281.7 billion in revenue, and strategic integration, Microsoft is recreating its Windows-era dominance. Strategic analyses confirm its competitive advantage, while its financial strength ensures sustained investment. Despite risks, Microsoft’s ecosystem and innovation make it the foundational platform for the AI economy.

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How DeepSeek's Challenge Actually Strengthened NVIDIA's Market Position

How DeepSeek's Challenge Actually Strengthened NVIDIA's Market Position

The emergence of DeepSeek, a Chinese AI startup, initially sent shockwaves through the market, raising fears of disruption to NVIDIA's dominance in the AI chip market. DeepSeek's claim of developing an advanced AI model with significantly lower computing costs triggered a sharp market correction, wiping out nearly $750 billion from NVIDIA's market capitalization in a week. However, this seemingly negative event has, paradoxically, strengthened NVIDIA's long-term market position.   

The Initial Shock and the Correction:

The initial market reaction was swift and decisive, with NVIDIA's stock price dropping by 6% while the Nasdaq remained relatively flat. This correction, while painful in the short term, has resulted in a more attractive valuation for NVIDIA. The forward P/E ratio has compressed from a lofty 40x six months ago to a more reasonable 32x. This reset makes NVIDIA more appealing to investors seeking a balance of growth and value.

Beyond the Headlines: Customer Spending Speaks Volumes:

While the market reacted to the DeepSeek news, the actions of NVIDIA's largest customers tell a different story. Amazon, Meta, and Alphabet, the very companies that would be most likely to benefit from a cheaper alternative to NVIDIA's hardware, have all announced massive increases in capital expenditure for AI infrastructure. Crucially, they've all reaffirmed their strong partnerships with NVIDIA. This demonstrates that despite the emergence of potentially competitive solutions, NVIDIA remains the preferred and trusted provider for these industry giants. Amazon's commitment of $100 billion in capital expenditure for 2025, with a significant portion earmarked for AI infrastructure, is a powerful testament to this reality.   

Financial Metrics Tell the Tale:

The financial impact of the DeepSeek event, coupled with continued customer spending, paints a compelling picture for NVIDIA:

• Valuation Reset: The stock price correction has created a more attractive entry point for investors.
• Sustained Growth: Despite the lower P/E ratio, NVIDIA's projected revenue growth of 53% significantly outpaces its mega-cap peers, who are expected to average around 12.2%.
• Unwavering Customer Commitment: The substantial investments in AI infrastructure by major tech companies underscore NVIDIA's indispensable role in their AI strategies.
• Dominant Market Share: Projected data center revenue of $113 Bn for the fiscal year further solidifies NVIDIA's market leadership.

Navigating the Blackwell Transition – A Short-Term Hurdle:

While the transition to the Blackwell chip family presents a potential short-term challenge, with some analysts predicting a slowdown in growth during this period, NVIDIA's CEO has stated that production is "in full steam." The anticipated high-volume shipments in the second half of the fiscal year should mitigate any negative impact and pave the way for continued growth. The lowered market expectations due to the DeepSeek news could actually work in NVIDIA's favour if they manage to exceed forecasts.

The DeepSeek Silver Lining:

The DeepSeek episode, rather than being a threat, has served as a valuable market correction, bringing NVIDIA's valuation down to more sustainable levels while simultaneously highlighting the company's fundamental strengths. It has reinforced the fact that even with advancements in AI model efficiency, the demand for high-performance computing infrastructure remains robust. The incident has also provided a stress test for NVIDIA, demonstrating the company's resilience and its deep entrenchment within the AI ecosystem.

Looking Ahead:

NVIDIA's future prospects remain bright. The company is poised to capitalize on the continued explosive growth of AI, driven by its technological leadership, strong customer relationships, and dominant market share. The DeepSeek challenge, while initially perceived as a threat, has ultimately served to strengthen NVIDIA's position, creating a more compelling investment case and solidifying its role as the backbone of the AI revolution.

Nvidia's Post-Earnings Boost is Ahead: A Breakdown

 Nvidia's Post-Earnings Boost: A Breakdown

Nvidia's upcoming earnings call on August 28th is highly anticipated due to several key factors that position the company for a potential share price surge.

Key Factors Driving Nvidia's Potential Post-Earnings Boost

1. Inventory Disparity:

   • Nvidia's low inventory levels compared to AMD's bloated stock suggest strong demand and efficient production. This indicates a healthier financial position and potential for higher revenue.
   • The contrast between the two chip giants highlights Nvidia's superior supply chain management and ability to capitalize on market demand.

2. Dominant Pricing Power:

   • Nvidia's H100 GPUs command a significantly higher price than AMD's competing MI300X, demonstrating exceptional pricing power.
   • This pricing advantage translates into higher revenue per unit and improved profit margins, contributing to overall financial strength.

3. LLM-Driven Demand Acceleration:

   • The burgeoning LLM market is a key growth driver for Nvidia, as these models require immense computational power provided by its high-performance GPUs.
   • The rapid expansion of LLM model sizes and training requirements indicates sustained demand for Nvidia's chips in the foreseeable future.

4. Outperforming AMD in Data Center Segment:

   • While AMD reported impressive growth in its data centre segment, Nvidia's superior inventory management and pricing power position it to potentially deliver even stronger results.
   • This outperformance could further solidify Nvidia's dominance in the AI chip market.

5. Valuation and Volatility:

   • Despite its high valuation, Nvidia's stock is characterized by significant volatility.
   • Positive earnings results could trigger a substantial upward movement in the share price, given the high investor interest in the company.

The Broader Tech Landscape: A Comparative Analysis

When compared to other tech giants, Nvidia stands out in terms of its focus on AI and high-performance computing. Companies like Amazon, Meta, Microsoft, and Google are investing heavily in AI infrastructure, as evidenced by their high CapEx to Operating Cash Flow ratios. Apple, on the other hand, appears to be taking a more cautious approach.

Nvidia's role as a critical supplier of AI hardware positions it as a key beneficiary of this industry-wide trend. Its ability to convert this demand into strong financial performance will be a key focus for investors during the earnings call.

In conclusion, the combination of low inventory, high pricing power, and the booming LLM market creates a compelling case for Nvidia's post-earnings share price appreciation. While the stock's valuation and market volatility introduce risks, the company's strong competitive position and the overall positive industry outlook make it a compelling investment opportunity.

Image Credit: Richad Jarc.

Tesla - Its not a Car, Its an AI Device on Wheels

Tesla - It's not a Car, It's an AI Device on Wheels

Tesla - Evolution

Tesla - AI Device on Wheels

Teslas Secret of Success

Anecdote:

In 2013, Elon Musk sent a meeting request to Apple's CEO Tim Cook, but Tim refused to meet him.  Musk confirmed this by tweeting in Dec 2020, that he had tried to sell Tesla to Apple for 1/10 of its current value during "the darkest days of the Model 3 program." 

It's still a mystery why Apple didn't end up buying Tesla, but there are a few theories out there. One possibility is that Apple's CEO, Tim Cook, just wasn't interested in acquiring Tesla. Another theory is that Cook was worried about some of the difficulties Tesla was going through at the time, like production delays and financial losses. Lastly, it's also possible that the two companies couldn't agree on a fair price for Tesla.

Occasionally, there is a rumour that Elon Musk aspired to become the CEO of Apple. Following Steve Jobs' passing in 2011, whom he admires and finds inspiring, Elon believed he would be a fitting successor due to their shared strategic vision. Notably, Jobs was passionate about electric vehicles and had expressed a desire to create one.

To date, Cook has neither denied nor commented on Musk's claim.

Tesla - Where is the Growth

Tesla's Fully Self Driving (FSD) Ambition

Tesla - Powered by AI-Enabled Dojo 

What is Dojo and its Purpose 

Dojo is a supercomputer and is deemed to be the 3rd fastest supercomputer in the world behind  the other two Frontier (Oak Ridge National Laboratory, USA) Fugaku (RIKEN Center for Computational Science, Japan)

These supercomputers are both capable of performing more than 1 exaflop of floating-point operations per second (FLOPS). An exaflop is equal to one quintillion (1 followed by 18 zeros) operations per second.

It is estimated to be able to perform more than 1 exaflop of training operations per second (TOPS). TOPS is a measure of the performance of a supercomputer on AI workloads.

Dojo uses a custom-designed chip called the D1 chip. Tesla has not released many details about the D1 chip, but it is known to be a very powerful and efficient chip for AI workloads. Dojo is expected to use tens of thousands of D1 chips. 

Tesla had previously utilised Nvidia chips, however, it remains uncertain if the Dojo system incorporates Nvidia's Accelerators. Historically, Elon Musk has expressed dissatisfaction with Nvidia's inability to meet its demands and scale accordingly.

The cost of building and running Dojo is significant and Tesla has not released the cost of building and running Dojo. However, they have mentioned it has invested more than $1Bn to build it.

My other posts on Generative AI and Strategic Analysis of Key Players

• AI Value Chain 
• Amazon the King of Retail -WhyAWS is the Crown Jewel
• Microsoft the King of AI in Software, Salesforce under the AI Cloud 
• Google the King of Search - What the Future Beholds in the AI World
• Taiwan Semiconductor - The Chipmaker That Runs The World
• Nvidia Godfather of AI - Why the Market is Bullish
• Generative AI can transform Telecoms, Energy and Utilities

Source: SeekingAlpha, Bloomberg, Martinfowler.com, Databricks.com, Nvidia, Google, AWS, Fourweek MBA Blog, Amazon, Ashwath Damodaran Blog, TSMC

Taiwan Semiconductor - The Chipmaker That Runs The World

Taiwan Semiconductor (TSMC) - The Chipmaker That Runs The World

Key Indicators 

• Domain: Semiconductors 
• Comp. (Chip Manf) – Samsung, SMIC, GFS, UMC
• Growth Segment – HPC AI Chips (Up)
• Economic Moat – Wide
• Cyclical - Yes

Key Customers (Total 530+)

• TSMC's revenue is made up of 26% from Apple and 7% from Nvidia. Apple has a 10-year partnership with the chip maker. 
• Apple designs chips for iPhones and Mac computers, while Google designs Tensor Chips for Pixel smartphones. Qualcomm and MediaTek design processors for Android phones. Nvidia designs Gaming and Artificial Intelligence (AI) processors, and AMD and Nvidia design advanced processors for Tesla. 
• TSMC chips are also used by major cloud providers like AWS, MSFT, Google, Oracle, and IBM for data centres, networking, and software. Broadcom designs chips for broadband and wireless markets.  

TSMC is able to offer its customers its manufacturing capabilities in the areas of Smartphones, High-Performance Computing (HPC), Internet of Things (IoT), Automotive and Digital Consumer Electronics. TSMC calls its Technology Leadership, Manufacturing Excellence and Customer Trust as the TSMC Trinity of Strengths.

TSMC is a major player in three of the top four semiconductor growth sectors, which include Silicon Carbide (SiC), Gallium Nitride (GaN), AI Compute Processors, and Generative AI.

Traditional Artificial Intelligence (AI)

AI servers are specialised computers designed for AI Training and Inference. Training involves adjusting the layers of the neural network based on results and can require a month of computational power. Inference uses trained neural network models to infer results. AI chips are used for applying trained AI algorithms to real-world data inputs, which is often referred to as "inference".

Specialised chips called Accelerators play a crucial role in the field of deep learning. There are two types of accelerators, Training Accelerators and Inference Accelerators. Training accelerators are optimised to facilitate the training of deep learning models by performing intricate calculations and processing extensive datasets. Inference Accelerators, on the other hand, execute trained models on fresh data with great speed, making them perfect for real-time applications such as image recognition in cameras or voice assistants in smartphones.

Generative Artificial Intelligence (Gen - AI)

Traditional AI relies on structured, labelled data for training and is confined to specific tasks such as image recognition, sentiment analysis, and recommendation systems. Generative AI, on the other hand, aims to simulate human-like creativity and generate content autonomously. It is versatile and capable of producing diverse outputs across various domains, including text, images, music, and even entire applications. The key aspect of Gen AI models is their ability to generate content that goes beyond the scope of their
training data.

Various types of Gen AI chips are

• GPU (Graphics Processing Unit)
• TPU (Tensor Processing Unit)
• FPGA (Field-Programmable Gate Array)
• ASIC (Application-Specific Integrated Circuit)
• Neuromorphic Chips

My other posts on Generative AI and Strategic Analysis of Key Players

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• Microsoft the King of AI in Software, Salesforce under the AI Cloud 
• Tesla - It's not a Car, Its an AI Device on Wheels
• Google the King of Search - What the Future Beholds in the AI World
• Nvidia Godfather of AI - Why the Market is Bullish
• Generative AI can transform Telecoms, Energy and Utilities

Source: SeekingAlpha, Bloomberg, Martinfowler.com, Databricks.com, Nvidia, Google, AWS, Fourweek MBA Blog, Amazon, Ashwath Damodaran, TSMC

Image generated by Open AI's Dall.e - 3

Google the King of Search - What the Future Beholds in the AI World

Google the King of Search - What the Future Beholds in the AI World 

Google's Evolution

 

Google King of Search - Where is the Growth

Google Search - Economics with Gen AI

Google Cloud's - AI and ML Play

Googles AI Journey 

Googles $100Bn Blunder

Google - Risks to its Growth

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• Salesforce under the AI Cloud 
• How Generative AI can transform Telecoms
• Energy and Utilities

Source: Seekingalpha, Bloomberg, Martinfowler.com, Databricks.com, Linkedin, Nvidia, Google, AWS, Fourweek MBA Blog, Amazon

Nvidia Godfather of AI - Why the Market is Bullish

 Nvidia Godfather of AI - Why the Market is Bullish

What is their Economic Moat

Where is the Growth

Dot Com Era - History Repeating Itself 

Nvidia - Under The Hood 

Two CEO's Personal and Professional Relationship

Risks to its Growth

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