Book - Gen AI The New Reality - How Key Players Are Progressing

Gen AI The New Reality - How Key Players Are Progressing

About the Book 

In the rapidly evolving realm of Generative AI, this book delves into the intricacies of this transformative technology, exploring its history, potential, and key players. It unravels the value chain, deployment models, and future trajectory of Large Language Models (LLMs) while shedding light on the growth opportunities in this domain.

Embark on a journey through the world of chipmakers, where TSMC reigns supreme, pioneering the most advanced chips, yet facing its unique challenges. Discover the driving forces behind Nvidia's dominance as the "Godfather of AI" and analyse the potential for a dot-com bubble resurgence.

Venture into the realm of Hyperscalers, where Microsoft stands as the undisputed king of AI in the cloud and software. Explore its strategic partnerships, the economics of training AI systems, and the inherent risks associated with its growth.

Delve into the world of Google, the search giant that's leveraging Gen AI to revolutionize its offerings. Examine its search economics, cloud play, diversification efforts, and the infamous $100 billion blunder.

Uncover the secrets behind Amazon's retail empire, where multiple flywheels drive its growth. Analyse the intricacies of AWS, the crown jewel of Amazon's offerings, and the company's pursuit of new flywheels.
Step into the automotive sector, where Tesla stands as a visionary leader, constantly reinventing its vehicles. Explore the company's secret sauce, its growth trajectory, its ambitious FSD plans, and the role of AI-enabled Dojo.

Discover how Oracle, the database leader, is transforming into an AI innovator. Understand the company's growth strategy, its focus on Gen AI, and the challenges it faces.

Dive into the world of Salesforce, a cloud and AI-powered CRM giant. Explore its growth trajectory, its evolving relationship with competitors, and the potential risks it faces.

Examine SAP, the ERP market leader, as it strives to become a one-stop shop for businesses. Understand the company's efforts to catch up with AI advancements and the challenges it faces.

Uncover the story of IBM, a tech giant facing growth hurdles. Analyse its history of misfires, its comprehensive AI play, and the factors that have led to its relative stagnation in recent years.

Key Takeaways

• Understand the evolution, hype, and potential of Generative AI.
• Discover the value chain, deployment models, and future growth of LLMs.
• Analyse the dominance of chipmakers like TSMC and Nvidia.
• Delve into the AI strategies of Hyperscalers like Microsoft, Google, and Amazon.
• Explore the AI innovations of automotive, software, and security companies.
• This book provides a comprehensive overview of the Generative AI landscape, equipping readers with the insights needed to navigate this transformative era.

The sample chapters on Hyperscalers and Chip Makers are available for download below.

The Future of Software is New SaaS

The Future of Software is New SaaS - powered by Services, AI Agents, Sharing

This POV is available for download below.

Aussie BroadBand on Acquisition Spree

First, what I wrote about ABB's FY23 Results last year.   

Update on ABB's Business 

ABB's Acquisition Spree - Ongoing Tussle and Drivers Behind it. 

My other post on NBN and its Economics

Australias Telecom Industry in Transition

Australia Telecom Industry in Transition - From Four Pillar to Six Pillar Model 

Australia Telecom Industry - Fixed Services 

Australia Telecom Industry - Fixed Internet Ranking  

Australia Telecom Industry - Mobile Services 

Australia Telecom Industry - Mobile Internet Ranking  

My previous post on the Global Telecom Industry Evolution to date.

Generative AI - Where is The Growth ?

 Generative AI - Where is The Growth? 

• The current state of the Gen AI industry shows that big tech companies, especially hyper scalers, dominate the scene. They are the primary drivers of innovation and growth, focused on achieving long-term sustainability by shifting their focus from selling computing to selling generic and specialised model services with higher margins. 
• This has led to increased interest from venture capitalists, resulting in numerous startups focused on selling model-based services and integrating with existing apps and services. The low barriers to entry make it easy for startups to grow in the short term, but sustainability is challenging without a unique proposition. Many startups will likely fail, with some being acquired by larger companies.
•  Software providers such as Salesforce, Oracle, and Workday are also integrating AI services, either by building or purchasing specialised services to defend and survive the industry changes.

Future of Language Models

• Large Language Models (LLMs) are incredibly resource-hungry (compute & finance), making them only accessible to a select few organisations. Besides their time to market duration is not desirable.
• Google, Meta, and other major players in the AI industry are actively working to make LLMs more efficient and affordable for wider 

Foundation Model vs Large Language Model

Large Language models (LLMs) are a type of machine learning model that can process and generate human language. They are trained on massive datasets of text and code and can be used for a variety of tasks, such as translation, summarisation, question answering, and creative writing.

Foundation Models are a newer type of AI model that is still under development. They are trained on even larger datasets of text, code, and other types of data, such as images and videos. Foundation models are designed to be more general-purpose and adaptable than LLMs, meaning that they can be used for a wider range of tasks.

Key Differences

Foundation models are multimodal, meaning that they can work with multiple types of data. This enables them to perform tasks that would not be possible for an LLM alone, such as generating images from text or translating videos between languages.

Besides, Foundation models are designed to be more transferable. This means that they can be easily adapted to new tasks without having to be retrained from scratch. This makes them more practical for use in real-world applications.

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 
• Tesla - It's not a Car, It's 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
• IBM - How it Lost Its Way
• 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

Generative AI - Framework to Identify Use Case and Investment

 Generative AI - Framework to Identify Use Case and Investment

Application of Framework - Use Cases for Telecom

Gen AI - Use Cases for Telecom

Palo Alto Networks - What is their Growth Template

Palo Alto Networks - Leader in Cyber Security  

Key Indicators 

• Market Cap – 73.07 Bn
• EV – 72.95 Bn
• Debt - $2.26Bn
• P/B – 41.79 (Goodwill from M&A) 
• P/E (Trailing) – 184.99 (Growth) 
• P/E (Forward) – 44.44 (Growth)
• Economic Moat: Wide (product, innovative)   

• Palo Alto Network provides network security solutions. The company's solution offerings spread across network security, cloud-native application protection, security operations, and endpoint security and are available across multiple key industries.
• Cybersecurity has 5 stage lifecycle - Identify, Protect, Detect, Recover and Restore. This cyclical process is essential for protecting an organisation from cyber threats. It helps to ensure that corporations are constantly prepared and able to respond to evolving threats.

Dominating Growth Strategy

Since Nikesh Joined as CEO in 2018, Palo Alto Networks has grown with a CAGR of 24.9% with market cap. surging by $48Bn to $73.1Bn. This growth is spurred by M&A activity, improving the top line and ensuring it outpaces both competitors and potential security threats from hackers.

Palo Alto Networks has spent nearly $4Bn in acquiring 17 businesses, to form its Cortex and Prisma Cloud businesses, since its inception. The company has consistently demonstrated its commitment to driving integrated organic growth through mergers, acquisitions, and partnerships with several well-known startups. Time and again, the company has proven its capability to integrate these acquisitions to enhance or complement its own product offering.

There are various activities taking place beneath the surface, including incoming acquisitions through M&As and partnerships, as well as ongoing spin-offs initiated by former employees. The company boasts of notable start-ups that have been founded by its alumni. 

The company strives to stay ahead of the competition by promoting a start-up culture internally and keeping a close watch externally for any potential opportunities. In the future, with Gen AI coming onto the centre stage it will focus on an AI and ML-centric acquisition that will have applications in cyber security.

The strategy of acquiring small and nimble players to ensure cutting-edge technology is available to its customers has proven effective. Nikesh, who has previously worked at Google, has adopted the same playbook from his former employer. However, due to the rise in short-term debt, the company's ability to generate FCF has decreased, making it difficult to pursue any big deals.

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 
• Tesla - It's not a Car, It's 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
• IBM - How it Lost Its Way
• 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