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Amazon Flywheel Phenomenon - AWS is The Crown Jewel

Amazon Flywheel Phenomenon - Why AWS is The Crown Jewel  -- Amazon Evolution   Where is the Growth   Amazons Flywheel Phenomenon Amazons Multiple Flywheels  AWS The Crown Jewel  Amazon Chasing new Flywheel My other posts on Generative AI and Strategic Analysis of Key Players AI Value Chain   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

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 My other posts on  AI Value Chain ,   Microsoft King of AI in Software ,  Salesforce under the AI Cloud , AWS is the Crown Jewel and how  Generative AI can transform Telecoms ,  Energy and Utilities

Microsoft - The King of AI in Software and Cloud

Microsoft's transformation under Satya is phenomenal, from a PC dominant player to being called the King of AI in Software and Cloud.   Where is the Growth Partnership with OpenAI Why Microsoft is called as The King of Generative AI in Software and Cloud.  My other posts on  AI Value Chain ,   Microsoft The King of AI in Software ,  Salesforce under the AI Cloud  ,  AWS is the Crown Jewel  and how  Generative AI can transform Telecoms ,  Energy and Utilities Source:  Seekingalpha ,  Bloomberg , Martinfowler.com,  Databricks.com  Linkedin,  Nvidia ,  Google ,  AWS

CEO Leadership - Amazon, Microsoft and Googles Evolution

 Amazon, Microsoft and Google Evolution  - How these three Cloud Giants have evolved in terms of market cap and CEO leadership. -

Salesforce - Under The AI Cloud

 Salesforce - Under The AI Cloud  -- Marc Known for his mission and marketing mantra, ”The End of Software" Revolutionised the CRM industry by introducing Cloud-based service.  Despite facing tough competition from companies like Oracle, SAP, and Microsoft, it has emerged as the leader in this field. Today it commands 23% of the Global Market Share. Strategically acquired companies like Tableau, Slack (Data & Analytics) and MuleSoft (PaaS) to build a platform-centric flywheel by integrating CRM and Business System applications. With a Market Cap of US $209Bn and a Robust Economic Moat, it can become a $500Bn company by the end of this decade. Strategic shift from Competition (MSFT 365, ORCL) to Cooperation (Google, OpenAI) Risks to AI Ambitions My other posts on  AI Value Chain , Microsoft Transformation - The King of AI in Software and Cloud   and how  Generative AI can transform Telecoms ,  Energy and Utilities Source:  Seekingalpha ,...

NVIDIA - Godfather of AI - Why the Market is Bullish

 NVIDIA - The Godfather of AI - Why the Market is Bullish Key Message Q2 FY23 -  The data centre business, driven by demand from cloud providers and internet companies, played a vital role, contributing $10.32 billion (76% of total revenue). Gaming revenue also grew positively to $2.49 billion. Products like the H100 tensor core GPU, DGX supercomputers, inference platforms, and AI Infrastructure-as-a-Service in the cloud are poised to transform AI delivery, partnered with major cloud providers. Today Nvidia commands 70% of the AI Chip market. Other Key players are AMD, ARM (Softbank) and Intel.  If you take the other Nvidia divisions separate them out and add them together, revenues were up by 9.9% to $3.18Bn. That’s another way of saying that the Nvidia datacentre business is now 3.2x bigger than the rest of Nvidia, and the datacentre business now accounts for 76.4% of the overall sales for the company. A single chip can cost upwards of US$40K  and Open AI used abou...

Why Amazon Web Services (AWS) is a leader in Cloud Computing

Why Amazon Web Services (AWS) is a leader in cloud computing? Response from ChatGPT (Generative AI Tool) -- Amazon Web Services (AWS) is considered a leader in cloud computing for several reasons, which have contributed to its dominant position in the cloud services market:  Comprehensive Service Portfolio: AWS offers a wide range of cloud services, including computing, storage, databases, machine learning, analytics, IoT, security, and more. This extensive portfolio enables organizations to address diverse business needs within a single cloud provider. Global Reach: AWS has a global presence with data centres (Availability Zones) in multiple regions around the world. This global network infrastructure allows customers to deploy their applications and services close to their end-users for low-latency access. Scalability and Elasticity: AWS provides scalable and elastic infrastructure, allowing users to easily adjust resources up or down based on demand. This pay-as-you-go model is...

How Generative AI Can Transform Communication Service Providers

How Generative AI Can Transform Communication Service Providers (CSPs)  The global generative AI in telecom market size was at US $150.81Mn in 2022 and it is expected to rise to US $4.9Bn by 2032, with a CAGR of 41% from 2023 to 2032. Response from GenAI tool:  -- Generative AI has the potential to transform Communication Service Providers (CSPs) in several ways, enabling them to enhance their services, improve operational efficiency, and stay competitive in a rapidly evolving industry. Here are some key ways generative AI can drive transformation for CSPs: Enhanced Customer Experiences: Generative AI can be used to create highly personalised customer experiences. Chatbots and virtual assistants powered by generative models can provide 24/7 customer support, answer queries, and offer personalized recommendations, improving overall customer satisfaction. Natural Language Processing (NLP): Generative AI models can process and understand natural language, allowing CSPs to offer ...

Why Telcos are Struggling for ROI

Why Telcos are Struggling for ROI  Response from GenAI Tool -- The telecom sector has faced several challenges that have made it difficult to generate a satisfactory return on investment (ROI). While these challenges can vary by region and market conditions, here are some key factors contributing to the struggles of the telecom sector in generating ROI: High Infrastructure Costs: Building and maintaining the physical infrastructure for telecommunications networks, including towers, fibre optics, and data centres, requires substantial capital investment. The initial costs of rolling out networks and keeping them up-to-date can be burdensome. Intense Competition: Telecom markets are often highly competitive, with multiple providers vying for the same customer base. This competition can lead to price wars, reduced profit margins, and increased spending on marketing and customer retention. Regulation and Compliance: The telecom industry is subject to complex and evolving regulatory ...

India China Strategic Play - Striving for Growth and Leadership

 India China Strategic Play - Striving for Growth and Leadership 

Telecom Companies using Generative AI Globally

Companies in the telecom sector are exploring and implementing AI generative technologies for various applications. However, please note that the landscape of AI usage in the telecom sector is rapidly evolving, and new companies may have emerged or existing ones may have expanded their AI initiatives since then. Here are some examples of companies that were known for using AI generative technologies in the telecom sector:  AT&T: AT&T has been actively utilizing AI for network optimization, customer service, and predictive maintenance. They have employed AI-driven generative models to optimize network traffic and improve the overall customer experience. Verizon: Verizon has been exploring AI and machine learning for network optimization, fraud detection, and customer service. AI generative models are used to generate synthetic data for testing and validating network configurations. Vodafone: Vodafone has been using AI to enhance customer experience and network management. Ge...

Aussie Broadband's FY23 Results

 Aussie Broadband's  (ABB) FY23 Results and Why it's Emerging as an Acquisition Target NBN Consumer 2019, 100K customers, unprofitable.  2023, 700K customers, NPAT of $37Mn. 65% of Revenue from the consumer segment. 12% of NBN customers are on >  $100/month plan and 40% of Aussie customers are in this segment. Aussie acquires more than half of all new high value subscribers to the NBN. Business & Govt Business segment grew by 8% to $90Mn Rev. EE is adding to margin growth >800 new deals – SMB is driving it  Both segments utilise wholly owned fibre and an internally built cloud platform to generate 50% GM against residential GM of 30%. As those segments grow, profits will disproportionately rise. Smallest telco to build its own fibre backhaul. Built in house software to manage data loads, billing and other core functions. Wholesale business offers white labelled telco services to 3rd parties like Origin, was its fastest growing segment and a genuine sur...

The Value Chain for Generative AI (Artificial Intelligence)

The Value Chain for Generative AI (Artificial Intelligence) Response from ChatGPT (Generative AI Tool) -- The value chain for Generative AI (Artificial Intelligence) involves a series of activities and processes that create, deliver, and capture value through the development, deployment, and utilization of generative AI technologies. Here's a simplified overview of the generative AI value chain: Research and Development (R&D): This is where the foundational work on generative AI takes place. Researchers and data scientists work on developing algorithms, models, and techniques for generative AI. This includes areas such as neural networks, deep learning, and reinforcement learning. Data Collection and Preparation: High-quality data is crucial for training generative AI models. This step involves sourcing, cleaning, and preparing datasets that the AI models will learn from. Data collection can involve structured data, unstructured data, or a combination of both. Model Train...