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
Friday, September 22, 2023
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
Wednesday, September 20, 2023
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.
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Friday, September 15, 2023
Salesforce - Under The AI Cloud
Salesforce - Under The AI Cloud
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- Marc, known for his mission and marketing mantra, ”The End of Software", Revolutionised the CRM industry by introducing a 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 Generative AI and Strategic Analysis of Key Players
- AI Value Chain
- Salesforce under the AI Cloud
- 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 Linkedin, Nvidia, Google, AWS
Wednesday, September 13, 2023
NVIDIA - 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.
Where is the Spend
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.
What is their Economic Moat
A single chip can cost upwards of US$40K, and OpenAI used about 10,000 of them to train ChatGPT. If you want to train a GPT5-level model, you apparently need somewhere on the order of 20K to 25K H100 GPU accelerators, and that is somewhere around two dozen $1 billion machines
Nvidia's Economic Moat is driven by its 1st mover advantage in the AI Chip segment, supported by the network effects of a multisided platform and a frictionless Flywheel. 
Nvidia's Economic Moat is driven by its 1st mover advantage in the AI Chip segment, supported by the network effects of a multisided platform and a frictionless Flywheel.
My other posts on AI Value Chain and how Generative AI can transform Telecoms, Energy and Utilities
Source: Seekingalpha, Bloomberg, Martinfowler.com, Databricks.com Linkedin, Nvidia, Google, AWS
Thursday, September 07, 2023
Why Amazon Web Services (AWS) is a leader in Cloud Computing
Response from ChatGPT (Generative AI Tool)
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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 cost-effective and flexible for businesses of all sizes.
- Reliability and Availability: AWS has a track record of high availability and uptime. It offers robust service-level agreements (SLAs) for many of its services, and its global network is designed for redundancy and fault tolerance.
- Security and Compliance: AWS places a strong emphasis on security and compliance. It offers a wide range of security tools and services, and its data centres meet rigorous security standards. AWS also provides compliance certifications for various industries and regions.
- Innovation and Technology Leadership: AWS is known for continually innovating and launching new services and features. It invests heavily in research and development, staying at the forefront of emerging technologies like machine learning, artificial intelligence, and serverless computing.
- Ecosystem and Partnerships: AWS has a vast ecosystem of partners, third-party integrations, and a vibrant developer community. This ecosystem supports a wide variety of use cases and enables customers to access additional tools and services.
- Cost Management: AWS provides cost management tools and features, such as AWS Cost Explorer and AWS Budgets, to help customers optimize their cloud spending and control costs.
- Customer Focus: AWS has a customer-centric approach, with a dedicated support team and resources to assist customers with their cloud initiatives. This focus on customer satisfaction has helped build strong relationships with organizations across industries.
- Hybrid and Multi-Cloud Capabilities: AWS offers solutions for hybrid cloud deployments, allowing customers to seamlessly integrate their on-premises infrastructure with AWS services. Additionally, AWS provides services for multi-cloud management and deployment.
- Educational Resources: AWS provides extensive documentation, training programs, and certification opportunities to help individuals and organizations build cloud skills and expertise.
- Market Leadership and Experience: AWS entered the cloud computing market early and has accumulated years of experience. This leadership position has allowed AWS to refine its services and infrastructure continually.
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:
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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 advanced NLP-based services. This includes sentiment analysis, language translation, and voice recognition, enhancing customer interactions and support.
- Content Creation and Recommendations: Generative AI can automate content creation for marketing and customer engagement. It can generate text, images, and videos, and offer content recommendations based on customer preferences, increasing engagement and revenue opportunities.
- Predictive Analytics: Generative AI can analyse vast amounts of historical data to predict network outages, service disruptions, and customer churn. This enables CSPs to proactively address issues, improve network reliability, and reduce customer attrition.
- Network Optimisation: CSPs can use generative AI to optimise network configurations, allocate resources efficiently, and enhance network performance. Predictive modelling can help with traffic management and capacity planning.
- Fraud Detection and Prevention: Generative AI can detect and prevent fraud in real time by analysing call records, transaction data, and network traffic. This minimizes financial losses and ensures network security.
- Data Monetisation: CSPs can monetise the vast amount of data they generate by offering data analytics and insights services to businesses. Generative AI can create valuable data-driven reports and recommendations for corporate clients.
- IoT and Smart Services: Generative AI can facilitate the growth of IoT services by optimising data processing and analytics for connected devices. CSPs can offer end-to-end IoT solutions for various industries, including smart cities, healthcare, and manufacturing.
- Voice and Video Quality Enhancement: Generative AI can be used to improve the quality of voice and video communications by generating missing data, reducing noise, and optimising network performance, leading to a better user experience.
- Cost Reduction and Automation: Generative AI can automate repetitive and time-consuming tasks, reducing operational costs and increasing efficiency in areas such as network management, customer support, and billing processes.
- Service Expansion and Innovation: With generative AI, CSPs can explore innovative service offerings in emerging areas such as 5G, edge computing, and augmented reality (AR)/virtual reality (VR) services, tapping into new revenue streams.
- Market Differentiation: Embracing generative AI can help CSPs differentiate themselves in a competitive market by offering unique and innovative services that cater to evolving customer demands.
- Cybersecurity: Generative AI can be used to strengthen network security by identifying and mitigating potential threats in real-time, and safeguarding sensitive customer data and network infrastructure.
By leveraging generative AI technologies, CSPs can not only transform their operations but also create new revenue opportunities, improve customer satisfaction, and position themselves as leaders in a rapidly evolving telecommunications landscape.
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My previous post on Generative AI Value Chain
Why Telcos are Struggling for ROI
Why Telcos are Struggling for ROI
Response from GenAI Tool
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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 frameworks, which can vary significantly from one region to another. Complying with regulations and licensing requirements can be costly and time-consuming.
- Technological Advancements: The rapid pace of technological change in the telecom sector means that companies must continually invest in upgrading their networks and services to remain competitive. This ongoing investment can strain profitability.
- Market Saturation: In many mature markets, the number of potential new customers has plateaued, making it challenging to expand the customer base. Telecom providers must focus on retaining existing customers and increasing revenue per user (ARPU).
- Shift to Data Services: With the increasing demand for data services, telecom companies have had to invest heavily in expanding their data networks & upgrading infra. to support the growth in data usage. This can be costly, & monetising data services can be complex.
- Rising Customer Expectations: Consumers and businesses expect high-quality, high-speed connectivity and a wide range of services. Meeting these expectations often requires continuous investment in improving network performance and service offerings.
- Debt and Capital Structure: Telecom companies often carry significant debt loads due to the need for extensive infrastructure investments. High levels of debt can lead to interest expenses that eat into profitability.
- Emerging Technologies: Emerging technologies like 5G, edge computing, and the Internet of Things (IoT) require substantial investment but may take time to yield significant returns. Companies must carefully balance these investments with revenue expectations.
- Over-the-Top (OTT) Services: OTT services like messaging and video streaming apps have disrupted traditional telecom revenue streams. These services often bypass traditional revenue channels like SMS and voice calls.
- Customer Churn: High customer churn rates can erode revenue as companies need to continuously acquire new customers to replace those lost. Improving customer retention can be more cost-effective than customer acquisition.
To overcome these challenges and improve ROI, telecom companies often focus on strategies such as diversifying service offerings, optimizing network efficiency, reducing operational costs, and exploring new revenue streams through partnerships and innovative services. Adapting to changing market dynamics and customer demands is crucial for long-term success in the telecom sector.
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Tuesday, September 05, 2023
India China Strategic Play - Striving for Growth and Leadership
India China Strategic Play - Striving for Growth and Leadership
Monday, September 04, 2023
Telecom Companies using Generative AI Globally
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 utilising AI for network optimisation, customer service, and predictive maintenance. They have employed AI-driven generative models to optimise network traffic and improve the overall customer experience.
- Verizon: Verizon has been exploring AI and machine learning for network optimisation, 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. Generative AI is used to predict network outages and optimise network performance.
- Ericsson: Ericsson, a major telecom equipment provider, has been incorporating AI generative models into its solutions to automate network management tasks and improve network performance.
- Huawei: Huawei has been investing in AI and machine learning for network optimisation and cybersecurity. They have explored generative models to enhance network security and predict network anomalies.
- Nokia: Nokia has been leveraging AI generative models for network automation and predictive maintenance. They use generative AI to analyse network data and generate insights for optimising network infrastructure.
- Telefonica: Telefonica, a Spanish multinational telecommunications company, has been using AI and generative models for network planning, optimisation, and customer service improvements.
- Cisco: Cisco has been incorporating AI into its networking solutions, including AI generative models for network automation and security.
- T-Mobile: T-Mobile has explored AI generative models to improve customer service and network performance, including predictive maintenance of network infrastructure.
- Orange: Orange, a French multinational telecommunications corporation, has been using AI and generative models for network optimisation and to enhance customer experience.
- In Australia, Telstra, TPG, and Singtel are in the early stages of discovery.
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 41.59% from 2023 to 2032.
My previous post on Generative AI Value Chain
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 surprise. Revenue rose 60% to over $100Mn and maintained 30% GM. Management expects margins to rise as more partners use the service.
- Cloud
- With 2 new data centres added to the business, growing fibre and a bespoke cloud platform, there is little doubt that Aussie aims to build a capability to sell cloud services to enterprise and government.
- This is ambitious and chances are this will fail. Globally Telcos have struggled with Cloud as HPS’s and Tier 2 Cloud players are far ahead in the game.
Acquisition Target
- ABB is to become a billion-dollar business in the next 2 years.
- Healthy balance sheet.
- Currently, the EV is $0.946 Bn and with good growth potential for Rev and Margin growth in the next 2-3 years.
- The market share of NBN is around 8%.
My previous on TPG and NBN Co FY23 Results and its strategic play.
Source: AFR, TPG, ACCC, ITnews, Reuters, ABB, UBS
The Value Chain for Generative AI (Artificial Intelligence)
The Value Chain for Generative AI (Artificial Intelligence)
Response from ChatGPT (Generative AI Tool)
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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 Training: In this stage, generative AI models are trained on the prepared data. This involves using machine learning frameworks and powerful hardware to optimize and fine-tune the models. Training may require significant computational resources.
- Model Validation and Testing: After training, the models are rigorously tested and validated to ensure they perform as expected. This includes assessing their accuracy, reliability, and
generalization capabilities. - Deployment and Integration: Once the models are ready, they are integrated into real-world applications. This can involve deploying them in cloud environments, on edge devices, or within specific software systems.
- Application Development: Developers and engineers build applications and solutions that leverage generative AI models. These applications can span various domains, such as natural language processing (NLP), computer vision, recommendation systems, and creative content generation.
- User Interaction and Experience: The end-users interact with the generative AI-powered applications. The user experience, including user interfaces and user-friendly interactions, plays a significant role in delivering value.
- Monitoring and Maintenance: Continuous monitoring of generative AI models and applications is essential to ensure they continue to perform well and adapt to changing data patterns. Maintenance and updates are regularly performed to address issues and improve performance.
- Data Security and Privacy: Protecting sensitive data and ensuring privacy compliance is a critical part of the generative AI value chain. This includes data encryption, access controls, and compliance with data protection regulations.
- Value Capture: Organisations generate value from generative AI through various means, such as subscription models, licensing, consulting services, or direct sales of AI-powered products.
- Feedback Loop and Improvement: Continuous improvement is key to the generative AI value chain. Feedback from users and performance metrics are used to refine models and applications over time.
- Ethical Considerations and Compliance: Ensuring ethical and responsible AI practices is crucial. This involves addressing bias, fairness, and ethical concerns associated with AI technologies and adhering to regulatory requirements.
- Research and Innovation: The insights gained from real-world usage and feedback can inform further research and innovation in the field of generative AI, leading to the development of more advanced models and applications.
The generative AI value chain is a dynamic process that involves multiple stakeholders, from researchers and data scientists to developers, users, and business executives. Successful implementation of generative AI can lead to a wide range of applications across industries, from healthcare and finance to entertainment and customer service.
My other posts on how Generative AI can transform Telecoms, Energy and Utilities
Source: Seekingalpha, Bloomberg, Martinfowler.com, Databricks.com Linkedin, Google, AWS
Friday, September 01, 2023
How to Analyse if the Business is Growing
My Quick Framework for Validating if the Business Is Growing
© Copyright Vishal
Key Message
- This 2x2 matrix is plotted to identify if the business is growing organically.
- X-Axis has Revenue Contribution which is the additional contribution from the previous comparison (like year or qtr)
- Y-Axis X has EBITDA Contribution which is the additional contribution from the previous comparison (like year or qtr)
Meaning of Each Quadrant
Black Hole
- In essence, when both contributions exhibit a downward trend, it can be inferred that the enterprise is undergoing a decline in terms of expansion and stability.
Sinking & Milking
- When the contribution to revenue is declining, but the contribution to EBITDA is growing, the business is still able to increase its EBITDA contribution despite shrinking. In other words, it is extracting more margin from a declining entity
Stressed Growth
- When revenue contribution increases while EBITDA contribution decreases, it indicates that the business is growing, but its EBITDA contribution is under pressure. As a result, margins are facing fierce competition.
Growth on Steroids
- The simultaneous growth of revenue and EBITDA is a reliable indicator of a robust and rapid business expansion. This signifies a positive trend in the company's financial performance and can be viewed as a favourable development by stakeholders.
Sunday, August 27, 2023
India and China's Dilemma
India and China's - Game of Thrones via Economic and Political Lens
Indias Conundrum
India is playing its part in the BRICS because of the following
India's trust in China has eroded due to 4 skirmishes at the Northern Border. India is wary of China's growing clout in the new world order. While BRICS is economically China-centric, it is ensuring that it doesn't become a Unipolar bloc in Asia and another India-bashing group like OIC used to be. India can no longer rely on Russia as it has been forced to become China's ally because of sanctions.
By consenting to an expansion, it is mounting pressure on China and other relevant parties to acknowledge its deserving position in the UN's Permanent Five + Group and refrain from raising objections.
Besides India's trade deficit with the New BRICS being around 69%, prompting a need for effective management. In response, India is seeking to promote the use of local currency in trade transactions, thereby reducing its reliance on the USD and conserving Forex reserves. Furthermore, UAE has agreed to do business in Indian Rs, a significant development in this regard.
In light of the ongoing conflict in Ukraine and the economic impacts of COVID-19, the New BRICS bloc, which now includes Argentina, Iran, Ethiopia, Egypt, UAE, and Saudi Arabia, is expected to have a combined GDP of $29.14 Tn. The New BRICS bloc has a population of 3.6 Bn, which accounts for 51.6% of the world's population, while G7 has a population of only 0.8 Bn, which accounts for only 10.9% of the world's population.
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Why China Wants to Invade Taiwan
- China has been threatening to invade Taiwan, claiming it as part of its One China policy due to cultural and historical reasons.
- Being a highly authoritarian country with minimal democratic values, it cannot afford to have an island nation located merely 150 kilometers away from its mainland that practices democracy.
- Taiwan holds a prominent position as the global leader in the semiconductor industry, with TSMC as its flagship company. Today Semiconductors are crucial components in the defense, space, and technology sectors.
- China's trade deficit is exacerbated by its reliance on Taiwan for advanced semiconductors, prompting it to seek industry dominance and supply chain control.
- The US, which has a $400Bn trade deficit with China, is preventing China from acquiring advanced semiconductor technology, fuelling further tension. Therefore, China has multiple reasons to invade, including cultural, political, and economic factors.
- Analysts argue that the US is feeling the pressure because they facilitated China's ascent as a manufacturing hub and are now seeking to diversify supply chains to mitigate risks in the post-COVID era.
- China's trade deficit with Australia is being driven by continued demand for its resources, while Australia post-COVID, is diversifying its import and export portfolio.
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