From Four to Six Pillars: The Evolution of the Australian Telecom Industry 

The Rise of Aussie Broadband: 

The Australian telecommunications landscape has witnessed a significant transformation in recent years, shifting from a traditional four-pillar model dominated by Telstra, Optus, TPG, and the NBN to a six-pillar model that now includes Vocus and Aussie Broadband. This evolution has been driven by a confluence of factors, including regulatory changes, evolving consumer demands, technological advancements, and strategic diversification. A "pillar" refers to any telecommunications operator with a revenue of $1Bn or more. A New Era of Competition Aussie Broadband, with its rapid growth and strategic acquisitions, has emerged as a key player in this evolving market. With revenue at ~$1Bn, the company is poised to solidify its position as the sixth pillar of the Australian telecommunications industry. (PE TTM - 35.7, PB - 1.7) 

Key Factors Driving the Transition Regulatory Framework: 

The ACCC's role in promoting fair competition and open access to the NBN has created opportunities for new entrants. Changing Consumer Needs: Australian consumers are increasingly demanding reliable, high-speed connectivity, personalised services, and cost-effective solutions. 

Technological Advancements: The rollout of 5G, cloud-based and AI-enabled services, and other innovations have lowered barriers to entry. 

Diversification and Consolidation: Providers are expanding their service offerings and gaining economies of scale through mergers and acquisitions. Aussie Broadband's Growth Strategy Aussie Broadband has been actively pursuing a growth strategy that involves both organic expansion and strategic acquisitions. The company's recent acquisition of Symbio, a leading provider of NBN services, is a testament to its ambition to consolidate its market position. 

Superloop: A Strategic Target: One of Aussie Broadband's most intriguing prospects is its potential acquisition of Superloop. With a nearly ~12% stake in Superloop, Aussie Broadband is well-positioned to capitalise on opportunities in the market. Analysts predict that such an acquisition could significantly enhance Aussie Broadband's capabilities and further solidify its position as a major player in the Australian telecommunications industry. 

Conclusion:  The Australian telecommunications industry is undergoing a period of dynamic transformation, characterised by increased competition, technological innovation, and strategic consolidation. Aussie Broadband's emergence as a significant player in this evolving market is a testament to its ability to adapt to changing market conditions and capitalise on new opportunities. As the company continues to grow and expand its reach, it is poised to play a pivotal role in shaping the future of the Australian telecommunications landscape. 

 Src: Excerpt from my book on NBN, AFR, WSJ 

 #australia #telecom #future #strategy #M&A

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.

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

IBM a Tech Giant - How it Lost its Way

 IBM a Tech Giant - How it Lost its Way

Key Indicators 

• Market Cap – $129.39Bn
• EV – $173.4Bn
• Debt - $57.5Bn, Cash - $17.9Bn 
• P/B – 8.57 
• P/E (Trailing) – 60.44 (Growth) 
• P/E (Forward) – 14.3 (Div. Centric,  No Growth)
• Economic Moat: Narrow (under threat)  

Where is the Growth

How it Lost its Way

• IBM a more than 100-year-old company that used to be a trendsetter in the technology space has become a laggard and is struggling to get its Mojo back. It is facing headwinds, and it is not clear how it will modernise its business. Today, IBM has 3 business segments, Infrastructure, Software and Consulting, and all of them are declining YoY. There are multiple reasons why IBM's revenues are declining except for the minor surge in 2021, and 2022. Let's look at the key reasons.
• Unlike its peer group players like Salesforce and ServiceNow which specialises in providing packaged application software in the Cloud (SaaS), IBM has no application software to offer. Instead, IBM's software offering is primarily in system software, such as middleware, database management systems, and operating systems, that are used to build applications, but it has no end-user applications to offer. To add further, the issue with IBM's system software business is that it is increasingly moving towards open-source software, like how its other peer, Oracle is facing headwinds in the Database domain. IBM's Red Hat Enterprise Linux is built on open source and is cheaper than the company's legacy proprietary software. 
  
• Microsoft, it's another peer, that competes with IBM in the Enterprise IT, has developed a mousetrap around Windows OS and its MS Office offering for both consumers and businesses. IBM, on the other hand, has no such product. Its other distant peer group players like Google and Meta, unlike IBM, earn most of its revenue from advertising.
• The IT spending in OPEX has been flat since the augment of Digital Transformation in the early 2010s. Most of the IT spending is CAPEX-centric for corporates to transform their businesses by rolling out customer-centric applications in the cloud and reducing the spending on system upgrades like Mainframes. In a way, the IT spending profile has significantly changed from being OPEX and IT-centric to CAPEX and business-driven. To add further, the Cloud first approach by businesses got a boost during the pandemic for resiliency and agility, ensuring that the likes of AWS and Microsoft extended their market share. In comparison, IBM has been relegated to a Cloud Consulting business where they help implement AWS, Azure and Google Cloud for their clients. This change is validated by IBM's Cloud market share decline from 25% in 2016 to 4% in 2022, indicating a lack of success in its effort to be a major player in this segment.
• IBM ventured into the AI industry during the early 2010s, introducing its Watson platform. However, the platform's performance was lacklustre, resulting in IBM selling its Watson Health initiative at a significant loss. The company is now making a fresh push into the market by relaunching Watson with the new name of watsonx, keeping in mind the current trend of Generation AI. IBM had previously rebranded its flagship database from DB2 to Db2 to rejuvenate it, but the move led to its downfall, especially among developers. IBM is now attempting a similar strategy with its AI offerings. It remains to be seen, whether rebranding will help IBM boost its AI efforts and achieve much-needed growth.
• During the mid-1990s, IBM decided to shift its focus from hardware manufacturing to the IT Managed Infrastructure Services sector, to drive growth in its software and consulting businesses by moving up the value chain. This strategic transition was necessary to meet the changing needs of customers who were moving away from mainframes and towards commodity hardware-enabled servers. However, in recent years, the IT Managed Infrastructure Services industry has experienced a decline due to the emergence of Hyperscalers and a change in IT spending. Today, most of IBM's original mainframe customers have shifted to the cloud or on-premise commodity hardware platforms for new application development, adding to the company's current challenge of finding ways to drive growth.
• IBM's IT Consulting segment has maintained a steady performance, with operating margins staying flat at 10-12%. The company has been pushed by market forces to shift its focus from providing consulting services solely based on its products to helping clients implement software from other companies. This shift is similar to the approach of a System Integrator. Despite IBM's attempt to emulate the cost arbitrage model of the leading Indian SI players, it has not been successful. Additionally, the margins in the consulting business have decreased, whereas the software business provides healthy margins due to the negligible marginal cost of selling an additional unit.
• To summarise, It's unsurprising that IBM is undervalued in comparison to its peers, given the various aspects that have been discussed. Wall Street analysts view IBM as a dividend stock with limited potential for capital growth, which is reflected in its forward PE of 14 and the market cap of $129Bn only, 18 times less than Microsoft's market cap of $2.4Tn.

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
• 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 - Changing the World, Key Players and Their Progress

 Generative AI - Changing the World, Key Players and Their Progress - Part One

 

Singularity - Humanity on the Cusp of Achieving It?

 What is Singularity?

A singularity is a theoretical condition that could arrive in the near future when a synthesis of several powerful new technologies will radically change the realities in which we find ourselves in an unpredictable manner. 

The term "singularity" was first used in the context of technology by mathematician and computer scientist John von Neumann in the 1950s. However, it was popularised by futurist and inventor Ray Kurzweil in his 1999 book "The Age of Spiritual Machines." 

In 2005 Ray Kurzweil further extended this concept and wrote in his book titled: "The Singularity is Near: When Humans Transcend Biology". 

In his book, Ray Kurzweil defines the "singularity" as "a point in the future when technological progress becomes so rapid and profound, resulting in unforeseeable changes to human civilisation." He believes that the singularity will be triggered by the development of artificial general intelligence (AGI), which is a type of AI that is as intelligent or more intelligent than humans. 

According to Kurzweil, the singularity is driven by three interconnected technological revolutions:

• Genetics (Bio-Technology) 
• Nanotechnology 
• Artificial intelligence AI

Let's look at the progress in these 3 domains and how far we have come:

Following are some of the capabilities that are so ahead of our times that make me believe that we might be closer to the singularity from an AI perspective than what we were anticipating

• Google's Bard, Gemini, BERT and T5 
• Metas RoBERTa and XLM-R 
• Tesla's Dojo enabled FSD
• Microsoft's and Open AI's, ChatGPT 4 enabled Bing and Co-Pilot
• XiaoIce and MT-DNN
• Amazon's Alexa, and Bedrock
• Apple's Siri
• IBM's Watson Assistant and Project Debater
• Rasa
• Hugging Face
• NVIDIA's Megatron and Triton 
• SalesForce's Einstein
• SAP's Conversational AI

Bsised The above transformative Natural Language Processing applications, there are many other free AI utilities that can effectively replicate human activities in many areas, including: 

• DALL.E3: Creates realistic images and art from a description in natural language
• FaceApp: The app generates highly realistic transformations of the human face
• Talk To Books: You type a query or statement in the search box, and it discovers books related to that query
• Magic Eraser: It allows you to remove any unwanted objects from your photo while extending the background
• Replika: Allows you to create an AI personality and build a relationship with it
• Elsa: Analyzes speech and acts as an English-speaking coach
• Socratic: Helps students with their homework by providing educational resources
• Character.AI: Users can create characters, including their personalities, and publish them to the community for others to interact with
• Point E: Generates a 3D image based on the text written
• Youper: Helps users deal with emotional struggles by presenting them with different psychological techniques

Another area where rapid technological advancements have taken place is in the field of robotics like Boston Dynamics robot's dancing, back-flipping and somersaulting. 

 Today Robots are used in almost every area of our lives, and these include: 

• Manufacturing (Tesla) 
• Healthcare Exploration and Science 
• Mining 
• Education 
• Entertainment M
• Military and Security and many more domains 

It is hard to find an industry where robots and automation are not playing a major role in its day-to-day operations. In short, the proliferation of AI in all aspects of our lives and is very difficult to find an area where AI has not taken big strides in replacing humans

--

Let's look at the advancements in the area of Bio-Technology 

Genetic advancements had the following events: 

 First Complete Sequence of the Human Genome: The Human Genome Project, completed in 2003, covered about 92% of the total human genome sequence. The final, complete human genome sequence was described in a set of six papers in the April 1, 2022, issue of Science. The benefits of sequencing the human genome include: 

• Advancing our understanding of genetics
• Identifying genetic predispositions
• Personalised medicine
• Advancing drug development, specifically, gene therapy
• Improving diagnostic accuracy

The mRNA technology was invented in the 1960s by Hungarian biochemist Katalin Karikó and American biochemist Robert Langer. However, it was not until recently that mRNA technology has been able to be used to develop vaccines and therapeutics. It is a molecule that carries genetic information from DNA and delivers that to the ribosome (cell), where proteins are made. 

One of the main benefits of mRNA technology is that it is very fast and efficient. mRNA vaccines can be developed and produced much faster than traditional vaccines, which makes them ideal for responding to pandemics and other emerging threats. mRNA vaccines are also very safe and effective.
Another benefit of mRNA technology is that it can be used to develop vaccines and therapeutics for a wide range of diseases. These vaccines are already being used to protect against COVID-19, and therapeutics are being developed to treat cancer, infectious diseases, and other conditions. 

Here are some specific examples of how mRNA technology is being used:

• COVID-19 vaccines: The Pfizer-BioNTech and Moderna COVID-19 vaccines are both mRNA vaccines. These vaccines have been shown to be very safe and effective at preventing serious illness, hospitalisation, and death from COVID-19. 
• Cancer vaccines: mRNA vaccines are being developed to treat a variety of cancers, including melanoma, lung cancer, and pancreatic cancer. These vaccines work by teaching the immune system to recognise and attack cancer cells.
• Infectious disease vaccines: mRNA vaccines are also being developed to protect against other infectious diseases, such as HIV, malaria, and Zika virus. 
•  Therapeutic vaccines: mRNA vaccines are being developed to treat a variety of conditions, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis. These vaccines work by teaching the immune system to repair damaged cells or to remove harmful substances from the body.

CRISPR Therapeutics (CRSP) has developed Clustered Regularly Interspaced Short Palindromic Repeats, a ground-breaking technology capable of precisely and effectively modifying genes. The CRISPR-associated (CAS) endonuclease, Cas9, operates as a "molecular scissors" by cutting DNA at a specific site designated by guide RNA. The impact of CRISPR/Cas9 on biomedical research is transformative, and it has the potential to pave the way for unprecedented medical advancements.

Some examples are

• Treating sickle cell anemia
• Preventing cancer, CRISPR-Cas9 gene editing has been used to prevent cancer in mice. In a study, researchers used CRISPR-Cas9 gene editing to disable a gene that is involved in cancer development. The mice that received the CRISPR-Cas9 gene editing treatment were less likely to develop cancer than the mice that did not receive the treatment.
• Improving crop yields

Precision Medicine or Personalised Medicine is an emerging field in healthcare that integrates the use of genomics, big data analytics, and population health. Recent advancements in genetics have enabled medical treatments to be customized according to a person's unique genetic makeup, leading to improved effectiveness and safety of treatments. The main difference between conventional approaches to complex health conditions and precision medicine is the greater emphasis on genetic data to determine specific treatment options. Gene therapy is a technique that employs genes to prevent, treat, or cure a disease. Recent progress in gene therapy has demonstrated potential in treating genetic disorders by introducing healthy genes into a patient's cells to replace defective ones. Gene therapy is also utilized as a treatment for certain illnesses to target defective cells such as cancer cells and disable their functioning, providing a viable treatment option.

Epigenetics: Epigenetics explores how the environment affects gene function. Unlike genetic changes, epigenetic changes are reversible and modify how the body reads DNA. Scientists are studying the role of epigenetic modifications in diseases like cancer and developing epigenetic therapies. 

--

Let's look at the advancements in the area of Nano-Technology 

The term "nanotechnology" was first used by Japanese scientist Norio Taniguchi in 1974. However, the concept of nanotechnology has been around for much longer. For example, Richard Feynman's famous 1959 lecture "There's Plenty of Room at the Bottom" is considered to be one of the founding documents of nanotechnology.
Nanotechnology is the manipulation of matter on an atomic and molecular scale. It is a rapidly developing field with a wide range of potential applications in many different industries. Nanotechnology is already being used in a variety of industries, including:

• Healthcare: Nanotechnology is being used to develop new drugs and therapies, diagnostic tools, and medical devices. For example, nanoparticles can be used to deliver drugs directly to diseased cells, which can improve the effectiveness of the drugs and reduce side effects.
Electronics: Nanotechnology is being used to develop new electronic devices that are smaller, faster, and more efficient than current devices. For example, carbon nanotubes can be used to make transistors that are much smaller and faster than silicon transistors.
• Energy: Nanotechnology is being used to develop new energy sources and storage devices. For example, nanomaterials can be used to make solar cells that are more efficient and less expensive than current solar cells.
• Environmental science: Nanotechnology is being used to develop new ways to clean up pollution and protect the environment. For example, nanomaterials can be used to remove pollutants from water and air.
• Food and agriculture: Nanotechnology is being used to develop new ways to produce and process food, and to improve the nutritional value of food. For example, nanomaterials can be used to make food packaging that is more effective at preserving food and preventing foodborne illness.

Here are some specific examples of how nanotechnology is being used in different industries:

• Healthcare:
  • Nanoparticles are being used to deliver drugs directly to cancer cells, which can improve the effectiveness of the drugs and reduce side effects.
  • Nanotechnology is being used to develop new diagnostic tools, such as blood tests that can detect cancer and other diseases early on.
  • Nanotechnology is being used to develop new medical devices, such as artificial implants that are more durable and less likely to be rejected by the body.
• Electronics:
  • Carbon nanotubes are being used to make transistors that are much smaller and faster than silicon transistors. Today TSMC has made a 3nm chip and is working on 2nm.
  • Nanotechnology is being used to develop new types of batteries that are more efficient and have a longer lifespan than current batteries.
  • Nanotechnology is being used to develop new types of displays that are brighter, more energy-efficient, and have higher resolution than current displays.
• Energy:
  • Nanomaterials are being used to make solar cells that are more efficient and less expensive than current solar cells.
  • Nanotechnology is being used to develop new types of fuel cells that are more efficient and produce less pollution than current fuel cells.
  • Nanotechnology is being used to develop new types of batteries that can store more energy than current batteries.
  • It is used in plasma-based tools in the recovery of oil and gas. These plasma processes are also used in additive manufacturing and 3D printing
• Environmental science:
  • Nanomaterials are being used to remove pollutants from water and air.
  • Nanotechnology is being used to develop new ways to clean up oil spills and other environmental disasters.
  • Nanotechnology is being used to develop new ways to reduce the environmental impact of industrial processes.
• Food and agriculture:
  • Nanomaterials are being used to make food packaging that is more effective at preserving food and preventing foodborne illness.
  • Nanotechnology is being used to develop new ways to produce and process food, such as using nanomaterials to deliver nutrients to crops.
  • Nanotechnology is being used to develop new ways to improve the nutritional value of food, such as using nanomaterials to encapsulate vitamins and minerals so that they are better absorbed by the body.

To summarise, the pace of technological advancement is so rapid that it surpasses human capabilities. It won't be long until singularity is achieved. Businesses that resist or fail to adapt to this change will likely struggle to survive in the future, similar to the impact that Digital transformation has had on industries such as Media, IT Software and Services, Banking, and Telecom.

That being said, businesses that have adopted or are currently undergoing technological transformation will enjoy advantages similar to those of Tesla (Auto), Apple (consumer and personalized devices), Amazon (Retail), and Google (Digital Advertising).

Technology is advancing rapidly and its economic impact is still to be fully experienced. The changes required to the economic model cannot be achieved using the current framework for economy and policy management.

My other posts on similar topics like 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

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

Tesla's 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 that 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

• 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, 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