Three Software Powerhouses of AI - Snowflake, Palantir, and Databricks

 Let's break down how Snowflake, Palantir, and Databricks work together in the AI world, using a technology stack analogy and real-world examples.

The AI Technology Stack

Think of building an AI-powered company like building a house. You need a solid foundation, a smart design, and skilled builders.

1. Foundation (Data): Snowflake

   • Layman's Terms: Snowflake is like the concrete foundation of your AI house. It stores all your data in one organised place, making it easy to access and use. It's not just storage; it's like a super-organised library where any information can be found instantly.  
   • Technical Function: Snowflake is a cloud-based data warehouse. It allows companies to store vast amounts of structured and semi-structured data, making it readily available for analysis and AI model training. It handles the messy work of data organisation and access.  
   • Example: Imagine a retail company. Snowflake stores all its sales data, customer information, inventory levels, and even website traffic data. Because it's all in one place and easily accessible, the company can quickly analyse what products are selling well, who their best customers are, and how to optimise their inventory.  

2. Design (Intelligence): Palantir

   • Layman's Terms: Palantir is like the architect of your AI house. It takes the data from Snowflake and uses it to design intelligent systems. It helps you understand what the data means and how to use it to make better decisions. It's like turning raw data into actionable insights.
   • Technical Function: Palantir is an operational platform that connects data, analytics, and operations. It uses AI to analyse data from Snowflake (and other sources) and create visualisations, dashboards, and predictive models that help businesses make better decisions. It focuses on turning data into action.  
   • Example: Using the retail company example, Palantir can take the data from Snowflake and build a model that predicts which customers are most likely to buy a certain product. It can then automate marketing campaigns to target those customers, increasing sales. Or, it can analyse supply chain data to predict potential disruptions and suggest alternative suppliers.  

3. Builders (AI Development): Databricks

   • Layman's Terms: Databricks is like the construction crew for your AI house. They use the data from Snowflake and the designs from Palantir to build and maintain the actual AI systems. They're the experts who know how to put everything together. They keep the AI models up-to-date and running smoothly.
   • Technical Function: Databricks provides a unified analytics platform for data science and machine learning. It allows data scientists to build, train, and deploy AI models at scale. It offers tools for data engineering, model development, and MLOps (machine learning operations).  
   • Example: For our retail company, Databricks would be used to build and train the AI model that predicts customer behaviour. They would use the data in Snowflake and work with the insights provided by Palantir to create a model that is accurate and effective. They would also manage the ongoing maintenance and updates to that model.

Diagram of the Stack

+-----------------+
|   Applications   |  (e.g., Marketing automation, Supply chain optimization)
+-----------------+
|   Palantir      |  (Intelligence Layer - AI-driven decision making)
+-----------------+
|   Snowflake     |  (Data Layer - Unified data storage and access)
+-----------------+
|   Databricks    |  (AI Development Layer - Model building, training, deployment)
+-----------------+

Example Flow

1. The retail company stores all its data (sales, customers, inventory, etc.) in Snowflake.  
2. Databricks uses this data to build an AI model that predicts which customers are likely to buy a new product.
3. Palantir takes the output of this model and uses it to create targeted marketing campaigns.
4. The results of these campaigns (new sales, customer engagement) are then stored back in Snowflake, and the process begins again, allowing the AI models to continuously learn and improve.

In short, Snowflake provides the data, Palantir provides the intelligence, and Databricks provides the tools to build and deploy the AI systems that drive the AI-native enterprise. They are the essential components for companies looking to leverage AI effectively

The Future of Enterprise AI: Palantir's AIP

 The Future of Enterprise AI: Palantir's AIP

Palantir's AI Platform (AIP) is revolutionising how enterprises harness data's power. By integrating, analysing, and visualising vast datasets, AIP enables organisations to uncover valuable insights and make informed decisions.

What Does Palantir AIP Offer?

At its core, Palantir AIP is an ontology-driven platform. This means it uses a structured knowledge graph to represent concepts, entities, and their relationships. This foundational layer allows AIP to:

Integrate diverse data sources: Seamlessly combine data from various sources, including structured and unstructured data.

Visualise complex relationships: Use powerful visualisation tools to explore connections and patterns within data.

Support decision-making: Provide actionable insights to drive strategic decisions and optimise operations.

Opportunities for Service Providers

• For service providers, Palantir AIP presents a wealth of opportunities:
• Skill Development: Invest in AI skills, like CUDA-driven libraries for Nvidia, to effectively utilise AIP's capabilities.
• Platform Expertise: Gain deep knowledge of AIP's semantics and architecture to build and manage applications on the platform.
• Commercial Insights: Position yourself as a trusted advisor, offering a commercial insight-centric pitch to highlight the value of AIP.

Positioning and Pricing

• When positioning AIP, consider a balanced approach:
• Commercial Insight: Focus on the tangible benefits and ROI that AIP can deliver to clients.
• Thought Leadership: Showcase your expertise and innovative solutions built on the AIP platform.

Pricing models for service providers can vary:

• Usage-Based: Charge based on the consumption of AIP resources.
• Outcome-Based: Tie fees to the achievement of specific business outcomes.
• Navigating the Australian Market.

While Australia may be more cautious in adopting new technologies, Rio Tinto is reaping the benefits of Palantir's Foundry. Sectors like Agriculture, Telecom, and Retail can benefit from its adoption.

To gain traction, service providers should:

• Build Strong Partnerships: Collaborate with key players in the industry to accelerate adoption.
• Demonstrate Value: Highlight the tangible benefits of AIP through compelling case studies and proof-of-concept projects.
• Address Security and Privacy Concerns: Assure clients about the robust security measures in place.

By leveraging Palantir AIP's capabilities and understanding the unique dynamics of the Australian market, service providers can unlock new opportunities and drive digital transformation.

PS: With >$0.5Bn in net income and a PE of $310. This stock has grown by ~3x since Aug this year.

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Telecom Network Evolution to Cloud Native

Telecom Network Evolution to Cloud Native 

 

Emerging Trends in Telecom Network

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Cloud-Native in 2025: A Comprehensive Overview of Trends, Opportunities, and Challenges

Introduction

As we approach 2025, cloud-native architecture has evolved from a cutting-edge approach to a mainstream strategy for enterprise digital transformation. This blog post explores the key trends, strategic importance, benefits, challenges, and future trajectory of cloud-native technologies.

Key Trends Shaping Cloud-Native Ecosystem

1. Cost Optimization: FinOps Takes Center Stage

Cloud-native architectures are becoming increasingly complex, making cost management crucial. The emergence of FinOps (Financial Operations) is transforming how organizations approach cloud spending. Key developments include:

• Tools like OpenCost providing granular visibility into Kubernetes spend
• Projects such as OpenTelemetry, Prometheus, and OpenSearch enabling precise resource consumption tracking
• Organizations focusing on reducing overall spend without compromising performance

2. Developer Productivity: Internal Developer Portals (IDPs)

To address developer friction caused by multiple cloud-native tools, Internal Developer Portals (IDPs) are gaining prominence:

• Backstage has become the de-facto standard for building IDPs
• Real-world example: Infosys implemented a Backstage solution for a US insurance company, resulting in:
  • 40% reduction in developer onboarding time
  • 35% increase in code deployment frequency
  • Improved time-to-production and customer satisfaction

3. Cloud-Native Powering AI

Cloud-native technologies are becoming fundamental to AI workloads:

• OpenAI has been running AI training on Kubernetes since 2016
• Key open-source projects supporting AI include:
  • OPEA: Cloud-native patterns for generative AI
  • Milvus: High-performance vector database
  • Kubeflow: Machine learning workflow deployment
  • KServe: ML model serving toolset

4. Observability and Open Standards

The cloud-native ecosystem is moving towards open observability standards:

• Addressing limitations of closed-source commercial vendors
• Projects like OpenTelemetry and TAG-Observability driving standardization
• Goal: Minimize vendor lock-in and reduce costs

5. Enhanced Security Approaches

Modern cloud-native security focuses on:

• Zero trust architectures
• Secure supply chain concepts
• Runtime security tools like Falco
• Policy-as-code implementations through Open Policy Agent (OPA) and Kyverno

6. Sustainability: Green IT Goes Mainstream

Sustainability is becoming a critical consideration:

• Projects like Kepler measuring carbon consumption
• Driven by legislation such as EU sustainability reporting rules
• Focus on reducing carbon footprint through intelligent resource management

Strategic Importance

Kubernetes: The Orchestration Backbone

• Kubernetes has become the standard platform for modernization
• Continuous improvement focusing on reliability, scaling, and security
• Enables dynamic, scalable, and efficient application deployment

Platform Engineering

A emerging discipline that:

• Designs reusable software platforms
• Provides standardized capabilities across infrastructure
• Enables faster delivery, improved quality, and increased scalability

Cost Benefits

1. Granular Cost Tracking

• Tools like OpenCost provide unprecedented visibility into cloud spending
• Enable precise allocation of resources and optimization of cloud expenses

2. Improved Developer Productivity

• Internal Developer Portals reduce onboarding time
• Standardized platforms decrease time-to-market
• Reduces overall development and operational costs

3. Resource Efficiency

• Dynamic infrastructure allows creating and destroying resources as needed
• Optimized resource allocation reduces unnecessary cloud spending

Challenges and Considerations

1. Complexity

• Cloud-native architectures are more complex than traditional monolithic systems
• Requires significant expertise and continuous learning

2. Tool Proliferation

• Multiple tools and frameworks can create developer friction
• Needs careful selection and integration of tools

3. Security Challenges

• Microservices architecture increases potential attack surfaces
• Requires sophisticated security approaches and continuous monitoring

Future Outlook

The cloud-native ecosystem is poised for continued growth, with key focus areas:

• AI and machine learning integration
• Enhanced observability
• Improved security frameworks
• Sustainability-driven innovations
• Further standardization of platform engineering practices

Conclusion

Cloud-native is no longer just a technology trend—it's a strategic imperative for organizations seeking agility, efficiency, and innovation. By embracing these technologies and methodologies, enterprises can build more resilient, scalable, and cost-effective digital infrastructures.

Key Players and Foundations

• Cloud Native Computing Foundation (CNCF)
• Linux Foundation
• FinOps Foundation
• Open Source Security Foundation (OpenSSF)
• LF AI & Data Foundation

Enterprises looking to embark on their cloud-native journey should start by:

• Assessing current infrastructure
• Implementing pilot projects
• Investing in platform engineering capabilities
• Focusing on developer productivity and tooling

Examples of Adoption by Enterprises:

• Infosys' implementation of Backstage for a US insurance company (increased developer onboarding speed and deployment frequency)
• OpenAI's use of Kubernetes for AI training and inference workloads

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