Strategic Foresight - Insights in Business and Technology

How DeepSeek's Challenge Actually Strengthened NVIDIA's Market Position The emergence of DeepSeek, a Chinese AI startup, initially sent shockwaves through the market, raising fears of disruption to NVIDIA's dominance in the AI chip market. DeepSeek's claim of developing an advanced AI model with significantly lower computing costs triggered a sharp market correction, wiping out nearly $750 billion from NVIDIA's market capitalization in a week. However, this seemingly negative event has, paradoxically, strengthened NVIDIA's long-term market position.     The Initial Shock and the Correction: The initial market reaction was swift and decisive, with NVIDIA's stock price dropping by 6% while the Nasdaq remained relatively flat. This correction, while painful in the short term, has resulted in a more attractive valuation for NVIDIA. The forward P/E ratio has compressed from a lofty 40x six months ago to a more reasonable 32x. This reset makes NVIDIA more appea...

Deep Seek's GPU Infrastructure Initially acquired 10,000 GPUs in 2021 Estimated to have grown to around 50,000 GPUs in total Used 2,000 H800 GPUs specifically for V3 model pre-training Share infrastructure with their quantitative trading fund operations Initial Export Control Framework US government initially restricted two parameters : Computing power (FLOPS) Interconnect bandwidth between GPUs This two-factor restriction created an opportunity for optimisation H800 GPU Restrictions and Adaptations H800 was China's version of the H100 GPU Two key restriction factors from the US government: Chip compute (FLOPS) Interconnect bandwidth H800 was designed with: Full FLOPS capability (same as H100) Restricted interconnect bandwidth Deep Seek developed specialized SM (Streaming Multiprocessor) scheduling techniques to work around interconnect limitations Managed to achieve full GPU utilisation despite interconnect restrictions Export Control Evolution First Phase: Dual restric...

 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. Foundation (Data): Snowflake Layman's Terms: Snowflake is like the concrete foundation of your AI house. It stores all your data in one organized place, making it easy to access and use. It's not just storage; it's like a super-organized 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 organization and access.   Example: Imagine a retail company. Snowflake stores all their sales data, customer information, inven...

  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 ...

  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 Produ...

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...

The Future of Software is New SaaS - powered by Services, AI Agents, Sharing This POV is available for download below.

 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 ...