How to lose 90% of your customers in 5 years?

In 1995, Netscape Browser was the best browser. I remember talking with my friend Andrew who had the idea to bring this business solution into Asia. 

1995: 90% market share

1996: 86%

1997: 51%

Netscape didn't just lose; they committed suicide. They had a 90% market share. And they threw it all away because they forgot the most important rule: Focus on what the customers want.

Here's how they blew it:

1. The Rewrite Trap: 

This was their biggest error. They re-wrote their code, meaning they stop new releases for 3 years to "clean up your code". While Netscape was busy, the world moved on. You don't win by being perfect later; you win by being better right now.

2. Software Bloat: Turned a browser into a "suite" of emails, newsreaders, etc. Became slow & buggy. Users want a tool that works.

3. Wrong Business Model: Trying to sell when the competitors were giving it for free. Microsoft realized the browser wasn't a product; it was a feature of the operating system. Google's free browser was a door to their world of fast searching and apps, while profiting from ad revenues. 

By the time Netscape realized they were in a fight, the fight was already over.

It's been 30 years. Has successful startups learnt this lesson? Or are they still bragging about their 1st-mover advantage that gave them a major share of the market (for now).

1st-movers in the past:

1. 1st successful PDAs: PalmPilot 1000(1996). 

2. 1st digital music player: MPMan F10. 

3. 1st digital camera: Kodak 1975.

These companies don't exist today. 

Marc Andreessen, Netscape cofounder: "Netscape will soon reduce Windows to a poorly debugged set of device drivers". this quote didn't age well..

Business is always personal in Asia.

We often assume business is a universal language, but the "dialect" changes drastically across borders.

In many Western contexts, the sales revenue or transaction is king. But looking through an Asian lens, all business is personal. It isn't just about contracts or invoices; it is about the human connection & lifelong trust behind them.

There is a profound preference for harmony. Rather than the "move fast and break things" approach—often viewed as the destruction of the normal order—there is a focus on stability, respecting hierarchy, and maintaining balance.

This "Let's not change anything" miindset heavily influences leadership expectations. Asian bosses typically prioritize deep, demonstrated loyalty from their existing staff over the ambitious new hires. However, new hires who can lead and execute the business plan will win over the bosses. It is not about the volume of your voice in the boardroom, but the depth of your commitment after you leave the room.

To navigate the global market, we must move beyond our own cultural defaults and appreciate these nuances in Asian markets. What works in one city may not in another Asian city..

How do you balance the drive for business transformation (disruption) with the need for harmony in your professional relationships? What happens when the firm faces a crisis unsolvable using existing leadership?

#DigitalTransformation hashtag

#ChangeManagement hashtag

#BusinessTransformation hashtag

#BusinessCulture hashtag

#GlobalBusiness hashtag

#Leadership hashtag

#Networking hashtag

#AsiaBusiness hashtag

#AsianCulture

When I was managing a team, we had this sacred rule: never, ever let the people who got us here feel like they’re worth less today than yesterday.

And yet, I see it happening everywhere in recent years. Companies hiring in a frenzy. Salary for new talent have exploded (sales, engineers, designers, product people). A fresh graduate suddenly makes 30% or 40% more than the person who’s been bleeding for the company for 5 years. Same role.

That’s not hiring. That’s a betrayal.

You know what it feels like when you’ve been at a place for years, you’ve shipped incredible products, you’ve worked nights and weekends, you’ve turned down better offers because you believed in the mission… and then some new person walks in, glances at the code you wrote, and gets paid dramatically more than you? It feels like a slap in the face. It feels like the company is saying, “Thanks for working long nights building this thing. Now step aside, the new guy’s more valuable than you.”

That’s how you destroy a culture. That’s how you kill the soul of a team.

I’ve seen it. I’ve felt it in the hallways. The best people (the ones with taste, the ones who care deeply, the ones who made the products insanely great) they don’t complain loudly. They just quietly update their résumé. And one day they’re gone. And when they leave, they take the magic with them.

So here’s what I would do:

1. Never let the market dictate your soul.

Yes, you sometimes have to pay crazy money to get the right person. Fine. But the moment you do that, you go back to every single person on the team who’s in a comparable role and you tell them that we’ll review their salary upwards in the next budget. You just do it. Because new people's pay might get disclosed (somehow).

2. Equity is the great equalizer.

Cash salaries get compressed and distorted by the market. Shares doesn’t lie. Give people real ownership that grows with the company they helped build. When the company does well, the people who were here in the begining will get insanely high stock valuations. That’s how you keep the early believers believing.(and staying)

Great companies aren’t built by the people who joined yesterday. They’re built by the people who stayed through the dark days, who shipped the impossible, who cared when it wasn’t cool yet.

If you let salary inflation turn those people into second-class citizens, you don’t just demotivate them; you lose them. And when you lose them, you lose everything that made your company special in the first place.

Take care of the people who took care of you.

It’s not complicated.  It’s the right thing to do.

Our 2025 Christmas message:

Dear Friends, Clients and Staff,

As we enter the heart of the Christmas season, a time often defined by twinkling lights, festive gatherings, and the exchange of gifts, I find myself reflecting on the deeper purpose of this holiday. I believe our success is not just measured by our year-end reports, but by the positive footprint we leave on the world around us.

The reality we must face is that while many of us have plenty — warm homes, full tables, and the security of a steady income—there are countless individuals just outside our doors who have nothing. For many, this season isn’t about celebration; it is about survival.

This year, I want to challenge each of you to give back to society. It is easy to get caught up in the consumerist whirlwind of buying expensive presents for people who already have everything they need. I am asking you to consider a different path this December.

Instead of purchasing that luxury item or another high-priced gadget, I encourage you to redirect those funds. Donate that money directly to the poor or to reputable charity organizations that are working on the front lines to alleviate suffering. Whether it is providing a hot meal, a warm coat, or medical supplies, your contribution can be the difference between despair and hope for someone in need.

We often hesitate to give because we feel our contribution is too small to make a difference. Please remember: a little bit is better than zero. You do not need to be a billionaire to be a philanthropist. A small sacrifice in your holiday budget can provide a monumental blessing to a family in crisis. Let us redefine what "abundance" means this Christmas. Let it not be the number of boxes under our trees, but the amount of help we extend to the helpless.

Thank you for your time reading this, and thank you for joining me in making this a season of true, ethical impact.

With gratitude and warmth,

Daniel Cheah



The AI Race is Shifting from Hardware to Software & Efficiency

Nvidia's dominance has been built on a simple premise - "to implement AI, you need immense computational power, and our GPUs are the best at providing it." For years, this was unquestionably true. Companies like Google, Microsoft, and OpenAI spent billions on Nvidia's H100 and B200 chips to train and run their massive models.

Google's recent "wins"—specifically with its Gemini model and the underlying Tensor Processing Unit (TPU) infrastructure—challenge this premise by proving that the game is no longer just about raw hardware power. It's about creating a more efficient, integrated, and cost-effective "full stack".

Here’s why that's bad for Nvidia's current business model:

1. The Vertical Integration Threat: Google's TPUs

This is the most direct threat. Instead of buying Nvidia chips, Google designs and uses its own custom AI chips called Tensor Processing Units (TPUs).

*  Purpose-Built Efficiency: TPUs are designed from the ground up specifically for the kind of linear algebra operations (matrix multiplications) that dominate AI model training and inference. This can lead to better performance-per-watt and lower cost than a general-purpose GPU for these specific tasks.

* Control the Stack: By controlling both the hardware (TPU) and the software (TensorFlow, JAX), Google can optimize them to work perfectly together. This software-hardware co-design is a significant advantage that a general-purpose chipmaker like Nvidia, which must cater to a wide range of customers, cannot easily replicate for any single one.

* Reducing Nvidia's TAM: Every major AI task Google runs on its TPUs is a task for which it does **not** need to buy a Nvidia GPU. As Google's AI services (Search, Workspace, Cloud, etc.) grow, its internal demand for TPUs grows, directly eating into Nvidia's potential market.

2. The Software Ecosystem Threat: Nvidia's "MoAT" is Being Challenged

Nvidia's true strength has never been just its silicon; it's its **software platform, CUDA**. For over a decade, CUDA has been the indispensable programming model for AI. If you trained a model, you did it with CUDA. This created a powerful "moat."

Google is building a compelling alternative with **JAX** and its ecosystem.

* A New Software Stack: JAX, combined with Google's TensorFlow and optimized for TPUs, is becoming a highly popular and powerful framework for cutting-edge AI research, especially for large-scale models. Many researchers now prefer it.

* Breaking the Lock-In: If the best and most efficient models (like Gemini) are built on a non-CUDA stack (JAX/TPU), it proves that CUDA is not the only game in town. This encourages other companies to explore alternatives, weakening Nvidia's strategic lock-in on the developer community.

3. The Inference Problem: Where the Real Money Is

AI has two phases:

1. Training - Building the model (requires massive compute, Nvidia's stronghold).

2. Inference - Using the model to answer queries (e.g., asking a chatbot a question).

While training is computationally intensive and gets all the headlines, inference is where the vast majority of the long-term computational cost and business revenue lies. Every Google Search, every ChatGPT query, every image generation is an inference task.

*  Inference Favors Specialization: Inference doesn't always need the brute power of a top-tier H100 GPU. It often runs better on cheaper, more specialized, and power-efficient chips—exactly what TPUs are designed for.

*  Cost is King: For a service used billions of times a day (like Google Search with AI), shaving off microseconds and fractions of a cent per query through a more efficient chip like a TPU translates to hundreds of millions of dollars in saved operational costs. Google's vertical integration gives it a massive cost advantage here.

4. The Cloud Power Shift: Competing with Your Supplier

Google Cloud Platform (GCP) is a major seller of Nvidia GPUs to its customers. But it's also the primary showcase for its own TPU v5e chips.

*  Offering an Alternative: Google can now offer cloud customers a choice: "You can rent Nvidia GPUs from us, or for many workloads, you can use our cheaper, more efficient TPUs." This positions TPUs as a direct competitor *within* Nvidia's own distribution channel.

*  The "Apple vs. Microsoft" Dynamic: This is akin to Apple controlling its entire hardware and software stack (like Google with TPU+JAX) versus Microsoft/PC makers relying on Intel (like other AI companies relying on Nvidia). The integrated model can often be more efficient and profitable.

Conclusion (Why Nvidia Isn't Doomed)

It's crucial to understand that this is a long-term threat, not an immediate collapse.

*  Nvidia is Still the King and the Pace-Setter: Nvidia's latest GPUs (like the Blackwell B200) are still arguably the most powerful AI chips on the market. The demand for AI compute is so immense that the market can support multiple winners for the foreseeable future.

*  The Broader Market: Nvidia sells to everyone: other cloud providers (Azure, AWS), sovereign nations, research institutions, and startups. Google's success does not directly impact these sales. In fact, it fuels the overall AI arms race, which benefits Nvidia.

*  Nvidia is Evolving: Nvidia isn't standing still. It's building its own cloud AI services (DGX Cloud), investing in software, and its hardware roadmap remains aggressive. It's also expanding into new areas like robotics and autonomous vehicles.

Google is proving that the path to AI dominance may not run exclusively through Nvidia's GPUs. By successfully vertically integrating with its TPUs and building a world-class software stack, Google is breaking Nvidia's perceived monopoly on high-performance AI computation. It demonstrates that superior algorithms and a tightly integrated hardware-software stack can be a more powerful and cost-effective advantage than simply buying the most raw compute power from a third party.

For Nvidia, this means the competitive landscape is shifting from being the sole provider of the "picks and shovels" in the AI gold rush to being one major player in a more diverse and competitive ecosystem. That, by definition, is bad news for a company that has enjoyed near-total dominance.

AI's Transformative Impact on Business Revenue: Opportunities and Challenges in 2026

Artificial Intelligence (AI) is reshaping the business landscape, driving unprecedented opportunities for the Asia Pacific market, especially. Strong AI adoption in many countries has led to revenue growth, increased lead generation, customer retention, and market expansion. However, it also presents challenges, such as AI talent shortages. Let's explore how this affects every business.

1. Boosting Business Revenue Through AI

AI is a powerful catalyst for revenue growth, enabling businesses to optimize operations, personalize customer experiences, and make data-driven decisions. According to recent industry insights, companies leveraging AI can see revenue increases of up to 20% by improving efficiency and customer engagement. Here’s how:

- Operational Efficiency: AI automates repetitive tasks, such as inventory management, supply chain optimization, and customer service, reducing costs and freeing up resources for revenue-generating activities. For example, predictive analytics can optimize pricing strategies, boosting profit margins by 5-10%, as seen in retail and e-commerce.

- Personalization at Scale: AI-driven tools analyze customer data to deliver hyper-personalized experiences, increasing conversion rates. Companies like Amazon use AI to recommend products, contributing to an estimated 35% of their revenue from personalized suggestions.

- Predictive Insights: AI’s ability to forecast trends and customer behavior helps businesses anticipate demand, reduce churn, and identify new revenue streams. For instance, financial services firms use AI to detect fraud, saving billions annually while enhancing customer trust.

 2. Revolutionizing Sales Lead Generation

AI is transforming how businesses generate and qualify sales leads, making the process faster and more precise. By leveraging machine learning and natural language processing, companies can identify high-potential leads with greater accuracy.

- Lead Scoring and Prioritization: AI algorithms analyze historical data, website interactions, and social media activity to score leads based on their likelihood to convert. This allows sales teams to focus on high-value prospects, increasing conversion rates by up to 30%, according to Salesforce data.

- Automated Outreach: AI-powered tools like chatbots and email automation platforms engage leads in real-time, nurturing them through the sales funnel. For example, Drift’s AI chatbot has helped businesses reduce response times and increase lead engagement by 50%.

- Behavioral Targeting: AI analyzes customer behavior across platforms, enabling businesses to create targeted campaigns. B2B companies using AI-driven lead generation tools report a 20% increase in qualified leads, as they can pinpoint decision-makers more effectively.

 3. Targeting Key Existing Customers for Upselling

Upselling and cross-selling to existing customers is a cost-effective way to boost revenue, and AI makes it more effective by identifying the right customers and tailoring offers.

- Customer Segmentation: AI clusters customers based on purchase history, preferences, and behavior, allowing businesses to target high-value segments with personalized upsell offers. For instance, Netflix uses AI to recommend premium plans, contributing to a 15% increase in subscriber upgrades.

- Predictive Upselling: AI predicts which customers are likely to purchase additional products or services based on their engagement patterns. Retailers using AI-driven upselling strategies report a 10-20% increase in average order value.

- Real-Time Personalization: AI enables dynamic pricing and real-time offer adjustments. For example, airlines use AI to upsell premium seats or ancillary services during booking, increasing ancillary revenue by up to 25%.

 4. AI Talent Shortages: A Critical Challenge

While AI offers immense potential, the shortage of skilled talent is a significant barrier. The demand for AI professionals—data scientists, machine learning engineers, and AI strategists—far outstrips supply, creating a competitive market for talent.

- Current Landscape: According to LinkedIn, AI-related job postings grew by 74% annually from 2020 to 2024, yet only 1 in 5 organizations has sufficient AI expertise. This gap delays AI adoption and increases implementation costs.

- Impact on Businesses: Companies without in-house AI talent often rely on third-party vendors, which can increase costs by 20-30%. Smaller businesses, in particular, struggle to compete for talent against tech giants offering high salaries and advanced projects.

- Solutions: To address this, businesses are investing in upskilling programs, partnering with universities, and leveraging low-code AI platforms that require less technical expertise. For example, Google’s AI training programs have helped over 2 million professionals gain basic AI skills since 2022.

 5. Market Potential and Revenue Opportunities

The global AI market is poised for explosive growth, presenting vast revenue opportunities for businesses across industries. According to recent projections, the AI market is expected to reach $1.8 trillion by 2030, growing at a CAGR of 37.3%.

Industry-Specific Opportunities: 

 - Healthcare: AI-driven diagnostics and personalized medicine are projected to generate $150 billion in annual revenue by 2026.

 - Retail: AI-powered e-commerce solutions, such as dynamic pricing and inventory management, could contribute $500 billion to global retail revenue by 2030.

 - Financial Services: AI applications in fraud detection, trading, and customer service are expected to save banks $447 billion annually by 2028.

- Emerging Markets: AI adoption in developing economies is accelerating, with Asia-Pacific and Latin America projected to contribute 40% of global AI revenue by 2030, driven by digital transformation initiatives.

- SMEs and AI: Small and medium enterprises (SMEs) are increasingly adopting AI through affordable SaaS platforms, with 60% of SMEs reporting revenue growth after implementing AI tools, per a 2024 Gartner study.

 Navigating the AI Revolution

To maximize AI’s impact on revenue, businesses must act strategically:

- Invest in AI Infrastructure: Adopt scalable AI tools tailored to your industry, such as CRM platforms with built-in AI or predictive analytics software.

- Focus on Data Quality: AI’s effectiveness depends on clean, structured data. Invest in data governance to ensure accurate insights.

- Address Talent Gaps: Partner with AI vendors or invest in training to build internal capabilities.

- Ethical AI Adoption: Ensure transparency and fairness in AI applications to build customer trust and comply with regulations.

In conclusion, AI is no longer a futuristic concept—it’s an immediately-available critical driver of business success. From using AI chatbots to AI Agents to the latest Agentic-AI apps, we are unlocking new market opportunities. AI offers a evolution in transformative potential for revenue growth. Businesses that strategically embrace AI will not only boost revenue but also gain a competitive edge in an increasingly AI-driven world.