Your AI Life Story

I have a slightly unusual habit whenever I travel abroad for work: I always pick up a local newspaper and turn straight to the obituaries.

It’s fascinating how much this single section reveals about cultural differences.

In Hong Kong, an obituary is typically formal and concise – often dominated by an extensive list of surviving family members and lineages. In many Western papers, the focus is deeply human: quirky anecdotes, formative struggles, and life lessons. It captures a life in full color.

Lately, this habit made me wonder: What will an AI-generated “Life Stories” look like for our generation?

If an AI agent synthesizes our digital footprint – our emails, commit histories, published papers, and social feeds – will it summarize us by our titles, pedigrees, and corporate milestones? Or will it actually uncover the quiet, defining moments that made us who we are: the projects that failed, the mentorship given off the record, the late-night problem-solving?

Technology often defaults to cataloging achievements. But what makes human history worth reading isn’t the clean outcome; it’s the friction along the way.

As we build tools that summarize and automate our world, we should remember to measure what truly matters. Celebrate the messy, authentic journey – not just the curated title.

What Are Useful AI Use Cases?

What are your AI use cases?

* Drafting emails?
* Summarizing 50-page industry reports?
* Generating social media content?
* Prototyping a quick game or building personal workflow tools?

Most of our day-to-day conversations around AI naturally center on personal productivity and speed – getting through our inbox faster, sharpening our code, or automating routine tasks. And that alone has transformed how many of us work.

But then you see projects that remind you of what AI can do on a planetary scale.

A fantastic example is Cathay Pacific’s collaboration with Google and Contrails.org (shoutout to Oliver Haas and the team for sharing this journey).

By applying AI models to predict where contrails – the heat-trapping cloud streaks left behind by flights – are likely to form, flight operations teams can make minor altitude adjustments to bypass them. It’s an elegant, data-driven approach to tackling real-world aviation climate impact in real-time.

Efficiency gains are great. But using AI to make our planet a better, more sustainable place to live? That is where the real inspiration lies.

Curious to hear from you all : Beyond everyday productivity and automation, what is the most impactful or unexpected real-world application of AI you’ve seen lately?

Play The Right Game

Success is no longer determined by getting better at playing yesterday’s game using AI. It’s defined by whether you’re playing the right game.

Almost everyone now has access to powerful LLMs, GenAI tools, and intelligent workflows. As a result, content is abundant: perfectly structured emails, solid analytical reports, polished slide decks, and slick video assets.

Yet, when output becomes effortless, distinctiveness disappears. Without clear human intent, much of what we see feels generic, pale, and transactional.

AI amplifies capability, but true differentiation comes from how we apply it.

I spent this past week for an intensive training series. We moved beyond simple prompting and coding, to focus on what matters most for scalable, enterprise-grade AI:

+ Output Quality & Authenticity: Bringing critical thinking and domain expertise back into AI-generated deliverables.

+ Agentic System Design: Moving from standalone prompts to multi-agent architectures.

+ Evaluation & Security: Ensuring governance, risk mitigation, and continuous quality measurement.

The tech landscape is evolving rapidly. The goal isn’t just to produce more, but to figure out what to do – it’s executing with quality, judgment, and human nuance.

Make The First Step Effortless

Some projects start with a plan. Others start with, “Let’s see where this idea goes.” 💡

We’ve all been there – falling into the trap of endless desktop planning, specification developing, and “what-if” scenario handling. Before you know it, momentum is lost, and the spark remains on paper.

The key question is: 𝗵𝗼𝘄 𝗰𝗮𝗻 𝘄𝗲 𝗺𝗮𝗸𝗲 𝘁𝗵𝗮𝘁 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 𝗳𝗶𝗿𝘀𝘁 𝘀𝘁𝗲𝗽 𝗲𝗳𝗳𝗼𝗿𝘁𝗹𝗲𝘀𝘀?

That’s where AI comes in to turn concepts into reality. It can:

 • 𝗗𝗲𝘀𝗶𝗴𝗻 𝘁𝗵𝗲 𝗳𝗹𝗼𝘄 and structure your workflow from scratch.
 • 𝗚𝗿𝗶𝗹𝗹 𝘆𝗼𝘂 𝗼𝗻 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗽𝗮𝗿𝘁𝘀 to identify gaps before they become issues.
 • 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗮𝗻 𝗶𝗺𝗮𝗴𝗲 𝗼𝗿 𝘃𝗶𝗱𝗲𝗼 to visually illustrate the vision.
 • 𝗖𝗿𝗲𝗮𝘁𝗲 𝗰𝘂𝘀𝘁𝗼𝗺 𝗮𝗴𝗲𝗻𝘁𝘀 and skills to test-run your idea.

The first step is now completely effortless.

Stop overthinking. Start doing. 𝗚𝗼 𝗳𝗼𝗿 𝗶𝘁!

#GenerativeAI #Innovation #Mindset

Both Sides Now – Cloud and AI

“I’ve looked at clouds from both sides, now 

From up and down, and still somehow 

It’s cloud illusions, I recall 

I really don’t know clouds at all”

Those hauntingly beautiful lyrics from Joni Mitchell – recorded back in her 1966 live performance at the Second Fret – resonate with me today in a way they never did when I first heard them.

It has been 15 years since I led my first cloud implementation. Back then, the “cloud” felt like a radical experiment. I have vivid memories of the early days – days when I had to use my own personal credit card to shoulder the team’s cloud expenses just to keep our projects running while we fought for internal buy-in.

Back then, I thought I knew exactly what the cloud was. I was wrong.

Today, looking back from 2026, I realize that “knowing the cloud” isn’t a destination; it’s a continuous, evolving journey. We have moved from simple infrastructure migration to complex, distributed architectures, and now, we are in the era of AI-driven cloud computing.

If the last 15 years taught me anything, it’s that the technology will always outpace our current understanding. The “cloud” isn’t just about servers or storage anymore – it’s the foundation upon which the intelligence of tomorrow is being built.

In this era of rapid AI acceleration, the biggest risk isn’t the technology failing; it’s our own willingness to stop learning. Staying relevant means constantly “looking at the clouds from both sides” – the cost side and the innovation side, the technical debt and the architectural opportunity, the legacy systems and the generative future.

I started with a credit card and a dream of agility. Today, I’m still learning, still iterating, and still finding that the more I know, the more I realize there is to explore.

How has your relationship with the cloud evolved over the last decade? Are you finding the AI era to be the most challenging (or exciting) shift yet?

#CloudComputing #ContinuousLearning #AI #DigitalTransformation #CloudJourney