Charlie Munger famously said: “All I want to know is where I’m going to die, so I’ll never go there.”
When it comes to implementing AI, most leaders are desperately trying to figure out how to be brilliant. They want to disrupt the market, revolutionize their workflows, and implement the most bleeding-edge models available.
But Mungerโs philosophy of inversion suggests a much more practical starting point. Instead of asking how to win, ask: How do we guarantee this AI project will completely fail?
If your goal is to kill an AI initiative, here is the exact playbook:
Start with the tech, not the problem: Buy an expensive enterprise LLM license first, then wander around the company looking for a vague use case to justify the cost.
Ignore your data foundation: Assume the AI will magically untangle years of undocumented, siloed, and messy legacy data. Garbage in, garbage out – at scale.
Remove the human immediately: Automate a high-stakes, customer-facing workflow end-to-end on day one without a “human-in-the-loop” to catch the inevitable edge cases or hallucinations.
Skip change management: Drop a powerful new AI tool on your employees’ desks without training them on how to use it, or adjusting their KPIs to reflect their new workflows.
In the rush to adopt AI, the tech world is obsessed with seeking brilliance. But in complex systems, avoiding stupidity is often the faster path to ROI.
Don’t ask how AI is going to make your company a genius. Figure out what will cause the implementation to die – and then just don’t go there.
Right now, we are living through an era of technological whiplash.
The advancements we are seeing in Artificial Intelligence aren’t just incremental; they are fundamentally rewriting how we work and live.
Consider the recent leaps happening right in front of us:
โข ๐ฃ๐ฒ๐ฟ๐๐ผ๐ป๐ฎ๐น ๐๐ด๐ฒ๐ป๐๐: We are moving past chatbots that simply answer questions to autonomous digital assistants that can plan, reason, and execute multi-step workflows on our behalf.
โข “๐ฉ๐ถ๐ฏ๐ฒ ๐๐ผ๐ฑ๐ถ๐ป๐ด”: We are shifting from writing strict, line-by-line syntax to guiding AI with natural language and intentโletting the machine build the software while we direct the vision.
โข ๐ฅ๐ผ๐ฏ๐ผ๐๐ถ๐ฐ๐: AI is rapidly breaking out of the screen and into the physical world, interacting with our environments in increasingly capable, autonomous ways.
The pace of this development is staggering. And naturally, it makes a lot of us want to cling to what we know.
We try to fit these paradigm-shifting tools into our existing, comfortable boxes. We treat powerful agents like fancy search engines, or we view AI coding through the rigid lens of traditional software engineering.
But here is the hard truth: ๐ข๐๐ฟ ๐ฝ๐ฟ๐ถ๐บ๐ฎ๐ฟ๐ ๐ฏ๐ฎ๐ฟ๐ฟ๐ถ๐ฒ๐ฟ ๐๐ผ ๐ฝ๐ฟ๐ผ๐ด๐ฟ๐ฒ๐๐ ๐ฟ๐ถ๐ด๐ต๐ ๐ป๐ผ๐ ๐ถ๐ ๐ป๐ผ๐ ๐ฎ ๐น๐ฎ๐ฐ๐ธ ๐ผ๐ณ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ถ๐๐ถ๐๐ ๐ผ๐ฟ ๐ฎ ๐๐ต๐ผ๐ฟ๐๐ฎ๐ด๐ฒ ๐ผ๐ณ ๐ป๐ฒ๐ ๐ถ๐ฑ๐ฒ๐ฎ๐. ๐๐ ๐ถ๐ ๐ผ๐๐ฟ ๐๐๐๐ฏ๐ฏ๐ผ๐ฟ๐ป ๐ฟ๐ฒ๐๐ถ๐๐๐ฎ๐ป๐ฐ๐ฒ ๐๐ผ ๐ฎ๐ฏ๐ฎ๐ป๐ฑ๐ผ๐ป๐ถ๐ป๐ด ๐ผ๐๐๐ฑ๐ฎ๐๐ฒ๐ฑ ๐บ๐ฒ๐ป๐๐ฎ๐น ๐บ๐ผ๐ฑ๐ฒ๐น๐.
To truly leverage this era of AI, we have to let go of HOW we used to do things and focus entirely on WHAT we are trying to achieve.
So, how do we escape the old ideas?
โข ๐๐ฒ๐ ๐๐ผ๐๐ฟ ๐ต๐ฎ๐ป๐ฑ๐ ๐ฑ๐ถ๐ฟ๐๐: Don’t just read about the tech. Pick one AI tool or agent and use it for a real task today.
โข ๐๐บ๐ฏ๐ฟ๐ฎ๐ฐ๐ฒ ๐๐ต๐ฒ ๐บ๐ฒ๐๐: You will prompt it poorly at first. It will make mistakes. You will get frustrated. That is a necessary part of the learning curve. Learn from those errors to understand how the machine “thinks.”
โข ๐๐๐ฒ๐ฟ๐ฎ๐๐ฒ ๐๐ผ๐๐ฟ ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐: Stop asking, “How can AI do my old process?” and start asking, “What does a completely new, optimized process look like now that I have AI?”
The future belongs to those willing to unlearn just as fast as they learn.
What outdated mental model are you trying to let go of right now? Let me know in the comments! ๐
Ten years might feel like an eternity in our daily routines, but in the timeline of #๐๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ถ๐ฎ๐น๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ, itโs a blink of an eye that changed everything.
Just ten years ago this week, the world tuned in to a historic confrontation: #๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ฒ๐ฒ๐ฝ๐ ๐ถ๐ป๐ฑโ๐ #๐๐น๐ฝ๐ต๐ฎ๐๐ผ ๐๐. ๐๐ผ ๐๐ฟ๐ฎ๐ป๐ฑ๐บ๐ฎ๐๐๐ฒ๐ฟ ๐๐ฒ๐ฒ ๐ฆ๐ฒ๐ฑ๐ผ๐น. It was a watershed moment that redefined our understanding of machine intelligence.
The match wasnโt just about a computer winning a game; it was about the profound “creativity” and “resilience” displayed by both sides:
โข ๐ ๐ผ๐๐ฒ ๐ฏ๐ณ (๐๐ฎ๐บ๐ฒ ๐ฎ): AlphaGo placed a stone in a location no human expert would have ever considered. It was a move so “inhuman” it shocked the commentators, yet it ultimately proved to be a stroke of strategic genius.
โข ๐ง๐ต๐ฒ “๐๐ถ๐๐ถ๐ป๐ฒ ๐ ๐ผ๐๐ฒ” (๐๐ฎ๐บ๐ฒ ๐ฐ): Just when it seemed the machine was invincible, Lee Sedol played Move 78 – a brilliant, unexpected wedge that confused the algorithm and secured a victory for humanity. It was a stunning display of human spirit and the ability to find a path where none seemed to exist.
Since that week in 2016, the pace of AI advancement hasn’t just continuedโit has accelerated exponentially. We are no longer just watching AI play games; we are working alongside it to solve complex global challenges.
If you haven’t seen it yet, I highly recommend watching ๐๐ต๐ฒ ๐๐น๐ฝ๐ต๐ฎ๐๐ผ ๐ฑ๐ผ๐ฐ๐๐บ๐ฒ๐ป๐๐ฎ๐ฟ๐ ๐ผ๐ป ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ. It is a gripping, emotional look at this turning point in history.
The last decade proved that AI can surprise us, but the “Divine Move” reminded us of the unique power of human ingenuity. Now is the time for us to work together, leveraging these tools to make the ๐ฏ๐ฒ๐๐ ๐บ๐ผ๐๐ฒ ๐ผ๐ณ ๐ผ๐๐ฟ ๐น๐ถ๐๐ฒ๐.
How has your perspective on AI changed since that 2016 match? Letโs discuss in the comments.
It is completely normal to feel overwhelmed by the sheer velocity of AI.
Every week brings a new model, a new feature, or a headline declaring that everything is about to change. When the landscape shifts this fast, figuring out where to begin can feel paralyzing.
Billionaire investor Howard Marks famously wrote a memo in 2001 titled: “๐ฌ๐ผ๐ ๐๐ฎ๐ป’๐ ๐ฃ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐. ๐ฌ๐ผ๐ ๐๐ฎ๐ป ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฒ.” While he was talking about financial markets, this philosophy is the ultimate playbook for navigating the AI revolution. Here is how to apply that mindset to get yourself ready for what is next:
If you try to predict exactly where AI will be in three years, you will exhaust yourself. Will AI replace software engineers or make them 10x more productive? Which AI company will dominate? What specific jobs will disappear?
The truth is, no one knows. If you tie your career strategy to a specific prediction, you are building your house on sand.
Preparation doesn’t mean knowing the future; it means building the resilience and adaptability to thrive no matter what the future looks like.
This brings us to the most crucial shift in how you should approach your AI education: ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ต๐ฒ ๐ฐ๐ฎ๐ฝ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐, ๐ป๐ผ๐ ๐๐ต๐ฒ ๐๐ผ๐ผ๐น.
Tools are fleeting. Their interfaces change, and eventually, they get replaced. Capabilities – knowing ๐ฉ๐ฐ๐ธ and ๐ธ๐ฉ๐บ to apply technology to solve a problem – last a lifetime.
Think of it like photography. Mastering a tool means memorizing the menus on a specific 2026 high-tech camera. Mastering the capability means understanding lighting, composition, and human emotion. The camera will be obsolete in three years; the eye for a great photograph lasts a lifetime.
Donโt just memorize where to click. Instead, master the underlying skills that make AI useful:
Problem Decomposition: AI struggles with massive, vague goals but excels at small, defined tasks. Learn to break big projects into bite-sized pieces an AI can actually execute.
Critical Evaluation (Taste): AI generates infinite content. The premium skill is no longer creation; it is editing – spotting errors, biases, and mediocrity.
Context Building: Models only know what you tell them. Master the ability to clearly articulate the specific constraints and goals of your problem.
How to Start Today
Pick one friction point: Don’t try to automate your whole life. Pick one annoying, repetitive weekly task to experiment with.
Experiment with curiosity: Treat AI like a brilliant but naive intern. When it fails, figure out why, adjust your instructions, and try again.
Double down on human skills: Empathy, strategic vision, and relationship-building are things AI cannot do.
You cannot predict what the AI landscape will look like tomorrow, but by focusing on timeless skills and daily experimentation, you can ensure you are ready for it.
I’d love to hear from you: What is ONE repetitive task you are trying to use AI for this week? Let me know in the comments!
We are incredibly lucky to have the privilege of two New Years. It usually serves as a grace period – a chance to review the resolutions we set on January 1st and tweak them if they aren’t working out by the time the Lunar New Year arrives.
Usually, itโs just a status update. A minor pivot.
We are just 6 weeks into the year, and the landscape hasn’t just shifted; it has completely transformed. This isn’t the time for an “update.” It is time for a total ๐ฟ๐ฒ๐๐ฟ๐ถ๐๐ฒ.
The pace of Agentic AI advancement in these last few weeks has been unprecedented:
โข ๐๐ผ๐ฑ๐ถ๐ป๐ด & ๐๐ฒ๐: We now have multiple autonomous models handling complex architecture.
โข ๐๐ฟ๐ฒ๐ฎ๐๐ถ๐๐ฒ ๐ฆ๐๐ถ๐๐ฒ: Video and music generation have leaped forward in fidelity and control.
โข ๐ง๐ต๐ฒ ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ ๐ฝ๐น๐ผ๐๐ถ๐ผ๐ป: Powerful agentic platforms are popping up from nowhere, automating workflows we thought required human oversight just two months ago.
If your strategy relies on how things worked in December 2025, you are already behind.
Look at your processes and redesign them from the ground up with an “AI-First” mindset. Ask yourself: If I were building this workflow today, with today’s agents, what would it look like?
