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[Alvin Foo @alvinfoo]

The biggest challenge of AI isn’t generating content.

It’s filtering it.

Just a few years ago, most information online was created by humans.

Today, AI can produce articles, videos, images, reports, research summaries, social posts, and entire websites in seconds.

The result?

An explosion of information.

But not necessarily an increase in wisdom.

We’re entering an era where the scarcest resource isn’t content.

It’s attention.

And the most valuable skill won’t be creating more content.

It will be distinguishing VALUE from NOISE.

Here’s the framework I use:

🔹 Credibility over popularity
Don’t assume something is true because it has millions of views.

🔹 Depth over volume
One high-quality source can be worth more than 100 AI-generated articles.

🔹 Original thinking over repetition
Most AI content is derivative. Seek people who contribute insights, not just summaries.

🔹 Signal over speed
Being first is less important than being right.

🔹 Human judgment over AI output
AI can accelerate analysis, but it cannot replace critical thinking.

The irony is that AI will make knowledge more abundant while making wisdom more valuable.

In a world where everyone can generate content, the winners will be those who can curate, interpret, and act on the right information.

The future belongs not to those who consume the most information.

It belongs to those who develop the best filters.

How are you separating signal from noise in the age of AI?

[Alvin Foo @alvinfoo]

The AI race is real.
The window of advantage is closing.

Every month, models get smarter.
Every month, costs get lower.
Every month, disruption accelerates.

Most people are focused on which AI model is best today.

They’re asking the wrong question.

The real question is:

What happens when frontier AI becomes a commodity?

For years, the U.S. held a significant lead in generative AI.

Today, that gap is shrinking rapidly.

📈 China’s top AI models are catching up in capability.

📉 Model costs are collapsing.

🌎 Adoption is accelerating worldwide.

The winners won’t necessarily be the companies building the largest models.

The winners will be the organizations and individuals who learn how to apply AI faster than everyone else.

History has shown this repeatedly:

* The biggest opportunity wasn’t the invention of electricity.
* It was what businesses built with it.
* It wasn’t the internet itself.
* It was the companies that leveraged it to transform industries.

AI is following the same path.

As models become more powerful, cheaper, and more accessible, the competitive advantage shifts from technology ownership to technology execution.

The question is no longer:

❌ “Which model should I use?”

The question is:

✅ “How do I redesign my business, career, and workflows around AI?”

Those who adapt early will compound their advantage.

Those who wait may find themselves competing against people who are operating at 10x speed, 10x scale, and 10x efficiency.

The AI race is no longer about countries.

It’s about capability.

It’s about execution.

And it’s about who learns fastest.

What do you think?

Will AI become a commodity like cloud computing, or will a handful of frontier labs continue to dominate the future?

[Alvin Foo @alvinfoo]

Everyone is talking about AI.

Very few people are building an AI stack that actually makes them more productive.

The reality is that AI isn’t one tool.

It’s a collection of tools that can help you think faster, write better, automate repetitive work, create content, analyze information, and make better decisions.

That’s why I put together this visual guide:

The AI Stack Every Executive Needs in 2026

⚡ Work Smarter.
⚡ Move Faster.
⚡ Stay Ahead.

Inside you’ll find some of the most useful AI tools across:

✅ AI Assistants
✅ Content Creation
✅ Image & Video Generation
✅ Productivity & Knowledge Management
✅ Automation & No-Code
✅ Software Development

The biggest mistake I see executives make is trying to learn every new AI tool that appears.

Instead, focus on building a stack that solves real business problems:

• Save time
• Improve decision making
• Increase productivity
• Scale your expertise
• Create leverage

The winners in the next decade won’t necessarily be AI experts.

They’ll be leaders who know how to combine the right AI tools with the right workflows.

Which AI tool has had the biggest impact on your work this year?

👇 Share it in the comments.

PS. Get a FREE AI Diagnostic and subscribe to our AI newsletter at https://t.co/I4zR8aXpsx

[Alvin Foo @alvinfoo]

Everyone is talking about how AI will create more ideas.

Fewer people are talking about what happens when everyone has ideas.

Before AI, the challenge was creation.

Today, creation is becoming abundant.

A founder can build what once required a team.
A marketer can create what once required an agency.
An executive can analyze what once required a department.

The distance between an idea and execution is collapsing.

But there’s a catch.

As AI increases the supply of ideas, products, content, and businesses, human attention does not increase at the same rate.

Attention remains finite.

Trust remains finite.

Time remains finite.

This creates a new reality:

When everyone can create, creation is no longer the advantage.

The advantage becomes:

→ Attention
→ Trust
→ Distribution
→ Judgment

AI can generate content.

It cannot generate years of credibility.

AI can create products.

It cannot instantly create a loyal community.

AI can help people sound smart.

It cannot replace wisdom earned through experience.

The great paradox of the AI era is this:

As intelligence becomes more accessible, human differentiation becomes more valuable.

As creation becomes cheaper, trust becomes more expensive.

As execution becomes easier, distribution becomes harder.

The winners of the next decade may not be those who create the most.

They may be those who build the strongest trust, the deepest relationships, and the most engaged communities.

Because in a world where everyone has access to AI, the scarcest resource is no longer technology.

It’s attention.

And attention follows trust.

This is why I believe personal branding is not becoming less important because of AI.

It’s becoming exponentially more important.

AI democratizes creation.

Trust determines who gets chosen.

[Alvin Foo @alvinfoo]

AI won’t eliminate the need for human skills, it will increase the value of the right ones.

McKinsey’s latest research shows that while many technical and routine tasks face growing automation exposure, skills like leadership, communication, coaching, negotiation, and problem-solving remain among the most resilient.

The winners in the AI era won’t be those who compete with AI.

They’ll be those who learn to partner with it.

The future belongs to human + AI, not human vs AI.

[Alvin Foo @alvinfoo]

This is the biggest Siri overhaul ever:

• On-screen awareness — Siri sees exactly what’s on your display (Instagram post, web page, notification) and acts on it instantly. No copy-paste.
• Personal context — It understands your photos, messages, calendar, and history while staying private (on-device + Private Cloud Compute).
• Agentic flow — Natural conversation + multi-step actions across apps (reminder → music → navigation → contacts). It doesn’t just answer — it does things for you.
• Deep ecosystem integration — New dedicated Siri app with history, synced across all your Apple devices.

Apple’s not first to conversational AI, but they’re making it feel native, reliable, and private on hundreds of millions of iPhones at once. If it delivers, this could finally make Siri the assistant people actually use daily.

Developer betas are out now, public release later 2026. Game changer or more vaporware?

[Alvin Foo @alvinfoo]

AI Must Change K-16 Education. Dr. Fei-Fei Li is Right.

Quoting Dr. Fei-Fei Li (Co-Founder & CEO, World Labs) on Bloomberg Tech with Emily Chang:

“AI must change learning. AI must change K-16 learning.”

“The most precious resource of our entire world is human capital.”

“When AI can answer standardized tests… and do better than average human, it’s not about humans are bad. It’s about we need to change the education system.”

“We need to change how we evaluate, we need to change the way we empower teachers… so students can use these tools, be empowered, and do things that we can never imagine.”

“All of the kids today shouldn’t be scared of AI. They should feel the human agency to lead AI, to use AI in the right way, and to make the impact that they want to make for the world.”

This is urgent. Standardized testing is obsolete. AI aces it all. Clinging to old metrics trains kids for a dead world, breeding anxiety over agency.

What changes now:
•Evaluation: Real projects, AI-augmented outcomes, not gameable exams.
•Teaching: Empower educators as AI orchestrators.
•Curriculum: Early AI fluency + human strengths (creativity, judgment, orchestration).
•Mindset: Command AI. Don’t fear it.

For Asia/SEA, this is existential. Fast movers dominate.

This is core to what I’m building with @10xme_biz Executive AI System, agentic frameworks that deliver 10x human potential today.

Parents, educators, leaders: Rebuild education for AI reality.

Kids must lead AI, not hide from it.

What needs the fastest overhaul, evaluation, curriculum, teachers, or mindset?

[Alvin Foo @alvinfoo]

Is AI Just Another Railroad Bubble? Scott Galloway’s Brutal Warning Hits Hard But the Track-Layers Will Still Win Big

Scott Galloway is asking the right question in this must-watch clip: AI is undeniably transformative, like the railroads or the late-90s telco buildout but will the companies laying the massive CapEx tracks actually earn a solid return on that capital, or are we headed for another painful crash?

He’s spot on about the risks. Hyperscalers, chip giants, and frontier labs are pouring hundreds of billions into data centers, GPUs, power, and infrastructure in 2026. History is littered with precedents: railroad overbuilds led to panics and bankruptcies; the dot-com fiber glut created “dark fiber” and destroyed trillions in value. Many CEOs still report zero measurable ROI from AI pilots. If demand lags or China’s open-weight models commoditize everything, margins will get crushed.

But Galloway’s analogy is incomplete.
Unlike physical rails or fiber, AI models have near-zero marginal cost at scale. The winners aren’t just building infrastructure, they own the data, distribution, applications, and monetization flywheels (Azure, Google Cloud, AWS, enterprise suites, ads). Revenue is already accelerating: OpenAI approaching $20B ARR, cloud AI contributions surging.

This isn’t passive infrastructure. It’s general-purpose intelligence compounding across every industry. Survivors of the inevitable shakeout (and there will be a shakeout with valuations correcting hard) will capture outsized returns, just as Amazon and Google did post-dot-com.

My take as an AI Automation Strategist : Focus ruthlessly on the application layer, the real money is in agentic workflows, custom integrations, and measurable 10x productivity gains for businesses, not raw infra bets.

The pipes will commoditize. The intelligent applications built on top will create durable moats and high-margin profits.

Galloway’s skepticism is essential discipline in the hype cycle. But AI’s software economics and data flywheels make this different and far more consequential than steel rails.

What’s your view? Are we overbuilding for a bust, or is this the asymmetric upside of a generation? Drop your thoughts below.

Credit : Prof G Media, Scot Galloway

[Alvin Foo @alvinfoo]

The Underdog Just Flipped the Script.
Watch this.

OpenAI starts with a rocket-ship lead, $1B ARR, then $2B, $3B, climbing fast on consumer hype and ChatGPT mania.

Anthropic? Barely a blip. Flatlining near zero while the world obsesses over the shiny consumer toy.

Then something shifts.

By late 2024, Anthropic begins its climb. Steady. Relentless. Enterprise-grade. B2B focused.
Fast-forward to 2026: Anthropic doesn’t just catch up. It surges past OpenAI, hitting $47B ARR while OpenAI stalls at $33B.

The orange line (Anthropic) doesn’t just cross the teal line. It leaves it in the dust.

This is what B2B discipline looks like in practice.
Consumer wins headlines. B2B wins balance sheets.

Consumer products chase virality and retention theater. B2B products deliver mission-critical reliability, security, compliance, and measurable ROI for Fortune 500s, governments, and serious operators who pay premium prices and stick around for years.

Anthropic didn’t try to out-meme OpenAI. They built the model enterprises actually trust at scale. They focused on safety, constitutional AI, and solving real workflow pain instead of chasing every Twitter trend. They played the long game and the market rewarded them with explosive, high-margin growth.

The lesson for every founder, strategist, and builder in 2026:
Stop romanticizing the consumer lottery. The real wealth, defensibility, and scale in AI right now is in B2B.
•Solve expensive problems for customers who have budgets.
•Obsess over integration, compliance, and outcomes.
•Build for the people signing $100K+ checks, not just the ones liking posts.

The next wave of AI winners won’t be the ones with the most viral demos. They’ll be the ones who become indispensable infrastructure for serious business.

If you’re building in AI, ask yourself: Are you chasing the next consumer dopamine hit, or are you engineering the quiet revolution that enterprises can’t live without?
The graph doesn’t lie. The orange line is still climbing.

Time to pick your lane and commit.

[Alvin Foo @alvinfoo]

Most people are asking the wrong question about AI.

Instead of asking:

❌ “What’s the smartest model?”

Ask:

✅ “What’s the smartest model for the cost?”

As AI adoption accelerates, cost-efficiency is becoming just as important as raw capability. A model that delivers 95% of the performance at 10% of the cost can dramatically change the economics of AI deployment.

Some interesting observations from the latest rankings:

• Chinese AI labs now dominate the cost-efficiency leaderboard.
• Context windows are rapidly expanding to 1 million tokens.
• Smaller, optimized models are delivering remarkable performance-per-dollar.
• The AI race is no longer just about intelligence—it’s about intelligence at scale.

The winners of the next decade won’t necessarily be the organizations using the most advanced AI.

They’ll be the ones that know which model to use, when to use it, and how to maximize ROI.

In the future, competitive advantage won’t come from having access to AI.

It will come from making better AI decisions than everyone else.

What surprised you most about this ranking?

👇 Share your thoughts in the comments.

P.S. If you’re trying to determine which AI models are best suited for your business, sign up for a free AI diagnostic and newsletter at https://t.co/I4zR8aXpsx.

[Alvin Foo @alvinfoo]

Warren Buffett on how to build a business

[Alvin Foo @alvinfoo]

Roberto P. Nickson recently posted a stunning ad for Claude, within days of being posted, it became one of the most viral Claude ads online.

[Alvin Foo @alvinfoo]

AI ROI Is the New Moat: Why the Companies Generating the Most Value Per Token Will Win

Everyone is asking:

“Will AI replace employees?”

I think that’s the wrong question.

The better question is:

Can AI generate more value than it costs?

The latest OpenRouter data shows something fascinating.

AI token consumption has exploded over the past 18 months, growing from under 1 trillion tokens per week to over 12 trillion. What’s even more interesting is that Chinese AI models have rapidly gained market share, not because they’re necessarily better, but because they’re often dramatically cheaper.

The market is voting with its wallet.

And that’s where the real AI conversation should be.

AI ROI.

Many companies assume AI automatically reduces costs.

In reality:

AI = Compute + Software + Integration + Governance + Human Oversight

For some organizations, AI bills are already becoming one of the fastest-growing operational expenses.

The winners won’t be the companies using the most AI.

They’ll be the companies generating the most value per token.

Here’s the framework I use:

🔹 Premium AI Models
Use for strategic thinking, board presentations, legal reviews, executive communications, and high-stakes decisions.

🔹 Mid-Tier Models
Use for research, summarization, internal reports, and workflow automation.

🔹 Low-Cost Models
Use for classification, extraction, translation, customer support, and high-volume processing.

Not every task needs the most expensive model.

Just like not every employee needs to be the CEO.

The future belongs to organizations that build an AI portfolio instead of relying on a single model.

My rule:

Before deploying AI, ask:

“If this AI costs $50,000 annually, can it create at least $500,000 in value?”

If the answer is no, it’s probably a science project.

If the answer is yes, it’s a competitive advantage.

AI is becoming like cloud computing.

The first phase was:

“Put everything in the cloud.”

The second phase became:

“Optimize cloud costs.”

We’re now entering the same phase for AI.

The companies that win won’t be those generating the most tokens.

They’ll be those generating the most value per token.

Credit : Professor Scot Galloway - https://t.co/qXpnHjwSGT

[Alvin Foo @alvinfoo]

Beyond the Surface, Lessons in Love and Loyalty

In a world obsessed with filters, "perfect" aesthetics, and the constant pressure to live up to societal beauty standards, Declan Rice’s story serves as a powerful reminder of what truly matters. Here’s what we can learn from his unwavering commitment:

1. True Beauty is Found in Shared History
It’s easy to stand by someone when they’re at their peak, but the deepest bonds are forged in the quiet moments before the fame. Declan reminds us that the person who was there when you had nothing is the one who truly deserves to be there when you have everything.

2. Love is Not a Public Performance
We often feel pressured to "curate" our lives and relationships for the approval of others. Declan’s refusal to let public opinion dictate his heart is a lesson in authenticity. Real love doesn't need a "like" or a "follow" to be valid; it only needs mutual respect and sincerity.

3. Character Over Comparison
The internet is a breeding ground for comparison, but comparison is the thief of joy. When we judge others based on their appearance, we miss out on the richness of their character. Declan’s public defense of Lauren wasn't just about her; it was a statement about his own values, loyalty, integrity, and strength.

4. Standing Up for Your Partner
In the face of bullying and harassment, silence can sometimes be seen as complicity. By speaking out, Declan showed that being a "star" isn't just about what you do on the field; it's about how you protect and cherish the people you love off it.

Let’s stop measuring worth by waistlines or follower counts. Instead, let’s value the people who see us for who we are, who support our dreams, and who stay loyal through every season of life.

True love isn't about finding the "perfect" person; it's about seeing an imperfect person perfectly.❤️

[Alvin Foo @alvinfoo]

Small minds discuss people

Average minds discuss events

Great minds discuss ideas

Source: Denzel Washington — AFI Life Achievement Award (2019)

[Alvin Foo @alvinfoo]

Barack Obama emphasizes a simple principle: real value comes from execution. Many people can identify problems. Far fewer step forward and say, “I’ll handle it.” The people who consistently deliver stand out immediately. His advice is practical: do not obsess over titles or chase the biggest responsibilities too early. Master the task in front of you. Repeatedly doing small things well builds trust, credibility, and visibility over time. The lesson is clear: reliability creates leadership.

[Alvin Foo @alvinfoo]

Most people use AI like a search engine.

Power users build an AI team.

The biggest productivity breakthrough isn’t writing better prompts, it’s creating a system where humans and AI work together repeatedly, with shared context, knowledge, workflows, and goals.

That’s where Claude Cowork changes the game.

Instead of starting from scratch every time, you can create dedicated workspaces, build knowledge bases, document team standards, assign workflows, and collaborate with AI as if it were another member of your team.

Here’s the framework we use:

✅ Create focused projects
✅ Build a shared knowledge base
✅ Document rules and workflows in Claude.md
✅ Break work into tasks and milestones
✅ Review, refine, and iterate continuously
✅ Turn outputs into reusable assets

The result?

• Faster execution
• Better decision-making
• Less repetitive work
• More consistent outcomes
• Teams that scale without adding complexity

The future of work isn’t Human vs AI.

It’s Human + AI teams outperforming everyone else.

I’ve put together a visual guide showing how to become a Power Claude Cowork User and build your own AI-powered operating system.

If you’re serious about working smarter with AI, save this post and share it with your team.

🚀 Bonus: Get a free AI Diagnostic and join our AI newsletter at https://t.co/I4zR8aXXi5 to discover where AI can create the biggest impact in your business.

[Alvin Foo @alvinfoo]

The Onion Principle of Success

Most people quit too early.

They see a problem, spend a few hours thinking about it, come up with a complicated solution, and convince themselves that’s as good as it gets.

But Steve Jobs believed something different.

“The first solutions you come up with are often very complex. Most people stop there. But if you keep going, peel more layers of the onion, you can arrive at elegant and simple solutions.”

I’ve found this to be true in almost every area of life:

* In business, the best strategies are often simpler than the first draft.
* In leadership, the most effective communication is usually the clearest, not the most sophisticated.
* In personal branding, the strongest message is often the one that’s easiest to understand.
* In life, clarity comes after confusion, not before it.

The people who create extraordinary outcomes aren’t necessarily smarter.

They simply stay with the problem longer.

While others are looking for shortcuts, they’re asking deeper questions.

While others are adding complexity, they’re removing it.

While others have stopped digging, they’re still peeling back another layer.

The lesson:

When something feels overly complicated, don’t assume complexity is the answer.

Stay with the problem.

Think deeper.

Ask “why” one more time.

The elegant solution is often waiting beneath the surface.

Most people never reach it because they stop at the first layer of the onion.

Don’t. 🧅

[Alvin Foo @alvinfoo]

The Unstoppable Rise of China’s Auto Empire: From Underdog to Global Dominator

Watch this chart animate the seismic shift that’s reshaping the world economy.

In 2014, China exported under 1 million cars annually, barely a blip while Japan, Germany, and the US dominated with 4+ million each. Fast-forward to December 2024: China hits 6.4 million vehicle exports, crushing Japan (4.22M), Germany (3.18M), and the US (1.43M). That’s not incremental growth. That’s conquest.

This isn’t just volume. It’s velocity. China went from “copycat” label to EV powerhouse in under a decade. Affordable, tech-packed vehicles, often loaded with AI-driven features, autonomous capabilities, and next-gen batteries, are flooding global markets. BYD, NIO, Xiaomi, Zeekr, and others aren’t just competing; they’re redefining mobility for billions who were priced out by legacy Western and Japanese brands.

Why This Matters And Why It’s Inspiring
• Disruption at Warp Speed: Legacy giants rested on decades of brand prestige and incremental tweaks. China combined manufacturing scale, supply chain mastery, government alignment, and breakneck innovation (especially in EVs and software-defined vehicles). Result? Price points that democratize quality cars while legacy players scramble with high costs and slower pivots.
• Asia/SEA Opportunity: For markets like Malaysia, this is fuel for transformation. Cheaper, smarter vehicles accelerate adoption, boost local jobs in assembly/distribution/tech integration, and position the region as a hub in the new auto order. No more dependency on expensive imports from distant powers.
• Broader Lesson in Execution: This proves what focused execution + technological leapfrogging can achieve. Start behind? Obsess over customer value, iterate relentlessly, integrate AI/automation deeply, and scale without apology.

To every founder, strategist, and executive watching: Stop romanticizing the old guard. The future belongs to those who ship faster, price smarter, and integrate intelligence deeper. China’s auto rise isn’t a threat, it’s a blueprint. The same forces (AI optimization, vertical integration, data-driven iteration) that propelled this export explosion are available to you right now.

Video credit: James Eagle

[Alvin Foo @alvinfoo]

China added 97 GW of clean energy capacity, nearly 10x more than either US or India.

Whether you view this through the lens of economics, geopolitics, manufacturing, or sustainability, one thing is becoming increasingly clear:

The global energy transition is no longer a future event. It is happening now.

China’s approach demonstrates what can happen when long-term national strategy, industrial policy, infrastructure investment, and execution align around a common objective.

The implications extend far beyond reducing carbon emissions:

⚡ Lower energy costs

🏭 Stronger industrial competitiveness

🔋 Leadership in batteries, EVs, and energy storage

🌍 Greater energy security

📈 New economic growth opportunities

While many countries continue debating the transition, others are already building it at scale.

The biggest lesson is not about China.

It’s about the power of execution.

Vision matters. Strategy matters. But ultimately, the countries, companies, and leaders that move fastest often shape the future.

What lessons can businesses and governments learn from China’s clean energy acceleration?