In tech circles, the mythical 10x engineer looms large. The narrative usually centers around two ingredients: talent and hustle. The genius who sees patterns no one else does, and who codes deep into the night, fueled by caffeine and sheer will. But this story, while seductive, is incomplete.

The real secret behind 10x engineers isn’t just brilliance or burnout-level effort. It’s the unglamorous, often invisible force of systems and discipline.

Grind and Talent Can’t Scale. Systems Can.

Sure, raw talent can give you an edge. And relentless grind can deliver results in bursts. But talent burns out. Grind hits limits. What keeps output consistent and scalable is having a system.

A 10x engineer doesn’t reinvent their workflow every time. They automate, they templatize, they standardize. From deployment pipelines to code review checklists, they remove decision fatigue and build repeatable excellence.

They don’t just do work — they build machines that do work for them.

Discipline Isn’t Fabulous. But It’s Invaluable.

Let’s be honest — there’s nothing flashy about writing tests first. About committing early, even when it’s just a stub. About deleting clever code for the sake of clarity. These acts rarely trend on Twitter. But they’re the invisible bricks in the foundation of legendary systems.

Discipline is what shows up when motivation takes the day off. It’s what keeps you writing clean interfaces, even when deadlines loom. It’s what stops you from shipping the hack and instead finding the fix.

Discipline looks boring. But it feels like shipping on time. Sleeping well. Watching your code thrive in production without drama.

Legendary Software Has Boring Roots

Every groundbreaking software system, every product that stands the test of time, carries the DNA of an engineer (or team) who cared about more than code. They cared about feedback loops, test coverage, CI/CD hygiene, monitoring, rollback safety, and naming things well.

Legendary software isn’t just born in lightning strikes of genius — it is grown in the patient light of consistency.

The 10x Equation Rewritten

So let’s rewrite the 10x equation:

10x = (Talent + Grind) × (Systems + Discipline)

Without systems and discipline, talent and grind give you spikes. With them, you get a slope — an upward trajectory that compounds over time.

If you’re chasing impact, don’t just train your brain. Train your habits. Don’t just learn algorithms — learn architecture. Don’t just sprint — build a track you can run forever.

Because in the end, it’s not just about how fast you code. It’s about how long your code lasts.

Where instinct failed, effort showed up – asking our guides, watching others, asking them, and stubbornly trying again when things wilted.

Gardens don’t just grow from talent; they grow from care, attention, and trying one more time.

Sometimes effort is the fertilizer.

#GrowthMindset #EffortOverTalent #GreenThumbInTraining Resilience #MSGivingBack

In a world flooded with ideas, the ones that rise, stick, and resonate are those with depth—dimensional ideas. While a flat idea may get a nod, a dimensional one gets remembered, funded, implemented, and even loved.

What Does It Mean to Make an Idea Dimensional?

A dimensional idea is more than a clever concept or catchy phrase. It is an idea that breathes, moves, adapts, and speaks to more than one layer of human experience. It’s anchored in insight, shaped by context, tested in practice, and imagined for scale.

Let’s break down the dimensions of a truly impactful idea:

1. The Emotional Dimension: Does It Make People Feel?

You can have the most logical, data-driven pitch in the world—but if it doesn’t move people, it won’t move forward. Dimensional ideas connect emotionally. They solve real human problems, tap into shared values, or ignite a sense of wonder or urgency.

Ask yourself:

• What emotion does this idea trigger—hope, excitement, trust, fear, pride?
• Will people care?

2. The Contextual Dimension: Where Does It Fit?

No idea lives in isolation. It needs to fit into a larger ecosystem of problems, platforms, people, and purpose. A dimensional idea knows its time, place, and relevance.

Ask yourself:

• Why now? Why here?
• What shifts—social, technological, cultural—make this idea possible or necessary?

3. The Practical Dimension: Can It Work?

Great ideas don’t just live in slideshows—they live in systems, habits, and code. Dimensional ideas are executable. They consider constraints and still manage to thrive. They aren’t allergic to trade-offs.

Ask yourself:

• Can it be built, launched, scaled?
• What’s the first real step?

4. The Narrative Dimension: Can It Be Told?

Every idea needs a story—a way for others to carry it forward. A dimensional idea is a story others want to retell in their own words. It has metaphors, use cases, heroes, and stakes.

Ask yourself:

• Can someone repeat this idea at lunch and still get it right?
• Is the “why” as strong as the “what”?

5. The Visual Dimension: Can People See It?

Our brains are wired for images. A dimensional idea has form—it can be sketched on a napkin, built into a prototype, or animated in a video. It is tangible, or at least imaginable.

Ask yourself:

• Can people visualize it?
• Can they experience it before it exists?

6. The Ethical Dimension: Should It Exist?

Some ideas are doable, even scalable—but ethically questionable. Dimensional thinking requires responsibility. The best ideas build trust, not just traction.

Ask yourself:

• Who benefits? Who might be harmed?
• What does success look like—not just for me, but for the community it touches?

Final Thought: Ideas Are Sculptures, Not Stickers

Flat ideas stick for a moment. Dimensional ideas shape the world.

So, the next time you’re brainstorming, pitching, or building—don’t stop at clever. Add layers. Add tension. Add humanity. Because in this noisy, fast-moving world, depth isn’t just desirable—it’s necessary.

Make your ideas dimensional—and they’ll move people, not just slides.

There’s a truth about human memory that too often gets overlooked in our metrics-driven, achievement-obsessed world:

People don’t remember what you said. They don’t always remember what you did. But they always remember how you made them feel.

This idea, often attributed to Maya Angelou, isn’t just poetic—it’s deeply human. And it’s the foundation of every meaningful relationship, every powerful brand, and every story worth retelling.

Feelings Outlive Facts

Emotions are sticky. They outlast logic, and they linger long after the details fade. When someone recalls a great mentor, a terrible boss, a magical vacation, or a painful breakup, they’re not quoting spreadsheets or recounting travel itineraries. They’re telling you how they felt in those moments—empowered, dismissed, alive, or lost.

In workplaces, people don’t just remember who gave the most polished presentation or met their KPIs. They remember who celebrated their growth, who made them feel seen in a tough moment, who created safety, or who left them out. It’s the emotional residue we leave behind that defines us.

These Become the Stories

Humans are storytellers by nature. We process our experiences through narrative. And the core of any story worth retelling isn’t just what happened—but how it felt.

• “She made me feel like I belonged from day one.”
• “That meeting left me feeling humiliated.”
• “I’ll never forget how calm he stayed when everything was falling apart.”
• “They treated me like I mattered, not just as an employee, but as a person.”

That’s what gets passed on. Not your resume. Not your credentials. Not your bullet points.
The feeling you gave someone—that’s your legacy.

What This Means for You

If you’re a leader: Your legacy isn’t your title: it’s how safe, inspired, and trusted people felt under your guidance.

If you’re a teammate: Your value isn’t just in the tasks you complete, but in the atmosphere you help create.

If you’re building a product, hosting an event, or running a business: You’re not just solving problems. You’re crafting experiences. Make people feel empowered, included, respected—and they will come back, and bring others with them.

If you’re a parent, friend, partner: The little things matter. Warmth, patience, presence—these things aren’t small. They’re everything.

So, Ask Yourself

How do people feel when they leave a conversation with you?
How do they feel after working with you?
What’s the emotional imprint you’re leaving behind?

Because that’s what they’ll share. That’s what will spread. And that’s what will last.

The best stories aren’t about what you did. They’re about how you made someone feel. And those are the ones that get remembered.

In the rapidly evolving landscape of artificial intelligence (AI), the allure of one-shot prompting—providing a single example to guide a model’s response—has captivated many. While this approach showcases the impressive capabilities of large language models (LLMs) in controlled settings, it often proves inadequate when addressing the multifaceted challenges inherent in real-world business scenarios.

The Complexity of Business Challenges

Real business problems are rarely straightforward. They encompass a myriad of variables, including market dynamics, regulatory considerations, organizational culture, and customer behaviors. Attempting to encapsulate such complexity within a single prompt often leads to oversimplified or generic responses that lack actionable depth.

For instance, consider a company seeking to optimize its supply chain in response to fluctuating global demands. A one-shot prompt might yield a broad strategy, but it would likely miss nuanced factors such as regional logistics constraints, supplier reliability, and geopolitical risks. Addressing these intricacies necessitates a more iterative and context-rich approach.

The Necessity of Iterative Prompting

Effective problem-solving with AI mirrors human reasoning—it requires iterative exploration, hypothesis testing, and refinement. Iterative prompting involves a dynamic exchange where prompts are continuously adjusted based on previous outputs, allowing for deeper insights and more tailored solutions.

This process is akin to a dialogue with the AI, where each iteration builds upon the last, honing in on the most relevant and practical outcomes. Such an approach is particularly valuable in complex tasks like strategic planning, market analysis, or product development, where initial responses serve as a foundation for further inquiry and elaboration.

Enhancing Accuracy Through Contextualization

Providing context is paramount in guiding AI models toward meaningful outputs. One-shot prompts often lack the necessary background information, leading to responses that are disconnected from the specific business environment.

By incorporating detailed context—such as company goals, industry specifics, historical data, and stakeholder perspectives—into the prompting process, AI models can generate more accurate and relevant recommendations. This contextualization transforms the AI from a generic information processor into a strategic partner attuned to the organization’s unique challenges and objectives.

The Role of Prompt Engineering in Business

Prompt engineering—the art of crafting effective prompts to elicit desired responses from AI—has emerged as a critical skill in leveraging AI for business applications. It involves understanding the model’s capabilities, structuring prompts to align with business goals, and iteratively refining interactions to improve outcomes.

Businesses that invest in developing prompt engineering expertise can unlock greater value from AI technologies. This includes enhancing decision-making processes, streamlining operations, and fostering innovation. Moreover, as AI models continue to evolve, the ability to effectively communicate with these systems will remain a vital competency.

Conclusion

While one-shot prompting demonstrates the remarkable potential of AI, it falls short in addressing the complex, nuanced problems that businesses face daily. Embracing an iterative, context-rich approach to AI interactions—underpinned by skilled prompt engineering—enables organizations to harness the full power of AI, driving more informed decisions and achieving strategic objectives.

In a world enamored with moonshots and billion-dollar breakthroughs, it’s easy to assume that innovation springs solely from genius or vast resources. But history, and the future, tell a different story. Innovation, more often than not, is the product of just collaboration.

What do we mean by “just collaboration”? It’s collaboration that is equitable, inclusive, transparent, and values-driven. Not merely working together, but working together right. When collaboration is built on justice—on fairness, mutual respect, and shared ownership—it becomes a powerful engine for ideas to move from spark to scale.

1. The Myth of the Lone Genius

While legends of solitary inventors persist, the reality is that innovation thrives in ecosystems. Thomas Edison had a team. Steve Jobs had Wozniak and a talented Apple crew. The greatest breakthroughs come from minds that connect and challenge each other. But when power dynamics stifle voices, or credit isn’t shared, that collaborative potential is wasted. Just collaboration ensures that the right ideas, not just the loudest voices, rise to the top.

2. Diverse Perspectives Spark Originality

True innovation requires cognitive diversity. When people from different backgrounds, disciplines, and life experiences collaborate with equal footing, they bring unique ways of seeing and solving problems. A just collaborative environment fosters psychological safety where everyone can speak up without fear. This isn’t just ethical—it’s a competitive advantage.

3. Trust Fuels Speed

One of the biggest killers of innovation is friction—delays caused by bureaucracy, mistrust, or misalignment. Just collaboration builds trust, the currency of innovation. When teams believe in each other’s integrity and intentions, they move faster. They iterate more freely. They recover from failures more resiliently. Trust doesn’t mean agreeing all the time—it means being able to challenge one another honestly, without collateral damage.

4. Shared Ownership, Shared Success

In unjust collaboration, ideas become currency—and often, theft is rampant. In just collaboration, ideas are shared, built upon, and credited fairly. That shared ownership leads to shared motivation. When contributors see how their effort matters—and how success is collective—they invest more, stay longer, and take bolder creative risks.

5. Sustainable Innovation is Just Innovation

Short-term wins built on exploitation, exclusion, or inequity aren’t real innovation—they’re disruption with a ticking clock. Just collaboration creates systems and products that last. It naturally prioritizes ethical considerations, long-term impact, and responsible stewardship.

How to Practice Just Collaboration

• Flatten hierarchies in brainstorms. Make room for introverts, junior members, and marginalized voices.
• Co-create goals. Let teams shape the definition of success.
• Audit credit. Who gets mentioned in writeups or promotions? Equity in acknowledgment matters.
• Resolve conflict with curiosity. Disagreements are gold—if you mine them well.
• Measure inclusion, not just participation. Who’s being heard, not just who’s in the room?

Final Thought

Just collaboration isn’t a soft virtue. It’s a strategic necessity. In a time when complexity is rising and no one person can hold all the answers, we need collective intelligence more than ever. And the way to unlock it isn’t just to collaborate—but to do so justly.

Because in the end, it’s not just collaboration that drives innovation—it’s just collaboration that does.

I advertised my session on Wednesday with a Sound of Music afterthought:

“🎶 Off to the Expo, my badge in my hand,
To talk about tech that’s incredibly grand!
Quantum entanglement, GenAI’s might,
And spatial computing that brings scenes to life! 🎵

On my way to the Morgan Stanley Tech Expo today – can’t wait to dive into all my favorite things. If you’re around, come geek out with me about the future! 🧠✨”

I and we had absolutely fun again!

Today we celebrate not just the humble towel (the most massively useful thing an interstellar hitchhiker can have), but also the joy of being gloriously geeky. From Star Wars Day to Pi Day, these quirky holidays remind us of the imagination, curiosity, and creativity that fuel both fandoms and innovation.

So whether you’re quoting Douglas Adams, wearing your Hogwarts house colors, or calculating how many parsecs to the next coffee shop—stand proud. The geeks have inherited the Earth.

The traditional landscape of Search Engine Optimization (SEO) is undergoing a seismic shift with the advent of agentic search. This new paradigm, powered by autonomous AI agents, is redefining how users interact with information online, rendering many established SEO practices obsolete.

Understanding Agentic Search

Agentic search refers to AI-driven systems capable of performing complex tasks autonomously, such as retrieving information, synthesizing data, and executing actions without human intervention. Unlike traditional search engines that provide a list of links based on keyword matching, agentic search delivers direct, conversational answers tailored to user intent. For instance, Google’s recent introduction of “AI Mode” exemplifies this shift, offering users synthesized responses and follow-up questions instead of standard search results.

The Disruption of Traditional SEO

The rise of agentic search challenges the core principles of SEO:

• Keyword Optimization: Traditional SEO relies heavily on keyword placement to rank content. Agentic search, however, focuses on understanding user intent and context, diminishing the importance of exact keyword matches.
• Link Building: Backlinks have been a cornerstone of SEO, signaling content authority. In agentic search, the AI’s ability to evaluate content quality and relevance reduces the reliance on backlinks as a primary ranking factor.
• Click-Through Rates (CTR): SEO strategies often aim to improve CTR by optimizing meta descriptions and titles. With agentic search providing direct answers, users are less likely to click through to websites, leading to a decline in organic traffic.

Emergence of New Optimization Strategies

In response to these changes, new optimization methodologies are emerging:

• Answer Engine Optimization (AEO): AEO focuses on structuring content to be easily interpreted and utilized by AI agents, ensuring that information is presented in a way that aligns with how these systems process and deliver answers.
• Generative Engine Optimization (GEO): GEO involves optimizing content for generative AI systems, aiming to increase the likelihood of being cited or included in AI-generated responses.
• Artificial Intelligence Optimization (AIO): AIO emphasizes enhancing content structure and clarity to improve its retrievability and interpretation by AI systems, focusing on semantic relevance and contextual authority.

Preparing for the Agentic Future

To adapt to the agentic search era, content creators and marketers should:

• Focus on User Intent: Develop content that addresses specific user needs and questions, moving beyond keyword-centric approaches.
• Enhance Content Clarity: Ensure that information is presented clearly and logically, facilitating AI comprehension and retrieval.
• Implement Structured Data: Utilize schema markup and other structured data techniques to provide context and improve content discoverability by AI agents.
• Monitor AI Trends: Stay informed about advancements in AI and agentic search technologies to adjust strategies proactively.

Conclusion

Agentic search represents a fundamental transformation in how information is accessed and consumed online. As AI agents become the primary intermediaries between users and content, traditional SEO practices must evolve to meet the demands of this new landscape. Embracing strategies like AEO, GEO, and AIO will be crucial for maintaining visibility and relevance in an increasingly AI-driven digital world.

For over a decade, the phrase “There’s an app for that” encapsulated the convenience and ubiquity of mobile applications. Today, a transformative shift is underway, heralding a new era where “There’s an MCP server for that” becomes the norm. This evolution signifies the transition from the traditional app economy to an emerging AI agent economy, fundamentally altering how we interact with digital services.

Understanding the Model Context Protocol (MCP)

Introduced by Anthropic in late 2024, the Model Context Protocol (MCP) is an open standard designed to facilitate seamless communication between AI models and external tools, data sources, and services. By standardizing these interactions, MCP enables AI agents to perform complex tasks autonomously, accessing and manipulating data across various platforms without the need for bespoke integrations.

Think of MCP as the “USB-C of AI applications” or “the new HTTP”—a universal connector that allows AI agents to interface with diverse systems efficiently. This standardization reduces the complexity of integrating AI into existing workflows, promoting scalability and interoperability.

The Shift from Apps to AI Agents

Traditional applications require users to navigate interfaces, input data, and interpret results manually. In contrast, AI agents equipped with MCP can autonomously perform tasks such as scheduling meetings, managing emails, or analyzing financial data by interacting directly with relevant services.

This paradigm shift is not just theoretical. Companies like Microsoft are actively developing platforms to support AI agents, aiming to transform their ecosystems into “agent factories” where businesses can build and deploy AI agents tailored to their specific needs.

Implications for the App Economy

The rise of AI agents and MCP servers poses significant implications for the traditional app economy:

• Reduced Need for Standalone Apps: As AI agents can perform tasks across multiple platforms, the necessity for individual apps diminishes.
• Shift in Monetization Models: Revenue streams may transition from app sales to service-based models, where access to MCP servers or enhanced AI capabilities becomes the primary commodity.
• Increased Emphasis on Data and Services: The value may shift towards the quality and accessibility of data and services that AI agents can utilize, rather than the apps themselves.

This transition suggests a potential decline in the traditional app economy, giving rise to an AI agent-driven market where services are consumed through intelligent intermediaries.

Challenges and Considerations

While the AI agent economy offers numerous advantages, it also introduces challenges:

• Security and Privacy: Ensuring secure interactions between AI agents and MCP servers is paramount, especially when handling sensitive data; this is something vendors are actively working on.
• Standardization and Compatibility: Widespread adoption of MCP requires consensus on standards to ensure compatibility across diverse systems and platforms.
• Ethical Considerations: The delegation of tasks to AI agents raises questions about accountability, decision-making, and the potential for unintended consequences.

Addressing these challenges is crucial to realizing the full potential of the AI agent economy.

Conclusion

The evolution from “There’s an app for that” to “There’s an MCP server for that” marks a significant transformation in our digital landscape. As AI agents become more capable and integrated through protocols like MCP, we are entering an era where intelligent systems handle tasks on our behalf, streamlining processes and redefining user experiences.

This shift presents both opportunities and challenges, necessitating thoughtful consideration and proactive measures to harness the benefits while mitigating risks. The AI agent economy is not just a futuristic concept—it is an emerging reality reshaping how we interact with technology.