In today’s rapidly evolving world, where artificial intelligence seems to dominate every boardroom agenda and headline, it’s easy to feel like human value is shrinking. The fear? That relationships will be replaced, intuition outmoded, and the art of connection reduced to an algorithm. But let’s pause the panic.

The Currency of Connection

Every opportunity, every job, every idea that turned into something more—it likely started with someone. Not something.

That coffee chat. That DM you responded to. That shared moment at a meetup or hackathon.

Human relationships still grease the gears of innovation. Trust, credibility, referrals, empathy—these are not downloadable packages. They are earned. Built. Grown.

And in a world oversaturated with information and automation, who you know becomes even more valuable—not less. People still hire people. People fund people. People trust people.

AI is Not the Rival

Let’s stop framing AI as the cold machine poised to out-network, out-negotiate, and out-human us. It’s not.

AI doesn’t replace your network. It helps you scale your value within your network.

It can write the first draft of your outreach. It can summarize a meeting so you can focus on the people in it. It can surface relevant insights so your next conversation is more thoughtful.

When used right, AI is like a hyper-efficient assistant who never sleeps. But it still can’t shake hands, look someone in the eye, or understand that subtle dance of mutual trust and timing.

Combine the Two: Power Multiplied

Here’s where it gets exciting.

AI won’t replace your job—but someone using AI effectively might.

Similarly, your network won’t grow just because you have one—it’ll grow because you nurture it with smart, thoughtful actions. That’s where AI becomes your amplifier.

• Use AI to prep before meetings with people.
• Use AI to follow up quickly and meaningfully.
• Use AI to remember what matters to others, not just to you.

That’s the winning formula: relational intelligence, multiplied by artificial intelligence.

Final Thoughts

In the end, the winners in this new economy will be the connectors who wield tools, not the solo technologists who try to replace connection.

So go build. Go connect. Go be human.

Because in this new world?

💡 Your network is your currency. AI is your multiplier. Not your rival.

In product meetings, we often talk about “the roadmap.” It’s aspirational. Strategic. Sleek slides with color-coded quarters and feature sets. But while you’re carefully planning the next shiny thing, there’s another roadmap forming — one you didn’t design, but that your users are already living. And it’s written in rage.

The Angry Blueprint

You’ll find it in your support tickets. In the bug reports marked “URGENT” three times. In caps-locked tweets and passive-aggressive comments on your changelogs.

That anger? It’s not noise. It’s insight with urgency. It’s your users telling you — with emotion — what they need right now to survive, not just what they might want in the future to thrive.

Support queues are brutal mirrors. They don’t flatter you with vanity metrics or celebrate your Q2 launch party. They timestamp your failures. They chronicle every moment you said “not yet” or “in a future release” and your users felt abandoned.

What Rage Really Means

Rage is a signal. It’s not just someone yelling into the void. It’s the customer who tried three times to onboard their team and finally gave up. It’s the power user who found your edge case and fell off a cliff. It’s a roadmap made of pain points — and pain points map directly to opportunity.

When people are mad, it’s because they cared. When they stop writing support tickets, that’s when you should worry.

Rebuilding the Roadmap

Your real product roadmap should be forged in the intersection of:

• 🧭 Strategy: What you want to become.
• 💔 Support history: What’s already broken.
• 💢 User emotion: Where frustration meets unmet need.

Start with the timestamps in your queue. Where are users consistently running into friction? What workarounds are your support teams repeating in perpetuity? What didn’t make it to your product backlog because it wasn’t sexy enough to pitch?

Use support tickets as weighted votes. The more painful, the heavier the vote.

Bonus: It Builds Trust

Nothing builds user loyalty like fixing something they asked for. Not “customer delight” in the abstract — but an honest “We heard you, we fixed it.” That’s magic. That’s how you win users back. That’s how rage becomes relief — and eventually, advocacy.

So the Next Time…

…you sit down to sketch out the next quarter’s roadmap, open two tabs:

• Your OKRs.
• And your support queue.

Read them side by side. Then ask yourself: “Are we building what matters, or just what we imagined?”

Because the real roadmap? It’s already written. In rage. And it’s timestamped. In your support queue.

(Plus — Announcing the Public Preview of the Microsoft Learn MCP Server)

What is an MCP server—and why it matters

Model Context Protocol (MCP) is an open JSON‑RPC-based standard, launched in November 2024 by Anthropic, that enables AI agents and applications to dynamically invoke tools and pull data from remote services like APIs or knowledge bases. Think of it as a universal connector—“the USB‑C of AI integrations”—that bridges LLM-powered agents with live, structured resources.

Unlike basic API docs, which only sit in a browser, an MCP server wraps your APIs into callable tools. When using agent‑enabled coding tools like GitHub Copilot or Claude Desktop, agents can invoke high‑level functions—like “search documentation for X” or “filter topics for Y”—rather than blindly guessing URLs or out‑of‑date examples. That means faster, more accurate, context‑aware responses driven by live data sources.

Benefits of layering MCP on API documentation

Announcing: Microsoft Learn MCP Server (Public Preview)

📣 Launch update from Microsoft Learn: The MCP Server for Learn is now publicly available in preview—open‑source, freely hostable, and designed for all MCP‑compatible hosts like Copilot, Cursor, Semantic Kernel, and more.

Highlights include:

• Document-level semantic retrieval: The server provides tools like microsoft_docs_search, enabling rich semantic queries over official docs, returning up to 10 high-quality content chunks.
• Simple activation: No need to build one from scratch—just point your IDE’s .mcp.json to https://learn.microsoft.com/api/mcp and start querying via agent mode.
• Custom hosting & embedding: You can self-host or embed the Learn docs server in your app, empowering your users with real-time access to Learn content without hitting REST or search APIs.
• Backed by Big Docs coverage: Covers Microsoft Learn, Azure, Microsoft 365, and more—using vector search and optimized chunking to deliver accurate and up-to-date info.

How to Get Started

1. Add the server to your MCP config in VS Code or Visual Studio: { "servers": { "Microsoft Learn Docs": { "type": "http", "url": "https://learn.microsoft.com/api/mcp" } } }
2. Use agent‑mode tools: In Copilot or Cursor, switch to agent mode and select the Learn server to query official docs directly.
3. Feel the synergy: Ask clear questions like:
   • “Show me Azure Storage CLI commands from Learn.”
   • “What’s the syntax for creating a PostgreSQL instance in Azure?”
   • “List Learn modules on role-based access control in AAD.”
     Agents will use the MCP interface to fetch actual docs snippets—and display clickable URLs and context.

TL;DR — Why this matters

By encapsulating your API documentation in an MCP server, you transform static docs into intelligent, callable tools. AI agents immediately gain access to precise, authoritative content—without hallucinations or outdated samples—and within a secure, governed framework.

Now that the Microsoft Learn MCP Server is live, everyone can plug it into their favorite IDE or platform and begin building smarter, documentation-aware agents—with zero effort required on the docs side.

Seize the moment: as AI agents go mainstream, Make your docs callable—power your users today with Microsoft Learn MCP Server.

In a world that increasingly commodifies ideas, it’s easy to forget a fundamental truth: the best ideas aren’t born out of competition, ego, or even brilliance — they emerge from generosity.

Generosity is the creative superpower we don’t talk about enough.

When we think of creators, we often imagine artists, inventors, coders, or entrepreneurs tirelessly honing their craft. But look closer — the most impactful creators are not just builders. They are givers. Their work is a form of contribution, not conquest.

The Hidden Engine Behind Creativity

True creativity stems from a desire to solve a problem for others, to share beauty, to illuminate understanding, or to connect humanity. That drive to give — to improve someone else’s experience — is where real impact begins.

• The teacher who builds free educational content.
• The developer who open-sources a helpful tool.
• The writer who shares vulnerable stories so others feel less alone.
• The startup founder solving a pain point they once faced and now want to spare others from.

All of them are powered by the same impulse: generosity.

Giving First, Without Expectation

The internet has taught us many things — not all of them good — but one clear lesson remains: the people who give consistently and without strings attached often build the strongest communities, reputations, and yes, even businesses.

Creators who give:

• Attract trust before they sell.
• Build relationships before they scale.
• Plant seeds long before they see results.

And when the results do come, they’re rooted in a deep foundation of value.

Scarcity vs. Generosity Thinking

Scarcity says: “If I give too much away, I’ll have less.”

Generosity says: “If I give value, I’ll create more for everyone — including myself.”

This shift in mindset is powerful. Instead of guarding every idea, fearing theft or imitation, generous creators share. They know ideas are abundant — execution, care, and community are what make them thrive.

Legacy Is Built Through Giving

If we’re lucky, our creations will outlive us. But they won’t do so because they were protected behind paywalls or patents alone. They’ll live on because someone, somewhere, felt seen, helped, or inspired by them.

The most enduring impact is never transactional — it’s emotional.

People may forget your name. But they’ll remember how your work made them feel, what it helped them do, and the doors it opened.

A Challenge for Creators

So here’s a thought: The next time you’re stuck on what to create, don’t ask “What will get me noticed?” Ask instead:

• “What do I wish someone had made for me?”
• “What’s one thing I can give today that will make someone’s life easier, richer, or brighter?”

That’s the creator’s compass. Not fame. Not virality. Not even profit. But service.

Because in the end, the most impactful ideas aren’t the ones that go viral — they’re the ones that go deep.

They begin not with a pitch, but with a gift.

There’s a saying that has quietly made its way from coaching circles into boardrooms, classrooms, and even dinner table wisdom: “You can’t read the label from inside the jar.”

It’s catchy. It’s visual. But most importantly—it’s true.

The Curse of Being Too Close

When you’re immersed in your own work, your own habits, your own company culture, or even your own thoughts, it becomes incredibly difficult to see what others can observe almost immediately. You start to assume, normalize, or even defend things that are no longer effective or aligned.

That’s because, metaphorically speaking, you’re inside the jar. And from that vantage point, the label—which often holds critical information like your strengths, blind spots, or how others perceive you—is invisible.

Labels Are for the World to See

Think about what a label does:

• It communicates what’s inside.
• It sets expectations.
• It creates clarity for others.

But none of that is for you, the contents. The label exists for someone else. Whether that’s your audience, your customers, your colleagues, or your community—it’s their perception of you that defines how you’re read, received, and remembered.

You might be strawberry jam with a label that says “mystery sauce.” That disconnect can cost opportunities, trust, and growth—simply because you’re unaware of how you’re coming across.

Why Feedback, Reflection, and Outside Input Matter

This is why feedback isn’t a threat—it’s a flashlight. It’s why mentors, advisors, therapists, editors, and co-founders can spot things you’ve missed for years. And why stepping away from your work for a day or two often leads to sudden clarity.

Sometimes it takes a coach. Sometimes it takes a crisis. Often, it just takes someone asking, “Do you realize how that comes across?”

The goal isn’t to fear the jar—but to occasionally climb out of it.

How to Get Out of the Jar (Without Breaking It)

1. Invite feedback regularly – not just when something’s broken.
2. Use reflection tools – journaling, retrospectives, 360 reviews.
3. Switch contexts – new environments often bring new perspectives.
4. Bring in outsiders – fresh eyes can spot what the familiar overlooks.
5. Test assumptions – ask: “What if I’m wrong about this?”

Final Thought: Become Label-Aware

Clarity comes from perspective, not proximity.

So if you’re feeling stuck, misunderstood, or like your efforts aren’t landing—maybe it’s not you. Maybe you’re just too deep in the jar to read the label. And maybe it’s time to let someone else read it to you—or help you rewrite it.

Because once you understand how you’re seen, you can decide how you want to be seen.

And that’s the power of stepping outside the jar.

In the tech world’s noisy celebration of breakthroughs in artificial intelligence, a subtler, more precarious trend lurks beneath the surface: the quiet fragility of AI concentration.

We are witnessing a historic consolidation of power, compute, and talent in the hands of a very few. A handful of companies control the largest AI models, the most critical training data, and the specialized hardware infrastructure required to push the frontiers of machine intelligence. These players also increasingly shape the rules, ethics, and expectations around how AI is developed and deployed. While this concentration brings short-term benefits—efficiency, speed, alignment of research goals—it carries with it a systemic vulnerability that few are eager to discuss.

The Illusion of Stability

Like any tightly coupled system, concentrated AI power looks stable until it isn’t. Think of a single towering skyscraper with all the power cables, servers, and pipelines running through it. It feels efficient, centralized, even inevitable—until an outage, a breach, or a geopolitical shift knocks out the foundation. Whether it’s a regulatory backlash, supply chain disruption, or simply a massive failure in model behavior, centralized AI poses a single point of failure for entire ecosystems.

And unlike traditional monopolies, where substitution is possible, foundational AI models often have no immediate alternatives. Training a frontier model from scratch is prohibitively expensive. Starting over is not a Plan B—it’s a financial and infrastructural moonshot.

Talent Gravity and the Innovation Ceiling

Another subtle fragility is the drain of AI talent into concentrated silos. The gravitational pull of big labs is immense: high salaries, massive compute, access to frontier models. But this concentration creates an intellectual monoculture. Independent research struggles to thrive in the shadows of closed APIs and guarded architectures. The more brilliant minds funnel into the same few organizations, the narrower the frame becomes for asking different, disruptive questions. The innovation ceiling quietly lowers.

Worse, these labs may—often unintentionally—gatekeep not just access, but perspective. What if the next paradigm shift in AI isn’t scale, but structure? Or culture? Or multilinguality from the ground up? Concentration makes it harder to find out.

Fragile by Design

Ironically, much of this fragility is the byproduct of success. Centralized AI models are optimized to deliver at scale—APIs, copilots, LLMs, agents. But they are not optimized for diversity of approach, accessibility, or experimentation. When the whole world builds on a few models, the downstream applications inherit the assumptions, blind spots, and even the bugs of those models. The risk isn’t just centralization of power—it’s centralization of error.

Rethinking the Narrative

This isn’t a doomsday warning. But it is a call to reframe the narrative. The future of AI should not hinge on whether a handful of companies can remain stable and benevolent. It should hinge on resilience—of ideas, architectures, incentives, and access.

We need:

• Open ecosystems where alternative models can emerge and be viable;
• Decentralized infrastructures to democratize training and inference;
• Shared governance models to align power with public interest;
• Global collaboration to ensure AI reflects the world, not just its wealthiest corners.

Final Thought

The danger of AI concentration isn’t in what’s visible. It’s in what goes unnoticed until it’s too late. Fragility rarely makes noise—until it breaks.

In a world where AI is becoming the operating system of society, we can no longer afford to confuse power with progress, or centralization with strength. The future must be quieter, broader, and more distributed—by design.

In the age of information, we’re told knowledge is power. But that’s only part of the truth. The deeper, more unsettling reality is this: intelligence has become capital—a tradable, investable, and increasingly exclusive asset. And just like any form of capital, it creates a divide between those who have it, and those who don’t.

From Sweat to Synapse: A Shift in Value

Historically, capital was land, then it was machinery, then data. Now, it’s intelligence. Not just raw IQ, but applied intelligence—how we reason, solve, create, and adapt. This includes:

• Analytical thinking
• Strategic foresight
• Pattern recognition
• The ability to interface with machines, especially AI

This kind of intelligence isn’t evenly distributed. And it certainly isn’t equally rewarded.

The Rise of Cognitive Capitalism

Cognitive capitalism is not science fiction. It’s already here. AI developers, prompt engineers, algorithmic traders, and strategic decision-makers trade in mental acuity. They’re not just workers—they are asset holders of intelligence. And in doing so, they accumulate power and influence at an accelerating rate.

Meanwhile, others are stuck on the wrong side of the divide. Their knowledge becomes obsolete, their skills less valued. In a world where an AI can do your job faster and cheaper, the question becomes not just “Can you think?” but “Can you think in ways machines can’t?”

Education: Bridge or Moat?

Education was supposed to be the great equalizer. But in practice, it’s become a moat—especially when access to quality learning, mentorship, and digital literacy is unequal. The privileged not only accumulate financial wealth but intellectual wealth. They train their children to analyze, question, innovate. Others are trained to comply.

The result? A feedback loop: cognitive capital breeds more opportunity, more access, more returns. Those without it? Less leverage. Less agency.

Intelligence as Rent

What happens when intelligence becomes a rental economy? We’re seeing it with AI copilots, consulting services, and decision-as-a-service models. You don’t need to be smart—you just need to pay for smartness. But therein lies the catch: the rich can afford it. The poor can’t. Intelligence, once a human attribute, is now a subscription model.

If this sounds like a dystopia—it is. One we’re slipping into, incrementally.

Reclaiming the Narrative

To resist the commodification of intelligence, we need to challenge the current structures:

• Redefine merit: Value diverse intelligences—not just computational or academic, but emotional, social, artistic, and intuitive.
• Invest in inclusive cognitive infrastructure: Community-based learning hubs, open-source intelligence tools, and AI-literacy programs can decentralize access.
• Reframe intelligence as a commons: The breakthroughs of tomorrow shouldn’t be locked in vaults but shared as collective advancement.

Conclusion: The Divide You Can’t See – But Feel

The unseen divide is not between the rich and poor, not entirely. It’s between those who possess, cultivate, and compound intelligence—and those who are priced out of it. And because intelligence looks intangible, the divide stays hidden, rationalized, normalized.

But make no mistake. Intelligence is capital. And like any capital, it shapes who wins, who works, and who watches from the sidelines.

We owe it to the next generation not to merely distribute knowledge, but to democratize the power to think, to question, to build. Not as a privilege. But as a right.

In an age where technology evolves faster than laws can be drafted, regulatory frameworks are at a breaking point. Traditional models—grounded in compliance—are struggling to keep pace with an economy increasingly shaped by algorithms, decentralized systems, and AI-driven decisions. It’s time for a shift. The future of regulation isn’t just about ticking boxes—it’s about cognitive engagement. It’s time we move from compliance to cognition.

The Problem with Compliance-Centric Regulation

Regulation has long been about enforcing behavior: define the rules, monitor adherence, penalize violations. This compliance mindset has served us well in more stable, predictable environments. But today’s world isn’t either.

In sectors like fintech, healthtech, autonomous vehicles, and AI, the rate of innovation has outpaced static rulebooks. Compliance systems that once relied on historical audits and checklists now face live systems that self-learn, self-optimize, and sometimes self-replicate.

This results in three major issues:

• Lag: Regulations are reactive and often outdated the moment they’re implemented.
• Opacity: Black-box systems make it difficult to understand how decisions are made, even when outcomes are observable.
• Box-Ticking Culture: Organizations focus on passing the test, not learning from the material.

What Does “Cognitive Regulation” Mean?

“Cognition” implies understanding, context, and dynamic reasoning. Cognitive regulation is about designing frameworks that can think, adapt, and interact—just like the systems they’re meant to oversee.

This doesn’t mean replacing regulators with robots. It means:

1. Continuous Learning Systems: Just as machine learning models retrain on new data, regulatory systems must evolve based on real-time market behaviors, risks, and feedback loops.
2. Behavioral Insight over Static Rules: Rather than prescribing every outcome, regulators should guide principles and measure intent, risk patterns, and systemic impact.
3. Collaboration over Control: Regulators become participants in innovation, not just gatekeepers. Think regulatory sandboxes, open dialogues with industry, and co-development of frameworks.
4. Explainability and Interpretability: Systems must not only function within boundaries, but also communicate why they do what they do. If AI is driving a decision, that rationale must be legible—not just legally compliant.

Case Study: Financial AI

In modern trading systems, algorithms execute millions of decisions in milliseconds. Traditional compliance would require post-trade audit trails. Cognitive regulation, on the other hand, could:

• Embed AI that flags behaviors as they emerge, based on anomaly detection.
• Cross-reference behaviors across institutions to detect systemic risks.
• Require explainability layers that summarize decision logic in human language.

This transforms regulation from an act of after-the-fact enforcement to a system of co-evolution with technology.

The Human Element

Moving from compliance to cognition doesn’t mean removing humans from the loop—it means elevating them.

• Regulators need retraining: in data science, systems thinking, and ethics.
• Companies need cultural shifts: toward transparency, ethical design, and internal governance.
• Public trust must be rebuilt: by showing that cognitive regulation doesn’t mean looser rules—it means smarter safeguards.

From Policing to Stewardship

Ultimately, rethinking regulation is about rethinking power: not as a mechanism to police behavior, but as a stewardship role to cultivate responsible innovation.

In a world run by AI, governed by distributed ledgers, and shaped by invisible code, regulation cannot be a blunt instrument. It must be a living, thinking system—just like the world it oversees.

It’s time to stop asking: “Are we compliant?” And start asking: “Are we understanding what we’re building—and why?”

In the early days of the digital age, companies competed on access to talent. Then, tools took center stage – whoever had the latest tech stack, frameworks, or infrastructure was ahead of the curve. But now? Talent is abundant. Tools are commoditized. The next great race – the one that separates thriving teams from obsolete ones – is for time.

Let’s unpack what that means.

Talent Is Everywhere

Thanks to open education platforms, remote work, and global communities, there has never been a more skilled generation of developers, designers, product thinkers, and entrepreneurs. Talent is not the bottleneck anymore. The right people exist – often more than one – for almost every job.

Sure, the best talent still makes a difference. But hiring a genius won’t help if they’re stuck in meetings all day or lost in an unprioritized backlog.

Tools Have Leveled the Field

Cloud platforms, AI copilots, no-code tools, CI/CD pipelines – every team has access to what once was enterprise-only. You can build a prototype in a weekend and scale it to millions with out-of-the-box infrastructure.

So why do some teams still lag?

Because tools aren’t the advantage – how you use your time with those tools is.

Time: The Ultimate Scarcity

You can’t buy more hours in the day. You can’t invent a new calendar. Time is the only resource that is distributed equally but spent wildly differently.

The winning teams, products, and companies are not the ones with the fanciest stack or biggest budget – they’re the ones who protect, prioritize, and multiply time.

That means:

• Saying no more than yes
• Automating everything that’s repeatable
• Aligning teams so that fewer meetings are needed
• Making decisions with 70% confidence rather than waiting for 100% certainty
• Shipping imperfectly and improving constantly

Speed is a Feature

In a world of continuous delivery and rolling releases, being fast is not a by-product – it’s a core feature of a healthy, competitive organization.

Time allows you to:

• Learn faster than competitors
• Recover from failure before others realize you slipped
• Move with customers, not behind them

A Call to Action

Ask yourself and your team: Where is your time going?

• Are you building or talking about building?
• Are you reacting or proactively designing?
• Are you measuring effort or outcomes?

The next race is already underway. It won’t be won with flashy résumés or shiny dashboards. It’ll be won by those who guard their time like a precious resource and wield it with precision.

Because in the end, talent without time is untapped potential. Tools without time are unused leverage. But time – when invested wisely – becomes impact.

TL;DR: In the age of democratized talent and tools, the edge lies in how well you manage your time. The next race isn’t for who has more – it’s for who wastes less.

Let the clock start.

Excited to share that I’ll be leading a dynamic discussion at the upcoming FINRA & World Economic Forum Joint Roundtable on the Metaverse and the Implications for the Financial Services Industry – happening June 17, 2025, at FINRA’s Brookfield Place office in New York City.

🎙️ What I’ll Be Covering: Immersive Technologies in Trading

1. From experiment to execution: How far are we? We’ll explore the current frontier of immersive tech – AR/VR/immersive environments – within trading workflows. What firms are already piloting, and what’s needed to move toward broader adoption? We’ll examine where we stand on the road to real-time, spatial trading platforms.

2. Forecasting prominence: Will immersive trading take off? We’ll weigh the factors shaping its future: potential benefits like enhanced situational awareness and decision speed, against barriers like regulatory hurdles, infrastructure costs, and user readiness. How likely is immersive trading to become mainstream?

3. Adoption drivers: Trading vs. other XR use cases We’ll compare immersive trading to other use cases – like gamified investor education, investor engagement, or XR applications beyond the financial securities industry. What advantages or constraints make trading a leading or lagging candidate for XR adoption? What will drive investment in one use case versus another?

🔍 Why It Matters

• Investor outcomes: Can immersive interfaces improve understanding, performance, or oversight by traders?
• Regulatory and governance implications: How might immersive channels alter compliance, transparency, or audit trails?
• Cross-industry perspectives: Learning from XR initiatives in education, healthcare, gaming- where could synergies or pitfalls lie?

Although it is too late now to join the roundtable – do let me know if you are interested to join our XR in Finance Guild – let’s unpack what immersive might mean not just for trading floors, but for the broader financial ecosystem.

Looking forward to a lively discussion alongside leaders from FINRA, the Word Economic Forum, XR Association, and financial institutions like Morgan Stanley, The World Bank, Mastercard, Fidelity and more.