Beyond the Jargon: The Powerful Truth About Why AI Leaders Must Still Code

Beyond the Jargon – AI leaders must still code because the speed, ambiguity, and architectural decisions in AI work can’t be delegated to slide decks. A leader who hasn’t shipped a model in two years can’t realistically scope a GenAI initiative, spot a hallucinating pipeline, or call BS on a vendor demo. After 30 years, from B.Com student to building “The Brain”, here’s what I’ve learned about why the keyboard matters as much as the boardroom. I recently sat in a famous coffee shop in Hong Kong, during my annual break, the sun hitting my desk, staring at two different screens that felt like two different lives.

• The Academic View: On one screen, a formal email thread from a group of brilliant students sharing their end-of-term project. They’ve proposed what I’d call the “Holy Grail of Hyper-Personalisation”—a framework featuring a full “Awareness Stack” for emotions, behaviour, and time-sensitive mobile use cases.
• The Strategic Foundation: Their work on “life-event detection” is high-level, academic, and strategically sound. It represents the “strong ideas” we need.
• The Roadmap Fit: It is precisely the kind of rigorous, theoretical thinking you’d expect to see anchored in a cross-org AI roadmap.

On the other screen was a terminal window and a Flutter environment. This is where Pulse lives. Pulse is my “off-clock baby” project and a dream of the last 22 years. It’s what I do when the meetings end, sundown, and the CTO title is set aside for the title of “Builder/Coder.” As I scrolled through the students’ recommendations, I felt a surge of adrenaline. It was a strange, humbling, and electric moment. It was a reminder that in this fast-paced AI era, the distance between “strategy” and “reality” is shrinking every single day.

I wasn’t just reading ideas; I was looking at things I had already built, coded, and tested in Pulse. Dream: I have a dream to build and achieve my own Machine Consciousness code, logic woven directly into the heart of the app. I want the system to operate on a primary feedback loop where it truly “understands” consequence, essentially fearing the punishment of failure and feeling the excitement of a reward for perfect execution.

The 30-Year Journey from B.Com to “The Brain”

I often look back at my path. I finished my B.Com in 1996, followed by a BCA, PGDCA and an MCA. I’ve spent over 30 years in IT, with the last 12+ of those in the CTO chair. I’ve seen the “dot-com” boom, generations of mobile telephony, the mobile money revolution, and now the AI explosion.

People often ask me, “Vinod, why do you still code? Why do you spend your weekends building microservices for a community app when you have a seat at the highest table in fintech?”

The answer is simple: To keep my logic razor-sharp, and yes, I drain my imagination without approvals or budgets or processes. In the corporate world, it is very easy to become a “Slide-Deck Architect.” You can talk about “Responsible AI” and “Stochastic Models” all day, but if you haven’t felt the frustration of a broken logic gate at 2:00 AM, you are leading from a distance. I lead from the trenches.

When I talk to my team about transferring the “full AI reasoning context” to a human reviewer—instead of just routing a blind ticket—I’m not quoting a textbook. I’m talking about a principle I’ve baked into the architecture of my own hands-on projects.

The “Electricity” of the Future

While I was processing the feedback from the Alberta University students, a young entrepreneur approached my table. He had seen my recent keynote and several of my blog posts where I pose the question that keeps me up at night: “Can AI beat Natural Intelligence?” He had caught onto my “Electricity” analogy, and our conversation quickly moved from abstract theory to the raw, moving parts of the future.

• The Electricity Standard: I’ve always maintained that the “Bank of the Future” shouldn’t feel like a bank at all; it should operate like an electricity board. You don’t walk into a room and celebrate when the lights turn on—you expect it. My goal is to build tech that is invisible, foundational, and always there.
• Money in Motion: The founder spoke about “sensing inflation” or prioritising buying an electricity token over a chicken burger, and the concept of “money in motion.” The idea is that a platform shouldn’t wait for a manual command; it should understand the global context and help migrate assets before the user even realises there is a need.
• The Pulse of Contextual Living: This shift moves us beyond “Fintech” into what I call Contextual Living. My daughter calls it “hyper-personalisation,” and the startup founder views it as an “Agentic Layer at the Traffic Robot,” but to me, it is simply The Pulse.
• Human-Centric Engineering: We are moving toward a heartbeat in the system—a platform that views the user not as a static data point, but as a human being navigating life events, whether that’s a career shift, a new child, or a rainy Tuesday afternoon.

This is the bridge between the academic “Awareness Stack” and the “razor-sharp logic” of a hungry startup. It’s about building a system that doesn’t just process transactions but understands the rhythm of human life. We aren’t just moving money; we’re powering existence.

The Reality of “At Work”

At work, we navigate complex missions. We build systems that handle millions of transactions for millions of people. The stakes are high. When we talk about “Responsible AI,” we aren’t just being cautious; we are being custodial.

One of the most profound ideas in the recent student output—and something I advocate for daily—is the Escalation Model. Traditionally, when an AI doesn’t know what to do, it “drops” the task to a human. The human starts from zero. That is a failure of design.

A truly intelligent system transfers its “thinking.” It says to the human, “I reached this conclusion because of X, Y, and Z. I am stuck at point A. Here is the context you need to take the wheel.” This is how we build trust. This is how we ensure that as we scale, we don’t lose the “human in the loop.”

Seeing this validated by external researchers was a “great minds think alike” moment. But the “shining” moment for me was knowing that we don’t have to wait for a three-year roadmap to see if this works. I’ve already seen the demo. I’ve already run the tests.

Why the “Hobby” Matters

There is a certain “magic” in the hobby project. When I build Pulse, I am my own Chief Product Officer, my own QA lead, and my own Developer. There are no “budget constraints” or “committee approvals”—there is only the logic and the user.

The “Pulse” Philosophy: From Builder to Boardroom

I believe every leader in tech should have a “Pulse.” Something that keeps them curious. Something that reminds them that technology isn’t just about “optimizing KPIs”—it’s about solving human problems.

The Road Ahead: From Theory to Demo

Later this month, I’ll be publishing our cross-org AI strategy and roadmap. It’s a big moment for the organization. It will outline how we use AI to reinvent payments, how we leverage blockchain for transparency, and how we bring “Electricity” to the consumer app.

• Theory vs. Reality: While the roadmap sets the vision, the demo on my laptop is the truth—it’s where “Money in Motion” stops being a concept and starts being a solution.
• The Intersection of Intellect: Success lies in fusing high-level university theory with the “razor-sharp,” aggressive logic found in hungry startups.
• Grounded Execution: I’m taking those raw ideas and filtering them through 25 years of hard-won IT and Fintech experience to ensure they actually scale.

The message to my team—and to anyone listening—is this: Don’t just wait for the future to be “routed” to you. AI is moving too fast for us to be passive observers. Whether you are a student, a founder, or a CTO, the only way to truly understand this “electricity” is to get your hands on the wires.

Final Reflections: The Human Touch

Technology, for all its “agentic” power, is still a human endeavour. We build it to make lives easier. We build it to help people save for their children’s university fees, to help them navigate inflation, and to help them connect with their communities.

As I look at my “Dr Vinod Sharma” goal—the dream of having that title as a symbol of personal fulfilment—I realise that the “Doctorate” isn’t just about a degree. It’s about a lifetime of learning. It’s about the curiosity that makes a 50-year-old male living in Kenya stay up late to learn a Korg PA-1000 keyboard or debug a new microservice.

To bridge where we are today with where we are going, I look at our growth through this lens:

We are all “Newborns” in the face of this AI revolution. We are learning to walk in a world that is changing faster than we can sometimes process. But being a beginner again at 50 isn’t a setback—it’s a superpower. It means our eyes are open, our egos are sidelined, and our capacity for wonder is intact.

And that is the most exciting place to be.

A Note on “The Electricity Analogy”

The truth is, nobody wakes up excited to “interact with an algorithm.” They wake up wanting to buy a home, send money to family, or grow a business. Electricity changed the world not because people loved wires, but because it disappeared into the walls and let life happen. For those who have asked, the analogy is simple:

1. Accessibility: You don’t need a degree to use it.
2. Reliability: It works when you need it, 24/7.
3. Invisibility: It doesn’t brag about its presence; it just powers your world.

If our AI isn’t doing those three things, we aren’t doing our jobs. We aren’t building a “Bank of the Future”; we’re just building a faster horse. Let’s aim for the light.

If our tech feels like a “feature,” we’ve missed the mark. It should feel like a utility—quiet, constant, and essential. We need to stop romanticising the complexity and start obsessing over the outcome. Let’s aim for the light.

Frequently Asked Questions

**Q: Do AI leaders really need to code, or is strategic understanding enough?**

A: Strategic understanding is necessary but not sufficient. AI systems fail in ways that don’t show up in architecture diagrams — data drift, silent degradation, prompt regressions. Leaders who code catch these earlier and scope more accurately. Leaders who don’t become dependent on whichever engineer last briefed them.

**Q: How much should an AI CTO or VP still code day-to-day?**

A: Realistically, 4-8 hours a week — enough to maintain fluency, not enough to bottleneck the team. Use it for prototypes, reviewing PRs, reproducing bugs, and evaluating new tools. The goal is to stay calibrated, not to ship production code.

**Q: What’s the difference between an AI leader who codes and one who doesn’t?**

A: The coder-leader makes faster, cheaper decisions because they can distinguish real limits from manufactured ones. The non-coder leader relies on consensus from engineers who may not agree with each other, resulting in slower decisions, bigger budgets, and more vendor dependency.

**Q: Isn’t AI moving too fast for senior leaders to keep up with the code?**

A: The opposite — AI moves so fast that non-coding leaders fall behind quarterly. Tools like Claude Code, Cursor, and modern IDEs make it easier than ever for experienced engineers to stay hands-on. The barrier is ego, not time.

**Q: What languages and tools should an AI leader know in 2026?**

A: Python (for ML/DS workflows), TypeScript (for AI-powered product work), SQL (for data), and fluency with at least one LLM SDK (OpenAI, Anthropic, Google). Familiarity with LangChain or LlamaIndex, a vector DB (Pinecone, Weaviate, pgvector), and one eval framework (Braintrust, LangSmith) is now table stakes.

**Q: What is the “Pulse Philosophy” mentioned in this post?**

A: It’s my framework for staying technically current as a leader — short daily coding sessions to keep your “pulse” on the stack, paired with weekly architecture reviews and monthly hands-on builds. Details are in the section above.

Conclusion – Staring at those two screens, I realised they represent the heartbeat of a complete leader: one reflecting the strategic vision of the boardroom, the other pulsing with the raw, executable logic of the “Builder.” Strategy without execution is a hallucination, but execution without strategy is a drift.

By staying in the trenches—debugging microservices while directing roadmaps—I ensure that the “Electricity” we build for our users is never just a slide deck. True innovation isn’t found in the jargon of hyper-personalisation; it’s forged at 2:00 AM when the code finally breathes. Let’s stop talking and start building.

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Points to Note

it’s time to figure out when to use which tech—a tricky decision that can really only be tackled with a combination of experience and the type of problem in hand. So if you think you’ve got the right answer, take a bow and collect your credits! And don’t worry if you don’t get it right.

Books + Other readings Referred

• Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you have any burning questions about Big Data, “AI & ML“, Blockchain, FinTech,Theoretical PhysicsPhotography or Fujifilm(SLRs or Lenses)? Please feel free to ask your question either by leaving a comment or by sending me an email. I will do my best to quench your curiosity.

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