Entrepreneurs Start Companies. Bureaucrats End Them

 

When companies are born, they rarely begin with grand org charts, multilayered governance structures, or 200‑page SOP manuals. They begin with a handful of people who are hungry, restless, and unafraid to get their hands dirty. People who don’t wait for permission. People who learn by doing, not by presenting. People who care about purpose, outcomes, and value, not optics, credits, or turf.

These early teams are made of Doers in the truest sense of the word. They take responsibility. They deliver. They improvise. They experiment. They fail fast and recover faster. They don’t hide behind process because there is no process to hide behind. They don’t obsess over structure because the only structure that matters is the one that gets the job done.

This is the spirit that births companies.
This is the spirit that builds movements.
This is the spirit that creates impact.

And then… scale arrives.

And with scale comes the inevitable: systems, processes, governance, measurement, compliance, and the dreaded bean‑counting. None of this is inherently bad. In fact, without these, organisations collapse under their own weight. Stability matters. Accountability matters. Repeatability matters.

But here’s the tragedy:
When the pendulum swings too far toward process, the organisation forgets why it exists.

The machinery becomes more important than the mission.
The rituals become more important than the results.
The compliance becomes more important than the customer.

And in this slow drift from purpose to process, a new species emerges inside the organisation, the Passenger.

The Rise of the Passenger

The Passenger is not incompetent. In fact, they are often articulate, polished, and excellent at navigating internal systems. They know how to write long emails, how to attend meetings, how to escalate, how to cover themselves, and how to stay “aligned.”

But they are not builders.
They are not creators.
They are not owners.

They are more invested in the machinery than the mission. They optimise for internal perception rather than external impact. They care more about credits than outcomes. They follow the rulebook even when the rulebook is outdated. They prioritise safety over speed, predictability over possibility, and optics over ownership.

Passengers don’t kill organisations overnight.
They kill them slowly, by draining the entrepreneurial spirit that once made the organisation alive.

And once Passengers dominate, the Doers either leave or get suffocated. That is the beginning of the end.

The Balance That Determines Survival

Every organisation eventually faces a fundamental question:

How do we preserve the entrepreneurial spirit while building the systems needed for scale?

This balance, or the lack of it, determines whether a company evolves or perishes.

The good news is that many large companies have found a way to keep innovation alive. When they want to open new growth avenues, they carve out a crack team — a small, empowered, entrepreneurial unit with the freedom to experiment, break rules, and move fast. A team that is intentionally kept away from the bureaucratic machinery.

This team is given:

• A mandate to think differently
• Freedom from the shackles of BAU
• Permission to experiment
• A leader who believes in speed, risk, and disruption

And once this team gains momentum, the mainstream organisation absorbs the learnings and scales the success.

But here’s the catch, and it’s a big one:

If this crack team reports to a BAU leader, the experiment is dead on arrival.

Because BAU leaders optimise for stability, predictability, and risk minimisation. They are not wired for entrepreneurial chaos. They don’t understand the value of a quick strike. They want plans, frameworks, decks, committees, and alignment before taking the first step.

That is how innovation dies, not because the idea was bad, but because the environment was hostile.

A Real Story: How Bureaucracy Kills Momentum

Recently, a company I know attempted such an experiment. They onboarded an entrepreneurial, high‑energy individual from outside , someone with the mindset of a commando, not a clerk. His mandate: open a new geography with massive potential.

He did exactly what a hunter would do.
He identified a powerful early linkage.
He moved fast.
He reached out to the leadership with excitement.

And then came the response, from a leader who had never been required to think like an entrepreneur. Someone steeped in the classic bureaucratic “CYA” culture.

The reply was a masterpiece of corporate paralysis:

“I appreciate your initiative in meeting people in this new territory, but for us to engage effectively, we need context, a structured plan, and alignment on what we are jointly looking to achieve. Without that, it becomes difficult for us to prioritise or commit resources, especially when nothing concrete has been outlined yet. Let us spend another four months studying the market and evolve a detailed implementation plan and then start.”

Brilliantly articulated.
Perfectly structured.
And absolutely spirit‑killing.

This is how you pour cold water on a go‑getter.
This is how you suffocate initiative.
This is how you turn a commando into a clerk.

Is the leader wrong?
Not entirely. Planning matters. Context matters. Alignment matters.

But this is not how hunters operate.
A hunter’s mindset is about the surgical strike , a quick, sharp opening salvo that creates early momentum while the larger plan evolves in parallel.

When the world is moving at breakneck speed, waiting four months to “study the market” is not strategy. It is self‑sabotage.

The World Has Changed. Many Organisations Haven’t.

We live in an era where industries are being disrupted in real time. New technologies, new behaviours, new competitors, everything is shifting faster than traditional organisations can comprehend.

In such a world, the companies that survive will be the ones that can:

• Experiment fast
• Learn fast
• Adapt fast
• Scale fast

The ones that cling to old models of planning, alignment, and risk‑avoidance will become irrelevant. They will become dinosaurs, large, impressive, and extinct.

The irony is that many organisations talk endlessly about innovation, agility, and transformation. They put these words in their annual reports, their town halls, their strategy decks.

But when a real entrepreneur walks in and tries to do something bold, the system reacts like an immune response, attacking the very thing that could save it.

The Choice Every Leader Must Make

Every leader, especially those running established businesses, must ask themselves a brutally honest question:

Do I want Doers or Passengers?
Doers disrupt the status quo.
Passengers defend it.

Doers take risks.
Passengers avoid them.

Doers create value.
Passengers create paperwork.

If you want innovation, you must protect the Doers.
If you want stability, you will attract Passengers.
If you want longevity, you must balance both, but never let the Passengers dominate.

The future belongs to organisations that can institutionalise entrepreneurship without descending into chaos. That can build systems without killing spirit. That can scale without suffocating initiative.

This is not easy.
But it is necessary.

Because in a world that is changing this fast, the companies that fail to metamorphose will not get a second chance.

They will simply disappear.

“The future belongs to the organisations that can reinvent themselves before the world forces them to.”

Powering with AI: Why AI-Native Redesign Matters More Than AI Enablement

 

When Jamsetji Tata, and later Dorabji Tata, introduced hydroelectric power in the early 1900s, culminating in the commissioning of the Khopoli plant in 1915, industries in Bombay were deeply skeptical.

Their resistance was not irrational. It was human.

Industrialists raised five broad concerns.

First, fear of unreliability. Hydroelectricity was new and untested in India. Mills were used to steam engines and coal-fired captive power systems they owned and controlled. Depending on an external supplier felt risky. A power failure could halt production entirely.

Second, perceived technological risk. Electricity itself was still novel. Factory owners did not trust that electric motors could reliably replace mechanical line-shaft systems that had powered mills for decades.

Third, economic uncertainty. Would electricity actually reduce costs? Or would it lock them into a monopoly supplier with unpredictable pricing?

Fourth, psychological and cultural hesitation. The familiar refrain: if it isn’t broken, don’t fix it. Early industrialists preferred systems they understood and could physically see and control.

And finally, high sunk costs. Mills had already invested heavily in boilers, steam engines, and mechanical transmission. Switching to electricity meant writing off existing capital, a very real, very quantitative cost-benefit dissonance.

The first four concerns were largely qualitative. The fifth was brutally financial.

This pattern is not unique to electricity. We see it repeatedly across geographies and throughout human history. The more dramatic the technological leap, the stronger the resistance.

The Same Mistake, Repeating with AI

Most organizations today are making the exact same mistake early industrialists made with electricity.

They are using a revolutionary technology to do old things slightly faster, rather than redesigning the system around the new capability.

Electricity did not transform factories when leaders merely attached electric motors to steam-era line shafts. Real transformation came only when they reimagined the factory layout itself.

AI is now at the same inflection point.

AI-Assisted vs AI-Native: The Core Difference

AI-Assisted (the old mindset)

• Take an existing, human-centric process
• Insert AI to speed up one step
• Keep the same approvals, handoffs, forms, and controls
• Celebrate “efficiency gains” while the system remains unchanged

This is equivalent to replacing a steam engine with an electric motor. while keeping the same belts, pulleys, and factory layout.

AI-Native (the new mindset)

• Start with the assumption that AI can perceive, reason, summarize, predict, and generate
• Redesign workflows around those capabilities
• Remove steps that existed only because humans had limits
• Shift humans to oversight, judgment, and exception handling
• Build processes that are continuous, real-time, and adaptive

This is the equivalent of redesigning the factory floor for distributed electric power, a complete re-architecture.

What AI-Native Redesign Actually Looks Like

1. From periodic to continuous
Monthly reporting becomes real-time dashboards.
Annual performance reviews become continuous feedback loops.
Batch underwriting becomes dynamic, cashflow-based risk assessment.

2. From hierarchical to distributed
Decision-making moves closer to the edge.
AI copilots empower every employee, not just managers.
Knowledge flows horizontally, not top-down.

3. From document-driven to data-driven
Instead of humans reading documents, AI extracts, interprets, and synthesizes information.
Humans intervene only where judgment, ethics, or ambiguity are required.

4. From compliance after-the-fact to compliance by design
AI monitors, flags, and enforces rules in real time.
Risk management becomes proactive, not reactive.

5. From siloed functions to integrated workflows
AI connects sales, operations, finance, and customer service.
Processes become end-to-end instead of departmental.

The Leadership Imperative

AI-native redesign requires leaders to ask a fundamentally different question:

“If we were designing this process today, knowing what AI can do, would we build it this way?”

In most cases, the honest answer is no.

This shift is not primarily about technology.
It is about letting go of legacy mental models.

Why This Matters Now

Organizations that merely add AI will achieve incremental gains.

Organizations that re-architect around AI will achieve exponential gains.

This is the same pattern we saw with electricity, cloud computing, mobile, and digital public infrastructure.

The winners were not the ones who automated old processes.
They were the ones who redesigned the system around the new capability.

“AI won’t transform your business. Redesigning your business around AI will.”

Artificial Super Intelligence and the Humble Human Part 1

 

“Science fiction warned us about machines that think; it never warned us how ordinary that moment would feel.”

For centuries, intelligence was the one domain humans assumed would remain uniquely ours. Strength could be mechanized, memory could be stored, speed could be amplified, but thinking felt different. Today, that assumption is quietly dissolving. Artificial Intelligence is advancing not in steady steps, but in accelerating leaps, challenging our definitions of reasoning, creativity, and even understanding itself. And yet, when we look closely, the distance between computation and humanity remains both profound and revealing.

Artificial Intelligence is progressing in leaps and bounds, practically on a day-to-day basis. In many areas, it already outperforms humans by a wide margin. To evaluate how close AI comes to genuine human reasoning, we rely on benchmarks such as ARC, which attempts to measure AI performance in comparison to humans.

ARC (Abstraction and Reasoning Corpus) is a benchmark created by François Chollet to measure general fluid intelligence in AI—the ability to learn new skills and reason abstractly without prior domain-specific training. It has evolved into multiple levels: ARC-AGI-1, ARC-AGI-2, and ARC-AGI-3 (preview). Current leading AI models perform far below human level on the hardest levels: humans solve nearly 100% of tasks, while even the best AI models struggle to reach 50% on ARC-AGI-2 and under 20% on ARC-AGI v2.

The current performance landscape looks like this:

• Humans vs AI: Humans consistently solve 100% of ARC tasks, highlighting the massive gap in reasoning ability.
• AI Progress: While ARC-AGI-1 was nearly “solved” by large models, ARC-AGI-2 exposed their limitations—brute force and scale are insufficient.
• Breakthroughs: GPT-5.2’s ~53% on ARC-AGI-2 is a major leap, but still far from human-level reasoning.
• Future: ARC-AGI-3 will test interactive reasoning, pushing AI beyond static puzzles into dynamic environments.

In summary, ARC is the North Star benchmark for AGI, measuring whether AI can truly think, generalize, and adapt like humans. Despite significant progress, leading models remain far from human-level performance, especially on ARC-AGI-2 and beyond.

While researching this topic, I was reminded of Data, the humanoid android from Star Trek: The Next Generation. Curious, I asked Microsoft Copilot how Data might score by comparison. This was its response:

ARC-AGI-1 (basic abstract reasoning puzzles)
Humans: ~100% | AI: ~75–87% | Data: Effortless.
Data routinely demonstrates the ability to infer rules, patterns, and transformations far beyond simple grid puzzles. He would solve these instantly, likely faster than humans.

ARC-AGI-2 (harder, resistant to brute force, requiring deep abstraction)
Humans: ~100% | AI: ~53% (best models) | Data: Near-perfect.
Data shows fluid intelligence: he learns new games (like poker or strategy simulations) without prior training, adapts to alien logic systems, and reasons abstractly across domains. He would likely match or exceed human-level performance here.

ARC-AGI v2 (leaderboard variant, where top AI scores are <20%)
Humans: ~100% | AI: ~15% (best) | Data: Essentially flawless.
Data’s ability to generalize across unfamiliar contexts—whether deciphering alien languages, solving engineering crises, or improvising in diplomacy—suggests he would achieve near-100% here, like humans.

ARC-AGI-3 (interactive reasoning, dynamic environments)
Humans: ~100% | AI: Not yet tested | Data: Beyond benchmark.
Data thrives in interactive, real-world reasoning: commanding starships, negotiating with adversaries, and adapting in real time. He embodies the kind of general, embodied intelligence ARC-AGI-3 aims to test.

At this point, I listened again to a discussion between Data, Geordi (Chief Engineer), and Dr. Pulaski (Chief Medical Officer), which reminds us how far away even the fictional android is from being human.

The context is that Data and Geordi enter the Holodeck to play Sherlock Holmes adventures. Since Data knows all the stories, he keeps jumping the gun and spoiling the fun. Geordie got frustrated and abandoned the game. They both came to the café and discussing the frustration about playing with Data and Dr Polaski was listening to this conversation.

“What we were doing You are wasting your breath ,lieutenant. saying that to data is asking a computer not to compute” Said Dr Polaski.

“Am I so different from your doctor.” Asked Data

“Are you able to cease speaking on command. In medicine I am often faced with puzzles that I do not know the answer.” Said Doc

“She's right there. you always know the answer. to feel the thrill of victory there has to be the possibility of failure and where's the Victory in winning a battle you can't possibly lose.” Georgie observed

“Are you suggesting there is some value in losing” Data asked

“ Yes yes that's the great teacher . we humans learn more often from a failure or a mistakes than we do from an easy success . not you. you learn by rote. to you all is memorization recitation .” Said Doc

‘I don't know about all that. Deductive reasoning is one of data strengths” Georgie commented

“Yes and Holmes is too. But Holmes understood the human soul; the dark flecks that drive and turn the innocent into the evil, that understanding is beyond data.” Said Pulaski

“Now you're just being unfair doctor” Quipped Georgie

“I don’t think so lieutenant. Your artificial friend doesn't have a prayer of solving a Holmes mystery that he hasn't read’

Being a Star Trek aficionado, I then asked Copilot to compare Data with another Next Generation–era AI: the Emergency Medical Hologram from Star Trek: Voyager.

Data would consistently outperform the Doctor in abstract, cross-domain reasoning.
The Doctor would rival or surpass Data in medical problem-solving and human interaction. His emotional growth gives him an edge in empathy-driven reasoning, which ARC-AGI-3 (interactive tasks) is designed to capture.

In short, Data is the embodiment of general intelligence; the Doctor is the embodiment of specialized intelligence evolving toward generality. Together, they illustrate two pathways AI could take. one built for universality, the other for depth and human connection.

AI may bring super intelligence soon, not a normal human being. And will this super intelligence empower the human or annihilate the human is the question.

The idea behind this post is not to be judgmental, but to invite you to join me on a quest on what more is to being human- better human. Atma with a link to Paramatma ?

“Intelligence may be measured in problems solved, but humanity is revealed in the problems we struggle with.”

Open Network, Open Beyond Commerce, Open Beyond Borders: A Vision for the Digital Continent

 

The digital age has given rise to a new kind of continent; one not defined by geography, but by connectivity, imagination, and shared protocols. This “digital continent” is built on the foundational ethos of openness: open standards, open participation, and open innovation. From the World Wide Web to India’s Open Network for Digital Commerce (ONDC), the journey of open networks is reshaping not just commerce, but the very architecture of digital society.

The Power of Open Protocols

The internet’s early success was rooted in open protocols. Tim Berners-Lee’s decision to make the web’s architecture freely available catalyzed a wave of global innovation. Email, powered by SMTP, became a universal mode of communication because it was interoperable, anyone could build on it, use it, and connect through it.

These protocols didn’t just enable technology; they fostered ecosystems. They allowed diverse actors, governments, startups, communities, to participate without permission or gatekeeping. The result was a flourishing digital commons.

Yet, when it came to commerce, the world veered toward proprietary platforms. Amazon, Alibaba, and others built closed ecosystems that centralized control. While efficient, these platforms created silos, limited choice, and concentrated power. The internet of transactions became fragmented.

ONDC: A Reference Model, Not the Destination

India’s ONDC emerged as a bold corrective. Conceived as a public digital infrastructure, ONDC is not a marketplace or app; it’s a protocol layer that enables interoperability across buyer and seller platforms. It allows any seller to be discovered by any buyer interface, provided they speak the same digital language.

ONDC’s early success, spanning groceries, mobility, financial services, and electronics, demonstrates the viability of open commerce. But its true value lies not in its scale, but in its replicability. ONDC is a reference model, not a global monolith. It shows that open networks can work, and that they can be federated across domains and borders.

Beyond Commerce: The Architecture of Open Networks

Open networks are not limited to retail. The same principles, interoperability, decentralization, and inclusivity, can be applied across sectors:

• Agriculture: Farmers, processors, and buyers can transact through open agri-networks, improving market access and price transparency.
• Tourism: Guides, accommodations, and transport providers can be discovered across interoperable platforms, reducing dependency on global aggregators.
• Healthcare: Patients, providers, insurers, and pharmacies can connect through open health networks, improving care coordination and reducing costs.
• Education: Learners, educators, and institutions can share resources and credentials across open learning networks, fostering lifelong learning.

Each domain can build its own network using shared protocols, tailored to local needs but capable of global interoperability. This is the architecture of the digital continent: a mesh of open networks, each sovereign yet connected.

Federation, Not Centralization

The vision is not to create one giant network, but many interoperable ones. Just as the internet connects websites across domains, open networks can connect commerce, health, education, and governance.

Federation allows for diversity. A tourism network in Bhutan can interoperate with a mobility network in Brazil. A farmer in Uganda can be discovered by a buyer in India. Innovation can flourish without centralized control.

This model also respects sovereignty. Countries and communities can define their own rules, data policies, and governance structures, while still participating in a global digital economy.

Inclusion by Design

Open networks are inherently inclusive. They lower barriers to entry by removing the need for proprietary integrations. A small seller, a rural entrepreneur, or a local cooperative can join the network once and be visible everywhere.

This is especially powerful for:

• MSMEs and informal sector players
•  Women entrepreneurs and self-help groups
•  Rural and tribal communities
•  Artisans and creators

By decoupling discovery from dominance, open networks democratize visibility. They allow participants to retain autonomy while accessing scale.

Iteration and Local Customization

Open networks are not static. They evolve through experimentation. ONDC’s early journey involved handholding, incentives, and protocol refinement. Other networks will need similar iterative approaches.

Local customization is key. Buyer apps can curate offerings for specific communities. Protocols can be adapted to local languages, payment systems, and regulatory norms. Feedback loops can drive continuous improvement.

This mirrors the spirit of the web: decentralized, diverse, and user-driven.

The Role of Policy Makers

To build and sustain open networks, policy makers must go beyond regulation. They must become architects of digital public infrastructure. This involves:

•  Investing in foundational layers: identity, payments, data sharing, and consent frameworks.
•  Promoting open standards: ensuring interoperability across platforms and domains.
•  Supporting capacity building: enabling small players to adopt and adapt technology.
•  Preventing proprietary lock-ins: ensuring that public services and subsidies are not tied to closed platforms.

The goal is not just to regulate platforms, but to enable alternatives. To seed ecosystems that are resilient, inclusive, and innovation-friendly.

A Global Movement

ONDC has inspired interest from countries across Asia, Africa, and Latin America. But the movement must go further. It must become a global conversation about digital sovereignty, economic inclusion, and protocol-based collaboration.

International organizations, development banks, and philanthropic institutions can play a catalytic role. They can fund pilots, convene stakeholders, and support capacity building. They can help create a global commons of open protocols—available to all, owned by none.

The Internet of Transactions

We are entering a new phase of the internet, not just as a network of information, but as a network of transactions. This “Internet of Transactions” will be:

•  Interoperable: connecting diverse actors across domains and borders.
•  Inclusive: enabling participation without gatekeeping.
•  Iterative: evolving through feedback and experimentation.
•  Infrastructure-led: built on public digital rails, not private silos.

It will offer choice without coercion, scale without centralization, and innovation without inhibition.

Conclusion: Building the Digital Continent

The digital continent is not a metaphor, it is a blueprint. It is a call to reimagine digital ecosystems as open, federated, and inclusive. ONDC is one landmark on this journey, but the path stretches far beyond.

As countries and communities build their own networks, they contribute to a global mesh of opportunity. They reclaim agency, foster innovation, and create resilient digital economies.

The future is not platform versus platform. It is network of networks. It is openness as infrastructure. And it is ours to build together.

 “Openness is not the absence of control—it is the redistribution of power.”

“The Matrix Isn’t Coming - We’re Building It.”

 

In 2011, my blog post titled “Looking for ‘the One’? A Cynic’s Fantasy” invoked The Matrix as a metaphor for our world ; where most people are treated as mere resources, while a powerful few extract wealth and power, often using distraction and control to keep the masses docile.

Today, as we stand amid the rapid rise of generative AI, autonomous systems, and mass-data platforms, I find that old metaphor eerily prescient , but the villain need not be the technology itself. The real danger lies in how it is exploited, and in our collective failure as society to resist that exploitation.

Consider these parallels:

• AI-driven recommendation engines can personalize content to the point of manipulation , redirecting our attention, shaping our world-views, and nudging behavior, much like the virtual reality in The Matrix.
• Advanced automation could optimize efficiency .  but if left unchecked, it risks reducing people to cogs in a system focused solely on profit and power.
• Powerful actors (big tech, corporations, political players) may lean on technology to amplify influence, extract value, and suppress dissent  not unlike the ruling class in the original metaphor.

But here’s the thing: technology is not the enemy. The problem is how we allow it to be used , or abused,  and the choices we make (or don’t) when we see injustice.

🔹 What if, instead of waiting for a “saviour” (a Neo or “the One”), each of us made small, conscious choices  championing transparency, demanding accountability, and supporting those who use technology for public good rather than private gain?
🔹 What if we recognized that real change comes from collective resolve, not isolated heroics? That means encouraging ethical AI, pushing for fair policies, holding companies accountable, and refusing to be pacified by convenience or fear.

In short: let’s not demonize AI,  let’s stay vigilant about how we use it, and who holds the levers of control. Because if we remain passive, we risk ending up in a very real-world version of The Matrix.

#AI #Ethics #TechForGood #Society #Responsibility #MatrixMetaphor #Leadership

 

Related Article  Looking for “the One”? A Cynic’s Fantasyl

The Paradox of Growth: When "Doing it Right" Kills "Getting it Right"

 

Every enterprise begins with the spark of entrepreneurial energy, the founder’s drive, ambition, vision. As the venture builds momentum, scales up, adds people, processes and systems, it injects stability, but also risk. Because without the right balance, what once felt alive can become weighed down.

A modern-day parable is Apple. Born of Jobs’ entrepreneurial fire, it soared. Then came the “establishment.” Jobs was ousted. The soul left the building. Apple drifted. Until the prodigal founder returned, not just to revive the company, but to re-infuse it with purpose. The rest is history.

Here’s what I’ve observed:
When you’re building, you’re agile. You do things. You experiment. You learn by doing. You focus on purpose, on outcome, on value created. You are a “Doer” in the truest sense of the word. In the words of my recent post, a Doer is someone who makes things happen, who cares about what’s good for the organisation and beyond, who takes responsibility and deliver.

Then as scale arrives, processes creep in. Structures to govern. Systems to measure. Beans to count. That’s not inherently bad, stability matters. But when the emphasis tilts too far toward process and loses sight of the raison d’être; the driving engine stalls. The “Passenger” emerges: someone more invested in the machinery, the compliance, the credits, the own agenda, rather than the outcome or the impact.

And here is the fatal tension:

• If you stay purely in “Doer” mode without systems, chaos reigns, decisions get missed, growth becomes fragile.
• If you over-systematise and let the “bean counters” dominate, you become rigid, unresponsive, blinded by process rather than purpose.
• Most organisations toggle somewhere in between. But the danger: the passengers gain ascendancy. They’re comfortable with status quo, minimal risk, personal gain—and often end up driving the ship. Meanwhile your true “Doers” drift away, disengage or leave. The outcome? The system survives for a while. but the life drains out of the enterprise.

So what’s the distilled wisdom?

• Keep founders’ spirit alive: Remind yourselves of the outcome, the customer-impact, the value you set out to create.
• Embed systems, yes; but don’t let them become the mission: Systems exist to enable, not to replace action or purpose.
• Promote Doer-thinking: Celebrate those who pick up the ball, who care deeply about the work, who challenge “this is how we’ve always done it”.
• Be alert to passengers: The ones who prioritise process over purpose, comfort over change, credit over contribution—they’re not the fatal enemy alone; the problem is when the governance structure rewards them.
• Balance is dynamic: The cycle will shift—start-up to scale to maturity. What you need is conscious recalibration: when you scale, build enough muscle to keep doing, keep adapting, keep delivering.

If you ask me, the real question is: Will your enterprise choose to be right, or will it continue to do right? Will it chase compliance, status, structure and miss the reason you started? Or will it hold fast to the why, even as it puts the what and how in place?

You may not disappear into the mythology of “passenger vs doer” in black-and-white. We all move along the continuum. What matters is the direction. If we respect ourselves as those who build, create, deliver, not just manage. we will pay the price for the choices we make. But more importantly, we’ll build something that lasts, that matters, beyond the next quarterly cycle.

Here’s to being a Doer. And to building enterprises that don’t just run , but roar.

“Systems should be the stagehands.
The moment they start demanding the spotlight, the show begins to fail.”

#entrepreneurship #leadership #growth #culture #systems #purpose

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To be or not to be - Part 8 'GET it Right or DO it Right ?'

India’s AI Moment: Foundational Models, Human Intelligence, and the Future of Work

 

The rapid proliferation of artificial intelligence has turned what were once speculative questions into urgent, everyday conversations. In boardrooms, classrooms, and chai stalls alike, we now ask: Has India missed the foundational model bus? Has AI surpassed human intelligence? And what happens to jobs as machines become smarter than us at most cognitive tasks?

These are not idle musings. They are existential questions for a country of 1.5 billion people, standing at the cusp of a technological revolution. And few voices articulate this moment with as much clarity and conviction as Vishal Sikka, former CEO of Infosys, veteran of SAP and Oracle, and now founder of Vianai Systems. His recent podcast conversation offers a treasure trove of insights, and I’d like to unpack and reflect on them here, in the spirit of strategic curiosity and national urgency.

---

Has AI Surpassed Human Intelligence?

Let’s start with the provocative question: Has AI become smarter than us?

Sikka’s answer is refreshingly grounded. He reminds us that today's large language models (LLMs) are essentially “lookup machines”, brilliant at pattern recognition, but devoid of true understanding. They can generate answers, yes, but they don’t know anything. They lack grounding in the physical world, in causality, in embodied experience. They are not sentient, and they are not superintelligent.

What they are, however, is astonishingly efficient. Consider this: our brain runs on about 20 watts of energy. Training GPT-5, by contrast, consumes energy orders of magnitude higher. somewhere between 10¹² to 10¹⁸ times more. That’s a trillion-fold gap in efficiency. And yet, despite this brute-force power, AI still stumbles on basic reasoning, context, and nuance.

So no, AI hasn’t surpassed human intelligence. But it has become a powerful tool, like a calculator, then Excel, then Google, and now this. The question isn’t whether it’s divine. The question is: What can we build with it?

Has India Lost the Foundational Model Story?

This is where the conversation gets interesting and controversial/.

Sikka is unequivocal: India must build its own foundational models. Not just because we can, but because we must. To be a passive consumer of AI built elsewhere is to surrender our agency in shaping the future. And India, he argues, is large enough, deep enough, and important enough to do it all. build the models, build the applications, and build the services.

We have unique advantages:

• India Stack: A digital infrastructure unmatched globally, offering rich, structured data.
• Linguistic diversity: Hundreds of languages and dialects, ripe for training multilingual models.
• Cultural archives: Manuscripts, documents, and oral traditions that no other country possesses.

And yet, the expertise to build frontier models is shockingly concentrated. According to Sebastian Thrun, only about 3,000 people globally can build such models, and 80% of them are in San Francisco and London. This is not just a talent gap. It’s a geopolitical vulnerability.

India must democratize this capability. Stanford teaches a course on building foundational models. Why shouldn’t IITs, IIITs, and NITs do the same? Why shouldn’t we have open-source frameworks, indigenous datasets, and public-private partnerships to accelerate this journey?

 

The Future of Jobs: Catastrophe or Opportunity?

This is perhaps the most emotionally charged part of the conversation. Sikka doesn’t mince words: Mass unemployment is a real and imminent risk. And paradoxically, it’s the educated class, those trained for certificate-based jobs like database administration or network maintenance, that are most vulnerable.

But here’s the twist: AI could empower artisans more than engineers.

Imagine a village woodworker using AI to design, market, and sell his craft globally. Imagine a weaver translating her product descriptions into 20 languages. Imagine a painter understanding global trends and adapting her style. These are not fantasies. These are real, empowering use cases.

The challenge, then, is not just technological. It’s societal. We must shift from training people to “make a living” to training them to “make a life.” That means teaching them how to use AI to augment their creativity, productivity, and agency, not just to pass certification exams.

And yes, while many jobs will become irrelevant, many new ones will emerge. Transitioning legacy systems and  reimagining business processes for AI enablement of existing enterprises,  managing AI ethics, curating datasets, fine-tuning models, these are all new frontiers. Services companies will play a pivotal role in this transformation, but they must evolve from body-shopping to capability-building.

 

What Should India Do Next?

Let me offer a strategic synthesis, drawing from Sikka’s wisdom and some of my reflections:

1. Invest in foundational models: Not just one, but many. Across languages, domains, and modalities.
2. Democratize AI education: From elite labs to vocational centers. Teach people how to build on and around it.
3. Empower the informal sector: Use AI to elevate artisans, farmers, and micro-entrepreneurs.
4. Reimagine job training: Move from certificate-based skills to capability-based learning.
5. Build public infrastructure for AI: Open datasets, ethical frameworks, and compute access must be national priorities.

 

Final Thought: The Building Is Not Smarter Than Us

Sikka ends with a beautiful metaphor: the building we’re sitting in is more powerful than us. But we don’t worship it. We use it. We live in it. We shape it.

AI is the same. It’s not God. It’s not superintelligence. It’s a tool. And like all tools, its value lies in what we do with it.

India’s AI moment is here. Let’s not squander it. Let’s build, with clarity, courage, and conviction.

“The true measure of intelligence is not in what we can automate, but in what we choose to preserve.” Anonymous

“AI hasn’t taken over the world yet—but it has taken over my browser tabs, my inbox, and my sleep.”Anonymous

 

Sources:
PM Modi and Vishal Sikka Chat About India's Bright AI Future
Vishal Sikka’s Advice for India on Foundational Models
A Visionary Meeting: Vishal Sikka & PM Modi Discuss AI's Future