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.”