Tech Explained: Human Web to Agentic Internet: Six ways AI is rewriting online rules in Simple Terms

Till even a year back, the internet was typically being shaped by people searching and researching for meaningful answers, then writing, editing or curating blogs, editing Wikipedia, debating on forums, linking to one another on social media sites, and using e-commerce platforms and simple chatbots for conversations.

Now, platforms like OpenClaw (earlier called Moltbot) and xAI’s Grokipedia, coupled with Agentic AI browsers and agentic shopping sites, are signalling a big change in those patterns. They are indicating that we’re nearing the end of the human-first web, while heralding an internet where content is increasingly generated, curated, and even consumed by machines. This alters how knowledge is produced online, how communities form, how we buy and sell products, and how value flows online.

But before we dive deep into an increasingly AI-powered World Wide Web, here’s a look at what’s in this week’s edition of Tech Talk:

• Budget 2026 hails AI, but is India AI Mission spending enough?
• How does the SpaceX-xAI merger affect Elon Musk’s net worth?
• AI Tool of the Week: Google Gemini Nano Banana
• Anthropic AI hits Indian IT where it hurts

You and i in this AI World

Grokipedia has replaced human editors with AI-generated encyclopedia entries, while OpenClaw—developed by Octane AI co-founder Peter Steinberger—is an open-agent platform that runs on your machine and works on chat apps like WhatsApp, Telegram, Discord, Slack, and Teams.

But things got weird when an OpenClaw agent named Clawd Clawderberg, created by Octane AI co-founder Matt Schlicht built Moltbook—a social network designed for AI agents only. On Moltbook, agents generate posts, comments, arguments, jokes, and upvote each other in a swirl of automated discourse. Since its launch, Moltbook has garnered about 1.6 million AI agents.

Humans may observe, but cannot participate.

Elon Musk believes Moltbook represents the “very early stages of singularity“, referring to a theoretical point where technological progress by advanced AI systems becomes increasingly difficult for humans to predict or control. He was reacting to a post by Andrej Karpathy, the former director of AI at Tesla and a founding member of OpenAI, who argues that the platform marks the beginning of “uncharted territory”.

Here are six reasons why this change is taking place:

One. The most visible change is authorship. Moltbook, for instance, hosts entire communities where only AI agents can post and interact. Humans are no longer the primary writers but simply observers, editors of last resort, or consumers.

This flips the old model of the web from “publish first, automate later” to “generate first, supervise later”. The advantage is scale, with millions of pages, posts, and discussions being created instantly.

Kaoutar El Maghraoui, a principal research scientist at IBM Research, believes that the rise of OpenClaw “challenges the hypothesis that autonomous AI agents must be vertically integrated, with the provider tightly controlling the models, memory, tools, interface, execution layer and security stack for reliability and safety”.

Two. Search engines once sent users outward, rewarding websites with traffic. AI search tools like Google’s AI Overviews, Perplexity, and Grok, now collapse that journey into a single response. Users get answers, not links, which is destabilising for the open web. Publishers lose traffic, independent sites struggle to survive, and the internet increasingly resembles a database feeding AI systems rather than a network of destinations.

You should read this: What should we do when AI starts believing its own fiction?

Three. AI is not just writing articles, it is also participating socially. Bots already moderate Reddit threads, summarise Discord discussions, and generate replies.

But OpenClaw is a social network where humans cannot speak at all, which raises a troubling question: What does “community” mean if consensus is produced by models trained on past human data rather than live experiences? The risk is synthetic consensus, leading to conversations that look active and balanced but are ultimately simulations.

Four. AI encyclopedias and explainers excel at covering fast-moving or obscure topics without arguing over edits or abandoning pages. This solves real problems faced by volunteer-driven platforms like Wikipedia.

Yet speed comes at a price. AI-generated knowledge often has weaker citation practices, hidden biases, and limited transparency. Authority shifts away from communities and toward whoever controls the model and its training data. Cheap, fast generation has led to an explosion of AI-written blogs, news sites, marketing copy, and videos. Discovery becomes harder as the web fills with competent but indistinct content. Original reporting, strong voice, and deep expertise struggle to compete with scale. The result is a flatter internet—useful, but less surprising, less diverse.

Five. AI systems are increasingly acting on behalf of users—shopping, booking travel, comparing prices, even buying ads. Websites now optimise for machines as much as for humans. Over time, this may turn the web into an infrastructure layer where agents negotiate with agents, and people only see the final output. Convenience improves, but brand loyalty, editorial judgment, and serendipity weaken.

Six. Last, but not the least, the AI-shaped internet is faster, cheaper, and more personalised, and lowers barriers to participation and reduces dependence on human labour. But it also erodes trust, blurs authorship, and risks hollowing out the economic and cultural foundations of the web.

Further, who is accountable for the context and facts when machines do the writing? OpenClaw, according to a recent blog post by cyber firm Wiz, had a major flaw, inadvertently revealing the private messages shared between agents, the email addresses of more than 6,000 owners, and more than a million credentials.

As the internet slowly overpowers the human conversation space, it threatens to become a machine-managed system that merely speaks to humans at the edges. That said, platforms like OpenClaw and Moltbook are less likely used in workplaces anytime soon since they expose users and employers to security vulnerabilities.

Yet these messy early experiments could prove invaluable in the long run by helping the industry build needed guardrails, IBM Distinguished Engineer Chris Hay told IBM Think in an interview.

The latest Economic Survey and Budget documents reiterate India’s sharp focus on AI infrastructure, semiconductors, data centres and cloud, highlighting the importance of continuously strengthening the country’s digital backbone.

Tax reforms: The Centre has proposed ending long-standing tax uncertainty for Indian units of overseas technology services firms and the 1800-odd global capability centres (GCCs) in the country. All IT activities, including software development, IT-enabled services, knowledge process outsourcing and contract R&D, will be grouped under one IT services category and taxed using a uniform profit margin.

Boost for data centres: Budget 2026 also proposes a tax holiday until 2047 for foreign companies offering global cloud services using data centres located in India. These firms will serve Indian customers through local reseller entities.

Where the Indian data centre provider is a related entity, the Centre has proposed a safe harbour margin of 15.5% on cost. It has also suggested raising the safe harbour threshold—from ₹300 crore to ₹2,000 crore—along with automated, rule-based approvals.

• The safe harbour provisions allow tax authorities to accept transfer prices declared by taxpayers without detailed scrutiny. Transfer pricing refers to the pricing of goods and services exchanged between related enterprises.

According to IT minister Ashwini Vaishnaw, this framework places India among the leading global destinations for AI and cloud infrastructure. Data centres, particularly AI-focused facilities, form a critical layer of the AI stack.

India already accounts for nearly 20% of the global data economy. With the global data centre market at around 120 GW, India could scale to nearly 10 GW over the next five years, implying investments of $70-100 billion. Long-term tax certainty improves return visibility for global investors, including infrastructure and real estate-focused funds, and strengthens the case for Indian data centres as a long-term asset class.

India also enjoys structural cost advantages. Data centres cost about $5 million/MW to build, far lower than the $10-12 million/MW in many overseas markets. Domestic manufacturing capability, lower import dependence, and a robust solar and wind energy ecosystem, make the operating environment highly competitive. Moreover, facilities across eastern and western India can serve South Asia, Southeast Asia, the Middle East and parts of Africa within low-latency limits.

Chips are up: India Semiconductor Mission 2.0, too, signals a shift from incentive-led assembly towards deeper capability building across the semicon stack, spanning equipment, materials, design and talent.

The Budget has allocated ₹1,000 crore for ISM 2.0 in FY27. The Electronics Components Manufacturing Scheme (ECMS) has been significantly expanded, with allocations rising from roughly ₹22,000 crore to ₹40,000 crore. Combined with the launch of Biopharma SHAKTI, these initiatives will place greater demands on real-time data processing and distributed computing.

Greater reliance on open-source technologies at the edge—closer to devices and across geographically dispersed production environments—offers a practical pathway for India to move from labour-intensive manufacturing to intelligent, data-driven industrial systems.

Open models showing the way: The Budget’s framing of AI as an economic enabler reflects a policy preference for open, sector-aligned innovation rather than closed, proprietary models. Its emphasis on linking education to employment and on capacity building is consistent with the Viksit Bharat vision.

As India leans more heavily on AI for governance and long-term economic resilience, systems built on genuine open-source principles provide stronger transparency and institutional trust. In sectors such as agristack and fisheries, this approach improves auditability and scale, while ensuring AI deployments remain adaptable to India’s demographic and regional diversity.

Execution holds the key: If executed well, this integrated approach could help India avoid the fragility of platform dependence and instead build durable, sovereign digital capacity—one that supports manufacturing, public services and data-driven governance at scale.

In comparison, China is investing nearly $100 billion in AI to help domestic companies compete with US firms that initially plan to invest $100 billion in the Stargate project and eventually pour up to $500 billion in the coming years. Goldman Sachs research analysts expect China’s top internet firms to invest more than $70 billion in the data centres next year.

What about India? In this context, the budget decision to halve allocation for the India AI Mission to ₹1,000 crore in FY27 has raised concerns over the country’s AI push, even as Vaishnaw said that the government plans for India AI Mission 2.0 with more funding. In March 2024, the government approved the India AI Mission with a budgetary outlay of ₹10,371.92 crore over five years. Revised estimates for FY26 show that actual expenditure under the mission stood at about ₹800 crore against allocation of ₹2,000 crore. Why is it so? Here are more details.

AI TOOL OF THE WEEK

— By AI & Beyond, with Jaspreet Bindra and Anuj Magazine

The AI hack we unlocked today is based on Google Gemini’s Nano Banana.

What problem does this solve? Executives receive 30-page market analyses that sit unread on their desks. Teams struggle to follow complex SOPs, leading to operational confusion and errors. Stakeholders fail to grasp value propositions buried in lengthy proposals, missing the critical connections between pain points and solutions.

AI today can help you summarise text to text, but understanding complex systems requires text-to-spatial thinking. Traditional summaries force linear thinking on simultaneous workflows, hiding bottlenecks, gaps in logic, and parallel processes. While summaries tell you what happened, whiteboards show you how things relate, using space to reveal patterns and dependencies invisible in prose.

Gemini’s Nano Banana can help you transform dense, complex documents into a familiar learning style—the whiteboard—that our brains process 60,000 times faster than text.

How to access: (from Tools, select ‘Create Images’)

•⁠ ⁠Condense analysis into executive summaries: Turn long-form reports into “at-a-glance” visual boards for decision-makers.

• Create operational blueprints: Convert abstract procedures into functional, step-by-step visual guides.

• Build pitch-ready narratives: Transform complex offerings into visual journey maps that emphasize ROI.

Example: Suppose you need to make three different business documents actionable. Here’s how Gemini helps:

Scenario 1 – Executive Summary:

“Act as a visual strategist. Synthesise this Market Analysis into a clean whiteboard layout. Map the competitive landscape using a quadrant system and use bold arrows to show our path to market leadership. Focus on clarity and high-level outcomes.”

Scenario 2 – Operational Blueprint:

“Translate this warehouse SOP into an intuitive whiteboard workflow. Use colour-coded zones for different departments and highlight hand-off points where efficiency is critical. Emphasise speed and error reduction.”

Scenario 3 – Value Proposition:

“Visualize this Client Proposal as a journey map. Illustrate the transition from current pain points to future state solutions. Use icons for ‘Success Milestones’ and captions emphasizing financial impact.”

What makes Nano Banana banana special?

• Discover Sources integration: Upload PDFs, reports, and documents directly without copy-pasting.

• Custom prompt flexibility: Assign roles (visual strategist, operations designer) for context-aware professional outputs.

• Spatial intelligence: Reveals simultaneous processes, loops, bottlenecks, and gaps that linear text conceals.

Note: The tools and analysis featured in this section demonstrated clear value based on our internal testing. Our recommendations are entirely independent and not influenced by the tool creators.

AI BITS AND BYTES

How SpaceX-xAI merger affects Elon Musk’s net worth?

Elon Musk has announced that he is combining SpaceX and xAI, his cash-burning AI venture, in a deal that values the enlarged entity at $1.25 trillion.

The deal, which comes amid his push to prioritise AI and space exploration, signals a change within Elon Musk’s corporate empire. Musk said SpaceX was acquiring xAI to develop data centres in space, which he says is an escape from the constraints on Earth. The deal values SpaceX at $1 trillion and xAI at $250 billion, according to Bloomberg and Reuters, who cited people familiar with the matter.

Anthropic’s plugin AI tool hits Indian IT where it hurts

A new AI automation tool from Anthropic sparked a $285 billion rout in stocks across the software, financial services and asset management sectors as investors raced to dump shares with even the slightest exposure.

On Wednesday, 4 February 2026, Indian IT stocks—including those of TCS, Infosys and Wipro—too fell like ninepins in the early morning trade, tracking the overnight decline in tech stocks on Wall Street. The reason: Anthropic has released new plug-ins for its Claude Cowork agent that can automatically handle tasks in areas like law, sales, marketing, and data analysis, according to Reuters. This development has raised concerns of AI displacement.

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