The Coming Shift: From Social to Agentic Web — and What It Means for the Poor

Marissa Dean

We are standing at the edge of another major shift in how the world connects, learns, and earns. For more than two decades, the internet has evolved through distinct eras, each one defined by what people could do within it.

In the beginning, Web 1.0 allowed us to read — to access information that had never before been so widely available. Web 2.0 invited us to write and share, ushering in the age of social media and user-generated content. Web 3.0 introduced the idea of ownership through decentralization, giving rise to blockchain and digital assets.

Now, we are entering what many describe as Web 4.0 — a world where users can execute actions through intelligent agents that understand intent and carry it out on their behalf.

AI, Commerce, and the Reinvention of WhatsApp

Meta is rapidly transforming WhatsApp from a messaging platform into an integrated, AI-enabled ecosystem for communication, commerce, and connection. At the same time, foundational model developers, including Meta, OpenAI and Anthropic, are expanding what chat interfaces can do at remarkable speed.

The interface of the future will not be a website or an app; it will be a conversation. Instead of typing into search engines, people will ask questions, give instructions and trust digital agents to act. Soon, many of us will simply say, “Order more toothpaste,” and the AI will find the product, compare prices, and complete the purchase — all within a chat thread presented visually or audibly.

The experience will feel seamless and intuitive. Yet, for those of us working in digital inclusion and livelihoods, it raises critical questions about who stands to benefit and who may be left behind as the web becomes increasingly agentic.

When Chat Becomes the Marketplace

Inside the chat-based platforms that now anchor daily life for millions, something new is taking shape. As AI integrates more deeply, commerce is being rebuilt around conversation. Product recommendations, comparisons, and purchases are no longer separate steps in separate apps. They happen within the same thread — in real time, guided by algorithms that learn what we want before we even know to ask.

This is what some have begun calling shoppable AI: a blend of conversation, personalization, and transaction. For users with stable connectivity and purchasing power, this promises convenience and time saved. For low-income users or microentrepreneurs, it may create both opportunity and risk.

Learning from the Past

We have been here before. Web 2.0 connected the world, but it also concentrated control and profit in a handful of platforms that monetized our attention and data. As AI becomes the organizing layer of the new Web, that pattern could repeat. This new layer could also unintentionally exclude those already at the margins: low-income microentrepreneurs who rely on visibility within WhatsApp to reach customers or non-English speakers navigating systems trained on English data, as examples.

For low-income users, particularly women and informal workers, this could take several forms:

• Exclusion from visibility. Agentic responses may prioritize large or paying merchants, pushing smaller sellers further to the margins.

• Rising costs of participation. Agentic features may begin to rely on heavier data use. For users with prepaid bundles or limited connectivity, participation in the “AI-rich” web may simply be unaffordable.

• Digital dependency. As monetization opportunities expand within WhatsApp, changes to platform policies or pricing could alter the economics for millions of livelihoods overnight.

• Loss of data privacy. This risk extends to all users, but it carriers sharper consequences for those with limited recourse. AI-driven chat interfaces collect rich behavioral and contextual data — tone, timing, emotional cues, and purchasing patterns—that can easily be monetized or misused without consent or understanding.

These are not abstract possibilities. They are near-term realities for people who use WhatsApp as their storefront, classroom, and community hub.

Stepping into the Next Chapter with Intention

The most hopeful part of this story is that we are still early enough to shape it. The decisions being made today — by AI labs, regulators, investors, and global development actors — will determine whether this next web empowers or excludes.

To build a more inclusive digital future, we will need to:

• Strengthen digital and AI literacy so that users understand what they are agreeing to and what data they are sharing.

• Advocate for fair visibility and discoverability for small sellers and women-led enterprises.

• Push for affordability in both data and tools so that adoption is not limited to those who can pay for premium access.

• Insist on transparency in how AI systems recommend, rank, and reward.

If we can get this right, the agentic web could open extraordinary opportunities — not just for convenience, but for empowerment. It could allow individuals, especially those historically left behind, to delegate routine tasks and focus more energy on creativity, care, and connection.

The challenge before us is not simply to innovate, but to do so with integrity and foresight. The web has always reflected human intention. The question is whether, this time, we will be intentional enough to ensure it serves everyone.

When Caution Is Risky

Bryan Pon

Many of the social-impact organizations we work with are taking a deliberate, cautious approach to adopting AI. Their concerns about model bias, data privacy, output errors, environmental impacts (and more) are absolutely valid, and we typically encourage our clients to take a conservative approach—after they establish an AI usage and governance policy.

Because if your careful approach to AI adoption is delaying you from establishing usage guidelines, that caution is actually creating a lot of risk.

While you wait, your staff are invariably already using AI tools, and that usage is growing week by week. Avoidance only creates a vacuum of best practices and practical guidance, which not only leaves your staff high and dry in terms of the training and policies they need, but also leaves you with no liability coverage or recourse were one of your staff to mishandle data with an AI tool.

For most organizations, taking a “wait-and-see” approach to large scale technological transformation makes sense. The move to cloud computing was a slow, inexorable transition that didn’t really punish laggards. But staff weren’t experimenting with cloud infra on the side without you knowing, putting your data and reputation at risk. In the AI era, the safe assumption is that all staff are using AI personally and probably professionally, whether explicitly or not.

With this in mind, one of the most important ways to reduce risk is to get out ahead of these practices with formal AI usage and governance policies that can support your employees and protect your organization’s most important assets.