When the Web Learns to Serve Machines: Reflections on Parag Agrawal’s Parallel

There is a small, disorienting thrill that comes when a familiar surface—like the web—suddenly reveals a different set of inhabitants. Parag Agrawal’s new company, Parallel Web Systems, feels like that moment: the internet I learned to navigate with clicks and attention is being reimagined for a user that never sleeps—AI agents.

Parag’s trajectory is part technical, part narrative. He rose through engineering ranks to lead Twitter, was forced out during the Musk takeover, and now returns with a bet: the next web will be built for machines, not humans (NDTV; Indian Express). The product at the centre of that bet—Parallel’s Deep Research API—claims to outperform humans and leading models (including GPT-5) on some hard research benchmarks (Entrepreneur; Cointelegraph). Venture capital has listened (about $30M raised, per several reports), which signals that others find this reframing plausible (LiveMint; Analytics India Mag).

Accuracy over latency: a different engineering gospel

What strikes me is the deliberate inversion of priorities. The human web optimized for speed—faster page loads, instant search results, immediate attention. Parallel argues that for agents, accuracy and depth matter more than milliseconds (Indian Express). That is not a small tweak. It is a rewrite of assumptions across the entire stack: crawl, index, ranking, and interfaces.

This change feels inevitable when you imagine thousands of software agents quietly performing multi-step reasoning across the open web on behalf of companies or individuals. Ganesh Kanade’s LinkedIn post captured that thought concisely: humans use the web a few hours a day; agents will use it a million times more—and so the rules must adapt (Ganesh Kanade — LinkedIn).

The promise: exponential research, better automation

There’s real beauty in the idea. If agents can reliably fetch, verify, synthesize, and attribute information at scale, whole classes of work—market research, legal analysis, scientific literature surveys, complex debugging—become orders of magnitude faster. Parallel’s Deep Research API, according to early reports, is already powering millions of research tasks daily and is being used by startups and public enterprises (Entrepreneur; Cointelegraph). That capability could amplify human creativity and decision-making in ways we are only beginning to imagine.

The hazards: sovereignty, economics, attribution

But every new infrastructure imprints power. If the web’s primary user becomes machine agents, questions that were once peripheral become existential:

• Who owns—and controls—the programmatic interfaces that agents rely on? If a handful of platforms or cloud providers gate access, the web risks becoming a machine-feudal landscape.
• How is attribution enforced and rewarded? Parallel talks about transparent attribution and open markets for contributions (NDTV), but markets are messy and incentives imperfect.
• What rights do content creators have when machines harvest, synthesize, and monetize their work at machine scale?

These are not technical wrinkles. They are political, legal, and economic design choices that will shape whether an AI-first web is equitable or extractive. As some commentators warn, a city built for machines still needs zoning, governance, and attention to sovereignty, or a few gatekeepers will own the skyline (comments on the LinkedIn thread echo this concern) (Ganesh Kanade — LinkedIn).

A personal frame: redesigning memory

I think of the web as humanity’s external memory. For decades we shaped that memory around human cognition—pages, links, attention economy. If we now redesign memory around machine cognition—structured retrieval, provenance, and composability—we are effectively rewriting part of our collective mind. That offers enormous potential: better research, fewer duplicated efforts, faster cycles of discovery. It also requires humility. We must intentionally architect norms for attribution, consent, and economic participation.

Parallel’s early wins and the $30M backing show that industry believes this is a real vector (LiveMint; Entrepreneur). My hope is that as we build systems optimized for machines, we do not forget to embed human values into their foundations: openness, fair attribution, decentralized opportunity, and governance that protects the many, not just the few.

The technical problem is thrilling. The societal question is harder—and ultimately more consequential.

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Regards,
Hemen Parekh