Parag Agrawal’s Parallel: Reimagining the Internet for the Age of AI

Former Twitter CEO Parag Agrawal launches Parallel Web Systems Inc., a startup building a programmatic web designed for artificial intelligence. Learn how Parallel is reshaping the future of the internet for machines.

Parag Agrawal’s Second Act: Building the Internet for AI

When Elon Musk acquired Twitter (now known as X) in 2022, one of his first high-profile moves was firing then-CEO Parag Agrawal. For many, that marked the end of Agrawal’s chapter in Silicon Valley’s spotlight. But as history shows, some endings pave the way for bigger beginnings. Just a year later, Agrawal quietly founded Parallel Web Systems Inc., a startup that is already attracting attention—and millions in funding—for its bold mission: to rebuild the internet, not for people, but for artificial intelligence.

Based in Palo Alto, Parallel is backed by powerhouse investors like Khosla Ventures, First Round Capital, and Index Ventures. With a 25-member team and $30 million in fresh funding, the company has unveiled its vision of a "Programmatic Web"—an internet that machines can use as effectively, if not more, than humans.

Why Parallel Exists: The Web’s Next User Isn’t Human

For decades, the internet has been humanity’s greatest knowledge engine. It allowed billions of people to publish, learn, collaborate, and innovate at scale. Every modern AI model, including GPT-5 and its predecessors, was trained on this massive pool of human-created content.

But as Agrawal argues, a shift is underway: the web’s next major user isn’t human—it’s artificial intelligence.

Unlike people, who may browse a handful of articles, search casually, or click through ads, AIs interact with the web differently. They process data at a massive scale, parsing thousands of documents in seconds, automating workflows, and even reasoning through complex problems. The traditional infrastructure of the web—built around human clicks, ads, and paywalls—doesn’t support this type of usage.

If the internet doesn’t adapt, it risks fragmentation, with valuable knowledge locked away in silos inaccessible to machines. Parallel’s mission is to prevent that outcome by designing a web optimized for AI.

Inside Parallel: A Vision of the Programmatic Web

Parallel frames its mission around a few core principles that define what the web should look like in an AI-first world.

1. Unified Infrastructure

Instead of just serving up static documents, Parallel envisions infrastructure that combines data, compute, and reasoning. This would allow the system to generate insights and actions—answers to questions, summaries, solutions—rather than just links or content dumps.

2. Declarative Interfaces

Rather than browsing or running manual searches, AI systems can simply declare what they need. Parallel’s platform then determines the most efficient way to fulfill that request, pulling information, verifying it, and structuring it.

3. Transparent Attribution

One of the web’s biggest challenges today is that sources often go uncredited, especially in the AI era where models ingest data without explicit attribution. Parallel insists on transparency: every source gets credited, and contributions can be tracked and measured.

4. Open Markets for Knowledge

Parallel wants to ensure that openness isn’t just a matter of goodwill but also of economics. By creating open markets where contributors are rewarded, the system incentivizes knowledge-sharing and prevents critical information from being locked behind restrictive paywalls.

Parallel’s First Breakthrough: Deep Research API

Earlier this year, Parallel introduced its flagship product—the Deep Research API. According to Agrawal, this is the first system to outperform both humans and leading AI models, including GPT-5, on two of the most challenging industry benchmarks.

The Deep Research API is already powering millions of research tasks daily. Its applications range from startups building new AI agents to public companies automating workflows with accuracy that exceeds human performance. For example:

AI coding assistants are using Parallel’s search capabilities to quickly locate documentation and debug issues.
Public enterprises are automating complex processes that once required human oversight.
AI research teams are integrating Parallel’s tools to accelerate large-scale online investigations.

Agrawal summarized the company’s impact on LinkedIn: “We already power millions of research tasks every day, across ambitious startups and public enterprises. Some of the fastest-growing AI companies use Parallel to bring web intelligence directly into their platform and agents.”

The Bigger Picture: Why the Web Must Adapt

Parallel’s thesis is simple but radical: the web was built for humans, but AI will be its primary user in the future. If the infrastructure doesn’t evolve, the internet risks becoming inefficient and broken for machines. This has several implications:

Ads and paywalls won’t sustain machine users. AIs don’t click ads or subscribe like humans, which means current business models tied to human attention are obsolete in an AI-first world.
Knowledge silos threaten innovation. Without open frameworks, critical knowledge might remain locked behind proprietary APIs or gated platforms, slowing progress.
Trust and verification become critical. With machines parsing vast amounts of data, ensuring sources are credible and traceable is more important than ever.

Parallel’s approach ensures the web continues to be a living, breathing system of shared knowledge—only now, one designed for both humans and machines.

Challenges Ahead

While Parallel’s mission is inspiring, it faces significant hurdles:

Scaling infrastructure: Building a unified platform capable of serving millions (eventually billions) of AI queries requires massive compute and bandwidth resources.
Industry buy-in: For Parallel’s open markets and attribution systems to work, publishers, enterprises, and developers must adopt its framework.
Competition: Tech giants like Google, Microsoft, and OpenAI are also exploring similar territory, and Parallel will need to carve a unique niche.

Despite these challenges, Parallel’s timing may be its greatest advantage. With AI adoption skyrocketing, the need for a machine-optimized web is no longer theoretical—it’s urgent.

A Founder’s Redemption Story

For Agrawal, Parallel is more than just another startup. It’s a statement. After being ousted from one of Silicon Valley’s most high-profile CEO positions, he’s returned with a mission that could reshape the very foundation of the internet. If successful, Parallel won’t just be another SaaS company—it could be the blueprint for how machines and humans coexist in the digital world.

Agrawal himself frames the mission around “building for abundance.” Rather than competing for clicks, attention, or paywalls, Parallel’s vision is about creating a web where both humans and machines thrive, where knowledge flows openly but sustainably.

Conclusion: The Future of the Web is Parallel

Parallel Web Systems is betting on a simple but transformative idea: the web must evolve to serve its second user—AI—or risk falling apart. With its Deep Research API already outperforming leading benchmarks and a growing roster of AI companies integrating its technology, Parallel may well be at the forefront of the next great internet transformation.

What started as a setback for Parag Agrawal at Twitter may become one of the defining innovations of the AI era. If the past internet was built for people, the next one may indeed belong to machines—and Parallel aims to be its architect.

Author’s Note:
The rise of Parallel highlights an important shift in how we view the internet. For decades, humans were the web’s main beneficiaries. But as AI systems become more capable, we must rethink the digital infrastructure to support them too. Whether or not Parallel becomes the ultimate solution, its vision sparks an essential conversation: the future of knowledge-sharing must work for both people and machines.

Sources:

LinkedIn Post by Parag Agrawal (2024)
Parallel Web Systems Blog
Coverage from Bloomberg and TechCrunch on Parallel’s funding and launch

The Financial And Tech Literacy