Fired by Elon Musk, Ex-Twitter CEO Parag Agrawal Launches ‘Deep Research API’ to Rival ChatGPT

When Elon Musk acquired Twitter in 2022, one of his first major actions was to oust then-CEO Parag Agrawal. Now, three years later, Agrawal is back in the media—though this time not in social media and instead in the space of artificial intelligence. On 15 August 2025, Agrawal unveiled his latest venture, Deep Research API, a Palo Alto–based startup aiming to beat humans and OpenAI’s GPT-5 on industry benchmarks. 

 This launch not only represents a personal rebirth but also a new page in the global AI race. Agrawal's project is not just a chatbot—it is marketed as a NLP tool that will change the way companies and researchers think about automating knowledge-heavy work.

Who is Parag Agrawal? A Quick Background

Parag Agrawal’s career has been anything but ordinary.

• 2006–2009: Researcher roles at Microsoft and Yahoo.
  
  

• 2009–2010: Joined AT&T Research Labs.
  
  

• 2011: Started at Twitter as a software engineer.
  
  

• 2017: Promoted to Chief Technology Officer (CTO).
  
  

• 2021: Named CEO of Twitter, just before Musk’s takeover.
  
  

• 2022: Fired by Elon Musk after the Twitter acquisition.
  
  

Agrawal attended the Atomic Energy Central School in India; earned a Computer Science degree at IIT Bombay; and, received his PhD in Computer Science at Stanford University.

After years in big tech, he is betting big on AI now.

What is Deep Research API?

Agrawal's announcement on LinkedIn mentioned that Deep Research API is expected to outperform both humans and the best AI models, including GPT-5, on two of the hardest benchmarks in AI research.

He wrote:

“We launched our Deep Research API—it’s the first to outperform both humans and all leading models including GPT-5 on two of the hardest benchmarks.”

Unlike ChatGPT, which focuses on conversational AI for general users, Deep Research API is aimed at enterprises, researchers, and startups who need automation in knowledge-heavy workflows.

Core Focus of Deep Research API

• Research automation - Automating tasks that usually need skilled human analysts.
  
  

• Enterprise use cases - Powering workflows for startups, corporates, and public institutions.
  
  

• Accuracy beyond human level - Claims of exceeding human-level accuracy on daily research tasks.
  
  

Agrawal also said his company already powers millions of research queries every day.

Deep Research API vs ChatGPT: What’s Different?

Here’s a quick comparison of Deep Research API and ChatGPT as they currently stand:

Key takeaway: Agrawal isn’t trying to build “another ChatGPT.” Instead, he’s positioning Deep Research API as an enterprise-grade research automation engine, designed to solve high-value, knowledge-driven problems.

Why Parag Agrawal is Betting on Enterprise AI

The enterprise AI market is booming. According to Gartner, global enterprise AI spending is projected to surpass $200 billion by 2030. While many of the consumer AI tools make the newspaper headlines, its the enterprise adoption that drives sustainable revenue.

Possible Use Cases for Deep Research API

• Startups - Automating competitive research, market analysis, and technical documentation.
  
  

• Enterprises - Knowledge management, internal research, and compliance automation.
  
  

• Public institutions - Large-scale data analysis, academic research, and policy planning.
  
  

By focusing on this niche, Agrawal’s startup avoids directly competing with consumer-facing products like ChatGPT and instead moves into the enterprise AI infrastructure space—where contracts are bigger and stickier.

The AI Race: OpenAI, Google, Anthropic vs Deep Research API

The launch of Deep Research API adds another competitor to the already crowded AI race:

• OpenAI (ChatGPT, GPT-5) - Market leader in generative AI, with Microsoft backing.
  
  

• Google DeepMind (Gemini models) - Pushing multimodal AI for search and enterprise.
  
  

• Anthropic (Claude models) - Building “constitutional AI” with safety-first principles.
  
  

• Meta (Llama models) - Focusing on open-source AI for scale.
  
  

• Mistral & Cohere - Strong challengers with smaller, faster models.
  
  

Agrawal’s startup is competing against giants with billions in funding. But being lean and specialized could be an advantage—especially if Deep Research API keeps outperforming benchmarks that even GPT-5 struggles with.

What This Means for India’s Tech Talent & Global AI

Agrawal’s Indian roots are a big talking point. He’s part of a long list of Indian-origin tech leaders in Silicon Valley (Satya Nadella at Microsoft, Sundar Pichai at Google).

Why this matters for India:

• Talent inspiration: Agrawal’s journey shows how Indian engineers are shaping the global AI ecosystem.
  
  

• Startup lessons: Indian AI founders may see opportunities to create “enterprise-first” AI solutions instead of chasing ChatGPT clones.
  
  

• Collaboration potential: Deep Research API could open doors for Indian companies to partner on research automation.
  
  

Risks and Challenges Ahead

While Agrawal’s bold claims are attracting attention, Deep Research API faces several hurdles:

• Proving benchmarks: OpenAI, Anthropic, and Google have deep credibility. Agrawal must back his claims with transparent data.
  
  

• Funding race: Competing with AI giants requires billions in R&D. Can Parallel Web Systems raise enough capital?
  
  

• Adoption curve: Enterprises move slower than consumers; breaking into corporate workflows is tough.
  
  

• Trust & ethics: AI systems must address issues of bias, security, and privacy before large-scale adoption.
  
  

Agrawal may have a first-mover advantage in research automation, but the question is: can he scale it fast enough?

Key Takeaways for Investors, Developers, and Businesses

1. For investors:  Deep Research API could be an early-stage bet in enterprise AI, but faces stiff competition from OpenAI, Google, and Anthropic.
   
   

2. For developers: Opportunity to build on top of the Deep Research API if the company expands its API ecosystem.
   
   

3. For businesses: If claims hold true, it could reduce costs in research-heavy workflows by outsourcing tasks to AI with higher-than-human accuracy.
   
   

4. For Indian tech talent: Agrawal’s move highlights that AI entrepreneurship doesn’t just belong to the big four; there’s room for lean challengers.
   
   

Final Word

Parag Agrawal represents a story of second chances. After being terminated by Elon Musk, he is back once again with a daring venture into artificial intelligence that strives to not only rival ChatGPT, but move past GPT-5 in research activities.

While Deep Research API needs to live up to its many claims at scale, the launch has ushered in a new era of the world-wide AI race; where enterprise automation might prove to be more salient than consumer chatbots.