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  5. DeepMind Alumni Startup Inherent Claims its 27B AI Agent Beats OpenAI and Anthropic in Science Tests
AIMAG 7
•
2026-08-22•2 min read

DeepMind Alumni Startup Inherent Claims its 27B AI Agent Beats OpenAI and Anthropic in Science Tests

Zubiqo Take
QuoteThreads

“The real breakthrough here isn't the science simulation, it's realizing you can use a cheap 27B model to orchestrate OpenAI's expensive coding tools and call it a proprietary product.”

DeepMind Alumni Startup Inherent Claims its 27B AI Agent Beats OpenAI and Anthropic in Science Tests
📷 Image Source: TechCrunch

Executive Summary

  • •London AI lab Inherent claims its Faraday agent outperforms Anthropic and OpenAI models at reproducing scientific papers.
  • •The agent runs on a 27 billion parameter Qwen 3.6 model while utilizing OpenAI's GPT-5.5 Codex for actual coding tasks.
  • •The startup recently emerged from stealth with a $50 million seed round and plans to double its King's Cross headcount this year.

Community Sentiment

1-Tap Vote

Key Developments & Data

London startup Inherent claims its new AI agent outperforms OpenAI and Anthropic at replicating research. The Google DeepMind alumni lab recently emerged from stealth with a $50 million seed round. Its Faraday agent runs on the Qwen 3.6 model, operating with just 27 billion parameters. The system claims to beat Claude Opus 4.8 and GPT-5.5 at independently reproducing published scientific papers. Rather than building internal coding tools, Faraday relies directly on OpenAI's GPT-5.5 Codex. "What was most interesting to us about this was not so much the result of beating those frontier agents — which of course we liked — but was actually the way we went about building this." — Edward Hughes Everyone wants to build an artificial scientist, but wrapping a cheap 27-billion parameter model around existing API calls proves that workflow architecture is what investors are actually paying for.

Zubiqo Strategic Assessment

Primary Impact

Frontier AI labs and the broader artificial intelligence research market, as smaller agentic workflows prove capable of beating monolithic models at specialized, high-value tasks.

Strategic Shift

The transition from relying purely on massive, general-purpose frontier models to utilizing smaller, highly orchestrated models trained via reinforcement learning for specific agentic workflows.

The Ripple Effect

Enterprise AI buyers will increasingly look to fund or adopt smaller, task-specific agentic architectures rather than paying a premium for frontier models to handle end-to-end reasoning tasks.

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This intelligence assessment is generated by Zubiqo's AI for informational purposes only.

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Read original on TechCrunch
Zubiqo MethodologyAI Synthesis

Synthesized from linked market reporting using AI extraction under Zubiqo's editorial standards. Have a correction? Contact our desk.

Event Magnitude7 / 10
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