Magic, a US-based startup specialising in artificial intelligence (AI) technology for software development, is in talks to raise more than $200m at valuation of approximately $1.5bn, reports Reuters.  

Investors, including Jane Street, are anticipated to join the funding round, which could see Magic’s valuation triple from its previous round, sources said. 

This is despite the startup having no revenue and no products currently for sale. 

Magic, which employs around 20 people, has raised $140m since its inception in 2022.  

Its backers include NFDG Ventures, co-founded by Nat Friedman and Daniel Gross, as well as CapitalG, a growth fund under Alphabet.  

When approached for comment, Magic declined, and Jane Street did not respond to inquiries, the publication said. 

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Magic’s fundraising efforts are indicative of a broader trend in the field of generative AI technologies, which are seen as a way to reduce costs associated with software developers.  

In April 2024, coding-assistant startup Augment raised $252m in April, while Cognition, which developed the AI-powered coding assistant Devin, secured $175m at a $2bn valuation in a round led by Founders Fund.  

The market’s potential is underscored by Microsoft-backed GitHub Copilot’s success, with GitHub reporting a 40% increase in year-over-year revenue in the latest quarter, driven by its 1.3 million paid subscribers. 

While existing products such as GitHub Copilot and OpenAI’s ChatGPT can assist with code completion, the next advancement is expected to be coding assistants that can independently design and write entire software applications.  

Magic and other startups are pursuing this goal by developing their own large language models tailored to coding tasks, although this requires significant investment in data, chips, and electricity. 

The funding Magic seeks will be channelled towards enhancing its models, particularly those that support long-context windows, allowing AI systems to process more data in a single query.  

According to sources, Magic’s approach enables it to understand and process large amounts of context simultaneously, surpassing the traditional “transformer model” used in models like OpenAI’s GPT.