In the early 1950s, Grace Hopper coined the term “compiler” and built one of the first versions with her A-0 system. The compilers that followed abstracted away machine code, letting programmers focus on higher-level logic instead of lower-level hardware details. Today, AI coding assistants are enabling a similar change, letting software engineers focus on higher-order work by generating code from natural language prompts. Everyone from big tech to well-funded startups is competing to capture this shift. Yesterday Google announced Antigravity, their new AI coding assistant, and the day before, AWS announced the general availability of their AI coding tool, Kiro. Last week, Cursor, the standout startup in this space, raised $2.3B in their series-D round at a valuation of $29.3B.
Two lines in Cursor’s press release stood out to me. The first:
We’ve also crossed $1B in annualized revenue, counting millions of developers.
This disclosure means Anysphere Inc. (Cursor’s parent company) is the fastest company in history to reach $1B in annual recurring revenue (ARR). Yes, faster than OpenAI, and faster than Anthropic.
Source: Yuchen Jin, Twitter/X, 2025
Engineers are trying every new AI coding tool. As a result, the AI-coding tool market is growing exponentially (+5x in just over a year). But it’s still early. As I wrote in Why Some AI Wrappers Build Billion-dollar Businesses, companies spend several hundred billion dollars a year on software engineering, and AI has the potential to unlock productivity gains across that entire spend.
Software developers represent roughly 30% of the workforce at the world’s five largest market cap companies, all of which are technology firms as of October 2025. Development tools that boost productivity by even modest percentages unlock billions in value.
In my view, this nascent market is splitting based on three types of users.
Source: Command Lines, wreflection.com, 2025
On one end is Handcrafted Coding. These are engineers who actively decline to use LLMs, either because of skepticism about quality or insistence on full control of every code. They argue that accepting AI suggestions creates technical debt you cannot see until it breaks in production. This segment continues to decline as the quality of AI coding models improves.
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