Artificial intelligence coding tools such as Anthropic's Claude Code and OpenAI Codex showed signs of slowing after surging earlier this year, with independent AI researcher Rihard Jarc citing infrastructure limitations and softer enterprise spending.
“It’s clear that growth for coding tools such as Claude Code has decelerated from the pace it was since the start of the year. It might be compute- constrain related or due to many clients blowing their full-year AI budgets,” Jarc said in a post on X Wednesday.
Claude Code, OpenAI Codex Growth Appears To Plateau
Daily installs of AI coding tools showed that Claude Code witnessed a massive acceleration from January through April, but the growth curve has recently flattened around 120,000 installs per day instead of continuing to climb sharply, according to data from Microsoft's (NASDAQ:MSFT) Visual Studio Code marketplace.
OpenAI Codex also appears to be plateauing at about 60,000 after rapid gains.
AI Coding Tool Adoption Begins To Normalize
While demand for coding assistants remains strong, the latest data suggests growth may be normalizing as enterprises manage AI budgets, infrastructure constraints and intensifying competition among platforms.
Consumption of existing installations continues to grow, even though demand for coding tools has decelerated, Jarc added.
Investors Question Sustainability Of The AI Growth Trade
"As AI moves rapidly from promise to deployment, we believe the central challenge for investors is determining where it will sustainably create value, and where market narratives are moving faster than fundamentals," UBS analyst Steve Magill said in a note last week.
Magill added that investor sentiment around AI has swung between optimism and fear as markets attempt to price the technology's economic impact despite limited hard data.
The slowdown in AI coding tool adoption comes as investors question whether the generative AI boom can sustain the explosive growth that fueled rallies in software and semiconductor stocks earlier this year.
Analysts have pointed to rising costs, GPU constraints and intensifying competition as key challenges, while the AI coding assistant market becomes increasingly crowded.
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