Three.js Water Pro Author Considers Quitting After AI Replicates 90% of His Work
Three.js veteran Dan Greenheck, creator of the Water Pro shader library, reveals he may leave the ecosystem after AI tool Astra matched his years of water-simulation work and a user replicated 90% of his demo from a video, prompting a crisis over the eroding value of deep technical specialization.
Why a Three.js Veteran Is Considering Leaving
On September 21, Dan Greenheck — author of the commercial Three.js Water Pro library and a recognized expert in WebGL, shaders, and real-time water simulation — published a long-form post opening with: "I think it's time to hang up my Three.js hat." He clarified the decision is not final; he plans to finish Water Pro v4 first.
The Trigger: Astra and Rapid AI Replication
Greenheck observed multiple water effects produced by Astra that match or exceed Water Pro quality.
A user fed his demo video into an AI agent and reproduced roughly 90% of the visual result .
Client referrals have dropped noticeably.
Revenue from courses and plugins has begun to suffer from AI competition.
The Three.js community is seeing a surge of "Vibe Coding" — games and demos assembled largely through prompt-driven AI agents rather than hand-written code.
Loss of Craft and Cognitive Side-Effects
Greenheck describes the shift in his daily work: instead of wrestling with Shader math, water physics, and performance optimization — problems that took months or years to master — his routine has become "write requirements, wait for output, tweak prompts, rerun." While throughput is higher, he finds the process far less engaging. He also reports a subjective decline in focus, comprehension, and critical thinking after prolonged reliance on AI-generated code.
Broader Implication: The Collapsing Technical Moat
The article frames Greenheck's case as representative. Frontend 3D development ( WebGL, Shader, Three.js) historically carried a high entry barrier; developers who invested years built defensible expertise. AI is now aggressively lowering that barrier. For newcomers this is enabling, but for specialists whose livelihood depends on accumulated depth, the disruption is severe. The piece closes with an open question: when technical implementation becomes trivially replicable by AI, what will differentiate programmers?
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