AI Won’t Take All Jobs — Yet: Developers Report Major Limitations in Rust and Tauri Ecosystems
PoriPurno News Desk | August 01, 2025
The tech world has been abuzz with fears that Artificial Intelligence will soon devour white-collar jobs, especially in software development. But how real is this threat—today?
While AI tools like ChatGPT, Gemini, and Claude have shown impressive performance in mainstream development environments like JavaScript, React, and Python, their limitations become glaringly obvious when applied to more complex or emerging stacks like Rust and Tauri. Recent hands-on experience from developers working in these domains suggests that AI still has a long way to go before it can truly replace skilled human programmers.
When AI Falls Short: The Rust & Tauri Struggle
For developers transitioning from JavaScript to Rust, the difference in AI support is stark. “When I worked with React, AI often gave decent solutions. Sometimes it even made me worry about job security,” said one developer working on full-stack cross-platform apps. “But once I started working with Tauri and Rust, the AI’s performance became borderline unusable.”
AI tools struggle not only with basic Rust syntax, but also with more advanced patterns and real-world application logic. In some cases, even after multiple attempts, tools like ChatGPT fail to produce syntactically correct code—let alone functional or optimized solutions.
The situation becomes worse when integrating Tauri, a framework used to build native desktop applications using Rust at the backend and web tech at the frontend. Given Tauri’s relative newness and the scarcity of high-quality documentation or StackOverflow posts, AI models frequently generate hallucinated functions, outdated APIs, or completely fictional modules.
Debugging AI Code: A Time Sink, Not a Time Saver
Instead of saving time, developers often spend extra hours debugging AI-generated code that turns out to be fundamentally broken. “Sometimes it’s not just wrong — it’s so far off that you start questioning where the model even got the idea from,” said another developer who recently migrated a project from Electron to Tauri for better performance and memory efficiency.
These frustrations point to a larger issue: while AI performs well in popular, mature, and well-documented ecosystems, it tends to fail in niche, low-level, or cutting-edge domains. In Rust—a systems-level language known for strict compiler rules and memory safety—the margin for error is low, and the AI’s guesswork quickly becomes a liability.
The Broader Picture: AI Hype vs Ground Reality
Despite the tech media’s relentless AI hype, the practical use cases for AI-generated code remain largely confined to boilerplate generation, basic scripting, and well-documented environments.
That being said, these limitations don’t mean AI won’t improve. Given the rapid pace of model updates and domain-specific fine-tuning, it’s likely that models will eventually catch up in Rust and Tauri as well. But for now, AI is no replacement for deep domain expertise.
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Conclusion: Human Developers Still Indispensable
The panic over AI “eating jobs” is, for now, exaggerated—especially in specialized or evolving tech stacks. AI might speed up certain tasks, but it lacks contextual understanding, fails in low-resource frameworks, and can’t match human intuition when it comes to debugging, architecture design, or production-level robustness.
In the words of one seasoned developer:
“In Rust and Tauri, the AI isn’t replacing me. If anything, it’s reminding me how valuable my experience still is.”
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