Muhammad Tayyab December 10, 2025
Collected at: https://datafloq.com/why-the-hardest-coding-languages-are-still-difficult-for-ai-in-2025/
There is no doubt that AI is transforming programming. Various AI tools, such as ChatGPT, GitHub Copilot, and DeepMind AlphaCode, are helping developers write and optimize code more efficiently. Still, in 2025, while dealing with the hardest coding languages, AI faces issues. Such languages need deep knowledge of programming, understanding, and careful reasoning. Although AI excels at predictable syntax and patterns and is well-suited to languages like Python and Java, it faces challenges with languages that are logic-intensive and abstract.
This article explains why AI is still struggling with the hardest programming languages by discussing its limitations with the 10 most difficult languages. Also, why is human expertise essential to master these languages?
Let’s get into the details!
Why Some Coding Languages are Challenging For AI
We are listing some key factors here that contribute to the difficulty of some hardest programming languages:
- A few scripting languages, like F# and Haskell, running on abstract concepts, make reasoning harder for AI.
- Programming languages relying on recursion, rules, and backtracking are conceptually different from usual languages and programming.
- The dependency on hardware needs precise instruction management.
- The complex syntax of certain hardest coding languages gives rise to unpredictability for AI.
Usually, AI relies on large datasets and pattern recognition. Coding languages with less training data, abstract logic, or intricate syntax are more challenging, specifically for the advanced AI models.
A Snapshot of AI Performance and the Difficulty Level of Languages
Here’s a quick overview of the AI accuracy of the 10 most difficult programming languages and their difficulty level:
| Programming Language | Difficulty Level | AI Accuracy for the Language (%) | Some Common Use Cases |
| Malbolge | Considered an extremely hard one for AI to write | 20 | Usually a conceptual or puzzle language Rarely used in real world projects |
| Assembly | Very high difficulty lt works close to hardware | 45 | Firmware Microcontrollers Kernel components |
| Prolog | Advanced one Logic-driven rather than imperative | 50 | Natural language tasks Early AI systems Reasoning engines |
| C++ | Complex syntax Manual memory handling | 65 | Performance intensive software Game engines |
| Haskell | A steep learning curve due to pure functional style | 60 | Financial modeling Research Academic tools |
| Rust | High difficulty but modern safeguards | 70 | System-level software Some game engines Secure apps |
| LISP | Unique structur Macros need deep understanding | 55 | AI algorithms High-level tooling Symbolic computing |
| Scala | Functional and OOP mix adds complexity | 65 | Large-scale data systems Distributed computing frameworks |
| Perl | Hard to read and master because of flexible syntax | 60 | Automation scripts Legacy backend systems |
| Erlang | A challenging concurrency model | 50 | Real-time communication apps Telecom-grade messaging |
10 Most Difficult Programming Languages in 2025 for AI
Let’s have a detailed understanding of how each given coding language is difficult for AI to handle, even in 2025:
Malbolge
The extremely complex nature of Malbolge makes it highly difficult to master. It also makes it nearly impossible to write; this language uses a confusing notation system and self-altering code. Even the most skilled full-stack developers struggle to program in Malbolge. Also, the obscure execution rules and extremely sparse training data make it challenging for AI.
Assembly Language
Direct interaction with CPU memory and instructions is offered by Assembly language. Providing such unparalleled control leaves no space for error. While developers work with Assembly, they have to handle memory addresses manually. Here, AI struggles because mistakes can crash systems.
C++
Combining generic, procedural, and object-oriented programming, C++ is a multi-paradigm scripting language. It’s a strong but highly complex coding language. AI mostly faces the challenge of optimizing C++ code or debugging legacy features.
Prolog
It’s a logic-based programming language and is used for natural language processing and AI. It operates on logical inference and rules instead of sequential execution. Those AI models trained in imperative languages find Prolog’s backtracking and recursion approach difficult to manage.
From sequential logic to declarative reasoning, the transition is a considerable barrier for AI to work properly with Prolog.
Haskell
This one is purely functional and one of the hardest programming languages, focusing on type inference, immutability, and lazy evaluation. Haskell introduces concepts like functors, monads, and algebraic data types-difficult to conceptualize for AI. Also, it is challenging for AI due to different functional paradigms in comparison with imperative code.
Rust
Rust is a modern coding language, and it focuses on performance and safety. Its advanced ownership and borrowing model, although it prevents common bugs, requires a new way of thinking about memory management. It is difficult to implement safe Rust patterns for AI without violating strict rules.
LISP
LISP focuses on treating code as data and recursion and is widely used in symbolic computing and AI research. For both humans and AI, the parenthetical syntax of LISP is challenging and unique.
Scala
This is also one of the hardest coding languages, combining object-oriented and functional paradigms. Its strong type inference, support for concurrent programming, and abstractions are behind its versatility and difficulty. Its performance nuances on the JVM and high-level abstractions make AI models struggle.
Perl
Perl is mostly used for system administration, text processing, and legacy enterprise apps. Although it is highly versatile, it is not easy to learn. Here, multiple correct solutions for the same problem cause trouble for AI in predicting the best approach.
Erlang
This one is an ideal language for concurrent, fault-tolerant systems. The actor-based model of Erlang is complex for both AI and humans. Also, a smaller developer community, intricated syntax, and limited learning resources are a challenge for AI for generating reliable code autonomously with Erlang.
What Future Holds for AI and the Hardest Coding Languages
Soon, although AI-powered coding will continue to evolve, full automation of the hardest coding languages is a distant dream. Here are some predictable future approaches:
- AI and human collaboration to generate and validate code.
- For niche languages, specialized datasets
- Besides generating code, explainable AI can help understand why a solution is crucial
- AI adaptation with evolving programming paradigms
Human Expertise Matters
Companies that hire full-stack developers who are equipped with deep programming knowledge hold a competitive edge even in 2025. Humans are important:
- To make design and architecture decisions
- For Performance optimization
- To take care of the development of innovative algorithms
- For handling debugging issues
Conclusion
Although AI has reshaped programming, the hardest coding languages are a challenge even in 2025. They require abstraction, contextual reasoning, and human intuition. If developers master these languages, they remain absolutely necessary, and AI collaboration makes the toughest programming tasks manageable.
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