Why AI Cannot Solve Complex Coding Problems

Seattle Web Design
9 min readNov 29, 2023

AI, or artificial intelligence, is the recreation of human intelligence in machines and computers to accomplish human tasks. It contains a range of capabilities, including input and output systems, machine learning, and information filtration. AI significantly impacts coding and the tech world, as various applications use AI in their systems.


However, innate challenges hinder its ability to solve complex code successfully. While AI has made strides in aiding coding tasks, its limitations in handling complex code are noteworthy. AI systems heavily depend on patterns and data from past experiences. In scenarios where code complexity exceeds the training data, AI may need help to provide solutions accurately and up to date. Complex and innovative problem-solving, abstract thinking, and understanding nuanced requirements remain challenges for AI in coding. We see this in problems that humans solve that AI cannot.

Complexity of Coding Problems

A complex coding problem is characterized by its intricacy and the involvement of numerous interrelated aspects. When a task demands a high level of conceptual thinking, demands a deep understanding of diverse technologies, or requires innovative solutions, we can assume it is a complex coding problem. “It measures how much effort is required to comprehend code.”

Several factors contribute to a coding problem’s complexity, including the codebase’s size and structure, dependencies between different lines or blocks of code, and the dynamic nature of software. Code with various interconnected modules increases complexity, as modifications in one section may have consequences in another. Using diverse technologies introduces complexity, requiring a broader skill set to address challenges effectively. Problems where changes in one part of the codebase have non-linear effects on other components add a layer of intricacy, making prediction and debugging more challenging.

We devised five general steps to solve a complex coding problem. We must first understand the problem we are trying to resolve. Clearly defining the problem, its requirements, and its limitations is crucial. When we understand the situation, we then have to break it down. Decomposing the problem into smaller, more manageable sections simplifies the solution process. Next, we devise an algorithm design. Coming up with different strategies to address each area increases efficiency and controls the scale of the project. Flowcharting and drafting what kind of code lines we need before implementing them into the project is essential, no matter the scale. Following this, we apply our plans in the code, ensuring it adheres to best practices and coding standards. Lastly, we must run our code and debug accordingly. Thoroughly testing the code for different scenarios ensures the solution meets the specific requirements.

Real-world examples of complex coding problems can include, but are not limited to, security in finance systems, optimizing performance in AI applications, and integrating various software systems in a large enterprise.

The Human Brain — How Humans Interpret and Solve Complex Problems

The human brain is so similar yet so different from AI. We both can process, learn, and solve problems, but how we do it, and the types of problems we solve differ. The human brain applies a comprehensive approach to complex problem-solving. We combine patterns, think critically, recall memories, and administer creative problem-solving. Humans can comprehend, interpret, and navigate challenging circumstances through these complicated cognitive processes.

One way that the brain effectively uses what it already knows to solve complex problems is pattern recognition — in which the brain already excels. It recalls past experiences and knowledge to identify similarities and differences. We can then handle the problem by relating new problems to old ones and then to familiar solutions. This process helps in breaking down complex issues into more manageable units. One example of this is doing your laundry. We separate the colors and the white and use different types of detergents according to which one we wash first. We recognize patterns, and by doing so, we change the course of our actions to cater to the scenario.

Critical thinking is another way the human brain approaches complex problems. Critical thinking uses objective analysis to further understand the situation and the paths we can pursue. This means analyzing information about both the problem and possible solution. We evaluate and organize our thoughts, assessing the possible biases or flaws in our thinking. By questioning assumptions and considering alternatives, we can navigate through complex problems with more clarity.

Critical thinking is a concept with many layers and steps to it. To successfully approach problems with critical thinking, we need past experiences and practice to build a solid foundation to handle any problem given to us.

Recalling memories goes hand in hand with critical thinking. The brain relies on memory recall to retrieve information from past experiences to aid in understanding the problem and applying what we already learned to it. These explicit memories are both a guide and a blueprint for your solution. We use memory recall in various settings, including exams, recreating structures such as buildings, telling stories, and even locating items.

Above all, the human brain has something special — creativity. Creativity is the cornerstone of the brain’s approaches to solving complex problems. It involves open-mindedness and thinking outside traditional boundaries. We, as humans, are used to exploring unconventional solutions and adapting based on where the problem takes us. Creativity allows us to devise unheard-of approaches and overcome problems with unprecedented solutions.

The Human Brain Solving the Creative Music Problem

One coding problem the human brain can solve is the creative music problem. Although music has always been seen as a form of human art, it is also considered a form of a complex coding problem. Music is highly technical, requiring intricacy and a high level of complex thinking. Creating music needs conceptual thinking and intertwined aspects, such as the bass, melody, rhythm, and more. Therefore, under the definition of a complex coding problem, music can be considered one of the most complex problems of all.

While the human brain also relies on what it’s encoded in music previously, there have also been instances where the product of the mind was completely original and novel. Somewhere after the year 1033, the musician Guido d’Arrezo created the quarter note and the system based around it. He created a notation still used today, showing how conceptual and creative the human brain can be.

We can also see how the human brain can go beyond what was given to it from the existence of different music genres. EDM, jazz, and pop are all so different yet are connected by the umbrella concept of music.

AI Algorithms — How AI Interprets and Solves Complex Problems

AI Algorithms use a multimodal approach that includes machine learning models, data representation, feature extraction, and pattern recognition to navigate and solve complicated problems. First, these algorithms transform data from the real world into a format they can understand. They then extract relevant features to highlight important parts of the problem. By identifying patterns within the data, all algorithms discern underlying structures crucial for understanding and decision-making.

In machine learning, supervised learning modes optimize parameters to forecast results, and probabilistic inference helps with reasoning in the face of uncertainty. Neutral networks, highly skilled at learning complicated data representations, are a key component of deep learning’s efficacy in solving tasks like speech and image recognition. Additionally, reinforcement learning allows algorithms to learn through interaction with an environment, demonstrated by applications such as AlphaGo mastering the board game Go. Algorithms for search and planning, ensemble approaches, and optimization strategies improve AI’s problem-solving abilities in various fields, showcasing AI’s flexibility and resilience in deciphering and solving complex problems.

AI Solving the Creative Music Problem

There are various examples of code AI cannot solve. The most infamous one would be the same creative music problem mentioned above.

Despite its ability to create complex and captivating melodies, AI-generated music has fundamental constraints due to its reliance on data. Large databases of previously recorded music serve as the foundation for the creative process, from which AI extracts patterns, structures, and styles. However, it needs to possess the true creativity, intuition, and human experience understanding that is necessary to create completely original and ground-breaking artistic expressions. When referring to AI-generated music, the term “original” must be interpreted to mean rearranging or merging previously learned patterns from prior songs.

Human curators who create algorithms, modify datasets, and direct decision-making fundamentally impact AI’s creative output. While AI can be a valuable tool for experimentation and collaboration with human composers, the ongoing debate centers around its capacity to achieve true innovation and emotional depth compared to human-created music.

Below is the code provided when ChatGPT, a well-known AI application, is tasked with the prompt: “Using code, write a unique music piece.”

Comparing the Human Brain and AI

The human brain has the upper hand when it comes to complex coding problems. As previously displayed, there are several differences between AI and the human brain’s processes and analyses, which is an intricate problem. Four key differences that AI portrays are the lack of intuition and creativity, adaptability, interpreting skills, and understanding of context.

Creativity is one of humankind’s most valuable traits when solving complex problems. We can create and produce ideas and solutions that may have been unexpected. There is an absence of this in AI. As AI and subfields of AI, such as machine learning, rely on data collection to operate, the solutions they construct can only go so far. AI falls behind compared to humans, who can creatively devise solutions, adapt strategies, and think outside the box.

Its dependency on data collection also makes AI vulnerable to novel situations, such as before pandemics and new virus outbreaks. AI systems are trained on data and patterns fed to them, making them proficient in known conditions. However, when presented with a never-before-seen problem, AI falters. If you asked an AI system back in 2020, “What is the cure for the Coronavirus,” it would not be able to give an accurate result. Compared to this, the human brain’s ability to adapt and apply existing knowledge from other experiences AI would not associate together with makes it shine. This gives the human brain a clear-cut advantage in solving complex coding problems that may not conform to existing patterns.

Human developers can also explain their thought processes and decisions while also being able to provide transparency and responsibility. AI systems do not function like this, making their behavior and decision-making processes extremely difficult to understand. We see this in different code types, such as functions and loops, which make understanding and editing code much more straightforward.

Lastly, understanding the broader context of a coding problem is another area where AI could be improved while humans excel. The human brain can interpret and incorporate information within the context of the situation or problem to better understand it. To create a well-thought-out solution, we can consider aspects of a problem, like what trade-offs are valuable. With explicit programming, AI can achieve this high level of decision-making, making it easier for AI to solve complex issues where that is needed.


AI advancements have made remarkable impacts on the coding and technological world. While AI has its strengths, we must recognize that it encounters problems and realizes it is not all-knowing. It faces challenges in fields such as creativity, understanding the context of a situation, interpretability, and adaptability. We see this difference when looking at music composition from AI and humans.

These specific drawbacks of AI make it difficult for AI to understand and solve complex code, as the intricacy of complex code calls for skills beyond analyzing mass data. This illustrates the limitations of technology, as the most advanced technology cannot solve dynamic and novel situations, yet the human brain can. Each has its domains in which it excels, and it’s just that AI specializes more in input and output while the human brain is in consciousness. Ultimately, while AI can be applied to solve many types of problems, nothing can replace the human brain when approaching complex coding problems.