Why Data Structures & Algorithms is so Underwhelming Nowadays

Prologue - A Quick Story

The other day, I was practicing implementing linked lists—specifically, a singly linked list and a doubly linked list. I started with the singly linked list by jotting everything down on paper, visualizing how appending, deletion, and various operations would work with nodes. With my notes in hand, I eagerly began coding my singly linked list. However, when I ran my test class, it failed. I quickly discovered there were numerous edge cases related to head and tail operations that I hadn’t accounted for, despite my visual intuition and careful planning.

Things didn’t improve with the doubly linked list, either. I had a clear image in my head, wrote everything down, yet the implementation turned out to be horrendous. Initially, I resisted using AI because I wanted to tackle it on my own. But sure enough, when I plugged my request into a chatbot, it generated a perfect doubly linked list implementation, complete with my coding style, that passed all my test cases.

I often use AI for my coding projects, not just in DSA but also in machine learning, data analysis, GUI development, and blogging. However, with all these fields—except DSA—the immediate support I receive from AI never feels like it undermines my work. For instance, in data analysis, I can simply prompt the AI with my questions and visualize the results. In machine learning, I specify which model to evaluate and what parameters to tweak, and I always feel in control, never overshadowed by AI. Even though I don’t know basic Python syntax, AI helps bridge that gap, allowing me to bypass the intricate syntax of countless packages. In short, I remain motivated to dive deep into the math and manual analysis within these fields; here, the code doesn’t feel like the heart of my work.

In stark contrast, once AI generates a flawless solution for a DSA problem, I’m left with a sense of dread. The excitement that initially drove me to tackle DSA vanishes, and I lose all motivation to engage with it further.

This experience leaves me with a few intriguing revelations:

• Why do I feel this way?
• Why do I only experience this sentiment regarding AI-generated code in DSA?
• What does DSA actually represent in today’s context?

This post aims to explore these questions and critique the current approach to teaching and comprehending DSA. I believe it offers various perspectives that can help connect the current state of DSA with the innovations in AI coding. My goal isn’t to undermine the significance of DSA or those who enjoy it; rather, I seek to provide a realistic view of the field, particularly in light of its current state and how it could be taught and viewed more effectively. Furthermore, I also want to speculate about the affect of recent innovations of AI in relation to DSA, and it’s implication to other fields outside of DSA.

I, Deadend Playground - Fun in a Cage

My journey into Data Structures and Algorithms (DSA) began with the overwhelming praise it receives from various sources. It is often hailed as the “heart of computer science,” a vital component that supposedly guarantees a job in the tech industry. I was enticed by promises of learning how to write efficient programs, think abstractly, and, most ambitiously, solve problems holistically. However, I now see this as somewhat deceiving.

The current landscape of DSA education feels primarily focused on coding interviews, often fostering a toxic environment. Many individuals in this space look down on those who struggle, creating a culture that thrives on intimidation rather than encouragement. This toxicity seems to be deeply ingrained in the nature of DSA itself, which often prioritizes competition over collaboration.

In my view, DSA should resemble fields like machine learning, where exploration and intuition-driven decision-making are encouraged. If the essence of DSA does not support this kind of exploration, then it begs the question: should it even be considered a standalone field? Rather, it might be more fitting as a foundational knowledge area within computer science.

The concept of abstract thinking within DSA often feels misleading. It is framed in the context of DSA jargon, making it appear abstract when, in reality, it is rooted in specific patterns and solutions that can feel overly rigid. This limitation, the failure to branch out and seek broader applications, creates a sense of confinement, as if I am navigating a cage rather than an expansive playground of ideas.

I recall spending countless hours grappling with challenging problems, only to realize that my inability to find solutions stemmed from not being aware of established techniques that seemed almost tailor-made for those specific challenges. This often left me feeling disconnected, as many of these insights are confined to the DSA realm and do not translate to broader applications or experiences.

To truly invigorate the learning experience in DSA, we must shift towards solving problems using natural language, where individuals are motivated to reason through challenges at their own pace. This approach could foster a supportive environment where constructive feedback is the norm, encouraging exploration rather than perfection.

In reflecting on my experiences with DSA, it becomes clear that the current educational landscape often resembles a cage—one that restricts creativity, collaboration, and genuine exploration. The allure of DSA lies in its potential to equip us with essential problem-solving skills and abstract thinking. However, when the focus shifts predominantly to coding interviews and toxic competitiveness, it stifles the very essence of learning.

This confinement leads to a disconnect between the rigid frameworks of DSA and the dynamic nature of real-world problem-solving. Instead of fostering an environment where curiosity and intuition thrive, we find ourselves navigating a maze designed by pre-existing solutions that may not even apply outside the realm of DSA.

To reclaim the excitement and purpose that should accompany our engagement with DSA, we must advocate for an approach that encourages exploration beyond the cage. By promoting natural language problem-solving and a culture of support and constructive feedback, we can transform the way we learn DSA. It’s time to unlock the cage and allow our curiosity to flourish, enabling us to appreciate the beauty of algorithms and data structures not just as tools for interviews, but as fundamental components of a vibrant and innovative field.

II, The Dilemma of False Creativity

In the realm of Data Structures and Algorithms (DSA), a pervasive issue emerges—what I call “false creativity.” DSA problems, especially on social media and coding platforms, often carry an air of exclusivity and cleverness, presented with a smug undertone that belies their true nature. While these exercises are labeled as opportunities for problem-solving, they frequently feel crafted with the solution in mind. Rather than encouraging genuine creativity, many DSA problems seem reverse-engineered to reinforce specific methodologies or patterns, leaving little room for an intuitive understanding of the problem itself.

The structure of these problems often disconnects them from real-world scenarios, relying instead on abstracted numbers and operations stripped of any practical context. Without analogies to actual systems, DSA exercises fall short of fostering adaptive thinking or preparing learners for the unpredictable nature of real-world challenges. A problem like “finding the longest increasing subsequence” or “maximizing stock trading profit” may seem applicable but is almost always presented as a sequence of numbers, divorced from the fluctuations and irregularities of a genuine trading system. This sanitized approach discourages learners from thinking dynamically, reducing the exercise to a pattern-matching game rather than an open-ended exploration of coding skills.

This disconnect becomes even clearer when comparing DSA to fields like machine learning, where challenges are often posed within the complexities of real data, inviting learners to develop insights and build on their intuition. By contrast, DSA feels more like an intellectual puzzle box, where “solutions” reward rote memorization and technique recall instead of fostering true ingenuity. As beginners work through problems labeled as “easy,” “medium,” or “hard,” they’re implicitly nudged to recognize specific patterns rather than thinking through the problem’s underlying principles. The labeling obscures the reality that each problem is designed to fit a particular solution, reinforcing a backward approach where solutions drive the problems instead of organically addressing actual needs.

This structure ultimately creates a false sense of creativity and mastery. DSA exercises often reward learners for spotting clues within a predetermined framework rather than cultivating adaptive problem-solving skills. Unlike open-ended exploration in other domains, DSA exercises tend to lead learners through a narrow maze of techniques, recycling familiar methods in slightly altered forms. This closed-loop system traps learners in a superficial sense of accomplishment, reinforcing an insular way of thinking that does not translate well into real-world coding.

The fundamental question here is whether DSA should be structured this way. If the objective of problem-solving is to build adaptable thinkers, then DSA’s design seems counterproductive. By anchoring problems to specific techniques, the system limits learners’ ability to explore beyond predefined paths, presenting an illusion of creativity while keeping true problem-solving within narrow bounds. It truly grinds my gears when I hinges on the question: “Should DSA even be a field like it is today in the first place?”.

III, The Demotivating Dread - When AI masters a Field

As AI begins to seamlessly conquer Data Structures and Algorithms (DSA), a once-daunting proving ground for software engineers is losing its symbolic value. DSA problems, long seen as benchmarks for programming skill, are often highly structured, crafted puzzles that don’t reflect real-world complexity but rather encourage rote memorization and pattern recognition. Ironically, this approach makes DSA feel more like an exceptionally clean-labeled dataset in machine learning terms—designed to maximize AI’s learning efficiency but not necessarily to cultivate genuine human intuition or problem-solving.

The structured, formulaic nature of DSA creates an ideal playground for large language models (LLMs), which can solve these problems with precision and speed. What was once a rigorous, challenging task is now a straightforward computational exercise for AI. In this environment, AI highlights the rigid, repetitive patterns that underlie many DSA problems, revealing how little these tasks require in terms of creative thinking or adaptability. This growing disparity creates a sense of “demotivating dread” for learners: why invest hours in mastering something AI can handle instantly, without effort?

The machine intuition promoted by DSA increasingly resembles a framework designed primarily for machines, where abstract methodologies are built for computational efficiency first and human understanding second. This shift is evident in how DSA concepts are often presented—more akin to the precise, axiomatic structure rather than to an intuitive approach that resonates with human learners. As a result, learners are left to draw connections retroactively, grappling with a system that feels increasingly alien to the flexible, analogical thinking typical of human problem-solving. This creates a disconnect, where the original intent of nurturing adaptive thinking becomes overshadowed by an emphasis on fitting neatly into a machine-centric paradigm.

The effect extends beyond individual learners to the hiring process, where DSA is used as a measure of coding proficiency. If AI can perform flawlessly on these tasks, what value do DSA interviews actually hold? The prevalence of AI shows that DSA doesn’t necessarily gauge one’s ability to solve real-world problems but rather one’s familiarity with a specific type of machine-idealized logic. For those who once viewed DSA as a pathway to sharpen their problem-solving skills, AI’s dominance underscores the artificiality of these challenges, emphasizing that DSA often serves the strengths of machines better than the nuanced skills of human intuition.

Rather than pushing forward critical thinking or fostering adaptability, DSA now seems to promote a “machine intuition” that feels divorced from the messiness and ambiguity of real-world problem-solving. If DSA is to remain relevant in this AI-driven landscape, the focus must shift toward fostering broader, adaptable skills that machines cannot easily replicate—skills that require genuine human insight, creativity, and flexibility. Only by evolving beyond its current formulaic structure can DSA reclaim its purpose, providing learners with challenges that develop enduring, real-world competencies.

Epilogue

The rise of AI poses a pivotal question: what does the future hold for Data Structures and Algorithms (DSA)? While AI excels at solving well-defined, structured problems, it often struggles with the ambiguity and adaptability required for real-world challenges. This suggests that DSA, when taught with a focus on human intuition, critical thinking, and problem-solving, can still be a valuable asset.

Perhaps the future of DSA lies in a shift away from rote memorization and towards a more holistic approach that integrates concepts with real-world applications. By focusing on the underlying principles and their practical implications, we can empower learners to think critically, adapt to new challenges, and leverage AI as a tool rather than a replacement.

Ultimately, the true value of DSA lies in its ability to cultivate a mindset that embraces complexity and encourages creative solutions. By embracing this human-centric perspective, we can ensure that DSA remains a relevant and empowering field in the age of AI.