DSA vs. Real-World Coding Interviews — and Where Does AI Leave Us?

For a long time, I have come across the debate on the right way to vet technical candidates: should interviews focus on Data Structures & Algorithms (DSA), or on real-world coding problems?

Two sides of the debate:
Some argue that DSA style interviews are not always applicable to actual engineering work, they can feel tricky, abstract, and sometimes unfair when trying to identify great software engineers.
Others argue that real-world coding interviews can focus too much on expertise in a particular language, framework, or tool. But a good engineer should be able to reason well, regardless of the tools.
Based on my professional experience vetting other candidates as well as going through vetting myself, I lean toward the former.

Why DSA still makes sense
When you really think about it, DSA is mostly about optimization: compute (time complexity) and storage (space complexity). Even though in real life you may never implement a "Trapping Rain Water" algorithm, the mindset of thinking in terms of optimizing these two limited resources is what matters.
This is similar to the classic debate about why students have to go through complex mathematics in school. It's rarely about solving differential equations in daily life rather, it's about training the mind to think critically about real-life challenges.

Why this matters at scale
After several years working on systems that operate at global scale, performance and efficiency optimization remain some of the most challenging and important problems.

• A small query optimization can sometimes have more business impact than a new feature
• The right data structure can make seemingly impossible storage constraints manageable
• Low latency can tremendously improve user experiences
• Real-time systems that deliver insights to drive sales, prevent harmful content before it reaches users, and support many other critical use cases

All of this is made possible not just by designing systems that work, but by designing systems that work efficiently.

So where does that leave us?
DSA may not be perfect, but in my view it is a better way — or at least it "was."
With AI now reshaping how engineers work, the industry seems to be looking to a new set of skills. TokenMaxxing 😊
I am curious to see what this stabilizes into.