Over the years, I’ve made it a deliberate habit to periodically engage with the job market.
Not because I’m constantly looking to leave. Not because interviewing is entertaining. But because it’s one of the most reliable ways to stay calibrated.
Engineering organizations can easily become self-referential. Teams develop internal standards. Companies evolve their own expectations of what “good” looks like. Over time, those standards can drift from the broader market without anyone noticing. One of the clearest real-time signals of where the industry is heading isn’t found in opinion pieces or hiring reports — it’s embedded in interview processes.
And over the last year, that signal has changed dramatically.
AI hasn’t just altered how we write software. It has quietly rewritten how engineers are evaluated.
The Evolution of the Technical Interview
Looking back, technical interviews have moved through distinct phases.
In the early days, interviews focused heavily on narrow, well-defined problems. You were given a compact task and expected to solve it logically while writing the code manually, often under time pressure. Implementation mattered a great deal. In some cases, even syntax accuracy carried noticeable weight. The implicit question was straightforward: can you write correct code in front of me?
The evaluation surface was relatively small. If you could reason through the problem and implement it cleanly, you were in a strong position.
Over time, interviews evolved into something more balanced. Companies began asking for small end-to-end solutions within a constrained domain. You still needed to write working code, but there was growing interest in how you structured it, how you handled trade-offs, and how you thought about edge cases. The signal began to shift from pure implementation toward engineering judgment — but implementation was still central.
What we are seeing now feels like a more fundamental shift.
With modern LLMs and agent-based coding tools, the weight placed on raw implementation has noticeably decreased. Code can be generated quickly. Boilerplate can be scaffolded in minutes. Entire flows can be prototyped with surprising speed.
As a result, interviews are increasingly structured around system-level thinking. The focus is less on whether you can produce code and more on how you reason about architecture, scalability, trade-offs, and long-term maintainability. The code still exists, but it no longer carries the same evaluative weight.
The signal has moved up a level.
The Compression Effect
One of the clearest patterns is what I think of as compression.
Tasks that previously might have required several days of effort can now be meaningfully executed in one or two hours with modern tooling. But companies haven’t simply shortened assignments. They’ve expanded them.
Instead of asking candidates to solve an isolated problem, interviews increasingly involve building a broader end-to-end slice of a system. You may be expected to think about API structure, data modeling, edge cases, and potential scaling concerns — all within a compressed timeframe.
The implementation becomes an artifact. The real conversation centers around why you designed it the way you did.
When the technical interview discussion begins, the emphasis is rarely on line-by-line correctness. Instead, the questions revolve around architectural decisions, trade-offs, alternative approaches, and how the system would evolve under different constraints.
In other words, the visible output is code. The evaluated skill is judgment.
Ownership in the Age of Agents
There’s an important tension here.
Modern tools can generate large portions of working code quickly. That’s powerful. But in the interview setting, you are still expected to defend architectural choices, explain design decisions, and connect those decisions clearly to the implementation.
If your understanding is shallow, this becomes difficult very quickly.
It’s one thing to prompt an agent to produce a solution. It’s another to fully internalize that solution, reason about its limitations, and articulate its trade-offs with clarity. The newer interview formats expose weaknesses in system design fundamentals and conceptual clarity far more directly than older formats did.
It’s no longer sufficient to produce something that works. You have to intellectually own it.
That ownership is increasingly what’s being tested.
The Automation of the Hiring Funnel
Parallel to the shift in evaluation criteria is a structural shift in how interviews are conducted.
More processes now begin with a short introductory call that primarily explains the steps ahead. This is often followed by a screening form or quiz, and then an asynchronous technical assessment. Candidates may be asked to record a walkthrough of their solution rather than presenting it live.
That recording can be transcribed and analyzed before any in-depth human conversation takes place. Previously, automation lived mostly at the resume screening stage. Now it is extending into technical evaluation itself.
Human interaction is increasingly reserved for higher-level discussions: deeper technical reasoning, alignment on collaboration style, and culture fit. From an efficiency standpoint, this is entirely rational. Senior engineering time is expensive, and processes are being optimized accordingly.
But it also means communication is no longer a secondary skill. The ability to clearly explain complex reasoning — without live prompting — becomes part of the filter.
What Has Actually Changed
If I had to summarize the shift in a single sentence, it would be this:
Technical interviews are moving from testing implementation to testing judgment.
Implementation still matters. Fundamentals still matter. But writing code is no longer the scarce capability in the room.
What’s scarce — and therefore increasingly valuable — is clear reasoning, strong mental models, system-level thinking, and the ability to articulate trade-offs coherently.
The presence of AI hasn’t reduced the bar. It has shifted it.
For engineers, this means that leaning solely on implementation fluency is no longer sufficient. If system design foundations are weak, that weakness becomes visible very quickly. If AI tools are used without understanding the architectural implications of their output, that gap surfaces during explanation.
For hiring leaders, there is a parallel responsibility. As processes evolve, care must be taken not to over-index on polish or presentation at the expense of depth. Broader, faster assessments risk selecting for confidence rather than competence if not designed carefully.
The challenge on both sides is the same: identifying genuine engineering judgment in an environment where execution has become dramatically easier.
Closing Reflection
Periodically engaging with the market has reinforced one consistent observation for me: the industry’s definition of signal is evolving.
AI hasn’t removed the need for engineers. It hasn’t diminished the importance of strong fundamentals.
But it has made shallow implementation skill easier to replicate.
The differentiator is increasingly architectural clarity, sound judgment, deep understanding, and clear communication.
Code remains essential.
But more and more, code is the output.
The interview is about the thinking behind it.
And that shift is already well underway.