AI Is Not Killing Software Developer Jobs. It Is Rewriting Them.

Tech hiring has cooled sharply since the pandemic-era boom, and it is tempting to blame generative AI for the whole story.   

According to recent findings from Indeed, data shows tech job postings have retreated and are down well below pre-pandemic levels, with analysts debating how much of the slowdown is macro conditions versus AI-driven efficiency. 

A bifurcation is underway 

We aren’t seeing the “end of coding,” but we are seeing a bifurcation in the skills companies are hiring for. The numbers tell a story of two different worlds: software engineer roles have tanked significantly over the last two years, yet programmer jobs remain where they were.  

Basically, if your value was just writing a boilerplate or fixing easy bugs, you’re in trouble.  

Based on the assessment data we are seeing at HackerEarth, analytical thinking, problem-solving, data visualization, and programming have emerged as top skills companies are hiring for. 

Back in 2022, a mid-level developer would spend all week grinding out maybe 1,000 lines of code for a couple of features. Today? That same developer starts Monday by wrestling with ambiguous requirements, spending hours just trying to turn a “vague vibe” into a real spec. They handle a larger portion of their time refining the specification. 

AI handles the boilerplate in minutes, but then the real work starts. The engineer spends time hunting for matching requirements to the code; the subtle logic flaws the AI may have missed. By midweek, they’re doing the heavy lifting of system integration, connecting parts where AI still gets lost. The output is higher, maybe five or six PRs a week, but they’re more complex and require way more “intent auditing” than before. 

It’s a shift from being a “writer” to being a “lead architect” of your own AI interns. An engineer’s job is to deliver code that is proven to work, so now they have to focus most of their time on evaluating the code. 

Reviewing AI code is a totally different beast. It’s “intent auditing.” You’re not checking for commas You’re checking for hallucinated dependencies or libraries that don’t exist. AI is famous for “security theater.” It’ll write code that looks secure but fails a real-world attack. We’re seeing a 4x jump in code duplication because AI just copy-pastes what works without caring about long-term debt. 

It also optimizes locally but ignores your overall system architecture. You have to bring the institutional memory to the table, knowing that we tried this two years ago, and it failed. You’re spending 200% more time on architecture alignment now than you used to. 

Who stays valuable among this software engineering shift 

Entry-level hiring at top firms is down 60%. If your job is just to translate a spec into code, then AI is coming for you. Maintenance programmers and generic feature developers are also in the splash zone. 

On the flip side, “Domain Experts” and “Staff-level Systems Designers” are more valuable than ever. Their work lives in that unverifiable space where there’s no objective right answer. Senior employment is stable, while junior roles are dropping.  

There is also a new kind of engineer emerging, the so-called “vibe coder.” This is enabling people experienced in different crafts to also produce software. For instance, a marketing manager could now use AI and generate code for a landing page, and it will do a decent job. 

The new core skills: Systems thinking, verification, and domain expertise 

Analytical thinking, problem-solving, and data visualization are the new gold standards, and you need to be a master of verification. “Systems-thinking” is not a buzzword. It is a daily practice. Think of it as looking at the “ripples” of a decision, not just the splash. An AI might suggest microservices because they’re commonly used, but a systems thinker looks at their 8-person team and realizes it’ll be a coordination nightmare. They see their team lacks Kubernetes skills or that the cost of maintaining a cluster, maybe $5k a month, isn’t worth a tiny latency boost.  

It’s about understanding the incentive structures of everyone involved, not just the code. AI can’t do that because it doesn’t understand your company’s team dynamics, your competitive landscape or the budget. An engineer with a “systems-thinking” mindset can think about these tradeoffs. 

Karpathy’s “generator-verifier loop” is real, and the speed at which you can prove AI output is safe, is now the main bottleneck. 

Then there’s “Domain Expertise.” You need to know your industry, whether finance, logistics or healthcare, deeply enough to provide the context AI lacks. Throw in some AI orchestration and a paranoid security mindset, and that’s the survival kit. 

But does that mean that foundational skills are not needed? The answer is a resounding no.  

A modern software engineer still needs to know the basics of code and design patterns, but to get the code proven to work, they need to fully understand AI-generated code. Companies continue to value these basic skills when hiring. 

Writing code requirements that AI can execute 

You have to stop being vague. “Make it fast” is useless; “API responds in <200ms” is a requirement. You need to be explicit about constraints like using specific libraries and document every weird edge case like null values or concurrent requests.  

Security is huge: don’t just say “secure it,” tell it to sanitize for SQL injection and set rate limits. And always specify what not to do. If a human engineer would have to ask you “what do you mean by this?” then the AI is definitely going to produce suboptimal code. You’re basically designing the objective function for the AI to solve.  

We have actually developed a platform to help engineers become good at writing such requirements. It’s called vibecodearena.ai. From the requirements provided by engineers, multiple LLMs produce code that is auto evaluated for metrics such as security, performance, quality etc. 

A 90-day playbook for mid-career engineers 

First, stop fighting the tools. Use Cursor or Copilot for 100% of your boilerplate for two weeks and see how much time you save.  

Then, pivot that time into systems thinking. Start writing design documents for everything you do. You need to show that you considered alternatives and trade-offs. 

Finally, build “proof artifacts.” These are projects where the AI did 70% of the output, but you provided the critical 30% of security and integration. The goal is to prove you’re an architect who uses judgment, not just an implementer who types fast. Companies will value engineers who are able to deploy the right LLM for the right task. 

Where AI helps most, and where it still breaks 

AI is basically an expert at anything you can fact-check instantly. CRUD operations, standard tests, documentation—it hits 80-90% automation there. But it doesn’t think adversarially, so it misses security holes, and it lacks the “operational context” of how users actually behave. It’s great at passing tests, but not so good at making high-stakes judgment calls. 

Agentic approaches are developing very fast and we are confident that in the future AI will use tools to execute, debug and keep iterating and improving the code until it arrives at an optimal solution. But it is not fully there, and it requires a human to guide it using prompts (the “vibe coding” way). After several steps of human and AI collaboration, the code that is written will typically work, but human judgement is still needed. 

The strongest counterarguments, and why the human role stays central 

The “doomers” say all jobs will vanish, but the rise of skills such as analytical thinking, problem-solving, data visualization, and systems thinking indicates that humans still have a key role to play in becoming the “eval” layer for the new engineering stack. AI isn’t just a productivity win if it’s creating 4x the code duplication and massive maintenance debt. 

That said, the AI agents are evolving fast. They will be able to run the “Generate-Verify” loop thousands of times and arrive at a solution, or maybe multiple solutions, and the AI agent can pick the one that is optimal for the business case. But it won’t be cheap. Companies will still have to pay for the token costs and have a system of checks and balances provided by senior engineers with systems thinking.