Hey, One specific skill you're prioritizing because of AI: Business Context Translation — the ability to understand messy client requirements and guide AI tools toward actual solutions, not just technically correct code. At Zibtek, we've seen AI generate perfect authentication systems when clients needed simple login bypasses. Our most valuable developers now bridge this gap between business needs and AI execution. They sit in client meetings, decode what's really needed, and translate vague requirements into specific prompts that produce useful code. This isn't about technical skills anymore; it's about interpretation and context-setting that AI simply can't do alone. How you spot it in candidates during interviews or evaluations: I give candidates messy, real-world scenarios like: "A restaurant owner says their 'online ordering is broken' but customers are placing orders successfully." Strong candidates don't jump to code — they ask clarifying questions about customer complaints, payment issues, or interface problems. At Zibtek, we do "client simulation" exercises where candidates interpret vague feedback and translate it into development tasks. We're looking for curiosity about business problems before technical solutions. The best candidates demonstrate they can work independently with AI tools without building features that completely miss the mark. Has AI changed HOW you hire developers, or is it just the same: Everything changed. We used to hire people who could code; now we hire people who can think, then use AI to code their thoughts. At Zibtek, we eliminated traditional coding tests for "problem-solving simulations" using real client scenarios with AI tools. We hire more "technical translators" — hybrids who bridge business needs and AI capabilities. Surprisingly, our best new hires often have less coding experience but stronger communication skills. Our interviews now include client role-playing and requirement gathering exercises. We're testing interpretation and AI collaboration skills, not algorithm memorization.
CTO, Entrepreneur, Business & Financial Leader, Author, Co-Founder at Increased
Answered 10 months ago
Designing the Future: The Skill Every Developer Needs in 2025 As a CTO in 2025, one of the skills I am beginning to prioritize while hiring developers is system design. With AI doing away with a lot of the mundane coding, what developers are really called on to do these days is design scalable, efficient and flexible systems. AI can write code, but it can't think critically about how different parts of a system will interact and scale in the future. That's where human judgment plays a role. Now when I hire I put more emphasis on candidates who can abstract away complex problems and design architecture with the future in mind. I like to ask them during interviews about specific examples in the real world when they would have to design a system from the ground up or optimize one. I will ask them to walk me through their "space of solutions" and the trade-offs they considered, how they dealt with scaling, and what they learned in the process. AI hasn't made hiring easier—it's actually altered how we evaluate developers. Technical knowledge is still important, but I increasingly search for problem-solving and big-picture thinking. AI tools are just that—tools. What does matter is how developers like that use those tools to solve tough problems that require a deep understanding of systems and design. The best candidates are those who understand how to use AI's strengths to complement their own creativity and expertise in solving real-world problems.
One skill we now actively look for is contextual thinking in debugging AI-assisted code. With tools like GitHub Copilot or ChatGPT helping generate a lot of logic, many junior and even mid-level devs rely too heavily on the suggestion without really understanding why it works or why it doesn't. AI is fast, but it's not always right. So debugging has become less about syntax errors and more about catching logic gaps, edge cases, and side effects AI might miss. We once had a strong candidate who aced all the DSA rounds and built apps using AI tools. But when we gave him a prompt to debug an AI-generated code that subtly misused async handling in Node.js, he couldn't figure out what the code was trying to do before trying to fix it. That's a red flag now. To test this, we give candidates a AI-written function and ask: "What do you think this was supposed to do?" Then, "What could go wrong?" We're not just testing debugging, we're testing how well they think like a human. So yes, AI has changed how we hire. We're not only hiring developers anymore, we're hiring AI editors, interpreters, and sense-makers.
We recently interviewed a developer for a healthcare app project. During a test, we handed over AI-generated code that looked clean on the surface. Most candidates moved on. But this one paused, they flagged a subtle issue: the way the AI handled HL7 timestamps could delay remote patient vitals syncing. That mistake might've gone live and risked clinical alerts. Thanks to that moment of scrutiny, we avoided what could've been a serious patient safety issue. We later found this fix would've prevented potential liability and saved us thousands in rework. Now, I look for developers who challenge AI outputs, ask 'what if this breaks?', and think about how code behaves in the real world. It's not about knowing more, it's about thinking deeper. If you're hiring in any field touched by AI, test how candidates respond when the machine is wrong. That's where the real talent shows up.
One skill we've started prioritizing in hiring is how well developers can think critically about AI-generated code. With tools like GitHub Copilot and ChatGPT in the mix, it's easy for someone to accept whatever output they get without questioning it. That's risky. We need people who can pause, review what the AI produced, and ask, "Does this actually fit our architecture? Is it secure? Could this break under certain conditions?" This ability to analyze and challenge AI suggestions is now just as important as writing clean code. In the interview, we often show the candidates a code snippet from the AI Tool and asks them to review it. The best ones do not just point out syntax issues. They notice design flaws, security gaps, or ways to improve maintainability. AI has not really changed who we hire, but it has changed how we evaluate. We now focus more on how candidates collaborate with AI, instead of treating AI as magic they can rely on blindly.
AI has profoundly changed how we hire developers, though perhaps in subtle yet powerful ways. It's undeniably less about assessing raw coding speed or memorized algorithms and far more about evaluating strategic thinking and adaptability. Our coding challenges now very often start with a fully functional AI assistant at the candidate's disposal. This allows us to keenly observe how candidates leverage the tool. Do they use it as a simple crutch, mindlessly relying on it, or do they see it as a powerful, intelligent extension of their own intellect, guiding it towards better outcomes? I actually remember one candidate who really impressed me recently, Ana, let's call her that. We'd given her this task, to build a pretty specific data processing pipeline, you know? Instead of just jumping straight into coding, she spent her first fifteen minutes—yeah, a full fifteen minutes—asking incredibly smart questions about data sources, expected volumes, how we'd handle errors, and even future scalability. Then, when she finally did use an AI tool, she made it quite clear she was concerned about a suggested for loop's efficiency for really large datasets. So, she intelligently re-prompted the AI, asking for a more optimized, vectorized approach. That right there, immediately showed me not just critical evaluation, but a profound understanding that truly went way beyond mere syntax. We're definitely shifting our focus from just assessing pure coding ability to evaluating deep problem-solving acumen, keen architectural foresight, and that nuanced, uniquely human ability to question, reason effectively, and adapt swiftly. Developers who can critically assess and skillfully guide AI, rather than just be passively guided by it, are the ones who will truly drive meaningful innovation and provide lasting, invaluable contributions in 2025 and beyond.
For us, critical thinking and healthy skepticism are now a priority. AI often gives "confident but wrong" answers — the developer must doubt. The key to quality is the ability to test assumptions. Without critical thinking, a developer can believe false results. At the interview, we demonstrate this skill as follows: we add errors or ambiguities to the task condition, we monitor whether the candidate asks clarifying questions, we ask to evaluate the code in terms of risks, not just functionality. The approach to hiring has changed: we give more open-ended tasks without clear instructions, the way of thinking becomes more important, not a template solution, we actively involve behavioral interviews to check resistance to "false confidence."
One skill that I've prioritized in hiring is product mindset. This is especially true for product engineers, who blend technical expertise with a deep understanding of business goals and user needs. AI tools like Copilot and Cursor have automated many routine coding tasks, freeing engineers to focus on more strategic aspects of product development. This shift requires a broader perspective as they now need to not only write code but also understand the business implications and user value of their work. I believe that the ability to see the big picture and align technical solutions with business objectives has become essential. When evaluating candidates, I look for their ability to understand and clearly articulate business goals and user needs, break down complex problems, define what actually needs to be done, and propose solutions from a product perspective rather than just a technical one. I want to see how they bridge the gap between business teams, designers, and analysts—connecting "what we want" with "how to achieve it"—and how they engage meaningfully in planning, prioritization, and strategic decision-making. In my experience, engineers who integrate business insights into technical decisions thrive in this environment. For example, one candidate I interviewed had collaborated with a Product Owner to assess market impact before developing a new feature, ensuring resources were invested wisely. I've also seen developers who focus solely on technical tasks without considering the broader business context struggle to adapt. The integration of AI in development has elevated product mindset from nice-to-have to essential, fundamentally transforming how I approach hiring to prioritize candidates who can think strategically and collaborate across functions to drive product success.
While AI is now capable of writing code efficiently, it still lacks the ability to frame the problems that need to be solved or determine exactly what to build. Because of this, the skill I prioritize most when hiring developers today, far more than before AI, is system design and architecture. My clients want candidates who can design robust, scalable systems that directly address core business problems. AI can help with implementation, but only humans can deeply understand business needs and translate them into effective architectures. That's why system design has become such a critical differentiator in 2025. To assess this capability, I use a blend of strategies during interviews. I ask open-ended system design questions and scenario prompts like, "How would you design for resiliency at scale?" I also incorporate whiteboard architecture problems to see how candidates structure their thinking. I listen for whether they approach the problem step by step, clarify assumptions, and thoughtfully consider trade-offs rather than jumping straight to code. I value candidates who take the time to truly understand the underlying needs before proposing a solution. The bottom line is that AI has shifted the focus of developer roles. Developers are no longer just responsible for typing out code; they're expected to act as architects and problem solvers who can frame problems clearly and design systems that align with those needs.
One specific skill we now prioritize is systems thinking. We look for developers who understand how code connects to broader architecture, user experience, and operational impact. Writing functions is one piece of the work. Designing durable, scalable solutions is what drives long-term value. Why it matters more now - AI handles basic code generation. Developers who think in systems create structure, reduce risk, and support growth. They make better decisions at every layer of the product. How do we spot it? We give real-world scenarios and ask candidates how they would design, build, and scale a solution. We listen to how they reason through complexity, define priorities, and anticipate impact. This shows how they approach real work. How AI has changed our hiring process : We focus on judgment, context, and tool fluency. Developers who understand how to integrate AI into their workflow move faster with precision. We prioritize those who lead with structure and understand the business impact of their technical choices.
One skill I'm prioritizing now is "critical thinking in code validation"—essentially, the ability to question and verify what AI tools produce rather than assuming it's correct. Why this matters now With AI assistants like Copilot or ChatGPT generating boilerplate and even complex code, developers are moving faster—but the risk of subtle bugs, security holes, or performance issues creeping in has gone up. What sets apart strong engineers now is their instinct to pause and ask, "Does this really do what I think it does? Could there be an edge case?" They're not just consuming AI outputs; they're auditing them. How I spot it during hiring I've adjusted interviews to include AI-assisted coding rounds. For example, I'll have the candidate use an AI tool to scaffold a solution, then ask them to explain potential failure points or suggest tests to validate it. I also like giving them slightly flawed AI-generated code and asking them to debug or refactor it on the spot. Those who can calmly dissect and improve on AI's output stand out. Has AI changed how I hire? Yes. I'm less focused on speed typing or memorizing syntax and more on design, reasoning, and their ability to collaborate with AI tools. The best candidates treat AI like a junior teammate: helpful, but not infallible. One great example: I hired a mid-level developer who impressed me by catching a subtle concurrency bug in code Copilot wrote. She sketched out how she'd test and refactor it—showing the exact mindset I now value.
One skill I now prioritize is systems thinking, the ability to understand how different parts of a solution work together and how to design for long-term performance and flexibility. AI tools like GitHub Copilot and ChatGPT have boosted developer productivity by up to 55%, but this shift has moved the real value away from just writing code. [ Source: https://resources.github.com/enterprise-octoverse/ ] Developers now spend more time debugging, integrating, and making architectural decisions. So, we look for people who think in systems. At Vitanur, we've seen cases where the code looked perfect but broke under real-world pressure due to overlooked issues like caching or database bottlenecks. That's why we don't rely only on technical tests anymore. In interviews, we focus on: 1. Real-world problem solving: "How would you scale a CRM module or reduce loading times during peak usage?" 2. Design reviews: "We show a flawed system plan and ask candidates how they would fix or improve it." The strongest candidates explain their thinking clearly and understand the trade-offs. We're also cautious with candidates who can only talk about tools or frameworks but struggle to explain the why behind their decisions. That's a red flag, especially now. According to the 2025 Manpower Report, 74% of employers are struggling to find developers with the right mix of technical and strategic thinking. [Source: https://go.manpowergroup.com/talent-shortage] So, in the hiring process, after AI rose, we also use modern assessment approaches, like simulation-based assessments. These better show how someone builds in context
What we prioritize now is a developer's ability to work effectively with AI tools. At our mobile app development company, tools like GitHub Copilot have become standard, so we've started prioritizing prompt engineering as an important skill when hiring developers in 2025. For example, the AI might generate UI code or suggest API handling methods. But not everything AI writes is usable in a project. Developers need to review, rethink, and sometimes rebuild it. That skill of knowing when to trust the AI and when to take control is something we actively test in interviews. In interviews, we ask candidates to solve small tasks using Copilot or a similar tool, and we watch how they guide it. Do they tweak their input using logic to improve the output? Do they spot mistakes in the suggestions? One of our candidate impressed us by iteratively refining a single prompt until the AI produced nearly production-ready UI code. That kind of thinking is now more valuable than raw speed. Overall, AI has definitely changed how we hire developers and what we look in developers now. We're no longer just looking for who can code fast; we look for developers who are good at decision making and know how to guide AI and how to debug when the suggestion goes wrong.
I find myself specifically looking for uniquely human skills like being a fast learner and thinking outside of the box. In the AI realm, one thing is certain right now and that is that AI is developing at an incredibly rapid pace. Tech companies are dealing with so much pressure to be the best that they are putting out new programs and tools so quickly (often before they are really all that ready for public use, to be honest) that it can be hard to keep up. So, I need developers who excel with that fast pace of AI development, who can learn new programs and practices quickly and anticipate those changes instead of always just reacting to them.
We now prioritize "AI-augmented code review"—the knack for questioning and tightening AI-generated stubs for logic, security, and resource leaks. In interviews, we have candidates spin up half a page of Copilot code, then hunt down and fix three injected bugs; those who immediately add proper error handling and edge-case checks stand out. This shift has made our new hires true AI collaborators—capable of challenging suggestions, preventing subtle runtime issues, and spending more time building features than firefighting.
As the owner of recruiting agency, the one skill I value most in 2025 when hiring developers is critical thinking, particularly for judging and validating AI-recommended code. While AI coding assistants speed up development, evaluating suggestions critically and even identifying elusive bugs or inefficiencies is now a necessity. This is a more critical skill nowadays because developers can no longer just accept what the AI comes up with—developers must now be able to question, edit, and tweak AI outputs to meet project needs safely and reliably. I observe this skill by having candidates examine in interviews AI-generated code snippets and request that they review, debug, and beautify them within a time limit. Artificial intelligence has changed the way I hire developers by shifting the focus from raw coding ability to deeper problem-solving and judgment capabilities that cannot be replicated by AI.
In 2025, the one skill I'm prioritizing when hiring developers is Emotional Intelligence (EQ) — especially the ability to collaborate effectively and think critically during debugging. In the age of AI, many technical tasks like code generation, unit testing, and even architectural suggestions can be assisted — or in some cases, executed — by AI tools. But what AI cannot replicate is human understanding, empathy, and contextual judgment. It cannot comfort a frustrated client, navigate a tense team meeting, or dig deep into a problem with a blend of logic, intuition, and communication. Great developers today must not only write efficient code — they must work across disciplines, communicate clearly, ask the right questions, and align technology with human needs. When bugs arise (and they always do), AI can point out possibilities, but it's a human's curiosity, calm, and collaborative mindset that leads to root-cause analysis and sustainable solutions. EQ helps developers embrace feedback, handle uncertainty, and build strong relationships with teammates and stakeholders — all of which are essential in high-stakes environments. In short, AI will amplify technical output, but human EQ will define successful teams. At our startup, we're building more than just scalable systems — we're building people who can thrive in this new, hybrid world of humans and machines.
After working with 100+ service businesses implementing AI workflows, I'm prioritizing **systems thinking over pure technical ability**. The developers who thrive now understand how their code fits into larger automated processes, not just isolated functions. I had two candidates tackle automating our client's payroll system that reduced errors by 70%. One built a perfect algorithm but couldn't explain how it would integrate with existing HR workflows. The other designed a solution considering data flow, error handling, and human oversight points - they got the job immediately. During interviews, I give candidates a real scenario from our client work: "Design a system where AI processes invoices, but humans handle exceptions." The winners map out the entire workflow first, identifying where automation helps versus where human judgment stays critical. They think like business operators, not just code writers. The shift is dramatic - I'd rather hire someone who understands process automation and can architect reliable systems than a coding wizard who builds in isolation. When your AI-generated code needs to integrate with legacy systems and handle real business workflows, systems thinking becomes everything.
CEO here building AI-powered systems for nonprofits through KNDR.digital. We've generated $5B in donations and consistently deliver 800+ donations in 45 days using AI automation. The skill I now prioritize is **stakeholder empathy mapping** - developers who can understand the human emotions behind data patterns and design AI systems accordingly. When we built our donor engagement platform, our strongest developer didn't just optimize for conversion rates. She recognized that a grieving family donating to a cancer nonprofit needs completely different AI-driven follow-up sequences than excited parents supporting their kid's school fundraiser. During technical interviews, I present real scenarios from our nonprofit clients. I'll describe how a disaster relief organization needs to process 10,000 emergency donations in 24 hours while maintaining donor trust. The best candidates immediately consider the emotional state of donors, regulatory compliance for crisis fundraising, and how to structure AI responses that feel genuinely human rather than automated. AI can analyze donation patterns and optimize campaigns, but it can't inherently understand that a $5 donation from someone's grocery money deserves the same thoughtful acknowledgment as a $5,000 corporate gift. Developers who can encode this emotional intelligence into AI systems create technology that actually serves people instead of just processing them.
CEO here building GrowthFactor.ai, an end-to-end real estate platform for retail brands. We've processed $1.6M in cash flow decisions and evaluated 800+ locations in 72 hours during major retailer bankruptcies like Party City. The skill I now prioritize is **business context translation** - developers who can interpret messy real business requirements and guide AI toward practical solutions. When we built our AI agent "Waldo" for site evaluation, our best developer didn't just code the demographics analysis. He recognized that a 12,000 sq ft western wear store has completely different traffic patterns than a bookstore, then structured the AI training to account for those nuances. During interviews, I present candidates with actual customer problems from our retail clients. Last month, I described how Cavender's needed to evaluate Party City locations for western wear stores. The standout candidate immediately asked about parking space requirements, competitor proximity, and regional demographic differences - then explained how they'd structure AI prompts to account for these variables rather than building generic location scoring. AI generates plenty of code, but it can't understand that a fireworks retailer's site selection criteria are totally different from a wellness brand's needs. The developers who succeed now are the ones who can bridge that gap between AI capability and real business complexity.