Critical thinking, creativity, and foundational programming are still invaluable
If you’re leading technology or product in your organization, you’re already seeing how fast generative AI (GenAI) is moving. It’s automating parts of software development. It’s good at writing boilerplate code. It’s fast. But let’s be clear: this doesn’t change what actually matters. The best software still comes from skilled people with solid judgment, an understanding of context, and the ability to think critically.
Lee Faus, Field CTO at GitLab, made this point directly: “The syntax doesn’t matter anymore.” He’s right. Syntax, the structure of code, is becoming less relevant. GenAI can handle that. What it can’t replace is the ability to think through systems, solve hard problems, and apply creativity to product development. These are human skills. And they remain non-negotiable in any engineering culture that cares about quality and scale.
As your teams adopt new tools, you don’t want them losing their edge. GenAI gives you speed, but speed without direction doesn’t really get you anywhere valuable. Critical thinking is how developers understand why something works, not just how to build it. Creativity gives them the ability to step outside the default and figure out what should be built next. And foundational programming keeps it all technically solid.
If you’re scaling product development, train your teams to see GenAI as a support system, not a substitute. Push for deep thinking within your org. You don’t want developers who can only follow prompts. You want people who shape the strategy behind what gets built.
GenAI is just a tool. The value still comes from the person using it.
Prompt engineering has emerged as a critical competency in modern software development
Most executives looking at AI right now are focused on outcomes, faster development, reduced cost, and higher throughput. That’s fine. But getting meaningful output from GenAI requires precision. And that’s where prompt engineering comes in.
Prompt engineering is a discipline. You’re asking an AI to generate software, and how you ask determines what you get. Vague input leads to vague output. Developers who understand how to craft accurate, specific, and context-rich prompts will always get better results than those who treat AI like a search engine.
Lee Faus at GitLab highlighted this frustration. Developers get poor code responses, not because the AI failed, but because they didn’t give it direction. Matching the right prompt with the right data source, especially tools like GitLab Duo or retrieval-augmented generation (RAG) that can incorporate up-to-date knowledge from places like Stack Overflow, drastically improves code accuracy and relevance.
At an operational level, the implication here is real. You can’t expect legacy workflows to translate over cleanly. Prompt engineering relies on natural language, not syntax. But it still demands clarity, context, and strategic thinking. And if you’re scaling teams globally, language precision becomes even more important.
For engineering leaders, this should trigger some immediate focus areas: invest in structured training for prompt formulation, create shared prompt libraries, and encourage cross-functional collaboration around AI input techniques. You do this, and your team can raise the quality of the codebase in every sprint.
GenAI is best used as a learning tool rather than a crutch
GenAI opens up new possibilities, but how your teams use it matters. If you position AI purely as a shortcut to outputs, you’re missing the actual advantage. The real value is in using it to learn faster, think better, and create more strategically.
Lee Faus, GitLab’s Field CTO, put it clearly, developers who are making the most of GenAI today are the ones using it to connect ideas, not replace effort. They’re assembling knowledge, seeing patterns, and amplifying their capabilities, not hiding behind machine-generated responses. These developers are shaping products that ship faster and make more sense to the customers.
Research from Ethan Mollick at Wharton, working with Procter & Gamble, shows that AI becomes significantly more impactful when treated as a teammate. It helps with critical thinking and complex problem-solving. That’s a different way of framing AI in the enterprise, not as a cost-cutting feature but as a capability expander. And that mindset shift aligns with long-term value, not short-term metrics.
Executives who assume GenAI is a replacement mechanism will end up with teams that automate the wrong things and underperform when it comes to innovation. Encouraging your engineers to explore, test, and learn from the results of each AI-assisted interaction leads to stronger teams and smarter systems.
There’s also a talent perspective. When GenAI becomes just another layer in the stack, alongside terminal commands and version control, it has potential to raise the standard of the entire dev pipeline. That only works if people are expected to grow alongside the tool, not get buried under it. The learning curve becomes a competitive advantage. Businesses that prioritize it stay ahead. Those that don’t, won’t.
Traditional programming must complement prompt engineering
GenAI changes how code gets written, but it doesn’t eliminate the need for traditional programming. It speeds things up, generates a lot of baseline code, and gets developers off the ground faster. That’s useful. But performance, reliability, and deep system logic still depend on people who understand the fundamentals.
Prompt engineering helps guide AI tools. It frames the goal, sets the constraints, and improves output consistency. But once the prompt delivers, it’s experienced engineers, and their understanding of actual code behavior—who make it work in production. Without solid knowledge of traditional programming, the risk of instability increases.
This is where a lot of organizations are misjudging the balance. They assume prompt engineering will flatten the skill curve. It doesn’t. It shifts emphasis. You still need high-level technical review. You still need developers who can debug, assess performance trade-offs, and understand why the AI-generated solution might not scale.
GenAI assistants like GitHub Copilot are advancing fast. According to GitHub’s internal tests, early adopter teams completed tasks 55% faster. That’s not trivial. But as with any compounding gain, it only works when paired with judgment. Code that functions at a basic level isn’t the same as code that’s production-ready, secure, and maintainable over time.
For C-suite leaders, the takeaway is straightforward: Invest in both. Build capabilities in prompt engineering, but continue to reinforce traditional code literacy. The most effective teams will know how to work across both disciplines. GenAI enhances delivery speed, but traditional development ensures that what ships actually works.
Team dynamics and development workflows are evolving to incorporate prompt engineering
Integrating GenAI into engineering workflows isn’t just about adopting a tool, it requires rethinking how teams operate. Prompt engineering introduces a level of variability that traditional development cycles aren’t optimized for. You’re no longer just writing code; you’re guiding intelligent systems to generate it. That changes the structure of collaboration across engineering teams.
Prashanth Chandrasekar, CEO of Stack Overflow, highlighted how complexity escalates when AI systems require context across multiple variables. Developers now need to understand not just what code to produce, but how to craft input that aligns with business goals, integrates external knowledge sources, and produces grounded, usable output. When this isn’t done well, productivity slows.
This impacts workflow structure. Teams need to get comfortable with iterative learning inside their pipelines. What worked for one sprint might not apply to the next release cycle if the AI model has changed. Prompts require continuous refinement, and that process depends on feedback loops, inside the product and within the team.
McKinsey’s research backs this up: AI adoption often stalls when companies underestimate the importance of human oversight and fail to build internal competence around prompt iteration and management. Without process adjustments, AI integration introduces risk instead of efficiency.
If you’re leading product or engineering, this is about operational readiness. Build systems that support rapid experimentation with prompt structures. Encourage documentation and transparency around best practices. Give your senior engineers the bandwidth to mentor prompt use, not just push code. When workflows evolve with AI in mind, velocity improves and your team’s strategic output expands. GenAI requires alignment at the process level, and that starts with leadership.
Overreliance on GenAI, especially by junior developers, can hinder essential skill development
AI is making some parts of development easier, but that ease comes with a cost if you’re not careful. For junior engineers, there’s a real risk: they might start relying on GenAI for answers without understanding the logic behind the solution.
Both Lee Faus, Field CTO at GitLab, and Prashanth Chandrasekar, CEO of Stack Overflow, have flagged this as a growing concern. GenAI can write code, generate commit messages, fill in gaps. But without internalizing why the output looks the way it does, or whether it’s actually the right solution, junior devs stop learning. Worse, they stop questioning. And when something breaks, they can’t fix it.
This has operational consequences. Teams that skip over foundational understanding don’t scale well. They struggle with edge cases, regressions, or anything outside the model’s comfort zone. That creates overhead as more experienced engineers have to step in and clean things up. And in the long term, it weakens your talent pipeline.
Charity Majors, a respected voice in software engineering, called out this exact issue. Software remains an apprenticeship-based industry. It takes time and hands-on experience to develop sound engineering intuition. You don’t accelerate that by automating it. You accelerate it by pairing learning with real ownership.
As an executive, it’s your call on how you set the standard. Make it clear that GenAI is a tool, not a replacement for understanding. Encourage code walkthroughs. Create space for debugging sessions. Push for technical reviews that check output and question the reasoning behind it. That’s where real growth happens. And that’s how you build resilient, capable teams that are ready for what’s next.
GenAI is democratizing programming by enabling non-technical colleagues to participate in development processes
One of the most immediate shifts driven by GenAI is accessibility. Technical workflows that were once limited to formally trained software engineers are now within reach for non-technical professionals. This is already happening inside organizations that are moving fast.
With natural language prompts driving automated code generation, product managers, designers, marketers, and others can now contribute directly to prototyping, documentation, and even light coding tasks. Lee Faus, Field CTO at GitLab, points to this shift as a strategic advantage, teams no longer rely solely on engineering to move an idea forward. Input is more distributed. Progress is faster.
Prashanth Chandrasekar, CEO of Stack Overflow, notes that even within his own teams, domain experts with limited coding experience are already pushing functional prototypes to support new business models, such as pricing tests or internal tools. This means your organization’s ability to innovate isn’t just tied to engineering headcount, it’s tied to how well you enable cross-functional collaboration.
This is further reinforced by Greg Benson, Professor of Computer Science at the University of San Francisco and Chief Scientist at SnapLogic. He sees a future where an entire generation can create software solutions without traditional programming backgrounds. While foundational knowledge still matters for scaling and long-term architecture, AI-assisted tools enable a broader range of people to participate in credible software development efforts.
If you’re a C-suite leader, this is a signal to build internal systems that empower, and regulate, this kind of access. Create frameworks that let cross-functional teams contribute safely, while still allowing senior engineers to maintain oversight. Encourage prototyping across departments, while keeping clear boundaries between exploration and productization. GenAI is unlocking new contributors. The smart move now is to design your software culture so that it gains from that expansion without losing coherence or control.
Expert oversight helps AI-generated code meet reliability, maintainability, and quality standards
GenAI can generate code quickly, but speed alone doesn’t guarantee reliability. Without meaningful review, the output might function on the surface, only to break under scale, integration, or edge cases. This is a problem if left unchecked.
Bill Harding, CEO of GitClear, made this clear in public commentary: while GenAI is capable of producing plausible code, it does not consistently support code reuse or safe modification. This introduces technical debt. When organizations treat AI-generated output as production-ready without review, they sacrifice long-term maintainability in exchange for short-term progress.
Enterprise caution around these issues is growing. Prashanth Chandrasekar, CEO of Stack Overflow, noted that organizations are wary of fully committing to automated code integration because trust in AI output is still limited. Stack Overflow’s own Developer Survey shows that while enthusiasm is high, only 43% of developers are confident in the accuracy of AI-generated code. Another 31% actively question its reliability.
For engineering leaders and executives, this isn’t something to delegate blindly. You need policies, testing frameworks, and human validation layers that are built into your CI/CD pipelines. AI might deliver code faster, but it won’t understand your security model, your architecture constraints, or your business logic unless someone adds that contextual check.
If the end goal is stability at scale, then expert oversight is operational risk management. Align your QA, DevOps, and engineering leads around a shared understanding: GenAI delivers output, but humans deliver quality.
Combining human validation with AI-generated content
GenAI output improves when paired with high-quality, validated data. Without that, the risk of inaccurate or outdated results increases sharply. Human oversight, combined with trusted knowledge sources, is how you raise the reliability standard.
Stack Overflow has built tools intentionally for this. Its Enterprise products and Knowledge Solutions integrate over 15 years of community-validated knowledge directly into the AI workflow. These are curated, controlled environments designed to ensure that both teams and AI systems reference correct, tested information.
Prashanth Chandrasekar, CEO of Stack Overflow, emphasized that organizations moving fastest with GenAI are the ones combining system automation with trusted, vetted content. When the AI is grounded in accurate knowledge, the quality of its output increases. This leads to fewer errors in production, faster onboarding for new developers, and clearer knowledge sharing across teams.
For product and technology leaders, this approach means building AI usage into existing knowledge management strategy. You don’t want GenAI generating random results. You want it operating with access to your organization’s trusted answers, inline with how your developers already work. That connection, from private knowledge to AI interface, is where the value compounds.
GenAI is capable, but when it’s supported with human insight and contextual accuracy, it becomes more effective. That’s the standard enterprises should be adopting. Use AI at scale, but never disconnect it from the people and platforms that carry your institutional knowledge.
Final thoughts
GenAI is already reshaping how software gets built. But speed and automation don’t replace strategy and skill, they amplify them. The organizations pulling ahead are rethinking workflows, building up prompt engineering capabilities, and reinforcing core developer competencies. They’re clear about one thing: AI is powerful, but it performs best with human context, oversight, and direction.
For decision-makers, this is a leadership call. Use GenAI to scale talent, not replace it. Push for clarity in how prompts get written, how outputs get reviewed, and how teams stay accountable. Equip your developers with support systems that raise quality across the board, from training and mentorship to trusted knowledge sources like Stack Overflow Enterprise.
AI won’t think for your team. But with the right structure, it will help them think better, move faster, and build stronger products. That’s where the real competitive edge is, getting more out of people, not just machines.
