Vibe coding as a transformative approach in software development
Vibe coding represents a meaningful shift in how AI is used by developers. Coined by Andrej Karpathy, former head of AI at Tesla and a founding architect at OpenAI, vibe coding is a method where developers step back and let generative AI take the wheel. It’s a hands-on collaboration with intelligent systems, where the developer’s role becomes more about guiding intent and less about writing every function line-by-line. Karpathy said it best: the code grows “beyond [his] comprehension.” When you offload code generation to AI with enough context, the results are fast, scalable, and sometimes, good enough to ship.
Tools like Cursor AI’s Composer, alongside emerging platforms like GitHub Copilot and JetBrains AI agents, are expanding from single-function completions to full-application design and multi-file management. That means fewer developers are needed to spin up prototypes, internal tools, or even full apps. And they’re doing it in hours, not weeks.
Viewed through a business lens, this is acceleration without adding headcount. It’s not just about speed, although speed is part of it, it’s about shifting from bottlenecks to throughput in software creation. That’s why venture-backed startups and product labs are already embedding this into their teams.
If your teams are still doing development the same way they did a year ago, you should be asking why. Because at scale, these new approaches to coding change the economics of product development. Vibe coding isn’t a theory. It’s already reshaping how digital products get made.
Generative AI is reshaping developer roles and workflows
The way software teams operate is changing, fast. And it’s being driven by systems that can now generate enterprise-level code at near-human quality.
According to Jared Friedman at Y Combinator, 25% of their surveyed startups report that over 95% of their codebases are AI-generated. That stat isn’t marginal. It suggests we’ve hit an inflection point where the majority of code doesn’t come from humans anymore, at least in certain contexts. At ObligeAI, for example, CTO Ken Schirrmacher manages a real-time pipeline during meetings, with five live instances of vibe coding running across his screens. His team isn’t writing from scratch. They’re validating, integrating, and scaling ideas at a pace older workflows can’t compete with.
This doesn’t mean developers are going away. It means their jobs are evolving. Instead of starting from zero, the new baseline is higher. Developers now iterate on AI-generated code, filtering out bugs, aligning output to business goals, and enhancing it for security and scalability. Time spent writing 40,000 lines of boilerplate code over weeks is reduced to making final revisions and integrating components that AI drafted in minutes.
That has implications across your organization structure. You’re no longer constrained by traditional productivity metrics. Your dev-to-productivity ratio can shift. You don’t need larger engineering teams to scale up technology, you need developers who know how to work with AI to get there faster.
You’re looking at a change in how coding works, but more importantly, in what “coding” even means. Removing the repetitive task load allows developers to spend more time designing functionality and less time creating building blocks. That’s a structural advantage for any company willing to adapt its culture and workflows.
Rising concerns over potential displacement of developer jobs
One of the loudest questions in the room right now is whether AI is going to make software developers obsolete. Let’s be clear: that concern isn’t coming from fringe voices, it’s being discussed at the top levels of the AI industry.
Dario Amodei, CEO and co-founder of Anthropic, projected that 90% of code could be written by AI within six months, and nearly all within a year. That hasn’t fully materialized yet, not at that extreme scale, but the direction is accurate. The pressure is building. Companies like Shopify and Fiverr have already started assessing new hires based on whether they can outperform AI tools. This shift reframes the hiring model: it’s no longer about skill alone, but about value differentiation above machine-level output.
At the startup level, these conversations are not theoretical, they are operational. Developers must now prove they can guide smart tools, not just code without help. Enterprises are beginning to do the same. Expect those evaluation filters to make their way into broader tech hiring practices over the next year.
The larger issue is strategic. If AI can deliver code faster, at lower cost, with acceptable quality, at what point do large engineering departments become economically inefficient? That’s the outcome being raised by Ken Schirrmacher, CTO of ObligeAI. He thinks companies with thousands of developers on payroll may not scale well into the next decade unless their tech talent moves to higher-value functions. It comes down to sustainability. Organizations that cling to outdated software development models risk falling behind those who restructure earlier around intelligent systems.
Most developers aren’t going away, but their jobs might not look anything like they did five years ago. And that should be part of every boardroom discussion right now.
Slower-than-expected enterprise adoption of AI-generated code
There’s a gap between what’s possible with generative AI and how it’s actually being used in large organizations. Predictions of machines taking over software development overnight haven’t come true, because enterprise systems don’t pivot that quickly, and the models still have limitations.
In a March 2025 interview, IBM CEO Arvind Krishna said he expects AI-generated code to plateau at around 30% of total output. Microsoft’s Satya Nadella and Google’s Sundar Pichai echoed similar figures, estimating that AI contributes between 20–30% of their code production.
AI-generated code still carries risks, accuracy errors, hallucinations, regulatory blind spots, and gaps in security. These issues are manageable, but they make full automation too risky for core systems. For now, many organizations use AI to accelerate prototype development or early-stage solutions, but actual production environments continue to depend on experienced human developers to validate, harden, and scale the output.
That reality was echoed by Diego Lo Giudice of Forrester Research. He’s seeing strong traction for AI in prototyping and ideation workflows, but for mission-critical projects, true developers still carry the load. Because when something breaks or fails compliance, the tool won’t take the blame, your company will.
Understanding the current ceiling of AI utility allows you to use it responsibly. You can benefit now by focusing on where AI adds speed and value, usually at the front end of the build cycle, while keeping core systems on solid ground.
Approach this with a clear head and structured planning, and you’ll gain a competitive lead. Just don’t assume that what worked in a hackathon will hold up in a regulated market or a production-scale SaaS product.
Increased responsibilities for senior developers amid AI integration
As AI becomes more embedded in the software development process, the workload hasn’t vanished, it’s shifted. The so-called productivity boost from AI-generated code is real, but it’s transferred significant operational responsibility to senior engineers. Instead of writing every feature from scratch, they’re now reviewing, refining, and validating AI outputs at scale.
Kyler Middleton, Principal Software Engineer at Veradigm, described reviewing ten times more code than the previous year due to the influx of AI-generated contributions. That’s not scalable unless teams change how they manage engineering bandwidth. The review volume is overwhelming, and velocity without quality doesn’t benefit the business in the long run.
Junior developers, meanwhile, are becoming less focused on fundamentals. As they rely more on AI tools to generate code, they’re skipping the hands-on coding experience that helps engineers develop long-term problem-solving capabilities. Rob Strechay, an analyst at TheCube Research, flagged this skill atrophy as a long-term risk to talent development. If your juniors start as prompt-tuners instead of engineers, your pipeline of future senior architects becomes weaker.
Executives should recognize this imbalance early and act on it. You’ll need to invest in more structured oversight, version control practices, and code validation tooling. More importantly, you’ll need to rethink how you train and scale junior talent. If your developers can’t eventually evolve into senior engineers without AI doing the thinking for them, you’re not building a resilient technical team, you’re creating dependency.
Use AI, but don’t assume it’s a replacement for core engineering competency. Ensure your team structure accounts for the critical thinking layer, because automation isn’t a substitute for sound judgment in system design, security, or software lifecycle management.
AI’s productivity gains are tempered by quality and process challenges
AI tools generate code fast, but fast doesn’t always mean efficient, especially when measured across the entire development lifecycle. In practice, teams are discovering that cleaning up after an AI can sometimes take more time than writing it yourself.
Rob Strechay noted that AI-generated code often requires double the time to validate and correct, especially when it’s used without rigorous review systems in place. These tools might handle simple logic well, but for larger, interconnected systems, they sometimes introduce subtle bugs, non-compliant patterns, or redundancies. Human engineers are then tasked with catching and resolving those issues after the fact.
DORA, an internal Google team focused on DevOps and engineering productivity, reported in April 2025 that while AI tools make preferred tasks faster, they don’t effectively reduce organizational overhead. Issues like excessive meetings, repetitive operations, and bureaucratic obstacles still erode productivity. Burnout levels were found to be largely unchanged, suggesting automation hasn’t addressed the underlying structural inefficiencies slowing down teams.
This needs to be understood at the leadership level. Productivity metrics shouldn’t be based solely on speed of code delivery. You need a full-picture understanding, how long does it take to deploy? How stable is it? What’s the failure rate in production? AI speeds output, but it doesn’t always improve outcomes.
Executives should focus on integrations that promote precision rather than just speed. And when adopting AI development tools, the oversight process should be built into the deployment pipeline. Used this way, AI becomes a multiplier, but only when grounded by senior engineering oversight, systems-level thinking, and repeatable best practices.
The potential for full AI automation in software development
Some leaders in the AI space are no longer asking whether AI can replace software development, they’re describing how and when it will happen. The emerging position is about full automation of the development cycle, powered by AI agents that only write code and validate, deploy, and sustain software systems without human input.
Humayun Sheikh, CEO of Fetch.ai, predicts this outcome based on active work in agentic platforms. His team is using blockchain-backed consensus models to validate AI-generated outputs, a system designed to reduce the need for manual review. He also expects quantum computing to accelerate this trajectory. The goal: eliminating redundant cycles across enterprises by using intelligent, autonomous AI agents capable of producing centralized outcomes with minimal variance.
This move toward total automation is not imminent. It depends on infrastructure that’s still being scaled and risks that are still being addressed, including model alignment, output interpretability, and failure transparency. For C-suite decision-makers, this is where the long-term roadmap begins. Smart planning now should include space for AI-native platforms that restructure how software is created and managed at scale.
The opportunity is to position your organization early to take advantage of that evolution, not only in terms of tooling, but also in how your team is structured, how you think about roles, and how you manage risk. Total automation won’t arrive overnight, but the architecture for it is already being built. Executives that anticipate and adapt infrastructure to accommodate AI-driven development cycles will be better positioned to compete over the next decade.
Evolution of developer roles and emergence of new disciplines
The rise of generative AI isn’t eliminating the need for developers, it’s changing what development means and introducing whole new disciplines in the process. The definition of a “developer” is widening to include citizen developers and business stakeholders who previously weren’t part of the software lifecycle.
Nick Cassidy, Lead Innovation Engineer at Stellarus Group, explained that new tools such as Red Hat’s InstructLab allow non-engineers to fine-tune AI models and build applications without deep technical experience. These AI-driven platforms expand access to development across disciplines, significantly reducing the time and technical barriers to action.
Developers are still essential, but the types of problems they’re solving are different. Today, developers aren’t just writing code. They’re managing the logic behind AI interactions, optimizing prompts, validating outputs, and ensuring that AI-generated applications meet standards of scalability, security, and regulatory compliance. This shift is already influencing how engineering and business teams interact.
David Strauss, CTO at Pantheon.io, shared how he’s using AI tools to prototype interface concepts and workflows before handing them off to engineering, enabling much tighter collaboration across departments. That’s becoming a common pattern. Meanwhile, new skill sets like prompt engineering are starting to become core to modern product teams.
For executives, this expansion of roles means rethinking traditional dichotomies between technical contributors and business users. If your organizational model doesn’t integrate these functions tightly, you’re leaving speed and value on the table. Managing this shift isn’t about staffing more engineers, it’s about providing the right interfaces and workflows so diverse teams can effectively use AI where it matters most.
Lars Maaløe, CTO and co-founder of Corti, cautioned that while AI can replicate repeatable logic well, it doesn’t produce true creative innovation. That still comes from people. And that’s something executives must preserve even as they scale AI, because velocity loses meaning without originality in product thinking.
In conclusion
We’re standing at the edge of a significant shift in how software gets built, and who drives that process. Generative AI isn’t just a tool for speeding up development. It’s restructuring team dynamics, redefining technical roles, and setting a new baseline for what productivity looks like. The startups already leveraging vibe coding and AI-assisted development aren’t waiting. They’re moving faster, hiring lighter, and building products in half the time.
That doesn’t mean every enterprise needs to scrap its engineering organization or chase every shiny model. But it does mean leadership needs a clear-eyed look at how AI is changing the inputs and outputs of software work. From hiring decisions to infrastructure choices, from training programs to platform spend, everything shifts when AI becomes a core collaborator in development, not just a side add-on.
Maintaining technical edge is no longer about raw headcount. It’s about how effectively your teams integrate with intelligent systems. And that means executives need to lead decisively, setting standards, asking harder questions, and being honest about what their teams need now versus what they’ve always done.
Most of the pressure is already here. The companies that adapt fastest will get further ahead. The ones that wait for certainty will lose ground they can’t afford to give up.
