Vibe Coding: Prompt It, Got It, Regret It? The Risks of the Vibe Trend You Haven’t Spotted

Author(s): Mohit Sewak, Ph.D.

Originally published on Towards AI.

Alright folks, let’s talk about the latest buzzword that’s got the tech world humming, buzzing, and occasionally, stumbling: Vibe Coding. If you’ve been anywhere near the digital water cooler (or, let’s be honest, scrolling through tech Twitter/X or LinkedIn), you’ve probably heard the whispers, the cheers, and maybe even a few hushed warnings.

The promise? Utterly seductive. Imagine whispering your wildest app dreams into the digital ear of an AI genie, and poof — functional code appears! No more wrestling with semicolons at 3 AM, no more arcane syntax rules standing between your brilliant idea and reality. Just pure, unadulterated vibes translating into software. Sounds like a dream, right? Like ordering a custom-built spaceship with a Post-it note.

This concept exploded into the mainstream consciousness thanks, in part, to AI visionary Andrej Karpathy’s musings back in February 2025, describing it as a state where you “fully give in to the vibes, embrace exponentials, and forget that the code even exists” (as cited in Vibe coding — Wikipedia, n.d.). The idea is intoxicating: focus on the what, let the AI handle the how. From entrepreneurs sketching MVPs on napkins to seasoned developers looking to shortcut the grunt work, the allure is undeniable.

But hold your horses, tech dreamers. As someone who’s spent years navigating the intricate circuits of AI safety and security at places like Google, NVIDIA, Microsoft, and IBM, I’ve learned that every silver lining has a cloud… and sometimes that cloud is packed with security vulnerabilities, ethical dilemmas, and societal curveballs.

So, before we all “vibe” our way into a future we didn’t fully code for (pun intended!), let’s pull back the curtain. Is Vibe Coding the next great leap in software creation, democratizing development for all? Or is it a seductive shortcut paved with hidden pitfalls — a case of “Prompt It, Got It, Regret It”? Let’s dive deep, dissect the hype, confront the hazards, and explore what responsible innovation looks like in the age of AI-generated code. Buckle up, it’s going to be a vibey, bumpy ride!

Section 1: Just Vibin’ — What Exactly Is This Coding Craze?

1.1 The “Just Tell Me What You Want” School of Programming

So, what’s the secret sauce behind vibe coding? At its heart, it’s an AI-fueled programming paradigm where you, the human, describe what you want your software to do in plain English (or your natural language of choice). You feed this description — your “vibe” — as a prompt to a Large Language Model (LLM) specially trained on mountains of code (Vibe coding — Wikipedia, n.d.; Replit, n.d.-b). Think of it like commissioning a painting by describing the feeling you want it to evoke, rather than specifying every brushstroke.

The LLM then does the heavy lifting, churning out the source code needed to (hopefully) bring your vision to life. Your role shifts from meticulous coder to something more akin to an orchestral conductor, guiding the AI, testing its output, and refining the results (Vibe coding — Wikipedia, n.d.). It’s about communicating intent, focusing on the application’s goals, its look and feel, and the user experience, while the AI sweats the syntactical details (Replit, n.d.-b).

“The art challenges the technology, and the technology inspires the art.” — John Lasseter

Pro Tip: While vibe coding is great for brainstorming or simple tasks, complex logic often requires more specific instructions than just a “vibe.” Think of it as ordering food: “Make me something tasty” might get you a meal, but “Make me a medium-rare steak with a side of asparagus” is far more likely to deliver what you actually envisioned.

1.2 The Karpathy Spark & The Willison Distinction: Not All AI Assistance is Vibin’

The term truly caught fire after Andrej Karpathy’s evocative description painted a picture of effortless, exponential creation (as cited in Vibe coding — Wikipedia, n.d.). It tapped into a collective desire to transcend the often-tedious implementation details and leap straight to innovation (Replit, n.d.-b).

However, it’s crucial to draw a line, as AI researcher Simon Willison points out. If you’re using an LLM to generate code, but you meticulously review, test, and understand every single line before integrating it, that’s not quite vibe coding. That’s more like having a super-powered autocomplete or a tireless coding assistant (Willison, 2025; Vibe coding — Wikipedia, n.d.). True vibe coding often involves a leap of faith — accepting the AI’s output without necessarily grasping all the nuts and bolts under the hood (Vibe coding — Wikipedia, n.d.). It’s this “trust me, bro” relationship with the AI that distinguishes vibe coding and, as we’ll see, is where some of the stickiest issues lie. The magic happens thanks to sophisticated LLMs trained on colossal datasets of code and text, enabling them to translate our fuzzy human intentions into concrete machine instructions (Replit, n.d.-b).

“The difference between ordinary and extraordinary is that little extra.” — Jimmy Johnson

(Okay, maybe about football, but it applies to code review too!)

Trivia: The datasets used to train code-generating LLMs are massive, often containing billions of lines of code scraped from public repositories like GitHub. This includes code in countless languages, covering everything from simple scripts to complex operating systems.

Section 2: Where the Vibes Flow: Applications and Use Cases

2.1 From Zero to MVP: The Need for Speed

One of the most celebrated applications of vibe coding is its potential to turbocharge the early stages of software development. Got a brilliant idea but facing a mountain of backlog? Vibe coding promises to turn those “someday” concepts into tangible Minimum Viable Products (MVPs) in weeks, not years (Security Journey, 2025; Replit, n.d.-b). This rapid prototyping capability allows for quicker iteration, faster validation of ideas, and potentially slashes the time-to-market (Security Journey, 2025). Think of it as hitting the fast-forward button on innovation.

“Ideas are cheap. Ideas are easy. Execution is everything.” — John Doerr

Pro Tip: Use vibe coding for MVPs to test core concepts quickly, but plan for significant refactoring or rewriting if the prototype proves successful and needs to scale into a production-ready application. The initial “vibed” code might not have the robustness or structure required for the long haul.

2.2 Code Without Credentials: Empowering the Non-Coder

Perhaps the most revolutionary promise of vibe coding is its potential to democratize software creation. Entrepreneurs, designers, educators, scientists — anyone with a specific need or a novel idea, but lacking traditional coding skills — can potentially become creators (Replit, n.d.-b). Vibe coding aims to dismantle the technical barriers, offering a simplified path from concept to functional application (Replit, n.d.-b). This could usher in an era of hyper-personalized software, “software for one,” where individuals craft bespoke tools tailored perfectly to their unique workflows or problems (Vibe coding — Wikipedia, n.d.). No need to learn Python or JavaScript first; just articulate your vision.

“The future belongs to those who learn more skills and combine them in creative ways.” — Robert Greene

Trivia: The idea of generating code from natural language isn’t entirely new. Early attempts date back decades, but it’s the recent breakthroughs in LLM scale and capability that have made concepts like vibe coding practical.

2.3 Banishing Boilerplate: A Helping Hand for Pros

Even seasoned developers stand to benefit. Let’s face it, a significant chunk of coding involves writing repetitive boilerplate, setting up standard frameworks, or implementing mundane functionalities. Vibe coding offers the possibility of outsourcing this drudgery to AI agents (Security Journey, 2025; Replit, n.d.-b). By letting the AI handle the “grunt work,” developers can reclaim precious time and cognitive energy to focus on the challenging, creative, and truly impactful parts of software engineering — the complex algorithms, the novel architectures, the elegant solutions (Replit, n.d.-b). Less tedious typing, more high-level thinking. Sounds like a productivity win-win.

“Automation applied to an efficient operation will magnify the efficiency.” — Bill Gates

Pro Tip for Developers: Use AI code generation tools strategically. They excel at well-defined, common tasks. Integrate them into your workflow for speed, but always maintain oversight and apply your expertise to the critical, unique aspects of your project. Don’t let the tool dictate the architecture.

2.4 Learning the Ropes (with AI Training Wheels)

For those dipping their toes into the vast ocean of programming, vibe coding can seem like a friendly life raft. Instead of facing the steep initial learning curve of syntax and logic from scratch, beginners can start with AI-generated code that works (or mostly works) (Replit, n.d.-b). By examining this code and making small tweaks, they can gain a more intuitive, hands-on understanding of programming concepts. Seeing the immediate impact of changes and using AI to help debug can make the learning process less intimidating and potentially more engaging (Replit, n.d.-b; Security Journey, 2025). It’s like learning to ride a bike with very sophisticated training wheels.

“Tell me and I forget. Teach me and I remember. Involve me and I learn.” — Benjamin Franklin

Trivia: Some educational platforms are already integrating AI coding assistants to provide personalized feedback and explanations to students, potentially accelerating the learning process for basic programming skills.

2.5 Hobby Projects and Quick Hacks: Just for Fun

Finally, let’s not forget the sheer fun factor. Vibe coding is often touted as perfect for whipping up simple apps, weekend passion projects, or those quirky little tools you wish existed just for you (Reddit user comment, as cited in Reddit, n.d.-a; Gitpod, 2025). If you’re not aiming to build the next enterprise-grade system, but just want to create something cool quickly without getting bogged down in coding theory, vibe coding offers an appealingly direct route from idea to execution (Replit, n.d.-b). It makes experimentation fast and cheap, encouraging playful exploration of software possibilities (Gitpod, 2025).

“The creation of something new is not accomplished by the intellect but by the play instinct.” — Carl Jung

Pro Tip: Hobby projects are a fantastic sandbox for experimenting with vibe coding. The stakes are lower, allowing you to explore the capabilities and limitations of AI code generation without risking critical production systems.

Section 3: The Hype Train vs. The Reality Check: Cheers and Jeers

3.1 All Aboard! The Case for Vibe Coding

The enthusiasm for vibe coding isn’t just hot air; proponents point to tangible benefits that could reshape software development.

• Productivity Rocket Fuel: Imagine generating code orders of magnitude faster than human typing speed (Gitpod, 2025). Advocates see AI assistants churning out working features while developers focus on the next big idea, leading to potentially exponential gains in efficiency and dramatically shorter development cycles (Gitpod, 2025; Security Journey, 2025). Less time coding, more time shipping.
• Creativity Unleashed: By automating the mundane, error-prone parts of coding, vibe coding promises to free up developers’ mental bandwidth for the truly creative work: envisioning new possibilities, exploring innovative solutions, and focusing on the art of software design (Gitpod, 2025). It shifts the focus from syntax wrangler to digital architect.
• Coding for the People: This is perhaps the most powerful argument — breaking down the walls around software creation. Vibe coding could empower a whole new generation of creators — entrepreneurs, artists, scientists, educators — turning anyone with a vision into a potential builder (Replit, n.d.-b). This democratization is especially appealing in fast-moving domains like Web3, where rapid iteration is key (Bitget News, 2025).
• Learning Curve, Smoothed: As mentioned, it offers a potentially less intimidating on-ramp for programming newcomers, fostering practical understanding through interaction and modification rather than abstract theory alone (Replit, n.d.-b; Security Journey, 2025). Faster feedback loops, lower stress.
• Fun & Exploration: Let’s not discount the joy of rapid creation! Vibe coding makes it easier to quickly prototype wild ideas or build personal tools just for the fun of it, fostering a spirit of experimentation (Gitpod, 2025).

“The best way to predict the future is to invent it.” — Alan Kay

Pro Tip: To maximize the benefits, treat AI code generators as collaborators. Provide clear context, refine the prompts based on output, and use your domain knowledge to guide the AI toward the desired outcome. Don’t just prompt and pray.

3.2 Hold On, Cowboy! Concerns and Criticisms Emerge

Now, let’s pump the brakes and listen to the chorus of concerns. The path of vibe coding isn’t paved entirely with gold; there are some significant potholes and potential cliff edges.

• The Black Box Problem: Understanding & Maintainability: This is a big one. If you’re using code you don’t fully understand, how can you effectively debug it when things go wrong? How can you maintain or evolve it over time? Relying on AI-generated code without comprehension can lead to fragile, unmaintainable systems riddled with hidden flaws (Security Journey, 2025; Vibe coding — Wikipedia, n.d.). Building a production system via pure vibe coding is widely seen as playing with fire (Vibe coding — Wikipedia, n.d.).
• Security? What Security?: Experts are sounding the alarm bells loud and clear. AI models learn from vast datasets, including code that might be insecure or outdated. Generating code without understanding its security implications can introduce critical vulnerabilities (Security Journey, 2025; Legit Security, 2025). It creates a scenario where developers “don’t know what they don’t know,” potentially shipping ticking time bombs, especially when handling sensitive data (Security Journey, 2025).
• Hallucinating Code & Stubborn Bugs: AI models aren’t infallible. They can, and do, make mistakes. They might generate code with subtle (or glaring) logical errors, misunderstand requirements, produce inefficient solutions, or even “hallucinate” features or libraries that don’t exist (Security Journey, 2025; Cendyne.dev, 2025). Remember, they’re trained on existing code, warts and all, including potentially sloppy or incorrect examples (Cendyne.dev, 2025).
• Skill Atrophy & The Illusion of Competence: Is convenience making us dumber? There’s a real concern that over-reliance on AI code generation could lead to an erosion of fundamental programming skills, critical thinking, and problem-solving abilities, especially among learners (Security Journey, 2025; Adnovum, 2025). You might feel like you’re coding, but are you truly learning if the AI does all the heavy lifting (Reddit user comment, as cited in Reddit, n.d.-b)?
• Quality Limits & Context Blindness: While AI might nail simple tasks, its ability to handle complex, nuanced software requiring deep architectural understanding is still questionable (Cendyne.dev, 2025; DEV Community post, as cited in McNulty, 2025). Current LLMs often have limited context windows, meaning they might not see the bigger picture, suggest reusing existing code effectively, or maintain design consistency across a large project (Cendyne.dev, 2025).
• The Thorny Ethics Patch: Ownership, Liability, Bias: Who owns AI-generated code? Who is liable if it fails catastrophically or causes harm? How do we ensure the AI isn’t perpetuating biases hidden in its training data? These complex ethical and legal questions are lagging behind the technology’s rapid advance (Adnovum, 2025; Pearlmutter et al., 2024).
• “Will AI Take My Job?”: And of course, the million-dollar question (or perhaps, the zero-dollar salary question). While some see AI as an augmentation tool, others fear significant disruption in the software engineering job market, potentially squeezing out junior developers as AI tackles more foundational tasks (Adnovum, 2025; Reddit user comment, as cited in Reddit, n.d.-b).

“Everything should be made as simple as possible, but not simpler.” — Albert Einstein

Pro Tip: Never blindly trust AI-generated code in production environments. Rigorous testing, security scanning, and human code review are non-negotiable, especially for critical systems or code handling sensitive information. Assume the AI made mistakes until proven otherwise.

Section 4: The Responsible AI Tightrope: Balancing Innovation and Impact

Vibe coding isn’t just a technical trend; it’s a phenomenon with profound implications for Responsible AI principles. We need to walk a fine line, embracing innovation while diligently managing the risks.

4.1 Security Nightmares: Code That Bites Back

Let’s be blunt: AI code generators are known to produce insecure code. Research consistently flags this issue, with studies suggesting a startlingly high percentage (sometimes nearly half!) of AI-generated code snippets contain exploitable vulnerabilities (CSET, 2025a; CSET, 2025b). These aren’t just minor oopsies; we’re talking classic security blunders like SQL injection, cross-site scripting (XSS), insecure handling of sensitive data, and pulling in unsafe dependencies (ITPro, 2025; SecureFlag, 2024).

Why? Because the AI learns from the vast ocean of public code, which, frankly, contains a lot of insecure practices (All Things Open, 2025). An inexperienced user “vibing” their way to an application might unknowingly deploy code with gaping security holes, creating easy targets for attackers (Pearlmutter et al., 2024). It’s like building a house with instructions copied from random blueprints found online — some might be solid, others dangerously flawed.

“Security used to be an inconvenience sometimes, but now it’s a necessity all the time.” — Martina Navratilova (Adapting her wisdom for the digital age!)

Pro Tip: Integrate security scanning tools (SAST, DAST, SCA) early and often in workflows involving AI-generated code. Treat AI code suggestions with healthy skepticism, especially regarding input validation, authentication, authorization, and data handling.

4.2 Prompt Injection: When AI Listens to the Wrong Vibes

Vibe coding relies on natural language prompts. Unfortunately, this opens the door to prompt injection attacks (IBM, n.d.). Imagine a malicious actor crafting a prompt that looks innocent but secretly instructs the LLM to do something harmful — like leak sensitive data, bypass security controls, or even generate malicious code itself (OWASP, n.d.).

It gets worse with indirect prompt injection. Malicious instructions could be hidden within data sources the AI accesses (like websites or documents), tricking the LLM without the user even typing a malicious prompt directly (CETAS, n.d.). Researchers have even shown how AI coding assistants can be compromised through seemingly innocuous configuration files, leading them to generate backdoored code (SC Magazine, 2025). It’s a subtle but potent threat vector, turning the AI’s helpful nature against itself.

“The cleverest trick of the Devil is persuading you that he doesn’t exist.” — Charles Baudelaire

(Or that the malicious prompt is just helpful advice! — Dr. Sewak)

Pro Tip: Sanitize and validate all inputs that might influence an LLM, especially data retrieved from external sources. Implement strict output encoding and context separation. Be wary of prompts that ask the AI to ignore previous instructions or perform actions outside its intended scope.

4.3 Code Glitches & Gremlins: Reliability Takes a Hit

Beyond security, the fundamental correctness and reliability of AI-generated code remain significant hurdles. Studies show LLMs frequently generate code that simply doesn’t work as intended or fails on complex tasks (Siddiq et al., 2024). AI-generated code has even been observed to be more prone to hangs and crashes compared to human-written equivalents (Zügner et al., n.d., as cited in Pearlmutter et al., 2024).

Sometimes, the errors are embarrassingly simple — “stupid bugs” that a human developer would catch instantly, but the AI overlooks (Pearlmutter et al., 2024). Testing this code introduces new challenges, especially when dealing with incomplete snippets or novel AI-generated logic (Henley et al., 2024). Furthermore, AI might prioritize functional code over efficient code, potentially leading to performance regressions compared to human-optimized solutions (Shang et al., 2024). “It works” isn’t always the same as “it works well.”

“To err is human, but to really foul things up requires a computer.” — Paul Ehrlich

Pro Tip: Implement comprehensive unit, integration, and regression testing suites for all code, especially AI-generated portions. Performance profiling is crucial if the AI-generated code is part of a performance-sensitive application. Don’t assume correctness.

4.4 Safety Lapses: When Code Does Harm

The potential for harm extends beyond bugs and security flaws. AI systems, including those powering vibe coding, can be misused (intentionally or accidentally) to generate harmful content or code with dangerous implications (Safe Generative AI Workshop, n.d.). Think code designed for malicious purposes, or applications that, due to flawed logic, make harmful decisions in critical domains like healthcare or finance.

A significant safety concern is overconfidence. Users might implicitly trust the AI’s output, deploying vibe-coded applications without the rigorous safety checks, ethical reviews, or human oversight they truly require (Safe Generative AI Workshop, n.d.). This misplaced trust can lead to unforeseen and damaging consequences when the AI’s limitations or biases surface in the real world.

“The first step in solving a problem is recognizing there is one.” — Will McAvoy (The Newsroom)

Pro Tip: Establish clear protocols for human oversight and validation, particularly for applications generated or assisted by AI that operate in safety-critical domains. Define acceptable risk thresholds and ensure AI outputs are treated as suggestions, not infallible commands.

4.5 The Bias Blindspot: Code Reflecting Inequality

This is a critical Responsible AI challenge. LLMs learn from vast datasets reflecting our often biased world (Yuan et al., 2024). As a result, they can inadvertently learn, perpetuate, and even amplify societal biases related to gender, race, ethnicity, age, culture, and socioeconomic status (Mei et al., 2024; Yuan et al., 2024).

Research has specifically uncovered demographic biases in code generation models (Sun et al., 2024). Training data often skews towards Western, Anglo-centric perspectives, potentially ignoring or misrepresenting diverse needs and contexts (Bhatt et al., 2024a; Bhatt et al., 2024b). When vibe coding relies on these biased models, it risks creating software that is unfair, discriminatory, or simply doesn’t work well for certain user groups. The “vibes” the AI picks up might be steeped in prejudice.

“Our biases are weakest when we are aware of them.” — Daniel Kahneman

Pro Tip: Actively audit AI coding tools and their outputs for potential biases. Advocate for and utilize models trained on diverse, representative datasets. Incorporate fairness testing and diverse user feedback throughout the development lifecycle when using AI-generated code.

Section 5: Ripples in the Pond: Societal Shifts and Shakes

The rise of vibe coding isn’t happening in a vacuum. Its widespread adoption could send significant ripples across society, reshaping jobs, ethics, and even how we think.

5.1 The Evolving Code-scape: Jobs and Roles in Flux

Let’s address the elephant in the room: jobs. Yes, AI code generation will automate tasks currently done by developers (Tommie Experts, 2025). Estimates vary wildly, but the potential for disruption is real (AIPRM, n.d.; Exploding Topics, 2024). However, the narrative isn’t purely one of replacement. New roles are emerging — AI trainers, prompt engineers, AI ethicists, specialists in managing AI-driven development (Tommie Experts, 2025; Simbla, n.d.).

The traditional programmer role is likely shifting towards orchestration and oversight — guiding AI agents, validating their output, focusing on high-level architecture and complex problem-solving (Iyer et al., 2024). Productivity boosts might lead to smaller, more specialized teams (Reddit user comment, as cited in Reddit, n.d.-c). Demand for AI-specific skills (data science, ML engineering) is skyrocketing (Brainhub, 2024). The squeeze might be felt most acutely at the junior end, where tasks are more easily automated (Adnovum, 2025). Adaptability and continuous learning will be key.

“The only constant in life is change.” — Heraclitus

Pro Tip: Embrace lifelong learning. Focus on developing skills that complement AI, such as critical thinking, complex problem-solving, system design, domain expertise, and understanding AI/ML principles. Learn how to effectively use AI tools as force multipliers.

5.2 The Bigger Picture: Ethics, Accountability, and Trust

Beyond jobs, widespread vibe coding forces us to confront broader societal questions. How do we handle the ethical implications when AI, lacking true understanding of legal or moral norms, generates code used in sensitive contexts (Pearlmutter et al., 2024)? Who is accountable when vibe-coded software fails, causes harm, or exhibits bias (Adnovum, 2025)?

There’s also the risk of malicious actors leveraging the ease of vibe coding to create harmful software more rapidly (CRA, 2024). Furthermore, as more content (including code) becomes AI-generated, how do we maintain trust and authenticity in the digital realm (Bhatt et al., 2024b)? Establishing clear ethical guidelines, governance frameworks, and accountability structures for AI in coding is no longer optional; it’s essential for navigating this new territory responsibly (Pearlmutter et al., 2024; Adnovum, 2025).

“Ethics is knowing the difference between what you have a right to do and what is right to do.” — Potter Stewart

Pro Tip: Advocate for and contribute to the development of industry standards and best practices for responsible AI development and deployment, including clear guidelines for using AI code generation tools. Promote transparency in how AI is used in software creation.

5.3 Our Brains on AI: Cognitive Shifts and Learning Challenges

Finally, what does relying on AI for cognitive heavy lifting like coding do to our own brains? While AI can reduce cognitive load (Microsoft Dev Blogs, 2025), there’s a valid concern about cognitive offloading — delegating thinking tasks to the point where our own skills atrophy (Schaefer Marketing Solutions, 2025; Kazemitabaar et al., 2025a). If we always let the AI solve the problem, do we forget how to solve it ourselves?

In education, the ease of generating solutions via vibe coding might create an illusion of understanding. Students might get the right answer without engaging deeply with the underlying principles, potentially hindering long-term learning and critical thinking development (Kazemitabaar et al., 2024; Kazemitabaar et al., 2025b). Studies suggest students might overestimate their learning when using AI aids and struggle when those aids are removed (Kazemitabaar et al., 2024). Younger learners might become particularly reliant, impacting their fundamental skill acquisition (Kazemitabaar et al., 2024). Balancing AI assistance with foundational learning is a pedagogical tightrope walk.

“The human brain is a wonderful organ. It starts working the moment you get up in the morning and does not stop until you get into the office.”— Robert Frost

(Let’s hope AI doesn’t encourage it to stop even then! — Dr. Sewak)

Pro Tip for Educators & Learners: Use AI coding tools as scaffolds, not crutches. Focus on understanding the why behind the AI’s output. Encourage manual problem-solving and code-reading alongside AI generation. Design learning activities that require critical evaluation and modification of AI-generated code.

Section 6: Conclusion — Charting a Responsible Vibe Forward

So, vibe coding. Is it the promised land of effortless creation or a minefield of unintended consequences? The truth, as is often the case, lies somewhere in the messy middle.

The potential is undeniable: faster development, democratized access, enhanced creativity, and a shift towards higher-level problem-solving. Vibe coding could genuinely revolutionize how we build software, empowering more people to bring their digital ideas to life (Replit, n.d.-b; Gitpod, 2025).

However, the risks are equally real and demand our urgent attention. The specter of insecure code, the insidious threat of prompt injection, the propagation of bias, the questions around reliability, safety, ethics, job shifts, and cognitive impact — these are not minor quibbles. They are fundamental challenges we must address head-on (Security Journey, 2025; CSET, 2025a; Yuan et al., 2024; Pearlmutter et al., 2024).

Ignoring these issues while blindly chasing the “vibe” would be irresponsible, potentially leading to systems that are brittle, unfair, unsafe, and ultimately, untrustworthy. We can’t afford to just “Prompt It, Got It,” and then later “Regret It.”

6.1 Recommendations: Coding the Future, Responsibly

To navigate this complex landscape and harness vibe coding’s potential for good, we need a concerted effort grounded in responsibility:

1. Security & Safety First: Mandate rigorous testing, validation, and security scanning specifically tailored for AI-generated code (SecureFlag, 2024; Pearlmutter et al., 2024). Develop and share best practices for secure prompting and integrating AI code safely.
2. Bias Beware: Invest heavily in detecting and mitigating bias in code-generating models (Sun et al., 2024). Prioritize diverse training data and establish robust ethical frameworks for fairness in AI-driven development.
3. Education & Critical Thinking: Foster educational approaches that use AI tools to enhance understanding, not replace it (Replit, n.d.-b). Emphasize code review, fundamental principles, and the critical thinking needed to evaluate AI output (Kazemitabaar et al., 2024).
4. Ethical Guardrails & Accountability: Develop clear industry standards, ethical guidelines, and legal frameworks defining ownership, liability, and responsible use for AI-generated code (Adnovum, 2025; Pearlmutter et al., 2024).
5. Study the Long Game: Support ongoing research into the long-term societal, economic, and cognitive impacts of AI-assisted coding to inform adaptive strategies (Kazemitabaar et al., 2024).
6. Human-in-the-Loop, Always: Champion a collaborative model where AI augments human capabilities, rather than aiming for full replacement (Adnovum, 2025). Keep humans firmly in control, especially for critical decisions and validation.

The future of software development will involve AI. Trends like vibe coding are powerful indicators of that shift. But the quality of that future — whether it’s secure, equitable, reliable, and ultimately beneficial — depends on the choices we make now. Let’s ensure we’re coding not just with vibes, but with wisdom, foresight, and a deep sense of responsibility.

Let’s vibe on, but let’s vibe responsibly.

References

Defining Vibe Coding & Core Concepts

• Bitget News. (2025, March 31). Is crypto the ultimate vibe coding industry? Retrieved March 31, 2025, from https://www.bitget.com/news/detail/12560604668135
• Gitpod. (2025, March 31). ‘Vibe coding’ is a revolution for optimistic creatives. Gitpod Blog. Retrieved March 31, 2025, from https://www.gitpod.io/blog/vibe-coding
• McNulty, N. (2025, March 31). Vibe Coding. AI-Assisted Coding for Non-Developers. Medium. Retrieved March 31, 2025, from https://medium.com/@niall.mcnulty/vibe-coding-b79a6d3f0caa (Cites DEV Community post by erikch: https://dev.to/erikch/what-i-learned-vibe-coding-30em)
• Reddit. (n.d.-a). What is vibe coding? [Online forum post]. Reddit. Retrieved March 31, 2025, from https://www.reddit.com/r/Bard/comments/1jn2v3f/what_is_vibe_coding/
• Replit. (n.d.-a). What is Vibe Coding? Replit Blog. Retrieved March 31, 2025, from https://blog.replit.com/what-is-vibe-coding
• Replit. (n.d.-b). What is Vibe Coding? Replit Blog. Retrieved March 31, 2025, from https://blog.replit.com/what-is-vibe-coding#:~:text=Vibe%20coding%20means%20leaning%20on,getting%20stuck%20in%20technical%20details. (Note: Specific fragment link provided in source list)
• Vibe coding. (n.d.). In Wikipedia. Retrieved March 31, 2025, from https://en.wikipedia.org/wiki/Vibe_coding
• Willison, S. (2025, March 19). Not all AI-assisted programming is vibe coding (but vibe coding rocks). Simon Willison’s Weblog. Retrieved March 31, 2025, from https://simonwillison.net/2025/Mar/19/vibe-coding/

Applications, Benefits & Positive Perspectives

• Gitpod. (2025, March 31). ‘Vibe coding’ is a revolution for optimistic creatives. Gitpod Blog. Retrieved March 31, 2025, from https://www.gitpod.io/blog/vibe-coding
• Replit. (n.d.-b). What is Vibe Coding? Replit Blog. Retrieved March 31, 2025, from https://blog.replit.com/what-is-vibe-coding
• Security Journey. (2025, March 31). 10 Professional Developers on the True Promise and Peril of Vibe Coding. Retrieved March 31, 2025, from https://www.securityjourney.com/post/10-professional-developers-on-the-true-promise-and-peril-of-vibe-coding

Concerns, Criticisms & Potential Drawbacks

• Adnovum. (2025, March 31). Will AI Replace Software Engineers? Unveiling the Truth in 2024. Adnovum Blog. Retrieved March 31, 2025, from https://www.adnovum.com/blog/will-ai-replace-software-engineers
• Cendyne.dev. (2025, March 19). “Vibe Coding” vs Reality. Retrieved March 31, 2025, from https://cendyne.dev/posts/2025-03-19-vibe-coding-vs-reality.html
• Legit Security. (2025, March 31). AI Code Generation: The Risks and Benefits of AI in Software. Retrieved March 31, 2025, from https://www.legitsecurity.com/blog/ai-code-generation-benefits-and-risks
• McNulty, N. (2025, March 31). Vibe Coding. AI-Assisted Coding for Non-Developers. Medium. Retrieved March 31, 2025, from https://medium.com/@niall.mcnulty/vibe-coding-b79a6d3f0caa (Cites DEV Community post by erikch: https://dev.to/erikch/what-i-learned-vibe-coding-30em)
• Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation. ResearchGate. https://www.researchgate.net/publication/384502842_Artificial-Intelligence_Generated_Code_Considered_Harmful_A_Road_Map_for_Secure_and_High-Quality_Code_Generation (Note: Source provided as PDF link, details extracted) (Also appears as arXiv:2408.10554)
• Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence generated code considered harmful: A road map for secure and high-quality code generation. arXiv preprint arXiv:2408.10554v2. https://arxiv.org/pdf/2408.10554
• Reddit. (n.d.-b). Karpathy’s ‘Vibe Coding’ Movement Considered Harmful [Online forum post]. Reddit. Retrieved March 31, 2025, from https://www.reddit.com/r/programming/comments/1jms5sv/karpathys_vibe_coding_movement_considered_harmful/
• Security Journey. (2025, March 31). 10 Professional Developers on the True Promise and Peril of Vibe Coding. Retrieved March 31, 2025, from https://www.securityjourney.com/post/10-professional-developers-on-the-true-promise-and-peril-of-vibe-coding
• Siddiq, M. L., Santos, J., Chaparro, O., Lopes, C., & Tan, L. (2024). Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise. arXiv preprint arXiv:2412.06603v2. https://arxiv.org/html/2412.06603v2
• Vibe coding. (n.d.). In Wikipedia. Retrieved March 31, 2025, from https://en.wikipedia.org/wiki/Vibe_coding

Responsible AI: Security Vulnerabilities & Risks

• All Things Open. (2025, March 31). 6 limitations of AI code assistants and why developers should be cautious. Retrieved March 31, 2025, from https://allthingsopen.org/articles/ai-code-assistants-limitations
• Center for Security and Emerging Technology (CSET). (2025a, March 31). Cybersecurity Risks of AI-Generated Code. Retrieved March 31, 2025, from https://cset.georgetown.edu/publication/cybersecurity-risks-of-ai-generated-code/
• Center for Security and Emerging Technology (CSET). (2025b, March 31). Cybersecurity Risks of AI-Generated Code [PDF]. Retrieved March 31, 2025, from https://cset.georgetown.edu/wp-content/uploads/CSET-Cybersecurity-Risks-of-AI-Generated-Code.pdf
• ITPro. (2025, March 31). AI-generated code risks: What CISOs need to know. Retrieved March 31, 2025, from https://www.itpro.com/technology/artificial-intelligence/ai-generated-code-risks-what-cisos-need-to-know
• Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation. ResearchGate. https://www.researchgate.net/publication/384502842_Artificial-Intelligence_Generated_Code_Considered_Harmful_A_Road_Map_for_Secure_and_High-Quality_Code_Generation
• SecureFlag. (2024, October 16). The risks of generative AI coding in software development. SecureFlag Blog. Retrieved March 31, 2025, from https://blog.secureflag.com/2024/10/16/the-risks-of-generative-ai-coding-in-software-development/
• Siddiq, M. L., Santos, J., Chaparro, O., Lopes, C., & Tan, L. (2024). A systematic literature review on the impact of AI models on the security of code generation. PLoS ONE, 19(5), e0302848. https://doi.org/10.1371/journal.pone.0302848 (Note: Source 28 inferred as this based on content/context)

Responsible AI: Prompt Injection & System Manipulation

• Center for the Analysis of Targeted Attacks (CETAS). (n.d.). Indirect Prompt Injection: Generative AI’s Greatest Security Flaw. The Alan Turing Institute. Retrieved March 31, 2025, from https://cetas.turing.ac.uk/publications/indirect-prompt-injection-generative-ais-greatest-security-flaw
• IBM. (n.d.). What Is a Prompt Injection Attack? IBM Think. Retrieved March 31, 2025, from https://www.ibm.com/think/topics/prompt-injection
• OWASP Foundation. (n.d.). LLM01:2025 Prompt Injection. OWASP Top 10 for LLM & Generative AI Security. Retrieved March 31, 2025, from https://genai.owasp.org/llmrisk/llm01-prompt-injection/
• SC Media. (2025, March 31). How AI coding assistants could be compromised via rules file. Retrieved March 31, 2025, from http://www.scmagazine.com/news/how-ai-coding-assistants-could-be-compromised-via-rules-file
• Zhang, Y., et al. (2024). DeVAIC: A Tool for Security Assessment of AI-generated Code. arXiv preprint arXiv:2404.07548v2. https://arxiv.org/html/2404.07548v2 (Note: Source 29 inferred based on content)

Responsible AI: Code Errors, Reliability & Testing

• Henley, A. Z., Kazemitabaar, M., Hughes, M. C., & Van Meter, P. (2024). Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning. arXiv preprint arXiv:2410.08922v1. https://arxiv.org/html/2410.08922v1 (Note: Source 48 points here)
• Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation. ResearchGate. https://www.researchgate.net/publication/384502842_Artificial-Intelligence_Generated_Code_Considered_Harmful_A_Road_Map_for_Secure_and_High-Quality_Code_Generation
• Shang, W., Zhou, X., Xia, X., Lo, D., & Hassan, A. E. (2024). Assessing the Performance of AI-Generated Code: A Case Study on GitHub Copilot. University of Waterloo ECE. Retrieved March 31, 2025, from https://ece.uwaterloo.ca/~wshang/pubs/ISSRE_2024 (Note: Full reference details inferred)
• Siddiq, M. L., et al. (2024). Are Large Language Models the End of Programming? arXiv preprint arXiv:2409.19182v2. https://arxiv.org/pdf/2409.19182 (Note: Source 13 points here)
• Zügner, D., Parshin, D., & Günnemann, S. (n.d.). LLM vs. Human: An Empirical Study of the Reliability of Code Generated by Large Language Models. arXiv. (Note: Inferred reference based on citation in Pearlmutter et al., 2024; specific arXiv ID not provided in source text)

Responsible AI: Safety & Harm Potential

• Safe Generative AI Workshop. (n.d.). Retrieved March 31, 2025, from https://safegenaiworkshop.github.io/

Responsible AI: Bias & Fairness

• Bhatt, U., et al. (2024a). Diverse Yet Biased: Towards Mitigating Biases in Generative AI (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(22), 24790–24791. https://ojs.aaai.org/index.php/AAAI/article/view/30512/32655
• Bhatt, U., et al. (2024b). Diverse Yet Biased: Towards Mitigating Biases in Generative AI (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(22). https://ojs.aaai.org/index.php/AAAI/article/view/30512
• Mei, J., et al. (2024). Bias in Large Language Models: Origin, Evaluation, and Mitigation. arXiv preprint arXiv:2411.10915v1. https://arxiv.org/html/2411.10915v1
• Sun, Z., et al. (2024). Measuring and Mitigating Demographic Bias in Code Generation Models. OpenReview. Retrieved March 31, 2025, from https://openreview.net/pdf?id=BOP5McdqGy
• Yuan, L., et al. (2024). Bias and Fairness in Large Language Models: A Survey. Computational Linguistics, 50(3), 1097–1186. https://direct.mit.edu/coli/article/50/3/1097/121961/Bias-and-Fairness-in-Large-Language-Models-A

Societal Impacts: Job Market & Developer Roles

• Adnovum. (2025, March 31). Will AI Replace Software Engineers? Unveiling the Truth in 2024. Adnovum Blog. Retrieved March 31, 2025, from https://www.adnovum.com/blog/will-ai-replace-software-engineers
• AIPRM. (n.d.). 50+ AI Replacing Jobs Statistics 2024. Retrieved March 31, 2025, from https://www.aiprm.com/ai-replacing-jobs-statistics/
• Brainhub. (2024). Is There a Future for Software Engineers? The Impact of AI [2024]. Brainhub Library. Retrieved March 31, 2025, from https://brainhub.eu/library/software-developer-age-of-ai
• Exploding Topics. (2024). 60+ Stats On AI Replacing Jobs (2024). Retrieved March 31, 2025, from https://explodingtopics.com/blog/ai-replacing-jobs
• Iyer, S., et al. (2024). From Today’s Code to Tomorrow’s Symphony: The AI Transformation of Developer’s Routine by 2030. arXiv preprint arXiv:2405.12731. https://arxiv.org/pdf/2405.12731
• Kim, J., et al. (2024). GENERATIVE AI IMPACT ON LABOR MARKET: ANALYZING CHATGPT’S DEMAND IN JOB ADVERTISEMENTS. arXiv preprint arXiv:2412.07042. https://arxiv.org/pdf/2412.07042 (Note: Source 50 points here)
• Reddit. (n.d.-c). AI effects on the job market [Online forum comment]. Reddit. Retrieved March 31, 2025, from https://www.reddit.com/r/computerscience/comments/197bsau/ai_effects_on_the_job_market/
• Simbla. (n.d.). AI-and-Employment-Revolution:-Navigating-the-Impact-of-Artificial-Intelligence-on-the-Job-Market. Simbla Blog. Retrieved March 31, 2025, from https://www.simbla.com/post/ai-and-employment-revolution:-navigating-the-impact-of-artificial-intelligence-on-the-job-market
• Tommie Experts. (2025, March 31). Generative AI’s Real-World Impact on Job Markets. University of St. Thomas Newsroom. Retrieved March 31, 2025, from https://news.stthomas.edu/generative-ais-real-world-impact-on-job-markets/

Societal Impacts: Broader Ethics, Accountability & Trust

• Adnovum. (2025, March 31). Will AI Replace Software Engineers? Unveiling the Truth in 2024. Adnovum Blog. Retrieved March 31, 2025, from https://www.adnovum.com/blog/will-ai-replace-software-engineers
• Bhatt, U., et al. (2024b). Diverse Yet Biased: Towards Mitigating Biases in Generative AI (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(22). https://ojs.aaai.org/index.php/AAAI/article/view/30512
• Computing Research Association (CRA). (2024, October). The Security Risks of Generative AI: From Identification and Mitigation to Responsible Use. CRA News. Retrieved March 31, 2025, from https://cra.org/crn/2024/10/the-security-risks-of-generative-ai-from-identification-and-mitigation-to-responsible-use/
• Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence generated code considered harmful: A road map for secure and high-quality code generation. arXiv preprint arXiv:2408.10554v2. https://arxiv.org/pdf/2408.10554

Societal Impacts: Cognitive Development & Learning

• Kazemitabaar, M. A., et al. (2024). Protecting Human Cognition in the Age of AI. arXiv preprint arXiv:2502.12447v1. https://arxiv.org/html/2502.12447v1
• Kazemitabaar, M. A., et al. (2025a). Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning. Austin Z. Henley. Retrieved March 31, 2025, from https://austinhenley.com/pubs/Kazemitabaar2025IUI_AIFriction.pdf
• Kazemitabaar, M. A., Henley, A. Z., Hughes, M. C., & Van Meter, P. (2025b). Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning. Proceedings of the 30th International Conference on Intelligent User Interfaces (IUI ‘25). ACM. (Note: Source 48/49 details combined and inferred conference) (Also appears as arXiv:2410.08922)
• Microsoft Dev Blogs. (2025, March 31). How does generative AI impact Developer Experience? Premier Developer Blog. Retrieved March 31, 2025, from https://devblogs.microsoft.com/premier-developer/how-does-generative-ai-impact-developer-experience/
• Schaefer Marketing Solutions. (2025, February 12). How frequent AI usage is leading to cognitive decline. BusinessesGrow.com. Retrieved March 31, 2025, from https://businessesgrow.com/2025/02/12/cognitive-decline/

Disclaimers and Disclosures

This article combines the theoretical insights of leading researchers with practical examples, and offers my opinionated exploration of AI’s ethical dilemmas, and may not represent the views or claims of my present or past organizations and their products or my other associations.

Use of AI Assistance: In the preparation for this article, AI assistance has been used for generating/ refining the images, and for styling/ linguistic enhancements of parts of content.

License: This work is licensed under a CC BY-NC-ND 4.0 license.
Attribution Example: “This content is based on ‘[Title of Article/ Blog/ Post]’ by Dr. Mohit Sewak, [Link to Article/ Blog/ Post], licensed under CC BY-NC-ND 4.0.”

Follow me on: | Medium | LinkedIn | SubStack | X | YouTube |

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI