The crisis of understanding arrived without fanfare, but its confession was public. On February 6, 2025, Jean Hsu sat down to build a “Trader Joe’s Snack Box Builder” and made a startling admission: “I didn’t even read the code that was generated.” Within two hours, she had deployed a functional application. “I didn’t edit a single line of code by hand, unless you count my OpenAI API key I copy/pasted.”

That same day, Andrej Karpathy—co-founder of OpenAI, former AI director at Tesla, a programmer whose expertise was beyond question—made his own confession that would redefine what it means to create software. His tweet about “vibe coding” described something unprecedented in the history of human craft: the ability to build functional, complex systems without comprehending how they work.

“There’s a new kind of coding I call ‘vibe coding,’” Karpathy wrote, “where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” He wasn’t describing a failure of understanding—he was announcing its obsolescence. The code he generated functioned perfectly. His applications ran smoothly. Yet he admitted, “The code grows beyond my usual comprehension, I’d have to really read through it for a while.”

The psychological weight of this shift became apparent in Karthik S.’s account of his transformation. After 27 years of programming, he found himself practicing “proper black box programming, where I was just looking at the input and output and had no clue what was in the middle.” The adjustment proved disorienting: despite AI-generated code appearing verbose and inefficient, he resisted optimization because it was “use and throw code.”

Within months, this moment of technological surrender had crystallized into something larger: a fundamental shift in the relationship between human intention and machine implementation, between what we want to create and how we understand what we’ve made. By March 2025, Merriam-Webster had added “vibe coding” to their dictionary as official slang. Y Combinator reported that 25% of their Winter 2025 startup cohort had codebases that were 95% AI-generated.

“The code grows beyond my usual comprehension, I’d have to really read through it for a while.” —Andrej Karpathy

The speed of this transformation suggests we’re witnessing more than a new programming technique. We’re watching the emergence of a new form of human agency—one where competence and comprehension have finally, decisively parted ways.

What Does It Mean to Build Without Understanding?

The philosophical implications of vibe coding become clear when examined against the backdrop of human craft traditions. For millennia, mastery meant deep understanding—the blacksmith knew metal, the architect understood stress and load, the programmer comprehended algorithms and data structures. Knowledge accumulated through practice, failure, and gradual insight.

Vibe coding represents a break from this tradition so radical it initially defied categorization. Simon Willison, a prominent developer advocate, struggled to define the boundary: “If an LLM wrote every line of your code, but you’ve reviewed, tested, and understood it all, that’s not vibe coding in my book—that’s using an LLM as a typing assistant.” True vibe coding, he argued, meant building software “without reviewing the code it writes.”

This distinction revealed something profound about the nature of human expertise—and the psychological cost of abandoning it. A LinkedIn post specifically addressed “beating imposter syndrome in a vibe coding world,” acknowledging that many developers are “quietly struggling with imposter syndrome in a world where AI tools are writing more code than we do.” The post recognized that “in an era of vibe coding, it’s easy to feel like you’re not a ‘real developer’ especially when GitHub Copilot or ChatGPT can build a working app in minutes.”

Traditional programming demanded what philosophers might call “transparent” knowledge—the programmer could trace every decision, explain every function, defend every choice. Vibe coding introduced “opaque” competence—the ability to achieve desired outcomes through systems whose internal logic remained hidden.

Andrew Ng, one of AI education’s most respected voices, recognized the psychological complexity of this shift. “It’s unfortunate that that’s called vibe coding,” he said, noting how the term misled people into thinking developers could simply “go with the vibes.” The reality proved far more demanding: “coding with AI is ‘a deeply intellectual exercise’” that left him “frankly exhausted by the end of the day.”

Ng’s exhaustion points to a paradox at the heart of vibe coding. While external observers saw developers casually directing AI tools, the practitioners experienced intense cognitive load. They were learning to think at a new level of abstraction—not about implementation details, but about system architecture, user experience, and the subtle art of communicating intent to artificial intelligence.

“In an era of vibe coding, it’s easy to feel like you’re not a ‘real developer’ especially when GitHub Copilot or ChatGPT can build a working app in minutes.”

The epistemological questions multiply when examined closely. If a developer can’t explain how their authentication system works but can verify that it functions securely, what kind of knowledge do they possess? When Replit reported that 75% of their users never write code manually, relying entirely on natural language instructions, were these people programmers or something else entirely?

How Did We Learn to Surrender Control?

The cultural adoption of vibe coding moved with unprecedented speed, suggesting it fulfilled a latent need that traditional programming had left unaddressed. The technology itself had been developing for years—AI coding assistants had evolved from simple autocomplete functions to systems capable of rewriting entire files and maintaining coherent architectures across complex projects.

But the philosophical breakthrough came when practitioners began accepting AI output without full comprehension. This wasn’t technological advancement—it was psychological evolution. Humans had learned to trust incomprehensible systems in other domains (few drivers understand their car’s electronic systems, fewer still comprehend their smartphones), but creative work had remained a bastion of transparent knowledge.

Business Insider dubbed vibe coding “Silicon Valley’s latest buzzword,” but the phenomenon transcended tech industry hype. It represented a new form of human-machine collaboration where humans provided vision and oversight while machines handled implementation. The division of labor seemed natural until examined philosophically.

The venture capital community’s enthusiasm reflected deeper economic pressures. As one investor told Business Insider, “This isn’t a fad
 This is the dominant way to code. And if you are not doing it, you might just be left behind.” This urgency revealed anxiety about competitive advantage in an era when AI capabilities advanced monthly.

“Replit built the whole app in less than 10 minutes. I could have grabbed a coffee or sent a couple of emails in the meantime; nothing was needed from me.” —Fadi Boulos

Educational institutions faced an existential challenge. If Anthropic’s data showed Claude Code users achieving 79% automation rates compared to Claude.ai’s 49%, what should computer science curricula emphasize? Traditional programming fundamentals or AI collaboration skills?

The market provided a stark answer. A vibe coding bootcamp emerged charging “$38k+” for four months of AI literacy training—significantly more expensive than traditional software engineering bootcamps. The program’s structure, where “students are expected to create their own curriculum using AI,” suggested a fundamental shift in how programming education might evolve. As one observer noted, “This says a lot about how the market is shifting to employers wanting ‘vibe coders’ rather than traditional software engineers.”

The startup ecosystem provided the perfect laboratory for this transformation. Unlike established enterprises constrained by legacy systems and risk management protocols, new companies could experiment freely with post-comprehension development. The results were dramatic: functional applications built in days rather than months, prototypes that validated business concepts before traditional development would have produced wireframes.

Yet beneath the productivity gains lay deeper questions about the nature of technological progress. Were we witnessing human augmentation or replacement? Enhancement or obsolescence?

Why Does the Loss of Understanding Matter?

The concerns raised by prominent developers about vibe coding’s rapid expansion revealed fears that extended far beyond software engineering. Simon Willison’s worry that the definition was “already escaping its original intent” reflected a deeper anxiety about maintaining meaningful distinctions in human expertise.

When people began applying the term “vibe coding” to all forms of AI-assisted programming, they were, as critics noted, “flattening important distinctions” between casual experimentation and serious engineering. This semantic drift mattered because it obscured the difference between tools that augmented human understanding and tools that replaced it.

Professional software development had evolved elaborate practices to ensure code quality, security, and maintainability precisely because the stakes were high. Security research revealed the costs of surrendering these safeguards: 58% of AI-generated APIs lacked proper parameter sanitization, enabling SQL injection attacks. Another study found that 40% of AI-built applications contained hardcoded credentials—fundamental security violations that occurred when developers accepted AI output without review.

The psychological cost became visible in community discussions. One Reddit user expressed visceral disgust: “Why ‘Vibe Coding’ Makes Me Want to Throw Up.” The responses revealed deep professional anxiety: “I’m happy, knowing that as a pre-LLM software dev my talents are going to become even more valuable the older I get, thanks to the mountains of broken slop that is going to be generated.”

“I’m frankly exhausted by the end of the day.” —Andrew Ng on AI-assisted coding

These weren’t merely technical problems—they were symptoms of a deeper epistemological shift. Traditional programming demanded that developers understand security principles well enough to recognize vulnerabilities. Vibe coding enabled functional applications without such understanding, creating systems that worked but couldn’t be properly secured or maintained.

Andrew Ng’s strong stance that advice discouraging people from learning to code would be “some of the worst career advice ever given” reflected recognition that foundational knowledge remained essential even when AI handled implementation. The question wasn’t whether AI could generate code, but whether humans could guide and evaluate it effectively.

The psychological implications extended beyond individual developers to entire professional communities. Senior engineers who had spent decades accumulating deep technical knowledge faced the possibility that their expertise might become less valuable than skill in directing AI systems. Junior developers wondered whether learning programming fundamentals made sense when AI could implement most requirements directly.

This transformation echoed historical moments when new technologies disrupted established crafts. But vibe coding presented a unique challenge: it didn’t replace human skills with machines, but with human-machine collaboration that operated beyond individual human comprehension.

What Happens When Tools Become Incomprehensible?

The deeper implications of vibe coding become apparent when viewed as part of humanity’s long relationship with tools that exceed our understanding. We already trust countless systems whose internal operations remain opaque—the GPS that navigates our route, the algorithm that curates our news, the financial software that manages our payments.

Vibe coding made this dynamic visible in creative work, traditionally considered a domain of human agency and understanding. When developers could describe functionality in natural language and receive working implementations, they gained unprecedented creative power while surrendering traditional forms of control.

The philosophical tension centers on the question of authorship and responsibility. If a developer directs an AI to build an e-commerce platform but can’t explain how the payment processing works, who bears responsibility for security vulnerabilities? When 25% of Y Combinator startups operate with 95% AI-generated codebases, what does it mean to be a technical founder?

These questions matter because they preview challenges that will emerge across knowledge work as AI capabilities expand. Doctors already use diagnostic AI they don’t fully understand. Lawyers rely on research tools that process legal databases beyond human capacity. Financial analysts depend on models that identify patterns invisible to human perception.

Vibe coding represents an early experiment in post-comprehension expertise—the development of professional judgment that operates above the level of detailed technical understanding. This isn’t ignorance; it’s a new form of knowledge that emphasizes system-level thinking, outcome evaluation, and the subtle art of human-AI collaboration.

The educational implications are profound. If programming becomes more about directing AI than implementing algorithms, should computer science curricula emphasize prompt engineering over data structures? If expertise increasingly means knowing what to build rather than how to build it, how do we prepare students for careers that don’t yet exist?

How Do We Define Human Value in an Automated World?

The emergence of vibe coding raises fundamental questions about the future of human expertise in an increasingly automated world. When machines can implement our ideas more efficiently than we can, what remains uniquely human? What forms of knowledge and skill retain value when AI can handle technical execution?

Research on team performance suggests that projects using architectural constraints reduced post-deployment bug rates by 41% compared to unrestricted vibe coding. This finding points toward a new division of labor: humans providing oversight, constraints, and high-level guidance while AI handles detailed implementation.

The most successful practitioners of vibe coding aren’t those who surrender all control, but those who learn to operate effectively at a higher level of abstraction. They develop intuition about AI capabilities and limitations, skill in crafting precise requirements, and judgment about when to accept AI suggestions versus when to intervene.

This evolution reflects a broader transformation in the nature of professional expertise. Traditional professional knowledge emphasized deep technical skills within specific domains. The AI era seems to demand broader systems thinking, cross-disciplinary understanding, and the ability to collaborate effectively with artificial intelligence.

Andrew Ng’s observation that everyone at his AI Fund knows how to code—from the CFO to the receptionist—illustrates this shift. They’re not software engineers, but coding literacy helps them “tell a computer what they want it to do” more effectively in their respective roles.

The economic implications extend beyond individual careers to entire industries. If software development becomes more accessible through vibe coding, what happens to the traditional software consulting industry? If non-technical founders can build functional prototypes, how does this change startup dynamics and venture capital evaluation criteria?

Yet the transformation isn’t simply about replacement or obsolescence. The most sophisticated vibe coding implementations combine AI capability with human judgment in ways that exceed what either could achieve alone. The human provides creative vision, contextual understanding, and ethical judgment. The AI provides implementation speed, pattern recognition, and the ability to handle complex technical details.

How Does Vibe Coding Actually Work in Practice?

The reality of vibe coding emerges most clearly through the experiences of those who’ve attempted it. Jean Hsu’s detailed account of building a “Trader Joe’s Snack Box Builder” captures the disorienting nature of this new approach: “I didn’t even read the code that was generated. The experience was both delightful and occasionally frustrating.” Her journey from skepticism to deployment took “under two hours from the time I downloaded Cursor and started brainstorming different product ideas to when it was deployed.”

Karthik S., a 27-year programming veteran, described his transition to “proper black box programming, where I was just looking at the input and output and had no clue what was in the middle.” The psychological adjustment proved significant: despite the AI-generated code appearing verbose and inefficient, he resisted optimization because it was “use and throw code.”

The workflow itself has crystallized into recognizable patterns. Fadi Boulos documented his approach: create a comprehensive Business Requirements Document, then feed it to AI tools. “Replit built the whole app in less than 10 minutes. Impressive, to say the least. I just watched as it explained what it was doing. I could have grabbed a coffee or sent a couple of emails in the meantime; nothing was needed from me.”

The technical infrastructure has evolved to support this hands-off approach. Modern platforms like Cursor Chat and GitHub Copilot can now “make real-time predictions about what you’re trying to do and offer intuitive suggestions,” enabling software creation “even if you’ve never written code before.” The progression from tools that completed single lines to systems that “can now rewrite an entire file for you, or create new components” represents a qualitative shift in human-AI collaboration.

“This was proper black box programming, where I was just looking at the input and output and had no clue what was in the middle.” —Karthik S., 27-year programming veteran

What Does Responsible Vibe Coding Look Like?

The distinction between reckless delegation and thoughtful AI collaboration emerges clearly in the practices of successful vibe coders. Research shows that teams using architectural constraints reduced post-deployment bug rates by 41% compared to unrestricted vibe coding, suggesting that some human oversight remains essential.

Simon Willison’s “golden rule” provides a framework for responsible practice: “I won’t commit any code to my repository if I couldn’t explain exactly what it does to somebody else.” This standard maintains human accountability while leveraging AI capabilities.

Professional developers have developed strategies for maintaining quality while embracing AI assistance. The “Three Layer Testing” protocol addresses AI’s tendency toward optimistic path coding by requiring comprehensive test coverage, integration validation, and security scanning. These practices preserve vibe coding’s speed advantages while mitigating its systematic weaknesses.

The most sophisticated implementations combine rapid iteration with selective human intervention. Practitioners learn to recognize when AI output requires scrutiny—typically around security boundaries, performance-critical sections, and integration points with existing systems. This selective attention allows developers to maintain oversight without sacrificing the creative flow that makes vibe coding appealing.

“My golden rule for production-quality AI-assisted programming is that I won’t commit any code to my repository if I couldn’t explain exactly what it does to somebody else.” —Simon Willison

The Dark Side: When Vibe Coding Goes Wrong

The enthusiasm surrounding vibe coding has obscured systematic problems that emerge when the approach is applied carelessly. Security research reveals that 58% of AI-generated APIs lack proper parameter sanitization, enabling SQL injection attacks. Another study found that 40% of AI-built applications contained hardcoded credentials—fundamental security violations that occur when developers accept AI output without review.

The human cost of these failures becomes apparent in community discussions. One Reddit user expressed visceral disgust with the practice: “Why ‘Vibe Coding’ Makes Me Want to Throw Up.” The post attracted responses revealing deep professional anxiety: “I’m happy, knowing that as a pre-LLM software dev my talents are going to become even more valuable the older I get, thanks to the mountains of broken slop that is going to be generated.”

The emergence of expensive vibe coding bootcamps—one charging “$38k+” for four months of AI literacy training—suggests market exploitation of the trend. The program’s structure, where “students are expected to create their own curriculum using AI,” raises questions about educational value versus marketing hype.

Technical debt accumulation happens at rates 3x higher than traditional development when vibe coding is applied without constraints. The debt manifests as duplicated logic, inconsistent coding patterns, and architectural decisions that complicate future development. Cost implications often remain hidden until deployment, with documented cases of cloud computing bills increasing tenfold due to inefficient AI-generated algorithms.

Cybersecurity analysis revealed how the same tools enabling legitimate development could be exploited for “VibeScamming”—using AI platforms to generate “production-ready phishing kits with zero pushback.” This dark mirror of democratized development suggests that lowering barriers to software creation also lowers barriers to malicious activity.

“I’m happy, knowing that as a pre-LLM software dev my talents are going to become even more valuable the older I get, thanks to the mountains of broken slop that is going to be generated.”

What Did We Lose?

The psychological toll of vibe coding’s rapid adoption becomes apparent in the authentic voices of practitioners grappling with professional identity crisis. A LinkedIn post specifically addressed “beating imposter syndrome in a vibe coding world,” acknowledging that many developers are “quietly struggling with imposter syndrome in a world where AI tools are writing more code than we do.”

The concern reflects deeper questions about the nature of expertise itself. When 75% of Replit users never write code manually, what distinguishes a programmer from a user? One Reddit commenter argued that “if your sole expertise is crafting prompts for a language model, then another model can easily handle that task for you.”

The intergenerational implications become visible in Cameron Adams’ account of his 11-year-old son’s natural comfort with vibe coding. The child’s patient typing (“c-o-u-l-d y-o-u p-l-e-a-s-e p-u-t t-h-e r-e-s-t-a-r-t b-u-t-t-o-n i-n t-h-e m-i-d-d-l-e”) demonstrated comfort with AI as a collaborative partner that contrasted with Adams’ need to guide and interpret the process.

Andrew Ng’s strong stance that advice discouraging people from learning to code would be “some of the worst career advice ever given” reflects recognition that foundational knowledge remains essential. Yet the practical question remains: if AI can implement most requirements directly, what specific knowledge should humans prioritize?

The loss extends beyond individual careers to institutional knowledge. When 25% of Y Combinator startups operate with codebases they cannot fully explain, the startup ecosystem faces unprecedented risks around maintenance, security, and technical leadership. The consequences may only become apparent when these companies need to scale, debug complex issues, or integrate with enterprise systems that demand transparency.

Perhaps most significantly, we may be losing the iterative learning process that builds programming intuition. Traditional development required wrestling with implementation details, debugging edge cases, and gradually building mental models of how systems work. Vibe coding shortcuts this process, potentially creating a generation of developers who can direct AI but cannot evaluate its output or recover when it fails.

“Over the last year, a few people have been advising others to not learn to code
 I think we’ll look back at some of the worst career advice ever given.” —Andrew Ng

Where Does Human Agency Go from Here?

The philosophical challenges raised by vibe coding preview questions that will define the next phase of human-AI collaboration. As AI capabilities expand beyond code generation to include design, analysis, research, and decision-making, we’ll face similar questions about the value of human understanding versus the efficiency of AI delegation.

The trajectory appears to be toward forms of human expertise that operate at higher levels of abstraction. Rather than knowing how systems work internally, professionals may need to become expert at evaluating outcomes, providing contextual judgment, and ensuring that AI-generated solutions align with human values and intentions.

This shift has precedent in other domains. Architects don’t need to understand the metallurgy of steel beams to design buildings, but they must understand structural principles well enough to work with engineers. Film directors don’t need to know how cameras work internally, but they must understand visual storytelling well enough to guide cinematographers.

Vibe coding suggests that software development might evolve similarly—toward a model where practitioners understand systems and user needs well enough to direct AI implementation, even without comprehending every technical detail.

The psychological adaptation required for this transition shouldn’t be underestimated. Professionals who built their identities around technical mastery must learn to find meaning in higher-level creative direction. The satisfaction that comes from solving complex implementation problems must be replaced by satisfaction in defining problems worth solving and ensuring solutions meet human needs.

Even Karpathy himself appears to be wrestling with these transitions, becoming “careful to separate ‘real coding’ and ‘AI-assisted coding’ from pure vibe experiments.” This suggests that even pioneers of post-comprehension development recognize the need for nuanced understanding of when transparency matters and when it doesn’t.

The long-term implications may be positive if we can navigate the transition thoughtfully. By delegating routine implementation to AI, humans might focus more on creative problem-solving, user experience, and ensuring technology serves human flourishing. But this outcome isn’t guaranteed—it requires intentional choices about how we structure education, professional development, and the division of labor between humans and machines.

What Kind of Future Are We Building?

The story of vibe coding’s rapid emergence reveals something profound about our historical moment: we’re learning to live with incomprehensible power. The AI systems that enable vibe coding represent tools more sophisticated than any in human history, yet we’re adapting to them with remarkable speed.

This adaptation has costs and benefits that we’re only beginning to understand. The democratization of software creation opens possibilities for innovation and entrepreneurship that were previously limited to those with extensive technical training. When 75% of Replit users can build applications without writing code, we’re witnessing a genuine expansion of human creative capability.

Yet the same transformation raises questions about the value of expertise, the nature of human agency, and our relationship with increasingly powerful but opaque tools. When we can achieve our goals without understanding our methods, we gain efficiency but potentially lose wisdom.

The resolution may lie not in choosing between human understanding and AI capability, but in finding new forms of collaboration that preserve what’s essential about human judgment while leveraging the unprecedented capabilities of artificial intelligence. This requires thoughtful consideration of what forms of knowledge we want to preserve and develop, even when machines can handle the technical implementation.

The emergence of dark patterns suggests the stakes of this choice. Cybersecurity research revealed “VibeScamming”—using the same AI platforms for malicious purposes, with some offering “production-ready phishing kits with zero pushback.” When tools that democratize creation also democratize harm, human judgment becomes more essential, not less.

Simon Willison’s “golden rule”—that he won’t commit code he couldn’t explain to someone else—represents one approach to maintaining human agency in an AI-augmented world. It’s a commitment to understanding that goes beyond efficiency to preserve the transparency and accountability that human systems require.

The future being built through vibe coding and similar developments will be shaped by choices we make now about the relationship between human understanding and machine capability. We can choose to preserve meaningful human agency while leveraging AI’s power, or we can drift toward a world where incomprehensible systems make decisions we can’t evaluate or contest.

The philosophical questions raised by vibe coding—about expertise, understanding, creativity, and human value—aren’t just technical concerns. They’re questions about what kind of beings we want to be and what kind of world we want to create with our increasingly powerful tools. The code may write itself, but the future still requires human intention, judgment, and choice.

In the end, vibe coding represents a moment of transition—not just in how we build software, but in how we understand our relationship with the tools we create. Whether this transition leads to human flourishing or diminishment depends on our wisdom in navigating the balance between capability and comprehension, between efficiency and understanding, between what we can do and what we should do.

The last coders may not be those who stop programming, but those who learn to program the programmers—to direct artificial intelligence with wisdom, maintain human agency in an automated world, and ensure that our most powerful tools serve our deepest human values.