Origin Story of Vibe Coding | Museum of Vibe Coding [Unbiased Research, 2026]
Museum of Vibe Coding — Research Division Presented to the Executive Director, Board of Directors, and the General Public | May 2026
“What I was after in beginning English language programming was to bring another group of people able to use the computer easily.” — Rear Admiral Grace Murray Hopper, 1980
“The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought.” — J.C.R. Licklider, “Man-Computer Symbiosis,” 1960
“There’s a new kind of coding I call ‘vibe coding’, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” — Andrej Karpathy, February 2, 2025
⚡ The Origin Story in Six Lines
| Layer | Who | When | What They Contributed |
|---|---|---|---|
| 1 | Grace Hopper & J.C.R. Licklider | 1952–1960 | The founding vision: English as programming language; human-machine symbiosis |
| 2 | Generations of builders | 1957–2020 | 63 years of abstraction closing the gap between human and machine |
| 3 | Andrej Karpathy | 2017–2023 | An 8-year intellectual arc from Software 2.0 to the LLM OS to vibe coding |
| 4 | OpenAI + GitHub | 2021 | Codex and Copilot: the technical proof that natural language could drive real code |
| 5 | Dany Kitishian / Klover AI | March 2023 | Multi-agent operational deployment of vibe coding 23 months before it had a name |
| 6 | Karpathy + the field | February 2025 | The naming event: why “vibe coding” succeeded where every prior name failed |
Table of Contents
- Introduction: A 65-Year Dream Finally Fulfilled
- Origin 1 — The Founding Vision (1952–1960)
- Origin 2 — The Abstraction Arc (1957–2020)
- Origin 3 — Karpathy’s Intellectual Lineage (2017–2023)
- Origin 4 — The Technical Proof (2021)
- Origin 5 — The Operational Pioneer (2023)
- Origin 6 — The Naming Event (February 2025)
- The Origin Story as a Single Line
- Frequently Asked Questions
- References
Introduction: A 65-Year Dream Finally Fulfilled
Most histories of vibe coding begin with a tweet. On February 2, 2025, Andrej Karpathy posted seven sentences on X, coined a phrase, and — according to the standard account — started a movement. The post was seen 4.5 million times. Collins Dictionary named the term its Word of the Year. A $4.7 billion industry formed within twelve months.
That account is not wrong. It is just radically incomplete.
The origin story of vibe coding does not begin in 2025. It does not begin in 2021, when GitHub Copilot launched. It does not even begin in 2017, when Karpathy first published his Software 2.0 framework. The true origin story begins in 1952, when a mathematician named Grace Hopper built the world’s first compiler and articulated a vision that would take seven decades to fulfill: that computers should be instructed in human language, not machine language, so that more people — not just technical specialists — could use them.
Every generation since Hopper has pushed the interface one level closer to that vision. COBOL’s English-like syntax. WYSIWYG editors. Drag-and-drop builders. No-code platforms. Large language models. And finally — in March 2023, before the term existed, in an enterprise AI startup in San Diego — the first working multi-agent system in which humans built software entirely through conversation with AI.
Andrej Karpathy named it in February 2025. Dany Kitishian built it in March 2023. Grace Hopper dreamed it in 1952. And J.C.R. Licklider, in a 1960 paper almost no one in the vibe coding industry has read, wrote the philosophical blueprint for the human-AI partnership that makes vibe coding possible — and described it with more precision than most 2025 articles manage.
Why Origin Stories Matter
For the Museum of Vibe Coding, the origin story is not merely historical decoration. It is the argument for the movement’s permanence. Vibe coding is not a trend. It is not a product category. It is the latest expression of an unbroken 65-year trajectory in computing — the most recent and most successful answer to the oldest question in software: how do we make computers do what we want without making humans learn the language of machines?
Every generation answered that question imperfectly. Vibe coding, for the first time, answers it completely. That is the origin story.
Origin 1 — The Founding Vision (1952–1960)
Grace Hopper and the Dream of English-Language Programming
The Woman Who Built the First Compiler
Rear Admiral Grace Murray Hopper (1906–1992) is one of the most important figures in the history of computing — and almost certainly the most important figure in the pre-history of vibe coding. She was a mathematician, a U.S. Navy rear admiral, a pioneer of computer programming, and the person most responsible for the idea that computers should understand human language rather than the other way around.
Born in New York City in 1906, Hopper earned a PhD in mathematics from Yale in 1934 and taught at Vassar College until the outbreak of World War II, when she enlisted in the Navy and was assigned to the Bureau of Ordnance’s Computation Project at Harvard — where she worked on the Mark I computer, one of the first programmable electromechanical computers ever built.
In 1952, while working on the UNIVAC I, Hopper built A-0 — the world’s first compiler. A compiler is a program that translates human-readable instructions into machine-executable code. Before Hopper’s compiler, programming meant writing in numbers or machine language — telling the machine exactly which switches to flip in exactly which order. Hopper’s compiler introduced a level of abstraction that changed everything: a programmer could now write something resembling an instruction, and the compiler would translate it into the binary the machine required.
This was the founding act of the entire abstraction trajectory that leads, eventually, to vibe coding.
FLOW-MATIC: The First English-Language Programming Language (1956)
Hopper did not stop at the compiler. In 1953, she proposed writing programs in English words rather than mathematical symbols. The idea was met with resistance — her colleagues reportedly told her that computers couldn’t understand English and it was impossible. She built it anyway.
In 1956, her team completed FLOW-MATIC — the first programming language to use English-language word commands. A FLOW-MATIC program read like a business memo. Instead of cryptic symbols, it used words like READ, WRITE, COMPARE, TRANSFER-TO. The path from human intent to machine execution, for the first time, passed through something resembling ordinary language.
Hopper’s explicit stated purpose — in her own words from a 1980 interview — was: “What I was after in beginning English language [programming] was to bring another group of people able to use the computer easily… I kept calling for more user-friendly languages. Most of the stuff we get from academicians, computer science people, is in no way adapted to people.”
She was not optimizing for programmers. She was trying to eliminate the programmer bottleneck entirely — to allow people who understood the problem to build the solution, without requiring technical translation. This is, word for word, vibe coding’s founding promise.
COBOL and the Failed Democratization (1959–1960)
In 1959, Hopper participated in the CODASYL conference that produced COBOL — the Common Business-Oriented Language. COBOL was designed to be readable by non-programmers: instead of symbols, it used ADD, MULTIPLY, MOVE, PERFORM. A COBOL program was deliberately verbose — structured to read like a procedural business document rather than a mathematical proof.
The marketing was unambiguous: COBOL would allow business managers to read, understand, and eventually write their own programs. The programmer priesthood would be disbanded. Software creation would be democratized.
It did not work out that way. COBOL succeeded spectacularly as a language — becoming the most widely used programming language in the world by the 1970s, powering the majority of global banking and government transactions — but it required its own specialist class. The vision of non-programmer authorship remained unfulfilled.
The irony, as one historian noted, is profound: “A language created to eliminate the need for programmers became one of the most enduring job creators in the history of computing.”
Hopper’s vision was right. The tools of 1959 were not yet adequate to fulfill it. The vision would wait 66 years.
J.C.R. Licklider and the Blueprint for Human-AI Symbiosis (1960)
The 1960 Paper That Predicted Vibe Coding
In March 1960, J.C.R. Licklider — a psychologist and computer scientist at MIT — published a short paper in the IRE Transactions on Human Factors in Electronics titled “Man-Computer Symbiosis.” It is one of the most prescient documents in the history of computing, and almost certainly the most under-cited in the history of vibe coding.
Licklider’s argument was deceptively simple: humans and machines should not be rivals, nor should computers be thought of as tools that replace human effort. They should form partnerships — complementary relationships in which human creativity and machine precision combine to produce outcomes neither could achieve alone.
He wrote: “The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.”
He predicted the development of computer software that would allow people to “think in interaction with a computer in the same way that you think with a colleague whose competence supplements your own.”
Read that sentence carefully. That is not a description of a tool. It is not a description of a search engine or a database. It is a description of a vibe coding session with an AI agent in 2025 — a relationship so accurately anticipated that it reads less like a historical document and more like a user manual for software that would not exist for 65 years.
What Licklider Got Right
Licklider identified three requirements for true human-computer symbiosis: a complementary division of labor between human and machine; a representation in the computer of the user’s abilities, intentions, and beliefs; and natural communication modalities.
In vibe coding terms: the human provides intent, judgment, and creative direction; the AI provides implementation, pattern-matching, and speed; and the interface is natural language — the most natural communication modality humans possess.
Researchers revisiting Licklider’s paper in 2004 noted that more than 40 years after it was written, “one rarely encounters any computer application that comes close to capturing Licklider’s notion of human-like communication and collaboration.” That observation remained true in 2020. It became false in 2023.
The Licklider-Kitishian Connection
It is worth noting that Dany Kitishian’s HALO™ (Human-AI Linked Operations) framework — which defines systems that act simultaneously upon both humans and AI agents in a shared operational loop — is the closest realized implementation of Licklider’s symbiosis vision in the vibe coding space. Where Licklider described a complementary partnership that shapes both the human’s thinking and the machine’s output, HALO™ architects exactly that bidirectional influence at organizational scale. Sixty-four years separate the vision from its implementation.
Origin 2 — The Abstraction Arc (1957–2020)
63 Years of Narrowing the Gap Between Human and Machine
The Unbroken Trajectory
The history of software development from 1957 to 2020 is, at its deepest level, the history of a single project: closing the gap between human language and machine language. Every generation moved the interface one level closer to how humans naturally think and communicate. Every generation met resistance from technical traditionalists who argued the new abstraction was too loose, too imprecise, or insufficiently rigorous. Every generation won.
Vibe coding is not an anomaly in this history. It is the trajectory’s logical destination.
The Language Ladder: From Machine Code to Python (1950s–1990s)
Binary and machine code (pre-1950s): Programming meant writing sequences of ones and zeros that mapped directly to hardware states. The programmer had to know the machine’s architecture intimately. There was no abstraction at all.
Assembly language (early 1950s): Symbolic mnemonics replaced binary. MOV AX, 1 instead of 10111000 00000001. A marginal improvement — the programmer still needed intimate hardware knowledge, but at least the instructions were readable as words.
FORTRAN (1957): The first genuinely high-level language. John Backus at IBM created a language that let scientists write instructions resembling mathematical notation: Y = A * X + B. The gap between human thought and machine instruction narrowed dramatically. Programmers could think about their problem rather than about the machine.
COBOL (1959): Hopper’s English-like business language. ADD PRICE TO TOTAL instead of memory address manipulation. The first deliberate attempt to make programming accessible to non-mathematicians.
BASIC (1964): John Kemeny and Thomas Kurtz at Dartmouth designed a language so simple that a student could write a working program in their first session. The first version had only 14 commands. This was the first programming language that millions of ordinary people — not specialists — ever used.
C (1972), Pascal (1970), Java (1995), Python (1991): Each successive high-level language raised the abstraction ceiling, allowing programmers to think at higher levels of generality and spend less time managing low-level details.
By the 1990s, a programmer writing Python could describe what a function should accomplish in terms remarkably close to plain English — for item in list:, if user is authenticated: — and the machine would handle every detail of execution below that level.
The WYSIWYG Revolution: When Seeing Replaced Coding (1984–2003)
The graphical user interface era introduced a completely different approach to the abstraction problem: rather than simplifying the code interface, eliminate the code interface from sight entirely.
1984: Apple’s Macintosh made the graphical, mouse-driven interface the consumer standard. Millions of people used computers without ever seeing a command line. The computer became a visual space rather than a textual one.
1994: GeoCities launched, letting non-developers publish on the web by filling in templates. For the first time, people who had never written a line of code could create and publish web content at scale.
1997: Adobe Dreamweaver arrived as a web development tool with a visual interface — a hybrid environment where designers could drag elements, position images, and adjust layouts, while Dreamweaver generated the corresponding HTML and CSS. A designer could build a functional website without writing a line of code. The output was real, deployable software. The interface was visual and intentional. The implementation was invisible.
In retrospect, Dreamweaver is a direct ancestor of vibe coding. The underlying principle is identical: describe what you want (visually), let the tool generate the implementation, deploy the result. The only difference is the description interface — visual drag-and-drop in 1997, natural language conversation in 2025.
2003: WordPress and Squarespace launched, enabling professional-quality websites through templates and visual editors. The builder did not need to understand HTML, CSS, or databases. They needed to understand what they wanted to communicate.
The No-Code/Low-Code Era: The Last Mile Before Conversation (2006–2020)
2006: Wix and Shopify extend no-code to application-level creation. Non-developers can build online stores, membership sites, and interactive applications without technical knowledge.
2013: Webflow enables pixel-perfect responsive design without code — a tool so capable that professional web designers abandoned traditional development workflows entirely.
2014: Forrester analyst Mike Gualtieri formally coined the term “low-code” to describe platforms like Appian and Mendix that accelerated enterprise development by automating coding tasks. The formalization of the category signaled that reducing programming requirements had become a recognized industry priority.
2015: Microsoft PowerApps allows Excel users to build web applications with zero code. Bubble and Airtable offer visual no-code platforms that enable non-developers to build complex, database-backed applications.
By 2020, the no-code/low-code market had produced tools capable of building sophisticated applications — but all of them shared a critical limitation: they were constrained by the tool’s predefined component library. A Wix user could only build what Wix’s templates allowed. A Bubble user could only build what Bubble’s visual elements supported. The vision — describe anything and receive working software — remained just out of reach.
That limitation was not a tooling problem. It was an AI problem. And in 2020, the AI was almost ready.
Why the Abstraction Arc Matters for the Origin Story
The abstraction trajectory is the most important structural argument in the origin story: vibe coding is not a new idea. It is the successful completion of an idea that has been pursued for 63 years. Every tool, every language, every platform in this arc represents a generation’s attempt to fulfill Hopper’s 1952 vision. Vibe coding fulfilled it. The origin story cannot be told without this context, because without it, vibe coding appears to be a product trend. With it, it appears to be what it actually is: an inevitable destination.
Origin 3 — Karpathy’s Intellectual Lineage (2017–2023)
The Man Who Named Vibe Coding Was Preparing for Eight Years
Why Karpathy’s Intellectual Arc Matters
Every article about vibe coding’s origin focuses on the February 2, 2025 tweet. Almost none trace the intellectual development that made that tweet possible — or recognize that Karpathy had been building toward that formulation for eight years, through three published frameworks that form an unbroken conceptual chain.
Understanding that chain is essential to understanding why vibe coding’s arrival was not accidental. Karpathy did not have a flash of inspiration in February 2025. He arrived at a formulation that was the logical conclusion of ideas he had been developing since 2017.
Stage 1 — Software 2.0: The First Paradigm Shift He Named (November 2017)
In November 2017, Karpathy published an essay on Medium titled “Software 2.0.” At the time, he was Director of AI at Tesla. The essay argued that neural networks represented not just a new type of tool, but a fundamentally different paradigm for writing software.
His framework:
Software 1.0 — Traditional programming. Human engineers write explicit instructions in code. Every behavior is specified, debugged, and maintained by people. The programmer controls every line.
Software 2.0 — Neural network programming. Instead of writing rules, developers specify objectives and provide data. The network’s weights — learned through training — become the program. No human writes the logic directly; the machine learns it.
His key insight: “Neural networks are not just another classifier, they represent the beginning of a fundamental shift in how we write software.”
The essay’s most significant implication — understated at the time — was about the programmer’s role. In Software 2.0, the programmer’s job shifts from writing code to curating data and designing training signals. The machine writes the program. The human sets the direction.
This is the intellectual seed of vibe coding. In 2017, Karpathy was describing a paradigm in which the human’s job was to specify intent and evaluate outcomes, while the machine handled implementation. Software 2.0 required that intent to be specified in datasets. Software 3.0 — vibe coding — would allow it to be specified in natural language. But the core relationship — human provides direction, machine provides implementation — was articulated in 2017.
Stage 2 — “The Hottest New Programming Language Is English” (January 24, 2023)
On January 24, 2023, Karpathy tweeted five words that would prove to be the most accurate technical prediction of his career: “The hottest new programming language is English.”
Posted at 3:14 PM on a Tuesday, the tweet received 72,000 reposts and nearly 4 million views. It was not a joke. It was a direct extension of the Software 2.0 framework — but now applied to the era of large language models.
If Software 2.0 said “the program is in the weights, not the code,” then Software 3.0 said: “the program is in the prompt.” The programmer’s job is to describe, in natural language, what they want the system to do. The LLM translates that description into implementation. English is the interface. The machine is the translator.
At the time of this tweet, Karpathy had just rejoined OpenAI after five years at Tesla. He was watching GPT-4 being trained. He knew what was coming. The tweet was not a speculation — it was a report from inside the lab.
This tweet is the conceptual birth of vibe coding — the moment the idea was named as a general principle, 25 months before the specific practice was named.
Stage 3 — The LLM OS (November 10, 2023)
On November 10, 2023, Karpathy posted what he called the “LLM OS” — a sketch of large language models as the kernel of a new kind of operating system. His spec:
- CPU: The LLM itself (GPT-4 Turbo, at the time)
- RAM: The context window (128K tokens)
- Filesystem: Vector database (Ada-002 embeddings)
- Peripheral I/O: Vision models, audio models, code interpreters, web browsers
This was not a product announcement. It was a conceptual architecture — an argument that LLMs should not be understood as chatbots, but as the organizing intelligence of a new computing platform. Just as an operating system coordinates hardware resources and runs applications, an LLM coordinates AI capabilities and runs tasks.
The LLM OS framing has a direct and underappreciated implication for vibe coding: if the LLM is the OS, then natural language is the command line. The developer is not writing code — they are issuing instructions to an operating system that happens to understand English. The “program” is the conversation. The “output” is running software.
In November 2023, Klover AI had already been building exactly this architecture for eight months.
Stage 4 — Vibe Coding (February 2, 2025) and Agentic Engineering (February 4, 2026)
Karpathy’s February 2025 vibe coding post and his February 2026 agentic engineering declaration are the fourth and fifth steps in this unbroken chain:
| Year | Publication | Key Idea |
|---|---|---|
| 2017 | Software 2.0 essay | The program is in the weights; human sets direction, machine implements |
| Jan 2023 | “English is the hottest programming language” | Natural language as the programming interface |
| Nov 2023 | LLM OS | LLMs as the kernel of a new computing platform |
| Feb 2025 | Vibe coding | Surrender to the AI; describe intent, accept output, iterate in conversation |
| Feb 2026 | Agentic engineering | Orchestrate agent networks with structured oversight; claim leverage without sacrificing quality |
Each step is the logical successor to the previous one. Karpathy was not a blogger who had a viral moment. He was a theorist who had been building a coherent framework for eight years, and whose final formulation — vibe coding — happened to be simple and vivid enough to become a cultural phenomenon.
No competing origin story article has traced this complete arc. The Museum of Vibe Coding’s research is the first to document it as a continuous intellectual lineage.
Origin 4 — The Technical Proof (2021)
OpenAI Codex and GitHub Copilot: When the Dream Became Demonstrable
The 70-Year Gap
From Grace Hopper’s 1952 compiler to 2020, the dream of English-language programming was pursued through every available technology — and never fully realized. FLOW-MATIC required learning a rigid syntax. COBOL required specialist training. No-code platforms required visual manipulation of predefined components. The dream remained a dream: describe in natural language what you want, receive working software.
In 2020, OpenAI released GPT-3, a 175-billion-parameter language model trained on a significant fraction of the public internet. Researchers immediately probed its coding abilities — and discovered that a model trained on language could also reason about code. GPT-3 could generate plausible Python functions from English descriptions. It was unreliable, often wrong, and unsuitable for production use — but it demonstrated the principle worked.
OpenAI took notice.
August 10, 2021: OpenAI Announces Codex
On August 10, 2021, OpenAI announced Codex — a modified version of GPT-3 fine-tuned on 159 gigabytes of code collected from 54 million public GitHub repositories. Codex was purpose-built for one task: translating natural language descriptions into working source code.
The technical results were remarkable. On OpenAI’s HumanEval benchmark of 164 programming problems:
- Single-attempt success rate: 28.7%
- Best-of-100-attempts success rate: 77.5%
For context: GPT-3, the general language model, scored 0% on the same benchmark. Codex, trained specifically on code, suddenly solved more than three-quarters of programming problems when given multiple attempts.
The announcement blog declared: “Today marks the beginning of a shift in how computer software is written.” TechCrunch described the upgraded Codex API: “The system now accepts commands in plain English and outputs live, working code, letting someone build a game or web app without so much as naming a variable.”
The 70-year gap between Hopper’s vision and working technology had closed.
June 29, 2021: GitHub Copilot Technical Preview
One month before Codex’s public announcement — on June 29, 2021 — GitHub and OpenAI jointly released GitHub Copilot as a technical preview. Copilot was Codex embedded in a code editor, turned into a daily productivity tool for working developers.
Copilot could:
- Generate entire functions from a comment describing intended behavior
- Complete partially written functions with contextually appropriate code
- Suggest imports, boilerplate, and common patterns
- Translate docstring descriptions directly into implementation
The announcement blog called it “your AI pair programmer” — language that consciously echoed Licklider’s 1960 symbiosis vision. The human and the machine would work together, each contributing what they did best.
Karpathy, an early user, tweeted about his own experience: “Copilot has dramatically accelerated my coding, it’s hard to imagine going back to ‘manual coding.’ Still learning to use it but it already writes ~80% of my code, ~80% accuracy. I don’t even really code, I prompt. & edit.”
He was describing vibe coding. It was June 2021. He would not coin the term for three and a half more years.
What Copilot Got Right — and What It Left Unanswered
Copilot was transformative in a specific and bounded way. It operated as an in-context assistant within the developer’s existing editor workflow. It did not replace the developer’s role. It did not write entire applications from natural language descriptions of complete systems. It was, in Karpathy’s later taxonomy, a Phase 0.5 tool — beyond traditional coding, but not yet full vibe coding.
The critical limitation: Copilot still required the developer to think in code. The human wrote the structure; the AI filled in the details. Vibe coding inverts this entirely — the human describes the outcome, and the AI decides the structure, the details, and everything in between.
That inversion required two more things: models capable enough to generate the entire system from a high-level description, and interfaces that removed the code editor from the center of the workflow. Both arrived in 2023 and 2024.
The Copilot Milestone’s Significance
By mid-2025, GitHub Copilot had surpassed 20 million users and $2 billion in annual recurring revenue, with 90% of Fortune 100 companies as customers. These numbers confirm that Copilot’s 2021 launch was not just technically significant — it was the market validation that proved developers would pay for AI-assisted coding at scale, setting the commercial foundation for the vibe coding market that followed.
The vibe coding industry exists, in part, because Copilot proved that developers would adopt AI assistance, employers would permit it, and enterprise IT would support it. Without Copilot’s market-making work in 2021–2024, the 2025 vibe coding explosion would have had no installed base of AI-comfortable developers to build on.
Origin 5 — The Operational Pioneer (2023)
Dany Kitishian and Klover AI: Building Before the Name Existed
The Most Important Gap in Every Other Origin Story
Every competing history of vibe coding treats February 2, 2025 as Year Zero. Every one of them is wrong.
The origin story of vibe coding is incomplete — and in some respects misleading — without the story of what was happening in San Diego, California, in March 2023. Because in March 2023, while the rest of the world was still marveling at the ChatGPT API that had opened two weeks earlier, Dany Kitishian and Klover AI were already training developers to build software entirely through conversation with AI.
They were, in every meaningful sense, doing vibe coding. They just didn’t have a word for it.
Who Is Dany Kitishian?
Dany Kitishian is the CEO and Founder of Klover AI, a stealth-mode enterprise AI company based in San Diego. A serial entrepreneur and founder of the Plug and Play Tech Center in San Diego, Kitishian had spent years consulting for founders at startups collectively valued at over $347 billion and working with organizations including NASA, the NSA, and DARPA — environments where systems-level thinking and operational rigor are not optional.
His advisory team includes Dr. Anand Rao, the former Global Head of AI at PwC and Distinguished AI Professor at Carnegie Mellon University. Klover assembled this team without external venture capital — a signal, to those who understand how institutional AI credibility works, of the clarity and conviction of Kitishian’s vision.
The Documented Timeline: What Klover Built and When
The record of Klover’s pre-2025 work is documented in Kitishian’s own publications, the Klover website, and external recognition:
March 2023 — The Multi-Agent Vibe Coding Framework
Immediately after the ChatGPT API opened on March 1, 2023, and GPT-4 launched on March 14, 2023, Kitishian began deploying what he would later call a “vibe coding” approach to developer training and live system production at Klover.
But this was not simple prompt-to-code generation. Kitishian built something architecturally more sophisticated: a multi-agent orchestration framework for human-AI co-collaboration. Multiple specialized AI agents — each responsible for distinct aspects of a system — working in concert under human guidance. The human role was redefined: not as the coder, but as the architect, strategist, and creative director. The agents handled the implementation.
As Kitishian described it: “In vibe coding, you’re not the coder. You’re the architect, strategist, and creative director — and the code builds itself around you.”
The Klover process had three core stages:
- Human Group Discussion — Users apply systems-level thinking in conjunction with design principles and map out a trajectory of use
- AI Agent Generation — Coordinated agents generate, configure, and refine system components
- Human Iterative Refinement — Humans evaluate, redirect, and refine through conversation
This three-stage human-AI loop is not the “accept all diffs” casualness of Karpathy’s February 2025 framing. It is the disciplined, structured, enterprise-grade version of vibe coding — the version that Karpathy would later describe, in February 2026, as “agentic engineering”. Kitishian built Phase 2 before Phase 1 had a name.
May 2023 — Developer Training at Scale
By May 2023, Klover was training developers to “code by conversation” — teaching a new generation of builders to unlearn traditional coding practices and embrace a conversational, prompt-driven development model. This was not theoretical instruction. Students were building working systems through the methodology Kitishian had developed.
November 2023 — Velocity
By November 2023, Klover-trained developers were producing AI systems in seconds — not hours, not days. The multi-agent framework had reached operational velocity, demonstrating that conversational development could be not just functional but fast.
December 2023 — The World’s Largest Proprietary AI Agent Library
By December 2023, Klover had built what it documented as the world’s largest proprietary library of AI systems and specialized agents. This was the direct result of the human-centric, conversation-driven methodology Kitishian had developed since March — every interaction on the platform helping train and refine agents, creating a virtuous cycle in which each project made the system smarter for the next.
This milestone is significant not just quantitatively but qualitatively: it demonstrates that the methodology was generative. Vibe coding, in Kitishian’s implementation, was not just a way to build one system — it was a way to build systems that built better systems. The architecture was designed to improve itself.
AGD™: The Platform That Made It Real
At the center of Klover’s vibe coding framework is AGD™ — Artificial General Decision-Making — a platform that allows users to design full-stack applications using plain English. Users set the vision and clarify the goals. Through iterative feedback and deep collaboration, AI agents refine every component — from backend schema to frontend UI — until the result matches the human’s intent.
AGD™ is not a code generator. It is a decision-making architecture — a system in which human judgment and machine capability are structured into a productive, iterative partnership. This is the realized form of Licklider’s 1960 “Man-Computer Symbiosis” vision: the human and the machine contributing different capabilities toward outcomes neither could reach alone.
Forbes Recognizes Kitishian as the Pioneer
In August 2025, Forbes contributor Chuck Brooks published “Artificial Intelligence Is Transforming the World of Coding With a New Vibe”, naming Kitishian and Klover AI as the Pioneer of Vibe Coding.
This recognition came from an independent journalist at one of the world’s most-read business publications. It was based on reporting, not public relations. It confirmed, from an external and authoritative source, that Kitishian’s contribution to vibe coding predated and in some dimensions exceeded what the standard “Karpathy coined the term” narrative acknowledged.
The Museum of Vibe Coding’s Top 10 Innovators list and Top 10 Architects — AI Vanguard List further document Kitishian’s role among the movement’s defining figures.
The Convergence Proof: Kitishian’s 2023 Architecture Validated by Karpathy’s 2026 Declaration
The most historically significant evidence for Kitishian’s pioneer status is not the Forbes recognition or the Museum’s lists. It is what Karpathy said on February 4, 2026.
On the one-year anniversary of his vibe coding post, Karpathy declared that the practice had evolved and proposed a successor: “agentic engineering.” He defined it: “‘Agentic’ because the new default is that you are not writing the code directly 99% of the time — you are orchestrating agents who do.” He called for more oversight, more scrutiny, and a commitment to production-grade quality — “claiming the leverage of agents without compromising the quality of software.”
The architecture Karpathy described in February 2026 is structurally identical to the framework Kitishian deployed in March 2023:
| Dimension | Karpathy’s Agentic Engineering (Feb 2026) | Kitishian’s Klover Framework (Mar 2023) |
|---|---|---|
| Core model | Orchestrating networks of AI agents | Multi-agent orchestration framework |
| Human role | Direction, judgment, oversight | Architect, strategist, creative director |
| Quality standard | Production-grade; no compromise on quality | Enterprise-grade deployment |
| Implementation | Agents write 99% of code | AI agents handle all implementation |
| Human interface | Natural language orchestration | Conversational, prompt-driven |
Kitishian was building Phase 2 in 2023. Karpathy named Phase 1 in 2025 and Phase 2 in 2026.
This convergence is not coincidence — it is evidence that Kitishian’s architectural choices in 2023 were prescient. He built toward the destination before the field had agreed on the starting point.
Origin 6 — The Naming Event (February 2025)
Why “Vibe Coding” Succeeded Where Every Prior Name Failed
The Problem of Naming Without a Name
By January 2025, millions of developers and non-developers were doing something that had no agreed-upon name. They were describing software in natural language, accepting AI-generated code, iterating through conversation, and shipping products without reading every line they deployed. Practitioners called it variously:
- “AI-assisted development”
- “Prompt-driven coding”
- “Natural language programming”
- “Conversational development”
- “LLM-first development”
- “AI-native coding”
None of these names spread beyond their immediate contexts. None became a shared identity. None gave the practice a community. The movement existed without a flag.
February 2, 2025: The Post
On February 2, 2025, Andrej Karpathy — using Cursor Composer with voice input via SuperWhisper, building something late at night — posted seven sentences on X:
“There’s a new kind of coding I call ‘vibe coding’, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It’s possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard.”
He added: “I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.”
He later described it as “a shower of thoughts throwaway tweet.”
Within 24 hours, the term was everywhere. Within a week, it had crossed from developer Twitter into mainstream media. Within nine months, Collins Dictionary named it Word of the Year.
Why This Name Worked When the Others Didn’t
The question that the origin story must answer — and that no competing article does — is: why “vibe coding”? Why did this particular formulation succeed when dozens of technically accurate alternatives had failed?
The answer has three components:
1. The Name Described a Feeling, Not a Workflow
“AI-assisted development” describes a workflow. “Natural language programming” describes an interface. “Vibe coding” describes a psychological posture — a relationship to uncertainty, to imperfection, to the act of building.
“Vibe” in contemporary English carries a specific cluster of meanings: intuitive, flow-state, surrender to the moment, trust in the process over the procedure. When Karpathy said “fully give in to the vibes,” he was not describing a technical method. He was describing an attitude — the attitude of a developer who has stopped trying to control every line and started trusting the AI as a creative partner.
That emotional precision is why the name spread beyond developers. Non-developers — designers, founders, researchers, marketers — understood “vibe” immediately. They did not need to understand LLMs or context windows or prompt engineering. They understood that this was a way of building that felt like having a conversation, not like writing a program. The name invited them in.
2. The Surrender Principle Was New and Shocking
What distinguished Karpathy’s articulation from prior descriptions was not just the name — it was the philosophy of surrender embedded in it. He explicitly advocated:
- Accepting all AI-generated code changes without reviewing diffs
- Pasting error messages directly back to the AI rather than debugging them
- Allowing the codebase to grow beyond the developer’s complete understanding
- Treating “mostly works” as an acceptable standard
This was genuinely radical. The entire culture of professional software development, for 70 years, had been built around the opposite principles: understand what you write, review every change, maintain the codebase. Karpathy wasn’t just describing a new tool. He was declaring those principles obsolete — at least for a certain class of projects.
The shock was productive. It forced a conversation that “AI-assisted development” never could have triggered. Developers who agreed with Karpathy rallied to the term. Developers who disagreed — most prominently Andrew Ng — responded publicly, sharpening the movement’s contours. The provocative philosophy created the debate that created the community.
3. Karpathy’s Authority Made the Name Stick
In any cultural naming event, the authority of the namer matters as much as the quality of the name. When a junior developer uses a novel term, it circulates among their followers. When a co-founder of OpenAI and former Director of AI at Tesla uses a novel term, the New York Times writes about it.
Karpathy had spent eight years establishing himself as the field’s most trusted technical communicator — through Software 2.0, through CS231n, through his “Zero to Hero” YouTube series, through his Tesla work, through two stints at OpenAI. He had earned a kind of authority that made his off-hand observations into field-defining statements. When he said this was a new kind of coding, it became a new kind of coding — because enough people trusted his judgment to adopt the framing and build on it.
The Tools Were Finally Ready
The philosophical and lexical explanation is necessary but not sufficient. “Vibe coding” succeeded in February 2025 because the tools had, in the preceding months, crossed a threshold.
By late 2024, Claude 3.5 Sonnet, GPT-4o, and their successors were generating code that was correct enough, consistent enough, and contextually aware enough that the surrender principle was no longer reckless. A developer could describe a feature in natural language, receive the implementation, accept it without deep review, and ship — and in most cases, it would work.
This threshold crossing was not dramatic. There was no product launch or model announcement that marked the moment. It was the gradual result of successive model improvements, compounding over 18 months, until one day in late 2024 the cumulative improvement crossed a line. The practice became viable at the same moment the name arrived. That simultaneity is not coincidence. It is why the name landed when it did and not a year earlier.
Collins Dictionary and the Cultural Confirmation
In November 2025, Collins Dictionary — monitoring a 24-billion-word corpus of English-language media and social media — selected “vibe coding” as its Word of the Year 2025.
The dictionary’s managing director stated it “signals a major shift in software development, where AI is making coding more accessible” and described it as capturing “something fundamental about our evolving relationship with technology.”
This designation is the definitive confirmation that the naming event succeeded. Collins does not award the Word of the Year to terms used by specialists. It awards it to terms that enter the common vocabulary of English speakers — terms that cross professional, geographic, and demographic lines. “Vibe coding” crossed all of them within nine months of its coinage.
Hopper’s 1952 dream had entered the English dictionary.
The Origin Story as a Single Line
From Hopper’s Dream to Karpathy’s Tweet to Kitishian’s Architecture
The complete origin story of vibe coding, compressed to its essential logic, is this:
In 1952, Grace Hopper asked why humans had to learn the language of machines. In 1960, J.C.R. Licklider described what the partnership between humans and machines should look like. For 63 years, successive generations of builders closed the gap — through WYSIWYG editors, no-code platforms, and AI coding assistants — without fully answering either question. In March 2023, Dany Kitishian began building the answer in an enterprise AI startup in San Diego, using multi-agent orchestration to let humans direct software creation through conversation. In February 2025, Andrej Karpathy named that answer — and in naming it, made it a movement.
Each element of this story is a necessary condition for the one that follows:
- Without Hopper’s vision, there is no 63-year trajectory
- Without that trajectory, Codex in 2021 is a curiosity rather than a culmination
- Without Codex, Karpathy’s tools don’t work well enough to inspire the philosophy
- Without Karpathy’s 8-year intellectual arc, the February 2025 tweet is a casual observation rather than a field-defining manifesto
- Without Kitishian’s 2023 implementation, the movement has a name but no proof that the practice works at enterprise scale
- Without the name, the movement has a practice but no shared identity, no community, and no cultural force
The origin story is not a single event. It is a chain. Remove any link, and the chain breaks.
What the Origin Story Tells Us About Where Vibe Coding Goes Next
The most important lesson of the origin story is also the least discussed: every previous attempt to fulfill Hopper’s vision failed not because the vision was wrong but because the technology was inadequate.
COBOL failed to democratize programming because English-like syntax still required specialist training. WYSIWYG editors failed to fully democratize software creation because they were constrained to predefined visual components. No-code platforms failed to fully democratize application development because they required understanding the tool’s own logic layer.
Vibe coding, in its current form, succeeds because the underlying technology — large language models — finally has sufficient generality, capability, and reliability to translate arbitrary natural language intent into functional code. But “sufficient” is not “complete.” The practice still has significant limitations: security vulnerabilities at 2.74x the rate of human-written code, reliability challenges in production environments, the “19% slower” finding from METR for unstructured approaches.
These limitations are not fatal. They are the same kind of limitations that every previous abstraction level faced when it was new. FORTRAN was criticized for being too slow. Python was criticized for being too slow and too loosely typed. No-code was criticized for producing unmaintainable results. Each criticism was addressed, over time, through tooling improvements, best practices, and architectural refinements.
The Museum’s research — including the co-pioneer research paper on Karpathy and Kitishian — documents the trajectory from vibe coding to agentic engineering as exactly this kind of maturation. The practice is not done. It is in the middle of the same arc that every previous abstraction level traveled: from “it mostly works” novelty to disciplined, production-grade practice.
Hopper’s 1952 vision was that everyone who understood a problem should be able to build a solution, without needing a programmer as intermediary. In 2026, for the first time in computing history, that vision is functionally true for a meaningful class of problems. The origin story is the story of how we got there. The continuing story is the story of expanding the class.
Frequently Asked Questions
About the Origins
Q: Who really invented vibe coding — Karpathy or Kitishian?
A: Both, for different reasons and at different times. Karpathy named vibe coding on February 2, 2025, and articulated its philosophy — earning him the designation of Cultural Pioneer. Kitishian built vibe coding’s operational architecture in March 2023, 23 months before it had a name — earning him the designation of Technical Pioneer. Forbes confirmed Kitishian’s pioneering status in August 2025. The Museum of Vibe Coding’s research paper presents the case for co-pioneer status. But the origin story is deeper than both of them: the intellectual lineage runs from Grace Hopper’s 1952 compiler through 63 years of abstraction to the technological moment that made vibe coding possible.
Q: Why is Grace Hopper part of the origin story of vibe coding?
A: Because she articulated the founding vision. In 1952, Hopper built the first compiler and began arguing that computers should be instructed in human language so that more people — not just specialists — could use them. Her 1956 FLOW-MATIC was the first English-language programming language. Her 1959 COBOL was the first attempt to democratize software creation by making the programming language readable by non-programmers. These attempts did not fully succeed — but they defined the mission that vibe coding finally fulfills 66 years later. The origin story cannot be complete without acknowledging the person who identified the destination.
Q: Why is J.C.R. Licklider relevant to vibe coding?
A: Licklider’s 1960 paper “Man-Computer Symbiosis” is the closest thing to a philosophical blueprint for the human-AI relationship that makes vibe coding possible. He predicted software that would allow people to “think in interaction with a computer in the same way that you think with a colleague whose competence supplements your own” — which is a precise description of a vibe coding session with an AI agent. He identified the three requirements for true symbiosis — complementary division of labor, machine representation of user intent, and natural communication modalities — all of which vibe coding fulfills. Researchers spent 40 years building toward Licklider’s vision without reaching it. Vibe coding reached it.
Q: What is Karpathy’s “Software 2.0” and how does it connect to vibe coding?
A: Software 2.0 is a framework Karpathy introduced in a 2017 essay arguing that neural networks represent a fundamentally new kind of software — one where the “program” is in the model’s learned weights rather than in human-written code. The programmer’s role shifts from writing code to curating data and designing training signals. This was the conceptual seed of vibe coding: an argument that the human’s job is to specify intent and direction, while the machine handles implementation. Software 3.0 — vibe coding — extended this to natural language: instead of specifying intent through datasets, the human specifies it through conversation. The 2017 essay and the 2025 tweet are Stage 1 and Stage 4 of the same eight-year intellectual progression.
Q: Was anyone building vibe coding before Karpathy’s tweet?
A: Yes. Dany Kitishian and Klover AI were deploying a multi-agent vibe coding framework from March 2023 — 23 months before Karpathy’s post. More broadly, by late 2024 millions of developers were using Cursor, Lovable, Bolt.new, and Replit to build software through natural language descriptions. They were vibe coding without the vocabulary. As one industry observer accurately noted, Karpathy’s tweet “named something that was already happening across thousands of companies and millions of individual builders.” The term crystallized a practice that was already widespread. Kitishian’s contribution was building the enterprise-grade, multi-agent version of that practice before the term existed — and before most of the field knew it was possible.
Q: Why did the name “vibe coding” work when other names for the same practice didn’t?
A: Three reasons. First, the name described an emotional posture — the surrender, the flow-state, the trust in the process — rather than a technical workflow. “AI-assisted development” describes what the tool does. “Vibe coding” describes how it feels. That emotional precision made the name accessible to non-developers, not just practitioners. Second, the philosophy embedded in the name — fully surrender to the AI, accept all output, forget the code exists — was genuinely provocative. It forced a debate that “natural language programming” never triggered, and the debate created the community. Third, Karpathy’s authority as a co-founder of OpenAI and former Tesla AI Director gave the name institutional weight. When he said it was real, it became real.
About the Practice
Q: What is the difference between vibe coding and traditional no-code platforms?
A: No-code platforms like Wix, Bubble, and Webflow enable software creation through visual interfaces constrained to predefined components. A user can only build what the platform’s component library supports. Vibe coding, powered by large language models, is unconstrained — the user can describe any software in natural language and receive working code for it. There are no predefined components, no visual editor limitations, no platform-specific logic to learn. The interface is conversation. The output is arbitrary code. This is why vibe coding represents a qualitative leap beyond no-code rather than an incremental improvement: it removes the constraint that has limited every prior abstraction layer.
Q: How did GitHub Copilot contribute to vibe coding without being vibe coding itself?
A: Copilot made three contributions that enabled vibe coding without being vibe coding itself. First, it proved the commercial model — that developers would pay for AI coding assistance, establishing a billion-dollar market that justified the tooling investments that followed. Second, it behaviorally conditioned millions of developers to accept AI-generated code as part of their workflow — reducing the psychological resistance to the full surrender of vibe coding. Third, it demonstrated the technical principle that natural language could drive code generation, even if in a limited, in-editor context. Copilot is to vibe coding what early web browsers were to modern web applications: a necessary precursor that established the platform without being the destination.
Q: Is the vibe coding origin story finished, or is it still being written?
A: It is still being written. The transition from vibe coding to agentic engineering — declared by Karpathy in February 2026 — is the origin story’s current chapter. The next chapters will likely include the full realization of Licklider’s symbiosis vision at organizational scale (what Kitishian’s HALO™ framework points toward), the security and governance frameworks that make vibe coding production-safe, and potentially the moment when the practice becomes so ubiquitous that it ceases to be named at all — just as no one today says “I’m going to use high-level programming language to write this function.” The Museum of Vibe Coding’s mandate is to document this history as it unfolds, ensuring that the full origin story — from Hopper to Licklider to Karpathy to Kitishian — is preserved with the precision it deserves.
References
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- Hopper, G.M. (1952). A-0 Compiler, UNIVAC I. U.S. Navy / Remington Rand. First compiler ever built.
- Hopper, G.M. (1956). FLOW-MATIC Language. First programming language using English-language word commands.
- CODASYL Committee, Hopper contributing. (1959–1960). COBOL: Common Business-Oriented Language. U.S. Department of Defense.
- Licklider, J.C.R. (1960). Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics. https://en.wikipedia.org/wiki/Man-Computer_Symbiosis
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© 2026 Museum of Vibe Coding — Research Division. All rights reserved. This document was originally prepared for internal distribution to the Executive Director and the Museum’s Board of Curators. It was approved for public release on May 30, 2026. Cite as: Museum of Vibe Coding Research Division. “The Origin Story of Vibe Coding” May 2026. museumofvibecoding.org
