AI Programming: The Overlooked Engine of the Business Model Revolution across Society
Editor's Note
Many managers in traditional industries may think that the revolution in AI programming has little to do with their businesses. This is a dangerous miscalculation. In the 21st century, software is no longer an independent industry but a "nervous system" that permeates every capillary of every industry.
In current business discussions, the focus of artificial intelligence (AI) mostly lies in application levels such as chatbots and content generation. However, a deeper and more disruptive change is quietly taking place. It doesn't target the end - points of enterprise marketing or customer service but directly hits the core of value creation - software development. This silent revolution is driven by AI programming. It is not just a simple efficiency tool but a brand - new production paradigm that is fundamentally rewriting the logic of value creation and foreshadowing profound changes in future enterprise forms and even social structures.
If the industrial revolution liberated physical labor, the AI revolution is liberating mental labor. When the threshold of tools collapses, creativity itself becomes the new productivity. As mental labor gradually breaks free from the threshold of tool skills, the scarcity in production shifts from "being able to do" to "being able to think and define". Competitive advantages shift from operational skills to the abilities of abstract modeling, aesthetic judgment, and posing complex problems.
The starting point of this change is marked by the popularization of tools like GitHub Copilot. They are like "intelligent co - pilots" for programmers, liberating developers from a large amount of repetitive work by understanding programmers' intentions and automatically completing code. Subsequently, AI - native development environments represented by Cursor have emerged, taking human - machine collaboration to a new height and giving birth to a brand - new way of working, which is called "Vibe Coding" in the cutting - edge practices in Silicon Valley.
"Vibe Coding": The Leap from Instructions to Resonance
One of the key points of the methodology of "Vibe Coding" is to let AI complete task decomposition and path planning under "intention traction". Especially in scattered and discrete real - world business scenarios, enhancing "traction ability" is often more crucial than "intention understanding". Ordinary people have limited abilities to ask questions and define problems. A good system should "lead those who can't ask the right questions to the correct results", which echoes the idea of Sarasvathy's Effectuation theory - do less prediction and more experimentation.
When the marginal cost of building complex software drops sharply, the problem is no longer just about efficiency but also about economics, organizational science, and sociology. When software can write software, capital starts to directly hire algorithmic labor. As the cost curve changes, the curves of organization and institutions also bend.
"Vibe Coding" no longer describes the process of humans giving precise and rigid instructions to machines but a state of highly tacit cooperation between humans and AI. In this model, developers play the roles more like "creative directors" or "band conductors". Through natural language, what they convey to AI is no longer "create a function that takes X as input and returns Y" but higher - level intentions, the "vibe" of the product, core logic, and expected user experience.
Many managers think that the progress of AI programming only means higher efficiency for programmers and faster software delivery. This is a linear and incremental understanding that completely underestimates the upcoming discontinuous disruption. The real significance of this revolution is not "doing the same thing faster" but fundamentally changing "who can do things" and "what things can be done".
When the marginal cost of creating a fully - functional software application drops from millions of dollars in team salaries and several years of time to the API call fees for a few cups of coffee and a few hours of "Vibe Coding", it is no longer just an efficiency issue. It is an economic problem, a social structure problem, and a strategic problem related to the survival and development of every industry. This also explains why the weight of aesthetics and narrative in engineering activities has increased. Creativity is not a lightning bolt of inspiration but a programmable power grid. Vibe Coding connects the power grid to the real - world system, illuminating every detail of the product.
Imagine a world where "creativity is the product":
An experienced retail purchasing manager can transform her unique insights into the supply chain into a more intelligent inventory management application than the existing ERP system over a weekend.
A fitness coach with millions of followers on a short - video platform can instantly generate and release a personalized fitness app with her unique training concepts, directly challenging Keep or Peloton.
A small restaurant owner who knows the local community well can easily create a highly customized takeout and membership system, with a user experience for his members even better than that of the Meituan app.
When all this becomes possible, the "democratization" of software development is no longer just a technical term but a huge force reshaping the market landscape. It means that any individual or small team with in - depth industry knowledge and unique creativity can bypass traditional capital and technical barriers and directly launch asymmetric attacks on existing market leaders.
Therefore, the evolution of AI programming is not just a storm within the programmer world but a business model revolution that will sweep across the whole society. It will profoundly affect every traditional industry that relies on software for operation, fundamentally changing the ways we design business models, gain competitive advantages, build organizations, and define the value of talents. To clearly understand the transmission path of this revolution, we will discuss it from the following three levels.
First - level Change: The Dual - track Revolution - Reconstruction of New Entrants and Crisis of Incumbents
The popularization of AI programming ability is opening up two completely different paths of change in the business world and triggering an inevitable conflict: one is the radical "reconstruction path" for entrepreneurs and new entrants, and the other is the difficult "transformation path" for traditional enterprises.
For new entrants, this is a "from 0 to 1" model reconstruction. In the past, the biggest challenge for entrepreneurs was how to transform a brilliant idea into a real product. This required a huge amount of start - up capital to form an engineer team and endure a long development cycle. AI programming is completely removing this core obstacle. Since the marginal construction cost is approaching zero, the bottleneck of entrepreneurship has shifted from "whether it can be made" to "whether it can be quickly tested for errors + whether there is a sustainable supply of creativity and aesthetics". In the past, the question was "can we do it"; now, the question is "is it worth trying". As the construction cost continues to decrease, speed and imagination become the new thresholds.
Case 1: The Rise of the "One - Person Unicorn" - Pieter Levels
Pieter Levels (online name levelsio) is the most well - known practitioner of this concept. He single - handedly creates and operates multiple highly profitable websites, including Nomad List for digital nomads and Photo AI for generating portraits using AI. According to the data he publicly shared, his personal annual income has long exceeded one million dollars. He widely uses AI tools to assist in programming, generate content, and handle customer service, leveraging his personal productivity to the extreme. His success proves that an individual with an excellent vision and the ability to harness AI can completely build a business empire that used to require a team of dozens of people to support.
Levelsio's practice reveals the essence of the new entrants' business model: prototype ideas at extremely low cost and high speed and directly verify them in the market. They have no historical burdens and no large organizational inertia. They can build their business models from the very first day around the rapid cycle of "creativity - AI implementation - market verification" on a blank slate.
For traditional incumbents, this is a crisis of "changing the engine on a flying plane". Many managers in traditional industries may think that the revolution in AI programming has little to do with their businesses. This is a dangerous miscalculation. In the 21st century, software is no longer an independent industry but a "nervous system" that permeates every capillary of every industry.
Whether it is the control system driving a smart factory, the supply - chain platform optimizing global logistics, the financial model for risk assessment, or the CRM system managing customer relationships, every aspect of modern business is defined and driven by software. Therefore, a fundamental logic emerges: a revolution that completely changes the way of software production will inevitably completely change all industries that rely on software for operation and competition.
Case 2: Hadrian's Disruption of Traditional Manufacturing
Hadrian, a startup in Silicon Valley, is using software and AI to completely disrupt high - precision manufacturing fields such as aerospace. They regard a precision manufacturing factory as a "software problem", using AI software to automatically convert customers' design drawings into the optimal processing paths and having robots carry out all - weather automated production. This shortens the delivery cycle of precision parts that used to take several months to just a few days. For traditional manufacturers, their competitors are no longer other factories but a software company. This new entrant is leveraging the productivity advantage of AI programming to launch a dimensionality - reduction attack on the existing business model.
This kind of "software - enabled manufacturing" not only compresses the delivery cycle but also rewrites the organizational boundaries: external design, simulation, prototyping, and compliance verification can flow into the production center through standardized interfaces, forming a "modular - pluggable" industrial division of labor. Traditional vertical integration is reorganized by a "task network". Interfaces are the new boundaries, and protocols are the new organizational laws. When manufacturing is software - enabled, the boundaries of the industry are drawn on APIs.
The huge challenge for traditional enterprises lies in "organizational inertia and model inertia". Cross - departmental coordination, standard processes, and compliance audits can control risks but significantly slow down the frequency of error - testing and the learning rate. They are like changing the engine on a plane flying at high speed. They need to maintain stability while achieving technological upgrading and are likely to fall into the trap of "using new technologies to do old things", making it difficult to unleash the disruptive potential of AI programming.
Second - level Change: Construction and Dissemination of AI - native Business Models
The productivity revolution brought about by AI programming has directly magnified the importance of business model design ability to an unprecedented level. When the cost and time of "building it" are no longer the main obstacles, the core strategic question shifts from "what can we do?" to "what should we do?" This requires enterprises to establish a brand - new and complete logic from model design to market defense.
First of all, business model design itself has changed from a "restricted art" to an "unlimited science". In the past, a brilliant business design would be shelved due to high development costs. Today, AI makes the "instant prototyping" of business models possible.
Case 3: The Arrival of Devin - Heralding the Era of "Creativity is the Product"
Devin, the world's first AI software engineer released by Cognition AI, is a glimpse of this future trend. The capabilities demonstrated by Devin have far exceeded "code completion" or "debugging assistance". In a public demonstration, it can receive a task briefing with vague requirements (for example, "create a website for a restaurant in New York"), then independently plan, search for relevant technologies online, write code, test, fix bugs, and finally complete the deployment of the entire project.
The emergence of Devin foreshadows a future where when an AI Agent can independently complete the whole process from an abstract business idea to a fully - functional product, the cost of business model design is almost only the cost of "coming up with the idea". An industry expert without a technical background can directly transform his unique business insights into a usable product prototype without going through long - term communication and an expensive development team. The verification cycle of business models will be compressed from "years" to "weeks" or even "days".
Parallel to this, "intention - traction" - type products will become an important route: through interpretable step - by - step navigation, default options, and question - and - answer guidance, even those who "can't define problems" can achieve complex goals, significantly reducing the "threshold of interacting with AI". Good questions are rarer than good functions. When an Agent can turn abstractions into applications, the question itself is half of the product.
The popularization of this ability has completely subverted the "Product - Market - Fit" (PMF) rule, which was once regarded as the golden rule in Silicon Valley. Since Marc Andreessen proposed the concept of PMF, finding PMF has become the core task of all startups and internal innovation projects in enterprises. Its underlying logic is that before investing large - scale resources (especially expensive engineering resources) in market expansion, it is necessary to verify whether the product meets a strong market demand in a small - scale scope. This linear process of "verify first, then expand" is essentially to minimize risks in an environment with limited resources and high development costs.
However, when AI programming reduces the marginal cost of software development to almost zero, the foundation on which this classic rule is based begins to shake. If the cost of creating a Minimum Viable Product (MVP) is no longer months of engineers' salaries but just a few hours of AI calls and creative thinking, the importance of the core requirement of "avoiding development risks" is greatly reduced. At this time, the old link of "predict - invest - expand" gives way to the new link of "diverge - parallel - screen". Instead of betting on a single point, it is better to let the market conduct "distributed A/B experiments". Dissemination is the experiment, users are the reviewers, and links are the channels. The market is unfolded in parallel at once rather than being "initiated" step by step. This is the so - called "dissemination > PMF", which reverses the traditional "causal logic" (find PMF first, then seek dissemination) into an "exploration logic" (discover PMF through dissemination). In the new link, the emotion - defining ability and emotional influence (aesthetics/narrative/persona) of creators can be more quickly converted into small - batch trial production and gradual volume increase on the manufacturing end after being amplified by AI. Dissemination behavior becomes the de - facto "market screener". AI enables those who can't write code to have the "right to manufacture" and those who can think to obtain the "right to generate power".
Therefore, the product development model of enterprises has changed comprehensively.
In the old paradigm, enterprises need to concentrate resources and spend months or even years to verify a product direction that is considered to have the greatest potential. This process is full of internal demonstrations, market research, and focus - group interviews.
In the new paradigm, enterprises will adopt a "wide - spread of creativity" strategy. Leveraging the powerful ability of AI programming, a very small team or even an individual can generate dozens or even hundreds of micro - product prototypes with different functions, positions, and design styles around a core insight in a very short time.
In the old paradigm, whether a product can enter the market is usually determined by the internal decision - makers in the enterprise - product directors, market experts, or even the CEO's intuition. This experience - based prediction is full of uncertainty and personal biases.
In the new paradigm, the spontaneous dissemination behavior of the market becomes the only judge. Enterprises will put a large number of product prototypes into real social networks and communities and then wait for the "natural selection" of the market. Products that can trigger spontaneous discussions, sharing, and viral dissemination among users are regarded as signals with PMF potential. To accommodate this "external selection mechanism", enterprises should implement "modular interfaces" and "open cooperation" internally: make data, processes, payments, and distribution into combinable interfaces, allowing external creators/suppliers to connect according to their abilities and interests, forming a task - cooperation network without employment.
In the old paradigm, product promotion relies on expensive and centralized marketing activities such as advertising and public relations.
In the new paradigm, dissemination is embedded in product design and highly depends on distributed media networks. The value of personal IPs and short - video platforms is greatly magnified. An entrepreneur with a strong personal IP can bring high - quality seed users to multiple product prototypes quickly generated by AI through his trust and influence in a specific community, achieving a very low - cost cold start. This kind of trust - based dissemination has a far higher conversion efficiency and feedback quality than traditional advertising. The algorithm - based recommendation mechanism of short - video platforms provides a perfect channel for "large - scale parallel experiments". Its algorithm can accurately push different product ideas to potential segmented user groups and provide instant feedback through users' interaction data.
Third - level Change: The Ultimate Transformation of Organizational Form - The Collapse of Old Institutions and the Rise of "Task - based Organizations"
The comprehensive changes in the production paradigm, strategic rules, and sources of competitive advantages will ultimately lead to a fundamental reshaping of the enterprise organizational form. The old corporate system established since the industrial revolution, with division of labor and management as the core, is becoming unprecedentedly redundant and cumbersome in the face of the extreme individual empowerment brought about by AI programming.
1. Redundancy of the Old Corporate System: When Coordination Costs Outweigh Value Creation
One of the core functions of traditional enterprise organizations is to coordinate a large - scale human resource to complete complex tasks. Project managers, department directors, product managers... The existence of these roles is largely to solve the "coordination cost" problems such as information asymmetry, task decomposition, and progress synchronization. However, when a core creative person can directly "talk" to an AI Agent and let the AI independently complete task decomposition, code generation, testing, and deployment, the roles of these intermediate management and coordination layers are greatly weakened.
AI reduces not the labor cost but the entropy of the organization. Hierarchy is a historical solution, not the default setting for the future. AI programming not only automates "execution" but also largely automates "management". AI significantly reduces internal coordination friction, and the reasons for the existence of many hierarchical levels are replaced by technology. If traditional enterprises do not sink into being "designers of interfaces/rules/incentives" but stick to "hierarchy/approval/person - based governance", they will be vulnerable in the competition with those extremely agile "