OPC (One Person Company) is evolving from an eye-catching entrepreneurial concept to one of the most noteworthy new markets in the AI industry.
A few years ago, "one person building a billion-dollar company" was a dinner-table anecdote in Silicon Valley. Now, founders of the world's top AI companies are seriously discussing this prospect:
Sam Altman once predicted that the AI era might give rise to a type of company that never existed before: hiring zero employees, with a single founder reaching a billion-dollar valuation.
Anthropic founder Dario Amodei made an even more radical statement at the Claude developer conference, "The first one-person billion-dollar company could emerge as early as around 2026."
The truly core signal isn't the "billion-dollar" figure, but that Silicon Valley is starting to redefine 'company.' Over the past few years, AI entrepreneurship asked if it could make programmers, designers, and operations more efficient. Now, the question is whether it can enable a single person or a tiny team to independently run the full business loop.
Capital is already placing clear bets: Replit raised $400 million in March 2026, valuing it at $9 billion, aiming to enable non-developers to turn ideas into software; Lovable raised $330 million in Series B funding in December 2025, valuing it at $6.6 billion, with a narrative of serving the 99% who have ideas but lack technical skills. They might not use the term OPC, but they are doing the same thing: enabling those who don't find building a tech team worthwhile to turn their ideas into operational businesses.
The OPC discussed in this article is not just a narrowly defined "company with only one person." It points more broadly to a category of small business nodes: individual creators, small merchants, SMEs who already know what to sell and to whom, but for whom maintaining a full tech/ops team is unnecessary.
I. OPC is Becoming a New Main Theme in AI Entrepreneurship
Over the past few years, the most common question in AI entrepreneurship was: Can AI make existing employees more efficient?
Now, the market is starting to ask another, more important question: Can AI enable a business to be viable with fewer people?
These two questions address different markets. The former increases output for existing organizations; the latter allows small-scale businesses that couldn't previously afford fixed costs to enter the market.
For OPCs, the value of AI is not just saving man-hours, but making previously unprofitable businesses profitable. Websites and sales materials can be generated at lower cost, and some repetitive processes can be gradually automated. As these costs fall simultaneously, the starting point for a company changes. Operators no longer need to first prove the business can sustain a team before gaining access to digital capabilities. They can first validate at a lower cost, then decide whether to expand based on actual revenue.
At the same time, amid the unemployment and layoff wave of the AI era, more former employees with industry experience are seeking income streams beyond the traditional "get a job at a big company". What AI provides is the execution layer to convert these personal resources into independent businesses.
Therefore, OPCs and AI business builders are not a short-term concept, but a new market naturally formed after AI lowers business costs. What AI changes is not just employee efficiency, but the minimum number of people required to start a viable business.
II. Replit and Lovable Prove: AI Coding Demand from Non-Technical Users is Real
Whether a market exists ultimately depends on whether users and capital are already paying for it.
Replit and Lovable provide the most direct validation. As mentioned at the beginning, both recently raised funds at valuations nearing ten billion dollars, attracting significant interest from prominent Silicon Valley institutions.
Their high valuations are not just because AI makes programmers code faster, but because software development capabilities are shifting from an engineer-exclusive skill to a service ordinary users can directly invoke. A person with an idea no longer needs to hire a development team first to have a chance of turning a need into a website or app. The roles of requester, user, and application creator, previously separate, are starting to converge in a single person or small team.
Behind this lies a market far larger than developer tools: a large number of users need digital tools tailored to their business, but lack the time and effort to learn programming deeply, and it's not worth assembling a tech team for every idea.
Replit and Lovable have proven this demand is not theoretical. AI coding is moving from a developer's efficiency tool to a way for a broader population to build new applications.
However, what they've validated is primarily the first half of the story: non-technical users are indeed willing to build applications directly.
What truly determines whether OPCs can appear at scale is the second half: whether these applications can run stably and sustainably, and whether they can carry real business.
III. Existing AI Coding Tools Still Have a Structural Gap
Current AI coding tools have significantly lowered code generation costs, especially the cost of "making a demo page/app to show on social media." But when a demo is to be truly deployed for business, it still assumes the user can manage the development process.
Users still have to break down business ideas into technical requirements, judge if the results are reasonable, then handle deployment and modifications. For developers, this is the normal process; for OPCs without a technical background, this is precisely the hardest layer.
An operator might know exactly what to sell and to whom, but not know how a shopping cart should manage order status, nor judge whether the backend and database are reliable. AI can quickly generate a page from a sentence, but when the page needs to integrate payment, record orders, or modify business rules, users still must make numerous technical judgments.
This is also the most easily overlooked distance between a demo and a business.
A demo only needs to run correctly during a presentation. A real business must handle continuous changes: products are updated, prices adjusted, customers make new requests. As long as every change requires re-understanding code, debugging environments, or finding outsourcing, the so-called "low-cost entrepreneurship" can hardly become truly viable.
Therefore, the current AI coding market has a structural contradiction:
Existing products have fairly well improved the efficiency of those with IT backgrounds like developers and product managers, making it possible to rapidly build and launch applications. However, they haven't fully solved the problem of completely replacing humans to use AI for long-term, stable business operations with zero barriers. They give increasingly powerful development capabilities to users but still require users to bear the responsibility for product definition, result acceptance, and continuous iteration.
For technical users, this freedom is an advantage; for non-technical OPCs, this freedom often means new learning costs or additional personnel/outsourcing costs.
The next stage of competition in this AI field may not be about who can generate more code, but who can further encapsulate the development process, truly and completely replace technology or outsourcing, and allow non-technical users to directly obtain operational business outcomes.
IV. xBubble's Entry Point: From Prompt-to-Code to SOP-to-Business
xBubble, launched by DAPPOS, does not directly compare coding capabilities with mature developer tools.
Its real entry point is changing the delivery unit of AI coding. Ordinary AI coding products primarily convert Prompts into code or applications, while xBubble attempts to convert business goals into an executable business path.
Users no longer start from technical architecture but from operational problems. They only need to state what product or service they plan to offer, target customers, and how they want the business to run. xBubble then uses SOPs (Standard Operating Procedures) to convert this information into specific processes, connecting pages, payment, and order backends.
This is the shift from Prompt-to-Code to SOP-to-Business.
The difference isn't that Prompts become shorter, but that more steps originally requiring user judgment are pre-organized. Ordinary AI coding gives users a development assistant; xBubble further undertakes requirement breakdown and process management, allowing users to start running their business without first learning to manage AI development.
For OPCs, this change is more important than merely increasing generation speed.
What they lack is not a more powerful code editor, but a technical execution system that is low-cost enough and can still be modified after launch.
xBubble's core judgment is: underlying model capabilities will continue to improve, but business needs won't automatically become standardized because of that. Users still need to express rules, styles, and outcome requirements. Truly valuable products are not just powerful, easy-to-use tools, but services that completely replace technical development or outsourcing companies by directly delivering results.
V. How xBubble Turns Business Goals into Operational Results
xBubble's core highlights are the SOP system and the third-party service provider network.
Here, SOP is not a longer Prompt, but a pre-organized execution process around specific tasks. It encapsulates models, tools, and result standards, and is then invoked by the system based on user needs. The user is responsible for stating the business goal; xBubble is responsible for converting that goal into software processes.
Take a small merchant selling World Cup merchandise as an example. They already have traffic, products, and potential customers, but lack an independent sales system. On the surface, they just want to "make an e-commerce webpage"; but when they actually need to acquire customers and fulfill orders, they need more than just a presentation webpage that looks okay at first glance. They need product materials with a unified style, pages that can complete transactions, and an order backend that can be continuously updated.
Using ordinary AI Coding, the user needs to add requirements item by item and judge whether each generation meets business needs. With SOP, the system can first recognize this as a merchandise store scenario, then complete the app build along the pre-organized process. The user still decides products, prices, and sales rules, but doesn't have to build the relationship between pages, orders, and backends from scratch.
The second change SOP brings is shifting the focus from single-generation to continuous stability.
For real business, making a product demo for the first time in the AI era isn't the hardest part. What truly affects the experience is whether the system continues to work correctly when subsequently changing products, adjusting prices, or modifying order flows. What OPCs need isn't a one-time impressive demo, but a delivery path that can be executed repeatedly and modified continuously.
Bubble Engine is responsible for generating and optimizing SOPs based on cases and result standards, consolidating validated business requirements and execution methods; Bubble Pilot is responsible for understanding current needs and invoking more suitable SOPs. Users face a business entry point; model selection and tool combinations remain inside the system.
Additionally, xBubble solves infrastructure issues from code to real business launch through third-party service providers.
Getting a website to actually run usually requires domain names, servers, and payment services. For non-technical users, even if AI can give instructions, purchasing accounts, configuring environments, and completing deployment are still unfamiliar processes.
xBubble does not lock all apps into a unified hosting platform. Instead, it separates software construction from infrastructure services. Users can choose trusted third-party service providers themselves, or AI can match suitable providers. Service providers handle resource procurement, environment configuration, and application deployment; xBubble continues to handle software generation, business processes, and subsequent modifications. Different service providers can use different cloud platforms, domain services, or payment solutions, and users can also know what resources they are using, who provides them, and the associated costs.
Notably, users can directly use xBubble credits to pay for these infrastructure services, achieving this in one step, rather than going through the hassle of registering accounts with various infrastructure providers and passing platform reviews.
(Relationship between users and service providers in xBubble, source: official blog)
In this system, service providers are no longer traditional outsourcing companies, but more like OpenAI/Anthropic's "on-site service engineers." Most repetitive development is handled by xBubble's SOPs, while user infrastructure needs and other tasks requiring manual service or external linking are handled by xBubble's service provider network.
Thus, what xBubble delivers is no longer just a generated application, but a more complete business launch path: the user states the business goal, SOPs complete software construction, third-party service providers handle deployment, and subsequent needs can still be modified via xBubble.
This is the full meaning of moving from Prompt-to-Code to SOP-to-Business.
(Technical comparison table between xBubble and AI coding companies like Cursor and Lovable)
VI. Why xBubble Has a Chance to Capture the OPC Market
Demand for AI tools targeting OPCs is growing rapidly, and non-technical users directly building applications has become a clear main theme drawing Silicon Valley product and capital attention. xBubble's opportunity lies in further pushing "building applications" to "launching businesses," thus precisely meeting a segment of OPCs who already have products, services, or customers but see no need to staff a tech team.
First, the OPC sub-group xBubble targets is clear and its size is not small.
xBubble targets not the most media-hyped "geek one-person companies," but a broader, more realistic category of small business nodes: they already have customer relationships, sales channels, or stable products/services, can sustain business based on understanding of niche markets, but technology is not their core competency. For these OPCs, the problem is usually not "what to sell, to whom," but how to convert existing commercial resources into sustainably running online operations at a low enough cost.
This is precisely the interval where xBubble's SOP model is most suitable and has the best chance of gaining market share.
Second, xBubble's SOPs have the opportunity to form accumulations independent of underlying model capabilities, making the user's business launch experience more friendly, mature, and stable.
Single-generation, agent-based coding workflows can easily be matched as base models improve; but a process repeatedly adjusted through real business contains not just code, but also requirement understanding and outcome standards. The more cases handled, the more SOPs can cover common issues in similar businesses, and subsequent delivery costs drop accordingly.
xBubble's service provider network distributes this accumulation. Many xBubble users subscribe to start their business after trusting a service provider who understands their industry and can demonstrate similar mature business cases. Service providers bring client needs into the system and bring mature SOPs to more similar clients.
Thus, product use and market expansion can form a cycle: more business brings more cases, more mature SOPs lower delivery costs, lower delivery costs make more small businesses worth launching.
Finally, xBubble's support for crypto-native payments also better matches the practical needs of some OPCs: small-scale operators targeting global users, digital services, or community transactions.
For them, the real difficulty is accessing payment, order, and settlement systems at low cost when business scale is still small. Wallet login, stablecoin payments, and on-chain reconciliation can be directly encapsulated into business processes, reducing the complexity of cross-region payment integration. xBubble further combines these capabilities with storefronts, backends, and SOP delivery, allowing operators to validate a crypto-native or cross-border business faster without deeply understanding Web3 technology.
Such capabilities won't replace all traditional payment methods, but can cover niche needs not easily met by general AI coding and standardized website-building tools, thus constituting another layer of differentiation for xBubble in the OPC market.
Of course, xBubble cannot create products and customers for users, nor will it replace professional teams needed for complex enterprise systems. What it truly needs to prove is whether SOPs can be stably reused across different users, whether businesses can be continuously modified after launch, and whether service provider involvement can significantly improve delivery efficiency.
If these conditions hold, xBubble is not just an easier-to-use AI coding product, but could become a business launch system for the OPC market, even the commercial infrastructure of the OPC era.
Conclusion
The OPC economy, particularly the trend of non-technical users participating in software creation, has become a main theme validated by both real usage and capital investment. Meanwhile, products like Replit and Lovable have made the market's next layer of gap clearer: applications can be built quickly, but business still needs to be organized and run continuously.
xBubble's opportunity comes from a different approach to this gap. It doesn't require OPCs to first learn the full AI coding process. Instead, it uses SOPs to convert business goals into execution paths, with service providers filling in parts not yet fully automated.
From this perspective, xBubble doesn't need to prove it writes better code than all AI coding products. It needs to prove that, before a small business earns its first revenue, SOP-to-Business is more valuable than a powerful blank input box.
Silicon Valley has proven AI is handing software creation capabilities to more people.
What xBubble needs to prove is whether this capability can let more people without tech teams truly start operating their businesses.
