"Ending 'I Have an Idea, But Can't Create It'—How AI Development Service Lovable is Transforming the Gateway to Entrepreneurship"

"Ending 'I Have an Idea, But Can't Create It'—How AI Development Service Lovable is Transforming the Gateway to Entrepreneurship"

The Era of Creating "Dynamic Businesses" Alone Has Arrived

Even if you've thought, "It would be convenient if there were such a service," few people actually proceed to start a business. One reason is the significant development costs between an idea and a product. Hiring engineers, outsourcing to a development company, and spending months solidifying requirements—all require substantial funds and time upfront before knowing if the product will succeed in the market.

Attempting to reverse this order is the AI development service "Lovable" from Sweden. By describing the desired website or app in natural language, AI assembles the screens, functions, and databases, making it operational in a browser. As you view the completed product and continue conversations like "change the color," "add a login feature," or "make this list searchable," you can modify the product.

If traditional no-code tools were about users arranging pre-made components, Lovable is more like a "collaborator that progresses from conveying intent to selecting and connecting components with AI." While you can start without knowing programming, actual code is generated behind the scenes. You can sync with GitHub to save and review the code, allowing engineers to take over if needed. For databases, authentication, storage, and server-side processing, you can use built-in backends or Supabase.

Lovable's official site states that a total of 60 million projects have been created, with 1.2 million new ones emerging weekly. These figures are disclosed by the operating company and do not mean all are operational businesses. Nonetheless, it indicates a shift from software being "something ordered from experts" to "something you can experiment with through conversation."


Until a Travel Service Dormant for 14 Years Came to Life

A symbolic example introduced by the German economic newspaper Handelsblatt is Nico Wittenberg's travel platform "Avara." The idea originated from a snowstorm 14 years ago. When his partner's flight was canceled, and after continuously searching for alternatives, they finally found a night train. He thought, "It would be great if you could search for all modes of transportation, from buses to planes, and book them directly."

At the time, when he shared this concept with colleagues at his former workplace, Google, he was told it would require a development team and millions of euros. Nevertheless, Wittenberg used Lovable last year to shape Avara on his own. According to him, it is used in over 125 countries, and inquiries from venture capitalists in various countries are coming in.

The important point here is not the optimistic story of "AI completed a global company overnight." It is that a hypothesis, which had been halted by financial barriers for 14 years, has reached a stage where third parties can interact with it. The value of an idea cannot be verified by mere explanation. Are the search results easy to understand? Is it straightforward to make a reservation? Are there people who genuinely want to use it? With a functioning product, real-world responses to these questions can be obtained.

The greatest effect of Lovable is not reducing development costs to zero but shortening the "time until learning begins."


The Enthusiasm on Overseas Social Media—Monetization and Spread Becoming Reality

On social media, reactions to Lovable are not simply divided into praise and criticism. What is common is the surprise at the speed with which the initial deliverables appear.

On LinkedIn, a user running a community business in Singapore reported the experience of turning a long-unrealized project into a web service overnight. While appreciating the ability to convey wishes in natural language and make corrections through conversation, they also mentioned encountering error messages despite updates and the need to purchase credits as more features were added. In the comments section, there was a remark that "AI was helpful because the person clearly understood what they wanted." This is fundamental. AI is not magic that automatically transforms vague business concepts into excellent businesses; it is a device that rapidly translates clear intentions into prototypes.

In the Reddit Lovable community, there is a post about a locally-targeted service created by a beginner that now earns $1,000 to $2,000 per month. In Greece, "Gyronomics," which shows how many pita gyros can be bought at minimum wage by age group, spread on social media and was featured on TV and in the local version of WIRED. The poster attributed their success to being able to quickly release their idea and present a visually polished dashboard from day one.

These are self-reported and not cases where revenue or access has been audited by a third party. There is also a bias towards posting only successful examples. Even so, it shows that a small web service that can be shared immediately moves people more than a highly polished business plan. Those who gain an advantage with Lovable are not those who incorporate the most features, but those who can release in a minimal form at the moment of heightened social interest and capture reactions.


The Weight of the "Last 20%" Revealed by Dissatisfaction on Social Media

Behind the enthusiasm, the same community also lists specific complaints. A prominent one is credit consumption. Lovable uses credits for AI requests, cloud usage, and in-app AI functions. Even the official pricing explanation notes that credit consumption varies between simple visual adjustments and adding authentication features. Users have voiced concerns that tasks have become more expensive than before, credits were lost due to repeatedly having AI fix unresolved issues, and database usage fees increased more than expected after publication.

Another point of discussion is the actual operation. Having a screen that works is not the same as having something customers can use with confidence. Can data from one account be accessed from another? Can payment amounts be altered on the browser side? Are duplicate processing issues avoided if the same payment notification is received multiple times? Does it fail safely when an external API stops? Can it truly be restored from a backup? On Reddit, practical advice is shared on conducting these tests before going live.

There is also a verification post about CPU load spiking even with a small number of simultaneous accesses due to insufficient database indexing. Another user was struggling with completely separating test and production databases, even when splitting code into branches on GitHub. This is not so much a defect unique to Lovable as it is an indication that the difficulties of software operation have not disappeared. AI creates visible parts astonishingly quickly. As a result, the importance of "invisible design," such as permissions, monitoring, failure response, and data migration, becomes apparent at an earlier stage.

Lovable itself offers basic and detailed scans, API key protection, row-level access control for databases, and dependency audits. However, the official documentation clearly states that scans do not guarantee complete safety and suggests considering additional reviews by experts when handling confidential data or critical functions. It is neither "dangerous because AI created it" nor "safe because there are security features." Depending on the data handled and the potential impact, it is necessary for humans to take responsibility and verify.


In Japan, More Effective "Before Approval" Than "Replacing Development Companies"

From a Japanese perspective, the value of Lovable may manifest in a slightly different form than for solo entrepreneurs overseas. In new business ventures within Japanese companies, it is common for planners to create specifications or slides, consult with the information systems department or development company, and go through estimates and approvals before creating a prototype. The earlier the hypothesis, the more ambiguous it is, yet detailed explanations and budget confirmations are required before starting development. As a result, internal coordination often exhausts efforts before showing it to customers.

Lovable enters before this structure. Before placing an order to "please make this," salespeople, store managers, marketers, researchers, and others who understand the issues on the ground can create prototypes. Instead of reading a list of features in a meeting, they can have people interact with the screen on a smartphone. If unnecessary features are identified in a day, weeks of waste can be avoided in formal development.

The "DX Trends 2025" by IPA addresses the use of generative AI, internalization of system development, and the quantity and quality of DX personnel as major challenges for Japanese companies. While Lovable does not instantly solve the shortage of engineers, it serves as a bridge for those with business knowledge to participate at the entry point of development. Especially for local SMEs, professionals, and sole proprietors, there is significant room to experiment with small tasks like reservations, estimates, customer diagnostics, internal applications, and inventory checks that were previously connected through Excel and email.

The ability to give instructions in Japanese also broadens the entry point. However, understanding Japanese does not equate to understanding Japanese business practices. Users must convey and confirm requirements themselves, such as address and name formats, Japanese calendar years, consumption tax, invoices, bank transfers, detailed smartphone operations, and inquiry handling. In the Japanese market, trust is built not on "roughly working," but on not causing anxiety during exceptions. There remains the task of refining wording, error displays, cancellations, refunds, and handling personal information.


Businesses That Suit Lovable and Those That Require Caution

Lovable is well-suited for web services where the primary goal is to verify customer reactions. Examples include simple business tools for specific industries, event reservations, member information sites, visualization of local information, diagnostic content, and internal management screens. When the core value lies more in understanding the business and connecting with customers than in proprietary algorithms, it is easier to directly transfer the founder's knowledge into the product.

On the other hand, areas such as medical decisions, financial transactions, administrative procedures, large amounts of personal information, children's information, and control related to life and property should not be published solely by non-engineers. The Personal Information Protection Commission also warns to confirm the scope of use and the use of machine learning by service providers when inputting personal data into generative AI services. Avoid using real customer names or consultation details in prompts, or copying production data directly into test environments.

The criteria are simple: "Is it an experiment where you can apologize and fix it if it fails?" or "Is it a system where failure would cause irreparable harm?" If it's the former, it's worth trying on a small scale. If it's the latter, limit Lovable to screen prototyping and work with experts on design, legal, security, and operations.


What Japanese Entrepreneurs Should Do in the First 7 Days

On the first day, don't write a grand business plan. Instead, create a single sentence that defines "who can complete what at what moment." For example, "A service that allows small restaurants to return potential dates and estimates for private bookings without using the phone." If the target audience and completion criteria are clear, instructions to AI will be less likely to deviate.

From the second to the third day, create only one flow. Instead of including registration, search, payment, chat, and analysis, choose the most important experience. Don't over-polish the appearance, and use fictional data. Sync with GitHub before changes to create a state that can be reverted. Rather than asking AI for numerous corrections at once, repeating one change and one confirmation makes it easier to manage the cause of issues and credit consumption.

On the fourth day, have 3 to 5 potential customers try it. Without explaining how to operate it, see where they get stuck. Instead of asking "Do you want it?" ask "What becomes easier compared to your current method?" "How much would you pay for it?" "What is the biggest reason you wouldn't use it?"

On the fifth day, decide on features to cut, not add. On the sixth day, use two test accounts to confirm that others' information is not visible and that operations cannot be performed after logging out. Avoid placing secret keys on the screen side, and also test what happens when external services fail. On the seventh day, release it to a limited audience. At this point, do not input large amounts of important information from actual customers.

If there is no response, it is not a failure of development but an inexpensive answer from the market. If there is a response, only then bring in experts to review data design, security, testing, monitoring, and pricing. Creating a prototype with Lovable does not eliminate the need for collaboration with engineers but makes it more specific about what should be created, enhancing the quality of collaboration.


What Entrepreneurs Need Is More "The Ability to Choose Questions" Than Code

As the cost of software development decreases, the rarity of merely having an idea also diminishes. If anyone can create similar services in a short time, the difference will be in how deeply one understands whose inconvenience and how to deliver it to users and build trust.

Lovable does not replace the profession of programmers. It replaces the old order of exhausting time and budget before verifying an idea. Founders can learn faster, and engineers can focus on difficult design and quality rather than translating vague requests. The common language connecting both is not just specifications but "something that works."

The story of Avara conveyed by Handelsblatt is not proof that anyone can easily create a million-dollar company. It is a signal that long-dormant ideas can now be presented to the market by the individuals themselves. For Japanese entrepreneurs and field personnel who have given up saying "I can't try because I can't create," this change is significant.

However, the publish button is not the goal. The business begins by listening to customer voices, imagining how it might break, and taking responsibility. What Lovable truly democratizes may not be programming but "the right to question one's own hypothesis in reality."


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