Richard de Silva argues that the declining cost of building software, thanks to AI-based code-generation and product-design tools, is restoring relevance to the bootstrapped-company model, also known as revenue-funded businesses. Instead of raising substantial capital first and then searching for product-market fit, a small team can start with a problem that customers are actually paying to solve, build an initial product, and decide later whether it needs additional investment.
This is a viewpoint written by de Silva as a guest contributor to Crunchbase News. He is the founder, managing partner, and chair of the investment committee at Lateral Investment Management, and previously served as a managing director at Highland Capital Partners and helped found IronPlanet. The argument should therefore be read as the opinion of an investor with experience in venture capital and middle-market companies, not as the result of an independent quantitative study.
Two Different Models for Building a Company
De Silva criticizes the common Silicon Valley path, in which a company begins with an ambitious idea, raises early funding, hires large teams, and then spends to reach a broad market quickly and test product-market fit. He says this path can succeed in exceptional cases, but often leads to repeated changes in direction or to the company disappearing when its appeal to investors declines.
By contrast, a bootstrapped company begins with a customer or industry that the founder already knows. The process involves solving an existing problem at a cost lower than the resulting revenue, finding other customers facing the same problem, and gradually improving operations. According to the author, this model places the customer and revenue before expansion, and makes hiring the team funded by profits rather than a bet on demand that has not yet been proven.
The article notes that the founders of these companies are often in the middle of their careers, with industry experience and existing customer relationships. This may mean slower, linear growth for years, but it focuses on profitability from the outset. The author cites Atlassian and Basecamp as examples of companies that began outside the traditional venture-capital path and achieved a significant presence in the technology sector.
What Is AI Changing in Practice?
Previously, a nontechnical founder often needed early external funding to hire engineers and build a usable product before having sufficient revenue. Today, de Silva believes that code-generation and product-design tools reduce the team size and spending required to reach a working application and deploy it with real customers.
This does not mean that AI has eliminated the need for capital. But, according to the argument presented, it has shifted the bottleneck from building the product to proving that sustainable demand exists for it. The author also argues that some early funding is instead used to buy additional time, accelerate sales and marketing, or support deployments that would not be economical for customers without subsidization. As a result, testing the market before raising a large round may become a more realistic option for a greater number of founders.
The article mentions Medvi, which it describes as a company providing treatment related to GLP-1 drugs and having reached $1 billion in revenue with a single founder, as an extreme example of what small teams can achieve. The text does not provide enough detail to verify this description or generalize from it, so it should not be regarded as evidence that small size guarantees success.
Limitations and Open Questions
De Silva is not calling for the elimination of venture capital. He acknowledges that untested, capital-intensive ideas, particularly when the founder does not have an existing customer base, still need external funding to finance the search for a market. Lowering the cost of software development also does not automatically solve the challenges of customer acquisition, compliance, support, product security, or building an advantage that is difficult to imitate.
The most important conclusion for the technical reader is that AI may expand the group of companies capable of reaching an initial product without an early funding round, but it does not prove product-market fit. The practical value of the bootstrapped model depends on the founder's knowledge of the problem, willingness to grow in line with revenue, and ability to turn AI tools into a service that customers actually rely on. Whether this will lead to a broad wave of new market leaders remains an opinion-based hypothesis that the source does not settle.