439: The Increasing Risk of Building in Public

Arvid reflects on how building in public used to be a highly effective strategy for transparency, community engagement, and validation, especially in the early days of a business when sharing financials and strategic details helped attract early adopters and investors. However, with the rise of AI and agentic tools, the landscape has changed dramatically. These tools enable anyone to clone and replicate business models quickly after viewing publicly shared information, such as product architecture, features, or even revenue insights. This new reality shrinks the safety threshold for sharing detailed business information, making traditional transparency potentially dangerous. Arvid emphasizes that the threshold for safe sharing has collapsed, and what was once manageable at $20-30K MRR is now a significant risk as competitors can use AI to replicate core aspects of your business almost instantly.

Arvid discusses how advances in AI and agentic systems have leveled the playing field, allowing entrepreneurs without technical skills to rapidly develop competitive products by using prompts and existing AI models. He warns that sharing specific operational details, architecture, dependencies, or configurations can provide breadcrumbs for competitors to clone a business. Therefore, entrepreneurs need to be more strategic about what they share publicly, focusing on interesting insights that are not easy to replicate. He also mentions that AI tools can act as mentors, making good coding, marketing, and product decisions accessible to non-experts, further increasing the speed at which competitors can catch up.

Arvid offers practical advice for founders aiming to maintain transparency while protecting their business. He recommends avoiding sharing revenue numbers, customer details, or detailed system architecture. Instead, founders should share lessons learned, operational insights, and general industry observations that are too vague or broad to enable easy copycat strategies. The key filter he suggests is: "Is it interesting, or is it easy to clone?" If a piece of information can be used by others to emulate or steal your business, it shouldn't be shared publicly. He emphasizes that some form of building in public can continue but must be more nuanced and risk-aware in the current environment.

Despite the risks associated with sharing technical details and revenue data, Arvid highlights the continued importance of building relationships with customers and developing trust—things that AI cannot easily replicate. Personal interactions, negotiations, and customer intimacy remain a significant competitive advantage and are safe from AI cloning. These human elements are crucial in acquiring large customers and maintaining a differentiated position. He cautions that early-stage businesses should keep sensitive information close until they reach a stage where their relationships and reputation form a robust barrier against automation and AI-based cloning.