Currently, nearly every new startup pitch includes the same two letters: AI
That makes sense. The technology is exciting, developing quickly, and opening up countless practical applications. However, it also presents investors, founders, and early employees with a harsh reality.
Many AI applications run the risk of becoming features of established companies or being completely outcompeted by open source and distribution.
If you are building a thin layer on top of a model, a prompt, or a simple automation, you are not really building a company. You are building a temporary advantage. Temporary advantages disappear quickly.
Large platforms already have the customers, the data, and the distribution. Open source models are getting better every month. What appears cutting-edge today can become a checkbox in a product roadmap tomorrow.
So what actually survives?
From what I’ve seen, the AI ventures with actual staying power tend to have one or more of the following traits:
Proprietary workflows
The winners don’t just generate content or summarize data. They embed themselves into how work gets done. They change a process, not just an output. When your product becomes the way a team operates, you move from nice-to-have to hard-to-remove.
Exclusive data rights
Data still matters more than models. If you have access to information that competitors cannot legally or practically replicate, you have leverage. Unique first-party data, historical datasets, or privileged integrations can create defensibility that algorithms by themselves never will.
Real network effects
Marketplaces, collaboration layers, or platforms where each new user makes the product better for the next user. If your AI tool delivers the same experience and outcomes no matter how many customers use it, you don’t have a network effect.
Regulatory positioning
Certain industries are harder to enter. Healthcare, fintech, government, and other highly regulated spaces create barriers that general-purpose tools struggle to overcome. Compliance, security, and audit requirements can become moats if you build for them intentionally while establishing deep industry relationships.
Deep integrations that create switching costs
The more your product connects into core systems and workflows, the harder it is to replace. When your product becomes part of how teams collaborate and actually get work done, ripping it out becomes risky and expensive. A clever MVP or demo can win a pilot. Embedded infrastructure wins renewals.
Pricing plays an important role here, too.
Outcome-based pricing is attractive in AI because it aligns value with cost. But it only works if you can justify a sustainable take rate. If your product can be replaced by the next model upgrade or an internal build, customers will not pay a premium for long.
The uncomfortable truth is that most AI startups will not fail because the technology didn’t work. They will fail because the business model didn’t hold, because ROI was never clearly proven, or because GTM execution didn’t match the promise of the product.
Powerful features struggle when companies can’t connect them to outcomes customers understand and care about. Without a shared understanding of value, deals stall, pilots lose steam, and budgets shift to other parts of the business with clearer needs or louder champions.
Great technology without distribution is a science project. Distribution without defensibility is a race to the bottom. Augmented reality solutions are still struggling to reach scale. On the other side, early chatbots and content creation tools gained fast traction, only to be undercut as identical capabilities appeared in every major platform.
The question every founder, prospective investors, and early employees should be asking is: if the models become free and the features become common, what remains that customers cannot easily replace?
Maintaining clear visibility into how key stakeholders continually evaluate build versus buy has never been more critical. It’s a core survival skill that needs to extend beyond Customer Success organizations and your closest client partners.
That answer, combined with disciplined execution and a clear path to measurable impact, is what separates vibe-coded experiments from companies that are built to go the distance.
Leave a Reply