Every other week, there’s a new wave of viral hype surrounding AI tools. This time around, it's
Everyone working with Software as a Service (SaaS) products must feel quite uneasy. The wave of “AI wrappers” already taken some casualties, but the response was quite obvious: integrate AI yourself, and your SaaS is safe. Agentic AI tools, if powerful enough, can threaten many SaaS products without any defensive play.
The idea of building a product moat must be reinvented in light of agentic AI tools. How (if at all) can your SaaS product niche be protected from AI?
AI and SaaS Business Model
In its simplest form, all SaaS companies follow the same formula: identify a specific user problem, solve it with software, and charge for it through a subscription. The model works as long as customers can’t replicate the solution themselves. All the classic product moats of switching costs, proprietary software, and network effects come into play.
Chegg, for example, has built a product around its proprietary homework solution database that cannot be accessed without a subscription. There are other notable functions of Chegg, but as we’ve seen from their
Even consumer-friendly network moats experienced disruptions. Stack Overflow saw a drop in traffic as developers began asking questions in AI chat rather than on its site. Stack Overflow created the community that provided the training data to enable its own disruption. Community moats might be easier to defend, but so far, the only proven strategy is integration.
Some SaaS tools quickly embraced the crisis and became “AI wrappers” themselves. Grammarly is a great example that integrated not only an AI text checker, but also AI writing features into its subscription bundle. Users who considered leaving due to AI offerings now have a different value proposition.
AI copycats further fueled the fire, allowing competitors to integrate new features or even create new products in already overcrowded niches. Established brands could fight them with their name, and it wasn’t seen as a serious problem for B2B niches.
Redefining Product Moats
Workflow tools like N8N, OpenClaw, Claude Cowork, and others have the potential to be even more disruptive. AI agents not only automate features in existing SaaS products but also raise the question of whether separate software is even needed. It requires rethinking what creates a product moat in the first place.
AI agents promise to learn your software interface and the steps needed to complete any given task. When OpenClaw launched, social media was full of users setting up Mac Minis to perform tasks like copying data and writing emails. It can create Jira tickets, update spreadsheets, and send Slack messages to inform coworkers.
Yet the power users who set them up still need Jira, Google Sheets, and Slack subscriptions. Such a workflow layer definitely adds value and may even lower switching costs, but unless the AI agent can create its own infrastructure, there is room for re-invented product moats.
As CEOs and product owners, we are forced to speculate here, as no one knows how AI agents will unfold. New product moats might no longer be based on knowledge barriers, but on execution and trust. There are at least three ways to think about it when planning a SaaS product’s defense.
Become The Wrapper
The wrapper strategy rests on the proprietary context that AI agents cannot replicate. Well-protected user data, internal workflows, and domain-specific rules can function as such a context. Established brands then supplement their functionality with an AI tool, and there’s still a reason to pay for a subscription.
Assuming you build upon the API layer, your SaaS becomes a specialized distributor of AI API tokens. As such, your profit margins shrink as every AI call costs additional money you pay to tech giants running large language models. A clever pricing model might make it viable, but the economics become different.
Such repackaging not only increases maintenance costs but also leaves your product without a defensive strategy. Once users become aware of AI tools, there’s little reason to pay for a subscription that just wraps the same chatbot. The economics of scale are no longer in your favor, and we have already seen wrappers like Jasper AI struggling because of this.
Despite the risks, some companies make wrappers work. The trick, it seems, is to avoid feature fatigue and use AI tools when they are actually helpful and can extend the functionality you already offer. Without the underlying value, your SaaS risks becoming just an expensive middleman.
The Infrastructure Moat
The best moats might be rooted in the physical world. Hardware, network access, regulatory approvals, and other necessary infrastructure parts create defensibility. AI can write a scraper or even run it from the cloud, but having your own partners with quality residential IPs all around the world won’t be automated.
The proxy server market is just one example. Almost every SaaS niche has an underlying infrastructure that serves as a moat for major companies. Payment processors like PayPal have spent years obtaining financial licenses. Salesforce doesn’t just provide a CRM; it has built extensive integrations to make it usable.
Infrastructure moats exist everywhere where a SaaS company owns something physical, regulated, or operationally complex. It isn’t flashy and takes years to acquire, but it creates absolute defence for your product. Owning infrastructure is close to the traditional moat of having an intangible asset.
Such legal monopolies allow companies to compete by excluding competitors through exclusive contracts, patents, or other constraints. The infrastructure moat is different because it focuses on operational efficiency. The user or its AI agent could do it themselves, but if your service is operationally complex enough, using your infrastructure and paying the subscription is worth it.
Human Accountability
The need for human accountability is commonly associated with highly regulated industries such as healthcare, finance, and law. With the rise of sloppy AI automation, the need to take credit for certain actions might become increasingly important. In some contexts, human verification may be a new form of luxury. In other cases, it is necessary.
B2B SaaS products are a great example of cases when decisions impact revenue. When mistakes cost, someone needs to be responsible. If you run your project entirely on OpenClaw or similar bots, you cannot sue the AI agent company for damages. The act of taking responsibility becomes a moat for competing SaaS products.
Such a defense might not work in low-stakes, price-sensitive niches, where customers tend to choose the cheapest solution. Consumers choosing a grammar checker will not care whether their text is proofread by a human if an AI tool is good enough. The biggest challenge here might be understanding exactly where your customers value human oversight.
In some cases, we might even expect legislation, such as the upcoming EU AI Act, to limit the use of AI tools for certain use cases. Critical decisions in fields such as HR, credit scoring, or infrastructure management might be entirely excluded from AI automation.
Conclusion
If AI agents and other tools continue to grow in the same direction, we can actually see tools like OpenClaw replacing traditional SaaS workflows. Whether it will be as apocalyptic as AI evangelists claim remains highly questionable, but CEOs would do well to reconsider their value proposition.
Successful SaaS products are valued not because of their shiny UI, but because users can trust that they will do the dirty infrastructure work behind the scenes. It always was like that. The new AI trends simply made the facade easier to replicate, and it might be for the better.