Your software isn’t your strongest asset – your data is. Here’s why: AI has made it easier than ever for competitors to replicate software features. What they can’t copy is the unique data your business generates every day, like customer interactions, user behavior, and workflow trends. This data, when structured and applied effectively, is what gives your product an edge.
Key takeaways:
- Software is now a commodity. AI can replicate software functionality at a fraction of the cost.
- Data is the new moat. Companies that turn their data into actionable insights gain a competitive advantage.
- Features don’t last; data-driven systems do. Build products that evolve and improve through user interaction.
If you’re not using AI to transform your data into a unique advantage, you’re at risk of being left behind. The future belongs to those who turn passive data into active intelligence.
Why Software No Longer Creates Defensibility
How AI Is Commoditizing Code
The game has changed. What used to take a team of ten engineers a year to build can now be done by two people in just three months. This shift is completely reshaping the economics of SaaS.
Take the e-commerce platform Dukaan as an example. In 2024, they replaced 23 customer support agents with an AI chatbot they built in just two days. The results? Support costs plummeted by 85%, and resolution times dropped from over two hours to a mere three minutes. Novo Nordisk saw a similar transformation. Their clinical study report writing process, which previously required 15 weeks and a team of over 50 people, was reduced to under 10 minutes with just three employees and an AI tool called NovoScribe.
The traditional "build vs. buy" model is crumbling. Enterprises can now replicate complex data systems for a fraction of the cost, making the old $200-per-seat SaaS pricing model seem outdated. By early 2026, 78% of enterprise software teams planned to create more internal tools instead of relying on SaaS providers. The barriers that once protected these companies are disappearing.
"Software is eating the world, and now AI is eating software." – Naval Ravikant, Entrepreneur and Investor
The numbers back this up. Over the past year, 35% of enterprises replaced at least one SaaS tool with a custom-built or AI-driven alternative. Klarna, for instance, dropped Salesforce and Workday in favor of an internal data stack and AI tools, saving $39 million in the process. Publicis Groupe invested €300 million in its CoreAI platform, cutting third-party licenses like Adobe Creative Cloud by half. Between 2023 and 2026, software company valuations have taken a $1 trillion to $2 trillion hit as investors question which companies can maintain pricing power.
This rapid commoditization of code is forcing a new approach to defensibility. It’s no longer about simply owning data – it’s about transforming that data into a proprietary asset that grows stronger over time.
What a16z Got Wrong About Data Moats
If software’s waning uniqueness is pushing companies to rethink their edge, then the way data is used becomes critical. Andreessen Horowitz (a16z) famously argued that just owning data isn’t enough to create defensibility. They’re partially correct. Raw data sitting idle is useless. But dismissing the value of data entirely misses the point: data becomes a moat when it’s proprietary, structured, and embedded in a feedback loop that improves with use.
The problem with a16z’s argument is that it treats all data as the same. It’s not. Take Bloomberg, for example. Their terminal still commands $24,000 annually – not because it has the most data, but because it has 40 years of proprietary financial data that’s structured in a way institutional traders rely on. This data is deeply integrated into workflows, creating high switching costs. Even when Bloomberg launched BloombergGPT in 2025, it didn’t create defensibility. Why? The AI model wasn’t central to the terminal’s core functionality and didn’t learn from real-time user behavior. This highlights a key truth: raw data only becomes defensible when it’s refined and embedded into the product itself.
Now, compare that to Cursor, an AI code editor. By early 2026, Cursor built a data moat by focusing on interactional data – tracking which code suggestions users accepted or rejected. This feedback loop trained their proprietary "Composer" model to outperform generic LLMs. The key here is that the data wasn’t just passively collected. It was actively structured into a system that improved the product with every interaction.
The takeaway? Passive data accumulation is worthless. Data only becomes a defensible asset when it flows through your product, generates feedback, and evolves into intelligence that competitors can’t replicate. Look at S&P Global. They generate $1 billion annually from licensing the S&P 500 Index data – not because of its complexity but because the data rights are proprietary and the index is embedded in financial products worth trillions.
"Owning proprietary data isn’t a moat. It’s a mirage… Moats form when data flows through product, through feedback, through behavior." – Dave Friedman
If your data isn’t actively generating new insights from user behavior, it’s not a moat. It’s just a liability.
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What Makes Data Defensible
The 4 Attributes of Defensible Data
Reaching $500,000 ARR is an exciting milestone, but many founders miss the bigger picture, treating operational data as a byproduct rather than a strategic asset. Truly defensible data has four key attributes: it’s unique to your company, grows in value with use, creates switching costs, and can be structured into actionable insights.
- Uniqueness: This data is born out of your specific customer interactions and workflows – no one else can generate it.
- Compounding: Each use improves its value. Abraham Thomas explains it as a feedback loop: "data moats reinforce AI advantages, and AI advantages reinforce data moats."
- Switching Costs: Years of behavioral history and workflow integration make duplication by competitors nearly impossible.
- Structurability: The data can be transformed into intelligence that directly enhances your product’s performance.
The real question is: What AI systems are you building to capture and activate this operational data? If you’re not sure where to start, consider checking out our AI Acceleration Newsletter for weekly insights.
The value of data isn’t static – it evolves. At the bottom of the hierarchy sits exhaust data (basic logs), moving upward to operational data (transactions), then interactional data (user choices), and finally learning data (explicit feedback). Many early-stage companies stop at operational data. But defensibility lies in capturing how users make decisions, not just the decisions themselves. This depth is where differentiation begins, especially in a world where software alone is no longer enough.
What Defensible Data Looks Like at $500K ARR
At $500,000 ARR, defensible data isn’t just a concept – it’s something you can measure. It’s found in the everyday patterns of how users interact with your product. For example, you might notice which sales follow-up messages get responses, where users struggle during onboarding, or how workflows reveal unexpected product usage.
Take a B2B sales tool at this stage. Every demo booked, objection handled, and follow-up email sent generates interactional data. By analyzing which email phrases lead to meetings, which demo sections close deals, and which touchpoints reduce churn, you’re building a dataset that reflects all four attributes of defensibility. This data is tied to your unique customer relationships and product experience, making it impossible for competitors to replicate. Transitioning from focusing on features to building a data-driven moat is what separates disposable tools from enduring companies.
Here’s a real-world example: In 2026, RunLLM applied these principles with its AI site reliability engineering tool. They tracked system incidents and identified which observability tools were most useful for root cause analysis – and, just as importantly, which were irrelevant. This "negative signal" data was inexpensive to collect yet incredibly valuable for refining their AI. Their data was unique (customer-specific incidents), compounding (every failure improved future diagnoses), high-friction (years of incident history couldn’t be easily migrated), and structured to enhance their AI’s performance.
How Defensible Data Creates Competitive Advantage
Defensible data doesn’t just provide insights – it creates lasting competitive barriers. Structured data powers both short and long feedback loops.
- Short loops: Real-time guidance, like an AI suggesting the next best action in a sales sequence based on past patterns.
- Long loops: Periodic model updates, where accumulated behavioral data helps outperform generic competitors over time.
The real magic happens when this data becomes an essential part of your users’ daily workflow.
"Advantage no longer comes from having data. It comes from turning data into intelligence faster than rivals can copy." – Alex Pawlowski
For example, imagine your tool predicts customer churn or prioritizes leads using behavioral signals unique to your system. That’s not just a feature – it’s defensibility.
Switching costs also come into play here, often referred to as "data viscosity." Moving years of historical records, workflow integrations, and learned behaviors to a competitor is no small task. This is why tools like Salesforce remain dominant. Their accumulated data and deeply embedded workflows make switching nearly impossible, even if competitors offer seemingly better features. These elements not only protect your position but also help shift your product from being a simple system of record to a dynamic system of action.
Moving from System of Record to System of Action
The Evolution from Systems of Record to Systems of Action
When founders hit $500,000 ARR, they often treat their product like a storage locker: data comes in, gets neatly organized, and then just… sits there. This is what’s known as a System of Record – a central hub for information, offering a single source of truth. Its defensibility lies in the hassle of migrating years’ worth of data and workflows to another platform. But here’s the catch: AI is eroding that barrier faster than many realize. The next big leap? Turning that static data repository into a tool that drives action.
The real shift is moving from a System of Record to a System of Action. It’s no longer about just storing data – it’s about weaving it into daily workflows to spark immediate, automated actions. Abraham Thomas, founder of Pivotal, puts it best:
"The value of data lies, solely and entirely, in the value of what can be done with it. You don’t want your SoR to be the place where data goes to die; you want to act on the data."
A System of Action doesn’t just highlight hot leads for a sales rep – it drafts the follow-up email, schedules the call, and updates the CRM based on the outcome. In this model, data isn’t the byproduct; it’s the driving force. So, what AI systems are you creating to turn your operational data into automated actions? If you’re unsure where to begin, join our AI Acceleration Newsletter for weekly tips on crafting revenue-generating automations.
Take GitHub as an example. It’s not just a place to store code – it’s an action platform that fuels collaboration, deployment, and constant improvement. While both GitHub and basic version control systems store code, GitHub layers in features like atomic commits, pull requests, CI/CD pipelines, and AI-powered code suggestions. The result? Data that doesn’t just sit idle but actively drives progress.
Looking ahead, the next phase is what Thomas calls Systems of Agents – software that doesn’t just assist humans but operates autonomously. For instance, SAP’s Cash Management Agent, launched in early 2026, automates cash reconciliations, cutting finance team workloads by up to 70%. Meanwhile, Salesforce’s Agentforce platform hit $800 million in ARR by March 2026 by positioning itself as the "operating system for the agentic enterprise." These autonomous agents use customer data to act independently, redefining what makes software defensible.
For founders at this stage, the key question is: What manual tasks can your data automate for your users? If your product identifies churn risks but forces users to switch to another tool to address them, you’re still stuck in the System of Record phase. But if your product triggers retention workflows, adjusts pricing, or escalates issues only when human input is needed, you’re building a System of Action. Without this evolution, you risk creating a feature, not a company.
Conclusion: Are You Building a Company or Just a Feature?
Here’s a simple way to find out: Can you explain, in one sentence, what unique data your company generates and how it grows stronger with usage? If you can’t, you might be building a feature – not a company. As product designer Bora puts it: "If a competitor can ship your feature in a week, it was never your moat." This idea highlights the critical difference between fleeting products and companies built to last.
The companies weathering the $1 trillion software valuation reset aren’t the ones with the sleekest UI or the flashiest pitch decks. They’re the ones that have shifted from merely storing data to creating active data loops. These companies evolve from being Systems of Record to Systems of Action, embedding themselves deeply in workflows where money, compliance, or physical goods flow directly through their software. This integration creates high switching costs that competitors can’t easily overcome. Curious about how to build such systems? Join our Founders Meeting to learn about M Studio’s approach to scaling with data-driven strategies.
The gap between a feature and a company is narrowing by the day. As foundation models chip away at interface value, the strongest edge left is proprietary, structured data that improves your product faster than others can copy it. Once you hit $500,000 ARR, the signals from your customers’ interactions will either build a defensible moat or fade into nothing. The decision is yours – and the clock is ticking.
Ready to turn your operational data into a competitive advantage? Join our Founders Meeting to explore how M Studio builds scalable, data-driven systems.
FAQs
What counts as proprietary data in my SaaS?
Proprietary data in your SaaS refers to exclusive patterns and signals generated by your company. These can include things like customer interactions, onboarding processes, usage habits, transaction patterns, and workflow telemetry. This data is unique to your business and becomes more valuable over time. When you organize it into models or products, it not only strengthens your position but also makes it harder for competitors to replicate, creating a natural barrier to switching and boosting your competitive edge.
How do I turn product usage into a data moat?
To create a strong data advantage from product usage, focus on designing your product to capture and analyze unique customer behaviors, workflow patterns, and engagement signals. Organize this information into a proprietary dataset that only your company can produce. Then, integrate it into a feedback loop where the data continuously improves your models, refines workflows, and tailors user experiences. This approach not only adds increasing value over time but also makes it more difficult for competitors to replicate your product, establishing a significant barrier to switching.
What’s the first step from System of Record to System of Action?
Embedding your proprietary data into your operational workflows is the critical first step. This integration transforms your data from static records into dynamic, actionable insights. By doing so, you create a closed feedback loop that refines the data over time, enabling smarter decisions and influencing behaviors. This process not only enhances its utility but also ensures continuous improvement, making your data a powerful tool for driving value.
