The One Moat AI Cannot Erode

The competitive landscape has shifted, and the prevailing narrative missed the mark. On April 23, 2026, ServiceNow lost 18% of its value in a single trading session. Worst day in the company's history. The cascade hit fast: Workday slid 9%, and Salesforce, HubSpot, Adobe, Intuit, and Oracle all dropped 6 to 9% in the same window. The headlines blamed AI fears. The deeper story is a moat reset, and the first sign just arrived.

The Warning Came for SaaS First

Per-seat pricing helped build the modern software industry. AI is now breaking that model, and the market is adjusting in real time. 

What used to take years is now happening in a matter of weeks. Press reports estimated roughly $285 billion in software market capitalization vanished in a 48-hour window in February 2026, after Anthropic launched Claude Cowork. Traders at Jefferies named the event the "SaaSpocalypse" before the market closed. Workday disclosed a 2% workforce reduction in February 2026, in the same week the SaaSpocalypse hit. The company recorded a $135 million restructuring charge in the same fiscal quarter. Even Atlassian, which beat Q3 earnings on April 30, 2026, traded 87% below its 2021 peak earlier in the month.

One company is already trying to get ahead of the shift. On its Q4 earnings call, Salesforce introduced Agentic Work Units, a new metric tracking AI-completed tasks rather than seats. That was a quiet but important signal: per-seat pricing has no long-term future. IDC projects that by 2028, 70% of software vendors will move away from per-seat licensing. 

That matters because one of the strongest moats in enterprise software just got reset in roughly twelve weeks. Per-seat subscriptions, switching costs, network effects, all of it looked durable. But if a moat that took twenty years to build can be eroded that quickly, no industry should assume it is protected. 

What Benioff and Huang Agree On 

This moment forces an uncomfortable question: what were these moats really made of? 

Two of the most powerful leaders in tech have publicly disagreed on whether SaaS is dying, and that disagreement helps clarify the answer. 

Marc Benioff used "SaaSpocalypse" at least six times on Salesforce's Q4 earnings call in late February 2026, per TechCrunch. His view was simple. The deeper your enterprise data is embedded in a system of record, the harder it is to rip out. 

Jensen Huang argued the opposite on CNBC that same month, saying the markets "got it wrong." His point was that agents will not replace the tools. Instead, they will use the tools.

But underneath the disagreement, they are saying the same thing. The real defensibility is not in the interface, the pricing model, or even the workflow. It is in the data and the systems of record underneath it. Everything else is negotiable. The data layer is not. 

This Is Not a Software Story

What just happened in software is going to happen everywhere else, often before the headlines name it. AI-driven customer service is already eroding service advantages that took years to build. Content libraries and domain authority, once the bedrock of marketing and SEO, are losing ground to AI that produces qualified content at near-zero cost. Product velocity gaps that once took a decade to open are now closing in months as AI-native companies catch up. 

Klarna is one of the clearest public examples. The company moved aggressively in 2024 to replace customer service with AI, then pulled parts of that strategy back in 2025 when service quality dropped. CEO Sebastian Siemiatkowski told Bloomberg that cost had become "too predominant" a factor, producing lower-quality service. 

The lesson is not that AI failed. The lesson is that the workflow was not ready to be replaced all at once. The companies that come out ahead will know the difference between a workflow AI can handle today and one that still needs rebuilding before AI can perform it well. 

The Moat Most Companies Skip

Most companies are not behind on AI investment. They are behind on the data work that makes AI investment pay off.

McKinsey's recent productivity paradox analysis, citing their late-2025 State of AI survey, found that nearly nine out of ten companies had deployed AI in at least one business function by the end of 2025, but 94% reported no significant value from those investments. Gartner projects that 60% of AI initiatives unsupported by AI-ready data will be abandoned through 2026. The bottleneck is not the technology. It is the data underneath the technology. These were not bad ideas. They were good ideas built on weak foundations. 

Legacy workflows are often running on infrastructure that cannot support real redesign. Most companies are constrained by the dataset they already have. I have made that mistake myself, moving slower than I should have and trying to force creative solutions into outdated systems.

One project has stayed with me in particular. My team and I were working on a healthcare affordability workflow. For us, patient affordability was never abstract. It is a real problem with real consequences. People do not fill prescriptions they cannot afford, and that reality has a way of sharpening your focus. 

We saw an opportunity to redesign the workflow. There was a vendor platform involved, API connections, data requirements, and operational changes. But somewhere in the middle of the project, it became evident that we were not building a workflow. We were identifying the most important pieces of data and using them to power one. The vendor platform mattered. The API mattered. But those were not the real story. The real story was what happened once clean data started going in and clean data started coming out. 

Everything accelerated. Operational teams had access to the data earlier. The workflow shifted from reactive to proactive. An internal automation tool worked better, not because the automation had changed, but because the data was finally ready to flow through it. All possible through data, not magic. 

That has shaped every AI workflow I have built since. Clean data does not just unlock new AI capabilities. It increases the value of investments a company has already made. That is the compounding effect many boardrooms still miss. 

And it points to a simple rule.

Workflow redesign should start with understanding how the data moves today. Not how the documentation says it moves. Not how the system was originally designed. How it actually moves, with all the friction included. Receipts before claims.

Healthcare Is Sitting on a Moat It Has Not Built Yet

Healthcare may be the clearest example of this pattern anywhere. Pharma, payers, pharmacies, and health systems sit on decades of regulated, proprietary data, the kind that new AI-native entrants cannot replicate or buy on the open market. The asset is real, but the readiness is not.

McKinsey's April 2026 healthcare survey found that half of US healthcare organizations have implemented gen AI, up from 25% two years ago. The barrier is no longer adoption. It is execution. The same survey ranked difficulty integrating tools into existing workflows as the top scaling roadblock, cited by 59% of healthcare leaders. The data readiness divide we have been tracking across industries is the same divide separating healthcare AI experiments from healthcare AI value.

In an earlier piece, I argued that the moat in healthcare is not the proprietary silo. It is the ability to connect to the broader ecosystem. Internal readiness is the prerequisite for external participation. Get the order wrong and you build connections to nothing. 

The Move on Monday Morning

You do not need to be a data architect to respond to this moment. You need to ask one question before implementing any AI workflow: How does the data move today?

That question works across industries. It works in regulated environments and unregulated ones. It works for technical teams and non-technical leaders alike.

The people who ask that question first will execute faster. The companies that ask it first will still be standing when this moat reset reaches industries that still believe they are insulated.

The reset has already started in software. It is coming for everything else.

The work is already sitting on your desk.

The companies that do it now will define the next decade in their industries. The ones that wait will be left explaining themselves on the next earnings call.

The data work is the work. Everything else is the outcome.