The SaaS Extinction Test

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The SaaS Extinction Test<br>Why your vibe-coded weekend project won't kill Salesforce

Vikram Sreekanti and Joseph E. Gonzalez<br>Aug 06, 2026

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Apologies for the unplanned summer pause for the last couple weeks — Joey’s been on family vacation and Vikram had his first kid. We’re slowly getting back online.<br>With the news of Airtable’s acquisition this past week, analyses of the previous generation of SaaS companies have started flying around the internet. Our opinions haven’t changed dramatically, so we thought we’d bring back this post from 6 months ago.<br>Stay tuned — we’ll be ramping back to regular content in the next couple weeks!

Projections about the demise of the SaaS industry have reached a fever pitch over the last few weeks. The general belief seems to be increasingly that there’s no moat in software anymore. Consequently, we have seen the stock prices of massive, entrenched incumbents take a significant beating.<br>The bear case for software-as-a-service goes something like this: As coding agents improve, the build-versus-buy calculus shifts permanently. An engineer at any company can now pick up a coding agent, build a prototype that meets the company’s specific needs, iterate on feedback, and deploy a bespoke tool that provides more value than a generic SaaS platform at a fraction of the cost.<br>The source of this concern is our collective recent experience with the improving quality of coding agents. We have all had Claude Code or Cursor scaffold an entire application from scratch for a few dollars in tokens. When you see something genuinely useful built in minutes, it is easy to assume the multi-billion-dollar incumbents are doomed. And if an individual can build a prototype quickly, surely a startup can build a strong offering with a few months and a few engineers?

Source: Gemini.<br>As with most hype cycles, however, the truth lands in the middle. While there are massive opportunities for disruption, the SaaS business model is not going to evaporate overnight. In our view, the distinction comes down to a quick survival checklist. If you meet one of these criteria, you are likely safe. If you miss on all of them, you should be worried:<br>Are you a system of record?

Do you do more than help humans automate a single workflow?

Are you mission-critical?

The Physics of Data Gravity

Data moats have long been the holy grail of enterprise software. Consumer products like Google or Instagram win on user behavior data, and enterprise titans like Snowflake or Datadog are powerful because they are systems of record. These companies do not just power workflows; they house years of historical data that companies have imported and structured.<br>Despite all the massive technological shifts in the last few years, the physics of data have not changed. Moving massive amounts of data is expensive, risky, and slow. This means that, as has been the case for fifteen years, the major cloud providers will continue to make their margins on networking and egress. The cost of moving data in and out of a third-party service — or your own cloud — remains incredibly high.<br>Beyond the operational cost, there is the issue of operational stickiness. If a product is collecting data for a core operational purpose, it becomes a load-bearing wall in the company’s architecture. You do not pay Datadog or Snowflake eight figures a year because storing logs is a nice-to-have feature. You pay them because when a mission-critical issue happens at 3:00 AM, you need a proven way to investigate, pinpoint, and fix the issue.<br>But realistically, your business will continue to exist if Datadog goes down for a little while. Things get much more difficult when we’re talking about truly mission-critical software.<br>The Mission-Critical Tautology

Even when companies don’t have the most interesting data, they’re likely safe if they are critical to the operations of their customers – the kinds of products that your business literally wouldn’t exist without. The obvious examples are Workday and Salesforce.<br>There is a certain recursive logic here: These companies are safe because they are big, and they are big because they are safe. No matter how shiny a rapidly scaffolded payroll system looks, a VP of HR is not going to risk payroll failing on a Friday morning. A CRO does not care how many new automations a startup offers if it means moving away from a Salesforce instance they have spent a decade customizing to their exact sales and revops motions.<br>When enterprises make these decisions, they are not just buying software – they are also offloading risk. A tech-forward company like Google or Meta certainly has the technical talent to build an internal payroll system. The question is not whether they can build it, but whether they want to own the risk of running it. Once they do the calculus, dedicating hundreds of engineers to a non-core area of expertise rarely makes sense.<br>By contrast,...