Source: Be Datable. Two axes define software's relationship with AI.
SaaS Reincarnated
Data + AI Is the New SaaS. The Model Is Changing, Not Dying.
Why This Matters for Your Business
$285 billion wiped from software stocks in 48 hours. Bloomberg called it the "SaaSpocalypse." Satya Nadella told an audience that SaaS applications will collapse in the agent era. The market took him seriously.
But SaaS isn't dying. It's being reincarnated. When anyone can build their own interface on top of their own data, the value shifts from the application layer to the underlying infrastructure. Paradoxically, that means more software gets built, not less.
Not all software faces the same risk from this shift. This framework maps where software sits across two axes to reveal which parts become stronger and which parts get consumed.
The Case Against SaaS
Zain Hoda built on Nadella's argument with a piece that went viral. His claim is that most enterprise SaaS products are surprisingly thin. The data inside them could fit on a thumb drive. The APIs are wide open. Once an agent pulls a full copy, the original application has nothing left to offer except a login screen nobody visits.
For single-purpose SaaS that stores static, structured data behind a web interface, agents really can replicate the contents and build a better experience around them. CRM records, HR profiles, project boards. These are small, structured, and copyable in seconds.
But this assumes that data is often static, which is increasingly not the case. Do you think AI systems will want more data in the future, or less? More real-time data, or less? More confidence in data accuracy, or less?
The Historical Pattern
Steven Sinofsky pointed out that we've watched this same prediction fail three times in a row. PCs were supposed to kill mainframes in the 1990s. Both grew. E-commerce was supposed to kill retail in the 2000s. Amazon and Walmart both hit a trillion dollars. Streaming was supposed to kill media in the 2010s. More media exists today than at any point in history.
Garry Tan invoked Jevons Paradox to make the same point from the demand side. When a resource becomes dramatically cheaper to use, total consumption goes up, not down. Steam engines didn't shrink the coal industry. They made coal so useful that demand grew by orders of magnitude. AI will do the same thing to software.
Where Value Concentrates Next
The $285 billion selloff is priced in a world where AI totally replaces software. The framework suggests a different outcome. AI reorganizes software, and the growth won't be evenly distributed.
It will focus on infrastructure rather than pure applications. The bottom-right quadrant is the most dangerous place to be, but it's also where entirely new product categories emerge if you're willing to rethink what the product is, rather than defend the old version of it.
The Four Quadrants
The Ground Truth: AI Foundation
LOWEST RISK
Deterministic infrastructure. AI depends on this layer to function. No model will improvise a payment amount or approximate an identity credential. As more agents run more workflows, demand for this layer increases.
Snowflake, Stripe, Segment (Twilio), Box
The Intelligence Layer: AI-Enhanced
MODERATE RISK
Probabilistic infrastructure. These products already work with probabilities, anomalies, and predictions. AI doesn't replace what they do. It gives them better tools to do it with.
Databricks, Palantir, Datadog, Amplitude
The Headless Threat: AI-Assisted
HIGH RISK
Deterministic applications. Agents can pull the data and route around the interface, but accuracy requirements keep humans checking the work. These companies still have time to adapt, but the clock is running.
Salesforce, Workday, ServiceNow, DocuSign, NetSuite
The Creative Collapse: AI-Disrupted
HIGHEST RISK
Probabilistic applications. These products generate probabilistic output, and that's exactly what generative AI now does natively. The overlap between what these tools produce and what a model can generate from a prompt is almost total.
Adobe Creative Cloud, Canva, Figma, Notion, Mailchimp, HubSpot
Common Mistakes to Avoid
Treating all SaaS as equally vulnerable
The risk depends on where you sit on both axes. Deterministic infrastructure is strengthened by AI, not threatened.
Assuming AI replaces software entirely
History shows technology expansions grow total demand. AI agents use software tools. More agents mean more demand.
Ignoring Jevons Paradox
When something becomes cheaper, consumption goes up. Steam engines didn't kill coal. AI won't kill software.
Defending the old UI instead of reimagining the product
The login screen nobody wants to visit is the symptom, not the disease. Rethink what value you provide.
How to Use This Framework
Map Your Stack
Place each tool in your software stack on the framework:
- Is this tool handling deterministic data (must be exact) or probabilistic data (variation is acceptable)?
- Is it infrastructure that other software builds on, or an application that end users interact with directly?
- Which quadrant does it land in, and what's the corresponding risk level?
Assess Your Exposure
- How much of your workflow depends on the bottom-right quadrant (Creative Collapse)?
- Which tools in your stack could be replaced by an AI agent with access to the same data?
- Where does your competitive advantage live: in the interface, or in the data underneath?
Act on the Framework
- AI Foundation (top left): Double down. You're what AI needs to function.
- Intelligence Layer (top right): Integrate AI tools aggressively. You're enhanced, not replaced.
- Headless Threat (bottom left): Open your APIs, build agent interfaces, make your data accessible before someone else does.
- Creative Collapse (bottom right): Reimagine the product. Don't defend the old UI. Build something AI can't do alone.
Quick Comparison
| Aspect | AI Foundation | Intelligence Layer | Headless Threat | Creative Collapse |
|---|---|---|---|---|
| Data Type | Deterministic | Probabilistic | Deterministic | Probabilistic |
| User Type | Infrastructure | Infrastructure | Applications | Applications |
| Risk Level | Lowest | Moderate | High | Highest |
| AI Relationship | AI depends on it | AI enhances it | AI routes around it | AI replaces it |
| Strategic Move | Double down | Integrate AI tools | Open APIs, add agents | Reimagine the product |
Common Barriers
Data Portability
Once agents can pull a full copy of your data through open APIs, your application layer has nothing left to offer except the interface.
Accuracy Requirements
Deterministic applications need 100% accuracy. The better AI gets, the harder it becomes to catch the remaining 1% of errors.
Infrastructure Lock-in
Companies in the AI Foundation layer benefit from switching costs. Moving payment processing or data warehousing is expensive and risky.
Creative Commoditization
Generative AI produces probabilistic output natively. Products that do the same face near-total overlap with what a model can do from a prompt.
Self-Assessment
Answer these questions to evaluate your position.
Where does each tool in your stack sit on this framework?
Bottom-right exposure is your most urgent vulnerability.
How much of your workflow depends on probabilistic applications?
These face the highest risk of AI replacement. Identify alternatives or transition plans.
Is your competitive advantage in the interface or the data?
If it's the interface, AI agents will route around you. If it's the data, you have staying power.
Are you building AI as a feature or rethinking the product around AI?
Adding a chatbot to an old workflow is defense. Rebuilding with AI as the interface is offense.
What happens if an agent can pull a full copy of your SaaS data in seconds?
If your product has nothing left to offer after that, you're in Zain Hoda's scenario.
The Core Principle
“SaaS isn't dying. It's being reincarnated around data and AI.”
The companies that own the infrastructure layer are the ones on which everything else gets built. AI reorganizes software. The growth won't be evenly distributed.
The part that's dying is the one-size-fits-all model, the version that forced every business into the same workflows and then required massive consulting practices just to make it fit. What's replacing it is simpler and more powerful.
“SaaS isn't dead. The model is being reincarnated around data and AI, and the companies that own the infrastructure layer are the ones on which everything else gets built.”
Our fear of the future is directly proportional to how small our ambitions are. If your plan is to keep doing exactly what you're doing, then yes, a machine that can do it faster and cheaper is terrifying. But if your plan is to do something dramatically bigger, then the machine is the best news you've ever gotten.
Sources & Further Reading
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