The Subsumption Window of AI
The Subsumption Window in AI refers to the period during which an AI product remains valuable before a more advanced foundation model renders it obsolete by incorporating its features.
As models like GPT-4o evolve rapidly, products built on narrow functionalities risk being subsumed, requiring teams to focus on defensible layers like unique data or specialised integrations to stay relevant.
This diagram illustrates the Subsumption Window from the perspective of AI product lifecycles, showing how specialised AI products face obsolescence when foundation models incorporate their capabilities.
Key Components
Product Lifecycle Curve (Blue)
Shows the typical journey of a specialised AI product — initial growth, market success, then decline as it gets subsumed.
Foundation Model Capability (Red)
Represents the rapid advancement of general-purpose AI models that eventually incorporate specialised features.
The Subsumption Window (Orange)
The critical period where:
The specialised product is still valuable and competitive
Foundation models are developing similar capabilities
There’s an overlap where both coexist before subsumption occurs
Real Examples
Grammar Checkers (like Grammarly), Subsumed by GPT’s writing capabilities
Translation APIs, Largely replaced by multilingual foundation models (NLLB)
Code Completion Tools, Being integrated into larger coding assistants
Image Generation, Currently in the window as foundation models add visual capabilities
A Critical Concept for Every AI Product Team
Foundation models like GPT-4o and Claude 3 improve fast. This raises a key question: What can you build today that stays useful tomorrow?
Early AI products often relied on simple prompts to add features.
Many of these have been replaced by updates in the models themselves. This process is called subsumption, where a new model absorbs what a product once offered.
Think of the early iOS days. Developers built flashlight apps. These apps earned money from ads and subscriptions. Then Apple added a flashlight as a standard OS feature. The apps became unnecessary. Users switched to the built-in tool. The same happens in AI.
What Is the Subsumption Window?
The subsumption window is the time between launching a product and when a foundation model makes it obsolete.
AI models evolve quicker than traditional software cycles.
In tech diffusion theory, this is disruptive innovation at work. Models advance every few months, while products take longer to develop.
For example, early tools for text summarisation or basic image editing used prompts on older models. Newer models now handle these tasks better out of the box.
Products built on narrow hacks lose their edge.
This is a real risk in product design.
Teams invest in features that vanish with the next model release. The window can close in weeks or months, depending on the task.
Why Your Moat Can’t Just Be the Model
Access to models is widespread. Anyone can use APIs from OpenAI or Anthropic. If your product only wraps a model with prompts, it has no defence.
Build around the model instead. Focus on these layers:
Differentiated Data
Collect unique datasets. Models can’t replicate proprietary or real-time data. For instance, a healthcare AI with patient records from specific hospitals holds value.
Infrastructure
Create systems for scaling, security, or integration. This includes custom pipelines that handle large inputs or comply with regulations.
User Interface (UI/UX)
Design intuitive experiences.
A simple chat interface might get subsumed, but a workflow tool tailored to engineers or marketers adds stickiness.
Reassess your subsumption window often.
Track model updates.
If a new release covers your core value, pivot. Move to harder problems that models can’t solve yet, like complex integrations or niche domains.
Designing for Moat Resilience
Treat data, UX, and vertical integration as core strengths. These aren’t easy for general models to replace.
Data as a Moat
Own the inputs. Models need training data, but your live, domain-specific data keeps you ahead.
UX and Integration
Build seamless tools. Embed AI into existing software stacks. This creates habits that users won’t abandon.
Vertical Specialisation
Focus on industries like finance or logistics. General models struggle with specialised rules or jargon.
This approach is “designing for moat resilience.” It shifts from model dependence to ecosystem building.
Enterprise Concerns
In enterprise AI, obsolescence and vendor lock-in matter.
Companies worry about tools that tie them to one model.
If that model subsumes features, switching costs rise.
Teams should plan for multi-model support.
Use abstraction layers to swap models easily.
Address lock-in by owning data and integrations.
The subsumption window forces constant adaptation.
AI product teams must monitor it to survive. Build defensible layers now, or risk becoming the next flashlight app.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. Language Models, AI Agents, Agentic Apps, Dev Frameworks & Data-Driven Tools shaping tomorrow.