The model layer is becoming like cloud compute: necessary, interchangeable, boring.
I’ve sat through dozens of enterprise AI strategy sessions this year. They all have the same agenda item: “Which foundation model should we standardize on?”
Teams spend months evaluating. They build elaborate benchmark suites. They negotiate enterprise agreements. They agonize over Claude vs GPT vs Gemini vs Llama.
Here’s what I tell them: it doesn’t matter.
Not because the models are identical - they’re not. But because the performance delta between top-tier models is shrinking fast, and the real differentiation has already moved up the stack.
The Convergence Is Real
Look at any capability benchmark over the past 18 months. The gap between first and fifth place has compressed dramatically.
In January 2024, the gap between the leading model and the fifth-best was 15 points. Today it’s 2 points. By the end of 2026, it will be noise.
This isn’t a prediction - it’s the inevitable result of competitive dynamics. Every major lab is training on similar data, using similar architectures, hiring from the same talent pool. Differentiation at the model layer is getting harder, not easier.
The Commoditization Pattern
We’ve seen this before. Multiple times.
flowchart TB
subgraph "Phase 1: Innovation"
A[Breakthrough Technology] --> B[Clear Leader Emerges]
B --> C[Premium Pricing]
end
subgraph "Phase 2: Competition"
C --> D[Fast Followers Catch Up]
D --> E[Performance Gap Shrinks]
E --> F[Price Competition]
end
subgraph "Phase 3: Commodity"
F --> G[Interchangeable Options]
G --> H[Differentiation Moves Up Stack]
H --> I[Infrastructure Becomes Invisible]
end
style A fill:#dbeafe
style B fill:#dbeafe
style C fill:#dbeafe
style D fill:#fef3c7
style E fill:#fef3c7
style F fill:#fef3c7
style G fill:#dcfce7
style H fill:#dcfce7
style I fill:#dcfce7
Cloud compute followed this arc. In 2008, AWS was genuinely differentiated. By 2015, compute was a commodity - the differentiation had moved to managed services, developer experience, and ecosystem.
Databases followed this arc. Oracle had a stranglehold. Then PostgreSQL got good enough. Then cloud-native databases emerged. Now the raw database engine matters less than the data platform built on top.
Foundation models are following the same arc - just compressed into 3-4 years instead of 15.
Where Differentiation Actually Lives
If models are converging, where does competitive advantage come from? It’s moved up the stack.
graph TB
subgraph "Low Differentiation (Commodity)"
M[Foundation Models]
end
subgraph "Medium Differentiation"
I[Inference Infrastructure]
F[Fine-tuning / Adaptation]
end
subgraph "High Differentiation"
O[Orchestration & Control]
E[Evaluation & Testing]
D[Data Context Layer]
A[Agent Architecture]
end
M --> I
I --> F
F --> O
F --> E
F --> D
O --> A
E --> A
D --> A
style M fill:#fee2e2
style I fill:#fef3c7
style F fill:#fef3c7
style O fill:#dcfce7
style E fill:#dcfce7
style D fill:#dcfce7
style A fill:#dcfce7
Orchestration and control - How do you coordinate multiple model calls? How do you implement fallbacks? How do you adjust behavior at runtime without retraining?
Evaluation and testing - How do you know if your AI actually works? How do you catch regressions? How do you compare approaches rigorously?
Data context layer - How do you get the right information to the model? How do you preserve semantic richness through your data pipeline?
Agent architecture - How do you design systems that reliably accomplish goals? How do you balance autonomy with control?
These layers are where enterprises will differentiate. The model underneath? Increasingly interchangeable.
The Strategic Implications
If you accept that foundation models are commoditizing, your AI strategy needs to change.
Stop Over-Investing in Model Selection
The perfect model evaluation that takes six months is a waste. By the time you finish, the landscape has shifted. Pick a tier-1 model that meets your security and compliance requirements, and move on.
Your time is better spent on the layers above the model - the orchestration, evaluation, and data infrastructure that will actually differentiate your AI capabilities.
Build for Portability
Lock-in to a single model provider is strategic malpractice. Your architecture should abstract the model layer so you can swap providers without rewriting applications.
flowchart LR
subgraph "Your Applications"
App1[App 1]
App2[App 2]
App3[App 3]
end
subgraph "Abstraction Layer"
GW[AI Gateway]
RT[Routing Logic]
FB[Fallback Handling]
end
subgraph "Model Providers"
P1[Provider A]
P2[Provider B]
P3[Provider C]
P4[Self-Hosted]
end
App1 --> GW
App2 --> GW
App3 --> GW
GW --> RT
RT --> FB
FB --> P1
FB --> P2
FB --> P3
FB --> P4
style GW fill:#dbeafe
style RT fill:#dbeafe
style FB fill:#dbeafe
This isn’t just about avoiding vendor lock-in. It’s about optionality. When a better or cheaper model emerges - and it will - you want to adopt it in days, not months.
Invest in the Differentiating Layers
The capabilities that will separate AI leaders from laggards aren’t at the model layer. They’re in:
Evaluation infrastructure. Can you rigorously test AI behavior? Can you catch regressions before they hit production? Can you compare approaches with statistical confidence?
Runtime control. Can you adjust model behavior without retraining? Can you implement guardrails that actually work? Can you intervene when agents misbehave?
Context engineering. Can you get the right information to the model at the right time? Is your data pipeline preserving the context that AI needs?
Agent governance. Can you track what your agents are doing? Can you enforce policies consistently? Can you audit decisions after the fact?
These are the capabilities that compound over time. Model selection is a point-in-time decision that becomes less relevant every quarter.
The Uncomfortable Corollary
Here’s the part that makes model providers uncomfortable: if models are commoditizing, model provider margins will compress.
The cloud infrastructure playbook applies. Compute margins fell as competition intensified. Storage became a race to the bottom. The profits moved to higher-level services.
Foundation model providers know this. It’s why they’re all racing to build platforms, not just models. They’re trying to create stickiness in the orchestration and tooling layers before the model layer becomes fully commoditized.
Your job, as an enterprise building on these models, is to not get locked into those platforms. Capture the value of commoditization by maintaining optionality.
What We Tell Our Clients
When clients ask us which model to use, we flip the question.
“What does your evaluation infrastructure look like? How will you know if the model is working?”
“What does your control plane look like? How will you adjust behavior when requirements change?”
“What does your data pipeline look like? Are you preserving the context that AI needs?”
Get those layers right, and the model becomes a pluggable component. Get them wrong, and it doesn’t matter how good your model is.
The model wars are exciting to watch. But the winners won’t be determined by which foundation model they chose. They’ll be determined by what they built on top.
Foundation models are converging. The differentiation is moving up the stack. Build accordingly.
Rotascale helps enterprises build the layers that actually differentiate - orchestration, evaluation, control, and governance. The model underneath is your choice. Let’s talk about what matters more.