Part 2: The real risk for OpenAI and Anthropic: LLMs Becoming Commoditised

In the first part of this analysis, we explored the recent reports and discussions suggesting that OpenAI and Anthropic are increasingly moving closer to enterprise deployment, operational integration, and AI-enabled services ecosystems - including reported partnerships and discussions involving private-equity-backed deployment structures and enterprise implementation capabilities.

At first glance, this appeared to signal a simple expansion into consulting or enterprise services.

But that interpretation may actually miss the much more important strategic story underneath. Because the real issue may not be services revenue at all.

The deeper issue may be that frontier AI labs increasingly realise that remaining “only” an LLM provider could eventually become strategically dangerous.

And that changes how we should interpret the entire evolution of OpenAI and Anthropic.

The Commoditisation Problem Nobody Wants to Talk About

For two years, the AI race has largely been framed around benchmark superiority, reasoning capabilities, parameter scale, GPU clusters, and model releases.

The implicit assumption was: the best model would naturally capture the most value. But technology history suggests things are rarely that simple.

The most powerful foundational technologies do not always become the dominant economic control points.

Semiconductors.

Servers.

Databases.

Telecom infrastructure.

Cloud hardware.

All became essential.

But much of the long-term ecosystem power often migrated upward toward operating systems, orchestration layers, enterprise platforms, developer ecosystems, and workflow control.

This may now be the hidden strategic fear emerging for frontier AI labs.

Because as models improve across the market performance gaps narrow, open-source ecosystems accelerate, enterprises increasingly adopt multi-model approaches, and switching costs at the pure model layer weaken.

If intelligence itself becomes increasingly accessible, then LLMs risk becoming:

critical infrastructure…but increasingly interchangeable.

And commoditised infrastructure layers rarely capture the highest share of long-term ecosystem value.

The future winners may be the companies that successfully prevent intelligence itself from becoming commoditised infrastructure.

The Real Battlefield Is Moving Up the Stack

Seen through this lens, the recent OpenAI and Anthropic moves suddenly make much more sense:

The objective may not be:

“becoming consulting firms.”

The objective may be: preventing the model layer itself from becoming commoditised underneath somebody else’s orchestration ecosystem.

This may explain why frontier AI labs increasingly push toward agents, workflow orchestration, memory, coding ecosystems, enterprise deployment, operational integration, and execution environments.

Because the future AI battle may not primarily be “Who builds the smartest model?”, but it may increasingly become: “Who controls where intelligence gets applied?”

That is a radically different strategic battlefield.

Historically, orchestration layers are where ecosystems consolidate. Not raw capability layers alone.

OpenAI May be trying to avoid becoming “LLM Inside”

This may ultimately become the clearest way to understand the strategic shift.

Intel powered the computing revolution. But over time, much of the ecosystem value migrated elsewhere: operating systems, cloud platforms, applications, developer ecosystems, and marketplaces. The risk for frontier AI labs may be similar.

Becoming:

essential…

powerful…

expensive…

…but ultimately replaceable infrastructure underneath somebody else’s operational layer.

In other words: “LLM inside.”

That may be the strategic nightmare OpenAI and Anthropic are increasingly trying to avoid.

Why Enterprise Integration Suddenly Becomes Strategic

This is why the initial news around enterprise integration and deployment capabilities matters more than many observers initially realised.

The “services” layer may not be the final business model.

It may simply be a strategic bridge toward:

• workflow embedment,

• ecosystem gravity,

• operational dependency,

• enterprise memory,

• and orchestration control.

  
  

Because once AI becomes deeply embedded into:

• enterprise workflows,

• approvals,

• coding environments,

• customer operations,

• business processes,

• and institutional decision-making,

…the model stops being just an API.

It becomes part of the operational fabric of the enterprise itself.

And historically, those embedded orchestration layers are where long-term power tends to concentrate.

The Real AI War may only be starting

If this interpretation is correct, then the AI industry may now be entering a completely new phase.

The first phase was: model creation.

The second phase may be: ecosystem capture.

And that second battle may ultimately become far more important.

Because the future winners of AI may not simply be the companies with the smartest models.

They may be the companies that successfully prevent intelligence itself from becoming commoditised infrastructure.

That is likely the much deeper strategic story behind the recent OpenAI and Anthropic moves.

And it may explain why frontier AI firms increasingly appear unwilling to remain “just” LLM providers.