I’ve been thinking a lot lately about the evolution of AI tooling, especially how we’ve gone from simple code generation to something much more nuanced.
We know the arc: first came basic prompting, then planning and editing agents, then orchestrators that toggle between different AI “modes.” But now we’re seeing signs of the next big leap: true multi-model collaboration.
Beyond Orchestrators
To be honest most so-called “orchestrators” today are really just glorified mode-switchers. They’re impressive, yes, but they function more like traffic controllers: “Now it’s coding mode’s turn. Now it’s planning mode. Now it’s research mode.”
What they’re not doing at least not yet is fostering a real back-and-forth between multiple AI systems, each bringing its own perspective, strengths, and assumptions to the table.
This is where I think things get really interesting.
I’ve been experimenting with a setup involving Claude (primarily Claude Code) and a meta coordinator which is called “Zen MCP.” What’s happening here is different from orchestration. It’s more like a team of collaborators working on a problem together.
Claude doesn’t just follow instructions, it pushes back. It suggests alternatives. And when paired with different models, which moderates and aligns their outputs toward a shared goal, it starts to feel like actual dialogue, not delegation. The productivity gains I’ve seen aren’t just marginal.
Why Multi-Model Collaboration Matters
I think that multi-model collaboration is the next major leap. The benefits are obvious:
- Different models have different strengths and blind spots
- Real-time idea refinement through AI dialogue
- More robust solutions through diverse AI perspectives
- Natural specialization where each model focuses on what it does best
The challenge is that most current systems are built around single-model paradigms with human-in-the-loop workflows. Multi-model collaboration requires rethinking the entire interaction architecture, shared context, conflict resolution, collaborative reasoning, distributed planning.
There's real value in AI systems that can actually think together rather than just take turns. The industry will likely move this direction, but it requires abandoning the simpler orchestrator model for something more complex and genuinely collaborative.
I think it would be a specialist-driven collaboration architecture, it just makes way more sense than trying to force one model to be everything. The specialisation approach really clicks for me because it mirrors how effective human teams operate.
You wouldn't have the same person doing initial creative brainstorming, UI/UX design, technical implementation, code review, vulnerability checking, and building deployment pipelines. Different cognitive strengths for different phases.
What I’ve found especially interesting is using different models not just as backups or alternatives, but intentionally for their actual strengths. So I might set things up like this:
- A reasoning-heavy model for problem decomposition and logic flow
- A code-focused model for implementation
- A review-oriented model for critique and refactoring
Building AI Orchestration Despite the Odds
The tricky part is the handoff protocols, how do you ensure context preservation and maintain coherent direction across the specialist chain? And how do you handle disagreements between specialists? (I think disagreements are great) The another major problem is that The major AI companies have strong incentives to keep users locked into their ecosystems, not to enable seamless multi-model collaboration.
Think about it from their perspective:
- If Claude can easily collaborate with GPT-4 and Gemini, why would you pay for Claude Pro when you could mix and match the cheapest/best models for each task?
- Their business models depend on usage volume and subscription stickiness
- They're racing to build the "one model to rule them all" - admitting you need multiple models undermines that narrative
OpenAI especially has shown they prefer vertical integration they want to own the entire stack from model to application. Anthropic talks about AI safety and collaboration, but they're still a business that needs to justify their valuations.
The reality is that multi-model orchestration will likely come from:
- Open source tools (like zen MCP setup)
- Third-party platforms that abstract away the model providers
- Enterprise solutions that need best-of-breed approaches regardless of politics
The big companies might offer token interoperability enough to check the "plays well with others" box, but they won't make it seamless or cheap. They'll probably structure pricing to make single-model usage more attractive.
Opportunity
Ironically, this creates a huge opportunity for whoever builds the best multi-model orchestration layer. If they can solve the context handoff, cost optimization, and reliability issues, they could become the new interface layer that sits above all the model providers.
In the end Zen MCP’s success suggests that building collaboration infrastructure first might be more effective than trying to make individual AI models smarter.
Their approach shows how relatively simple coordination mechanisms can unlock complex behaviors from existing models.
Future platforms could extend this infrastructure-first philosophy where we can build an Universal AI workspace protocols that any model can join and Standardized memory interfaces that work across different AI architectures