The AI race is quietly changing
Months ago, most discussions revolved around one question:
“Which model is the smartest?”
Today, I’m seeing a different pattern.
The conversation is shifting toward:
How do we orchestrate multiple models, tools, and workflows effectively?
Look at where the industry is investing.
It’s no longer just about improving the model itself. The focus is increasingly on long-running tasks, delegated execution, tool use, coding assistants, planning, memory, and specialized sub-tasks working together.
That’s not a coincidence.
The model is becoming one component of a much larger system.
As engineers, we’re spending less time debating benchmarks and more time designing the layer around the model:
• Context management
• Routing requests to the right model
• Memory and continuity
• Tool orchestration
• Verification and evaluation
• Recovery and fallback strategies
This is why I believe the next competitive advantage won’t simply be having access to the “best” LLM.
It will be building the best AI Harness, the engineering layer that coordinates models, tools, context, and decision-making into a reliable system.
Cloud computing went through a similar evolution.
Eventually, the infrastructure became a commodity, while orchestration became the differentiator.
I think AI is heading down the same path.
In a few months, we may stop asking:
“Which model are you using?”
and start asking:
“What’s your orchestration architecture?”
I’m curious, are you seeing the same shift in your AI workflows, or do you think model capability will remain the primary differentiator?
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