Series: AI Isn’t an Engineering Problem Anymore (Part 7)
It’s a cost problem—and most teams don’t realize it yet.
What is a Control Plane?
Imagine a world with hundreds of thousands of planes in the sky, but no airports, no central communication systems, and no coordination for routing, landing, or takeoff scheduling.
Sounds chaotic, right?
Air traffic control acts as a control plane.
The planes do the actual work, but the control systems coordinate:
routing
scheduling
communication
and safety
Now let’s refocus back to AI.
Most AI products today are surprisingly simple underneath.
At their core, many of them are essentially:
User input → LLM API call → Response
At a small scale, this works perfectly fine.
But as AI adoption grows, raw API calls alone stop being enough.
The hidden assumption
A lot of current AI workflows assume:
“Since the model is smart, the system will scale.”
But intelligence alone does not solve:
cost visibility
repeated reasoning
workflow duplication
routing
attribution
memory growth
governance
or context management
Those are infrastructural problems.
What happens inside organizations
Most businesses already use AI to some extent.
Maybe not officially at the organizational level yet, but when:
developers use Codex
teams use ChatGPT
support uses Claude
designers use AI generation tools
employees automate tasks independently
then the organization is already relying on AI operationally.
The problem is:
Most of this usage is happening without coordination.
The duplication problem scales quietly
At small scale:
repeated prompts
retries
overlapping reasoning
duplicate workflows
feel harmless.
At an organizational scale, they compound.
Different people may:
solve the same issue repeatedly
regenerate similar reasoning
feed the same context multiple times
independently rediscover the same solution paths
Without realizing it.
This starts looking less like “usage”
And more like:
distributed inefficiency.
Why APIs alone are insufficient
An API call only answers:
“Can the model respond?”
It does not answer:
Have we solved something similar before?
Should this request even hit the model?
Is this the optimal model for this task?
Is this repeated work?
Is this context unnecessarily large?
Which team is driving the cost?
Which workflows are inefficient?
What should persist?
What should expire?
Those questions exist above the model layer.
This is where a control plane becomes important
AI systems eventually need something closer to a CONTROL PLANE, not just raw model access.
A layer responsible for:
routing
caching
attribution
observability
governance
context optimization
and intelligent reuse
Because once AI becomes operational infrastructure, organizations eventually need visibility into:
where compute is going
why it is being consumed
and whether the work being performed is actually necessary
The cloud parallel
Cloud computing went through something similar.
At first:
spinning up compute felt magical.
Then eventually organizations realized:
compute sprawl exists
waste compounds
visibility matters
governance matters
optimization matters
I think AI is approaching a similar phase.
The difficult part
We have previously shown that human interaction with AI is inherently messy.
Humans:
revisit ideas
refine prompts
change direction
retry tasks
explore uncertainty
So unlike traditional deterministic systems, the boundaries between:
“new work”
and
“repeated work”
become much harder to detect.
Why this matters
Because if organizations do not eventually solve:
reuse
coordination
attribution
and context efficiency
then scaling AI usage may become significantly more expensive than most people currently expect.
Especially for:
startups
vibecoders
small engineering teams
and companies heavily integrating AI into daily workflows
My Opinion
I think the companies that win long term won’t just be the companies with the best models
But the companies that best understand:
orchestration
memory management
workflow efficiency
and intelligent compute allocation
What I’ll explore next
In the next post, I’ll talk about something that makes this even harder:
why you can’t simply cache everything
Especially once:
privacy
enterprise trust
and sensitive data
enter the picture.
👉 Part 6 is here: (https://dev.to/joshua_chukwu_ccb92f05a94/why-similarity-matters-more-than-exact-matches-in-llm-systems-46pa)
Closing thought
The first phase of AI adoption was:
“Can we make this work?”
The next phase may become:
“Can we make this scale efficiently?”
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