šš„ When Systems Grow Faster Than Meaning
- Sarah Gruneisen
- Mar 22
- 5 min read
(What Rejekts revealed when you listen between the talks)
I went to the Rejekts conference for the talks.
Kubernetes.
AI.
Platform engineering.
Observability.
But the real story wasnāt in any single talk.
It was in what they all had in common.
The Red Line, We Keep Adding Layers to Avoid Facing the Same Problem
Every talk, in a different way, was solving this:
āHow do we manage the complexity we already created?ā
Not reduce it.
Not question it.
Manage it.
Sveltos ā Manage clusters of clusters
Hypervisors ā Add isolation to orchestration
Container runtimes ā Mix paradigms in one system
OpenTelemetry ā Make fragmented systems observable
AI loops ā Manage systems we no longer fully control
Different topics.
Same pattern.
We Didnāt Lose Control All At Once
We lost itā¦ layer by layer.
First:
We abstracted hardware
Then:
We abstracted infrastructure
Then:
We abstracted deployment
Then:
We abstracted observability
And now:
We are abstracting decision-making (AI)
Each step made sense.
Each step solved a real problem.
From virtual machinesā¦
to containersā¦
to Kubernetesā¦
to multi-cluster orchestrationā¦
Even bringing hardware back again, GPUs, TPUs, because abstraction wasnāt enough anymore.
But together?
We created systems where cause and effect are no longer visible
And This Is Where It Becomes Human
Because the real issue isnāt tooling.
Itās that the system itself is no longer intuitively understandable
š§ Our brains were not designed for this
Humans build understanding through:
š direct cause ā effect
š fast feedback loops
š patterns we can recognize and feel
But modern systems break that.
Instead we deal with:
š¤ indirect signals
š¤ delayed feedback
š¤ invisible dependencies
š¤ multiple layers of abstraction
š§ What happens then?
Cognitive load doesnāt just increase.
It compounds
Because now the brain has to:
š„ hold multiple system models at once
š„ simulate what might be happening
š„ constantly switch context
š„ fill in gaps with assumptions
And neuroscience is clear:
š working memory is limited
š context switching is expensive
š uncertainty increases stress
So what do engineers experience?
āI need more observabilityā
āI donāt fully trust what I seeā
āSomething feels offā¦ but I canāt pinpoint itā
Thatās not a skill issue.
Thatās cognitive overload
Sveltos Was a Signal of This Shift
It exposed something deeper:
Platform engineering is no longer about enabling teams
It is about containing complexity
The promise:
š one control plane
š automated reactions
š simplified multi-cluster management
a dream of simplicity
The reality (hidden in the talk):
š¤ teams are overwhelmed by tooling
š¤ platform teams struggle to keep up
š¤ training gaps remain
š¤ resistance doesnāt disappear
Because the real issue isnāt tooling.
Itās that the system itself is no longer intuitively understandable
OpenTelemetry Showed the Next Layer
It exposed the next layer.
We created:
š„ logs
š„ metrics
š„ traces
Then needed:
a system to connect them
Then realized:
even if data is standardizedā¦ understanding is not
You can move data anywhere.
But you cannot move:
š context
š mental models
š meaning
And hereās the subtle trap:
standardization gives the feeling of control
So what did we do?
š„ dashboards for dashboards
š„ pipelines for pipelines
š„ visibility for systems no one fully sees
Not because weāre doing it wrong.
Because intuition no longer scales with the system
The Kubernetes Talks Said It Without Saying It
This line stood out:
Kubernetes is not built for multi-tenancy
And yetā¦
we are forcing it to be
So what do we do?
š„ add hypervisors
š„ add runtime layers
š„ add virtualization inside orchestration
We didnāt question the foundation.
We adapted reality around it
And that has a cost:
š more teams
š more coordination
š more cognitive overhead
And Then AI Walked In
The RALPH loop talk said:
AI thinks it completed the taskā¦ but it didnāt
Everyone laughed.
But that was the most honest moment of the day.
Because our systems already behave like that:
š¤ deployments succeed but value is unclear
š¤ dashboards are green but users struggle
š¤ pipelines run but no one questions why
AI didnāt introduce this problem.
It revealed it
š§ The Memory Parallel No One Talked About
AI systems have context windows.
When they fill up:
š¤ information gets compressed
š¤ details get dropped
š¤ earlier context is rewritten
To keep going, AI:
š¤ approximates
š¤ fills gaps
š¤ optimizes for completion
Humans do the same.
Under cognitive pressure:
š¤ working memory overloads
š¤ details fade
š¤ we simplify reality
š¤ we rely on patterns instead of precision
We start:
š¤ assuming instead of knowing
š¤ shortcut
š¤ reacting instead of understanding
š The Parallel Is Uncomfortable
AI hallucinates under pressure.
Humansā¦ approximate under pressure.
And both are trying to do the same thing:
keep the system moving forward
even when full understanding is no longer possible
The Hidden Shift No One Said Out Loud
We used to build systems we understood.
Now we build systems we:
š observe
š orchestrate
š react to
But donāt fully grasp end-to-end.
And that changes leadership.
Because the question is no longer:
āCan we build it?ā
But:
āCan humans still understand, trust, and operate what we built?ā
This Is Why Everything Feels Harder
Not because engineers got worse.
Not because tools are bad.
But because:
the gap between action and understanding has widened
And when that gap grows:
š¤ feedback loops weaken
š¤ ownership blurs
š¤ confidence erodes
So we compensate.
With:
š„ more tools
š„ more layers
š„ more automation
Until We Reach This Point
Where we are now:
building systems
to manage systems
to understand systems
to control systems
And somewhere in that stackā¦
meaning gets diluted
The Most Important Learning From Rejekts
Not Kubernetes.
Not Sveltos.
Not OpenTelemetry.
Not AI.
Complexity is no longer just technical
It is:
š cognitive
š human
š systemic
What Leaders Can Do, Turning Toward the Light
We donāt need to remove all complexity.
We canāt.
But we can change how we relate to it.
š Design for human understanding, not just system performance
Ask: Does this make the system easier to understandā¦ or just easier to run?
š„ Shorten the distance between action and meaning
Make cause ā effect visible again.
Clarity reduces cognitive load faster than any tool.
š Create space for thinking, not just reacting
Because under pressure:
both humans and AI approximate instead of understand
And Maybe Thatās Why Rejekts Mattered
Because in the middle of all this complexityā¦
Rejekts did something simple.
It wasnāt:
š¤ another platform
š¤ another abstraction
š¤ another layer
It was:
ā¤ļøāš„ people
ā¤ļøāš„ showing up
ā¤ļøāš„ sharing
ā¤ļøāš„ connecting
A community event.
Built in a few months.
By volunteers.
Not perfect.
But real.
And that mirrored everything this day revealed.
In a world where:
š¤ systems are layered
š¤ feedback is delayed
š¤ understanding is stretched
Rejekts brought something back:
š direct interaction
š immediate feedback
š shared understanding
Gratitude
Thank you to:
šš² the organizers
šš² the volunteers
šš² the speakers
šš² the sponsors
You didnāt just create a conference.
You created a space where meaning could catch up again
šš„ If This Resonatesā¦
This is exactly the work Iāve been exploring deeper in:
š The Leadership Leap: Now Without Crash Landings, where I break down how leaders can reconnect:
ā¤ļøāš„ systems to value
ā¤ļøāš„ people to purpose
ā¤ļøāš„ complexity to clarity
And in my programs, like Leadership Landing and Team Accelerator, we go further:
translating these insights into
real team practices, real decisions, real impact
Because this isnāt about rejecting complexity.
š Itās about leading it
without losing ourselves in it
Final Thought
We will keep building.
We will keep scaling.
But we can choose this:
systems that expand human capability
instead of systems that quietly exhaust it
And if we keep creating spaces like Rejektsā¦
meaning wonāt get lost in the system
Because we will carry it
together šš„š
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