Reality is more complex.
Language models do remove work from people. That is true. But above all, they remove work that is mechanical, repetitive, and text-based.
In software development – and knowledge work more broadly – a large portion of time is spent producing text:
documentation
code
tests
backlog issues
change descriptions
pull request summaries
reporting
This work is necessary. But it is rarely the part people find most meaningful.
The Level of Abstraction Is Rising
When the mechanical production of text shifts to machines, the human role moves to a higher level.
The central questions become:
How does the system work as a whole?
How do the different components connect?
Where is value created, and for whom?
What problem are we actually solving?
What are the risks and constraints?
A language model can write code.
A human must decide what the code should do, why it should be done, and how to ensure the outcome is safe and appropriate.
This is not about the disappearance of work. It is about a rise in abstraction.
Agents Do Not Operate in a Vacuum
When we talk about agents, we are talking about a system in which a human defines:
the objective
the boundaries
the context
the acceptance criteria
the supervision model
The agent produces suggestions and implementations. The human evaluates, approves, guides, and carries responsibility.
Without clear boundaries and quality assurance, an agent can produce a lot very quickly – but not necessarily the right thing.
That is why the essential issue is not the technology itself, but how it is integrated into the existing operating model.
This Is Not Just About Prompting
One of the most persistent misconceptions is that the new skill required is simply writing better prompts.
In reality, what is required is much broader:
systems thinking
architectural understanding
building quality assurance mechanisms
risk management
the ability to break down problems into clear structures
the ability to connect technical implementation to business value
When machines produce a large share of text and code, human responsibility does not decrease. It increases.
If the overall system is not understood, mistakes scale faster than before.
An Organizational Question, Not Just a Technical One
Leveraging language models is not an individual developer’s trick. It is an organizational transformation.
A technical and social environment must be built where:
responsibilities are clear
review and approval processes function
competence is developed systematically
people understand their role in the new whole
Without this, language models remain isolated tools.
When properly integrated, they become accelerators of productivity, quality, and learning.
What Does This Mean in Practice?
The question is not whether machines will take jobs.
The question is, above all, one of competence.
Technology advances rapidly. Organizational capability does not automatically advance at the same pace.
If language models are used without systems-level understanding, without clear operating models, and without training, the result is uncontrolled automation. In that scenario, risks grow faster than benefits.
If, on the other hand, an organization invests in competence – the ability to define problems clearly, build supervised agent models, understand architecture, and lead change – language models become a strategic strength.
This is fundamentally a competence issue:
Can we raise the level of abstraction at which people operate?
Can we train developers to orchestrate rather than merely implement?
Can we build an environment where responsibility and supervision are clear?
Those companies that invest systematically in developing competence will grow stronger.
Those that see this merely as cost-saving automation are taking a significant strategic risk.
Machines are not taking our jobs.
They are taking the boring work.
What replaces it is a more demanding role: understanding systems as a whole, modeling value creation, and building seamless collaboration between humans and machines.
This is not an easier world.
It is a world where the required level of competence rises.
And that is why this is not primarily a technology investment.
It is an investment in human capability.
