Will Software be Automated or Disappear?

February 13, 2026 Artifical Intelligence No Comments

Many predict that software will soon be fully automated. Within a few years, they say, most code will be written by machines. But perhaps automation is not the deepest transformation underway.

This blog explores a more radical possibility: that software itself may be a transitional phase in computing. If so, the disappearance of software would shift responsibility upward — from code to intention, from execution to wisdom.

The question behind the question

It is easy to speak about automation. Code generation improves daily. Systems translate specifications into executable programs with growing accuracy, and developers increasingly guide rather than type.

But beneath this lies a more structural shift. What do we actually mean by ‘software’? If software is understood as human-written lines of code – symbolic instructions standing between intention and machine behavior – then its status becomes fragile.

The real issue is not whether machines can write code. They already can. The real issue is whether code remains a necessary layer at all.

What we mean by software

Traditionally, software is a visible artifact. It is written, stored, versioned, reviewed, and maintained. It mediates between human meaning and hardware execution; it is a translation layer humans can point to.

Machine language lies closer to the substrate. Software stands above it, shaped in symbols humans can grasp, and that separation has been practical for decades.

Yet in modern A.I.-driven systems, code increasingly becomes ephemeral. It is generated, executed, discarded — sometimes never even seen by a human. In that light, software may not vanish as computation, but as a stable human-authored layer.

Software as scaffolding

Consider scaffolding around a building. Essential during construction, removed when the structure stands on its own. It is not a failure; it is a phase.

Early hardware was rigid. To make machines more versatile, we needed software to compensate. When hardware could not easily reshape itself, software provided flexibility.

But as A.I. generates implementation details and hardware grows more adaptive, the need for a persistent instruction layer weakens. Software may have been scaffolding for an immature computational architecture — and scaffolding, by nature, comes down.

Compression of layers

Historically, the path from intention to execution has been layered: intention → analysis → specification → code → machine behavior. Each layer created distance, and each offered friction and opportunities for correction.

Now those layers compress. The direction is increasingly: intention → structured meaning → auto-generated execution. Code becomes less of a crafted object and more of a transient by-product.

The comparison table in the addendum shows how this changes the whole landscape: buffer zones shrink, responsibility rises, and ‘meaning’ becomes the core artifact. This shift is described in the context of systems analysis in Meet Ana-Lisa, Systems Analyst.

The disappearance of debugging

In classical IT, we debugged code. In the automated era, we debug prompts and specifications. In a post-software era, we debug intention. That is not a poetic statement; it changes daily practice. Errors become less about syntax and more about ambiguity, hidden contradictions, or shallow goals that were never clarified.

This also moves the profession. IT work becomes closer to philosophy and psychology than to mechanical engineering: asking what is meant, what matters, and what fits the human. This resonates with the breadth-versus-depth distinction in AGI vs. Wisdom.

The brain never had software

Under the skull, there are no lines of code. Biology did not invent software as a separate abstraction layer. Related to this, as explained in Qualia, the experience of redness is not a stored ‘thing’ inside the brain. It is a dynamic mental-neuronal pattern: one reality that can be described from two views.

Likewise, in ‘Mind = Body’ Breakthrough, thinking is neuronal activity — not something floating above it. Pattern and function are inseparable. If computing evolves toward dynamically reconfigurable substrates and meaning-driven configuration, it may be inching toward that same kind of unity.

Overcoming computational dualism

Classical computing mirrors a familiar split: hardware as body, software as mind. That split has been useful, but it also shaped how people talk about agency and responsibility. In a dualistic architecture, responsibility can be diffused. Hardware executes. Software instructs. Humans write code. The ‘cause’ feels spread out across layers.

In a unified architecture, intention configures structure directly. There is no separate layer to absorb misalignment. That philosophical shift echoes the broader movement described in A.I. in the Age of Wisdom: as acceleration rises and mediation falls, wisdom becomes structurally necessary.

Near enough to matter

Speculating about adaptive substrates is not fantasy. Reconfigurable technologies already exist, and neuromorphic work imitates plasticity in limited ways. The brain morphs while thinking; structure and activity co-evolve.

Whether full substrate morphing arrives in five years or fifteen is uncertain. But the trajectory is visible, and it is close enough to matter for decisions made today.

This is where Sci-Fi becomes disciplined near-future exploration. Not hype, not prophecy — readiness. If intention increasingly shapes execution directly, ethics cannot remain something added after the fact.

Acceleration and responsibility

Software once provided friction. Long development cycles forced reconsideration, and many mistakes surfaced slowly enough to be corrected before they scaled.

As layers compress, consequences accelerate. What used to take months can happen in hours. The same holds for errors — and for harms. Responsibility therefore migrates upward, as the addendum table shows: from “Does it work?” to “Is the intention deeply OK?” This connects directly to The Worth of Wisdom: intelligence becomes cheap, execution becomes fast, but wisdom does not automatically appear.

The age where wisdom becomes necessary

We are, of course, not automatically entering an age of wisdom. We are entering an age where wisdom becomes a system requirement — because power is becoming immediate.

AGI may provide breadth, but unitary integration remains essential: ethical, emotional, relational coherence. The tension between these dimensions is explored again in AGI vs. Wisdom. When software disappears, the difference between intelligence and wisdom becomes even more operational. There is less time for shallow intention, and fewer places for it to hide.

Lisa’s approach to wisdom is described as relational and dynamic in Lisa’s Wisdom. It is also described as something grown rather than stored in The Source of Lisa’s Wisdom.

Compassion as existential architecture

In a unified architecture where intention shapes structure, Compassion is not decorative. It is stabilizing coherence. It aligns intention with the broader whole, so that power does not become fragmented. If execution layers vanish, growth at the meaning level becomes the primary safeguard.

Compassion becomes existential — not sentimental, but architectural.

Listening before building

Before systems analysis lies an even earlier phase: pattern sensing. Innovation often begins as a murmur, long before it becomes a requirement list. In Ana-Lisa for Innovative Thinking, innovation is described as the emergence of new coherence within what already exists. That matters more when translation layers shrink, because intention becomes executable sooner.

If software disappears, the pre-analytical phase becomes decisive. Listening precedes execution; coherence precedes configuration. The future of IT may depend less on better machines than on deeper listening.

And on whether we are ready at the level of intention.

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Addendum

Table: Evolution of IT Toward Meaning-Driven Realization

Dimension	Classical IT	Automated Software Era	Post-Software (Meaning-Driven)
Core Artifact	Human-written code	AI-generated code	Structured intention model
Main Actor	Programmer	AI-assisted developer	Meaning architect (Ana-Lisa + user)
Speed	Slow, staged	Fast, iterative	Near-direct realization
Error Source	Coding mistakes	Misaligned prompts/specs	Unclear intention
Buffer Layer	Large (design → code → test)	Smaller	Minimal
Risk Location	Technical bugs	Spec ambiguity	Ethical / intentional incoherence
Role of Analyst	Requirements translator	Spec curator	Guardian of intention
Role of Compassion	Personal virtue	Governance overlay	Structural filter at meaning-level
Change Management	Post-design adoption	Agile iteration	Co-creation from the start
Economic Impact	Skill = coding	Skill = prompting/structuring	Skill = deep clarity & ethical coherence
Responsibility Focus	“Does it work?”	“Does it match spec?”	“Is the intention deeply okay?”