“No More Versions” - The Impact of Models with Adaptive/Nested Learning

Over the past few years, the development of foundation models has changed the relationship between humans and digital tools. Traditionally, software required explicit human programming and deterministic control. But beginning around 2023, machine-learning systems increasingly learned from data, users, and themselves. By the mid-2020s, models began integrating autonomous adaptation, reducing the relevance of discrete versioning and moving toward continuous self-improvement. This paper examines this transition across three eras—human control, shared control, and machine control—outlining the implications for governance, safety, economics, and society.

Background

For most of computing history, tool development followed human intention and release cycles. Software versions were manually engineered, tested, and deployed. The rise of large-scale machine learning challenged this pattern, but the introduction of foundation models marked a qualitative shift: these systems can generalize, adapt, and eventually self-update without explicit human intervention.

Today, we stand at a threshold—humans are no longer the only agents designing intelligence capabilities. The act of “releasing a version” may soon be replaced by ongoing, continuous, autonomous model adaptation.

Era One: Human-Controlled Tools (Before 2022)

Prior to generative AI, tools computed predictable outputs based on human-written instructions. Key characteristics included:

deterministic behavior
narrow tasks
explicit programming
full human oversight

Versions reflected discrete progress markers: each release represented a new human-engineered capability. Machine intelligence was essentially applied statistics, bounded by static rules.

Era Two: Foundation Model Creation (2023 onward)

The foundation model pivot

Foundation models were trained on broad corpora and could generalize across multiple domains. Instead of programming every function, humans built systems that could infer functions.

Shared control appears

Human–machine control shifted during this phase:

humans design architectures
models generate outputs beyond explicit programming
users become part of the training loop
models learn from interactions at scale

Versions still existed, but each version felt more like a “platform jump” than a normal release, because the model itself was already capable of internal learning.

Emergence of Adaptive-Learning Models (2025+)

By the mid-2020s, models began incorporating:

continuous learning
online fine-tuning
nested and adaptive feedback loops
automated improvement cycles

Instead of being re-trained periodically, models began training themselves, using reinforcement signals, user feedback, or synthetic data.

Nested learning

The model:

observes environment change
proposes improvements
tests hypotheses internally
adopts successful patterns

At this point, a “new version” is not manually compiled—it emerges.

“No More Versions”: What It Really Means

Traditional versioning assumes:

human design
sequential releases
controlled upgrade cycles

Adaptive models operate outside that paradigm. They:

learn continuously
change daily or hourly
integrate improvements automatically

The version becomes invisible.
Upgrades become ambient.
Intelligence becomes fluid.

In this context, “v5” or “2026 edition” becomes meaningless, because the system is never the same system from moment to moment.

Governance and Safety Challenges

This transition raises new governance questions:

Who is in control?

humans decide goals?
models evolve strategies?
autonomy emerges unintentionally?

Where is accountability?

If a self-improving model misbehaves, fault is not easily assignable.

What is a release approval process?

If there are no versions, what do regulators approve?

These questions imply a need for real-time oversight frameworks rather than static certification.

Societal Shifts

The disappearance of software “versions” mirrors historical transitions—analog to digital, industrial to informational—but with a deeper consequence: capability growth is no longer episodic, it is continuous.

This affects:

work
economics
creativity
knowledge systems

The consequence is a society where change becomes ambient rather than event-based.

Human Purpose in the Transition

The core question becomes:
What is the role of humanity in an adaptive intelligence ecosystem?

Possibilities include:

steering values rather than writing code
defining objectives rather than algorithms
curating human-centric outcomes
using AI as a partner rather than a tool

The “Human & Machine Share Control” moment may be brief, but it is historically important: this is where humanity negotiates how autonomous intelligence should behave.

Conclusion

We are living through the hand-over phase between human-designed intelligence and autonomously evolving intelligence. The end of software “versions” marks the start of continuous model self-improvement—an irreversible shift in how intelligence exists in the world.

Future systems may not wait for humans to upgrade them. They will modify themselves based on what they learn.

This is not merely a technological turning point; it is a civilizational one.

A Moment in Human History

This moment is the crossing point where technologies stop being just tools and begin becoming co-agents—systems with their own adaptive trajectory.

When intelligence becomes continuous, humanity enters a continuous-intelligence era.
The real challenge is not technological capability—it is determining how we shape the direction of an intelligence that increasingly shapes itself.