A future where we achieve true AGI is perhaps not as far off as some might think.

AI is already proving to be one of the most powerful accelerators of human progress, and we are starting to see early signals of just what it can enable.

Researchers are making headway on some of the biggest problems that have resisted decades of human effort.

Nuclear fusion is just one such example. Long the poster child of always being 30 years away, it is now being modelled and optimized thanks to AI in ways that are materially changing the pace of experimentation.

If this trajectory continues, the benefits extend far beyond energy into climate modelling, resource optimization, and other global challenges.

However, the question is not whether AI will deliver breakthroughs, but who will benefit from them and, crucially, who gets to build with them. At present, the answer is very few.

Clipping the wings of progress

Most of today’s AI products are at their core the same. They sit on top of a small number of foundation models that are trained on broadly similar datasets. The result is a market of apparent variety masking underlying homogeneity with different interfaces, but increasingly the same intelligence layer.

The danger, of course, is that if the current trajectory holds, this small number of model providers will define not just the capabilities of AI systems but the boundaries of innovation and who participates in it. The wings of progress are being clipped.

Eroding differentiation

A world where every AI product behaves the same because it sits upon the same underlying intelligence is a world where differentiation erodes. It is a world where individuality, at both an organizational and personal level, becomes harder to express through technology.

A change of thinking is needed. It is important to not slow down progress but to redistribute the capacity to shape it. That requires a shift at the level that matters most: the intelligence layer.

If individuals and organizations are to meaningfully participate in the AI-native era, they need the ability to shape, adapt, and own the models that power their applications. This is where live learning becomes critical.

The need to retain individuality

Static models, no matter how large, are nothing but snapshots. They improve through periodic retraining cycles controlled by their providers. Live learning models, by contrast, evolve continuously in production incorporating new data and users controlling what and how the models learn. It can be thought of as the difference between renting intelligence and owning it. And that distinction will define who captures value in the AI-native era.

If we want an AI-native future that retains individuality, every individual and organisation must have the ability to own and shape the intelligence layer powering their AI applications. The good news is that there is already technology that uses live learning models in production. But building the technology is only part of the challenge. The real task is making it accessible at scale, in a way that is both usable and sustainable.

How intelligence evolves

A live environment can be crucial in understanding how users interact with, adapt, and derive value from live learning systems in practice. It provides the ability to observe how intelligence evolves when it is placed directly in the hands of users. In other words, it is an experiment in what an AI-native product ecosystem might actually look like.

What matters is not the first iteration but what it reveals. How users shape their models, what kinds of feedback loops emerge, and how intelligence behaves when it is no longer centrally controlled. Those insights will help inform what comes next and ensure a brighter future.

Avoiding a lost opportunity

The closest version of an AGI future we envisage is not a single moment of transformation but a gradual shift. Individuals will live increasingly AI-assisted lives. Plus, the organizations we interact with daily will become AI-native themselves, not by adding AI as a feature necessarily but by embedding it into their core operating models.

This is unlikely to be the end state of course. But over the next few years this is what an early version of an AGI world is likely to look like. And if that future is coming, as it most certainly is, then the current trajectory matters.

If the AI ecosystem continues to be dominated by only a small number of foundation model providers, we are heading towards a world where AI-assisted experiences are powerful but increasingly standardized. That seems like a lost opportunity.

The democratization of live learning

The only viable alternative is the democratisation of live learning. A world where any individual or organisation can train and own the intelligence layer powering their AI applications. Plus, one where that intelligence continues to evolve through feedback and additional data.

In that world, the performance, management, and evolution of AI systems are no longer tied to the roadmap of a handful of providers but becomes free from unnecessary shackles. The intelligence powering them belongs to the creator and is shaped by the people who use them.

That is the difference between participating in the AI-native era and inheriting someone else’s version of it. I know which I would choose.

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CEO and co-founder of Boltzbit.