A future where we achieve AGI is not as dystopian as one 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 what it can enable.
Researchers are making headway on problems that have resisted decades of human effort. Nuclear fusion, long the poster child of “always 30 years away”, is now being modelled and optimised with AI in ways that materially change the pace of experimentation. If this trajectory continues, the implications extend far beyond energy into climate modelling, resource optimisation, and other global challenges.
The more immediate question, however, 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.
Most of today’s AI products are structurally the same. They sit on top of a small number of foundation models, 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.
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 itself and who is allowed to participate in it.
A world where every AI product behaves similarly because it is powered by the same underlying intelligence is a world where differentiation erodes. More subtly, it is a world where individuality, at both an organisational and personal level, becomes harder to express through technology.
The alternative is not to 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 organisations are to meaningfully participate in the AI-native era, they need the ability not just to use AI, but to shape, adapt, and own the models that power their applications.
This is where live learning becomes critical.
Static models, no matter how large, are snapshots. They improve through periodic retraining cycles controlled by their providers. Live learning models, by contrast, evolve continuously in production incorporating new data and improving through direct interaction. In practical terms, this is 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.
In the short term
We have developed this technology and already have 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. That is the research we are conducting today for the greater good.
It is very much a live environment. It is an attempt to understand how users interact with, adapt, and derive value from live learning systems in practice. It allows us 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 itself, 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 inform what comes next.
In the long term
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. The organisations we interact with daily will become AI-native, not by adding AI as a feature but by embedding it into their core operating models.
This is unlikely to be the end state but, over the next five 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 a small number of foundation model providers, we are heading towards a world where AI-assisted experiences are powerful but increasingly standardised.
The only viable alternative is the democratisation of live learning. A world where any individual or organisation can customise and own the intelligence layer powering their AI applications and where that intelligence continues to evolve in production, through feedback and new data.
In that world, the performance and evolution of AI systems are no longer tied to the roadmap of a handful of providers. 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.
