We’re taking a look at the trends that matter most for leaders navigating the next 12–36 months.

We organize trends into three categories based on their maturity and impact timeline:

Game Changers — Trends reshaping entire industries right now. These affect how businesses operate, how value is created, and how competitive advantage is built.

Foundational Breakthroughs — Scientific and engineering advances unlocking new possibilities. These create strategic optionality for the decade ahead.

Weak Signals — Early indicators of transformations that will dominate the late 2020s. These require positioning now, even if mainstream adoption is years away.

Today, we’re diving into a Foundational Breakthrough that could redefine what AI is capable of.

Living AI: Models That Learn As They Go

Every AI model you’ve used, from the chatbot on your phone to the copilot in your enterprise software, knows only what it was taught during training. Ask it about something that happened after its cutoff date, or something outside its training data, and it simply doesn’t know. It’s frozen in time.

That’s about to change.

The next frontier in AI is about models that keep learning after deployment. Continuous, real-time learning would let AI systems absorb new information, adapt to shifting environments, and refine their own reasoning without waiting for the next training run. It’s a small conceptual shift with enormous implications: it’s widely considered one of the key unlocks on the path toward AI superintelligence.

Where This Is Emerging

Liquid AI is rethinking the neural network itself. An MIT spinout founded by Ramin Hasani and Daniela Rus, it builds “liquid” networks from neurons whose underlying equations keep changing at inference time, so the model adapts to new data on the fly instead of staying frozen after training — an architecture inspired by the 302-neuron nervous system of the C. elegans worm. The efficiency gains are striking: in one autonomous-driving test, just 19 liquid neurons matched the lane-keeping performance of a conventional network with more than 100,000 neurons, while drawing only 1 to 5 watts of power.

Safe Superintelligence (SSI) represents a new class of AI company built around a singular focus: systems that can learn and improve continuously, without compromising on safety, alignment, or human oversight. Founded in June 2024 by former OpenAI chief scientist Ilya Sutskever — co-creator of GPT-3 and GPT-4 — alongside Daniel Levy and Daniel Gross, SSI is a bet that today’s dominant recipe of more compute and more data will eventually plateau, and that reaching superintelligence will require fundamentally new ways of learning. It is a pointed wager from the person who did as much as anyone to prove that scaling works. With roughly 20 employees, no product, and no revenue, SSI has been valued at $32 billion — the market pricing a thesis rather than a business, and a signal that some of the smartest capital in AI believes continual, self-improving learning is the real path forward.

AMI Labs takes yet another route. Founded by Turing Award winner Yann LeCun after his departure from Meta, it is pursuing “world models” through an approach called JEPA — AI that learns how the physical world actually works, its cause, effect, and physics, rather than just the statistical patterns of text. The idea has drawn serious backing: a $1 billion seed round and a $4.5 billion valuation as of March 2026, from investors including Nvidia, Samsung, Jeff Bezos, and Eric Schmidt.

Why It Matters

Continual learning is the missing piece for AI superintelligence. Today’s models are static snapshots that are remarkably capable, but locked in place the moment training ends. A model that learns continuously can do something fundamentally different: discover new insights on its own, adapt to environments its creators never anticipated, and potentially develop reasoning capabilities that go beyond what it was explicitly trained to do.

That’s the shift from AI as a tool that recalls information to AI as a system that can genuinely innovate and invent.

For leaders, the strategic question is how quickly and where the first real advantages show up. Companies building products, security systems, or research tools on top of adaptive AI today are positioning themselves for a very different competitive landscape tomorrow.