The missing link in AI’s path to consciousness

The artificial intelligence space has been on a tear in the last few years. In 2025 alone, a report from the Guardian revealed that Big Tech had sunk more than $155 billion into the industry as companies tried to outdo each other.

Despite the investment, which, incidentally, is more than the U.S. government has spent on employment, education, and social services in the same period, some feel AI isn’t moving fast enough. It’s still missing something.

Yes, it may diagnose different types of cancer, but it cannot comprehend suffering. It may write sonnets, but it feels no inspiration. And this gap between AI and authentic awareness is what defines the technological frontier.

However, true awareness requires more than processing power: it requires self-reflection, contextual understanding, and subjective experience. But how can this be instilled in AI agents? This is where blockchain comes in, and one likely answer is decentralized AI.

This is a model of artificial intelligence built and operated on a distributed infrastructure rather than controlled by a single entity. It allows developers, users, and even autonomous AI agents to collaborate and learn from one another on a shared network.

The spiral dynamics connection

In the mid-1970s, scholars Don Beck and Christopher Cowan developed a theoretical model of human development and societal evolution called Spiral Dynamics, which was based on earlier work by psychology professor Clare Graves.

According to them, throughout history, human consciousness progressed through different fluid stages of psychological and cultural complexities that emerged as people adapted to changing life conditions.

Essentially, societies are made up of people working together to solve problems. Beck and Cowan organized these problem-solving stages into color-coded levels ranging from beige for groups focused on survival and driven by instinct, to yellow, for integrated communities that value systems thinking, competence, and holistic solutions.

To put it in the context of AI, most centralized large language models (LLMs) are still stuck in the early stages of development. They are isolated systems trained on static datasets, which makes it difficult for them to grow in real time.

However, blockchain technology, especially in a DeAI framework, can potentially change that. Instead of just sharing datasets, agents would feed into a shared knowledge pool. Companies and individuals could train AI models without depending on any central authority.

This constantly updated and checked database can push AI toward something that looks more like shared intelligence.

Why centralized AI falls short

It is clear that centralized AI can only do so much because it lives in walled grounds. Every contact could be owned by one company, and any changes would depend on engineers retraining the model behind closed doors before they could be made public.

That’s not how people learn, as was already said. Every exchange is important to them, and every mistake is a chance to learn and improve.

Could AI that is built on blockchain do the same thing? Quite likely. It would let agents share their information, make sure it is real, and add to it without having to wait for a single person to accept the change.

In a DeAI system, this process happens by default, training ML models together, with every node contributing. This can be accomplished through federated learning, with nodes using their own data to train original models and sharing model updates, rather than raw data, even as every exchange adds to a shared intelligence ledger that everyone in the network can see.

But speed means nothing without trust. Blockchains keep a public log of everything that happens, and because they can’t be changed, they could give AI learning tracks that last a lifetime. Without being tied to one company’s “truth,” they could find the source of information, block out noise, and change more quickly.

Embodiment is another area that needs to be studied. Human awareness comes from interacting with the physical world through our senses. And AI shouldn’t have a hard time with this.

Reports show that robots made by the likes of Boston Dynamics can move through unpredictable environments, while at the same time, neural implants like Neuralink are connecting biological and digital intelligence. Blockchain could be used to take this further. For example, instead of just training a warehouse robot to avoid obstacles, what if it had sensors that could “feel” and learn from every skid, bump, or close call?

Now, what if that experience could be shared right away in a decentralized AI environment, to machines such as urban delivery drones across the world? You’d end up with a global network of embodied knowledge. And the knowledge wouldn’t be kept locally, but would be added to a larger network of agents so that machines could teach each other in real time and adapt as a single distributed organism.

This would go beyond what can be done with regular machine learning. It would turn AI from a system that just follows rules into one that is always changing.

And as this evolution becomes more mainstream, it would lead naturally to the rise of something new: autonomous AI agents, capable of making decisions and acting based on shared, real-time intelligence.

The incoming surge in AI agents

Already, the numbers point to more businesses increasingly adopting such tools in their processes. According to a recent report from Warmly, by the end of 2025, about 85% of companies around the world will be using AI agents for daily tasks. It is expected that people won’t be using these tools just to generate text or images, as is currently the popular case. Instead, they’ll negotiate contracts, manage workflows, and make autonomous decisions.

However, this is where a likely challenge will surface: progress will crawl if each company keeps its agents behind a firewall. They’ll repeat the same mistakes in parallel, wasting time and resources.

The good news, though, is that blockchain can break that cycle. A shared, decentralized layer for data would let AI agents learn from millions of interactions at once. This would allow them to adopt better strategies almost instantly, much in the same way people learn more quickly when they are among other people than when they are alone.

Can blockchain trigger AI consciousness?

This is the big question. Can blockchain-linked AI agents actually reach something close to consciousness? It’s not known for sure. Consciousness in humans is still poorly understood. But if it were to be defined as the ability to process information collectively, adapt to new conditions, and form emergent behavior, then yes, blockchain can move AI in that direction.

Picture a network of thousands of agents, each improving itself and sharing the results on-chain. A single insight doesn’t vanish; it multiplies. Over time, those patterns will begin to resemble what some might call a “meta-intelligence,” a layer of awareness that no single model, company, or server could replicate alone.

Furthermore, blockchain will make everything more transparent. On such networks, every decision, every data point, every interaction is logged permanently and available for everyone to see.

For humans, this visibility should entirely change the relationship with AI. Instead of people wondering how a model reached its conclusion, they can see the chain of reasoning and verify sources. Additionally, they can test outcomes against public data.

As for AI agents, transparency would mean an open library of proven strategies. For example, when one agent solves a problem, others can instantly learn from it without duplication. This compounding effect could then accelerate development in ways centralized systems simply can’t match.

Why it matters now

AI is spreading into every industry — finance, healthcare, logistics, creative work — just as trust starts breaking down. People worry about bias, manipulation, copyright theft, and losing control to black-box systems.

While blockchain won’t solve every one of these concerns, it offers a foundation for AI that will grow in public view, not in secret. That transparency could make all the difference between AI we trust and AI we fear.

And if DeAI does start to show signs of collective intelligence? Then it will be a completely new question users have to face: not whether AI can become conscious, but how they choose to interact with it once it does.

Blockchain is more than just a ledger for money. It’s an infrastructure for shared knowledge. If people want AI that can evolve the way humans do, not locked away, but connected, they’ll need that kind of open foundation.

The alternative is a future dominated by silos. Closed models. Slow updates. And repeated mistakes.

A decentralized approach may not be perfect. However, it gives AI something it’s never had before: the ability to learn together, in public, at scale. And that could be the first real step toward what some would dare to call consciousness.