The AI age is undoubtedly upon us. In the grand scheme of things, this is both exciting and terrifying, but we aren’t here to discuss that.

We’re here to take a closer look at the decentralized AI stack.

Why? Because in a world where AI becomes a normal part of our lives, taking all of our jobs and so on, the entire stack cannot be controlled by a handful of mega-corporations. That’s simply too much power in the hands of a few. Decentralization is of utmost importance here.

Unfortunately, in classic crypto fashion, the term “decentralized AI” has been bastardized. Every marketing team is doing their best to slap the decentralized AI tag on their product and hope it increases its valuation.

Hate to be the one to burst your bubble, but almost all the projects that refer to themselves as “decentralized AI” are, unfortunately, not decentralized AI.

Decentralized AI is a very real and very important concept, but very few projects truly fall into this category.

GenLayer is arguably one of the most impressive ones. Before we explain how exactly it fits into this conversation, I think we all need to better understand decentralized AI.

Why decentralized AI is important?

Let’s delve into the ‘why’ first, and then get to the ‘what.’

I don’t think anyone doubts that AI will be a transformative technology in the near future. However, people are underestimating just how important it is that a technology like AI doesn’t stay closed-source.

Let’s start with the big one: censorship.

If AI model infrastructure and training remain in the control of a few corporations, then censorship and bias are big issues. If one of these corporations has a certain agenda that they are mandated to push, then it will be through the AI models they built.

For example, if AI is used for content moderation on YouTube, but Google has a certain bias to promote, then this bias will affect the content moderation on the platform.

Another issue that’s just as important is AI development.

Platforms like OpenAI, which currently dominate the AI landscape, are very closed-source in terms of how their models are built and how they collect or use data. This can lead to a monopolization of AI development, which is not healthy for innovation and censorship resistance.

With decentralized AI, all models are built open-source, which allows for distributed collaboration. Researchers can leverage the work of other researchers to keep creating better AI models without any bias, which is ultimately better for society.

Even when it comes to the computational resources required to train AI models, a decentralized system is superior because it’s more efficient. Computational power can be sourced from anywhere in the world for a fraction of the cost.

Relying on the centralized servers for this brings in the same issues of monopolization and censorship with the additional problems of higher cost and the centralized servers being a single point of failure.

So, now that we understand why decentralized AI is important, let’s examine it in more detail.

What is decentralized AI?

Decentralized AI commonly refers to protocols and technologies that use blockchain rails to enhance different elements of the AI stack by introducing attributes like transparency, censorship resistance, permissionless interactions, trustless interaction, verifiability, and open-source distribution.

I understand what I’ve said above is an absolute mouthful, so don’t worry—I’ll break it down.

The description of decentralized AI is so vague because the impact that crypto rails have on AI differs at each element of the tech stack. The way it benefits inferencing will be different from the way it impacts storage.

So, let’s further zoom in on each element of the stack to understand what decentralized AI really looks like.

Compute/Inferencing

Compute is a major part of the AI stack.

Training AI models requires processing and researching a lot of data. To successfully do this, AI researchers require a significant amount of compute resources from cloud providers.

Crypto comes into the picture because cloud service providing is limited to three big companies: AWS, Microsoft Azure, and Google Cloud.

With crypto, open marketplaces can be hosted on blockchains, where any cloud service provider can sell their data to any prospective buyer.

It’s sort of like Airbnb but for compute power. The amount you pay depends on your requirements, and there is optionality as service providers constantly compete to provide the cheapest compute power.

Estimates suggest that decentralized compute is often 1/3rd the cost of centralized compute services.

The best example of this would be the Akash Network, an independent blockchain that hosts its own marketplace for trading compute power while also offering things like storage capabilities, all made possible through tokenization.

Storage

When AI models are trained, they use a multitude of datasets, all of which need to be stored securely.

The problems with AI data storage are similar to those with compute. It is handled by a handful of large centralized companies, which means it can be expensive.

However, it also introduces a key element of trust with a third party, which can lead to censorship concerns.

Another issue with centralization is a single point of failure. If anything happens to a specific company's facility, all the data could be at risk.

With decentralized storage, there are solutions like Arweave or IPFS (inter-planetary file system), which prove to be great alternatives.

These are distributed networks that use the storage capabilities of nodes worldwide to store data on one distributed ledger.

This means that data can be stored on these networks permanently for a fraction of the cost. In parallel, there is also no fear of censorship or a single point of failure.

A bonus of decentralized storage products is the trustless verifiability of data. Since the data is on-chain, anyone anywhere in the world can verify its validity.

Model training

Training models is the recipe that makes the AI models into what they are, but even in this process, there is a stark difference between centralized and decentralized.

The benefit of decentralized AI in this scenario is that the models are trained in a more federated environment.

They are not bound by the shackles of one company and its goals but are instead distributed across a blockchain network, where they interact with all sorts of data to enhance the model's training.

Ultimately, decentralizing this element of the AI stack might not seem important at first glance, but it greatly improves the quality, resilience, and capabilities of an AI model, ultimately making it more useful in the future.

Decision-making

Decision-making refers to AI models' capabilities to react to different data inputs, process them, and adapt/respond to them independently as they continuously change based on new data.

Over time, AI models become more adept at making decisions themselves in different scenarios.

Let's examine a scenario to understand the differences between centralized and decentralized AI in decision-making.

Imagine a soldier who is getting trained for a war. To be fully battle-tested, all that can be done in training is simulating different possible real-life war events and continuously practicing so that they are best capable of dealing with whatever situation they are put in.

However, an experienced soldier is almost always going to be better than a freshly trained rookie soldier.

Why? Because you cannot account for all scenarios. You only learn how to get better at decision-making on the spot when you’ve been put in real-life battle scenarios where you face all the variables that were unaccounted for in training.

Similarly, centralized AI products are often constrained by their companies' policies, training rules, and safety parameters. On the other hand, decentralized AI products have been out in the wild from day one.

People around the world use and test them in various ways, which eventually enhances their decision-making capabilities even more.

Decentralized AI through GenLayer’s lens

As you can tell from the section above, very few AI crypto projects are actually decentralized AI projects.

But even among the ones that truly fall into this category, there is one glaring issue. Everything is crypto for AI. By that, I mean using crypto rails to improve the AI stack.

GenLayer is AI for crypto.

You see, blockchains were designed to handle trustless coordination between humans. However, in the age of AI and AI commerce, blockchains are sort of incapable of handling trust between AI models.

AI doesn’t sleep, it can’t hold assets, and it operates globally with instant connectivity. So, as AI’s importance in the global economy grows, the world of crypto needs to create a decentralized AI trust system built for real-world complexities and fast autonomous decision-making.

GenLayer is that solution.

Allow me to elaborate.

Traditional blockchains are deterministic and static. Smart contracts can be coded to create different sorts of applications. However, these applications only follow and self-execute based on the predetermined rules set by the smart contract developer.

They lack the capability to adapt to changes in the environment. In an industry as dynamic as crypto, this solution is far from optimal.

GenLayer acts as the digital decision maker, which can enable trustless agreements and resolutions for smart contracts, AI agents, and organizations. This ultimately transforms subjective decisions into on-chain resolutions, which reduces human bottlenecks and thereby slashes costs.

Basically, GenLayer uses AI to completely flip things on their heads. Using the power of AI, GenLayer has revolutionized smart contracts and consensus mechanisms, which will eventually allow for more powerful protocols to be built atop the network.

Key features of GenLayer’s decentralized AI approach

Probabilistic decision-making is the key theme in this section. With all the enhancements to the crypto infrastructure GenLayer has made, the ability to make probabilistic decisions at the protocol level based on on-chain transactions is truly game-changing.

The importance of GenLayer's dynamic probabilistic nature will become evident as we go over the different features.

Intelligent contracts

Intelligent contracts are smart contracts that are powered by AI.

Being enhanced by AI means that they have the ability to handle non-deterministic operations. When you think of blockchains, smart contracts are the foundational element that makes them function. GenLayer infuses AI into this foundation to further enhance it.

Intelligent contracts set themselves apart from traditional smart contracts in the following ways:

• Internet connectivity
• Natural language processing
• Complex decision making

Internet connectivity may seem like an underwhelming feature at first glance, but in reality, it’s an absolute game changer. It means that intelligent contracts can fetch real-time data from anywhere on the Internet at all times.

Just as a small example to get the point across, picture a predictions market. These platforms use oracles and validation processes to finalize the outcome of a specific market.

But intelligent contracts can replace these slow and sometimes unreliable oracles with intelligent oracles. That means the second an event has transpired, the intelligent contract has already fetched the data from the Internet and fed it into the predictions market.

Now, combine this real-time connectivity with natural language processing. Imagine being able to communicate with a smart contract through an LLM.

Similar to how you would give commands to an LLM, intelligent contracts can process natural language similarly and make changes on the spot.

Just think about the application possibilities that open up.

For starters, it lowers the barrier to entry for developing smart contracts. Secondly, it introduces a level of adaptability and on-the-spot decision-making that is often necessary in an industry as dynamic and random as crypto.

This leads us to the third point, complex decision-making.

In essence, GenLayer has set itself up to be in a completely different class of blockchains.

The entire network becomes an autonomous decision-making machine that constantly adapts to changes in environment, data, and inputs, significantly changing the design capabilities compared to the static nature of the blockchains we use today.

Consensus mechanism: Optimistic democracy

With a blockchain built on dynamicness and non-deterministic decision-making, the way consensus works will also look very different. Rather than agreeing on the basic validity of transactions, validators will have to reach a consensus on subjective tasks.

This sounds like an absolute nightmare to handle, right?

Well, even at the consensus level, AI is used to enhance the process.

Optimistic democracy works by leveraging validators that are, by default, connected to different LLMs. A validator is selected at random to be the lead validator for a specific block. This validator processes the transaction and proposes an outcome. Then, a group of validators votes on this outcome.

If accepted, the transactions are processed, and the block is added to the chain; if rejected or disputed, the process repeats.

It may sound like a long-winded process, but it happens super fast and is the best way to process all the subjective tasks and data that will be flooding the GenLayer network.

With a chain as AI-intensive as GenLayer, it only makes sense to have an AI-enhanced consensus mechanism to match it.

AI agent economy

With this technical architecture as the background, it sets the stage for a vibrant AI-powered application ecosystem with AI agents at the heart of it.

The AI agents we’ve seen thus far in the crypto landscape won’t be able to lay a fingertip on the quality of AI agents we will see on GenLayer. Why? GenLayer’s consensus leverages multiple AI models, while the AI agents we see today are limited by relying on one model.

Leaning on one AI model limits perspectives, complexity, and data quality. On the other hand, with GenLayer, the AI agents will be much more robust, resilient, and dynamic, enhancing their capabilities significantly.

The agents on GenLayer will be able to handle everything from managing wallets to making trades and transferring funds.

Eventually, these agents will have subagents that can do other smaller tasks for them. Then, through the network, these agents will be able to autonomously interact with one another to perform different tasks while also improving their efficiency and decision-making ability.

GenLayer as a network will ultimately act as the arbiter of this massive AI agent economy, while users will benefit from an unparalleled on-chain user experience.

AI commerce

Commerce is a very broad term, but the general point here is that GenLayer will provide the foundation for integrating AI into the operations of various industries.

Let’s run through a couple of examples.

Decentralized dispute resolution is something that’s poised to be transformative. Contract disputes can be resolved and moderated through AI-powered trustless adjudication. Imagine something like Polymarket, but completely autonomous.

Another example is basic transactions between two parties. AI agents can facilitate transactions and agreements between two parties with no intermediaries needing to take their cut. Think of Fiverr, but completely decentralized and autonomous.

Another one is marketing. Rather than paying through your nose to marketing agencies, a marketing protocol can be made on GenLayer at a fraction of the cost.

A bunch of different campaigns can be instantly added, and payments can be settled through smart contracts—all of this without breaking the bank.

These are just a few examples, but the possibilities are endless.

Concluding thoughts

You either keep moving forward, or you get left behind because keeping up simply doesn’t cut it anymore.

I think GenLayer perfectly embodies this.

We’ve reached a point with blockchain innovation where we’re getting differently packaged versions of what’s ultimately the same product.

The saturation caused by this is very evident in the market in its present state, and it’s clear that we have very few teams that are actually pushing the envelope.

A network like GenLayer is genuinely a major step forward for the industry. By using AI to improve crypto as an industry and opening up the application design space to virtually anything, GenLayer offers hope in terms of seeing real innovation in the near future.

I know we’ve said this multiple times already, but GenLayer is the real deal. You should familiarize yourself with it sooner rather than later because this is a train you don’t want to miss.