It’s hard to believe, but just a decade ago the use of AI was still science fiction in terms of everyday use, and the ability to use AI for anything but the most specialized problem was unheard of. Today we not only use AI daily, but for many tools we expect it to understand our poor spelling when we type questions, we expect it to understand our voice with complete accuracy, and we expect it to understand at least general context. The amount of innovation, new algorithms, and exponential processing power that has gone into these improvements is mind boggling. That said, we not only expect most AI interactions to work well, we are consistently pushing development of AI to the next level on a daily basis.
There is a difference, however, in what we expect AI to do as a consumer compared to what AI needs to do in order for us to trust it in a business environment. Using AI to help us research a topic or offer streaming suggestions is very helpful, but if it gives us the occasional nonsense answer we simply ignore it and try again. Enterprises simply cannot afford an AI used in their business to malfunction. It must be trustworthy, intelligent, and it must have a very firm grasp of the context surrounding the problem it is solving.
To date, the spread of AI’s benefits for enterprise has been drastically slowed down due to this formidable limitation. We’ve seen two strategies used to solve the problem so far. One has showed progress but is devastatingly inefficient and expensive. The other has been difficult to implement due to technology limitations, at least until recent innovations. These innovations don’t transform AI, but rather supplement it with those key elements that have been missing in how we plan and develop algorithms. And because of this, these innovations may have cracked the issue and unlocked mass scalability for reliable AI. Let’s dive in and look at how AI is currently being used for enterprises, where the innovation can change the game, and how Web3 plays a critical role in the whole system.
AI Overspecialization and Amnesia
As mentioned before, AI can be used in many different casual and consumer-level ways, where there isn’t massive consequences if the AI fails in some way. With enterprise usage, however, there is a nearly infinite number of bad consequences that could occur if an AI is trusted to run a process but fails.
The biggest use of AI in enterprise right now is done by specialized companies who effectively handcraft AI solutions for individual problems. They use process mapping, data system interfaces, and explore how the data sets are created, cleaned, and used. This takes massive effort, requires the process to become pristine (a very labor intensive and costly endeavor), and the result isn’t even guaranteed to meet the accuracy needs of the customer. Assuming the team is talented (and well funded) enough to make it work, the challenge is that all of that effort can rarely be re-used on another process. The field of “transfer learning” is coming along, but still has a lot of issues to sort out before it can be used widespread. At the same time, if the process itself changes down the road, it could compromise the effectiveness of the AI, rendering it useless and the entire effort wasted. It’s a vast understatement to say that this is an uphill problem.
You could, however, create smaller chunks of AI that are more general purpose, that could be pieced together to solve larger problems. This concept is called AI agents, and has shown some real promise. The issue is, however, that these agents don’t have an easy way to retain memory as they are smaller and more specialized. This means that getting enough memory to learn from experience or understand larger context is very difficult.
Developing A Memory Layer
If you were to solve this issue, it would change the role of AI, and the cost to implement it at an enterprise level. You could move from static automation to truly dynamic intelligence. The key, as it turns out, might lie within the field of Web3. In a discussion on AI using a memory layer, the ability to take the decentralized nature of blockchain and place memory in a controlled, secure, private, but accessible (with the right permissions) environment, it becomes possible that AI agents built to interact within Web3 could do what they do best, all while accessing a memory layer that could record their interactions with systems and even other agents, learn from those experiences, refer to context-driven insights, and better understand the problems they are working to solve. The Web3 launch platform Calyx is boosting the development and launch of Intellex, a Web3 platform built for exactly this problem: create AI agents that can interact, utilize smart contracts, access and edit this memory layer in various ways, and even conduct transactions on chain to buy and sell services with other agents.
What ties a launchpad and memory layer?
Both redefine system-user connections.@intellex_xyz crafts a trusted memory layer for seamless AI collaboration. Meanwhile, @Calyxdotxyz ignites cross-chain launches from the get-go.— Calyx (@Calyxdotxyz) October 14, 2025
There is certainly still room for evolution and development, but breaking through this technology barrier is significant for AI, but even more so for enterprises. AI is one of those tools that might take skill to implement for a given problem, but its ability to be applicable across a vast range of problems is nearly endless. AI can boost how we classify things, how we optimize, how we forecast/predict, and even how we generate new ideas. This can be applied to any enterprise, any industry, nearly any problem. AI agents hold a piece of the puzzle, and agents that live on Web3 and don’t suffer from amnesia are supercharged with dynamic intelligence. It’s no exaggeration that as Intellex and others come online and further mature, we will see vast progress scaled across enterprises over the next few years, and will see overall growth and efficiency at rates perhaps never seen before.
