AI Has a Memory Problem. Decentralization and Privacy Might Have a Solution. Part 1

We are on the cusp of an AI revolution. AI agents today are building social networks, negotiating contracts, and even contributing to creativity, like making music. All aspects of our lives are affected every day, as we can choose from a range of AI models and assistants for our interactions and experiences. But there is a chronic problem with AI that often flows under the radar. AI has a memory problem.

Anyone who uses an AI solution has faced it. It does not matter whether you are using Claude, GPT, or anything else; it does not matter whether you are a developer or an end user. If we ever need to switch models, we have to start again from scratch, rebuilding the context of our conversations every time. The same forgetfulness can also happen across different sessions or between different chats in the same model.

Here in this 3-part series, I will discuss what AI memory is, how it works, what types there are, the pain points regarding its architecture, and a potential solution through decentralization and privacy, with Oasis technology as a reference. I will also mention a few working use cases tackling the AI memory problem.
In the first part, I will cover AI memory and its classification.

What is AI memory?

AI memory is the ability of an AI system to retain, recall, and use information from past interactions. While human memory is associative due to being organic, AI associations come from data engineering and code-based architecture. In other words, Large Language Models (LLMs) do not and cannot remember anything. Every interaction is processed fresh. So, what appears to be memory is basically an association achieved through engineering and algorithms.

• Human brain: a biological organ where memory is always on, where learning, remembering, and forgetting happen organically.
• LLM: a codebase that learns and forgets, but can be made to remember by being given contextual refreshers manually or by smart engineering on a short-term basis.

The Context Window

Before expanding on the different types, it is useful to note that the context window is the most basic form of AI memory. This is quantifiable and measured in tokens or the amount of text that any given model can process in a single interaction. Approximately 150 tokens can constitute about 100 words. And every word in an interaction, those you send and those the AI responds with, together make up the context window limit.

How Context Works

Most conversations with an AI model consist of multiple prompts. What we do not know as an end user is that the system does not remember previous messages. So, what happens? The system typically resends the entire conversation history as part of each new request. Take this example.

1. You send “Hello” -> AI sees: [Hello]
2. You send “How are you?” -> AI sees: [Hello, AI reply, How are you?]
3. You send “Tell me about crypto” -> AI sees: [Hello, AI reply, How are you?, AI reply, Tell me about crypto]

As the conversation carries on, the token count also increases. On reaching the context window limit, the older messages get dropped. This is where AI forgetfulness and hallucination begin.

Context Window Sizes (Typical in 2025)

Any long, detailed conversation can thus stretch and exhaust even high token capacity models. That is why we need more advanced memory systems.

In 2026, the trends indicate that 1M+ token capacity is going to become common, with more focus on scale and performance. For example, top-tier models (e.g., potential Llama 4 iterations) can reach up to 10 million tokens.

Active Models:

• Qwen3-Coder-480B: Designed for coding with a 256K to 1M token range.
• Gemini 2.5 Pro: Offers 1M+ token windows, with 2M+ expected.
• GPT-4.1 Turbo: 128K–1M tokens with advanced "Context Compression" to manage efficiency.

The context window size increase without "smarter" context management is, however, also problematic, as it can lead to "needle in a haystack" failures where models struggle while searching for specific information within a vast context.

The Taxonomy of AI Memory

The analysis of AI memory architecture reveals the various components according to their temporal duration and functional purpose. All modern systems use a multi-tiered memory hierarchy that can balance real-time processing speed with the need for vast, durable storage.

Working memory is at the vanguard of this architecture. Its capacity is limited and critical for achieving coherence through raw token processing and immediate inference building.
Short-term memory is the next stage that strings together working memory. It builds and maintains context across a specific session through sliding windows or summarization techniques.
Long-term memory is the embodiment of continuity. It can be externalized into durable storage systems, acting as an unbounded index of knowledge.
Interesting to note that long-term memory can encompass and go beyond procedural memory, which is fixed within learned logic and inference.

In the next part of the series, I will discuss at length the short-term and long-term AI memory, and also the security and privacy risks associated with AI memory architecture.