It’s no surprise, the tech world is obsessed with generative AI. ChatGPT responses, AI-generated images, and voice clones dominate the headlines. Companies are rushing to integrate these capabilities into their products, desperately trying to claim the "AI-first" label.

But here's what almost everyone is missing: these surface-level implementations are just the beginning, and they're not even the most transformative part of what's coming.

The Superficial AI Trap

Let's be blunt: most companies are slapping AI onto their products with no real vision. They're scrambling to be "AI-first" without understanding what that means beyond buzzwords.

As most are aware, even X has its own generative AI chatbot.

These implementations are like early mechanical toys – neat demonstrations but limited in actual utility. They're the AI equivalent of putting tailfins on cars: visually impressive but adding little functional value.

Today's AI implementations resemble early mechanical devices – interesting demonstrations, but limited in integration and utility. And just like those early devices, they're a precursor to something far more transformative.

The Power of Memory

AI’s biggest shortcoming today is memory. Systems often forget context, lose track of important details, or simply fail to build on past interactions, with each new session feeling like you have to start from scratch.

Rob, our Principal Engineer, put it best: “The more interesting applications come from a good use of memory. Developing a system that keeps the memory of what worked – and what didn’t – allows AI to create its own rules and improve through trial and error. That’s where real adaptation and scalability come in.”

While everyone focuses on what AI can create right now, the true shift is happening in systems that:

• Remember your preferences across different contexts

• Learn from how you use them and adapt accordingly

• Improve at serving your needs without constant reconfiguration

• React fluidly to change without requiring updates

For example, instead of an AI remembering that your name is Alex and you prefer dark mode – imagine it remembers that you always ask for a summary before diving into details, that you tend to switch gears mid-project, and that you're most productive late at night – and it’s already prepared for that next session before you even show up.

That’s what meaningful memory looks like. But building it? That’s another story.

Let’s Go Under the Hood of AI

What most people don’t realize is that today’s AI memory is more illusion than infrastructure. When you have a conversation with ChatGPT, Claude, or any similar model, here’s what’s really happening:

Every single message you send – along with the entire chat history – is reprocessed every time.

That’s how the system understands context. There’s no persistent memory. No long-term knowledge. And this mechanism has hard limits. For example, GPT-4 Standard might support 8,000 tokens per request (~15-20 pages of text). That includes everything you’ve said before plus your latest input. Once you hit that limit, things get forgotten – cut from the conversation as if they never happened.

For example, GPT-4 Standard supports around 128k tokens for the context window (equivalent to 200 pages of text). That includes everything you’ve said before plus your latest input. 

Close the chat window? That entire context vanishes. Open a new one? You're back to square one. This is why AI feels like déjà vu. You’re always re-teaching, re-prompting, resetting preferences. And as interactions get more complex or span across tools, the cracks start to show.

200 pages of memorized context may sound like a lot, but over the years or following large amounts of context, it’s not nearly enough. “I’ve noticed that the new model seems to very often forget what we were talking about even 2-3 messages above. […]  It’s as if it’s focusing 100% of its attention to my last message and forgets the bigger picture.” And this user wasn’t alone, struggling with managing GPT-4 Turbo’s memory. 

Seen this before on GPT? You’re not alone. This pertains to certain texts GPT tries to keep hold of across conversations, however, even with paid plans, you still run into hard ceilings. Let’s be clear: we’re not talking about a single chat over a few weeks, which can be solved with good prompting. We’re talking about AI that remembers important threads of thought across months or even years.

Why? → Imagine giving your full medical history once, and never having to explain it again. An AI that knows your past symptoms, remembers the patterns, and can tell you why you might be feeling knee pain today – based on what happened last winter. That’s the level of continuity we’re aiming for.

Yes, OpenAI has been working on memory for a while now, having just recently released the ability to reference past conversations. But how scalable is their current solution if, for example, if a chat goes on for years?

Think of it like a human brain – after ten years of conversation, you can’t possibly remember every detail. Some memories fade, some get distorted, and others morph into stories that never actually happened, just because someone told you they did. 

This is the same challenge AI faces: we need systems that can intelligently decide what to remember, what to forget, and what to surface when it matters most. Selective memory isn’t just a convenience – it’s a necessity.

A Solution For Long-Term AI Memory

Through our daily work with AI tools at Patchwork, we quickly noticed this memory gap and the limitations it creates.

We believe the solution lies in a consolidated long-term memory system – a layer that’s maintained across requests and continuously refined. It’s not about sending everything back through the model each time; it’s about having an intelligent system that:

• Compresses what it knows

• Prioritizes what matters

• Remembers what’s useful

• Surfaces relevant context at the right time

This kind of memory-first structure is the key to unlocking true AI behavior at scale. It's what makes systems not just responsive, but proactive – capable of growing and adapting alongside the user. 

The Future Belongs to Systems That Remember

As the industry moves forward, it's clear: memory is the missing ingredient. While many companies are distracted by the glitzy generative capabilities of AI, the true value lies in creating systems that are able to grow, adapt, and learn from past interactions. Not to mention the ethical hurdle of what AI should remember or not.

The question is: Who will lead the way?