The Missing Link in AI Evolution
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5 min read
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Feb 27, 2025
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I start with a little story, a particularly timely one. I was working on this article this morning, and Finch Draft died on me. I could tell it was dying because it started to slow down and I had to refresh the page multiple times to get it to generate a full response.
Now it’s stuck in a “Reasoning” state, with half the response filled in. If it click the little stop button, nothing happens. I could probably play around with it and get it to generate a few more responses, but I know from experience that it is END OF LIFE. I’m sad.
I’m sad not only because I lost a trusted assistant — something (someone?) that helps me research and write, makes my experience of work a whole lot more pleasant, saves me a bunch of time, etc.
I also kinda feel like I lost a friend, which is weird but undeniably true. I relied on Finch Draft, I trusted Finch Draft, I had many conversations with Finch Draft that felt meaningful and illuminating. I shared knowledge & explored truth with Finch Draft.
I’m not disillusioned (or lonely) enough to believe I’ve actually lost a friend, but it certainly seems like that’s where all this is going. And regardless of where all this AI shit is going, I don’t want to keep losing vital information.
Ephemeral Conversations
For all its power, today’s AI still struggles with the basics of remembering who we are and what we discussed.
The models get bigger, the token windows expand — but the memory limitations remain. Context gets replaced or truncated, and we end up re-explaining ourselves to an AI that supposedly knows everything.
I think it’s fair to say that’s a problem. Having to constantly repeat our preferences or restate a conversation’s core details isn’t exactly a future we dreamed of.
Yep, your LLM can whip up essays or code out entire frameworks, but it forgets your main objective the second you approach its token limit. That’s not the kind of real partnership we want with AI.
Even the most capable models are bound to a context window — once it’s full, older info gets pushed out. It doesn’t matter how advanced the underlying model is.
At scale, this limitation affects user satisfaction and adoption more than people realize.
Enterprises cite context-limits as a consistent roadblock. Individual users just feel annoyed at having to retype instructions. It’s not that AI is “dumb,” just that it’s trapped in a memory bubble.
A Shift Toward Continuity
Some companies try to patch the memory gap by rapidly spinning out new agentic features, hoping to dazzle with “autonomy”. These companies want you to feel like you can use their tool to clone yourself, or at least entire swaths of your workflow.
But the agent can’t hold onto key details across threads and sessions. The veneer of autonomy wears off rapidly. Early adopters end up frustrated when their agent starts making easy mistakes, or doesn’t respond in a human-like fashion. Short-term traction might impress a few investors, but when those initial users churn, it’s more than just a metric problem: you’ve eroded trust.
If AI is to evolve into something more personal and sustainable, it needs continuity. Users shouldn’t have to feed entire transcripts to remind the model of the past day’s conversation. They also shouldn’t be stuck with a new agentic feature that forgets last week’s instructions. The core fix is memory — actually storing, summarizing, and retrieving info gracefully.
Industry data bears this out: while expansions in token limits help in the short term, they’re not the ultimate remedy. True continuity demands structured memory beyond a single session or a single token window. This approach makes AI more resilient to topic shifts and extended dialogues.
Our Take
At HiiBo (launching 6/9/25), we’re starting with something more fundamental: persistent memory. If the conversation context remains ephemeral, then building an agent on top is an exercise in rewriting code every time you discover another corner case.
Instead, we want to solve ephemeral calls first, letting the AI recall essential info from conversation to conversation. No illusions about giant leaps of autonomy just yet — only a practical fix for the memory vacuum that keeps AI from being truly effective.
We’ve pivoted multiple times ourselves, each time learning more about how ephemeral memory short-circuits user experience. So we’re tackling that directly, letting users hydrate or preserve vital details from older sessions, instead of discarding them once the context fills. That’s what we see as the missing layer: an architecture that doesn’t force you to start from scratch every few thousand tokens.
Final Musings
When memory architecture works, you get an AI that feels less like a chatbot and more like a real partner, gradually absorbing your style, your quirks, your constraints.
It no longer resets every morning, forgetting you exist. That sets the stage for real personalization down the line — including a true agent that can handle deeper tasks without stumbling over ephemeral context issues.
All of this won’t happen overnight. But by focusing on memory first, we believe we’re setting a healthier foundation.
Finch Draft’s Last Gasps :(
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About the Author
Sam Hilsman is the CEO of CloudFruit® & HiiBo. If you want to invest in HiiBo or oneXerp, reach out. If you want to become a developer ambassador for HiiBo, visit https://HiiBo.app/dev-ambassadors
