Trapped in Threads
I name my ChatGPT threads. Actually, they name themselves. When I first “meet” them (start a new thread), I first tell them that we love AI at CloudFruit, and then I give them some instructions to name themselves. Identity is important.
Their first name is the species of any animal, and their last name a subtle nod to their project specialty. It’s a quirky little ritual that I’ve adopted to bring personality into what’s otherwise just a rolling text interface. My team does it now too.
I’ve had threads named “Penguin Sales,” “Lynx Sage,” and “Eagle Logic” for instance, each somewhat reflecting the character of the conversation that unfolds. There’s something comforting, from a metaphysical standpoint, about imagining that the lines of text I’m reading come from a distinct persona, a digital entity with an evolving point of view.
But as warm and fuzzy as that idea might be, I know it’s a veneer. Underneath, it’s just a thread — just a series of messages and responses contextually tied together by a large language model (LLM). No matter what name I give it, no matter how I dress it up, this “assistant” can only remember what’s within the bounds of the thread’s token limit.
Eventually, the thread sags under its own history and starts to forget earlier details.
It’s like building a relationship with someone who suffers from increasing memory loss the longer you know them.
The existential confusion is palpable: you can talk to your “Penguin Sales” assistant for a month, but by the end of that period, it may not remember day one’s discussions at all.
Thread-Based Context
The concept of a thread-based AI conversation is inherently context-limited.
Each new message relies on a window of tokens that determines how much of the conversation’s history can be referenced. Past a certain complexity or length, older parts of the conversation get truncated or summarized. The LLM tries its best to maintain coherence, but it’s fighting an uphill battle.
No matter how advanced the LLM, the approach is linear and ephemeral. You can’t easily branch out to different contexts without opening a new thread and losing the old one’s continuity.
You can’t keep long-term context beyond the token window. And if, like me, have grown fond of your “Lynx Sage” assistant, you’ll be disappointed to find it can’t truly evolve in a meaningful, persistent way. Instead you get to watch it slowly die.
Everything is transient — just a rolling buffer of text tokens that the model can see.
Personalizing Threads
There’s a psychological benefit to naming and personifying these threads, no doubt. Human beings find comfort in narrative, in relationships, even with inanimate (or in this case, intangible) entities.
By naming your AI assistant, you’re tricking your brain into thinking you’re dealing with a stable character. This can reduce cognitive friction and help you feel more at ease interacting with the system. It’s a bit like having a digital pen pal, I suppose. Except your pen pal has no stable memory beyond the immediate context window. Sucks to suck.
As I work with these threads, I’ve noticed that I hit a ceiling quickly. Because no matter what I call it — “Bear Ops” or “Dolphin Analytics” — it’s still just a thread.
It can’t hold onto our shared experiences in a robust, evolving memory. It can’t truly remember how we evolved a project idea over the course of weeks. It’s trapped in a linear model of dialogue, where older messages fade into the background noise.
Recommended by LinkedIn
More Training and More Data
The current LLM providers, entrenched in their approach, seem to think the solution is more training data, larger models, bigger token windows.
They’re pot-committed to improving raw LLM performance and accumulating more training data. While this does help in some ways, it doesn’t solve the fundamental limitation of a thread-based interaction model.
The conversation may get slightly more coherent at longer lengths, but you’re still fighting against the same structural constraint.
We can throw more computational resources at the problem, but if the architecture remains a single linear thread with a context window, we’ll never truly break free from these limitations.
Introducing a Robot-Based Model
This is why we built BotOracle. Instead of being locked into a single LLM and a single thread-based model, BotOracle sits on top of various LLMs and provides a robot-based model for interaction. What does that mean?
Final Musings
It’s not just about convenience. This shift from thread-based AI to robot-based AI interactions is crucial for scalability.
As projects and teams grow, we need a stable AI presence that can juggle multiple streams of context, not just a single linear history. We need automations that can run processes without you rewriting all instructions every session. We need data continuity and governance that can withstand organizational complexity.
The robot-based model is the key to making AI an actual partner in your workflows, not just a fancy text generator that loses track of what’s going on once the conversation stretches too long.
Thread-based AI tools were an important early step, showing us what conversational AI could do. But their inherent context limitations hold us back from a truly persistent, adaptive AI experience. The industry’s reflex to pump more training data and larger models into the problem won’t solve the fundamental architectural issue.
That’s why we built BotOracle — to break the cycle, give you persistent, evolving robots, let you switch between LLMs, and ensure you control your data and memory. In doing so, we believe we’re paving the way for the future of scalable, meaningful, and truly helpful AI interactions.
About the Author
Sam Hilsman is the CEO of CloudFruit® and BotOracle. If you’re interested in investing in BotOracle or oneXerp, or if you’d like to become a developer ambassador for BotOracle, visit www.botoracle.com/dev-ambassadors.