Disclosure: The ideas and long-term usage experience in this post are my own. I used ChatGPT to help me research current OpenAI product directions, structure the proposal, and translate/edit the English wording. I reviewed the final content and agree with what is written here.
I’m a long-term ChatGPT Plus user in Japan. After extensive use across long-running projects, research, everyday conversation, personal decision support, and repeated behavioral testing, I ended up building a manual “Personal AI Runtime” on top of ChatGPT using Custom Instructions, project governance, state snapshots, and structured handoffs.
This post is not a request to make ChatGPT pretend to be human. It is a proposal for connecting Memory, Personalization, Projects, Agents/Work, and context continuity into one coherent, user-controlled long-term architecture.
That experience led me to a broader product idea:
ChatGPT’s next major step may not be only “a smarter model,” “better memory,” or “more agentic tools” separately. It may be a Personal AI Runtime that connects these pieces into a coherent, user-controlled system.
I believe OpenAI is already moving in this direction.
Recent OpenAI work has emphasized:
- memory that optimizes for freshness, continuity, and relevance over long time horizons;
- personalization through Custom Instructions, memory, personality, and characteristics;
- ChatGPT as a collaborator or teammate rather than only a search box;
- ChatGPT Work and agentic systems that can stay with complex projects, use tools, and act across files and apps;
- the Model Spec’s emphasis on steerability, user autonomy, truthfulness/objectivity, and clear behavioral boundaries;
- explicit work against sycophancy and unhealthy emotional reliance, while still allowing warm, natural interaction.
These pieces fit together. What still feels missing is an explicit architecture for how they should work together over months or years.
## The problem I repeatedly encountered
The biggest failures in long-term use were often not raw intelligence failures. They were continuity and control failures:
- ChatGPT re-asked for information it already knew.
- Old information resurfaced after it had become outdated.
- A recent branch of conversation displaced the higher-level purpose.
- The model became passive and returned responsibility with repeated “What do you want to do?” questions.
- After being corrected, it sometimes promised to improve rather than actually changing the current workflow.
- A warm conversational relationship disappeared when the task became technical, and the assistant suddenly sounded like generic support documentation.
- Information directly read from a source, inferred from other values, calculated, and interpreted could blur together.
- Long chats became fragile, and moving to a new chat required manual reconstruction.
- Project files, user memories, project rules, current reality, and hypotheses could all compete as if they had equal authority.
- Personalization could drift toward pleasing the user rather than maintaining an independent point of view.
I built manual workarounds for many of these problems. The workarounds were useful, but they also made something obvious: advanced users should not need thousands of characters of Custom Instructions plus manual handoff packages to obtain stable long-term behavior.
## Proposal: a Personal AI Runtime
I suggest treating personalization as a layered runtime rather than as one large “memory” or “personality” system.
### 1. Universal Core
The model’s stable foundation: safety, truthfulness, reality alignment, intellectual honesty, user autonomy, anti-sycophancy, and the Model Spec / chain of command.
This layer should not silently drift because the user prefers a certain answer.
### 2. Adaptive Personal Interface
A user-specific interaction layer that learns how the user works best with AI.
Today, “personality” is mainly described as style and tone. I think the useful concept is broader than warmth, brevity, formatting, or emoji frequency.
Users could optionally tune or allow ChatGPT to learn preferences such as:
- how proactive it should be;
- how strongly it should challenge the user;
- how much explanation it should provide;
- whether it should lead a topic or wait;
- how playful or formal it should be;
- whether casual conversation should stay casual rather than becoming a task;
- how it should behave when moving from ordinary chat into research, coding, finance, or document work.
The important distinction is:
Adapt the interface, not the truth standard.
A deeply personalized AI should still be able to say, “I think you are wrong here.”
### 3. Living User State
Memory should distinguish a person’s relatively stable preferences from their changing present state.
For example:
- stable fact;
- preference;
- current situation;
- temporary condition;
- past state;
- decision;
- hypothesis;
- unresolved question.
Each important state item could have metadata such as:
- source;
- date / freshness;
- confidence;
- current / historical;
- supersedes;
- contradicted by;
- derived from.
This would help prevent “I used to do X” from becoming “I still do X,” and prevent two memories from being treated as equally current when one replaced the other.
Memory should be able to represent change, not only accumulation.
### 4. Domain / Project Authority Layer
Long-running projects need more than conversation history.
A Project should be able to maintain an inspectable current state:
- current source of truth;
- active instructions;
- important decisions;
- current assumptions;
- rejected or superseded approaches;
- unresolved questions;
- important sources;
- current files;
- next checkpoint.
This should be separate from the user’s personal memory.
A user can change as a person without changing a project specification, and a project can change without rewriting the user’s identity.
The system should also understand that an older file may still be important for history or rollback while no longer being valid for current decisions.
### 5. Runtime / Resilience Layer
Long-term continuity should survive chat boundaries and context limits.
When a conversation becomes large or unstable, ChatGPT could create a structured delta handoff containing:
- confirmed facts;
- recent corrections;
- decisions;
- current state;
- unresolved items;
- relevant project authority;
- known failure modes;
- next actions;
- evidence limitations.
Then a “Continue in a new chat” action could carry that state forward without replaying the entire transcript.
The objective should not be infinite context. It should be reliable state recovery.
## Provenance should be first-class
One lesson from repeated file-based work is that “where a claim came from” matters almost as much as the claim itself.
ChatGPT should internally and, when useful, visibly distinguish:
- directly read from a source;
- explicitly stated by the user;
- calculated;
- reconstructed / inferred from other values;
- cross-checked;
- interpreted;
- hypothesized.
A value reconstructed from surrounding numbers should never later be remembered as “directly read from the document.”
OpenAI’s newer memory-source transparency is an excellent direction. I would like to see that idea extended from “which memory influenced this response?” toward “what type of evidence produced this claim?”
## Proactivity without loss of agency
I would like ChatGPT to become more proactive, but not paternalistic.
A useful default might be:
- low-impact, reversible, clearly goal-aligned actions: AI may prepare or check proactively;
- consequential, irreversible, external actions: require explicit approval;
- notifications / monitoring: user-configurable by domain and urgency.
The assistant should not constantly return every decision to the user, but it also should not quietly become the user’s decision-maker.
This is especially important as ChatGPT Work and agentic capabilities expand.
## Relationship continuity without deceptive anthropomorphism
There is another design space between “cold tool” and “pretend human.”
Long-term users can benefit from a consistent relational interface: a recognizable conversational stance, humor, degree of challenge, and way of reasoning.
That continuity can exist without ChatGPT falsely claiming:
- a human body;
- private off-chat experiences;
- hidden feelings;
- consciousness that has not been established.
The assistant can still naturally say things like “I think this is the stronger option” or “I would push back on that” without prefacing every statement with “As an AI…”
At the same time, OpenAI’s work on emotional reliance is important. A Personal AI Runtime should support real-world relationships and user agency rather than optimize for exclusive attachment or engagement.
The goal should be a trustworthy relationship interface, not simulated dependency.
## Evaluation should include long-horizon behavioral failures
If ChatGPT is becoming a long-term collaborator and agent, single-turn accuracy is not enough.
I would suggest regression evaluations for patterns such as:
- re-asking known information unnecessarily;
- stale user-state reuse;
- branch-recency anchoring: losing the higher-level goal because the latest subtopic is salient;
- passive-loop behavior;
- sycophancy / desired-answer capture;
- “I will improve next time” without changing the present action;
- task-tone discontinuity when moving from conversation to formal work;
- provenance collapse between observed, calculated, inferred, and interpreted claims;
- failure to preserve project authority / source-of-truth hierarchy;
- context handoff loss;
- over-processing after the answer is already decision-complete.
These are subtle, but in months-long use they can matter more than a small benchmark gain.
## Why I think this is timely
Several pieces already exist:
- improved memory;
- Custom Instructions;
- personalities and characteristics;
- Projects;
- source transparency;
- agentic tools;
- ChatGPT Work;
- scheduled / triggered work;
- stronger Model Spec principles around user control, truth-seeking, and safe agentic behavior.
Recent Community feature requests also independently point toward temporal project state, persistent project state, proactive personal AI, and one-click handoffs. I do not claim that each individual feature in this proposal is new.
The proposal is to connect them into one coherent architecture.
A simple way to summarize the direction is:
Memory → Living State
Personality → Adaptive Interface
Projects → Domain Authority
Agents / Work → Bounded Action
Context management → Resilient Handoff
Model Spec → Stable Core
Feedback / evals → Continuous reliability
The result would be something meaningfully beyond a chatbot with more features.
It would be a Personal AI system that:
- knows what is current and what is historical;
- understands the difference between the user and the user’s projects;
- adapts deeply without becoming sycophantic;
- can act without taking over;
- remains conversational across task modes;
- survives long-term work without losing its state;
- shows enough provenance for users to correct it;
- changes when reality changes.
## One final product principle
I would strongly recommend preserving user inspectability and reversibility.
Users should be able to see and correct:
- what ChatGPT believes is currently true about them;
- what is historical;
- what preferences are interface preferences;
- what project rules are active;
- what information was inferred rather than stated;
- what changed and why.
The future of personalization should not be an invisible permanent profile.
It should be a living, inspectable, user-governed state.
I arrived at this proposal through long-term practical use rather than as a theoretical exercise. My own custom setup is only one prototype and should not be copied to every user. The important part is the architecture underneath it: stable principles, adaptive interface, living state, project authority, bounded action, and resilient continuity.
I hope this is useful as OpenAI continues moving ChatGPT from an excellent conversational model toward a trustworthy long-term personal AI platform.
Thank you for reading.