Using multiple AI tools sounds efficient.
I might use ChatGPT to explore an idea, Claude to turn it into a structured plan, and Gemini when I want to continue the work from another angle.
Each tool can be useful for a different part of the job.
But there is a problem that becomes obvious as soon as the work gets serious:
Every time I switch AI tools, I have to explain the project again.
Not just the goal.
I have to explain what has already been tried, which ideas were rejected, what decisions were made, which constraints are fixed, and what the next step is supposed to be.
At some point, using multiple AI tools starts creating a second job:
managing context between them.
The problem isn't switching AI tools
Imagine you've already spent an hour working through a product idea with one AI.
During that conversation, you might have established:
- who the product is for
- what problem you're actually trying to solve
- which approaches you've already considered
- why some of those approaches were rejected
- technical or business constraints
- decisions that should not be reopened
- what needs to happen next
Then you open another AI.
From its perspective, none of that working context necessarily exists in the conversation you're starting.
So before you can continue the work, you have to reconstruct it.
You copy parts of the previous conversation.
You write a summary.
You explain the project again.
And if the summary is too short, important reasoning disappears.
If it's too long, you're basically pasting another conversation into the new one.
Neither feels like a good workflow.
Copy-paste works — until the project gets complicated
For a simple question, copying a few sentences is fine.
The problem appears when AI conversations become part of a longer workflow.
Suppose a team initially considers both B2C and B2B.
After customer interviews and a discussion about development resources, the team decides to validate B2B first.
A short summary might say:
Current strategy: B2B first.
That captures the decision.
But it doesn't capture why the decision was made.
Was B2C rejected because there was no demand?
Because the team lacked development resources?
Because the purchasing process favored B2B?
Or was B2C simply postponed?
Those distinctions matter.
Six months later, the correct decision may depend on the reasoning behind the original decision, not just the final sentence.
This is why transferring AI context is not simply a matter of transferring more text.
The valuable part of an AI conversation is often the reasoning
When I return to an old AI conversation, I rarely need every message.
What I usually need is something more specific:
the point where the thinking changed.
Maybe that's where:
- an assumption was challenged
- one option was rejected
- a constraint was discovered
- the project direction changed
- a decision was made
- a useful explanation appeared
- the next step became clear
Those moments are much more useful than an entire transcript.
This changes the question.
Instead of asking:
How do I move this whole conversation to another AI?
A better question might be:
What context does the next AI actually need in order to continue this work?
You slowly become the API between your AI tools
Without a shared workflow, the user becomes responsible for context transfer.
The process often looks something like this:
AI A
Idea → discussion → alternatives → decision → constraints
↓
You
Find the relevant messages → copy them → summarize them → remove unnecessary parts → explain what's missing
↓
AI B
Reconstruct the situation → ask clarifying questions → continue the work
The more AI tools you use, the more noticeable this overhead becomes.
The problem isn't that using ChatGPT, Claude, Gemini, or other AI tools together is inherently inefficient.
The problem is that the continuity between those conversations is still largely managed by the user.
What should actually move between AI tools?
For many workflows, a useful handoff doesn't need the entire conversation.
It may only need five things:
1. Goal
What are we ultimately trying to accomplish?
2. Current state
Where is the work right now?
3. Key decisions
What has already been decided, and why?
4. Constraints
What assumptions, requirements, or limitations should remain fixed?
5. Next task
What exactly should the next AI do?
This creates a much cleaner boundary between conversation history and working context.
The transcript can remain the record.
The handoff can contain only what is needed to continue.
A practical multi-AI handoff
Suppose I finish an ideation session in ChatGPT and want to continue planning in Claude.
Instead of pasting the entire conversation, I could carry something like this:
Goal
Design an onboarding flow for a B2B SaaS product.
Current state
The target user and primary onboarding problem have been defined.
Key decisions
Start with B2B rather than B2C because the current validation process and development resources favor a narrower initial market.
Constraints
Do not redesign authentication.
Do not expand the initial target market.
Keep the first version small enough for the current team.
Next task
Turn the current direction into a four-week implementation plan with milestones and dependencies.
Claude doesn't need every sentence that came before.
It needs enough context to understand where the work is and how it got there.
That's a very different problem from conversation storage.
Context transfer should follow the work, not the chat
This is the part I've become increasingly interested in.
Most AI interfaces organize information around conversations.
But real work doesn't always follow conversation boundaries.
One project might move through:
ChatGPT → Claude → Gemini → another ChatGPT conversation → back to Claude.
The conversation changed several times.
The work did not.
So perhaps the useful unit isn't always "this chat."
Maybe it's:
this point in the thinking that I want to return to or continue from.
That distinction becomes increasingly important as multi-AI workflows become normal.
This is the problem we're exploring with 5BY.AI
This context-continuity problem is also what we're working on with 5BY.AI.
The goal isn't to treat every AI conversation as something that must be preserved forever, nor to have AI automatically decide which parts of your thinking matter.
Instead, we're exploring a user-controlled approach.
A user can identify a useful point in their work and treat it as an Anchor — a point they may want to return to later.
When they want to continue that work in a new conversation, a Handoff lets them explicitly carry the selected context forward.
The important distinction is control.
The user decides:
- what matters
- what should be carried forward
- when the handoff happens
- where the work should continue
The objective is not to move everything.
It's to move enough of the right context to continue thinking without starting over.
Multi-AI workflows need continuity, not just better models
AI models will keep improving.
And people will probably keep using more than one of them.
Different tools will remain useful for different tasks, interfaces, workflows, and preferences.
That means one of the important questions may not be:
Which AI should I use for everything?
It may be:
How do I keep my work coherent when it moves between AI conversations?
For simple prompts, this barely matters.
For projects that last days, weeks, or months, it matters a lot.
Because the real cost of switching AI tools isn't opening another tab.
It's reconstructing the thinking that already happened.
https://chromewebstore.google.com/detail/5by/phhhbjlmcocfiofogckafjemklbhocle
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