Count the AI tools you used this week.
More than you expected? Probably.
ChatGPT for brainstorming. Claude for analysis. v0 for quick UI mockups. Cursor for code. Notion AI for documentation. Midjourney for images. Maybe Lovable or Bolt for prototypes.
Each tool excels at something. Each tool knows nothing about the others. You are the integration layer. Your brain holds the context that connects these fragments into a coherent product.
Which one holds the truth? You do.
This is the 2025 AI tool fragmentation problem. More capabilities than ever. Less coherence than ever.
Does this feel familiar? It does.
The Context Tax Compounds
I tracked a PM's workflow for a day. She was building a feature for a fintech product.
Want the messy version? Here it is.
9 AM: Brainstorms feature approach in ChatGPT. Pastes key insights into a doc.
10 AM: Opens Claude to analyze competitor approaches. Re-explains the product context. Re-describes the users.
Re-explains again? Yes, again.
11 AM: Opens v0 to generate UI mockup. Re-describes the feature differently. Gets mockup that does not match the design system. Spends 30 minutes adjusting.
1 PM: Opens Notion AI to draft PRD. Re-explains everything again.
3 PM: Shows mockup to stakeholder. "This doesn't look like our product." Back to v0.
By end of day, she had used five AI tools. She had re-explained her product five times. Each re-explanation slightly different. Each output reflecting a different interpretation.
How many tools is the tipping point? Five feels exhausting.
The 2024 Reforge State of AI report found product teams use an average of 4.3 distinct AI tools. The cumulative context tax exceeds the time savings.
The Integration Layer Problem
You are not using AI tools. You are the glue between AI tools.
Copy from one. Paste into another. Translate between formats. Reconcile inconsistent outputs. Remember what each tool was told.
Are you the integration layer? Yes.
This is cognitive overhead that scales with tool count. Three tools, manageable. Five tools, exhausting. Seven tools, you spend more time managing context than doing product work.
The irony is thick. AI was supposed to reduce cognitive load. Tool fragmentation increased it.
Why Unified Context Matters
What if every AI interaction knew what every other interaction had established?
The brainstorming session understands the product. The analysis builds on the brainstorming. The prototype reflects both. The PRD synthesizes all three. No re-explaining. No context loss.
What changes with unified context? Everything downstream.
When you make decisions in one session, future sessions recall them. Why did we choose the collapsible approach? Because the last session explored three alternatives and documented the trade-offs.
Memory is not a feature. It is the foundation that makes AI genuinely useful for sustained product work.
Is memory optional? No, it is the foundation.
The Multi-Tool vs Unified Approach
Multi-tool workflow: Five tools. Five context loads. Five interpretations. You reconcile.
Unified workflow: One persistent context. One understanding of your product. Outputs that build on each other.
A PM at a Series C company described the shift: "I used to spend the first fifteen minutes of every AI session re-establishing context. Now I walk in, and it already knows."
The Basic Gist
Tool fragmentation is the hidden cost of the AI revolution. More tools, more capabilities, more context management, less coherent output.
The winners will not be teams with the most tools. They will be teams with the most coherent context.
In Short
Your tool stack might be your bottleneck. The time you spend translating between AI tools is time stolen from product thinking.
Is your stack the bottleneck? It might be.
Consolidate where you can. Prioritize tools that remember. Stop being the integration layer.
→ Try Figr, one context that compounds across every feature