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The Cold Start Problem

Why every AI tool forgets your product — and what compounding context changes.

The takeaway

AI tools forget your product after every session. You re-paste context, get generic advice, and make decisions that contradict last week's. The fix isn't better prompts — it's persistent context that compounds over time.

You open ChatGPT. You start typing your product description. Again. For the third time this week.

Every session starts from zero. The AI doesn't know your architecture, your past decisions, or why you rejected approach B last Tuesday. So you paste in your PRD, re-explain everything, and get advice that's... fine. Generic. The kind you'd find in any PM blog post.

You end up managing the tool instead of the tool managing your knowledge.

What forgetting actually costs you
Contradictory decisions
March: "self-serve onboarding." April: AI suggests "request demo" as CTA. Neither session knows about the other.
Redundant exploration
45 minutes on WebSockets vs SSE. Two weeks later, same question. Neither you nor the AI remembers the conclusion.
Shallow advice
"Consider the user's mental model" vs "Your ops managers think in shipments, not orders — use shipment-centric nav."
Missed constraints
AI recommends microservices to a 4-person team. Suggests enterprise sales to a pre-revenue startup.
0
tokens remembered
after session ends

LLMs process text in a context window — short-term memory. When the session ends, it all evaporates. ChatGPT's "memory" stores your job title. Not your architecture decisions, trade-offs, or why you rejected Option B.

What compounding context looks like
Session 1
"What does your product do?"
"Who are your users?"
"What's your tech stack?"
80% context-setting · 20% work
Session 10
"We need multi-carrier tracking."
"Given your event-driven architecture and the 3 carrier APIs, I'd suggest a unified webhook handler. Aligns with the latency decision from January."
5% context · 95% actual work

Each conversation deposits knowledge. Each future conversation withdraws from that growing balance. The 50th conversation draws on everything from the previous 49.

What changes with persistent context
Catches contradictions — "This needs batch processing, but you chose event-driven in January for latency reasons."
Uses your language — If your team says "workspaces" not "organizations," every spec and story uses your terminology.
Knows your constraints — 4-person team? Won't suggest microservices. Pre-revenue? Won't recommend enterprise sales.
Connects dots across time — "Churn data correlates with the onboarding issue from January. Users who skip Jira import churn 3x more."
Context compounds, value accelerates
Session 1 Session 10 Session 50 Session 200+

The PM who uses persistent-context AI for a year has an external memory more reliable than their own. A decision log that's automatically maintained. Institutional knowledge that doesn't walk out the door.

The cold start problem isn't about the first 5 minutes of friction. It's the ceiling on how useful AI can ever be without persistent context.

This is the core thesis behind DISKO.

We call it DNA — a persistent knowledge graph that compounds with every conversation.

Join the beta →