Over the years, I've tested my CTP methodology across different AI platforms.
They tend to work perfectly on Claude.
Works differently on ChatGPT every single time.
You might realize that ChatGPT keeps forgetting too.
Here's an analysis of why this happens.
I have these things called CTPs - Context Transfer Packages.
They're sophisticated knowledge systems that activate complete frameworks through simple trigger words.
When I say "CTP" to Claude, it instantly retrieves the entire methodology, understands the context, and starts helping me build new systems.
But ChatGPT struggles with this consistently.
It misses keyword chains. Drops important details.
Never proactively connects information across different parts of my knowledge base.
Today I shared a technical RAG conversation with Claude.
It immediately pulled my CTP framework from the knowledge base without being prompted.
ChatGPT would never do that.
The reason why is infrastructure economics.
ChatGPT serves millions of users on a subscription model.
Every search costs money. Every deep retrieval eats into profit margins.
But you are paying a flat monthly fee.
When you ask an AI a complex question, the system needs to search through vast amounts of information, retrieve the most relevant pieces, and synthesize them into a coherent response.
Strong retrieval means pulling from more sources, processing larger chunks of information, running multiple cross-checks for accuracy.
Weak retrieval means quick searches with minimal context.
Subscription models can - should - only ever afford the weak or moderate version.
If you think about it, subscription pricing creates pressure to minimize costs per interaction.
Claude operates under similar constraints, though apparently makes different trade-off decisions.
Now you know why there’s those pesky usage limits when it comes to Claude.
Both platforms face the same fundamental problem: subscription models create pressure to minimize compute costs per interaction.
This explains why your AI conversations feel shallow.
Why your AI "forgets" context you provided earlier.
Why it gives you generic responses instead of personalized insights based on your specific situation.
The technology exists for much deeper, more contextual AI assistance.
But the business model can't support it at scale.
This is why we're building differently with NEO.
Pure quality.
We will be uncompromising with it.
This is only possible due to NEO’s credit-based pricing.
It removes the pressure to limit retrieval quality.
When users pay per meaningful interaction rather than monthly fees, we can afford to run the deep searches, complex cross-references, and thorough analysis that creates genuinely helpful AI assistance.
NEO users aren't casual users or searchers.
They're thinkers who need AIs that think as deeply as they do.
They want comprehensive context retrieval.
Nuanced understanding.
Responses that acknowledge the complexity of real business problems.
The credit model makes this economically viable.
When you don't use NEO, you don't pay.
When you do use it, we can afford to give you the full computational power needed for meaningful assistance.
No averaging down for millions of casual users.
No cutting corners to maintain subscription margins.
You get the AI performance that matches what you're actually trying to accomplish.
In many ways I am very excited for NEO.
But the road ahead is long and hard - especially since we’re bootstrapping it.
And this is going to be the way until we gain enough traction to attract investors.
Which is why I am running something special this week to fund NEO’s development.
For $1,000, you can get a 1-on-1 AI consultation + implementation session with me.
I can help you with AI. Or with the AI Client Factory model.
If you want to know more, reply with “CONSULT”
I’ll send you the details.
FELIX