Most organizations think AI is an intelligence problem.
It is increasingly becoming a representation problem.
AI systems fail when reality is fragmented, stale, disconnected, and poorly represented.
#AI#EnterpriseAI#AIGovernance#RepresentationEconomy
The most important AI battle may not be model vs model.
It may be:
Who gets to represent reality for machines?
Because once a system controls the representation layer, it shapes:
decisions,
visibility,
trust,
automation, institutional action
#EnterpriseAI#representationeconomy
Everyone says AI will transform the economy.
I think AI will transform something deeper:
Representation itself.
The next generation of powerful companies may not just control computation or models, they will control representation.
#AI#EnterpriseAI#ArtificialIntelligence
My gmail account has been compromised.
If you receive any emails, or requests from this address, please do not respond or click on any links.
Please treat any communication from that account as unauthorized.
If something urgent comes up, please reach out to me directly.
Enterprise AI doesn’t fail because models are weak.
It fails because intelligence wasn’t designed to scale responsibly.
That’s why AI is becoming a fabric & why Services-as-Software is inevitable.
Read👇
tinyurl.com/2nb22m7k#AIArchitecture#AgenticAI#aifabric#enterpriseai
🚀 What is Inference-Time Scaling?
The next big #AI scaling law after #Kaplan & #Chinchilla might not be about training bigger models… but about reasoning longer at inference.
🧵 Let’s break it down:
I wrote a detailed piece on this → 👉 “What Is Inference-Time Scaling? The Next AI Scaling Law After Kaplan and Chinchilla”
Read here: tinyurl.com/5n8hh9dx
🔄 RT if you believe reasoning > raw size is the future of AI. #AI#ScalingLaws#AIFuture
🤖 Most people think AI hallucinations are random glitches. They’re not.
They follow predictable chains of cause → effect.
This is where DAG Thinking (Directed Acyclic Graphs) comes in.
🧵 A short thread 👇
A DAG is just a map of causes and effects.
➡️ “Prompt ambiguity → partial recall → pattern filling → hallucination.”
No loops, just forward-moving chains.
This makes it a powerful lens to see how AI adoption & errors really happen.
💡 Example: A bank launches a chatbot. Satisfaction rises. Leaders say: “The chatbot worked.”
But DAG thinking shows: Launch → media buzz → employee motivation → better service.
It wasn’t just the chatbot—it was the chain.
🧩 Neurosymbolic AI: The missing link between language and logic.
It blends deep learning’s pattern power with symbolic AI’s reasoning skills.
The result? More explainable, trustworthy, and human-like AI.
🔗 Full post here: 👉 linkedin.com/posts/raktimsin…#AI#NeurosymbolicAI