Peter Ndukwe’s Post

Why Self-Learning AI Won’t Come From the Giants Who Built Today’s AI Before I dive in: yes, this will trigger pushback. Because trillions, careers, and reputations are now welded to one bet: brute-forced, frozen models pushed as intelligence. Self-Learning AI is AGI. 1) Data Obsession: For over twenty years, data was mined and monetized like oil, powering search, ads, social, and e-commerce, and creating today’s tech giants. Scaling data brings more clicks, not real understanding. Data is vital yet useless alone for AGI. Think of AGI like a car: Data is fuel. Cognition is the engine. Intelligence is the motion. No matter how much fuel you have, the car won’t move without an engine. Today’s AI giants keep hoarding fuel, hoping the car moves. It won’t. Cognition is that engine - it turns data into intelligence just as an engine turns fuel into motion. That's why cognition is the real source of intelligence not data. LLMs simulate the dashboard: flashy signals, no motion. Without cognition there's no intelligence. 2) Incumbents Rarely Lead Paradigm Shifts: History shows giants rarely invent what replaces them. Kodak didn’t make digital cameras. Blockbuster didn’t build Netflix. IBM didn’t birth the PC. LLM factories are no different. They profit by scaling data and hype but AGI needs cognition. To do it, they’d have to cannibalize the current paradigm that built their empires. That’s the classic Innovator’s Dilemma. 3) No Theory of Intelligence: You can’t build intelligence without understanding it. Today’s AI labs don't start with theory of intelligence, they start with how much data/compute/energy can we get our hands on. Without cognitive science, psychology, epistemology, and theory of mind, AI stays stuck producing statistical echoes of the past. 4) Frozen Model vs. Evolving Minds: LLMs are frozen once trained. They can’t learn or adapt once deployed. AGI must learn continuously, in real time like a growing mind. Static architectures can't do that. 5) From Human Control to Autonomy: Today’s AI needs constant human micromanagement. That’s not thinking, it’s puppeteering at scale. AGI must self-direct, self-correct, and handle tasks independently. 6) Don’t Brute Force - Let It Evolve: Break the egg from the outside, you get an omelet. From the inside, you get life. Real intelligence is the same - it grows from within. AGI needs developmental intelligence, like a child, step by step to autonomy. 7) Ontologies Over Statistics: Statistical mimicry isn’t understanding. AGI needs basic core knowledge with which it builds an inner model not just match patterns but create meaning, understanding the context, the intent, the actions and the outcomes. Bottom line: Self-Learning AI is a paradigm shift, not an upgrade. Oil powered the Industrial era. Data powered the Internet era. Cognition powers the Intelligence era. In the end, only Cognition will finish what Software started. Software ate the world. Cognition will rebuild it.

Why Self-Learning AI Won’t Come From the Giants Who Built Today’s AI Before I dive in: yes, this will trigger pushback. Because trillions, careers, and reputations are now welded to one bet: brute-forced, frozen models pushed as intelligence. Self-Learning AI is AGI. 1) Data Obsession: For over twenty years, data was mined and monetized like oil, powering search, ads, social, and e-commerce, and creating today’s tech giants. Scaling data brings more clicks, not real understanding. Data is vital yet useless alone for AGI. Think of AGI like a car: Data is fuel. Cognition is the engine. Intelligence is the motion. No matter how much fuel you have, the car won’t move without an engine. Today’s AI giants keep hoarding fuel, hoping the car moves. It won’t. Cognition is that engine - it turns data into intelligence just as an engine turns fuel into motion. That's why cognition is the real source of intelligence not data. LLMs simulate the dashboard: flashy signals, no motion. Without cognition there's no intelligence. 2) Incumbents Rarely Lead Paradigm Shifts: History shows giants rarely invent what replaces them. Kodak didn’t make digital cameras. Blockbuster didn’t build Netflix. IBM didn’t birth the PC. LLM factories are no different. They profit by scaling data and hype but AGI needs cognition. To do it, they’d have to cannibalize the current paradigm that built their empires. That’s the classic Innovator’s Dilemma. 3) No Theory of Intelligence: You can’t build intelligence without understanding it. Today’s AI labs don't start with theory of intelligence, they start with how much data/compute/energy can we get our hands on. Without cognitive science, psychology, epistemology, and theory of mind, AI stays stuck producing statistical echoes of the past. 4) Frozen Model vs. Evolving Minds: LLMs are frozen once trained. They can’t learn or adapt once deployed. AGI must learn continuously, in real time like a growing mind. Static architectures can't do that. 5) From Human Control to Autonomy: Today’s AI needs constant human micromanagement. That’s not thinking, it’s puppeteering at scale. AGI must self-direct, self-correct, and handle tasks independently. 6) Don’t Brute Force - Let It Evolve: Break the egg from the outside, you get an omelet. From the inside, you get life. Real intelligence is the same - it grows from within. AGI needs developmental intelligence, like a child, step by step to autonomy. 7) Ontologies Over Statistics: Statistical mimicry isn’t understanding. AGI needs basic core knowledge with which it builds an inner model not just match patterns but create meaning, understanding the context, the intent, the actions and the outcomes. Bottom line: Self-Learning AI is a paradigm shift, not an upgrade. Oil powered the Industrial era. Data powered the Internet era. Cognition powers the Intelligence era. In the end, only Cognition will finish what Software started. Software ate the world. Cognition will rebuild it.

As enterprises’ use of large language models (LLMs) evolve from generating text to driving decisions, the path to the answer has come to matter as much as the answer itself. Enterprises are moving beyond AI that merely generates responses toward systems for that reason, justify, and allow for inspection. This shift drives the need for AI that can ground explanations in each enterprise’s complex information environment, which requires the system to perform explicit reasoning over organizational, transactional, and behavioral relationships. #MicrosoftFabric #MSFTAdvocate

[AI in motion] Most companies are asking the wrong question about AI. - They ask: “Which model or tool should we use?” - But the real question is: “Are we mature enough to make AI actually useful for the business?”   Because here is the reality: | Models are improving every month. | Enterprise understanding is not.   And that is where most AI initiatives quietly fail. AI maturity is not about access to powerful models like Claude, Google Gemini, OpenAI GPT, Cursor composer and many more It is about whether your organisation has built the foundations that allow AI to reason, decide, and act inside a real business environment.   From what I have observed across organisations, AI maturity stands on three critical pillars. 1. Orchestration: The ability to get work done 2. Context: The missing memory of organisations 3. Semantics: The language of the enterprise ______________________________________________    1. Orchestration - the execution layer of AI. This is where agents, workflows, and tools come together to perform real tasks. It connects models to systems - databases, analytics platforms, and internal applications. | Without orchestration, AI remains merely a chat interface. | With orchestration, AI becomes a system that actually executes autonomously. - Trigger workflows - Analyze datasets - Generate reports - Perform RCAs - Enable decisions in real time   2. Context - the memory layer Most AI systems today operate with a very "shallow understanding" of business context. They know the data. But they do not know: - Why decisions were made - What assumptions existed - How metrics evolved - what trade-offs leaders considered This is where context engineering becomes critical. Context is the organisation's decision memory. It captures institutional knowledge, business logic, operational history, and the evolving narrative behind data. | Without context, AI throws numbers. | With context, AI understands the business.   3. Semantics - the meaning of the data Semantics ensures AI understands what the data actually means. In most companies, the same metric is defined differently by different teams. | Tables store numbers. | But the meaning often lives only in people’s heads. A semantic layer translates raw data into consistent, shared definitions that machines and humans can interpret reliably. ✅ Results: AI produces trustworthy insights.   Why these pillars matter Each of these layers solves a different problem: • Orchestration solves execution • Context solves understanding • Semantics solves meaning   Yet many organisations try to jump straight into agentic AI. ❗ The result is predictable. Impressive demos. Fragile systems. ‼️   What is missing in your #AIstack today? #SystemsThinking #Contextgraph #Ontology

Happy to share you all that I have successfully created my first AI Agent as below: Use case: Building Conversational AI Agents with RAG Building effective conversational AI agents that can retrieve information from a knowledge base and generate helpful responses is a key challenge in AI development. Our approach leverages Retrieval Augmented Generation (RAG) to achieve this seamlessly. Understanding Retrieval Augmented Generation (RAG) RAG combines data retrieval from a knowledge base with Large Language Models (LLMs) to generate accurate and user-friendly responses. This process involves two core components: 1. RAG Pipeline: This pipeline is responsible for ingesting and preparing data for retrieval. 2. RAG Agent: This agent interacts with the processed data to answer user queries. The RAG Pipeline: Getting Data into the Vector Database The RAG pipeline focuses on transforming your knowledge base into a searchable format: Vector Database (e.g., Supabase): We utilize a vector database like Supabase, which handles crucial steps such as chunking, embedding, and distributing data in a multi-dimensional space. Embeddings & Document Nodes: Within the Vector Store, we connect a "Document" node with a default data loader to chunk input documents. For "Embeddings," we use an Open AI embedding model to create numerical representations (embeddings) of the document data. Below is the pipeline skeleton to load the knowledge base using Document chunking and embeddings. Knowledge Base ---> Use Chunkings & Embeddings to store the documents in Vector DB. Below is the RAG pipeline along with the AI Agent built -

Most people think AI just answers questions. That's not how modern AI agents work. Here's a visual breakdown of the 3 core patterns powering today's autonomous AI systems — from a simple loop to full multi-agent pipelines: 🔵Claude's Internal Agentic Loop - The brain behind a single AI agent Most AI tools stop at "ask → answer." An agentic AI goes further: You give it a goal (not just a question) The LLM reasons about what needs to happen It calls tools — searches the web, runs code, reads files, hits APIs It observes what came back It reflects — "Did that solve the problem? Or do I need to try again?" It loops until the goal is actually met — then delivers the final answer 💡 Think of it like a developer who doesn't just Google once — they keep iterating until the bug is fixed. 🟣 Multi-Agent System (Orchestrator + Sub-Agents) When one agent isn't enough Some tasks are too big for a single agent. This is where teams of AI agents come in: You give a high-level objective (e.g. "Write a full market research report") An Orchestrator Agent breaks it into subtasks and delegates Specialized Sub-Agents work in parallel: 🔎 Research Agent — gathers information 💻 Code Agent — writes and runs code 📝 Writer Agent — drafts the content An Aggregator merges all results A Validator checks quality and accuracy If something fails — the orchestrator re-delegates automatically ✅ 💡 Think of it like a project manager assigning tasks to specialists, then reviewing the final deliverable. 🟢 RAG Pipeline (Retrieve · Augment · Generate) How AI answers from YOUR data, not just its training LLMs are trained on general knowledge — but what about your internal docs, knowledge base, or real-time data? That's where RAG comes in: User sends a query The query is converted into a vector embedding (a mathematical representation) A Vector Database finds the most semantically similar documents The top matching chunks are injected into the prompt as context The LLM generates a grounded, accurate response using that context The answer comes back with sources cited — no hallucination ✅ 💡 Think of it like giving the AI a "cheat sheet" from your own knowledge base before it answers. 🧩 Why does this matter? PatternBest ForAgentic LoopSingle-task automationMulti-AgentComplex, multi-step workflowsRAGKnowledge-grounded Q&A The future of AI isn't just smarter models — it's smarter architectures. Whether you're building internal tools, automating workflows, or deploying AI assistants — understanding these 3 patterns is your foundation. 💬 Which of these patterns are you already using — or planning to build? Drop it in the comments 👇 ♻️ Repost if this helped you understand agentic AI better — your network will thank you. #AgenticAI #ArtificialIntelligence #LLM #RAG #MultiAgent #AIEngineering #Automation #MachineLearning #AIArchitecture #FutureOfWork #TechLeadership #AIStrategy #Claude #GenerativeAI #AIDevelopment

What’s Actually Working in AI (Explained Without the Buzzwords) After reading a lot of AI research lately, here’s the simple truth: The best AI systems today aren’t the ones doing the most. They’re the ones doing the right amount. Let me explain with real-life examples 👇 1️⃣ “Mixture of Experts” doesn’t mean hiring 5 AIs to argue. Top labs like Google DeepMind and OpenAI use smart architectures where only parts of a model activate when needed. But many teams try this instead: “Let’s ask 5 AIs and vote.” That’s not efficiency. That’s a group project where everyone talks and nobody saves time. 😄 2️⃣ Not every question needs a PhD-level analysis. If you ask: “What’s 2 + 2?” And the system launches into deep multi-step reasoning… Congratulations, your AI is overthinking. Smart systems decide: Easy question → quick answer Hard question → think deeper Like a human would. 3️⃣ More AI agents ≠ better results. Imagine asking for a dinner suggestion. Instead of one helpful friend, you invite: A nutritionist A chef A food critic A philosopher And someone debating all of them By the time they finish arguing, you’re not hungry anymore. In many real-world apps, better instructions and clearer context beat complex AI debates. 4️⃣ Generating 10 answers doesn’t magically create genius. For math? Sure, voting helps. For normal conversations? Two or three thoughtful options are usually enough. Otherwise, it’s just AI saying: “Let me overthink this 9 more times.” 5️⃣ Safety should be smart, not dramatic. First: simple rule checks. Then: one AI review if needed. If your safety system has 6 layers debating each other… You’ve built airport security for a lemonade stand. 📚 For those who like to check the source code: • DeepSpeed (Mixture-of-Experts implementation) – GitHub: https://lnkd.in/gmqUjUXj • Megatron-LM (large-scale transformer + MoE research) – GitHub: https://lnkd.in/gBkTmYgb • Transformers (production LLM tooling) – GitHub: https://lnkd.in/gqUQxtfS Because in AI, simplicity is often the real innovation. 🔎 The big lesson? Smaller model + smart routing + good design often beats bigger model + brute force. The next generation of AI won’t win by being the loudest. It’ll win by knowing when to think — and when not to. #AI #LLM #Tech #Innovation P.S. If your AI requires a committee, a whiteboard, and a ritual chant just to answer “Hi,” you might be overthinking things… let the AI sleep, and grab a coffee instead. ☕

AT&T just cut AI costs by 90%. The lesson: the future of enterprise AI isn’t bigger models — it’s smarter systems. A recent report showed AT&T reduced AI costs by ~90% by redesigning its AI stack and shifting most tasks from large language models to smaller, specialized models. (PYMNTS.com) Instead of running everything through massive frontier models, they built a multi-agent architecture: • Large models act as coordinators • Smaller models handle most domain-specific tasks • The system processes more tokens, faster, and far cheaper (Let's Data Science) This isn’t just a clever optimization. It’s a preview of how enterprise AI will actually be built. ### The shift happening right now For the last two years, the industry narrative has been simple: > Bigger model = better AI. But production systems are telling a different story. Most enterprise workloads don’t need a trillion-parameter generalist. They need reliable intelligence tuned to their data and workflows. That’s why we’re seeing a new architecture emerge: - Small, domain-specialized models - Agent orchestration - Task-specific pipelines - LLMs used only when necessary AT&T’s Chief Data Officer put it bluntly: > The future of AI is “many, many, many small language models.” (Maverick Studios) ### This is exactly where open AI infrastructure becomes critical Building systems like this requires something most enterprises don’t have yet: A reproducible platform to experiment across models, workflows, and data. Not just prompting a black-box API. But running the full stack: - model selection - domain adaptation - evaluation - orchestration - deployment That’s the gap platforms like Oumi are designed to solve. Our thesis is simple: The companies that win in AI won’t just use models. They’ll build domain intelligence systems. Systems that are: • cheaper (smaller models) • faster (efficient inference) • more reliable (trained on proprietary data) • fully controllable (open infrastructure) In other words: Enterprise AI isn’t about the biggest model. It’s about the best architecture. And increasingly, that architecture looks a lot like: Open models + domain data + modular workflows. Exactly the direction the industry is now moving. Curious how others are approaching this shift from “big model thinking” to system design for AI? We launch 🚀 end of March Oumi.ai https://lnkd.in/e_vmkiB6