Trends in AI Model Architectures

Large Language Models (LLMs) are powerful, but how we 𝗮𝘂𝗴𝗺𝗲𝗻𝘁, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 them truly defines their impact. Here's a simple yet powerful breakdown of how AI systems are evolving: 𝟭. 𝗟𝗟𝗠 (𝗕𝗮𝘀𝗶𝗰 𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲)   ↳ This is where it all started. You give a prompt, and the model predicts the next tokens. It's useful — but limited. No memory. No tools. Just raw prediction. 𝟮. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻)   ↳ A significant leap forward. Instead of relying only on the LLM’s training, we 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗳𝗿𝗼𝗺 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 (like vector databases). The model then crafts a much more relevant, grounded response.   This is the backbone of many current AI search and chatbot applications. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠𝘀 (𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 + 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲)   ↳ Now we’re entering a new era. Agent-based systems don’t just answer — they think, plan, retrieve, loop, and act.   They: - Use 𝘁𝗼𝗼𝗹𝘀 (APIs, search, code) - Access 𝗺𝗲𝗺𝗼𝗿𝘆 - Apply 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗰𝗵𝗮𝗶𝗻𝘀 - And most importantly, 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝗻𝗲𝘅𝘁 These architectures are foundational for building 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀, 𝗰𝗼𝗽𝗶𝗹𝗼𝘁𝘀, 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀. The future is not just about 𝘸𝘩𝘢𝘵 the model knows, but 𝘩𝘰𝘸 it operates. If you're building in this space — RAG and Agent architectures are where the real innovation is happening.

Why Compound AI Systems Are Taking Over ⭐ We’re moving beyond single-model AI into an era where Compound AI Systems—modular, flexible, and powerful—are setting a new standard. But what does this mean? And why should AI leaders pay attention? 🔍 𝗪𝗵𝗮𝘁 𝗔𝗿𝗲 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Unlike traditional AI models that work in isolation, Compound AI Systems integrate multiple components—LLMs, retrieval mechanisms, external tools, and reasoning engines—to solve complex problems more effectively. Instead of relying on one massive model, these systems: ✔️ Combine multiple AI models for specialized tasks ✔️ Use retrieval mechanisms to fetch real-time, relevant data ✔️ Leverage external tools (APIs, databases, or symbolic solvers) to enhance reasoning ✔️ Improve adaptability by dynamically selecting the best approach for a given problem This modular approach enhances accuracy, efficiency, and scalability—giving AI systems the ability to think beyond their training data and operate more intelligently in real-world environments. 🏆 𝗪𝗵𝗲𝗿𝗲 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗔𝗜 𝗜𝘀 𝗪𝗶𝗻𝗻𝗶𝗻𝗴 ↳ Google’s AlphaCode 2 Generates millions of programming solutions, then intelligently filters out the best ones—resulting in dramatic improvements in AI-driven code generation. ↳ AlphaGeometry Combines a large language model (LLM) with a symbolic solver, enabling AI to solve complex geometry problems at an expert level. ↳ Retrieval-Augmented Generation (RAG) Now a standard in enterprise AI, RAG models retrieve relevant data in real-time before generating responses, significantly boosting accuracy and contextual relevance. ↳ Multi-Agent Systems Startups and research labs are developing AI "teams"—where multiple models communicate and collaborate to solve problems faster and more efficiently than a single model could. 💡 𝗪𝗵𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗔𝗿𝗲 𝗕𝗲𝘁𝘁𝗶𝗻𝗴 𝗕𝗶𝗴 𝗼𝗻 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗔𝗜 This isn’t just a research trend. It’s an industry-wide shift. ↳ Microsoft, IBM, and Databricks are already pivoting their AI strategies toward modular, system-based AI architectures. ↳ Fireworks AI is leading the GenAI inference platform with Compound AI Systems ↳ Even OpenAI’s CEO, Sam Altman, emphasized the transition: "We’re going to move from talking about models to talking about systems." 𝗧𝗵𝗲 𝗕𝗶𝗴 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 𝗳𝗼𝗿 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 The implications are massive: ✔️ AI performance will increasingly depend on system design—not just model size ✔️ Custom AI solutions will become the norm, allowing businesses to tailor AI systems for specific needs ✔️ Efficiency will skyrocket, as compound systems reduce computational waste by dynamically choosing the best approach for a given task ----------------------- Share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources to keep you up-to-date about the AI space!

Microsoft, Google, and Meta are making unprecedented bets on AI infrastructure. Microsoft alone plans to spend $80B+ in 2025. By 2027 their collective AI infrastructure investment could exceed $1T. The assumption driving these investments: bigger models equal better AI. But here’s the data: → OpenAI's Orion model plateaus after matching GPT-4 at 25% training → Google's Gemini falls short of internal targets → Training GPT-3 uses about 1,300 megawatt hrs of electricity, equivalent to the annual needs of a small town → Next gen models would require significant energy resources The physics of computation itself becomes a limiting factor. No amount of investment overcomes these fundamental barriers in data, compute, and architecture. Researchers are pursuing new architectures to address the limitations of transformers: → State Space Models excel at handling long-term dependencies and continuous data → RWKV achieves linear scaling with input length versus transformers' quadratic costs → World models, championed by LeCun and Li, target causality and physical interaction rather than pattern-matching DeepSeek’s efficiency breakthrough reinforces this trend: AI’s future won’t be won by brute force alone. Smarter architectures, optimized systems, and new approaches to reasoning will define machine intelligence. These constraints create opportunities. While tech giants pour resources into scaling existing architectures I’m watching for founders building something different.

𝐋𝐚𝐫𝐠𝐞 𝐨𝐫 𝐒𝐦𝐚𝐥𝐥: 𝐖𝐡𝐢𝐜𝐡 𝐢𝐬 𝐁𝐞𝐭𝐭𝐞𝐫? Get your minds out of the gutter, in this instance I am asking in regards to the models powering AI. For years, the AI industry has been obsessed with scale. Large Language Models (LLMs) like GPT-4 have dominated the conversation, boasting massive capabilities driven by billions of parameters. But here’s the question we should be asking: Is bigger always better? It’s time to think smarter, not just bigger. Enter Small Language Models (SLMs) and modular neural architectures—a strategy that focuses on building specialized, efficient, and collaborative AI systems rather than relying on one massive model to do it all. Here’s why this approach makes more sense: 1. ⚡ Efficiency Over Scale: LLMs are resource-intensive, demanding enormous computational power. SLMs are lightweight, faster, and more adaptable—ideal for edge devices, cloud systems, and even embedded applications. 2. 🎯 Specialization Beats Generalization: LLMs are generalists, but SLMs can be hyper-specialized. Imagine a system where one model handles cybersecurity, another manages predictive maintenance, and another focuses on language analysis. Each model excels in its niche, creating a collective intelligence more powerful than any single model. 3. 🔗 Scalable Modularity: Building AI systems with multiple SLMs is like assembling a high-performance team—each model contributes its unique expertise. This modular design is scalable, adaptable, and resilient, much like how different regions of the brain work together. 4. 💸 Cost-Effective Innovation: Training and maintaining LLMs comes with a massive price tag. SLMs reduce costs and make innovation accessible to startups, research teams, and solo developers. AI development becomes more democratic and agile. 5. 🔒 Enhanced Security & Privacy: SLMs can process sensitive data locally on edge devices, reducing reliance on centralized systems and lowering security risks. Smaller models can mean safer data. This shift from building colossal models to designing purpose-driven, collaborative systems could be the key to more sustainable and intelligent AI. The future of AI isn’t about one model knowing everything. It’s about many specialized models working together to solve complex problems. Sometimes, smaller is smarter. And smarter is what we need. #slm #ai #future

🏆 My curated list of the top Generative AI papers from January 2025 is now live on my repository! (A little late, but well 😅 ) I’ve compiled 45+ top papers with links and abstracts, catch up on the latest research in generative AI. This month’s research marks a clear shift from last year’s focus on the application layer—we’re seeing a return to more model-level advancements. Here are the key patterns: ⛳ Advanced Reasoning & Self-Correction: LLM research is moving toward active reasoning and self-correction, with reinforcement learning and process supervision improving accuracy and generalization. The focus is shifting from just producing answers to reasoning through problems. ⛳ Multi-Modal & Agentic Systems: An expected trend—more work on integrating text, vision, and interactivity, along with a rise in domain-specific and multi-agent research. ⛳ Scalable Inference & Efficient Computation: New techniques in test-time computing and scaling inference efficiently. This trend ties closely to reasoning models, optimizing compute without simply making models bigger. 💡 Compared to Q4 last year, which was heavily focused on agent applications, the current shift is toward reasoning, self-correction, and efficient inference. I see this trend sticking around for a while given that reasoning models have started this new wave of model-level optimization research. I’ll be sharing a deeper analysis on Substack soon. Link: https://lnkd.in/e229UbMa

🌟 The Future Architecture of AI Agents: A Blueprint for Smarter Systems 🤖 AI agents are rapidly transforming how we interact with technology, creating opportunities for unprecedented efficiency, automation, and insight. But what does the future of these agents look like? Here's a comprehensive look at their architecture: Key Layers of Future AI Agent Architecture 1️⃣ Input Layer: The foundation of AI agents lies in their ability to process diverse inputs, including: Future Predictions: Anticipating trends and outcomes. Real-Time Data: Staying updated with live inputs. User Feedback: Learning and adapting from user interactions. 2️⃣ Agent Orchestration Layer: The layer where tasks are coordinated and managed, featuring: Dynamic Task Allocation: Assigning tasks efficiently across agents. Inter-Agent Communication: Seamless collaboration between AI entities. Monitoring & Observability: Ensuring transparency and performance tracking. 3️⃣ AI Agents Layer: This is where the core intelligence resides: Planning and Reflection: Thoughtful execution and improvement. Tool Use and Self-Learning: Constant evolution through feedback loops. Diverse Models: Specialized models tackling specific challenges. 4️⃣ Data Storage/Retrieval Layer: The knowledge backbone, enabling access to: Structured + Unstructured Data Vector Stores for efficient retrieval. Knowledge Graphs for contextual understanding. 5️⃣ Output Layer: Delivering value through: Enriched/Synthetic Data: Advanced insights for decision-making. Customizable Outputs: Tailored to user needs. Knowledge Updates: Constantly refining and improving data. 6️⃣ Service Layer: Connecting the agents to real-world applications via: Multi-Channel Delivery: Seamless integration across platforms. Automated Insights: Actionable intelligence for users. Non-Negotiable Pillars Building the future of AI agents requires careful attention to: Safety & Control: Ensuring reliability and risk mitigation. Ethics & Responsible AI: Upholding trust and fairness. Regulatory Compliance: Aligning with global standards. Human-AI Collaboration: Working alongside people, not replacing them. Interoperability: Seamless integration with existing systems. Versioning & Evaluation: Continuous improvement and accountability. Why It Matters AI agents are poised to revolutionize industries by automating complex processes, enhancing decision-making, and enabling smarter workflows. This architecture is not just a technical roadmap—it’s a vision for how AI can empower businesses and individuals alike. How do you see AI agents shaping the future of your industry? Let’s discuss in the comments!

“Composite AI, or combining multiple AI techniques, will emerge as the next step in extending the AI revolution. Because Large Language Models (LLMs) are starting to hit natural performance ceilings when used in isolation, technologists will turn to Composite AI architectures that use LLMs as the orchestrating layer, seamlessly integrating them with specialized AI components like knowledge graphs, symbolic reasoning engines, and traditional machine learning models." https://lnkd.in/g6rysXjU

NVIDIA had an interesting announcement this month regarding its new “Cosmo” AI model, which builds an internal representation of the physical world in order to optimize outputs. This shift from analysis to simulation is powerful, as we have also seen with Physics-Informed Neural Networks (PINNs), which our portfolio company Basetwo AI uses to revolutionize process manufacturing. Why does this matter? Because world models, PINNs, and similar AI architectures represent a paradigm shift in AI, moving from passive pattern recognition to active reasoning and decision-making. While challenges remain, these models could be a huge step forward in making AI more adaptable, explainable, and effective in applications that impact the real world. For business leaders, understanding this shift is crucial, not just to leverage AI’s potential, but to prepare for a future where machines do more than just analyze data. You can read more about it in my latest AI Atlas:

What is coming next from AI models? We’re in the middle of a pretty big shift in AI models. GPT 2, 3, 3.5, and 4 were all LLMs that got better at instruction following and conversation. Context length got a bit longer, conversations could keep going, hallucinations were reduced, people were writing better emails - great! Then we start to get natively multimodal models (ex: GPT-4o), reasoning models (o1/o3/o4-mini/Claude 3.7 Sonnet), and agent-ready instruction models like GPT-4.1. And now, we’re at another moment of change. The next generation of model imo will be less of a model and more of a system. That means a heavy lift on orchestration and tool calling. That means much - much - longer memory. That means multiple layers of abstraction that haven’t been needed yet. That means more money, more infrastructure investment, and still a growing skills gap. In my conversations with AI researchers and AI labs, I am not seeing a slow down, I’m seeing pivots - some new algorithm research to prioritize better scaling or higher accuracy, and a lot of systems thinking. We shall see...