We Have Reached Peak LLM: Here”s What the Next Phase of AI Looks Like

Key Takeaways

• LLMs are becoming commoditized infrastructure, not the primary source of innovation
• System-level AI combines memory, reasoning, orchestration, and world models
• The next competitive advantage is in assembling complete systems around LLMs
• Agentic systems with memory and reasoning can execute complex business tasks

The AI industry has spent the past two years in an arms race over model size, focusing on more parameters, longer context windows, and more training data. But while everyone”s been watching the horsepower wars, a more consequential shift has been happening: Large language models (LLMs) are becoming foundational infrastructure, not the primary source of innovation.

The Shift From Models to Systems

The next AI breakthrough isn”t the next frontier model. It”s the realization that LLMs, powerful as they are, were never meant to work alone. They”re car engines, not complete vehicles. And the companies that will win with AI will adopt entire systems around them.

This is a fundamental shift in how AI delivers value. LLMs generate text very well, but lack native long-term memory of past conversations. And because they”re predictive rather than logical, they often struggle to reason through complex, multistep problems reliably. On their own, they also can”t learn or update their internal knowledge during a chat.

What Is System-Level AI?

System-level AI is the integration of LLMs with specific capabilities that transform them from chatbots into complete business systems. This includes memory architectures that enable continuity; reasoning modules that handle complex logic; simulation environments that continuously improve performance; multimodal capabilities that understand text, images, video and spatial reasoning; and orchestration layers that coordinate it all.

“LLMs, for the most part, have matured and become commoditized,” said Itai Asseo, senior director of incubation and brand strategy at Salesforce AI Research. “An LLM, on its own, is powerful, but it doesn”t give a company a complete solution.”

The Four Key Components

1. Long-Term Memory

One of the problems with standalone LLMs is that they”re stateless by default: Each new conversation starts without a memory of the last. It”s like Groundhog Day for data. System-level AI adds a memory architecture that remembers. This creates continuity and allows the AI to pick up exactly where the last conversation, whether with a human or an AI agent, left off.

Salesforce scientists have developed a “block-based extraction method” that maintains the accuracy of long context while dramatically reducing costs. The approach works in two phases: parallel extraction breaks conversation history into manageable chunks and extracts relevant memories from each in parallel; smart aggregation combines those snippets into a briefing for the AI to use in its response.

2. Reasoning and Planning

A reasoning engine is the executive function of an AI system. While standard LLMs predict the next likely word, reasoning-enhanced systems pause to plan multistep approaches before responding. They digest information, apply business logic, and map out a multistep plan before taking action, just as any businessperson would.

This capability can be built into the LLM itself or operate as a separate orchestration layer. The key is that the system moves from prediction to planning, enabling complex problem-solving that LLMs alone cannot reliably achieve.

3. Action and Orchestration

This is the layer where AI moves from talking to doing. Through APIs and orchestration, the system interacts with your enterprise software, bridging organizational boundaries. For example, one agent could check inventory, while another updates a customer record, or processes a refund.

The orchestration layer for tying that all together will become more important than any single model. Salesforce describes a semantic layer—a protocol that lets AI agents from different organizations communicate with each other—that interprets intent, verifies, and negotiates terms without human intervention.

4. World Models

LLMs are trained on text and, more recently, images and video. But we live in a three-dimensional environment, and looking at a video is not the same as understanding the real world within it. World models will enable spatial intelligence: AI”s ability to perceive, reason about, understand, and interact with the physical world.

For example, on a factory floor, a world model could see and predict that a robotic arm was about to collide with a human, and change its trajectory. In a broader sense, world models allow AI to simulate physical outcomes before they happen. Instead of predicting what would normally happen based on past patterns, they can model what would happen under certain conditions.

The Strategic Implications

As LLMs become more homogenized and commoditized, available to everyone through APIs, the new frontier of innovation is assembly. Building a transformative AI system means moving past the chatbox and integrating specific capabilities that turn that engine into a far smarter and more valuable business system.

“The algorithms gave us the basic concepts to be able to do this, but now we”re going to see more purpose-driven models that are just not language models, pure reasoning models, pure action models, or pure memory models,” said William Dressler, senior director of delivery leader at Salesforce.

How to Prepare

The shift to system-level AI doesn”t replace the LLM; it completes it. Your LLM is a car engine: powerful, but useless without a chassis, wheels, and a driver. Memory, reasoning, and orchestration are what turn that raw engine into a vehicle that can navigate complex business goals.

Here”s how to think about system-level AI: Don”t ask, “Should we add memory to our model?” Ask if your use cases require continuity across sessions. Let the business problem drive which system components you need. Do your customer service teams need past interaction context? If yes, look into memory. If your teams need more in-depth analysis or troubleshooting, you probably need reasoning capabilities.

The Human Element

The tech infrastructure is only half the story. The biggest opportunity is the mental shift you need to work alongside system-level AI. This means treating AI not as a chatbot you prompt, but as a team member that can reason and execute complex tasks.

The companies that figure out this organizational and cultural shift will be the ones that realize value from system-level AI. As Salesforce CEO Marc Benioff noted, “the task before us is not to predict which LLM will win in the marketplace, but to build systems that empower AI for the benefit of humanity.”