I’m becoming more convinced that LLMs are moving toward the same structure as payment networks. The models will be incredibly important. But the largest value will not be captured by the raw model layer alone. It will be captured by the layers above it: routing, evals, RAG, MCP, memory, orchestration, agentic workflows, vertical applications, and trust infrastructure.

As a founder and developer, this pattern feels familiar to me.I previously built a fintech company that routed transactions across multiple rails and 100+ payment methods around the world. It was eventually acquired by Visa. In payments, Visa, Mastercard, and AmEx were critical rails. But Stripe, PayPal, Adyen, PlaySpan (acquired by VISA) and others created enormous value by abstracting those rails, optimizing routing, managing risk, improving developer experience, and owning the merchant workflow. I think the same thing is happening with LLMs.

At the bottom, we will likely have a small number of frontier model providers: OpenAI, Anthropic, Google, and a strong open-weight ecosystem. They will remain valuable. They will set the capability frontier. But for most production apps, the model will increasingly become a pluggable inference rail. The value moves up the stack.

Layer one: model gateways and routing.

OpenRouter, LiteLLM, Bedrock, Together, Fireworks, Groq, and internal enterprise gateways are making model access interchangeable. A developer can route a request to GPT, Claude, Gemini, Llama, Mistral, DeepSeek, Qwen, or a fine-tuned model depending on cost, latency, context length, modality, privacy, or benchmark performance. This is where the “LLM as rail” abstraction begins.

Layer two: RAG and context engineering.

The hard problem in enterprise AI is not generating fluent text. It is assembling the right context at the right time. A useful AI system needs to know the patient record, contract clause, support ticket, lab result, CRM object, claim history, policy document, API schema, prior memory, and user permission boundary. RAG is evolving from “vector search over PDFs” into a full context layer: hybrid search, graph retrieval, tool retrieval, memory retrieval, structured database queries, re-ranking, summarization, and dynamic context packing. The LLM is only as good as the context substrate around it.

Layer three: MCP and tool connectivity.

MCP makes the harness layer much stronger because it standardizes how agents discover and call tools. Instead of every app building custom glue code for Gmail, Slack, GitHub, Postgres, EHRs, CRMs, calendars, and internal APIs, MCP gives agents a more consistent interface to external systems. This is a big deal.

Once tools become discoverable and composable, the agent is no longer just a chat interface. It becomes a workflow runtime that can read, reason, act, verify, and update state across systems.

Layer four: agentic orchestration.

This is where frameworks like LangGraph, LlamaIndex, LangChain, CrewAI, AutoGen, Semantic Kernel, and custom orchestration layers matter. The future agentic app will not call one model once.

It will use one model for planning, another for coding, another for extraction, another for medical reasoning, another for summarization, and another for cheap classification. It will make these decisions in real time based on task type, latency, cost, reliability, and safety constraints. One task may go to Claude for long-context reasoning. Another may go to Gemini for multimodal input. Another may go to GPT for tool use. Another may go to a local or open-weight model for cheap classification. Another may run through multiple models in parallel for consensus, critique, or ensemble evaluation.

This is exactly how payment orchestration worked. You didn’t hard-code one rail. You routed dynamically based on geography, fees, approval rates, fraud risk, currency, merchant category, and availability.

Layer five: evals, trust, and governance.

This is where I think platforms like TrustModel.ai become important. If the application can route across multiple LLMs, the system also needs a way to continuously evaluate which model is right for which task. Not just “which model is smartest,” but which one is safest, cheapest, fastest, most compliant, most consistent, most robust against prompt injection, best at structured output, best at domain reasoning, and least likely to hallucinate.

A serious agentic system needs multi-dimensional evals across models and workflows. It needs to test safety, quality, bias, factuality, privacy leakage, tool-use reliability, refusal behavior, cost, latency, and auditability. That eval layer becomes the control plane for selecting models and keeping applications safe across changing model providers. This is not optional in healthcare, finance, legal, or enterprise AI.

Layer six: vertical workflow applications.

This is where the most durable value gets created. A healthcare agent that closes care gaps is not valuable because it uses one specific LLM. It is valuable because it understands clinical workflows, patient context, lab data, insurance constraints, escalation paths, HIPAA boundaries, and provider operations. A revenue cycle agent is valuable because it knows claims, denials, CPT codes, payer policies, appeal letters, and EHR workflows.

A legal agent is valuable because it knows contract structures, risk positions, fallback clauses, negotiation playbooks, and approval workflows. The model is necessary. But the system, data, workflow, distribution, trust, and feedback loop create the moat. This is why I do not think “which model wins?” is the most interesting question. The better question is: who owns the orchestration layer between the model and the workflow?

My bet is that most serious applications and agents will be multi-model by default. That is already how I’m building. I’m working on agents that use five different LLMs in parallel, each selected for the task where it performs best: reasoning, extraction, summarization, coding, evaluation, or low-cost classification. The system should optimize in real time, just like a payment router optimizes transaction success, cost, and risk across multiple rails.

LLMs are becoming intelligence rails. The value will accrue to the builders who turn those rails into reliable systems.