The early days of the internet suggest what lies ahead for the AI revolution. In the late 1990s, capital poured into e-commerce sites, content portals and online services. The assumption was that the best application would win, yet most of those companies failed. But one of the world’s most recognizable brands arose from the wreckage of that period: Google.
Google succeeded because it looked beyond the application battle and built an orchestration layer that organized the web’s chaos, making it easier to navigate. The structural dynamics are similar in AI. The Google of the AI era will solve a coordination problem rather than building the smartest model. Engineers and platform builders who understand this early will be positioned for success. Those who wait for the platform to emerge will be stuck doing expensive catch-up.
What Does the AI Orchestration Layer Do?
- Routes Requests: Decides which model or combination of models should handle a specific task.
- Provides Context: Feeds models accurate company data, documents, records and history.
- Enables Action: Connects models to enterprise tools and systems to perform multi-step work.
- Maintains State: Tracks task history across steps so multi-step processes hang together.
- Enforces Guardrails: Ensures AI only accesses authorized data and actions.
The Current State of AI
Orchestration is increasingly viewed as the next significant platform category in AI as more organizations recognize the value of improving how AI systems interact with data, tools, workflows, business rules and people. Currently, however, most AI investment capital continues to flow into compute infrastructure and model capability, while the orchestration layer between them and actual enterprise value creation remains underfunded. This is understandable given that the models available today are shockingly capable. But here’s the thing: It’s easy to forget how isolated they are out of the box.
A model may know a lot about the world in general, but it understands little to nothing about a company’s world in particular. It can’t see a company’s data or touch a company’s systems, and it can’t actually do anything. The moment a task takes more than a step or two, it ceases to be effective. By itself, a model is just a very smart intern without login credentials, access to the file cabinet or memory of anything that happened five minutes ago. The orchestration layer is essentially everything built around the model to fix these issues. It turns raw capability into a product that does useful work inside a company.
The orchestration layer decides which model (or combination of models) should handle a given request and feeds the model the right context of actual documents, records and history, so that the model reasons over real information instead of “vibes.” It also provides the model with the tools and systems to conduct research or take action, and it keeps track of the system’s state across steps to ensure that a multistep task hangs together. The orchestration layer also enforces guardrails to guarantee that the model only access information and performs actions it’s authorized to use. Effective orchestration is the difference between a cool demo and a product that truly works.
The power of the orchestration layer is evident in some of the most successful and influential companies in the world. Tesla’s autonomous driving system can simultaneously gather information from camera feeds, maps, vehicle sensors, environmental data and more. Amazon’s fulfillment network orchestrates inventory systems, demand forecasts, warehouse robotics, transportation networks, delivery routing and supplier management. Uber’s marketplace platform coordinates riders, drivers, maps, pricing, traffic data, payment systems and fraud controls. The list doesn’t stop there; large banks, healthcare providers and entertainment companies like Netflix are recognizing the value of the orchestration layer.
Despite those companies’ successes, the orchestration layer remains a challenge for most businesses. The landscape of AI today is like the early days of the internet when all of the pages already existed. The information was out there, but it was just a scattered, unusable mess. Google didn’t create the content. Instead, it built the layer on top that made the internet findable and reliable, and that layer is what unlocked the web.
Companies are at the same spot with AI now. The raw capability has arrived, but the orchestration layer that makes it dependable at scale isn’t quite here yet. The organization that nails that layer will become this era’s Google, making this a more interesting problem than squeezing out another model that’s 5 percent better on benchmarks.
Who Will Become the ‘Google of AI’?
What’s next for the orchestration layer, and which company will emerge to be the “Google of AI”? The winning company’s orchestration layer will coordinate models, agents, tools, data sources, business rules and human approvals as seamlessly as Google coordinated billions of web pages back in those early days of the internet. It will feature superior retrieval and context management, along with governance and reliability. It will also learn from outcomes, not just interactions.
The winner likely won’t be one of today’s successful model providers, some of whom may simply absorb this layer themselves. They’re already moving in that direction, building agent frameworks, tool use, memory and protocols like the model context protocol (MCP).
Despite this, there are structural reasons to doubt these providers will own the orchestration layer. That’s because it has to be model-agnostic by design; its job is to route each task to the best model, this year and next. A company whose economics depend on its own models has every incentive to keep others using them. This conflict makes it difficult to trust a model provider as the neutral layer sitting above all models.
A harder problem is also the less glamorous one: deep integration with messy enterprise data, identity, governance and decades-old business processes. That is systems work, not model work, and it occurs alongside the customer’s data and workflows rather than upstream in the model.
Google didn’t win the web by writing the best pages; it won by organizing everyone else’s. The same logic puts the orchestration layer in the hands of whoever sits above the models, not the models themselves.
Although model builders may be strong in intelligence generation, scaling and benchmark performance, they’re traditionally weaker in deep enterprise integration and cross-system orchestration, which is essential in the new orchestration layer. The winning system will turn intelligence into operation, which is why the winner is more likely to come from a systems company rather than a model company.
Preparing for What Comes Next
Infrastructure leaders who want to benefit from the next stage to anticipate rather than react to it. The value is shifting, just as it did on the web, from creating content to organizing it. This time, however, the raw materials are models, agents, tools and enterprise knowledge, not web pages.
Today, the challenge is coordinating enterprise knowledge, agents, tools, workflows, human approvals, and real-time events. This is remarkably similar to the circumstances that gave rise to Google. Just as the web created browsers and search engines and cloud computing created orchestration platforms such as Kubernetes, AI is following a similar pattern. To stay current on the platform’s evolution, engineers and builders can monitor five areas:
1. Inference Cost Trajectory
When the cost of intelligence approaches the marginal cost of compute, competitive advantage shifts permanently to the orchestration layer.
2. Protocol Standardization Velocity
When the integration protocol reaches critical mass, the platform layer has a foundation to build on.
3. Shadow AI Migration Patterns
The shift from AI as an assistant to AI as an autonomous worker occurs when usage consolidates around agent-based frameworks rather than simple chatbots.
4. Enterprise AI Abandonment Rates
S&P Global documented the jump in AI abandonment rates from 17 to 42 percent in a single year. Continued growth signals that the current tool-centric approach is structurally broken.
5. Venture Capital Allocation Shifts
When capital flows into orchestration and integration startups rather than foundation model companies, the platform transition is underway.
The most significant signal of the orchestration platform transition will be easily identifiable: when organizations treat models as components and orchestration as the product. Engineering teams will be able to swap models with relatively little disruption, while replacing the orchestration layer will require redesigning business processes, integrations, workflows, governance and operational logic. At that time, companies that define what the orchestration ultimately becomes will start to separate from the rest of the market.
As AI evolves, builders can take several steps to position themselves for the coming transformation. First, they can build model-agnostic architectures and use abstraction layers and standard protocols to keep AI investments portable. MCP is a standard way for AI models to communicate with the tools, data, and systems they need to perform useful work, similar to the role that the hypertext transfer protocol (HTTP) played for the early web.
Just as HTTP gave browsers and websites a common language for exchanging information, MCP provides AI models, tools, applications and data sources with a common language for exchanging context and taking action. Once a protocol like this reaches critical mass, every new model and enterprise system becomes useful to others by default.
Second, tech leaders can invest in data quality, integration infrastructure, identity management and governance. Finally, they can build internal expertise in orchestration and integration rather than just model training.
Going to the Next Level With AI
Engineering and infrastructure personnel must understand that the current AI leaders may not be long-term winners, even though the five largest technology companies account for 30 percent of the S&P 500. According to MIT’s NANDA initiative, roughly 95 percent of enterprise generative AI pilots fail to deliver measurable business impact, and that rate has held because better models are not the primary roadblock.
The defining company of the AI era will be the one that builds the platform that makes all models useful and addresses the data gravity problem. That platform will offer end-to-end task completion, persistent memory across systems, multi-agent coordination, reliability and verification and a universal orchestration layer that allows users to connect once and use everywhere.
When that company emerges, and its platform gains widespread adoption, AI will move beyond being a “tool” and instead become a true orchestration layer. Today, AI is used inside applications. Soon, it will be the layer above those apps that routes work between them. In this environment, efficiency and profitability will soar for fast-moving companies that have closely monitored the orchestration layer progress and are ready to take full advantage of the new developments. It could be the difference between marketplace domination and falling behind the competition.
