Introduction

Over the past few years, we’ve experienced a meteoric rise in AI capabilities, largely fueled by increasingly powerful large language models (LLMs). These models, developed by major AI labs such as OpenAI and Anthropic, rapidly grew in capability—referred to as their “raw IQ.” From roughly 2023 to 2024, each new release brought a substantial leap in general intelligence.

Application developers seeking to harness AI in their products had a straightforward strategy: integrate the latest and greatest APIs from these leading providers, chain them together when needed, and wait for the next major release to see big improvements in performance. However, toward the end of 2024, the landscape began to change. The raw IQ of these frontier models stopped improving at the same breakneck pace, and new releases offered more incremental, specialized gains rather than seismic jumps in general intelligence.

In this post, we’ll explore the implications of this shift. Instead of relying purely on centralized APIs and waiting for large labs to release groundbreaking updates, application developers now need to roll up their sleeves and tailor existing models to their unique use cases. We call this the AI Sprawl era—a time when dozens, if not hundreds, of specialized models will be used within organizations, each tuned for optimal performance in a particular use case.‍

The Centralized API Era (2023–2024)

Rapidly Rising IQ‍

The hallmark of the Centralized API Era was the blistering pace of model improvements. With each new generation of LLMs, the community witnessed striking growth in overall “raw IQ,” with impressive improvements in capability. If your application was limited by your existing language model, the best thing you could do was simply plug into the newest API and watch your performance metrics skyrocket.

Chaining together these APIs with your prompt of choice was a fairly straightforward way to achieve good results, but it was heavily reliant on external innovation. All you had to do was wait a few months for the next big release from a major lab, and your application would naturally get a boost in quality. However, this era is coming to an end.‍

Why the Shift?

Slowing Growth in Raw IQ‍

By late 2024, progress in raw intelligence started to plateau. The new frontier models still showed improvements, but these gains were relatively modest. Instead of making leaps in generalized intelligence, labs focused on enhancements in specific areas—reasoning, Retrieval-Augmented Generation (RAG), or improving efficiency for smaller model sizes.

As a result, the gap between one generation of a frontier model and the next isn’t as dramatic as it used to be. Rather than offering a universal leap in capability, newer models often deliver targeted improvements or incorporate specialized optimizations. This means that simply integrating the latest central API no longer guarantees substantial performance benefits.‍

The Limits of Generalist Language Models‍

Another reason behind the shift is the inherent limitations of generalist language models. While they excel at generating text in broad or generic contexts, it’s much more difficult to build specialized solutions for complex or domain-specific problems (like the ones that exist in the enterprise) solely on these high-IQ models.

You might be able to create a basic chatbot, but building a deeply customized system that addresses a unique business challenge often requires more context than a general LLM can provide. Retrieval-Augmented Generation (RAG) was an early attempt to supply this context, but there are limits to what basic RAG alone can achieve. Ultimately, more specialized approaches—ranging from advanced fine-tuning to domain-specific data pipelines—are required to build truly specialized AI applications.‍

The Emergence of AI Sprawl

A New Approach for Application Developers‍

In this new era, application developers can no longer rely on a single, all-encompassing model that does everything well. Instead, the focus shifts to how these models can be fine-tuned, specialized, and improved for a specific end use case. Instead of waiting for the next big frontier model, developers are actively gathering domain-specific data, running reinforcement learning experiments, and applying fine-tuning techniques to shape base models into specialized agents.‍

Specialized Models, Everywhere‍

Enter AI Sprawl. With each application—be it in healthcare, finance, manufacturing, or customer service—developers will fine-tune frontier models to excel at one particular use case. The result is a growing, sprawling ecosystem of models, each carefully calibrated to a different task. While large frontier models remain the foundation, the value is created in the last-mile differentiation.‍

How to Thrive in the AI Sprawl Era

Data Collection and Curation‍

Carefully curated data for your particular use case and application is essential for building a high-quality specialized model.‍

Model Fine-Tuning‍

Fine-tuning a good base model can significantly improve performance for a specific use case. There are many examples where fine-tuned models have outperformed GPT-4o for specific applications (e.g., Digits has outperformed GPT-4o for accounting use cases).‍

Reinforcement Learning and Human Feedback‍

Using reinforcement learning to reward (or penalize) certain outputs can dramatically improve the relevance of a specialized model. Human-in-the-loop feedback remains crucial: domain experts can guide the model toward better responses. We anticipate far more use of RL over 2025.‍

Infrastructure and Deployment‍

As you develop dozens—or even hundreds—of specialized models, your infrastructure must scale accordingly. Deploying these models in a cost-efficient manner is not trivial. For most, deploying hundreds of accompanying GPUs is not feasible; therefore, using techniques such as serverless LoRA deployment is essential. In this world, investing in a scalable deployment platform like TitanML will allow you to enjoy AI Sprawl without suffering from GPU cost sprawl.‍

Monitoring and Continuous Improvement‍

Once these specialized models are deployed, continuous monitoring of their performance is essential. As the models sit in production, the production data can be gathered and used to further improve their performance.‍

Conclusion‍

We’re entering an era of AI Sprawl, where the raw gains in model intelligence have begun to plateau—the path to significant performance improvements lies in specialization rather than general leaps. For application developers, this means taking ownership—shaping, tuning, and strategically deploying AI models tailored to their particular needs.

In the AI Sprawl era, those who master the art of data collection, fine-tuning, and domain-focused engineering will reap the benefits of truly specialized and differentiated applications. Instead of plugging in an ever-more-intelligent centralized API, outlier success now hinges on skillfully adapting existing AI models to serve highly specific goals.

It’s a more complex landscape, but also one filled with opportunity. The age of one-size-fits-all AI is giving way to a tapestry of specialized models—each meticulously crafted and refined. Whether you’re a startup looking to stand out in a crowded market or an enterprise seeking deeper insights, the AI Sprawl era is your invitation to innovate, iterate, and create.