Explosion of AI Models

The landscape of AI models is increasingly competitive and fractured. According to the Stanford Human‑Centered AI Institute AI Index Report (2025), nearly 90% of new significant models in 2024 came from industry, up from 60% in 2023. Training compute is doubling approximately every five months, while dataset sizes double every eight. Meanwhile, the number of “frontier” models meeting high compute thresholds is forecast to grow super-linearly through 2028. The global large-scale AI models market was valued at USD 9.24 billion in 2025 and is projected to reach USD 18.98 billion by 2032 (CAGR ~13.1%).

However, for startups and agile teams, this rapid expansion has created a "Velocity Paradox": the more models available, the slower the path to a viable product (MVP). The operational burden of managing this ecosystem is stalling development in three critical ways:

• Integration Friction Stalls Feature Delivery: The single-model era is over. Teams now need to integrate an average of 3+ distinct models to balance reasoning and speed. Only 13% of organizations report being fully "infrastructure-ready" to manage this complexity, citing interoperability as a primary bottleneck. However, wiring up multiple fragmented endpoints requires complex, brittle engineering. Instead of shipping user-facing features, developers are bogged down in backend orchestration, significantly delaying the initial MVP release.

• The "Glue Code" Trap: With new foundation models releasing weekly, technical debt accumulates faster than product value. Engineering teams report spending up to 50% of their time on "glue code"-updating SDKs, managing context windows, and patching prompts-just to keep the system running. This constant maintenance forces teams to pause product iteration, preventing them from reacting to early market feedback.

• Inability to Capture Efficiency: A fierce price-performance war is underway. "Efficient Frontier" open models (e.g., Llama 3.1 70B) can now deliver ~95% of the practical utility of proprietary flagships for less than 1/10th of the cost. Yet, most organizations lack the routing infrastructure to dynamically leverage these savings. This process significantly delays critical architecture decisions and pushes back the subsequent timeline. So the chaotic abundance of models forces teams to trade shipping speed for infrastructure management, causing startups to miss critical market windows while wrestling with models. NativelyAI breaks this deadlock. Acting as a unified acceleration layer, it abstracts away integration chaos, empowering teams to bypass backend heavy-lifting and focus on slashing the time from concept to live MVP for startups.