145 AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising …At the platform level, a proliferation of foundation models has created a new kind of flexibility. Developers can now choose between dozens of models – OpenAI’s ChatGPT, Meta’s Llama, Mistral’s Mixtral, Anthropic’s Claude, Google’s Gemini, Microsoft’s Phi, and others – each of which excels in different domains. Some are optimized for reasoning, others for speed or code generation. The result is a move away from vendor lock-in. Instead of consolidating under a single provider who can gate access or raise prices, developers are distributing their efforts across multiple ecosystems. This plurality of options is empowering a new wave of builders to choose the best-fit model for their technical or financial needs. What’s emerging is a flywheel of developer-led infrastructure growth. As more developers build AI-native apps, they also create tools, wrappers and libraries that make it easier for others to follow. New front-end frameworks, embedding pipelines, model routers, vector databases, and serving layers are multiplying at an accelerating rate. Each wave of developer activity reduces the friction for the next, compressing the time from idea to prototype and from prototype to product. In the process, the barrier to building with AI is collapsing – not just in cost, but in complexity. This is no longer just a platform shift. It’s an explosion of creativity. Technology history has shown – as memorialized by then-Microsoft President Steve Ballmer’s repeat Developers! Developers! Developers… at a 2000 Microsoft Developers Conference (link) – the platform that gets the most consistent developer user and usage momentum – and can scale and steadily improve – wins.