Tldr; “innovation in the application layer”

Airbender is based on the following 12 principles:

1. There are no longer any frontier models, but rather 10+ tier 1 foundation models (OpenAI, Anthropic, Gemini, Cohere, Mistral, LLaMA, Grok, DeepSeek, Tülu3(AI2), Ernie(Baidu)) and many more tier 2 foundation models (Qwen, Yi, Phi(Microsoft), Nemotron(Nvidia), Nous(Databricks), GLM-4, InternLM2, OpenChat, Gemma, Mixtral, etc.)
2. The “AI-Wrapper Application" IS THE MOAT, not the risk. It’s the models that don’t have a moat.
3. Tool-use and fine-tuning against benchmarks show application progress, not model progress. Tool use is a demo, not a real advancement.
4. Reinforcement learning and small open models** perform better in operations than large models.**
5. An agent is simply a unit of GenAI compute. In today’s GenAI world: LLM + instruction/prompts + memory (short and long-term) + tool use.
6. Scaling reasoning is increasing hallucinations, not decreasing them. This is makes sense since chains of thoughts without a world model would compound hallucinations.
7. Measured AI progress has been in 1) fixed answers (math) and 2) general accuracy (accepting of many equivalent answers- e.g. an essay or blog post on a topic). Unfortunately, real-world AI operations is based on 3) predictive accuracy in qualitative domains is the most important measure for agentic operations (so we can reduce the cost and time for decision making). Whoops. Explains the lack of applications, doesn’t it?
8. As a result, autonomous agent strategies will mostly fail.  Thankfully, the value of building the AI application layer is looking good.
9. Autonomous low-code agents are mostly wishful thinking from vendors that can sell platforms but don’t have services organizations with scaled innovation, experience, and workflow practices.
10. Assuming a 30% inherent Noise factor in human systems we are integrated AI into, and we have a 10-30% error rate inherent in our agentic systems. Put another way, if physics can’t solve the Three-body Problem, how do we solve the organizational n-body problem of siloed workflows and incentives?
11. Agentic operations built around the application layer offer a 2-3x near-term productivity lift for key workflows that companies invest the reengineering and iteration against. 10X with tuning and transformation.
12. The cost and capacity of for deploying AI at scale will drive the ecosystem. The overfunded model startups will be be the mainframes of yesteryear as agile and locally optimized AI innovation is deployed years ahead of speculative data center build outs.

So what is the 2025 plan? Build and deploy the agentic application layer, and scale on that foundation.

Unfortunately, this requires fixing all of your front-end workflow challenges of the last 10 years. Your most important AI vendor? My pick is Vercel and the AI-enabled frontend cloud. 

A digital experience platform (DXP) built on leading edge frontend cloud services and managed with airbender.io is the key rapidly building your AI application layer and achieving AI iteration velocity  in production- the only metric you should be measuring in 2025.