A (very) brief history of generative frontier models
In just five days, ChatGPT went from 0 to 1 million users. In sixty days, it jumped from 1 million to 100 million. Today, it's used by more than 800 million people. No consumer technology in history has been adopted faster than Generative AI.
There are two reasons for this.
First, access to intelligence is a fundamental human need. Without it, our capabilities are limited; with it, we are constrained only by capital and creativity.
Second, getting the answer is better than searching for the answer. Search — the most profitable internet business of all time — suddenly found itself disrupted.
But ChatGPT is just the interface. The real shift is powered by generative frontier models — GPT, Claude, Gemini, Llama, and others. These models are incredibly good at taking a prompt and generating high-quality output, which is the foundation of modern AI agents and agentic applications.
The first breakout use case was access to basic intelligence — math, history, science.
The second was software engineering — coding assistance that compresses years of development progress into minutes.
As these models advance, the universe of possible use cases for consumers, businesses, governments, and academia grows exponentially. And every layer of the technology stack is being rewritten around them:
- CPU infrastructure is giving way to GPU-accelerated compute
- Classic SaaS apps are shifting into agentic applications and custom AI agents
- Mobile operating systems are being redesigned for on-device AI
In three years, more than $3 trillion has been deployed by NVIDIA, Google, OpenAI, Anthropic, Meta, and Amazon — the largest infrastructure buildout in human history.
And over the next decade, organizations of every kind will build and refine agentic systems to expand human capability.
The case for AgentGraph — a marketplace for elite AI engineers building custom AI agents
Building agents is hard. Deploying production-grade agents is even harder. Users have rapidly updated their expectations because of ChatGPT, but the businesses serving them are still trying to catch up.
Why did it take your company a decade to ship consumer-grade mobile apps?
Because mobile developers were scarce and expensive.
We are now entering a similar moment with AI agent engineers — specialists who understand how to design, build, and evaluate production-ready AI agents.
Agentic coding models like Anthropic's Sonnet 4.5 have mastered traditional web and mobile development, making deterministic software easier to build. But custom AI agents are not deterministic. Both the user and the agent generate inputs and outputs across a statistical distribution. This makes agents fundamentally different from apps — and much harder to get right.
Put simply:
Code generation has commoditized software — but not agents.
Today, the only reliable way to build high-quality, production-ready AI agents is to work with elite AI engineers — people who blend product intuition, full-stack engineering, and data science.
Just as the 10x full-stack developer powered the rise of internet companies from Google onward, the full-stack AI engineer will be essential in the multi-trillion-dollar agentic internet and the broader AI agent development ecosystem.
AgentGraph exists to bridge this gap.
We are building a curated network of elite AI engineers — freelancers and small teams — who want to work on the most exciting agentic application and custom AI agent use cases across enterprises, labs, startups, blockchains, and research institutions.
AgentGraph focuses on one thing:
Custom AI agents that you design, deploy, and fully own.
We connect buyers with the engineers who can take an agent from 0 → 1 and integrate it into their product, business, or workflow.
If you'd like to join AgentGraph — as an agent buyer or a builder — you can register at:
👉 https://app.hireagentgraph.ai/register