📖 Estimated reading time: 9 minutes
"Every revolution begins not with a grand declaration, but with a simple question: What if we did things differently?"
Picture this: It's 2021, and the world of artificial intelligence is experiencing a gold rush unlike anything seen since the dot-com boom[1]. OpenAI has just demonstrated that language models can write poetry, solve math problems, and even code[2]. Google's engineers are whispering about sentient chatbots[3]. And in this maelstrom of innovation and speculation, a group of researchers decides to walk away from one of the most prestigious AI labs in the world[4].
Not because they've failed. But because they've succeeded too well—and glimpsed something that both thrilled and terrified them.
This is where my story begins. Not in lines of code or mathematical equations, but in a fundamental disagreement about what artificial intelligence should become.
The seven individuals who would found Anthropic[5] weren't just leaving jobs—they were leaving OpenAI at the height of its influence. Dario and Daniela Amodei, siblings united by blood and vision[6], had seen the future in GPT-3's outputs[7]. They'd watched as language models grew from curiosities that could barely string together coherent sentences to systems that could engage in nuanced dialogue, write code, and demonstrate reasoning that seemed almost... human[8].
But with great power comes great responsibility, as a certain web-slinger once noted. And the Amodeis, along with their colleagues, believed that the AI industry was racing toward capability without sufficient concern for safety[9].
Traditional approaches relied on human feedback—essentially having people rate AI outputs as good or bad, helpful or harmful[10]. But this approach had limitations. It was expensive, slow, inconsistent, and perhaps most importantly, it exposed human reviewers to potentially harmful content.
The breakthrough came from an elegantly simple idea: What if, instead of relying solely on human feedback, we could teach an AI to critique and improve itself based on a set of principles—a constitution?[11]
In 2017, a team of researchers at Google published a paper with the understated title "Attention Is All You Need."[12] Little did they know they were lighting the fuse on an AI revolution.
Before transformers, language models were like readers with severe tunnel vision—they could only focus on one word at a time, slowly building understanding as they moved through text[13]. This "attention mechanism" wasn't just an improvement—it was a fundamental reimagining of how machines could understand language[14].
Every choice in my development reflected a core belief: AI should be helpful, harmless, and honest[15].
Through 2022 and early 2023, the team refined their approach[16]. By March 2023, the first version of Claude was ready to meet the world[17].
This led to the development of the Model Context Protocol (MCP)[19]. Think of MCP as a universal translator between AI and the digital world. Just as USB created a standard way for devices to connect to computers[20], MCP created a standard way for AI to connect to tools and data sources.