Following the breakout success of OpenAI’s ChatGPT, large language models (LLMs) and the AI applications built on them have taken the world by storm. ChatGPT, made available for public use in November 2022, was built on the powerful LLM GPT 3.5, which features over 175 Billion parameters. Within a matter of months, in February 2023, Meta released the model technology (software code and weights) behind Llama, an impressively performant LLM given more modest parameter counts. The question of whether powerful AI models should be openly released has been presented ever since, even as several other LLMs meeting or exceeding the capabilities of GPT 3.5 have followed.

One key goal of the AI industry is to build AI agents that can execute complex tasks in both the digital and physical worlds. The digital and physical worlds each present different product design and technical execution challenges. Each also presents overlapping but distinct legal challenges relating to risk and potential liability. The prospect of building effective AI agents to operate at scale for thousands if not millions of users will require a myriad of problem solving. The goal of this article is to frame the goals of agentic systems, flush out the various ways that AI agents can be implemented to have far reaching impact in the world, and outline some of the current challenges that must be addressed to make such prospects a reality.

In this piece I examine both the flawed corporate structure of OpenAI, and the neglect of governance evolution during a year of immense change. Those things led to the chaos that is still unfolding around the firing of Sam Altman. I conclude that alternative governance structures aren’t the issue, as some may readily surmise. Nonetheless, how OpenAI’s particular structure was set up and managed left it vulnerable to calamity. Other alternative structures, such as those instituted by Anthropic, were more thoughtfully put together, better harmonize the interests of multiple stakeholders, and should be more resilient to slow divergences of goals or sudden organizational shocks.