The CEO Must Be the Chief AI Officer
In a Nutshell
The CEO must personally lead AI adoption as chief AI officer rather than delegating it, because only the CEO has authority to redesign company architecture, processes, and self-identity from first principles instead of layering AI onto existing workflows. The core shift is treating AI as an agentic loop with tools and security boundaries like network-level proxies, then building harnesses that let both engineers and non-technical teams operate virtual employees rather than just chatbots. Companies that pause to ask how they would rebuild every function if starting today—with the same opportunity but different technological possibilities—will capture discontinuous advantages; those that don't will be left optimizing outdated systems.
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The CEO needs to be the chief AI officer rather than delegating this to an engineering or product team. A good proxy for how to spend your time is focusing on things that only you can do that the models cannot do. This includes refounding the very concept of the company's self-identity.
Pedro Franchesci is the co-founder and CEO of Brex, which started in the Winter 17 batch and became one of the most important fintech companies of the last decade. After meeting Pedro at YC for lunch, the team went down a rabbit hole of building on their own AI setups. The lunch generated significant token consumption and was the precursor of G Brain. The speaker was still working on GStack as a 2013 web 2.0 engineer who time-traveled to the AI tools of January 2026.
The craziest realization was that most people in software had been treating the LLM like a very precious and expensive thing. This led to putting the agent inside a Foxcon factory with excessive control and if statements. The alternative approach is giving agents more freedom, described as the Esalen Institute rather than Foxcon's factory. Every single good AI product is fundamentally an agent loop with tools, skills, and a model.
The first encounter with LLMs came during the pandemic when someone gave API access to GPT-3, which felt like a research project. Interest grew with ChatGPT, but things became truly interesting with reasoning models and tools. December marked the point where electricity was invented, referring to OpenAI 4.5 and open models. This was the tip of the spear where coding harnesses actually started to work. Cloud Code had existed for about a year before but wasn't valuable until this point.
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