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Listen to the audio, watch the video, or check out the Show Notes for a summary, key takeaways, and links to people, content, and tools we mention.
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Summary
It’s easy to describe AI in human terms. It takes on work we would historically have assigned to humans, and chats with us in a human form factor. But framing AI as an employee shapes the way we think about both the technology and the people we work with.
In this episode, Eric and John outline the three most common types of AI employees that people put to work (personal assistant, researcher/analyst, and advisor/coach), giving real examples of how they have implemented each one.
Then they step back and ask the hard question about the metaphor: why is thinking about AI as an employee dangerous? The answer is that it can foster wrong thinking about both AI and humans, leading you to believe that AI is more than it is, and defining human value through the lens of cost, efficiency, and ease of management.
The answer is using AI with deliberate intention, trying to harness its full power to increase human creativity and productivity, not displace it.
They end with practical advice, including multi-player human/agent workflows and using fun, fictitious names for the agents you build.
Key takeaways
The AI employee metaphor is dangerous: framing AI in human terms can subtly lead you to anthropomorphise machines and devalue humans.
Know what kind of agent you are building: a personal AI assistant for daily tasks is different than an AI advisor, and you need to be aware of the risks.
Think about leverage, not displacement: AI can make us more efficient, but that can lead to a displacement mindset. The better goal is creating leverage by unlocking more human creativity.
Don’t underestimate the power of AI: personal assistants are great, but if that’s your primary use case, you can underestimate how capable AI is beyond menial tasks.
Notable mentions and links
OpenClaw and Hermes Agent are used as examples of the first wave of highly capable AI assistants, primarily used by people with deeper technical knowledge.
Consumer-focused personal assistants were also mentioned, including Grok Bot, Meta Muse, Instinct, and Poke.
ChatGPT’s data analysis capability is mentioned as an example of a productized AI employee.