Episode Details

BONUS: The Hidden Dangers of AI at Work With Ari-Pekka Skarp

AI is usually sold as a productivity tool, but Ari-Pekka Skarp argues that the real story is what it does to the conversations, skills, and purpose that hold teams together. In this BONUS episode, Ari-Pekka explores why organizations are rushing to use AI "as efficiently as possible" without defining what efficiency means, and what that rush is quietly costing us.

Organizations Are Conversations

"The organizations are actually conversations, conversational patterns between people."

Ari-Pekka's path from software engineering in 1999 to psychology, psychotherapy, and change leadership was driven by one thread: how the mind works, both individually and socially. Meeting Ralph Stacey, Esko Kilpi, and Douglas Griffin at Nokia changed how he saw organizations. Instead of a machine made of parts, an organization is a living pattern of conversations — people responding to each other's gestures, again and again. George Mead added the idea that the human mind itself is not individual but relational. This matters for AI because a large language model is a new kind of player in those conversations, not just a tool that moves data between them.

The Efficiency Fetish

"It's like how much people are pressing the acceleration pedal in the car. It doesn't tell anything where the car is going."

Many organizations are trying to use AI "as efficiently as possible," but Ari-Pekka points out that few have defined what efficiency means. What he sees instead is measurement of AI usage itself — how many people are prompting, how many tokens are flowing. He calls this tokenmaxxing. The car metaphor is the key: pressing the accelerator harder says nothing about direction, and going fast in the wrong direction is more costly than going slow. Efficiency only has meaning against a purpose, and purpose is itself a conversational achievement — something a team has to talk its way into.

De-Skilling Is the Hidden Cost

"If there's nobody in the room who could review what AI has produced and say whether it's correct or not, it's not an AI strategy. It's a liability."

The risk Ari-Pekka worries about most is de-skilling. When we offload cognitive work to AI, we lose the friction that builds learning. There is neurological evidence that people who rely heavily on AI do not develop the same brain structures as those who work through challenges manually. Some skills are fine to lose — nobody needs machine code anymore — but the ability to review and judge AI output is critical, and it is exactly what erodes when we skip the slow work. The result is a double bind: senior experts burn out under the review burden of fast-produced AI output, while juniors never get the time to build the expertise they would need to review it.

We Need Speed Limits for AI

"We can't optimize individual going as fast as possible... we need a collective... boundaries for individuals."

Ari-Pekka reaches for a historical analogy. Our biological rate of processing information is roughly ten bits per second, and it is not going to change. When we only