Getting Productivity Wrong in the Age of AI [Newsletter #52]

Hello, AI enthusiasts from all around the world! Welcome to this week’s newsletter for the AI and the Future of Work podcast.

If you hear someone say they’ve been the top performer at their company, you’d probably think that’s a good thing.

After all, they’re hitting every KPI. They’re number one.

But what if that kind of performance isn’t always positive?

As strange as it sounds, this week’s guest encourages us to shift the conversation around efficiency and return to a more essential question: how do I solve the problem in front of me?

Let’s dive into this week’s highlights! 🚀

🎙️ New Podcast Episode With Brian Elliott

You don't have to be faster than a bear. You just have to be faster than the next slowest person.

It's still a competition, but you don’t need to be first.

You might read that and wonder, what are they talking about?

“If I'm not first, I'm not outperforming,” you might say to yourself. And that's exactly the issue.

Brian Elliott has seen this mindset repeatedly. After years working with leaders and teams, his take on our obsession with outperforming is simple: it’s not good.

And he believes the responsibility starts at the top.

When companies set KPIs and track every metric obsessively, people will perform to beat the numbers. That might sound reasonable, but it comes with a cost.

It fuels internal competition. People start showing up early, staying late, and doing whatever it takes to stay ahead.

Teams become obsessed with efficiency, which leaves out a critical aspect: discretionary effort.

This narrative is more dangerous than ever, especially with AI, which promises to make work many times more productive.

But using AI just to stay busy and do more doesn’t lead to a net gain.

Instead, it creates a culture of fear, where the constant call for "efficiency, efficiency, efficiency" turns into anxiety about losing your job to AI.

Brian Elliott believes there’s a better way.

Leaders and companies need to know when to press forward and when to ease off. And it’s up to them to shift the narrative with transparency.

It starts with asking the right questions:
- How do we grow our business sustainably?
- What are we going to do for our customers?

Brian is the CEO of Work Forward and one of the most recognized voices on the future of work.

He advises senior leaders on how to build better organizations. He’s also the bestselling author of How The Future Works: Leading Flexible Teams to Do the Best Work of Their Lives.

His work has appeared in publications like Fortune and Harvard Business Review.

This week, PeopleReign CEO Dan Turchin sat down with Brian to talk about why changing the narrative around performance, trust, and AI is more urgent than ever. They also explored these topics:

• AI has exerted so much pressure on employees that they use it secretly, out of fear of repercussions.

• Trust plays a critical role in AI adoption and in how employees can use it meaningfully for innovation.

• Leaders must recognize they don’t have all the answers, and that those answers don’t hide behind metrics.

• If organizations require employees to "learn AI," they must provide the space to do so. Otherwise, it becomes a burden.

• How team-led experiments can normalize AI and encourage open dialogue around the new way we work.

🎧 Our latest conversation with Brian Elliott inspired this issue. 

🎧 Listen to the full episode to hear more about Brian Elliott's perspective on a healthy future that integrates AI and employee growth.

📖 AI Fun Fact Article

It was the “Super Bowl of AI.” More than 25,000 attendees packed the SAP Center in San Jose, California for the annual NVIDIA GTC developer conference.

There, NVIDIA CEO Jensen Huang spoke about computing infrastructure as something that now shifts between critical functions.

Ashu Garg, from Foundation Capital (and a former podcast guest of ours), breaks down the event in his blog B2BaCEO. You can read it here.

The conference covered many of the most relevant topics, from new chips to robotics and performance gains. But it was one topic that caught Ashu’s attention.

Jensen Huang explained that the role of computing infrastructure has changed. What was once a cost center is now a production system. He calls it the new “AI factory.”

In these modern factories, the inputs are electricity and data. The outputs are tokens.

As Ashu puts it, tokens are the atomic units of prediction, reasoning, and generation that power AI systems.

The metric is tokens per second per watt. It tells us how fast a chip can generate intelligence, and how efficiently it can do so at scale.

This shift enables software to be created on the fly.

Dan Turchin, PeopleReign CEO, believes Ashu once again delivers sharp analysis that explains why AI infrastructure vendors are racing to own the entire stack: chips, systems, and software.

GTC announcements were NVIDIA's way of claiming ownership. In the coming months, expect similar announcements from OpenAI, Microsoft, Amazon, Meta, and Apple.

The race is on. 

🎥 Worth a Watch!

📣 Share your Thoughts and Leave a Review!

👋 Until Next Time: Stay Curious

That's a Wrap for This Week!

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