Hello, AI enthusiasts from around the world.

Welcome to this week's newsletter for the AI and the Future of Work podcast.

Is too much data a bad thing? It can be, especially when companies focus on the wrong things. 

If we go back to 2014, there was a data fever. Everyone was collecting data at incredible speed, hoping to use it someday.  But there was no clear answer to one essential question: what were we going to do with all of it? 

Fast forward to today, and things are becoming much clearer. As AI processes data faster than any human team could, its true value starts to emerge. But so does another challenge. 

Teams and individuals need the right mindset to face this new era of data, one that can truly elevate our potential. 

Let’s dive into this week’s highlights 🚀

🎙️New Podcast Episode With Ariel Assaraf, CEO and co-founder of Coralogix

The value of data has evolved before our very eyes. 

But now, we might have a problem with too much data. 

That’s because we haven’t fully entered a new phase yet. 

If we go back to 2014 or 2015, the entire world was talking about one thing: big data. Entrepreneurs, startups, and large companies were collecting and storing data at an ever-increasing pace. 

It’s what Ariel Assaraf called the Oil Phase: keeping data for future use. 

The years passed, and by 2020, these enterprises had matured their collection and entered the Landfill Phase. It was a critical stage because, as Ariel explains, many companies now faced a terrible dilemma.

Ariel Assaraf is the CEO and co-founder of Coralogix, a leading observability platform founded in 2014 that serves more than 4,000 customers, monitors more than 500,000 applications, and processes over 3 million events per second. The company most recently raised $115 million at a unicorn valuation. 

Ariel sat down with PeopleReign CEO Dan Turchin to explain that, after collecting and maturing data, most, if not all, companies did not know where to go. In fact, many of them had one question: what were they going to do with so much data?

That’s where AI entered the picture. By accelerating data processing capabilities, enterprises are able to extract more value from data and see the true value of years of relentless data collection. 

So, what happens next? As Ariel calls it, we’re now entering the Brain Phase. 

In this conversation, we discuss:

• How the AI-driven Brain Phase has made telemetry the most valuable raw material for business decision-making. 

• How Coralogix separates the data plane from the control plane, storing data in an open format on the customer’s own infrastructure to enable data ownership, infinite retention, and freedom from vendor lock-in. 

• Why Ariel invested over $100 million in R&D to build query engines that return answers every time. 

• How reactive incident responders evolve into autonomous operators as AI agents take over incident triage, root cause analysis, and narrative generation. 

• The emerging role of the AI-forward product manager, who sits between customer needs and autonomous agents, reshaping how software gets built, priced, and sold in real time. 

• How Ariel thinks about linear versus exponential impact as a leadership principle, and why the intuition to prioritize exponential value is something agents will never replicate. 

Listen to the full episode to hear Ariel explain how AI helps with data processing, and why companies will only succeed when employees become more autonomous and begin exploring how to exploit AI. 

📖 AI Fun Fact Article

Solow’s productivity paradox was born nearly 40 years ago, and it’s as relevant now as it was then. In 1987, economist and Nobel laureate Robert Solow observed that computers were producing too much information. 

The result was a heap of agonizingly detailed reports that someone, somewhere, had to print for someone else to quickly read and then discard. 

Technology was not improving productivity as much as people had predicted. Against all odds, productivity growth slowed, from 2.9% in 1948 to 1.1% after 1973. 

If you want to learn more about Solow’s paradox, you can read the paper here. 

But why are we talking about this paradox now? 

As Sasha Rogelberg writes in Fortune online, thousands of CEOs have admitted that AI has had no impact on employment or productivity. 

History repeats itself. Or does it? 

The author explains that data show that, despite 374 companies in the S&P 500 recently mentioning AI in earnings calls, often in positive terms, those references have not translated into broader productivity gains. 

C-suite executives across the board openly recognize that AI has not helped much, as one study published by the National Bureau of Economic Research found. 

Among 6,000 CEOs, chief financial officers, and other executives, two-thirds reported using AI, but nearly 90% said AI has had no impact on employment or productivity. You can read the study here. 

To make matters even more interesting, and challenging, trust is also playing a key role, though not quite in the way you might expect. This 2026 Global Talent Barometer by ManpowerGroup surveyed nearly 14,000 workers in 19 different countries. Their regular use of AI increased by 13% in 2025, but their confidence in AI’s utility plummeted 18%. 

Persistent distrust prevails, but the paradox can reverse. After all, the IT boom of the 1970s and ’80s eventually gave way to a surge in productivity in the 1990s and early 2000s

PeopleReign CEO Dan Turchin reminds us that we shouldn’t measure the impact of AI on society one quarter at a time. Previous technology shifts, such as spreadsheets, personal computers, and the internet, enabled the automation of transactional tasks.

AI enables the automation of life. Expect the real impact of AI to be felt when it doesn't just make it easier to do what we already do, but also enables us to do things we never dreamed of doing before. 

Expect real societal impact when, for example, the hospital comes to your aging parents, where they are, so they can age in place for, say, an extra decade. Or when AI tutors bring world-class education to every child in, say, sub-Saharan Africa. Or when international diplomacy is conducted in ways that prevent loss of human life because AI eliminates language, geography, and cultural barriers. 

That’s a future where we’ll grow beyond judging AI based on antiquated measures of employee output per hour. Let’s each take responsibility for accelerating what humans plus AI can become.

Worth A Read 📚

Until next time, stay curious! 🤔

We want to keep you informed about the latest happenings in AI.

Here are a few stories from around the world worth reading:

• A Nobel laureate admitted to using AI in her latest novel, but the way she used it sparked debate. Read more here. 

• The Musk-Altman trial revealed the egos driving the business side of AI. Here’s more. 

• The Trump administration is changing its views on AI. Here’s why. 

That's a Wrap for This Week!

This week’s conversation explored a once-immovable perspective: we used to see data as critical, as valuable as oil. 

The reality is that data itself does nothing. It’s what we extract from it that becomes helpful. 

Monitoring, understanding, and experimenting with data is the only way we truly grow as professionals and as enterprises. 

We hope today’s conversation inspires you to move beyond merely collecting data, open your mind, and find new ways to use it with the help of AI. 

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