Jyunmi

All right.

Right, right.

I think that's an interesting framework or facet or perspective to look at it is because it kind of encapsulates the thinking behind the workforce and job market and things like that is if they don't have to, then they won't.

Right.

So, yeah.

And yeah, it's that's I'm my gears are turning on that right now.

That's that's an interesting perspective to look at it because it.

OK, let's see here.

I do have a couple more stories.

And I think I think the last one I'll leave it on is the last one I've got is a quick story from IBM.

So we haven't or I haven't heard too much of IBM in the AI space other than their Watson and in the deep hardware and software solutions that they offer.

But IBM has agreed to acquire Confluent.

It's a data streaming company built around Apache Kafka and an all cash deal that values Confluent at about $11 billion.

Data streaming is the practice of moving and processing data as a continuous flow rather than in slow batches.

IBM says the goal is to create a smart data platform that connects, governs, and delivers real-time data to applications and AI agents running across hybrid cloud environments.

The deal is IBM's largest since Red Hat in 2019 and continues its pivot towards software automation and generative AI services under CEO Arvind Krishna.

Confluent has been growing revenue but remains unprofitable.

IBM is betting that integrating Confluent with its data fabric, WatsonX.AI stack, and consulting arm would both improve margins and make it easier for enterprises to wire up operational data to GenAI co-pilots and agents.

Analysts see the move as a strong strategic fit, but note execution risk and the challenge of retaining Confluence developer community inside a much larger company.

So why does this matter?