Eric Lealos - Test Bench to Tech Stack: Applying AI in Manufacturing

You know, it's interesting.

You talked about the internet was developing really fast, and I was around during those times too, man.

Like the mid to late 90s, everyone was trying to build websites as fast as they could, but then we were doing a lot of... programming as well.

I mean, back in the early days of Pearl and I was writing Java, applets and all sorts of stuff.

And then I, my career kind of then moved into, first it was like a lot of full stack development and then moved into mobile.

So I feel like there's been these, at least multiple waves, whether it's been the internet, mobile development.

And now my question to you is, I mean, do you feel like we're at this cusp of another big expansion here with AI? Like, is it exciting right now in your mind as it was back then?

There are a lot of things that I don't really understand about what's going on right now, but I think ultimately it's unbelievably exciting.

I don't know if things just haven't quite caught on, but I see the development of AI approaches and technology and solutions currently in the phase that really reminds me of the kinds of changes that the iPad and iOS brought to the world.

As an example, Once iOS came out and the iPad came out, suddenly there was this platform that people could make all kinds of applications.

And we saw lots of business applications built on top of it.

Things that could be built for tens of thousands of dollars that would have required... tens of millions of dollars earlier.

And so suddenly like you see people in retail operations using an iPad and we don't need fancy cash registers and point of sale systems from really proprietary application or hardware manufacturers that were super expensive and taking tons of money out of those operations.

I think AI is in the same boat.

So like for all of my career, When we were doing machine learning types of projects, there was just

a

huge overhead in terms of data engineering, feature engineering.

And there was like these monstrous disconnects between the data team, the data science team, the people on the business side.

And as a result, there never was like a big payback for a lot of stuff, except for a few cases.

And then they also just drifted right away.

And all of a sudden we have to keep doing this.

I'm going to spend $300,000 a year to save $310,000 a year on some sort of operation.

It was worth doing.

It just was never like the big smash hit that everyone expected.

Whereas now it feels between all of the things we've learned through data engineering and all the other technology that surrounds it, things like Snowflake, Databricks, dbt, ingestion pipelines, our ability to just write code easily.

You know, it's interesting.

You talked about the internet was developing really fast, and I was around during those times too, man.

Like the mid to late 90s, everyone was trying to build websites as fast as they could, but then we were doing a lot of... programming as well.

I mean, back in the early days of Pearl and I was writing Java, applets and all sorts of stuff.

And then I, my career kind of then moved into, first it was like a lot of full stack development and then moved into mobile.

So I feel like there's been these, at least multiple waves, whether it's been the internet, mobile development.

And now my question to you is, I mean, do you feel like we're at this cusp of another big expansion here with AI? Like, is it exciting right now in your mind as it was back then?

There are a lot of things that I don't really understand about what's going on right now, but I think ultimately it's unbelievably exciting.

I don't know if things just haven't quite caught on, but I see the development of AI approaches and technology and solutions currently in the phase that really reminds me of the kinds of changes that the iPad and iOS brought to the world.

As an example, Once iOS came out and the iPad came out, suddenly there was this platform that people could make all kinds of applications.

And we saw lots of business applications built on top of it.

Things that could be built for tens of thousands of dollars that would have required... tens of millions of dollars earlier.

And so suddenly like you see people in retail operations using an iPad and we don't need fancy cash registers and point of sale systems from really proprietary application or hardware manufacturers that were super expensive and taking tons of money out of those operations.

I think AI is in the same boat.

So like for all of my career, When we were doing machine learning types of projects, there was just

a

huge overhead in terms of data engineering, feature engineering.

And there was like these monstrous disconnects between the data team, the data science team, the people on the business side.

And as a result, there never was like a big payback for a lot of stuff, except for a few cases.

And then they also just drifted right away.

And all of a sudden we have to keep doing this.

I'm going to spend $300,000 a year to save $310,000 a year on some sort of operation.

It was worth doing.

It just was never like the big smash hit that everyone expected.

Whereas now it feels between all of the things we've learned through data engineering and all the other technology that surrounds it, things like Snowflake, Databricks, dbt, ingestion pipelines, our ability to just write code easily.