Building software factories and making the transition from a pre-AI to post-AI world as a large company w/ Zohar Einy #271

Jerry Li (0:02)

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Zohar Einy (0:51)

The best way to build a company that can go, go, go and run fast is to believe in people with very high potential. So this is an element of our hiring process. And it's not just about age. It's also people that had a specific career transition or career process. And now they want to take another step and they want to pivot their career.

Jerry Li (1:10)

And you give them a chance. You recognize their potential.

Zohar Einy (1:13)

I love to give them the chance because it's a win-win. They are excited about it and it's a win-win for us.

Patrick Gallagher (1:19)

Hello and welcome to The Engineering Leadership Podcast, brought to you by ELC, The Engineering Leadership Community.

Jerry Li (1:25)

I'm Jerry Li, founder of ELC.

Patrick Gallagher (1:28)

And I'm Patrick Gallagher, and we're your hosts. Our show shares the most critical perspectives, habits, and examples of great software engineering leaders to help evolve leadership in the tech industry.

Jerry Li (1:40)

Zohar, welcome to our show. I was really looking forward this day to hear from you face to face about your perspectives and your experience in the world of software building. Can you tell us more about who you are and your past experience?

Zohar Einy (1:57)

Yeah.

First of all, thank you for having me. I'm excited to be here in San Francisco in this beautiful office. So I started my journey as a software developer and a researcher. Before AI was a thing, I spent most of my time mainly in the cybersecurity area. Thereafter, I spent time in the venture capital as an investor. I joined one of the portfolio companies as one of the first employees. I wanted to get the look and feel of a startup, which was really exciting. The company got acquired to Dynatrace. Port is my second company. The first company I started called Aporia, which got acquired to Corelogics. And we started Port around five years ago. And yeah, super exciting. And I love everything about developers and their experience.

Jerry Li (2:35)

Five years can mean a long time, especially in the past two or three years, when everything is so accelerated. What is your biggest focus right now? You're building a company at five years old, and I'm sure you have customers to support. Finding a way to get adapted to this world of AI, especially how REPLH changed the way of software engineering. So maybe you can tell us more about Port, what it's do and what's your current focus right now.

Zohar Einy (3:00)

So my biggest focus right now is actually right before I came to meet you today, there was like a big, big noise about the software factories on X and on LinkedIn, and now Gartner also published a new category.

So right now, my biggest focus is to build this category and to work with the industry towards software factories. So this is my primary focus right now as this category is starting. There is a lot of conversation about software factories, but there is still a lot of open questions about what it is. Software factory, in simple words, means we are moving from an era where we are using AI as a coding assistant and starting to use AI as an engineering-led methodology, where AI is taking all the work across the SDLC and the humans are operating it and working alongside it and not as a coding assistant. So eventually, I think it's the moment where all the industry waited for, right? Where AI is taking the lead and we are managing it.

Jerry Li (3:54)

How does that mean differently day to day for people building software, for engineers, for product managers?

Zohar Einy (4:00)

I think that for a couple of months or maybe more than one year, the industry was all about, okay, developers are now going to be the operators, approving, declining, be a human in the loop. But we believe that the new role of engineering will be maybe 10 percent of that, 90 percent of their time will be building the factory. It means building the system that writes the features and not writing the features on their own. What does it mean? It means that they need to be very good data engineers. They need to understand how to architect context. They need to measure the quality of the factory. They need to be able to set the right guardrails. They need to be really software factory engineers rather than just writing the features themselves.