S11 E21: Raja Tabet & Synopsys: The Future of AI & How We Are Building It

Code Story: Insights from Startup Tech Leaders

Noah Labhart - Startup Founder & CTO

Tech News, News, Business, Technology, Entrepreneurship, Careers

4.6 • 216 Ratings

🗓️ 14 October 2025

⏱️ 40 minutes

🔗️ Recording | iTunes | RSS

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Summary

Raja Tabet lives in Austin, TX, but grew up overseas in Lebanon. When he migrated to the states for his education, he did not speak English, and had to go through the process of learning the language to fully integrate. He studied computer science for undergrad, and computer engineering for graduate school. And eventually, went to work for companies like IBM, Freescale, and others, prior to landing in his current role. Outside of tech, he has been married for 35 years, and has 3 kids. He and his wife are empty nesters, so they love to travel, hike and explore new areas.

In 2019, Raja joined Synopsys, specifically in their custom design and manufacturing group. A few years ago, and alongside the advent of AI, he changed roles and began building an AI powered solution for electronic design automation, or EDA.

This is Raja's creation story at Synopsys.

Transcript

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0:00.0	This episode is sponsored by CodeCrafters. As an engineer myself, I know that the best way to learn how to code is to build something new. But sometimes it's hard to come up with project ideas as well as find a community of folks on the same learning path. That's where CodeCrafters comes in. With CodeCrafters, you can build something that actually stimulates your mind while strengthening your engineering fundamentals.
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0:57.0	Okay guys, I got a serious update for you.
1:01.0	You heard me talk about C-M-A and the Product Roadmap Radar.
1:04.0	I just caught up with Matt, who's the founder there, and he gave me the latest on what they are building.
1:09.0	And my mind is blown. The team at SEMA are taking
1:13.1	things to the next level. Built around the core of the product roadmap radar, i.e. code
1:18.1	and Jira analysis, they are building agent orchestration across the product lifecycle.
1:23.5	Picture this. You hook up this system same as before, i.e. integrate your repository and your
1:28.3	backlog, Jira today. From that point, CMA's agent analyzes the code base and makes suggestions
1:33.7	as to what needs to be done next and what could improve, optimize the product. Then, if those tasks are
1:40.2	routine, the agent assigns the task to a coding agent, think something like Devin, to
1:45.4	complete the minutia while assigning the human-in-the-loop tasks to engineers themselves.
1:51.0	What you end up with is engineers focusing on building core competency features while AI handles
1:56.4	all the rest. If you're not salivating right now, I don't think you fully understand what this
2:00.8	means. But here's what I would suggest. right now, I don't think you fully understand what this means.
2:01.7	But here's what I would suggest. Right now, their product is in a closed beta, and SEMA is looking
	...

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