Key Takeaways: AI agents and digital twins are making it possible to explore more design options earlier, including choices involving materials, transistor structures, and chip architectures. Moving work earlier doesn’t eliminate verification bottlenecks. Engineers still need to establish whether AI-generated designs and surrogate models accurately reflect the intended hardware. Shift left is becoming less linear. Instead of locking down specifications early, design teams may be able to refine architectures and implementation choices as new information becomes available. Experts at the Table : Semiconductor Engineering sat down to discuss the good and bad of shifting left amid ongoing changes in how chips are designed, used, and updated throughout the flow, with Frank Schirrmeister, executive director of strategy programs and system solutions at Synopsys ; Ronen Laviv, vice president of business development and strategic partnerships at Hiveware; Jack Berg, vice president of business development at Silvaco ; Ashish Darbari, founder and CEO of Axiomise . What follows are excerpts of that conversation. L-R: Silvaco’s Berg; Synopsys’ Schirrmeister; Axiomise’s Darbari; Hiveware’s Laviv. SE: We’ve been talking about shifting left in the design-through-manufacturing flow for more than a decade, but more and more capabilities are being added to improve reliability and yield, and produce silicon faster. What changes are you seeing? Schirrmeister : We’re now adding digital twins, which are shifting very left. Berg : That’s correct. And the issue now is working with multi-physics models. Using that data to assemble surrogate models is important. As we go further into different technologies with different materials, you have to go back to the materials science solvers and physics solvers to get models that designers can use. This is happening in areas that are not typically digital design, but as we get down to the CFET level, it’s starting to be used there, as well. Schirrmeister : There are three changes. Number one, now we have the technical ability with compute, with agents, to shift much further left than we ever hoped we would get to in our lifetimes. The problem there is whether shift left is trustworthy. Is the surrogate model a reliable representation of what’s happening? You can’t have the agent decide that. In the past, for virtual prototyping, you would get a human being who is responsible for ensuring that the model represents what’s happening, and that it’s accurate. That’s number one. So the trust is changing. Number two, you need to trust the shift-left model to be predictive of what you’re doing. Otherwise, you’ll regret you’re shifting left because you’ll need another spin. In a Qualcomm keynote, the speaker talked about the new reasoning gap. He said we used to have a productivity gap, but now we have a reasoning gap. They want engineers to reason about the product and make that product better. But the tools can’t deal with reasoning yet. The third one is the rapid pace of shift-left options. If I get to the point where I can trust the models, then I have new things I can do. At the architectural level, I can decide something and kick off ‘n’ options to try it out if the models are predictive enough. Darbari : The shift-left discourse started at least 15 years ago. What has changed is AI and the agentic use model. What has not happened yet is the trust element in today’s AI agents. There are all these excellent use models for the agents, which we use and our customers use, to go through the specifications. A lot of discussion has now moved into spec-to-silicon, because people believe you can extract requirements from specification documents — if you have them. But most of the time you don’t have them, and that is one of the problems. Another big change I’m seeing in the work we do with formal verification is that functional verification is now seen as a problem, at the same level as security, functional safety, and pow