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Put to work on different tasks, AI can do a good many specialist skills that even just a few months ago wouldn\u2019t have been thought possible. AI is beginning to take on some of the work required to design the chips that power AI, way beyond simple optimisation. But that brings a potentially expensive problem: what happens if an AI ends up generating a design that infringes somebody else’s patent, which then ends up in thousands of chips rolling off production lines? Tom’s Hardware Premium<\/em> spoke with experts like Domenec Forte, professor of electrical and computer engineering at the University of Florida, and Simon Moore, professor of computer engineering at the University of Cambridge, to learn more about a looming issue that could broadly impact the AI chip design market.<\/p>\n This week, Synopsys announced a suite of AgentEngineer tools capable of carrying out long-running tasks that could verify and implement chips, as well as planning analog design and manufacturing. The company said it had more than 50 customer engagements already underway, and planned to offer the tech for general availability by the end of the year.<\/p>\n Synopsys isn’t alone in discovering the ability of AI to do such tasks. Competitor Cognichip says its ACI platform can generate specifications and RTL, then automatically produce verification plans and testbenches. And in China, Empyrean Technology recently claimed an AI agent reduced the time taken to complete one circuit layout task from four weeks to one. Even the big AI labs are getting in on the act: OpenAI says its models helped it and Broadcom develop Jalape\u00f1o, its first custom inference chip, from initial design to tape-out in just nine months.<\/p>\n Latest Videos From<\/span>Tom’s Hardware<\/span><\/span><\/p>\n It all poses difficult questions for the chip design industry \u2013 though not just about whether the sector will remain strong. It\u2019s also about whether AI is a suitable replacement for human ingenuity. The industry relies heavily on intellectual property, and it\u2019s not yet clear what happens when an AI designs something that somebody else already owns. Rather than designing every component of a chip from scratch, companies routinely license processor architectures, controllers, and other memory technologies rather than reinvent the wheel. Arm alone generated nearly $5 billion in its 2026 financial year, which came roughly half from licensing and other revenue and half from the royalties on that. Synopsys makes money that way too, selling a substantial portfolio of “silicon-proven” IP alongside its chip design software.<\/p>\n \n (Image credit: Synopsys)<\/span><\/p>\n But by putting AI to the task, some of those blocks could become far easier to create \u2013 and working out where they came from far harder. “It mostly amplifies existing problems,” said Domenec Forte, professor of electrical and computer engineering at the University of Florida, whose research includes AI-enabled chip-design tools and semiconductor IP protection, in written comments to Tom\u2019s Hardware Premium<\/em>. “AI can spread a copied design or infringed patent across thousands of chips before anyone notices and without anyone even intending it,\u201d he said.<\/p>\n Copying wouldn\u2019t be as blatant as feeding an Arm core or another piece of proprietary RTL directly into a model. Instead, AI\u2019s skill is in ingesting huge amounts of literature from academic papers and patent applications, then constructing lookalike designs. And in a space where power constraints, performance challenges, and a limited physical area all combine alongside mandated standards all have to follow, there are only a finite number of feasible designs that AI can reach quicker \u2013 inadvertently copying others\u2019 homework. “Even a circuit that adds two numbers draws from a well-documented catalogue of textbook designs,” said Forte. “Ask for the fastest one, and you’ll likely land on a design someone has already published.”<\/p>\n That creates a provenance problem for chips, because human engineers generally document and can explain where their ideas came from and which IP was licensed. AI might not have that same traceability. Existing tools can identify close copies, Forte said, but designs can be rewritten or run through synthesis tools that transform their implementation while leaving the underlying function intact.<\/p>\n Forte suggests IP owners could eventually deposit encrypted versions of their designs into a shared repository, allowing trusted agents to check whether newly generated hardware overlaps with existing IP without exposing the originals. He\u2019s less convinced by watermarking, which he argues could be removed or forged. International standards could help somewhat: IEEE 1735 defines methods for encrypting electronic-design IP and managing the rights attached to it. But Forte argues standards like it may now need to go further, defining what AI agents can access, retain, and learn from while operating inside electronic design automation tools.<\/p>\n That all might suggest there\u2019s a free-for-all when it comes to AI designing new chips, and potentially ripping off \u2013 advertently or not \u2013 other designs. But that\u2019s not the case, for a simple reason. The semiconductor industry is unusually cautious about new design techniques because software can be patched, whereas fabricated silicon can\u2019t. \u201cOnce you ship the chip, you ship the chip, and you can’t change the transistors,\u201d said Simon Moore, professor of computer engineering at the University of Cambridge, said in an interview with Tom\u2019s Hardware Premium<\/em>.<\/p>\n Moore estimates verification now accounts for more than half of the effort involved in getting many chips out of the door, while established verification tools can tell engineers whether an AI-generated test actually improves coverage. Getting that AI to conduct tests, probe possible failure points, and check results at scale is \u201ca bit of a no-brainer,” said Moore. However, allowing AI to make architectural decisions, which are much harder to undo, is where most manufacturers are drawing the line.<\/p>\n That same caution explains why licensed IP may survive the onslaught of AI-generated content, even if the tech is capable of generating technically similar blocks. Buying a block from Arm, Synopsys, or another established vendor comes with the history of the companies, and the assurance that it\u2019s gone through the relevant verification and standards compliance checks.<\/p>\n So could AI reduce reliance on licensed IP? \u201cFor routine building blocks, probably yes,\u201d said Forte. \u201cBut a licensed IP block is much more than its design files.\u201d<\/p>\n And if engineers increasingly have to ask not only whether a design works, but where it came from and whether somebody else already owns part of it, provenance may become something semiconductor companies are all the more willing to pay for. \u201cI’d treat an AI designer like a brilliant new hire,\u201d said Forte. \u201cFast and talented, but everything it produces gets checked.\u201d<\/p>\nThe AI copied my homework<\/h2>\n
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\n <\/span><\/p>\nAn abundance of caution<\/h2>\n