Put to work on different tasks, AI can do a good many specialist skills that even just a few months ago wouldn’t 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 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.

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.

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 Jalapeño, its first custom inference chip, from initial design to tape-out in just nine months.

It all poses difficult questions for the chip design industry – though not just about whether the sector will remain strong. It’s also about whether AI is a suitable replacement for human ingenuity. The industry relies heavily on intellectual property, and it’s 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.

The AI copied my homework

But by putting AI to the task, some of those blocks could become far easier to create – 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’s Hardware Premium. "AI can spread a copied design or infringed patent across thousands of chips before anyone notices and without anyone even intending it,” he said.

Copying wouldn’t be as blatant as feeding an Arm core or another piece of proprietary RTL directly into a model. Instead, AI’s 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 – inadvertently copying others’ 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."

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.

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’s 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.

An abundance of caution

That all might suggest there’s a free-for-all when it comes to AI designing new chips, and potentially ripping off – advertently or not – other designs. But that’s 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’t. “Once you ship the chip, you ship the chip, and you can't change the transistors,” said Simon Moore, professor of computer engineering at the University of Cambridge, said in an interview with Tom’s Hardware Premium.

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 “a 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.

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’s gone through the relevant verification and standards compliance checks.

So could AI reduce reliance on licensed IP? “For routine building blocks, probably yes,” said Forte. “But a licensed IP block is much more than its design files.”

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. “I'd treat an AI designer like a brilliant new hire,” said Forte. “Fast and talented, but everything it produces gets checked.”

The memory chip that makes a router remember how to be a router is a world away from the sleek GPUs that are attracting eye-popping investments and alarming valuations, as well as sending stock markets shooting upwards. They’re small, historically have been cheap, and are based on technology that has been around for years.

But despite being a world away from GPUs, the price of these often overlooked chips is skyrocketing, thanks to the all-encompassing memory price crisis caused by the AI boom.

While public and press attention has focused on the expensive chips, there’s an equally large impact beginning to be felt on older, less attractive memory chips. HBM is vital for AI accelerators, while DRAM and high-capacity NAND are being swallowed up by rapidly expanding data centres. A June report from Morgan Stanley reckons memory prices have risen more than sixfold over the last year, breaking with decades in which memory became steadily cheaper as production increased.

It’s not just HBM and DRAM that’s being affected. The crunch is also spreading down into much older forms of memory, including NOR flash and single-level cell, or SLC, NAND. Morgan Stanley expects NOR flash to remain undersupplied through 2026, while JPMorgan has warned its forecasts don’t fully capture a potential supply crunch in SLC NAND. The effects are already showing up in prices. TrendForce says contract prices for both NOR flash and SLC NAND rose by more than 100% during the first half of 2026, while it expects SLC NAND prices to rise another 120% to 170% in the second half of the year compared with the first half.

Picking winners

An increase in prices will have an impact on the tech we use day in, day out. NOR flash is commonly used to store boot and program code, and is a core part of automotive, industrial, and networking equipment. SLC NAND is deployed across a number of uses because of its reliability and endurance when placed in embedded hardware with long lifespans.

Both are vital. And both are being overlooked in favour of higher-margin chips — pushing the supply crunch to tech that previously never faced any issues. “The SLC NAND market is probably under a billion dollars a year,” said Jim Handy, a semiconductor and SSD analyst at Objective Analysis, in an interview with Tom’s Hardware Premium. That tiny scale adds up to a big problem, because it disincentivises any new investment.

“What you've got going on is a purely economic phenomenon,” said Handy. Hyperscalers and cloud providers are “all trying to outspend each other”, pouring unprecedented sums into semiconductors to build AI infrastructure. That willingness to spend big means the most profitable customers naturally move to the front of the queue.

Companies including Nvidia, Broadcom and Marvell need huge amounts of semiconductor manufacturing capacity for chips destined for AI systems. “They’re sucking up all of the wafers,” says Handy. “And then the companies who make NOR flash and SLC are having a hard time getting wafers to build their product, and so they have to raise prices.”

The problem is even starker in the NAND market. Bryan Ao, research manager at TrendForce, told Tom’s Hardware Premium in an interview that major NAND manufacturers, including Micron, Kioxia and SK Hynix, have been cutting the wafer capacity devoted to SLC because they can make considerably more money using it for newer NAND technologies. Ao estimates that a 12-inch wafer devoted to mainstream NAND can ultimately generate close to $20,000 in revenue. Use the same space to produce SLC and the figure is closer to $6,000 to $8,000.

Even if they wanted to, smaller SLC suppliers in China and Taiwan can’t just spin up new production. Lead times for some semiconductor manufacturing equipment have stretched to between 12 and 15 months, said Ao. The result is what he calls “severe undersupply”.

Big prices, big returns

BNP Paribas forecasts the average NAND price will hit $279.50 per terabyte during 2026, up from $73.10 in 2025. JPMorgan expects the memory shortage to persist for at least another two years, with customers getting just 70% to 80% of their orders fulfilled. TrendForce says manufacturers are shifting capacity towards advanced, higher-value memory products, with mature processes increasingly squeezed as a result.

There are some alternatives available. Kioxia says its serial SLC NAND is an alternative to NOR flash. But moving an existing industrial or networking product onto a different chip can itself require engineering work and qualification. Nor is there much incentive for memory manufacturers to fix the problem by building new SLC capacity – which means manufacturers are unlikely to invest billions in capacity whose useful market may disappear. That creates an unusual trap: there may not be enough demand to justify new factories, but there is still more demand than the shrinking supply can satisfy.

Hardware manufacturers can eat the higher component bill and accept lower margins, or pass it on. “We'll just have to either have lower margins, or we'll have to raise the prices to the consumer,” Handy said.

An ongoing issue

The problem is one that seems to have no solution – at least in the short term. Ao expects memory prices to remain high over the next five years and does not expect them to return to 2023 or 2024 levels. That broadly fits with the structural nature of the shortage identified by TrendForce, which says there are no significant capacity expansion plans for NOR flash or SLC NAND.

Handy sees one possible way out, but it is hardly reassuring. “As long as the race between the hyperscalers keeps up to spend, then it will continue to be an issue,” he said.

Handy compares the AI buildout to the internet infrastructure boom of the late 1990s. Rather than enough capacity eventually arriving to restore balance, he thinks spending may simply overshoot what the market can economically support.

“I'm expecting the same kind of a thing to happen here that we've got too many people spending too much money on AI, and not really making any return on it yet,” he said.

Until then, the least exciting memory chips in a computer may become some of the hardest to replace. Or, as Ao put it: “Pretty much we have to get used to this high price, no matter which segment of memory.”