Why I Think Synopsys and Cadence Look Cheap Today (Relative to Where They Were)

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{"summary": "Author argues Synopsys and Cadence look cheap relative to recent prices because the AI-disruption narrative misunderstands their real moat: silicon-validated design flows, foundry partnerships, and the high cost of chip failure.", "reason": "The author provides a fundamental rationale (moat based on validated flows and foundry partnerships) for their directional judgment that the stocks are undervalued.", "ideas": [{"symbol": "SNPS", "direction": "long", "thesis": "Author argues Synopsys is undervalued relative to recent prices because the AI-replaces-EDA narrative is flawed; the real product is silicon-validated flows and foundry partnerships, not just code, and the high cost of chip failure makes customers unlikely to switch to unproven AI tools. The AI threat mainly hits low-end hobbyist/student use, which contributes little revenue, while China's well-funded efforts have failed to break the duopoly. No specific catalyst or timeframe is given; main risk is that a sufficient AI solution could eventually disrupt the business, though the author disputes this.", "thesis_short": "AI threat mispriced; EDA moat intact", "quote": "The market is pricing Synopsys and Cadence as if AI represents an existential threat. I think that misreads what these companies actually sell.", "confidence": 0.85, "sentiment": 0.8, "timeframe": "unspecified"}, {"symbol": "CDNS", "direction": "long", "thesis": "Author argues Cadence is undervalued relative to recent prices because the market wrongly fears AI will replace EDA; the durable moat is decades of validated flows, foundry certification, and the huge cost of a failed leading-edge chip, which makes switching to unproven AI software irrational. The revenue at risk from AI is only the low-end hobbyist segment, which is negligible for Cadence and Synopsys. No catalyst or timeframe is provided; the main risk is a faster-than-expected AI disruption, which the author considers unlikely.", "thesis_short": "AI fears overblown; EDA moat durable", "quote": "That's why, relative to where these stocks were trading not long ago, I think today's prices look like an opportunity rather than a warning.", "confidence": 0.85, "sentiment": 0.8, "timeframe": "unspecified"}], "model": "gemini-3.1-flash-lite", "failure_count": 0, "verified": true, "extraction_model": "deepseek-flash"}

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Why I Think Synopsys and Cadence Look Cheap Today (Relative to Where They Were)

# 

Let me be clear about what this post is and isn't. This is not a fair value analysis. I'm not going to build a DCF or tell you what Synopsys (SNPS) or Cadence (CDNS) is "worth." This is a post about relative valuation: why I think these two stocks look undervalued today compared to the prices they were trading at not long ago, and why I believe the narrative pushing them down is wrong.

# The Narrative

The story weighing on both stocks is simple: AI is coming, and it will replace them.

The logic goes like this. We now have AI systems powerful enough to attack the hardest problems in mathematics, possibly even the Millennium Prize Problems. If AI can do that, surely it can handle electronic design automation (EDA), which is not nearly as hard. Once AI can write EDA software, the Synopsys and Cadence duopoly collapses.

Here's the thing: the first part of that argument is true. A sufficiently powerful AI almost certainly can solve EDA problems. I'm not disputing that.

What I'm disputing is the conclusion.

# First, What Do These Companies Actually Do?

Before we can judge whether AI threatens these businesses, we need to understand how they make money.

Synopsys and Cadence make the software required to design and manufacture semiconductor chips. Almost anything you own that contains a chip was touched, somewhere along the way, by their tools or their IP.

They sell to two broad groups of customers. The first is chip designers such as Nvidia, Qualcomm, AMD, and Apple, who use the tools to design, simulate, verify, and lay out their chips. The second is chip manufacturers and memory makers such as TSMC, Samsung, Micron, and Intel, who work with these tools to develop process technologies and to make sure designs can actually be built on their fabs.

That's the business. Now let's return to the narrative.

# The China Question

Ask yourself this: why is there a duopoly at all?

EDA is a lucrative business. It is also, increasingly, a national security issue. The US has used access to these tools as a lever in its technology competition with China. If there were ever a country with the motivation, the money, and the talent to build a homegrown alternative, it's China.

And China has tried. It has well-funded domestic EDA companies, enormous state support, and a deep pool of highly skilled engineers. I'm talking specifically about engineers trained within China, working with the data China can supply them, not people with access to US-based know-how. The lack of a competitive Chinese alternative at the leading edge is definitely not due to a lack of knowledge, skill, or ability.

So if capable, motivated, well-funded engineers haven't been able to break the duopoly, what exactly is stopping them? It isn't the ability to write software. And if the barrier isn't writing software, then why would AI, which is essentially a very good software writer and problem solver, change the picture?

It doesn't.

# Where EDA Sits in the Chip Economy

The second point is about where EDA sits in the chipmaking process and how much it costs relative to everything else.

EDA sits at the very core of chipmaking, yet as a fraction of the total spend to bring a leading-edge chip to market, it's a small amount. The bulk of the money goes to engineering teams, masks, wafers, fabs, and years of development time.

Now consider what happens when something goes wrong. If there's a fault in the EDA tooling, you usually don't find out cheaply. You find out after tape-out, after the masks are made, after the wafers come back, after months of time and enormous amounts of money are already gone. At the leading edge, a single respin can cost tens of millions of dollars and, worse, can cost you your market window.

Modern chips rely on billions of transistors and countless subsystems working together with little to no tolerance for error. There is a combinatorial explosion of possibilities that must be sorted out to get a functioning device at the state of the art. That is simply the reality of chipmaking, and it stays the reality even if an AI hands you a free EDA tool.

So picture yourself as the head of silicon at a major chip company. You can save a rounding-error amount of money by switching to unproven software, and in exchange you take on the risk of a catastrophic, extremely expensive failure. Nobody rational makes that trade.

# The Real Product Isn't the Software

This brings me to the heart of my argument.

Most of the value Synopsys and Cadence offer is not the software itself. It's the flows: the decades of accumulated tweaks, calibrations, workarounds, and validations that make designs work economically, at scale, at the leading edge.

These flows are co-developed with foundries on each new process node. They're certified against real silicon. They encode countless lessons learned from real chips that did and did not work. Every major customer has built internal methodologies, scripts, and sign-off processes around them. When TSMC brings up a new node, the reference flows are built and qualified with these tools. When a design house signs off a chip, it's signing off against results everyone in the ecosystem trusts.

An AI can write a place-and-route engine. What it cannot conjure out of thin air is the years of silicon-validated trust, the foundry qualification, and the integration into every customer's workflow. That comes from shipping real chips, node after node, and it can't be downloaded.

So it would be quite foolish for anyone operating at commercial scale to use software that hasn't been engineered and proven to work with current state-of-the-art processes. AI may well make these tools better, and Synopsys and Cadence are already building AI into their own products. But being "replaced" by AI is a very different claim from being improved by it.

# So What Revenue Is Actually at Risk?

If AI-generated EDA tools do take hold somewhere, it'll be at the low end: hobbyists, students, makers, and very small projects where the stakes are low and a failed design costs little.

And if you look at the financial statements of Synopsys and Cadence, you'll see they make essentially no money from that category. Their revenue comes from large semiconductor companies, foundries, and system companies working on complex, high-stakes designs, the exact customers for whom switching away is least rational.

In other words, the slice of the business that AI could realistically disrupt is a slice that barely exists on the income statement.

# Bottom Line

The market is pricing Synopsys and Cadence as if AI represents an existential threat. I think that misreads what these companies actually sell. The moat was never the code. It's the trust, the validated flows, the foundry partnerships, and the enormous cost of being wrong. AI doesn't change any of those things, and if China's well-resourced effort hasn't cracked that moat, I don't see why a better code generator would.

That's why, relative to where these stocks were trading not long ago, I think today's prices look like an opportunity rather than a warning.

*Disclaimer: This article reflects my personal opinion and is not financial advice. I am not a licensed financial advisor. Do your own research before making any investment decisions.*

*Disclaimer: Written with the help of the Claude Opus (arguments are mine, writing is LLM)*

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