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"Physics gets a vote." Cameron McCord learned this viscerally in his time at Anduril. "Simulate everything" became industry gospel, but it turns out testing is still critical in hardware. That experience became the thesis behind Nominal. He and co-founders Jason Hoch and Bryce Strauss built Nominal around testing because...

14,724 views • 5 months ago •via X (Twitter)

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When I sold my software company in 2010 and became an angel investor, one word could smother a pitch on the spot: “Hardware”. A hardware component made you close to unfundable. The VCs called you a hybrid company, and it wasn't a compliment. The refrain was: “Hardware is hard, hardware is slow, hardware eats cash and stretches timelines. We want pure software”. That was the era. It took two shocks to break the spell. First, COVID broke supply chains, and soon everyone in Washington was doing the math on where things actually get made. For example, the US hasn’t produced tungsten domestically since 2015 . (Aside: this is changing, cc Taylor Sulik ⛏️ ) Rare earth magnets: almost all bought from countries that may or may not be on our side. The ability to make critical materials at home was a national capability that we’d spent decades letting rot. That lit a fire under a lot of folks. Then AI showed up and knocked the legs out from under pure software entirely. The nightmare scenario for a lot of SaaS companies is now the next model dropping and a customer rebuilding your product in-house. So the same VCs who said "we don't want any hardware in this" are now asking founders where the hardware is. Which brings me to moats. A moat has always been a rebuild-time question: how long would it take a well-funded competitor to rebuild what you have? Software used to buy you years. AI is shrinking that number toward months, and a moat measured in months is more like a wooden fence. The physical world doesn't compress that way, since time is baked into its advantages. A factory takes three to five years to stand up regardless of how smart your tools get. Then permits, construction, supply chains, testing data… Another part is that AI trains on the written-down world, and high-moat physical knowledge lives largely offline… public AI models have not yet ingested it, and no prompt can reproduce the underlying empirical record. Anyway, hardware is still damn hard. And that's exactly the point.

Adam Rossi

11,855 views • 1 month ago