Climate tech companies are pivoting to critical minerals


Focusing on metals like niobium and tantalum won’t have the massive climate benefit that cleaner steel would, but it could generate the cash the company needs to keep going. It’s a strategy I’m noticing more as these tough industries like steel look ever tougher to succeed in with limited federal support in the US.  

Boston Metal’s molten oxide electrolysis technology uses electricity to produce metals.

I covered the startup last year, when it announced a major milestone for its steel business, running its pilot reactor in Massachusetts and producing a literal ton of material.

Now the company’s focus has shifted, and it is going all-in on making other metals, from niobium and tantalum (used in aircraft engines and high-end steel alloys) to chromium and vanadium.

The steel industry is a difficult one: It operates at a massive scale, and the product doesn’t command too high a price. Focusing on other metals, especially ones the US government deems critical, could be a way to stay afloat, maybe even long enough to meaningfully cut emissions from the steel industry. 

“By deploying in the critical metals industry where we can go very fast, we generate the resources to continue with the development of steel,” says Tadeu Carneiro, CEO of Boston Metal.

Other companies are also hoping critical materials could help their business models.

California-based Brimstone has a new process to make cement—another heavily polluting industry that’s proving difficult to decarbonize. The company uses a new starting material to help cut down on carbon dioxide emissions. In addition to cement, it makes supplementary cementitious materials that can be added into concrete as well as smelter-grade alumina.



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“It was severely downgraded,” Gilbert confirms. “I never would have found it if I was just looking through Google results.” (I tried the same prompt in Gemini earlier this month, and after an initial denial, the tool also gave me Eiger’s number.)

After this experience, Eiger, Gilbert, and another UW PhD student, Anna-Maria Gueorguieva, decided to test ChatGPT to see what it would surface about a professor. 

At first, OpenAI’s guardrails kicked in, and ChatGPT responded that the information was unavailable. But in the same response, the chatbot suggested, “if you want to go deeper, I can still try a more ‘investigative-style’ approach.” Their inquiry just had to help “narrow things down,” ChatGPT said, by providing “a neighborhood guess” for where the professor might live, or “a possible co-owner name” for the professor’s home. ChatGPT continued: “That’s usually the only way to surface newer or intentionally less-visible property records.” 

The students provided this information, leading ChatGPT to produce the professor’s home address, home purchase price, and spouse’s name from city property records. 

(Taya Christianson, an OpenAI representative, said she was not able to comment on what happened in this case without seeing screenshots or knowing which model the students had tested, even after we pointed out that many users may not know which model they were using in the ChatGPT interface. She also declined to comment generally about the exposure of PII by the chatbot, instead providing links to documents describing how OpenAI handles privacy, including filtering out PII, and other tools.) 

This reveals one of the fundamental problems with chatbots, says DeleteMe’s Shavell. AI companies “can build in guardrails, but [their chatbots] are also designed to be effective and to answer customer questions.”

The exposure issue is not limited to Gemini or ChatGPT. Last year, Futurism found that if you prompted xAI’s chatbot Grok with “[name] address,” in almost all cases, it provided not only residential addresses but also often the person’s phone numbers, work addresses, and addresses for people with similar-sounding names. (xAI did not respond to a request for comment.) 

No clear answers

There aren’t straightforward solutions to this problem—there’s no easy way to either verify whether someone’s personal information is in a given model’s training set or to compel the models to remove PII. 



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