In 2015, Popular Mechanics reported on the retirement of Larry Zottarelli, the last original Voyager engineer still working on the project. He was 80 years old. The article noted that keeping Voyager alive required knowledge of old languages and systems, including assembly language and Fortran.
Larry Zottarelli was the last original engineer still working on Voyager and I sometimes wonder if there were other pre-launch Voyager engineers still at JPL but working on different projects in 2015. Hard to track down, I imagine.
The "assembly language and Fortran" job requirement was made up by the Popular Mechanics headline writer and widely repeated in the press and online unfortunately. In the body of the 2015 article, Suzanne Dodd, the Voyager project manager, simply says, "Although, some people can program an assembly language and understand the intricacy of the spacecraft, most younger people can't or really don't want to." (The 2026 maketecheasier article kind of, sort of clarifies this.) Ultimately, Zottarelli was replaced by Lu Yang, an existing JPL engineer who can be seen in the It's Quieter in the Twilight movie and in photos of the current Voyager team.
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The writing is a bit ambiguous, but if I'm parsing it correctly it's only a big deal to the small subset of physicists who care about mathematical rigor: proof that Quantum Field Theory (i.e. what theorists have been using since the 1940s or so) is mathematically sound.
This part,
The existence proof pertains to the simplest grand unified theory that includes the gauge fields and fermions of the Standard Model
We reached out to external experts and asked them to act as shepherds, organizing groups to understand the new results. They advised three different release strategies:
[...]
For the physics result, form an external group of collaborators and defer to them on the release of materials.
[...]
The group requests that all communications with any and all members of the group regarding this effort be directed to the following email account: YANGMILLS4D@gmail.com.
Mathematicians are both frustrated and excited by OpenAI’s work. Sarnak, for his part, said in an email that he won’t be satisfied that it’s “a Theorem” until humans check and understand all the details. But “the general feeling is one of relief,” Radziwiłł said. There are scores of results across not just number theory but also geometry and theoretical computer science that the proofs of quasi-Riemann will, in a swift stroke, establish as mathematical truths.
Annual growth over that period has been ~2.2%, sometimes a little faster, sometimes a little slower. The doubling from 50 in 1982 to 100 in 2017 (35 years) averages to 2% per year.
Even if the overall productivity boost due to AI "only" turns out to be 40%, the lowest among your numbers, it's equivalent to 17 years of growth at 2% per year.
> Even if the overall productivity boost due to AI "only" turns out to be 40%, the lowest among your numbers, it's equivalent to 17 years of growth at 2% per year.
Two reasons to question the 40% number:
- Anthropic probably represents the extreme case of AI adoption, if they're not the absolute leader. I would see 40% as the absolute max for that category without knowing anything else.
- Anthropic is a tech company, willing to change their workflows rapidly, having maximal enthusiasm for the technology, and maximum knowledge of it. The vast majority of the world's companies don't come close to it for these aspects and therefore adoption and speed should lag significantly compared to Anthropic.
The numerator on that is “Real Value-Added Output for All Workers” [1], which is based on revenue (subtracting some costs), and the denominator is “Hours Worked for All Workers” [2].
Anthropic’s revenue has seen enormous growth [3]. They have few employees. Obviously, as these things are measured, they are highly productive! It might be hard to reproduce that sort of productivity outside the tech industry, though. We can’t all be Anthropic.
Brilliant. Now compare it to real wage gain, purchasing power, or other markers of societal well-being. Or income inequality!
I don’t see how we’d get to 40% unless a) humans are out of the loop, or b) we hire lots more humans which would make AI cost-ineffective. What am I missing?
The article used $16.8B. Not a big difference.
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