Yes. For instance, Stripe uses Bazel internally for ~all of its builds. https://stripe.com/blog/fast-secure-builds-choose-two

For other users, you might peruse the Bazelcon 2025 schedule, which happened earlier this month: https://bazelcon2025.sched.com/

https://statmodeling.stat.columbia.edu/2020/03/24/why-we-sle...

I do see generic statements like "boosting each other", and I see vaguely-drawn lines in the primary diagram with no further explanation, but that hardly counts as network analysis, right?

https://xcancel.com/SebastienBubeck/status/19581986678373298...

Comments URL: https://news.ycombinator.com/item?id=44468707

Points: 3

# Comments: 0

Here is the primary author of the timelines forecast:

> In our website frontpage, I think we were pretty careful not to overclaim. We say that the forecast is our "best guess", "informed by trend extrapolations, wargames, ..." Then in the "How did we write it?" box we basically just say it was written iteratively and informed by wargames and feedback. [...] I don't think we said anywhere that it was backed up by straightforward, strongly empirically validated extrapolations.

> In our initial tweet, Daniel said it was a "deeply researched" scenario forecast. This still seems accurate to me, we spent quite a lot of time on it (both the scenario and supplements) and I still think our supplementary research is mostly state of the art, though I can see how people could take it too strongly.

https://www.lesswrong.com/posts/PAYfmG2aRbdb74mEp/a-deep-cri...

Here is one staff member at Lightcone, the folks credited with the design work on the website:

> I think the actual epistemic process that happened here is something like:

> * The AI 2027 authors had some high-level arguments that AI might be a very big deal soon

> * They wrote down a bunch of concrete scenarios that seemed like they would follow from those arguments and checked if they sounded coherent and plausible and consistent with lots of other things they thought about the world

> * As part of that checking, one thing they checked was whether these scenarios would be some kind of huge break from existing trends, which I do think is a hard thing to do, but is an important thing to pay attention to

> The right way to interpret the "timeline forecast" sections is not as "here is a simple extrapolation methodology that generated our whole worldview" but instead as a "here is some methodology that sanity-checked that our worldview is not in obvious contradiction to reasonable assumptions about economic growth"

https://www.lesswrong.com/posts/PAYfmG2aRbdb74mEp/a-deep-cri...

In fact the model and technical work has basically nothing to do with the short story, aka the part that everyone read. This is pointed out in the critique, where titotal notes that a graph widely disseminated by the authors appears to be generated by a completely different and unpublished model.

This is really, really great. I hope it gets as many eyeballs on it as AI 2027 did.

The same post on the author's Substack is here: https://titotal.substack.com/p/a-deep-critique-of-ai-2027s-b...

> I like when my software gets used by a lot of people and people send me nice emails. In this case, people weren't sending me the nicest emails, unfortunately. But they also didn't really know what I was doing. They saw DOGE, weren't a fan of certain things that they were associated with. But I think at the end of the day, like, the role of the U.S. Digital Service is to improve the UX (user experience) of being an American, which is pretty exciting. And anyone who lets me do that, I will try to work for, even if my friends and family aren't huge fans.

People weren't mad at him because he was trying to improve UX. They were mad at him because DOGE was doing a bunch of other stuff like "harassing government employees who talked about climate resilience", which he actively contributed to but conveniently omits from the interviews he's been giving recently:

https://github.com/slavingia/va/blob/main/eos/analyze_eos.py...

Comments URL: https://news.ycombinator.com/item?id=43577610

Points: 1

# Comments: 0

> significant parts of our federal government have abandoned key precepts of outcome-driven problem-solving. (Just try navigating the IRS’ Free File tax return tool. I gave up and paid for H&R Block instead.)

It seems deeply weird to me to pick this example, of all things, where things are getting better because of outcome-driven problem-solving. (You might also want to consider that "I gave up on Free File" might score you anti-points with your audience.)

Comments URL: https://news.ycombinator.com/item?id=43552448

Points: 2

# Comments: 0

> We were restricted from disclosing the partnership until around the time o3 launched [...] Our contract specifically prevented us from disclosing information about the funding source and the fact that OpenAI has data access to much but not all of the dataset.

> Regarding training usage: We acknowledge that OpenAI does have access to a large fraction of FrontierMath problems and solutions, with the exception of a unseen-by-OpenAI hold-out set that enables us to independently verify model capabilities. However, we have a verbal agreement that these materials will not be used in model training.

Here, you can read "large fraction" as meaning "everything but the holdout set" - and my understanding is, they haven't disclosed performance on the holdout set.

The claims come from an MBA student graduating from Stanford this year, who plans on starting a developer tooling company. [1] The faculty member (a professor of psychology) who supported this work doesn't comment at all on the claims made by the student; their single quote in the article instead emphasizes how difficult it is to measure productivity.

The claims are based on, from their own description, unpublished ongoing research. There's no description of what data they gathered, what their methods were, or how they assessed accuracy anywhere. (For starters: how did they identify who a software engineer was in each of the companies in their dataset? How did they exclude data scientists, or technical writers, or other engineering-adjacent folks who in some companies need to commit infrequently? How did they identify remote workers, and handle workers transitioning between roles or working arrangements?)

Without this extremely basic information, I think it makes sense to just ignore it. It's possible that it'll eventually pan out as real! But definitely not yet. Wild that people are taking it seriously.

[1] https://poetsandquants.com/2023/02/21/from-middle-school-dro...

https://web.archive.org/web/20210421025041/http://theinciden...