djoldman

https://blog.google/products-and-platforms/products/educatio...

> We believe schools should have control over AI in their classrooms and in providing administrators with controls to help them manage their approach. After listening to schools, teachers, leading researchers, and policymakers, we are now updating our approach to controls for Gemini, Gemini Notebook, Gemini in Classroom, and Google Search. Access to these products will be entirely off for K-12 student accounts until school administrators explicitly turn them on.

Easy to find email addresses of famous people are unlikely to respond to you to say the least.

and here is a direct link to the pdf without all the rigamarole:

https://www.microsoft.com/en-us/research/wp-content/uploads/...

That seems true.

But also: generate all you like and then have humans edit, and keep a record of it, seems plausibly defensible too.

The current state of a lot of AI generated content material, not all / not necessarily program code, seems to really benefit from human editing.

https://lab1908.com/

Much more likely to actually get to market is diamond as the heat dissipating substrate.

I like to check out everyone's first commits :)

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.

This seems to be a common and continuing objection to the OpenAI math repo release. Specifically that at least some, perhaps many of

  * the natural language proofs are low quality or otherwise extremely difficult to understand (although possibly correct?)
  * the lean proofs are too long, convoluted, or otherwise extremely difficult to understand

And specifically: iterate on the OpenAI repo to produce "better" proofs that prove the same things.

  Commits per engineer 5.1x
  Queries per data scientist 3.1×
  Contracts per legal employee 3.1x
  Deals per salesperson 1.7x
  Hires per recruiter 1.4x
  Invoices per AP processor 1.4x

The first 3 stats, commits+queries+contracts, are secondary/meta statistics that could possibly go up 1000x and have very little affect on Anthropic's business, whereas that's much less likely to be the case with the last 3.

AI can make it very easy to do more and more busywork that doesn't move the needle.

If the above is true, AI econ must rely on a second order effect here in that busywork/BS is recognized more quickly and discontinued.

For example, if an organization paid an employee to compile a monthly report and it took them a very long time. But with AI, they can make that report every day. Now the organization realizes that they don't even need it.

This may not be saying much at all. So much goes into deciding how fast to hire. Instagram had 13 employees / 1B rev at acquisition?

Also revenue per employee can vary a lot depending on the industry.

A better metric to understand AI effects might be revenue per employee at companies that have been around > 5 years that are AI-enthusiastic.

  Anthropic (Claude models): Claude Code
  OpenAI (GPT models): Codex
  Google (Gemini models): Antigravity CLI
  Meta (Muse models): Muse Code

One thing we're learning is that harness is HUGE. The difference in performance for the same underlying model can change dramatically across:

1. harnesses

2. allowed tools (no web search/fetch - how does the model refer to documentation?)

3. thinking levels

https://arxiv.org/pdf/2610.12970

Shortest promoted paper I've seen from a FAANG.

> This breaks my students’ brains. If it’s so ubiquitous, it must be clearly legal…right?

Do they not drive? Driving slightly faster than the speed limit is ubiquitous in my experience.

Can someone explain why there wouldn't be arrows from all three types of solutions back to human understanding?

> Now we come to the question of whether or not the difference that LLMs bring to the table is sufficiently overwhelming to make their adoption inevitable, and on this I think the answer is a clear no. As established above, LLMs just can't do top-flight work at all: at the heights of basically any discipline, we're still very much relying on unaugmented human work.

The clear flaw here is that cost isn't taken into account. A new technology needs to be better than the status quo at a given cost/price point.

Examples: mass produced textiles, photography, IKEA. Ostensibly not the highest quality, but the cheapest at a price point.

1. "...these open problems were curated in order to organize a field around, most to all of them only have/had value in so far as they promoted study of the subject" - I don't understand, how were the open problems curated? Do you mean that mathematicians put a lot of work into coming up with the problems and now all that work is somehow worthless now?

2. "OpenAI is functionally reducing the communities ability to ask real questions." How? Let's assume all the remaining proofs are correct (big assumption): how does resolving conjectures reduce the ability to pose new problems? This only makes sense to me if the assumption is that there are a relatively small number of possible problems to solve and so it's like a game that's coming to an end with no more interesting areas to explore. Surely math is bigger than these?

1. Reacting from the POV of the individual person: disappointment, disillusionment, and/or grief from those who've worked on some problem for years and now don't have something to work on; it's been a part of their identity. As well as those whose career tracks and plans were thrown in disarray.

I wholeheartedly sympathize with the above.

2. Reacting from the POV of the entirety of mathematics as a field of study/research:

  "If OpenAI wanted to destroy the mathematical community, this would be a great way to go about it."

  "...it will create conflict in the mathematical community;"

  "I feel that solving so many problems in such a short time may damage the math community and profession"

  "They can simply release multiple 100+ page papers claiming to have solved this or that problem, often with redundant arguments, ... — in other words, slop. And we are then expected to go through it, check it, clean it up, simplify it, and explain what is actually going on. This comes at a considerable cost to us ...And, of course, the credit remains theirs."

  "Open problems are a resource that the mathematical community developed over decades or even centuries"

What if a human dropped all of these without AI? I suspect there wouldn't be the same reaction for some reason.

I sympathize with those who've worked on some problem for years and now don't have something to work on; it's been a part of their identity. I also especially sympathize with those whose career tracks and plans were thrown in disarray.

That being said, I absolutely cannot understand how one can't be excited and happy and enthused about these advances in one's field. Assuming just that the ones with formal lean proofs are actually true, these are reportedly huge advances. Even if folks don't understand it YET.

This section makes the reader think: why would I not pick Sonnet 5.5 instead of Haiku 5.5?

740M total (270M text, 170M vision, 300M audio)

> Claude also verified this paper’s main results using the Lean 4 proof assistant with the Mathlib library.

They plan to pay for most of it through excess cash flow, aka profits.

Follow on question: "they make $100s of billions per year? what did they do with all that previously?"

Answer: share buybacks, nothing (aka put it in the bank), acquisitions.

These companies make a ton of money and have a ton of margin over expenses.