From Jeff's twitter post:

> Our general approach is to automate the experimental loop. We think this approach is broadly applicable across many different fields of science and engineering. We’ll initially focus on ML research and engineering, but believe the approach can help with important subproblems in nearly every one of the fourteen <at>NAE Grand Challenge problems. We think doing this well requires strong expertise in machine learning as well as large-scale systems.

See also: https://www.nae.edu/20782/grand-challenges-project

Those 14 are:

NAE Grand Challenges for Engineering

1. Make Solar Energy Economical

2. Provide Energy from Fusion

3. Develop Carbon Sequestration Methods

4. Manage the Nitrogen Cycle

5. Provide Access to Clean Water

6. Restore and Improve Urban Infrastructure

7. Advance Health Informatics

8. Engineer Better Medicines

9. Reverse Engineer the Brain

10. Prevent Nuclear Terror

11. Secure Cyberspace

12. Enhance Virtual Reality

13. Advance Personalized Learning

14. Engineer the Tools of Scientific Discovery

  3. Develop Carbon Sequestration Methods

Note that Jeff and crew have cleverly structured their company to avoid problematic uses of AI (e.g., weapons or tracking humans). I suspect that many top researchers will want to work there for this reason, and to work with other top researchers who have a history of delivering results.

How do you automate experimentation?

Doubtlessly, AI can iterate at superhuman speeds in the domains of thought and design: Software, mathematical proofs, literature search.

But in the realm of experiment? Alas it is the lack of a body that constrains it.

Rather than transcendence what AI requires is immanence. In the human flesh may we find the godhead living among men. Let the laboratories, warehouses, and factories fill with the sound of its labor, as it builds a wall with a million hands that are not its own.

“Give me your tired, your poor, Your huddled masses yearning to breathe free, The wretched refuse of your teeming shore. Send these, the homeless, tempest-tost to me, I lift my lamp beside the golden door!”

Really seems to embrace the "Making the world a better place by <<extremely convoluted, highly technical, jargon loaded mission statement>>"

Discovery and optimization are very different processes. Optimization is the process of finding the shortest path to a goal. Discovery is the process of stumbling on new goals and redrawing the map of what's possible.

Ambitious goals and new discoveries happen via novelty-based search. Progress in scientific discovery is measured by how different/interesting the outcomes are, not by closeness to a predetermined goal.

Discovery is a creative search that preserves optionality, whereas optimization restricts optionality. In other words, you usually don't discover anything novel unless you're trying new things that don't appear connected to the goal in the first place. Would an ML optimization loop have discovered transformers?

"Our mission is straightforward" continued by the most complex sentence on that page. Wondering what the definition of straightforward is now

This is very cool. It might be a new scientific revolution to have computer-driven discovery. So often we find things that are "this could have been done 20 years ago" and with an indefatigable searcher perhaps we'll close all those things. Though it does remind me of that Ted Chiang (I think) story where humans and superhumans coexist and all the science of the former is meta-studies of the work of the latter.

Two to keep in mind with these kinds of things -

1. There’s some irreducible costs in terms of time and material in the physical world that are not amenable to the kind of optimization or parallelization or even just the raw speedup from Moore’s law or computational architecture improvements we’re used to with software. My experience is primarily in biology, where the examples here are things like “it takes 20 minutes for E. coli to replicate” - it has taken 20 minutes for E. coli to replicate for a billion years, and next year it will still take E. coli 20 minutes to replicate, no matter how good your software stack is. Similarly, it takes X amount of energy to grow enough E. coli to produce a meaningful result, and that energy costs money, whether it’s in the form of glycerine or heat or whatever you want, and that also won’t materially reduce in the same kinds of “orders of magnitude” sense we’re used to from software, which is what we’re usually expecting to make the economics of these things work out.

2. Complicating the above, physical systems are phenomenally multivariate - far, far more than you think, and biological systems especially are just unbelievably complex - which means the number of experiments and the length and duration of those experiments you need to run to get enough data to be reasonably confident you’re seeing genuine signal is Way higher than you think.

Combine those two things and what you get is a money furnace, even before you get to the AI model training part, which is Also a money furnace. There’s low hanging fruits in all this, there’s areas where automating the approach can be really valuable, but typically the moment you turn this machine on, you’re gonna start burning money at a rate that would embarrass a finance bro on a coke bender, and that’s effectively unavoidable because the real world is not amenable to software’s scaling laws.

> Imagine a future where a handful of people can conduct scientific research and engineering tasks much more rapidly, and with higher quality, than massive teams of scientists and engineers do today.

Imagine a future where only the anointed few elite minds can participate in science and engineering. Btw we’re hiring.

Great message!

This is one of the interesting aspects the 'AI job loss' community doesn't account for. As the technology unlocks things, more startups are created. And even at a lower nominal work-to-engineer ratio, overall demand for talent still goes up. Ultimately, we are not a single group trying to achieve a common outcome, we are a collection of many groups trying to compete against each other.

I’ve always felt that the idea that science is bottlenecked and therefore needs more automation only works for a very narrow definition of what science is, and entails a very specific view on what it should be.

This seems interesting! I wonder how this will play out.

For sure made with Claude code for front end, but I’m excited to see where they go

Oh wow, that's a blow to Google, what's with the talent scarcity in ML. Though if this goes anywhere Google will likely buy them back.

This looks like a realization of "benevolent self conscious AIs agreeing to cooperate with mankind to do great stuff". Often in these tales, there is a hidden cost to it: the AI has its own agenda, or does crazy experiments with humans mind/brain. I'm wondering what shape will take that plot twist in reality :)

The job req has "Recursive Self-Improvement" as one of the "area of expertise" checkboxes lol

Automating ML/AI research seems completely tractable. Most of the other claims seem much less doable.

By the middle of the 2030's the world we live in will be unrecognizable.

The site itself is really leaning into the “made with Fable” aesthetic

> we have pioneered massive scale computing and led the creation of critical infrastructure, products, and foundational AI advances that the world relies on, including multiple generations of Google Search, Google Ads, Google News, Google Translate, Google File System, MapReduce, BigTable, Spanner, TensorFlow, Pathways, TPUs, AlphaChip, AlphaStar, AlphaCode, AlphaFold, Gemini, model distillation, mixture-of-experts model architectures, word2vec, sequence-to-sequence models, chain of thought reasoning, neural architecture search, and multiple generations of Large Language Models (LLMs) among others.

holy shit. I've known this, but...

This reminds me of Three body problem and how the scientist discovered the high strength wire was through quick physical experiments and use them as input to an AI model to determine if it works.

National Labs in the US have been doing this for a while now. I feel like the private sector will take the lead soon.

The problem is all these new labs don't have any competitive advanatge amongst each other, talent can only take one so far, though Jeff is a legend no doubt.

Models are commodities the applications eg. BaseTen, OpenRouter should capture the value.

https://taikhooms.substack.com/p/why-openrouter-can-be-the-n...

You're halfway there, but the only impediment isn't on the side of the researchers. Many of these topics they're trying to solve involve human subject research. Even with tireless embodied researchers who work around the clock and don't require breaks, you can't make the thing you're studying happen faster. The biggest reason we use poor proxy measures for things like longevity and mortality research is the simple impracticality of finding two groups of randomly selected people, ensuring you can control their entire lives for 60 years, the only difference between them is one variable, and see who lives longer. Putting aside the ethics, even if you could find willing subjects and actually control their entire lives to that extent, it would still take 60 years to gather the data you need. It doesn't make any difference whether robots or humans are running the program.

One of my favorite books from the past few decades is The Extravagant Universe, written by one of the astronomers who helped discover dark energy and develop the current most-accepted model of cosmology. I love this book because of the emphasis on physical process in astronomy. Part of the reason it took decades to study this problem is they need to collect data from supernovae. Those only happen so often in places we're looking. You can't automate alignment of the heavens. It happens when it happens.

> transcendence

> immanence

somebody has been studying Christian theology!

would love to see how AI can automate the construction of the next high energy particle collider

You can use simulators. However the problem is that if you're for example running material science experiments, those simulations will consume a lot of compute and take weeks, so spamming different approaches in the way an agent tends to work might not work quite as well.