Those are all fake, part of an internal Google narrative that overstates individual contribution, and obscures the work of large engineering teams.
Here are some Jeff Dean well sourced facts:
- Already part of engineering of Google indexing systems that lacked basic checksums and ran on non-ECC hardware, allowing silent data corruption.
- One of the authors of LevelDB a database with so many documented crash-consistency, recovery, and data-loss weaknesses for years. Just check their Github project. LevelDB current tracker contains unresolved crash consistency, recovery and corruption reports going back almost 12 years on GitHub
- In AI engineering technical lead, let TensorFlow lose researcher mind share to PyTorch, and caused Google fragmented landscape across TensorFlow and JAX.
- Had the people at Google who invented the Transformer architecture, but failed, to turn that lead into the first dominant public LLM.
- As AI engineering and VP management let Google Brain and DeepMind remain duplicated and internally competitive for too long.
- Let Noam Shazeer leave and then spent heavily to bring him back with nothing to show for.
- Part of Technical VP leadership who had Bard rushed to launch with factual errors in Google own promotional material.
- The first Gemini demonstration overstated how real-time and interactive the system actually was, being basically a fake.
- Part of the VP and AI technical leadership who had Google AI Overviews launched with weak source quality controls and repeated satire and low-quality web content as factual advice.
- Part of teams that launched AlphaChip performance claims that were difficult for outside researchers to reproduce and remain technically disputed.
- Jeff Dean public explanation of Gebru departure was contested and damaged confidence in Google scientific governance.
- Jeff Dean was part of the team at Google that removed or marginalized prominent internal AI ethics critics shortly before many of their warnings became product problems.
- Jeff Dean was one of the managers behind Project Dragonfly supporting censorship.
- Jeff Dean is part of the VP technical leadership approving Project Nimbus supporting an ongoing genocide.
As LLM coding agents plateau— at least for the average engineer without tens of thousands of dollars or swarms of agents to run —I’d say that, from here on it’s going to be about ASICs, specialized LoRA/or-equivalent models, or a Ruby on Rails for LLM context engineering and orchestration, which LangChain and others seems well position, including Google as they own the entire stack. LLM free lunch has been over for a while, perhaps since the ReAct loop, and has been official since Ilya mentioned it at NeurIPS.
I feel the most exciting development these days is self-evolving agents. Especially if you have a way to verify their outputs with a formal system, or with a system developed since the 60s by armies of PhDs.
DeepMinds Gnome is a good example, where they use DFT to verify outputs. Approximating NP-problems is always fun for those who dare.
I am also building in this space. Its a mix between HPC, AI, and hard science. Pretty fun compared to waking everyday to LLM news that seem more like marketing stunts.
LLM coding isn't even close to plateauing. Right now, the major players are in a consolidation step, focusing more on economic efficiency but still not at the point where we're ready to start burning models to hardware and freezing the line.
They are straddling the line between pushing it forward, and justifying the business case. It's hard to do both at the same time.
LLMs are hungry for tokens. Every day I see more startups claiming token usage at 50-100k per month. You can always brute-force your way in - just see HuggingFace's recent attacks.
If throwing more money at inference while accumulating compounding technical debt is the new norm, then we are not solving the problem, and the solution space is already covered.
Perhaps there are marginal gains at the expense of quadrillion-params LLM models with 10x the cost and energy. We are simply making inefficiency more expensive, camouflaged by VC money and great marketing.
If that is not plateauing, then I guess I will have to reconsider what plateauing means.
This is such unbelievable revisionism! Can you imagine in 2022 saying "Oh of course you can brute force your way to AGI if you spend enough money per month". Nobody thought that! Come on!
To be honest, this feels more like a lifestyle business (aka hobby) than a startup. They truly deserve it, but I don't expect a huge success as a business.
That said, I hope they write cool papers with various peers across the industry without worrying too much about the competing dynamics. That'd be a blessing for humanity, and good for their spirit.
A public benefit corp shouldn’t be a start up. The primary goal of a start up is to grow as quickly as possible which is rarely benefits the public.
Very silly to call every non start up a lifestyle business. It’s just a business. Start up are the weird thing that almost always an obscene waste of time and money, but sometime creates google.
> The primary goal of a start up is to grow as quickly as possible which is rarely benefits the public.
Why is that so? Fast growth, when achieved honestly, is a result of solving user pain that others haven't. Maybe you think so because users != the public, but I think in totality the public is a collection of users who all have needs they want met.
For certain amount of fast growth: yes. Then there's continued "growth-hacking" and enshittification to keep fast revenue growing after the pain point has been solved with dark patterns and questionable tactics.
Why? Because investors poured a bunch of money in to support fast growth and now they want their money back. And incremental growth won't do. Since 9 out of 10 of the investments fail, the surviving one has to continue to growth-hacking revenues.
A PBC is not a charity or a non-profit. PBCs are for-profit businesses with the goal of making money. In day-to-day business they're indistinguishable with other for-profit corporations, including fundraising and investment. The only real practical difference is that they give directors a little more leeway in their fiduciary duties to say "no" to doing evil things.
The advantage to a PBC is protecting founders from a serious problem with standard corporations: you might bring on investors who could subsequently demand you pollute, exploit people, and/or do other immoral activities for profit. You don't have to do these things to grow a business quickly.
> The only real practical difference is that they give directors a little more leeway in their fiduciary duties to say "no" to doing evil things.
I’d like to provide maybe a clarification here that there is zero existing fiduciary duty in regular corporations to say yes to evil things, or even to turn a profit at all. A for-profit C corporation can legally sell stock, lose money every year, and go out of business, if the board of directors approves that strategy. Fiduciary duty exists primarily in areas of accurate communication and the avoidance of crime, fraud, etc.
A B corp basically is a C corp, but one that has formally published that their strategy includes a commitment to some social benefit. But if a C corp wanted to publish the same message to shareholders it could, and shareholder recourse would basically be to either try to replace the board, or sell the stock.
Fiduciary duty absolutely does go beyond accurate communication and fraud.
Consider the eBay/Craigslist case, eBay Domestic Holdings v. Newmark:
> When director decisions are reviewed under the business judgment rule, this Court will not question rational judgments about how promoting non-stockholder interests—be it through making a charitable contribution, paying employees higher salaries and benefits, or more general norms like promoting a particular corporate culture—ultimately promote stockholder value. Under the Unocal standard, however, the directors must act within the range of reasonableness. Ultimately, defendants failed to prove that craigslist possesses a palpable, distinctive, and advantageous culture that sufficiently promotes stockholder value to support the indefinite implementation of a poison pill. Jim and Craig did not make any serious attempt to prove that the craigslist culture, which rejects any attempt to further monetize its services, translates into increased profitability for stockholders.
This is where a PBC would have been different. With a PBC, courts are directed to balance the the stockholders interests with the company's stated public benefit.
I'm skeptical of any Engineering loop that doesn't include reality (as in touch grass) feedback. Pure logic and reasoning is the domain of Maths and Science (philosophy). Surely it will work, but it will not "be able to solve any learning loop".
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.
> only works for a very narrow definition of what science is
And so does academia. It's just that instead of AI and robotics, PhD students are thrown onto problems that are in large parts slightly tweaked reconfigurations of similar experiments.
Especially in chemistry, biochemistry, material sciences there is a large space of discoveries that are barely "novel" in an intellectually stimulating way, but still highly valuable that can be explored orders of magnitudes faster than is currently the case.
Yep. A communications professor where I did my MS says a 200usd/mo claude sub (which ant gives for free) does as much work as 5 grad students. It's mostly like you said, trying out new ideas rapidly.
It's 90% to advance science via cheap labor and 10% to train a small group of future experts who will hire grad students to 90% advance science via cheap labor etc.
...
Yes. They have grad students too. This is just like having more grad students that don't need to be trained so the work you can get done is not bottlenecked by the number of people you can train.
> 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.
> 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.
Sometimes you got to find a way to buy the silence of your top employee, to prevent them from going to the competition. This "start-up" is shallow as hell
Given the resources that would be available to them at Google: compute resources, data, etc. It's clear they want absolute freedom. Good for them. At this revolutionary turning point in history, I want the smartest people working in whatever area they want.
Many of these problems don't seem scientific at all, but rather a problem of political will.
As you said, Solar power is incredibly economical. There are plenty of ideas around putting them over farms, or parking lots en-masse to provide cleaner energy.
Access to clean drinking water, while certainly scientific in some situations, is also a problem of political will and money.
Restore and Improve Urban Infrastructure - It's infrastructure week!
It is not economical compared to alternatives that is why you have to have government to force people to do things. In many places such as Pakistan where solar power is not economical on paper is actually very successful in practice because it is actually profitable.
To make solar power practical and economical you need may a square foot of solar panel being able to get enough energy to power and entire home for a week
Not sure what you’re talking about here. We can’t replace all energy needs with solar but it’s clearly one of the cheapest energy sources and with the added benefit of low capital expense to get started so you can set it up in distributed grids without the massive expenditure to support nuclear installations.
Why not just add 'mind control' while you're at it.
Only death stops stagnation in the end. Without death, especially if death can be avoided by the rich and powerful but not the poor, life will get much, much worse for the average person (until only the rich and their automated capital remain I suppose, in which scenario they will simply turn on each other).
When one of them dies, they want to leave behind a puzzle so complex that entire groups of the population dedicate their lives to solving it within the virtual world. They look old enough to have a lot of favorite 1980’s and 90’s pop culture references, so those will probably be the clues.
I'm guessing this might be about "teleoperation" (like remote surgery via robots + VR) and being able to remote training as well. The binocular vision VR gives you compared to flat screens help a lot with depth perception for precision of incisions for example.
> I do not see how the second sentence follows from the first.
Point 1 on the list is "Make Solar Energy Economical".
Solar is economical today - mostly due to China investing (and heavily subsidising) in solar for the past couple of decades; but compare to the US where certain particular big-businesses (oil companies, mostly) were instead cynically funding disinformation efforts and getting into bed with the Republican party (which dovetailed with the GOP's allying with other science-denying movements of the Bush Jr era like creationism and public-health matters with abstinence-only sex-ed and defunding gun safety research efforts) - means we're decades behind where we could have been...
Consider an alternative past, where the GOP had the backbone to resist the oil industry's corruptive influence and instead made a big bet on American Solar; it's entirely possible that instead of MAGA today we'd instead have a right-wing coalition strongly supporting solar and wind energy because they align nicely with American rugged individualism - whereas the current situation on the right is an unprincipled farce with inconsistencies in policy positions at every turn.
This is not a particularly new struggle: Jimmy Carter installed American-made solar water panels on the White House in 1979, then Reagan tore them out.
> The solution to most of these problems lies in policy, not in new tech advancements.
That is not mutually exclusive. If technological advances result in a given technology becoming cheaper, more scalable, and easier to deploy, they also make it easier to advocate for and implement the relevant policies.
You can think of it like this: our "political technology" is not good enough to use solar energy at its current prices to replace fossil fuels as fast as we would like. Well, what about if we cut the price of solar by a factor of five? Perhaps it will be good enough then.
That sounds like just playing further and further into the game of the corrupt leaders. Who do you think would profit off that 5x margin? Would that margin come more likely from a scientific breakthrough of via some new exploitation of natural resources or human labor?
It's yearsss past time that our leaders should have changed policy.
> If technological advances result in a given technology becoming cheaper, more scalable, and easier to deploy, they also make it easier to advocate for and implement the relevant policies.
Hey now, our biggest companies do more than suck up to science denying wackos, don't be so unfair... sometimes our biggest companies fund their wacko superpacs!
Making progress requires first not dismissing your ideological outgroup along such lines, and instead trying to understand what actually motivates them.
We have a good understanding of the function (and more importantly dysfunction of) kidneys, lungs, heart, etc. from high level to cellular level.
For brain, our understanding is fuzzy, more like "this part is important for that behavior" or "here is how neuron works" but we don't have a holistic understanding.
If we had that, we could more easily diagnose and treat neurological disorder.
To understand, same reason you reverse engineer anything. Doesn't have to have a further goal than that, understanding the brain better helps in so many ways. But like most technology, obviously can be used for bad too. Should we just skip researching some topics then?
I'm all for alleviating psychiatric/mental health disorders, but yes, some topics are worth skipping research on. For example chemical/biological/nuclear weapons, human cloning, and unethical gene modification.
I just like to challenge myself, as an engineer, with the idea that not everything has to be engineered and optimized. What if we simply left some things unexplored and mysterious, and trusted nature and our own human capabilities?
A better way of alleviating psychiatric/mental health disorders might just be to focus on societal factors.
People on HN keep saying it is, but I'm still not seeing it. Companies are building datacenters in vast, sun-blasted deserts and still choosing to power those with natural gas. This in turn makes people complain about emissions pledges being reversed, but if it were economical, no pledge would be needed.
Of course, USA has cheaper oil/gas than other countries. But if you look elsewhere, rich countries are subsidizing solar, poor ones are basically not using it.
In the chart for section 3, how many countries have seen their share of electricity being generated by fossil fuels increase in the last 5 years? Only Canada.
Take a look at the charts for Pakistan, Australia, Nigeria, and China for the last few years. Pretty dramatic drops for fossil fuels generation.
They cherrypicked 15 countries. And still, some of those still had renewables decrease since 2000 like Nigeria, others saw an increase but it's still way less than fossil, and others like China are heavily subsidizing solar. I don't doubt that it's economical for individuals when the govt is subsidizing it.
Look at Australia then. Millions of homes already using solar yo basically power their homes for free most of the time. Yes it was subsidized, like oil was and still is. Solar without subsidies is already miles better than oil and gas.
Solar is dirt cheap in China, where 85% of panels are produced. The problem is they're made in China and face tariffs/import bans in the US/Europe.
Nat gas is preferred for AI DCs because it has faster time-to-market, doesn't have the intermittency issues. Training on solar + storage is an issue because of network synchronization.
I get the gas turbine for semi-temp power when there's not enough grid support, but they're talking about doing this long-term: https://www.gstatic.com/marketing-cms/79/80/fb229abf40efa81e... . Not a single mention of "solar" or "renewable" in there. Are they just trying to appease Trump administration?
Data centers need lots of power 24/7 and regardless of cloud cover. Solar is great to reduce your daytime bills but you still need other methods to cover the downtime.
I would be surprised if data centers didn't put in gas _and_ solar.
Very honorable effort, but a lot of these seem to touch heavily regulated industries impeded by unwise or outdated policy no less than by the lack of clever engineering - medicine, education, urbanism, energy. I wonder if they've given some thought to the key blocking factor as well.
They make many bold promises, but their core goal is neatly encapsulated on the website:
"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."
This is the same goal as every other AI company out there. Automate away the human employees and let a small number of "people" (note that they do not say scientists or engineers for this part) take the credit and financial rewards for every good thing this human-free system produces.
Also, wouldn't anyone with half a brain use the human-free system to produce another human-free system that was no longer controlled by the "small number of 'people'"?
Why do you think you'd be given access and permission to do this? If a company genuinely cracks this human free system problem, why would they open it up, instead of simply outcompeting everyone that doesn't have their product?
My main point is that people shouldn't just swallow the noble and lofty sounding PR. These guys are the same as everyone else in the sector. Don't ignore the harmful or scummy things they'll inevitably do. Hold them accountable. If they are as noble as they sound, they should agree with me.
Start with separated sewage/wastewater and stormwater drains. Then accredited and highly scrutinised wastewater treatment and discharge into water bodies (or see below for a high-tech solution). As for clean water to the home, direct those stormwater drains to new reservoirs which sustain freshwater aquatic life. Protect aquifers from over-drainage, and build pipelines from water-abundant regions to water-scarce regions.
To reclaim waste water or treat unknown water sources back to potable/semiconductor standards we have ultrafiltration, reverse osmosis, UV treatment, pH adjustment, fluoridation, desalination, softening (which is generally obviated by RO...). This is basically Singapore's NEWater.
Good sanitation is a financial and political problem. The engineering has been solved for decades now.
I've seen tiny tiny hints from the outside that Jeff Dean was dealing with too much internal BS. Two examples that come to mind: Having to deal with Timnit Gebru fiasco, and even chips in the TPU series getting marketing names (Trillium and Ironwood) before switching back to more standard numbering.
I have no doubt that internal Google friction is one of the reasons they are moving. But the Gebru incident was almost seven years ago now. It is very unlikely to be a proximate cause.
I doubt numbering vs names on TPU releases even crosses Jeff's radar. It's not the kind of thing he cares about.
Yes. I'm not privy to any real insider gossip, but I read all his tweets and watch all his public speeches. He made an offhand comment about the TPU naming. I probably overinterpreted that, but I took it as a sign. There should have been a team around Jeff Dean that acted as an absolute shield for any BS. If Jeff disagrees with anyone at Google outside Sundar/Sergey, the strong onus should be on the other person to justify their stance.
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.
Gemini has done absolutely nothing for me. I can't even shut off the navigation feature on my phone using only hands free, when I get close to my destination. I have to take my eyes off the road, look down, and tap to exit.
Google's advanced AI cannot even exit a mobile app.
Antigravity + Gemini Pro absolutely RIPS through fullstack react + react-native apps / systems. I pay ~$20/month and I basically don't have to do my real work anymore. My time is freed up to learn systems programming and blender.
Oh nice have things stabilized with models/quotas and Antigravity is usable with the Pro plan again? I was getting a crazy amount of value out of Gemini CLI for “free” on my Pro plan but after the shutdown struggled to make agy work with the updated quotas/bigger models without instantly being rate limited and just started doing everything on Codex/Opencode Go.
Yeah can't remember how long ago but it was running out after less than an hour - then they announced they were loosening restrictions and I've been able to easily get everything I need to done without hitting limits.
I don't do huge automatic project wide hands-off agent loops though. I spent a lot of time architecting my systems to be easy to generate code on top of with pointed & detailed prompts. So I'm not abusing context... YMMV
> Between us, 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.
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.
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.
Model routers - send all of your data through a third party who totally swears not to peek at it.
If youre doing anything high value (advanced research, classified work, high value industrial research, health data) then sending your data through a third party like that is insane.
Best option by what metric? For which enterprises. I say this having worked at an “enterprise” where this was not a good option. For (lack of) talent/expertise, budget, infrastructure, and actual value relative to the eventual bottom line.
Why are people so sour about this?? I can read the site easily, its clear, performs well on mobile, what else do you want? Why is so offensive to people that models trained on tailwind or whatever?
If this was a design firm, it might matter. But this is mostly a hiring ad for engineers, and a landing page for VC. I'd judge them more if they actually put effort into it.
To many people, myself included, who have to wade through huge amounts of low-quality AI slop which is actually a negative value: no-one benefits when non-experts fire-off one-shot LLM/agent prompts to produce PRs, reports, documentation or "journalism" riddled with imagined truth and factual errors - and the more that people like me have to evaluate these inputs for our job and how wrong they are it pisses us off - but also it means we pick-up on the hallmarks, tells and cliches of these low-effort, no-respect submissions - and now the litany of tells includes this beige-themed, blurred-backdrop-navbar corporate website look: theirs site looks like the 4 or so other LLM-generated, negative-value slop-farm sites I've wasted time on recently - all over the past few weeks.
So I'm saying that, without having known anything about what "Discovery Loop" is - or is not - but landing on their site and and seeing that beige colour and blurred-backdrop navbar, I immediately moved to close the tab; what kept me here was seeing the HN thread had over 100 comments by now and read more about it; if not for that then I wouldn't have given it further thought.
Having "that" beige site look with same the overused looks is either an unintentional indication that the site's author used a low-effort AI prompt to generate the site and that the content within is likely to be low-quality, low-value - or it's an intentional lure to appeal to those who uncritically share in the AI psychosis and so, I assume, are a good target to seek investment from even if it means losing the audience of cynical Internet critics like myself because they know people like me won't be breathlessly repeating their vision-statement on LinkedIn and throwing money at them - kinda like how scam emails intentionally include mistakes for better audience selection. And both possibilities have unpleasant implications.
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Anyway, regardless of the background of the team behind it, the way the project is described sounds exactly like the recursive-self-improvement and simulated-science thought-experiments from _that other website_ - it's the kind of thing I expect Angela Collier to brutally takedown in an amusing video.
Some parts are pretty annoying to read... the paragraph beginning in "Our mission is straightforward:" has many lines with just 2-3 words, massive font, and tons of unused whitespace to the right. Changing the page/browser zoom doesn't seem to help much either.
"The site itself demonstrates the team is spending their money in the places that matter, and using quick solutions for the stuff they need but isn't mission critical"
Right. What about the scientific hardware (instruments, sensors, robotics)? Partnerships with existing research institutions? Dealing with restricted data?
Modernizing science is a lot more complicated than just optimizing the inner experimental loop, but their hiring page implies it's a pure ML lab focused mainly on model development.
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 :)
Only California employers with 15 or more employees have to post a pay range (Senate Bill 1162 [1], effective Jan 1, 2023) [2][3]. Discovery Loop employs only four people (that we know of), so it isn’t required to disclose a salary range in its job posts, of which there is only one [4].
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 engineer-to-work 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.
What percentage of people work at a startup though? Not just new/small business, which could include restaurants, local services, etc., but tech/science startups that would meaningfully benefit from AI.
I'd bet you could 10x the number and still be in low single digit percentages of the US workforce. And it seems pretty likely that AI-enabled startups will also employ less people per-startup.
If AI causes a white-collar jobs apocalypse, I don't think startups are picking up the slack, although it'll plausibly cushion the blow somewhat for top-performing tech workers.
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!”
"You're absolutely right! I shouldn't have pushed the anti-mass spectrometer to 105% power, causing a resonance cascade. This was a major oversight on my part."
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.
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.
I wish them well, but this firm will likely fail miserably. The reason is that the value is in having access to real world hardware platforms that AI can control, not in the harness that controls them. There exist plenty of harnesses already. These people couldn't even get Google to build a top LLM. Before you dismiss and downvote, I dare you to counter it.
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.
Do you believe that securing cyberspace is problematic solely because it has military implications? I mean, everything has military implications. That fact doesn't imply, however, that those things are bad for society.
In essence I agree with that, it's just that cyber-security has particularly been the focus of recent military discourse.
Just yesterday I was reading an article here in the Romanian mainstream media about how Constanta Port's (our biggest port at the Black Sea) IT infrastructure has been under constant cyber attacks (presumably by the Russians) so as to hinder the export of Ukrainian grains through it. And this is just one of the many such (relatively) recent examples.
Securing cyberspace matters to everyone. Defending critical infrastructure or design of tactical cyber-offense is reasonably in scope for military work.
However, reducing (or rather limiting the increase of) PII leakage and impact of ransomware activities is much closer to day-to-day mainstreet of most people.
Anyone committed to advancing science should care about this regardless of its potential contributions to defense.
So does more efficient cooking methods, but that is not the primary focus.
As opposed to say weapons systems or targeting systems, which are really only for military use.
The military needs a lot of things that other people need, and some things that only the military needs. If you don't work on the things only the military needs, I think you're in the clear.
In short, they are in the business of using AI to automate the discovery of useful knowledge, not to sell general-purpose AI capabilities that could be directly used in troubling ways.
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?
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.
I mean what are they doing right now at Google? Optimizing data centres? Pretty lame compared to this. Even if they completely fail, i'm sure there'll be good lessons.
Seems like Karpathy was largely focused on ML / SWE research rather than the other domains this group is after. Still, hard to imagine they were not influenced by autoresearch.
Andrej, if you're around, please share your thoughts on Discovery Loop.
That is also what I understand from that page. Autoresearch is the closest thing we (mainstream audience) know of but I am sure there is already an active research literature around it.
You know when the page has all-caps "01 — THE APPROACH" that it is slopified. I guess I shouldn't be astounded, but I am, that world-class talents with world-class backing are just taking default LLM output and saying, "okay looks fine".
Maybe this is what happens when someone with Jeff Dean's standing tries to quit?
TBH, I'd rather have Jeff Dean working on the creepiest-possible tech for ICE than joining the race to automate AI research. Automating AI research is terrifying.
If you check out some sub-tweets from people in the org, it wasn't really all butterflies internally for a while. Sorry, really don't want to name people and give examples.
That founding team is insane. Very excited to see what happens here. I really like that they do not mention AGI or anything like that. Their mission statement reads pretty pragmatic compared to other AI companies (the bar is very low…)
Yah, by funding and how we award it, not by an imaginary lack of undergrad and grad students. Scientific funding requires a shotgun approach and many national science funds try to pick winners as opposed to funding broadly. When the folks who researched bacteria in volcanic vents or the molecular biology of the Gila monster they never could have imagined the industries and markets they'd create let alone the lives they'd impact (i.e., PCR and GLP-1 agonists). Lots of grants require you to explain how the work is "translational" or has some sort of economic application (even if not explicitly), but that'll just get us faster horses or whatever the Ford quote is.
I truly believe if we took a measely $50b out of the LLM world we could create trillion dollar economies from basic research within 10 years. I personally know folks who have intuitive understanding of things that can't get funding to be studied. If we could keep the money away from university upper management, it'd cost $10b max.
Oh and while we're at it, $20b a year would house every homeless person in the US - there's a hell of a lot of extremely high intelligence and low social cohesion folks who can't handle the extractive punitive system we have. Our ability to deliver opportunity to create lucky situations for ourselves is getting worse and worse
$50B is essentially 100% of the annual NIH budget, which funds the vast majority of JUST life sciences basic research. So you may want to update your beliefs
e: oh and while we're at it, California spent over $24 billion over a five-year period (2019–2024) specifically targeting homelessness
I have used something similar. I set up a team of agents that researches, proposes, builds and audits. Then rinse and repeat. I have used it for different topics. It hasn’t made me a millionaire, but I haven’t lost money either - so that’s some sort of win, right? But I would not have been able to ideate, test at that speed and quality without an LLM.
Jesus, he left Google to do what everyone else is already trying to do? He must be so insulated he doesn’t realize what the real world is actually up to. I mean, organizations started on this exact same mission three or four years ago. Or longer. I suppose it’s better to wake up later than never.
drcongo | 4 hours ago
ValentineC | 4 hours ago
[1] https://github.com/LRitzdorf/TheJeffDeanFacts
hoyd | 4 hours ago
I had not read this before, but told many students the same about my PIN code and I a quiz about the last digits. Love it.
soVeryTired | 3 hours ago
tcp_handshaker | 3 hours ago
Here are some Jeff Dean well sourced facts:
- Already part of engineering of Google indexing systems that lacked basic checksums and ran on non-ECC hardware, allowing silent data corruption.
- One of the authors of LevelDB a database with so many documented crash-consistency, recovery, and data-loss weaknesses for years. Just check their Github project. LevelDB current tracker contains unresolved crash consistency, recovery and corruption reports going back almost 12 years on GitHub
- In AI engineering technical lead, let TensorFlow lose researcher mind share to PyTorch, and caused Google fragmented landscape across TensorFlow and JAX.
- Had the people at Google who invented the Transformer architecture, but failed, to turn that lead into the first dominant public LLM.
- As AI engineering and VP management let Google Brain and DeepMind remain duplicated and internally competitive for too long.
- Let Noam Shazeer leave and then spent heavily to bring him back with nothing to show for.
- Part of Technical VP leadership who had Bard rushed to launch with factual errors in Google own promotional material.
- The first Gemini demonstration overstated how real-time and interactive the system actually was, being basically a fake.
- Part of the VP and AI technical leadership who had Google AI Overviews launched with weak source quality controls and repeated satire and low-quality web content as factual advice.
- Part of teams that launched AlphaChip performance claims that were difficult for outside researchers to reproduce and remain technically disputed.
- Jeff Dean public explanation of Gebru departure was contested and damaged confidence in Google scientific governance.
- Jeff Dean was part of the team at Google that removed or marginalized prominent internal AI ethics critics shortly before many of their warnings became product problems.
- Jeff Dean was one of the managers behind Project Dragonfly supporting censorship.
- Jeff Dean is part of the VP technical leadership approving Project Nimbus supporting an ongoing genocide.
bonsai_bar | 3 hours ago
root-parent | 3 hours ago
twister2920 | 3 hours ago
dekhn | 2 hours ago
shawn_w | 3 hours ago
Johnny_Bonk | 4 hours ago
calufa | 4 hours ago
I feel the most exciting development these days is self-evolving agents. Especially if you have a way to verify their outputs with a formal system, or with a system developed since the 60s by armies of PhDs.
DeepMinds Gnome is a good example, where they use DFT to verify outputs. Approximating NP-problems is always fun for those who dare.
I am also building in this space. Its a mix between HPC, AI, and hard science. Pretty fun compared to waking everyday to LLM news that seem more like marketing stunts.
deeviant | 4 hours ago
They are straddling the line between pushing it forward, and justifying the business case. It's hard to do both at the same time.
calufa | 4 hours ago
If throwing more money at inference while accumulating compounding technical debt is the new norm, then we are not solving the problem, and the solution space is already covered.
Perhaps there are marginal gains at the expense of quadrillion-params LLM models with 10x the cost and energy. We are simply making inefficiency more expensive, camouflaged by VC money and great marketing.
If that is not plateauing, then I guess I will have to reconsider what plateauing means.
bpodgursky | 2 hours ago
This is such unbelievable revisionism! Can you imagine in 2022 saying "Oh of course you can brute force your way to AGI if you spend enough money per month". Nobody thought that! Come on!
zuzululu | 4 hours ago
aoeusnth1 | 4 hours ago
flakiness | 4 hours ago
That said, I hope they write cool papers with various peers across the industry without worrying too much about the competing dynamics. That'd be a blessing for humanity, and good for their spirit.
canes123456 | 4 hours ago
Very silly to call every non start up a lifestyle business. It’s just a business. Start up are the weird thing that almost always an obscene waste of time and money, but sometime creates google.
flakiness | 3 hours ago
valleyer | 3 hours ago
mgfist | 2 hours ago
Why is that so? Fast growth, when achieved honestly, is a result of solving user pain that others haven't. Maybe you think so because users != the public, but I think in totality the public is a collection of users who all have needs they want met.
markstos | 2 hours ago
Why? Because investors poured a bunch of money in to support fast growth and now they want their money back. And incremental growth won't do. Since 9 out of 10 of the investments fail, the surviving one has to continue to growth-hacking revenues.
kube-system | 2 hours ago
The advantage to a PBC is protecting founders from a serious problem with standard corporations: you might bring on investors who could subsequently demand you pollute, exploit people, and/or do other immoral activities for profit. You don't have to do these things to grow a business quickly.
snowwrestler | an hour ago
I’d like to provide maybe a clarification here that there is zero existing fiduciary duty in regular corporations to say yes to evil things, or even to turn a profit at all. A for-profit C corporation can legally sell stock, lose money every year, and go out of business, if the board of directors approves that strategy. Fiduciary duty exists primarily in areas of accurate communication and the avoidance of crime, fraud, etc.
A B corp basically is a C corp, but one that has formally published that their strategy includes a commitment to some social benefit. But if a C corp wanted to publish the same message to shareholders it could, and shareholder recourse would basically be to either try to replace the board, or sell the stock.
kube-system | 8 minutes ago
Consider the eBay/Craigslist case, eBay Domestic Holdings v. Newmark:
> When director decisions are reviewed under the business judgment rule, this Court will not question rational judgments about how promoting non-stockholder interests—be it through making a charitable contribution, paying employees higher salaries and benefits, or more general norms like promoting a particular corporate culture—ultimately promote stockholder value. Under the Unocal standard, however, the directors must act within the range of reasonableness. Ultimately, defendants failed to prove that craigslist possesses a palpable, distinctive, and advantageous culture that sufficiently promotes stockholder value to support the indefinite implementation of a poison pill. Jim and Craig did not make any serious attempt to prove that the craigslist culture, which rejects any attempt to further monetize its services, translates into increased profitability for stockholders.
https://courts.delaware.gov/Opinions/Download.aspx?id=143440
This is where a PBC would have been different. With a PBC, courts are directed to balance the the stockholders interests with the company's stated public benefit.
tonfa | 4 hours ago
They're also incredibly productive and can build/deliver really good stuff, so who knows :)
sarjann | 2 hours ago
wavemode | an hour ago
1970-01-01 | 4 hours ago
XenophileJKO | 4 hours ago
1970-01-01 | 4 hours ago
https://en.wikipedia.org/wiki/Sense#Artificial_sensation_and...
LarsDu88 | 3 hours ago
montebicyclelo | 4 hours ago
https://www.ycombinator.com/library/Vy-jeff-dean-the-1-rule-...
xnx | 4 hours ago
melodyogonna | 4 hours ago
jfrbfbreudh | 4 hours ago
FailMore | 4 hours ago
IAmGraydon | 3 hours ago
mosfets | 4 hours ago
stephantul | 4 hours ago
hobofan | 4 hours ago
And so does academia. It's just that instead of AI and robotics, PhD students are thrown onto problems that are in large parts slightly tweaked reconfigurations of similar experiments.
Especially in chemistry, biochemistry, material sciences there is a large space of discoveries that are barely "novel" in an intellectually stimulating way, but still highly valuable that can be explored orders of magnitudes faster than is currently the case.
porridgeraisin | 3 hours ago
teamonkey | 2 hours ago
pickleRick243 | 2 hours ago
porridgeraisin | an hour ago
stephantul | 3 hours ago
Then again, gassing rats and taking biopsies is not something you can do with AI.
roughly | 3 hours ago
Also, like, let’s maybe _not_ make the “gassing and cutting living organisms open” AI? Let’s just leave that particular genie in its bottle?
smcg | 2 hours ago
tcp_handshaker | 3 hours ago
flakiness | 4 hours ago
holy shit. I've known this, but...
cjbarber | 4 hours ago
> 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
tcp_handshaker | 3 hours ago
tgma | 3 hours ago
Gotta compensate them somehow.
DataDaoDe | 3 hours ago
dude250711 | 3 hours ago
ex1fm3ta | 3 hours ago
returnInfinity | 3 hours ago
hiddencost | 2 hours ago
tgma | 43 minutes ago
xnx | 36 minutes ago
LogicFailsMe | 3 hours ago
But also, solar power is already economical.
elicash | 3 hours ago
staplers | 3 hours ago
twothreeone | 3 hours ago
rush86999 | 3 hours ago
marcosdumay | an hour ago
glaslong | 3 hours ago
podgietaru | 3 hours ago
As you said, Solar power is incredibly economical. There are plenty of ideas around putting them over farms, or parking lots en-masse to provide cleaner energy.
Access to clean drinking water, while certainly scientific in some situations, is also a problem of political will and money.
Restore and Improve Urban Infrastructure - It's infrastructure week!
RajuChacha108 | 2 hours ago
To make solar power practical and economical you need may a square foot of solar panel being able to get enough energy to power and entire home for a week
snaking0776 | an hour ago
Not sure what you’re talking about here. We can’t replace all energy needs with solar but it’s clearly one of the cheapest energy sources and with the added benefit of low capital expense to get started so you can set it up in distributed grids without the massive expenditure to support nuclear installations.
Marha01 | an hour ago
Not if you include the cost of needed storage.
gfarah | an hour ago
https://www.iea.org/data-and-statistics/charts/lcoe-and-valu...
UncleOxidant | an hour ago
xnx | 47 minutes ago
la64710 | 3 hours ago
hiddencost | 2 hours ago
What the fuck man? I really don't want some tech startup trying to "fix" my neurodivergence.
koolala | 3 hours ago
epicureanideal | 3 hours ago
Reverse human aging.
(Maybe a sub-topic under "Engineer Better Medicines".)
dag100 | 2 hours ago
Only death stops stagnation in the end. Without death, especially if death can be avoided by the rich and powerful but not the poor, life will get much, much worse for the average person (until only the rich and their automated capital remain I suppose, in which scenario they will simply turn on each other).
mrdependable | 3 hours ago
dbgrman | 3 hours ago
Sivart13 | 3 hours ago
cheschire | an hour ago
embedding-shape | an hour ago
xnx | 45 minutes ago
cm2012 | 30 minutes ago
Sivart13 | 3 hours ago
Maybe if our biggest companies did something other than suck up to science denying wackos, some progress could be made in these areas.
podgietaru | 2 hours ago
tbrownaw | 2 hours ago
I would think the claim in the second sentence would only be relevant in case of the inverse of the claim in the first sentence.
DaiPlusPlus | 2 hours ago
Point 1 on the list is "Make Solar Energy Economical".
Solar is economical today - mostly due to China investing (and heavily subsidising) in solar for the past couple of decades; but compare to the US where certain particular big-businesses (oil companies, mostly) were instead cynically funding disinformation efforts and getting into bed with the Republican party (which dovetailed with the GOP's allying with other science-denying movements of the Bush Jr era like creationism and public-health matters with abstinence-only sex-ed and defunding gun safety research efforts) - means we're decades behind where we could have been...
Consider an alternative past, where the GOP had the backbone to resist the oil industry's corruptive influence and instead made a big bet on American Solar; it's entirely possible that instead of MAGA today we'd instead have a right-wing coalition strongly supporting solar and wind energy because they align nicely with American rugged individualism - whereas the current situation on the right is an unprincipled farce with inconsistencies in policy positions at every turn.
slongfield | 2 hours ago
holmesworcester | 2 hours ago
Arainach | 2 hours ago
xnx | 49 minutes ago
marcosdumay | an hour ago
xnx | 50 minutes ago
Marha01 | an hour ago
That is not mutually exclusive. If technological advances result in a given technology becoming cheaper, more scalable, and easier to deploy, they also make it easier to advocate for and implement the relevant policies.
You can think of it like this: our "political technology" is not good enough to use solar energy at its current prices to replace fossil fuels as fast as we would like. Well, what about if we cut the price of solar by a factor of five? Perhaps it will be good enough then.
qwertygnu | an hour ago
It's yearsss past time that our leaders should have changed policy.
bossyTeacher | 38 minutes ago
Your thinking reminds me of this https://xkcd.com/538/
ivanovm | an hour ago
It also happens to be the favorite pretext for people to seize more political power and launder more money through nonprofits though.
floatrock | an hour ago
zahlman | 33 minutes ago
sajithdilshan | 3 hours ago
merona_io | 2 hours ago
lovlar | 2 hours ago
For what purpose? To replace humans? To make social media more addictive? To master brain manipulation?
RajuChacha108 | 2 hours ago
fcarraldo | 2 hours ago
willy_k | 2 hours ago
tantalor | 2 hours ago
For brain, our understanding is fuzzy, more like "this part is important for that behavior" or "here is how neuron works" but we don't have a holistic understanding.
If we had that, we could more easily diagnose and treat neurological disorder.
captainclam | 2 hours ago
embedding-shape | an hour ago
To understand, same reason you reverse engineer anything. Doesn't have to have a further goal than that, understanding the brain better helps in so many ways. But like most technology, obviously can be used for bad too. Should we just skip researching some topics then?
lovlar | 52 minutes ago
I just like to challenge myself, as an engineer, with the idea that not everything has to be engineered and optimized. What if we simply left some things unexplored and mysterious, and trusted nature and our own human capabilities?
A better way of alleviating psychiatric/mental health disorders might just be to focus on societal factors.
darth_avocado | 2 hours ago
Isn’t it already?
Marha01 | an hour ago
glenstein | an hour ago
frollogaston | an hour ago
Of course, USA has cheaper oil/gas than other countries. But if you look elsewhere, rich countries are subsidizing solar, poor ones are basically not using it.
jarpschope | an hour ago
https://www.pewresearch.org/short-reads/2026/07/20/how-globa...
In the chart for section 3, how many countries have seen their share of electricity being generated by fossil fuels increase in the last 5 years? Only Canada.
Take a look at the charts for Pakistan, Australia, Nigeria, and China for the last few years. Pretty dramatic drops for fossil fuels generation.
frollogaston | an hour ago
blahblaher | 48 minutes ago
frollogaston | 42 minutes ago
zahlman | 25 minutes ago
UncleOxidant | an hour ago
I don't think that's true: https://rmi.org/resources/the-global-souths-cleantech-revolu...
frollogaston | an hour ago
ravst3s | an hour ago
Nat gas is preferred for AI DCs because it has faster time-to-market, doesn't have the intermittency issues. Training on solar + storage is an issue because of network synchronization.
frollogaston | 56 minutes ago
jay_kyburz | 43 minutes ago
I would be surprised if data centers didn't put in gas _and_ solar.
vovavili | 2 hours ago
dgellow | 2 hours ago
vanviegen | an hour ago
jay_kyburz | 40 minutes ago
beloch | an hour ago
"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."
This is the same goal as every other AI company out there. Automate away the human employees and let a small number of "people" (note that they do not say scientists or engineers for this part) take the credit and financial rewards for every good thing this human-free system produces.
sejje | an hour ago
Also, wouldn't anyone with half a brain use the human-free system to produce another human-free system that was no longer controlled by the "small number of 'people'"?
nwienert | 53 minutes ago
If you grow up in the right place at the right time, how much should you be in control of everyone else's life?
terryf | 17 minutes ago
The universe has no need to be fair.
monknomo | 11 minutes ago
a2ff6eeb0 | 43 minutes ago
hahahaa | 10 minutes ago
beloch | 19 minutes ago
zahlman | 34 minutes ago
delta_p_delta_x | 30 minutes ago
??? We don't need any AI for this.
Start with separated sewage/wastewater and stormwater drains. Then accredited and highly scrutinised wastewater treatment and discharge into water bodies (or see below for a high-tech solution). As for clean water to the home, direct those stormwater drains to new reservoirs which sustain freshwater aquatic life. Protect aquifers from over-drainage, and build pipelines from water-abundant regions to water-scarce regions.
To reclaim waste water or treat unknown water sources back to potable/semiconductor standards we have ultrafiltration, reverse osmosis, UV treatment, pH adjustment, fluoridation, desalination, softening (which is generally obviated by RO...). This is basically Singapore's NEWater.
Good sanitation is a financial and political problem. The engineering has been solved for decades now.
michaelbarton | 21 minutes ago
xnx | 4 hours ago
compiler-guy | 4 hours ago
I doubt numbering vs names on TPU releases even crosses Jeff's radar. It's not the kind of thing he cares about.
xnx | an hour ago
asimpletune | 4 hours ago
an0malous | 3 hours ago
arjie | 4 hours ago
PaulDavisThe1st | 3 hours ago
And ... it might not.
arjie | 3 hours ago
jbmchuck | an hour ago
wy1981 | 4 hours ago
Sometimes I couldn't resist wondering if I'll ever do work that has a tenth of the impact of theirs.
gosub100 | 3 hours ago
Google's advanced AI cannot even exit a mobile app.
allthetime | 2 hours ago
qlte | 2 hours ago
I should give it another try…
allthetime | 2 hours ago
I don't do huge automatic project wide hands-off agent loops though. I spent a lot of time architecting my systems to be easy to generate code on top of with pointed & detailed prompts. So I'm not abusing context... YMMV
16bytes | 2 hours ago
Jeff was a ACM Fellow in 2009 and published the massively influential MapReduce paper in 2004.
parthdesai | 24 minutes ago
jimbokun | 40 minutes ago
Not a bad combined CV.
syntaxing | 4 hours ago
Taikhoom2010 | 4 hours ago
Models are commodities the applications eg. BaseTen, OpenRouter should capture the value.
https://taikhooms.substack.com/p/why-openrouter-can-be-the-n...
compiler-guy | 3 hours ago
malux85 | 3 hours ago
If youre doing anything high value (advanced research, classified work, high value industrial research, health data) then sending your data through a third party like that is insane.
adfm | 3 hours ago
Taikhoom2010 | 3 hours ago
willy_k | 42 minutes ago
Taikhoom2010 | 11 minutes ago
make3 | 3 hours ago
deerstalker | 4 hours ago
pphysch | 3 hours ago
bezko | 4 hours ago
sidcool | 4 hours ago
searine | 3 hours ago
ChrisArchitect | 3 hours ago
Jeff Dean leaving Alphabet
https://news.ycombinator.com/item?id=49184746
swalsh | 3 hours ago
kingofthehill98 | 3 hours ago
If I had to bet my money, it would be on "for worse".
dude250711 | 3 hours ago
swalsh | 3 hours ago
roughly | 3 hours ago
warkdarrior | an hour ago
claiir | 3 hours ago
pelagicAustral | 3 hours ago
make3 | 3 hours ago
swalsh | 3 hours ago
slopinthebag | 3 hours ago
DaiPlusPlus | 2 hours ago
To many people, myself included, who have to wade through huge amounts of low-quality AI slop which is actually a negative value: no-one benefits when non-experts fire-off one-shot LLM/agent prompts to produce PRs, reports, documentation or "journalism" riddled with imagined truth and factual errors - and the more that people like me have to evaluate these inputs for our job and how wrong they are it pisses us off - but also it means we pick-up on the hallmarks, tells and cliches of these low-effort, no-respect submissions - and now the litany of tells includes this beige-themed, blurred-backdrop-navbar corporate website look: theirs site looks like the 4 or so other LLM-generated, negative-value slop-farm sites I've wasted time on recently - all over the past few weeks.
So I'm saying that, without having known anything about what "Discovery Loop" is - or is not - but landing on their site and and seeing that beige colour and blurred-backdrop navbar, I immediately moved to close the tab; what kept me here was seeing the HN thread had over 100 comments by now and read more about it; if not for that then I wouldn't have given it further thought.
Having "that" beige site look with same the overused looks is either an unintentional indication that the site's author used a low-effort AI prompt to generate the site and that the content within is likely to be low-quality, low-value - or it's an intentional lure to appeal to those who uncritically share in the AI psychosis and so, I assume, are a good target to seek investment from even if it means losing the audience of cynical Internet critics like myself because they know people like me won't be breathlessly repeating their vision-statement on LinkedIn and throwing money at them - kinda like how scam emails intentionally include mistakes for better audience selection. And both possibilities have unpleasant implications.
------
Anyway, regardless of the background of the team behind it, the way the project is described sounds exactly like the recursive-self-improvement and simulated-science thought-experiments from _that other website_ - it's the kind of thing I expect Angela Collier to brutally takedown in an amusing video.
throwaway0123_5 | an hour ago
swalsh | 3 hours ago
"The site itself demonstrates the team is spending their money in the places that matter, and using quick solutions for the stuff they need but isn't mission critical"
lrae | 2 hours ago
"The team of AI pioneers lacks the basic prompt-writing capability to make their marketing landing page not look like AI slop."
I, personally, don't hate it. It's a decently clean site, but it does invoke those thoughts in me too.
IshKebab | 3 hours ago
npilk | 3 hours ago
(Though, I do wish people would use just a few extra prompts to break out of the 'vibe-coded' look.)
ramon156 | 3 hours ago
pphysch | 3 hours ago
Modernizing science is a lot more complicated than just optimizing the inner experimental loop, but their hiring page implies it's a pure ML lab focused mainly on model development.
snitty | 3 hours ago
numbers_guy | 3 hours ago
danielmarkbruce | 3 hours ago
For some of the other things, undoubtably yes.
Noe2097 | 3 hours ago
pelagicAustral | 3 hours ago
pickleRick243 | 2 hours ago
Grosvenor | 51 minutes ago
It certainly increases shareholder value.
guessmyname | 3 hours ago
brcmthrowaway | 3 hours ago
guessmyname | 3 hours ago
[1] https://hr.ucmerced.edu/hr-units/talent-acquisition/senate-b...
[2] https://www.adp.com/spark/articles/2023/03/pay-transparency-...
[3] https://www.jazzhr.com/blog/pay-transparency
[4] https://jobs.ashbyhq.com/Discovery-Loop
jiveturkey | 55 minutes ago
GodelNumbering | 3 hours ago
throwaway0123_5 | 2 hours ago
I'd bet you could 10x the number and still be in low single digit percentages of the US workforce. And it seems pretty likely that AI-enabled startups will also employ less people per-startup.
If AI causes a white-collar jobs apocalypse, I don't think startups are picking up the slack, although it'll plausibly cushion the blow somewhat for top-performing tech workers.
drivebyhooting | 3 hours ago
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!”
moelf | 3 hours ago
scrlk | 3 hours ago
EstanislaoStan | 2 hours ago
mbonnet | 3 hours ago
> immanence
somebody has been studying Christian theology!
cute_boi | 3 hours ago
DaiPlusPlus | 2 hours ago
numbers_guy | 3 hours ago
danielmarkbruce | 3 hours ago
flatline | 2 hours ago
nonameiguess | 2 hours ago
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.
OutOfHere | 3 hours ago
constantinum | 3 hours ago
danielmarkbruce | 3 hours ago
claiir | 3 hours ago
paxys | 2 hours ago
recursive | 2 hours ago
meindnoch | 3 hours ago
tmoertel | 3 hours ago
paganel | 3 hours ago
> securing cyberspace,
which has clear military implications, at least in today's age.
tmoertel | 2 hours ago
paganel | 50 minutes ago
In essence I agree with that, it's just that cyber-security has particularly been the focus of recent military discourse.
Just yesterday I was reading an article here in the Romanian mainstream media about how Constanta Port's (our biggest port at the Black Sea) IT infrastructure has been under constant cyber attacks (presumably by the Russians) so as to hinder the export of Ukrainian grains through it. And this is just one of the many such (relatively) recent examples.
bredren | 2 hours ago
However, reducing (or rather limiting the increase of) PII leakage and impact of ransomware activities is much closer to day-to-day mainstreet of most people.
Anyone committed to advancing science should care about this regardless of its potential contributions to defense.
jedberg | an hour ago
As opposed to say weapons systems or targeting systems, which are really only for military use.
The military needs a lot of things that other people need, and some things that only the military needs. If you don't work on the things only the military needs, I think you're in the clear.
2001zhaozhao | 2 hours ago
Do you have more sources/info on this?
moralestapia | an hour ago
All the "bad guys" of today were the "good guys" at some point in time. You even cheered for them back then.
tmoertel | 37 minutes ago
https://xcancel.com/JeffDean/status/2085034604172603724
In short, they are in the business of using AI to automate the discovery of useful knowledge, not to sell general-purpose AI capabilities that could be directly used in troubling ways.
wavemode | an hour ago
4lx87 | 3 hours ago
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?
kulsumshannan | 3 hours ago
roughly | 3 hours ago
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.
galoisscobi | 3 hours ago
Imagine a future where only the anointed few elite minds can participate in science and engineering. Btw we’re hiring.
Great message!
cwoolfe | 3 hours ago
AIorNot | 2 hours ago
https://www.geekwire.com/2026/the-startup-idea-that-convince...
Sathwickp | 2 hours ago
thisoneworks | 2 hours ago
thatsadude | 2 hours ago
bredren | 2 hours ago
In March Karpathy described this direction:
Tweet is protected but in SERP caches: https://x.com/karpathy/status/2030705271627284816Seems like Karpathy was largely focused on ML / SWE research rather than the other domains this group is after. Still, hard to imagine they were not influenced by autoresearch.
Andrej, if you're around, please share your thoughts on Discovery Loop.
ozgung | 2 hours ago
eamag | 23 minutes ago
m3kw9 | 2 hours ago
aaronharnly | 2 hours ago
m3kw9 | an hour ago
holmesworcester | 2 hours ago
https://turntrout.com/why-i-left-google-deepmind
Maybe this is what happens when someone with Jeff Dean's standing tries to quit?
TBH, I'd rather have Jeff Dean working on the creepiest-possible tech for ICE than joining the race to automate AI research. Automating AI research is terrifying.
asadm | 2 hours ago
what why?
tokioyoyo | 2 hours ago
dgellow | 2 hours ago
mem1nce | an hour ago
Wonnk13 | an hour ago
jszymborski | an hour ago
Yah, by funding and how we award it, not by an imaginary lack of undergrad and grad students. Scientific funding requires a shotgun approach and many national science funds try to pick winners as opposed to funding broadly. When the folks who researched bacteria in volcanic vents or the molecular biology of the Gila monster they never could have imagined the industries and markets they'd create let alone the lives they'd impact (i.e., PCR and GLP-1 agonists). Lots of grants require you to explain how the work is "translational" or has some sort of economic application (even if not explicitly), but that'll just get us faster horses or whatever the Ford quote is.
gtirloni | an hour ago
vanviegen | an hour ago
woeirua | an hour ago
tjwebbnorfolk | 51 minutes ago
taurath | 58 minutes ago
Oh and while we're at it, $20b a year would house every homeless person in the US - there's a hell of a lot of extremely high intelligence and low social cohesion folks who can't handle the extractive punitive system we have. Our ability to deliver opportunity to create lucky situations for ourselves is getting worse and worse
eamag | 20 minutes ago
e: oh and while we're at it, California spent over $24 billion over a five-year period (2019–2024) specifically targeting homelessness
puttycat | an hour ago
ggcr | an hour ago
as founding members is crazy !
omederos | an hour ago
varjag | 44 minutes ago
xuehaohu | 40 minutes ago
maCDzP | 36 minutes ago
skinfaxi | 34 minutes ago
I'm curious if that is before or after token costs?
7e | 24 minutes ago