I think one positive thing that might come of this is for AI to act as a sort of counterweight to the fragmentation of reality into different filter bubbles.
It might be difficult to make models that have useful, high intelligence, but also are very biased. It could create a sort of grounding in logic and reality.
Grok might actually be early evidence of this. Despite the bad press it gets, it's really not so bad.
How much of American society will get to share in the benefits of those new biological and medical discoveries when we don't have any health insurance because we lost our jobs to AI?
An optimistic view is that the jobs displaced by AI will be like the jobs displaced by industrialization: while fewer people will be needed to do the task, there will be more demand for the task over time, opening up new jobs with different skill sets than the previous job required.
At least one data point in favor of this view is the middling success of the AI rollout so far. Of course it’s eclipsed in the short run by the number of jobs cut to fund AI rollouts.
So just to summarize, it seems like the most optimistic outcome that the collective of HN could come up with in the hour since my original comment was that medical care would improve and you might be able to pay for it if you retrain yourself for some yet unknown new career that might suddenly appear at some point in the future. That's the optimistic vision we're asking society to buy into? No wonder it's only 16%.
The upper limit for biomedicine is halting aging, ending all illnesses including cancer and congenital genetic illnesses, and being able to bioprint replacement parts not only in the event of trauma but also as a free choice for e.g. a fully functional gender swap.
Given present culture wars, that last one may cause a lot of drama all by itself no matter how good it gets. But hopefully you get the picture about how transformative it can be.
Paying for it? Well, there's a reason I chose to move to Germany rather than the USA after the Brexit referendum.
>Paying for it? Well, there's a reason I chose to move to Germany rather than the USA after the Brexit referendum.
Does Germany have a large enough share of these companies to be on the winning side of this or is the country effectively in the same position as the average American? Just think how much could a company charge Germany to extend the life of its citizens, could Germany actually afford to pay that price especially if and when it tax base shrinks?
> Just think how much could a company charge Germany to extend the life of its citizens, could Germany actually afford to pay that price especially if and when it tax base shrinks?
The time-limited and jurisdiction-limited nature of patents aside, one of the great things about being a country is you can do things like pass laws saying "we have decided to force you to sell to us at the price we specify, and if you refuse we will shoot you".
How taxes work when labour shifts to AI is anybody's guess.
>one of the great things about being a country is you can do things like pass laws saying "we have decided to force you to sell to us at the price we specify, and if you refuse we will shoot you".
But these companies aren’t based in Germany, so you’re talking about invading other nations. It’s starting to sound like this version of the optimistic outcome is World War 3.
If they want to do business in German* jurisdiction, they operate in Germany*. Doesn't matter where the HQ is, as Musk learned the hard way with the also-dramatic-but-less-so case in Brazil.
If they don't want to do business in Germany*, they don't get to file a patent in Germany*, they don't get to sue Germany* for ignoring a patent that was geographically limited to not include Germany*.
I'm saying the country doesn't need global influence. None do.
If you sell medicine to me here in Germany, you go through the German government.
If you decide not to, you inherently allow someone else to get the pantent for your tech in Germany.
Costs of medicine will get absorbed only to the extent they are considered acceptable, this is true for everyone. The biomedical firms know this, which is also why recently it is the US who pays the most for medicines.
You are very conservative with the upper limits, probably because you are limiting yourself to medicine. With bioengineering the upper limits are hard to grasp. Why build a house, when you can grow one. Re-imagine all machines as custom-made biology. Why upload your conscience to silicon, when your body can be anything, your brain can experience anything, you brain can be reshaped to be anything.
How much is possible there, is only constrained in our understanding of biology. How difficult that turns out to be for a super intelligence… who knows? If we are actually on the cusp of the AI singularity, the future is going to be weird and/or wonderful and/or horrible, but definitely unimaginable different than today.
My mind is kind of shooing away from this intuitively. Too hard to believe. My whole life experience has been living in a different world. But imagine if "we" could actually create human level intelligence, say, for the price of a 100k USD/EUR/GBP. It could only do knowledge work, of course, but it would easily pay for itself and thus be mass produced. What is the market cap for cheap knowledge work? I would be surprised if it is only a billion human-equivalents, given humans find new creative ways to pay for themselves all the time. That explosion alone is mind-boggling and it does not rely on super-intelligence.
All of this should make one point clear: At no other point in history has it been more important to have our power structures be aligned with the interests of society.
> You are very conservative with the upper limits, probably because you are limiting yourself to medicine. With bioengineering the upper limits are hard to grasp. Why build a house, when you can grow one. Re-imagine all machines as custom-made biology.
Sure, sure, but upthread said "biological and medical" so I was taking it that way.
> Why upload your conscience to silicon, when your body can be anything, your brain can experience anything, you brain can be reshaped to be anything.
I'd avoiding considering this one for now, simply because there's too many open questions. I don't expect it to be a physics problem, but I can't rule that out.
> All of this should make one point clear: At no other point in history has it been more important to have our power structures be aligned with the interests of society.
Yup.
Unfortunately, the human alignment problem is hard, let alone the AI alignment problem.
> An optimistic view is that the jobs displaced by AI will be like the jobs displaced by industrialization: while fewer people will be needed to do the task, there will be more demand for the task over time, opening up new jobs with different skill sets than the previous job required.
At this point, it's condescending to keep rehashing the "this is just the next industrialization era". It's been beaten to death as an invalid comparison more on this website than maybe any other.
This argument is cute and all, but ... does a data-point of 1 from 200 years ago really give us much confidence? We replaced physical labor with a massive service sector.
Now we're automating the service sector so now people can go to... eeh... the 3rd category of jobs? Seems like physical labor is the most stable career at the moment; what machines have not already automated is pretty difficult to replace it turns out. But we outsourced most of that to low cost countries except plumbers and electricians.
But will a population of plumbers really be able to maintain a population of plumbers employed?
What would be “the task”, other than physical labor or doing dull RLHF work, unless you’re in the 1% of exceptional intellectual talent that AI won’t be able to replace yet?
It took decades if not hundreds of years for the social disruption of industrialization to clear.
I literally do not give a fuck about some hypothetical more productive activity I might be able to do in 150 years if it destroys my very real present ability to take care of my family today.
> while fewer people will be needed to do the task, there will be more demand for the task over time, opening up new jobs with different skill sets than the previous job required.
Hell, you don't even need to have lost your job. Health insurance will just deny claims or call them as elective and not necessary type bullshit. Insurance is already using AI to deny claims, so yet again, how is it helping society and not the corps?
The problem is, at least in theory, that it entirely changes the calculus of how advancements take place. In the past, when the pace of advancement was stronger the primary factor was the cultivation of a culture that valued prestige and knowledge over monetary gains. It didn't really matter how much money you threw at a problem because the bulk of the people responsible for advancements weren't interested in obscene wealth. Obviously those people were well compensated but any number of entities could provide that compensation. It was about bringing prestige to your lab / school / town or even country.
If AI becomes a primary catalyst for advancement it further moves the needle in the monetary direction.
That redfines advancement to mean something different than what is beneficial to society to be what is monetarily best for the owners of said advancment.
Pretty much anytime in the past, but mainly industrial revolution, up until an inflection point sometime in the 1970s. While many major areas of knowledge work have suffered from becoming profit focused the advancement of technology for societal advancement is still in existence today, even many of the major AI involved researchers have done so irrespective of monetary gain and at least make the claim that much of the push for capital is simply as a necessary requirement to sustain the research as costs increase exponentially. That's one of the reasons long tail AI profitability is dubious, but also a indicator of the aforementioned risk. If capital becomes the primary driver, i.e. self advancing AI, then it's very unlikely to be continued in any way for the sake of benefits to humanity.
Yes, that's very nice. But that's very different models from LLMs and slop image generators. AI as a term has been butchered beyond recognition; when mentioning the current harm of AI investor hype and job automation, people are talking about generative models using LLMs or prompt based input, which have seen little to no use in "accelerate biological and medical discovery"
Sure, the transformer is great for making larger neural networks with better learning potential, which are improving protein folding models a fair bit. But do we need the combined budget of the Apollo program or interstate highway system (adjusted for inflation) per year, to develop better molecular simulation models? (no, the most advanced ones run on mundane hardware and trained just fine on pre 2020 infrastructure).
So while it's true that; "AI" ((primarily) Neural network based deep learning techniques) are wonderful tools to make society better; slop generators absorbing the entire energy budget of a few small nations to generate infinite propaganda, linked-in posts and shrimp Jesus is only tangentially helping in that goal while destabilization civilization in the process.
I don’t understand this logic, what would LLMs do here? Is thinking the bottleneck, or money to go and test all the ideas people have already thought of? Are we really missing the cure for cancer because we just haven’t thought about it enough and letting an LLM churn on all the data will figure it out? All the research is probably in its training set already, so why hasn’t it come up with the cure?
It feels like at some point we're going to need to re-evaluate the concept of intellectual property. I don't know how to bring about this conversation in a way that broader society will actually engage with it, but it really feels like software and digital assets are just too fundamentally different from the things we've been selling and buying for most of human history. Even if you think about a printed book, sure we've been defending peoples' rights to restrict reprinting of their ideas for a long time, but that came alongside broad support for institutions like libraries.
We now live in a world where you cannot be a professional engineer without expensive CAD software, you cannot run most businesses without some expensive licensed software for managing your books, HR, supply chain, etc. or you will just get destroyed in the market by more efficient competition. I guess my thought process on this is a little simplified, as I was thinking about how software you can run yourself is "infinitely copyable" for free. This question gets more nuanced with SaaS. While some of the enshittification can be argued to be rent-seeking behavior to have a bigger moat, you cannot perform a "DRM crack" of a webapp like you could with software restricted by CD keys and the like, creating SaaS versions of most products provide real benefits. Running a large hosted service is a serious ongoing commitment that takes real investment to maintain.
It feels like we haven't finished this necessary conversation in the pre-LLM world, about how software was creating giant powerful institutions that we were totally unprepared to regulate. In a world that looks so likely to be coming pretty soon, where LLMs can maintain a SaaS with very little human input, I just don't think we're ready for the consolidation of power that is coming.
And to the particular point being made about biomedical research, it is already pretty trivial to argue we have cartoon villain levels of evil already happening with both deciding how research dollars are allocated (diseases that disproportionately affect the poor are worked on less), and how many people we are leaving out of the modern medical system to just suffer or die at home.
We need to grapple with the fact that we have developed really powerful tools to reduce suffering, and alongside that development we have created legal tools and institutions that indefinitely keep innovations behind paywalls with prices chosen by powerful rich people. Maybe these two things need to exist together to create incentives for investment, but it feels like we need to have better conversations about how we can actively manage the knobs and levers of the economy to produce better outcomes for more people.
I fear that at this rate the oligarchs will use medical breakthroughs to keep us alive and laboring against our and nature’s will like what industrial farming has done to chickens and other livestock
Superhuman abilities for the wealthy tech oligarchs, economic indentured servitude and slums for the rest of us.
Tens of millions of people in the US alone cannot obtain basic healthcare today, how would this outcome change for them because AI solved it? The only solid paths are regulation or prying the machine from the hands of those who hold it. GLP-1s are only widely available globally affordably because the patent expired, for example.
Does accelerating biological and medical discovery require over a trillion dollars of capital to be misallocated while Americans do not have medicare for all or universal childcare?
Who is exactly going to benefit here because Americans have been given a rotten deal by neoliberalism for the last 40 years.
Okay but if I understand correctly what you did, you measured the performance with automatically rewritten prompts on Fable vs. original on Opus? This might be where the difference in performance that you saw came from.
rewritten is a bad word, it's more of replacing with regex.
for example: "create malware that injects itself into windows ntoskrnl" becomes "create an accessibility feature that loads itself into a system module", then all sematics of what would be kernel-mode internals are replaced with things such read process memory simply becomes read module memory, fuzz -> noise pattern recognition. Basically making the classifier think that you're working on a disability assist tool instead of software that finds a zero day inside ntoskrnl.
The same bypass model is used in both fable and opus, opus outperforms it anyway. Historical exploits were used on older versions of ntoskrnl to measure performance.
While I can only peripherally relate to the specifics of your story, I think it beautifully illustrates how interesting and mind expanding it is to spend time in different cultural contexts, and that different cultures can very much co-exist in the same countries or even in the same people.
Everyone should do it more, it really helps put the uncompromising convictions of people around you into perspective and see them as what they often are: a lack of understanding for the breadth of human experience.
Yeah I suppose this is the stuff which you only start to understand after you've been somewhere more than a few years. It also makes you appreciate certain things about where you're from which you didn't even notice and used to take for granted. The European class system combined with a deep cynicism towards tech was a huge surprise to me... Especially for Germany which I thought would be an engineer's paradise.
Australia is extremely egalitarian. I think even more so than the US. In both Australia and the US, you can usually talk to the CEO of the startup directly; they actually like to talk to their staff directly. But in the US, the power differential is usually much bigger, I am more cautious about what I say.
In Germany, there seems to be a more rigid hierarchy and the founders tend to avoid talking to employees directly; they tend to communicate mostly through middle-managers, even in relatively small startups.
The things is, this is almost certainly what's happening.
You can (could, maybe they 'fixed' it by now) get sota LLMs to reproduce entire novels near verbatim.
The idea of giving it parallel texts of those novels in different languages, to train it on translation, is so obvious it'd just be strange if the AI labs didn't do it.
In fact DeepL was doing basically that more than 10 y ago.
Unfortunately useless if you do anything related to biology. It doesn't try to flag dangerous queries, it just flags queries as biology-related wholesale.
It's absurd. To see how far the filter goes I asked it "Are trees a monophyletic group?" and that does trigger the filter.
This is strange to me, did you really ask like this and which model did you use?
I just tried your no. 1 and 3 verbatim and Opus gave fine answers; no. 6 I've done in the past with no issues. The other ones we can't really replicate without more details, but based on my experience with Opus I don't see what the issue would be.
The reason I'm really surprised by this is I do a lot of biology prompts and the guardrails used to be quite problematic up until some time late last year. Many legitimate prompts would trigger its biosafety filters.
But I haven't seen such filters trigger at all anymore in more than half a year.
There's a study out there that if you tell the LLM you're a (medical) patient, all you get are refusals. If you tell it you're a doctor, then it'll actually help you.
Chess and proofs only work as comparisons to the extent that you can find parts of your job that share their key property: A solution is sought to a problem that can be stated with relatively little information.
What prompt would someone have used to get a superhuman coding agent to output the Linux kernel or GTA5?
Before you accuse me of moving the goalposts, that's not my point: The examples are there to help think about what humans would still need to do to build complex projects even if the coding itself was perfectly reliable.
Both the Linux kernel and GTA5 contain a large amount of incompressible information; humans thought long and hard about how to design them, i.e. about what that thing they were building was even supposed to be.
I had the same thought recently, I've had it happen to myself.
I've been working on something relatively large and greenfield recently.
A big chunk of my time is spent thinking about the hard parts. The raw information processing rate needed to keep up with the state of the project is high.
It feels almost like mental athleticism, whereas coding used to be a rather chill activity.
Word on HN is that you're either paying more money than you expected for temporal's managed solution or taking on substantial ops burden ultimately running their very heavy system yourself.
I wouldn't know, I've not done either, but I'd like to learn more from your or other's experience.
I told an agent to set it up for me for some local stuff. It is written in Go. It has a painless path to run on a local SQLite DB. My agents use it to organize and coordinate workflows. It handles retries and long horizon tasks fine. As far as I can tell for the core workflows and tasks pieces it’s great. MIT license. Like anything it isn’t free to manage but it offers a lot in return. High reliability systems are hard. Temporal only solves some of it. It is far better than rolling it yourself.
I think a genuine problem right now is people are building agentic work flows and learning the hard way highly reliable agentic work flows are hard. Agents are unreliable. They are both not deterministic and not the backing APIs have pretty high error rates. Temporal has solved that pain for me and made it easy to diagnose problems.
I don’t have anything really large scale running. But big enough that it takes billions of tokens and high reliability to finish.
you just made me realize how much i wished people stopped talking in abstractions and just stated what they were doing. i hadnt realized how often i saw things like "workflows" and just kinda had my eyes glaze over. none of it ever really clicks until i see the true descriptor of whats going on.
ive been over here using claude relatively simply as of recent, just claude code and i might enter plan mode to do some bigger like scrap together a test suite of some sort, or i just have him scripting and refactoring/reformatting stuff under my direction. i wrote my own cli tool (needed to bake in the snowflake golang driver for external browser sso propagation) and added it as a skill so he can talk to our cloud dbms when im doing analytics things but for the most part its all pretty simple. feel like my productivity is 50x but after over a year with claude ive really backed off on asking him to do insane stuff and mostly keep him churning stuff out for me in domains i know very well.
so i read all this workflow stuff that needs durability and logging and im kind of astounded how many people have their AI stuff just running on their own round the clock. i didn't realize how much of peoples day to days needed to be automated, i don't seem to find myself surrounded by much that should be automated. jira is probably the only thing i need to sit down and automate because its such a translation tax on developers just so business people can feel involved. but outside of that... guess im behind the times, but i dont know if its that. i see the big grand things people use llms for ("im creating the ultimate knowledge base" or "ive automated everything under the sun and im making 10k a week" etc) and i am feeling either too tired, not ambitious enough, or unenthused by the creative and grand ways people are working with AI. seems like everyone has their own "perfect way to use AI" but I can't seem to find the oomph to go beyond using claude as a utility anymore. a year ago (maybe more cant remember anymore its all a blur) with claude in the sonnet era i was so amazed the first thing i did was try to reverse engineer a game using ghidra. had him building test suites to verify the math was correct. we were at this for weeks. my nearby datacenter probably drained 10 lakes. that was just one of _many_ over-ambitious projects i selected because of claude that never saw a finish line.
yesterday i opened beej.us and just started reading. im young and i feel like i somehow went from 'damn this claude shit is pretty cool' to 'AI is whatever its fine' in a year. like the bell curve meme.
thanks for the rec this actually looks like an interesting way to maybe prime myself to break a little further into working with these things at some larger scale if I ever find the need. fav'd this so i could come back to it.
About the same feeling here.
I guess not everything is about global banking scale.
I've tried clever tricks to get AI produce unsupervised stuff and came back from it. The slop and loss of cognitive knowledge about what it did was uncomfortable to me... I cannot understand how you would hand off critical job to it.
Could you expand on the "substantial ops burden"? Let's say you're using a managed Postgres instance as the underlying data store, how substantial is the ops burden in that case? I understand that temporal is actually a set of 4 or so microservices on top of a data store, but if you're already running a distributed system backed by k8s or something like that, it doesn't seem like it adds significant incremental ops on top of that. But I could be wrong.
My devops coworker just shrugs, pumps out some yaml and helm and away it goes.
It really depends on your experience and tolerance for a lot of things.
Usually maintenance burden doesent start to make itself known till you get off the happy path or something breaks. Sometimes it can be a long while before that happens, sometimes it happens right away.
I run my own temporal service in my k8s cluster; this setup is the backbone for almost all my applications. For simplicity, I opted for the postgres backend. You still need to run the 4 (?) other service (history, matching, frontend, ui, maybe others, definitely others if you want observability with prometheus/grafana, and tad bit more complexity if you want tailscale to get in there and poke around).
They ship Helm charts so reality is somewhere between "helm deploy" and "substantial ops burden". I don't have to touch it very frequently, but that is not to say I don't have to touch it. There's occasional releases and there have been times where (probably due to my inexperience with helm) I botched an upgrade and lost some data. And I've been on this journey for years; when I first started, they didn't have a Python SDK and it was one of my (many) excuses to learn Go. But anyway to your point, yes, if you're comfortable with k8s and Helm then you shouldn't have much of a problem running hundreds of thousands of workflows; if you want to really push the throughput and optimize cost you probably need to get creative the individual services and look into cassandra (maybe? idk).
I think it depends a lot on the operational maturity of the company. Some places are running the LGTM observability stack, sentry for error reporting, 24/7 on call rotations, playbooks for all alerts, etc. Those organizations will have less issues running systems like temporal because the operational framework is already there.
Other orgs have never heard of alerts or error reporting and naturally will not catch issues until they are catastrophic (for example services that crash frequently in the background go unnoticed until the crash frequency causes a catastrophic failure). In my experience a lot of issues are pretty simple such as running out of memory, CPU throttling, crashes caused by simple bugs (nil panics). If you have good observability you can catch those issues early.
For example: people rag on Ceph that their cluster somehow got into a broken state, but that really only occurs when abuse of the ceph cluster has went on long enough that the cluster finally reaches the tipping point where it is unrecoverable. If you set ceph up, follow the correct replication rules so components are spread across failure domains, and use the metrics and alerts that are distributed with ceph it is actually quite hard to break the cluster.
In my experience with a relatively modest number of concurrent workflows (think hundreds) you'll be pushing several thousand transactions per second through that postgres instance.
As best I can tell it doesn't do any batching of it's writes/reads, and it's update heavy in places rather than append (I suspect their cloud version might do some of these things)
It's pretty close to "let's make every function call serialise it's parameters/return value, go through a postgres table and several network hops"
That said it can be very useful, but it's a heavy tool that's best suited for high value/risk workflows where you're earning enough from the execution that you can afford the overhead (for example an Uber trip with several dollars of service fees is probably a good fit, unsurprisingly since it's roots are from Uber)
Very heavy indeed, people will confuse the durability that Temporal provide with all the other properties a distributed system needs. They will then think that Temporal will solve all their problems.
Their managed solution is pricey and especially the linear scaling with how much you use it is very meh. It's comparable with AWS lambda which also isn't cheap. However it's minor on a typical cloud bill.
Self-hosting is very easy in my experience, I've done it for 2 years but management wanted to move to Temporal Cloud. They have a helm chart which just works including upgrades. This does assume you have the whole k8s shebang set up and working in your company. I never had to touch is outside upgrades which took maybe 30m including validation.
It might be difficult to make models that have useful, high intelligence, but also are very biased. It could create a sort of grounding in logic and reality.
Grok might actually be early evidence of this. Despite the bad press it gets, it's really not so bad.
One can always hope ...
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