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#cancer#don#long#cure#anthropic#forever#real#world#claude#cells
Discussion (95 Comments)Read Original on HackerNews
I guess the improvement loop is tighter and they have more control over how discoveries can be used for marketing?
But, in my mind, it begins to feel like they are setting themselves up to be “everything” companies instead of focusing on their core product…
They'll continue to burn model for marginal model improvements in the next few years all the while having no moat _and_ having Open-Weight / Local models eat their lunch.
The only way for them to stay relevant as a company is to expand beyond simply providing the models.
There was a pitched battle over features like row-level locking as competitors like Sybase, Ingress and Oracle scrapped it out. New features arrived on a monthly cadence, with immense engineering effort behind them. The winners (Oracle mostly) won a great moat which led to them to where they are today.
The fact that so many AI companies can produce amazing coding tools so quickly shows there is no moat, supporting your theory.
I run into this all the time - we have such powerful functionality available to our users, and further we provide the elements that undergird all of it, so it’s totally possible for clients to take the services they buy from us and reconfigure them to make their own tools, better even than the ones we have built, purpose-built for their workflows…
And 9/10 clients will just click on the one thing they know and recognize and are familiar with and comfortable with… and then stop thinking about it.
It’s crazy how much of our job is not only building our product, but interrogating our clients over what they need, so we can demonstrate how our tools solve their problem. The users simply are not interested in figuring it out for themselves.
Given the prestige of the AI labs, the recent explosion of math proofs, the literal millions they can throw around, it seems very likely they can attract then fund small research projects across a broad range of science. And like startup math, it only takes one or two ground breaking results from a hundred attempts to pay back in the PR/hype.
No doubt that curing cancer would help, but I think the timeline might be a little too long. Even RSI AGI will not be able to get new medical treatments to market instantly. Real world testing takes a long time and is an unavoidable part of the process.
I want to live forever (or until I'm bored of it) and I don't have kids. I'm not sure what that has to do with trustworthiness.
Edit: And, you're saying you want to die. Is that more trustworthy than not wanting to die? I suppose if you are religious, you might believe you're going somewhere good when you die, in which case, you don't actually believe death exists, so we're having different conversations. I believe death exists and is permanent, and I'd like to not do that.
what a weird bias
I have reduced trust in people who make judgements about the value systems of others based on fairly meaningless characteristics.
Why are you only allowed to live forever if you have kids?
Seems like someone seeking immortality should be willing to do for the elixir if they want it even a little bit...
How so?
For everyone else confused: Think of all the people throughout history we would prefer would not have lived forever. Then multiple that by A LOT. Then consider how greedy and sociopathic most of the billionaire class is already.
Now, we could spend time getting distracted by childless. I don't think it matters.
Your dramatization of society's ills are not tethered to reality
https://www.cancer.gov/news-events/cancer-currents-blog/2024...
https://jitc.bmj.com/content/8/2/e000848 (careful: Figure 1 can be very graphical, but it shows the huge positive impact of this therapy)
We also have therapies based on monoclonal recombinant antibodies conjugated with chemotherapeutics. Simply put, we can produce antibodies that are specific for markers present in the surface of cancer cells, and we can attach drugs that can kill those cells. The antibody part is what makes this type of therapy very effective (you target only cancer cells, and not healthy cells) and also very expensive.
https://www.cancer.gov/about-cancer/treatment/research/car-t...
https://www.cancer.gov/about-cancer/treatment/types/immunoth...
https://en.wikipedia.org/wiki/CAR_T_cell
https://www.theguardian.com/society/2026/may/10/cancer-treat...
https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...
Now on real world testing, you think the rule applies? I tell you it doesn't. Human life might be precious, but human life in practice is also not precious. We waste so much of it. In some countries regulations will stop/slow it, but there are plenty of places around the world that will turn a blind eye for a fistful of dollars. Countries will go to those locations if it means gaining an edge.
https://news.ycombinator.com/item?id=49329717
It’s the top rated comment in the thread. Somebody tried to do something good, this is the response.
This pisses me off severely.
He isn’t wrong. But selling potential cures for cancer won’t cut it.
Not if it's a virus
I think its much simpler than that. Anything actually useful for people would be a good solution.
Obviously image gen and code gen is not the case, as though it does increase productivity, it doesn't make anyone's life actually better. If it led to 4 day work week - sure. Otherwise it could easily be net negative.
They'll need to show their goal is to help humanity and that all the other peoole arent acceptable collateral damage. Since those other people get to vote.
Is it unavoidable, though?
Generally speaking, hiring an army of influencers to shill for you results in bad PR, and comments like this one.
Is there an equivalent headline for Anthropic of this?: https://www.businessinsider.com/inside-open-ai-influencer-ma...
Caveating I'm not a biologist, but my understanding of the way this kind of thing works right now is a basic three-step process:
1) Find molecules and DNA/RNA sequences in the wild and catalog them.
2) Discover interesting subsequences among these.
3) Figure out whether any useful applications can come from what was discovered.
All three of these generally take a long time. Systematic automatic analysis of known databases speeds up and removes some of the luck from 2. But 1 and 3 are still long poles. 1 has the further issue that we usually discover these in existing organisms. I recall much of the outcry over tropical deforestation back in the 90s and replacing of rainforests with palm oil monoculture today is that the vast majority of terrestrial biodiversity is found in rainforests, and destroying them at industrial scale risks losing potentially useful molecules forever. 3 has the problem that you need to conduct physical experiments, and are limited by the speed of biochemical reactions no matter what and by the speed at which human subjects can be found and ethically experimented on assuming we care about being ethical.
A lot of good can come of this, but I don't see a path to singularity here, assuming we're talking the original Kurzweil meaning there of all technological progress that will ever happen all happening at once. Data collection and experimentation on living subjects, human or not, can only happen so fast, regardless of automation. It's not computational. Whenever you have to interface with the real world, you're now working at the speed of the real world, not the speed of electricity. CRISPR was discovered in 1987 and first used to edit a gene sequence in a human zygote in 2015. I'm sure there are plenty of ways to make the candidate discovery to human application step not take three decades, but it's never going to be three months, either.
At Google/OpenAI/Anthropic level you have clusters of LLM agents working with clusters of ML agents doing all kinds of tasks. A lot of this falls into proto-RSI where the LLM can improve the ML agents output based on analysis of said ML.
This isn't much different from how people work, you can't dump even part of DNA context in a human mind and get anything useful out. We has humans have to use and build tools to find answers because of scaling efficiencies of different computation types.
There are so many people involved on this yet we still say things like "Claude did", we need to start waking up and being more real about how we are still in "AI + Human" land.
What's wrong with saying "A team of researchers backed by Anthropic using Claude discovers a novel enzyme system with CRISPR-like repeats" or, ffs, mention the lead researcher in the headline?
Not saying that they were right or wrong, but that single moment sullied all AI-driven breakthroughs that came after it, and I don't think it was ever particularly relevant, at least not nearly to the degree that it was presented in the media. But I guess it ended up being a convenient outlet for AI anxiety in the end.
The LLMs that make this stuff possible weren't created by the AI labs from whole cloth. They crept up and jumped onto the shoulders of giants, basically the collected (non-consensually, of course, but jingles keys look at this pelican riding a bicycle!) works of humanity. Every discovery LLMs enumerate in this fashion rightfully needs to have a billboard-sized asterisk regarding the provenance of the discovery. "Claude" didn't discover this, everyone who worked to produce the internet that Anthropic siphoned into their dataset belongs on the credits.
It's great that it happened, and I wish them the best of luck in using our work to make the world a better place. Just don't forget who the rightful owners are.
The people that say "It's just a next word predictor" might as well be saying "Well, it's just a long rage nuclear missile".
With the level of compute they have they aren't stuck with frozen models like you are.