The cost of manufacturing drugs is not where pharma makes it’s profits. Look at the margins for competitive generic drugs and over the counter medicine. Margins are tight.
Pharma makes bank on drugs that are patent protected. Squeezing the manufacturing cost is a small gain on top of that.
Yip, drugs are similar to software in this regard. Expensive to develop, very cheap to replicate. It'd make vastly more sense as a nationalized industry, because I think a profit motive is not only unnecessary, but actively harmful because the drugs that help society the most, and the drugs that make the most money, have little overlap.
If a big pharma had the choice between an expensive treatment that could help treat any cancer, or an extremely cheap treatment that could prevent any cancer - which would they pick? The answer when you're motivated by profit is unfortunately different than the one when you're motivated by social benefit. And this is more than just a thought experiment because this issue is going to drive which type of treatments are pursued.
Problem with those expensive treatments is that there are not that many cases. So profit from each is big but overall they add up to less. Getting everyone who can afford it on planet on some relatively cheap drug would be lot more profitable overall.
Get million out of thousand people or ten out of billion people... Later would be preferable.
Well there's also the third option of get a million out of a billion people. And that's only one side of the perverse equation. The other is rent-seeking. A treatment that needs to be redone or repeatedly boosted is far more profitable than a treatment that simply solves something once and for all, especially if that 'solving' treatment can be done cheaply.
Hearing people talk about the “information superhighway” was exceptionally tedious.
The biggest difference to me is the tone. The dot-com era was brimming with optimism and enthusiasm. People were getting rich (on paper). The demand for someone who could write html/perl/java (or fake it) was high, and lots of smart or savvy people got pulled into tech.
The thing I hate about all of the HN posts on AI is the underlying doomerism. There’s always someone gleefully proclaiming that white color workers are super screwed and that we’re destined to be sad serfs to our AI overlords.
Drug design is hard because it’s a system problem not a molecule-protein interaction problem.
At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I’m not sure how wide-spread that is any more; it used a lot of reagents. And it ran into the real bottleneck: lead generation tells you little to nothing about the efficacy, toxicity, and side effects of a drug or biologic in actual humans.
In silicon will never do a better job at lead generation than combinatorial chemistry. It could help with the real bottleneck. But… there just isn’t a lot of high quality training data on this.
They’ve been pushing ML approaches to drug design since at least the early 2010s. The approach has evolved with the trends in ML. Overall, I think the results have been very mixed; much like the traditional pharma pipeline.
> At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I don't think this was ever the gold standard. I mean, some companies did employ these approaches, but they were generally viewed as low-quality, brute-force approaches (at least at the handful of companies I was associated with).
It’s marketing, but in a global good will sense of “look how much OpenAI cares about secure open source.”
This works for established profitable brands that want to build / maintain a good public image.
This isn’t the marketing needed by an unprofitable startup that wants to show hyper growth before its IPO. They need the “use our product or get pwned” kind of marketing.
"Daybreak enables vetted enterprise customers and cybersecurity practitioners to use our most capable models for dual-use cybersecurity work."
They explicitly give out access to those who will make a big announcement of bugs found. OpenAI does not want projects to fix bugs themselves so it can have more bugs and better marketing.
> Software engineers are getting laid off because of advancements made by AI.
This is said by a lot of CEOs trying to make layoffs look good, but I haven’t seen anything that suggests that it’s true.
> Yet I see far more of these blogs about the plight of mathematicians, not enough about the plight of software engineers.
Really?!? We’ve been living through 3 years of blog posts, LinkedIn hot takes, and podcasts talking about the plight of software engineers in the era of AI. There isn’t a week that goes by without an HN post about it.
I can feel empathy for the practitioners in a field that may have been profoundly changed, the same way I can feel empathy toward software engineers who are struggling with the changes in their own field.
In the end, we’re all trying to figure out where this leads. And how this will affect our lives and our work.
> Assume the model which initiates the worm is a high powered model.
The disconnect here is that highly powered models have billions or trillions of parameters and have compute requirements that scale with the parameter count.
If this became feasible with smaller models, we’d be in a very different world. One where you’d have your own local Fable/Opus/Astra on your desktop.
If we get there, this becomes somewhat believable. Right now it’s just too many orders of magnitude of memory and flops between the many H100s that run the current models and what smart devices have.
Your typical ESP32 chip, which is really beefy for embedded, tends to come with <1 MB of RAM and similar amounts of NVRAM. It runs two cores at a few 100s of MHz and does a bit of SIMD but it was designed for embedded IO (I2C buses, etc) not matrix multiplication. And its network speed is nothing like an Infiniband interconnect. You’re lucky getting 802.11b Wi-Fi.
> Instead of infrastructure being more easily shared with the collective, or valued enough to build a business, now companies can tailor even major infrastructure to their own needs, and avoid all that coordination.
Large companies have always done this. Tons of early 2010s open source projects came out of reimplementing white papers from closed-source Google projects.
It’s also the major downside of this kind of thing. Once someone gets acclimated to how things are done at a large company with custom tools, it’s not always easy to switch to another process, especially a simpler one. There are tons of stories about FAANG engineers joining a startup and being useless.
There’s real value in having consistent tools across an industry. It makes hiring and training easier. Even if you’re convinced that a LLM could crank out a payroll or ordering system that’s perfect for your org, you still have to train the accountants and admin assistants to use it. And you’ve now become the customer support.
The bar is lower with version control, but it’s still more friction than git + GitHub + a well-known workflow.
> I hypothesize that the kind of reader who wants a physical copy of a book is much happier to get a hardcover.
Nope. I vastly prefer paperback. I don’t like ebooks much, and hardcover books are too large, too heavy, and take up too much space on a bookshelf. For books I know I’ll re-read, I might go for one, and I’ve certainly made some impulse HC buys that I regret, but I vastly prefer paperbacks.
One of the best things about mass market paperbacks was you could fit one in a jacket pocket, and easily carry 1-2 around with you.
Pharma makes bank on drugs that are patent protected. Squeezing the manufacturing cost is a small gain on top of that.
reply