moregrist
GoT was a late-comer to this. For me, William Gibson’s _Neuromancer_ (1984) was my first exposure to dark and morally-compromised Sci-Fi. I am sure it predates this in novels. _Neuromancer_ inspired a couple of movies in the 90s: The Net and Johnny Mneumonic come to mind.
In US TV shows, I think Babylon 5 started the lean into the dark tone, and the reimagined Battlestar Galactica of the 2000s is a better example than GoT.
We generally call it “enterprise software,” and it’s usually abysmal because the people buying/funding it are VP-level executives who care about compliance/reporting/visibility and who never have to actually use the software.
This actually comes up on HN pretty regularly. Or used to; like many topics it’s taken a back seat to AI.
But performance isn’t a single thing. If you prioritize features over pen testing, you’re optimizing one kind of performance and neglecting security, which is an another kind.
For a company whose whole selling point is security, it’s a bad trade-off.
Pharma makes bank on drugs that are patent protected. Squeezing the manufacturing cost is a small gain on top of that.
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.
I miss feeling good about tech.
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.
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.
It’s not clear to me what else one can do to protect this kind of IP if you want or need to do the processing on-device.
It’s not ideal. It has serious drawbacks. It makes development and updates a PITA. But it does make it much harder to decompile your firmware.
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.
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.
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.
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.
Peer review is then done _in private_ before publication as a check on quality and significance.
Retracting a paper is pretty embarrassing. And not considered science as usual.
In one case by asking Astra to review it.
Not exactly encouraging that they did their homework before publishing results.
Which seems like a pretty good argument to me, given the amount of slip out there and how increasingly performative these pieces are.
A waste of time. Every. Single. Time.
It wasn’t quite as needlessly performative. But it’s never been good.
It’s just not particularly common (or helpful) to view them that way.
Long term, this only works if you have a non-commodity, and if the higher tier is actually more profitable. We'll eventually learn whether both are true. For OpenAI right now, it's probably enough to just increase revenue, even if the higher tier is even less profitable.
You have some startup, the founder is either young or doesn't want to do the CEO stuff. Things kind of eek along until the founder either steps aside or is removed by the board because a) it's time for a "grown-up" CEO; b) the CEO needs real sales experience; c) the founder overpromised and under-delivered; d) board/VC politics make it helpful to install a buddy as CEO; e) etc.
Then the CEO clock starts, typically they have 18 months to get lucky and hit their metrics. They do a lot of glad-handing. They hire "their team" of sales/marketing/etc people. They spend A LOT of money. And I mean A LOT. They talk about OKRs or SMRTs and KPIs. Out of nowhere a small army of project managers show up and try to tell you how to do your job and why you can't just talk to the <thing X> team directly but have to go through them for "efficiency" and "visibility."
In 3-6 months, senior engineering and R&D staff starts to leave. Whatever culture you had slips away. HR has "culture" meetings to "find the right company culture."
Sales/product can't sell and points the finger at R&D, maybe even for the right reasons. You OKR/KPI harder, but it doesn't matter because nothing addresses whatever the underlying problem is. Multiple senior people have pointed loudly to the problem and are ignored; they're often not managers so it's unclear if they were even heard.
At some point there are one or more rounds of layoffs; sometimes these are announced, sometimes it's just a gradual attrition.
Eventually the CEO clock runs out. They don't get lucky. Nothing they did helped, and some of it hurt. They collect their $1M severance, get to keep their stock, get 9-12 months of health insurance, and move on. In a year or two we hear about them joining a new company as CEO.
In the meantime, you've either moved on or have a new CEO with a new 18-month clock.
Almost like the Star Trek mechanisms can infer perfect intent.
Like there’s a hidden script or something.
More seriously, I think there’s real value in an automatic door that behaves consistently rather than one that tries to infer messy human intent. Real life isn’t a TV show and there’s both ambiguity in how people behave and how they even intend to behave. It’s mostly not hard to understand how a proximity sensor door will function. Using a black-box classifier to improve that won’t necessarily make people like it more. And calling up to the cloud for every sensor event, ignoring privacy issues, adds weird latency and a huge failure mode during data center outages.
Being unemployed is hard and can be really demoralizing. Try thinking “I’m probably helping someone pay rent until they find a job listing that works for them” when you’re rejecting obviously unsuitable applications. It at least makes the “wtf”-ness of some of them seem a little less weird.
This is wrong. Reporters frequently don’t understand the science or the nuance in the science.
Reporting and science are two very different disciplines. Reporters rarely have a deep background in science and almost never have a background in the specific area that they’re reporting on.
Hell, even scientists have trouble accurately describing the work of a different scientific discipline.
Don’t invent bad faith motivations; they exist but most of the time it’s just two people slightly talking past each other.
If the OS knows that a thread is waiting for a lock, the scheduler will not bother to schedule it until the lock is available.
In general, it’s tempting when you’re bound by lock latency to skip the syscall overhead of sleeping. But a lot of the time that’s a code smell that there are other inefficiencies in the system and you should rethink how you’re scheduling work.
The dystopian part of me suspects that this is just a temporary blip as Amazon pivots from ads designed for humans to ads designed for agents. Which, in its truest form is just Amazon being a middle-man for bribing or poisoning agents.
In the end, it’s hard to not view everything through a lens of back dealing and anticonsumer enshitification.
Or maybe I’ve watched just one too many influencer videos on how the AI bots are either going to destroy humanity or take all the jobs.
It’s so common that there’s an xkcd about it:
The job market sucks, but there are so many confounding factors: pandemic over hiring, the loss of ZIRP, an economy that would be recession if it weren’t for data center investment, and may be in recession soon even with it.
This industry has been in down cycles before. It’s just been a while. So far it feels like a down cycle, not the sky falling.
It’s not clear what the future will hold, but I’m 100% sure it won’t evolve the way Anthropic predicts. Both because they have strong biases and because there's just no way to predict how this technology will play out. Humans are, if anything, remarkably adaptable.
I was with you up to “usually by coincidence”.
There’s a long and sordid history in areas of chemistry and areas of biology of holding up a competing paper in review so you can scoop them. I’m sure it exists in physics as well. Certainly biophysics, but probably most subfields.
Often it’s a famous labs that can steamroll review or even just dump the work into PNAS as a “member contribution.”
At least one author of a famous inorganic chemistry textbook was rumored to do this routinely.
And I know of at least one National Academy member who swore off arxiv prepublication after getting scooped.
None of this makes it all right. But plagiarism and academic theft is old and definitely not always accidental.
In a stable, profitable company, you need sales to keep the business solvent. And understanding the sales pipeline is pretty essential to effectively running a business.
Where this breaks down is when the C-suite forgets that you also need a product, and you need to keep innovating on that product in ways that bring value to current and future customers. This seems to happen pretty frequently.