5 ms·
The giants knew this was coming, and soon 95% of AI tasks will be able to be done by open models (coding, research, cowork style work). So why pay a premium? Wh
by Jackobrien 3mo ago
The giants knew this was coming, and soon 95% of AI tasks will be able to be done by open models (coding, research, cowork style work). So why pay a premium? Why use them at all? This leaves the labs with two options:
1) push the frontier in a way only massive scale can, and cash in on it (mythos level cyber security, recursive training, frontier science work). There’s big money for never before possible capabilities.
2) own the app layer with their edge in reputation and powered by their infrastructure. Be apple where everyone else is Linux. Do design, coding, research, SMBs, legal, finance, healthcare and more (they are doing all of this).
Will it be enough to justify a Google level valuation? We’ll see how fast they can push it.
- ed_elliott_asc 3mo agoWon’t all they need to do is say “best in class, latest models, fastest” and wine and dine a few execs and those enterprise deals will be signed? In this case the people tasked with using the product won’t actually mind.
- actionfromafar 3mo agoYes, exactly that. Be Azure and Office 365 and Sharepoint and AWS where everyone else is Debian Stable on a USB thumbdrive.
- fragmede 3mo agoOffice 365? Ew, Google docs, please.
- NitpickLawyer 3mo agoNo one is getting fired for using SotA.
- spwa4 3mo agoIf the price difference is 2x? Sure. If the price difference is 50x? No way.
- brainwad 3mo agoSo long as the benefit:cost ratio is still sufficiently high, I don't think anyone gets fired for not scrimping. Better to encourage positive EV behaviour by your employees than to scare them away by firing them for not being perfectly optimal.
- ThunderSizzle 3mo agoThe CEO won't get in trouble, but the employee who can't justify a bad result/prompt?
- RobotToaster 3mo agoTell that to Oracle
- watwut 3mo agoAccenture says "yeah totally CEOs will pay a lot for literal nothing"
- dualvariable 3mo agoLaughs in 2005-era VMWare and EMC...
- saltcured 3mo agoWell, getting laid off during the bankruptcy spiral is a form of firing. But that is months away, so not my problem?
- sofixa 3mo ago> own the app layer with their edge in reputation and powered by their infrastructure. Be apple where everyone else is Linux. Do design, coding, research, SMBs, legal, finance, healthcare and more (they are doing all of this). The problem with this is that there are incumbents in all those spaces doing their own AI agents / platforms, and they're the ones choosing the models they use internally and they sell to their own customers. The margins and the possibility to fine tunie using open weight models, as well as the guarantee they'll keep running at predictable costs (no US orders yanking access), make them a very appealing option. And if you're a company that needs an AI powered legal software, would you buy it from OpenAI/Anthropic, or from someone who you've already bought legal software from before and has the domain knowledge?
- fredley 3mo ago3) Buy all the RAM, increasing the barrier to entry to push back the tide a bit, in time for a juicy IPO.
- clickety_clack 3mo ago4) Make it illegal to use anything but regulated models.
- forshaper 3mo agoa: If making it illegal fails, make it a Federal procurement requirement to use regulated models. Come up with an audit standard that only fits regulated models. Watch the preference trickle down.
- rectang 3mo agoLicense the training corpus and encourage copyright suits against outputs from models trained on unlicensed corpora.
- amanaplanacanal 3mo agoThis won't work if the courts decide that training is fair use, which certainly seems the direction they are going.
- rectang 3mo agoOutput is a separate issue from training. Courts will never decide that a identical copy spit out by an LLM is non-infringing simply because it went through an LLM stage. Copyright laundering is wishful thinking by tech folks.
- julosflb 3mo agoI like to think of llms as seamless plagiarism machines.
- vlian2088 3mo ago
- ForHackernews 3mo agoGoogle already owns the app layer, and hardware, and they are a frontier-level AI research firm. I don't see how Anthropic or OpenAI survives being eaten by DeepSeek et al from the bottom of the stack and Google from the top.
- dubbie99 3mo agoThe only reason people use google apps is because they are cheap and reliable. The user experience is awful. Have you ever tried to find a document you had open yesterday in drive?
- hobo_mark 3mo agoUh? Recently and frequently opened documents always show up on the first screen as soon as I open the app or website.
- PunchyHamster 3mo agoI used their enterprise chat the other week coz one of the clients used it It is truly amazing how bad it is. Made me miss using MS Teams. No software should make anyone miss using MS Teams
- nickthegreek 3mo agoYou just got to https://drive.google.com/drive/u/0/recent https://drive.google.com/drive/u/0/recent
- dualvariable 3mo agoAnthropic is at least renting their datacenters, not owning, so all the capital accounting bullshit is getting laundered by someone else, who will wind up holding that bag. And Anthropic is currently cornering the enterprise coding market, and they were smart to avoid video. Under current economic conditions they're a lot closer to being profitable than anyone else, and they can take advantage of crashing prices for compute if we hit a datacenter-buildout-glut.
- orwin 3mo agoMythos was outperformed by small, specific local models in multiple oss project.
- RugnirViking 3mo agoi'd love to hear about this! do you have examples?
- orwin 3mo agohttps://aisle.com/blog/aisle-discovers-6-new-cves-in-curl-including-the-oldest-issue-ever-reported https://aisle.com/blog/aisle-discovers-6-new-cves-in-curl-in...
- rmunn 3mo agoI see "LLM discovers vulnerability in curl" and I get skeptical, given how Daniel Stenberg has talked about the flood of claimed vulnerabilities that weren't real issues once he looked into them (as most HN readers already know, I'm sure). But it looks like these 6 were real issues, that curl patched once they received the reports. Five ended up rated low and one medium, but given the amount of attention curl gets, I'd honestly be surprised if there were any high-severity issues; in fact, having even one medium-severity issue remaining is slightly surprising to me.
- orwin 3mo agoTo be fair (and for people who didn't click the link), i think most of the vuln were in libcurl, not in curl itself.
- kyleomalley 3mo agoIt might be kind of overlooked when people read about the big scary results from mythos; the real breakthrough was probably just as much the application of the (very decent) model through a well engineered wrapper (harness). Other models including codex or glm result in significant findings as well. Harness example: https://github.com/evilsocket/audit https://github.com/evilsocket/audit
- AnthonyMouse 3mo ago> Be apple where everyone else is Linux. Apple and Linux barely even compete in the same markets. Linux runs on the servers and embedded devices, Apple on the smartphones. Android is technically Linux but not in the "is a good analogy for open weight models" sense because Android is so deeply under the thumb of Google. The main place Linux and Apple actually compete is for PCs and laptops, and that's the market where the thing with 65% market share is Microsoft.
- deleted 3mo ago[deleted]
- Gud 3mo agoApple tried to make servers(they were awesome btw) but lost to Linux. Linux are on more phones than iOS.
- pseudosaid 3mo agoyoure missing the point entirely and opted to entertain your own framework
- AnthonyMouse 3mo agoIt's meaningless to suggest doing what Apple does when faced with Linux when the vast majority of Apple's business isn't competing with Linux. The majority of Apple's revenue is from hardware when Linux is software -- that can run on Apple's hardware.
- christkv 3mo agoYou forgot 3. Try to get the government to "certify models" to cause regulatory capture which is what both Anthropic and OpenAI has been pushing. No certification no use in business.
- CuriouslyC 3mo ago#1 isn't going to happen because we're actually data limited, not compute limited. You can throw all the compute in the world at bad data and it won't make a difference, but an undertrained model with perfect training data will absolutely slay. #2 isn't going to happen, because these labs have shown they have limited app/design sense, and they also lack the industry connections and domain wisdom to execute. The way things are actually going to go is that these labs will set up partnerships with huge biotech/engineering/etc firms, and do custom training/inference on specific tasks that promise to be wildly profitable with them, then take royalties on the creation in perpetuity. Why sell inference when you can partner with Pfizer to make a version of Ozempic that also makes people freaky jacked, or partner with Bectel to make a radically safer, more efficient Nuclear power plant?
- dominotw 3mo agowhat is 'bad data' and 'perfect data' according to you?
- CuriouslyC 3mo agoWorst possible bad data is where the data is orthogonal to the task, so increasing the data never provides information on the task. Perfect data is where the data exactly encapsulates the task being trained.
- Schiendelman 3mo agoI don't think "data limited" is true anymore outside of very specialized cases (for instance: https://arxiv.org/abs/2510.01631 https://arxiv.org/abs/2510.01631). As weird as it sounds, training improves a lot with synthetic data. You do need business development to create those relationships. Saying they "have limited ___" mostly means they "haven't yet hired people who are good at ___". That's been changing already; the Claude app is steadily improving and handling more use cases simply through understanding which tools to use, Anthropic is building more relationships to create more tools, and all the frontier model companies are building relationships with companies that have specialized data and want specialized solutions. I think we're also seeing the frontier model companies offer partners their own ability to run RL on their own data, and then retrain new models on the same data. That's going to make those relationships VERY sticky in ways that won't be obvious from the outside.