Workers against Deno Deploy? And even if you don't count Deno as a whole in the same market as Cloudflare it's still shady as hell. They had plans to buy out a company with the explicit intent to shut down their main product.
You would expect them to integrate Deno with Workers, make a new official managed service that would probably become profitable in no time with Cloudflare's cost optimized infra, or at least commit to basic security fixes until the community finds new maintainers.
What they pulled off instead is the most toxic form of acqui hire ever invented.
> They had plans to buy out a company with the explicit intent to shut down their main product.
I understand why it looks that way, and we knew it would be hard to combat this perception.
But it's simply not true.
The actual story is simply this: The Deno team made a strategic decision to refocus on celld, and we (Cloudflare) are excited to support this work, for the reasons I explained in the blog post: https://blog.cloudflare.com/deno-joins-cloudflare/
There isn’t a moat in the sense of self hosting but you need a reason for people who don’t want that to stay on your platform. Customers save time and effort managing payments easier this way. However it’s a race to the bottom price wise.
Going to be all about branding and platform stickiness for OpenAI to make investors and creditors whole.
The response to Jev should be the nail in the coffin over whether or not the AI business is a commodity market.
Out of no where Jev appeared as the next round of the price wars. Jev showed the value of System One models. A fast yes/no/confidence score not only is cheaper but also often all people want. Open source versions flood hugging face and now the big players are giving up a potentially big driver of output tokens to keep customers and race to the bottom price wise.
If I were OpenAI or Anthropic I’d be racing to make their products as sticky as possible bc ppl will flock to what’s cheapest otherwise.
At my job people discuss almost weekly what the best models are for various price/quality points, with our CTO in particular wanting us to make sure we're being cost-effective in terms of what we're using for a given task (his words are basically "I don't want you to use these tools less, I just want you to use them effectively")
A lot of places already subscribe to both. Indeed, in large enterprises it isn’t uncommon to simultaneously subscribe to Copilot, Claude, OpenAI, Cursor, AWS Bedrock, Gemini, etc — you might subscribe to different ones for different teams/projects/employees/etc, but often the approval by legal/IT/etc is generic not scoped to whoever is using it right now
If you are charged based on usage, you can “soft switch” between them really quickly.
Amen. My employer recently switched Cursor -> Claude, and the whole thing stalled out for around 3 weeks while a BAA (Business Associate Agreement, required for HIPAA compliance) was negotiated and signed.
That's pretty much all enterprise software though, it's pretty much how Microsoft stays in business, and even then if the question of switching is even humoured (the idea of a big company switching away from Office would be inconceivable to many), that'd imply its not sticky enough
We built our harness around swapping endpoints on demand. We can send to one model, many models, cloud or on prem infra. Certainly, not everyone has, but this is the future imho.
It's a problem for the security, legal, and IT teams, but not that much for developers, unless they get really particular about their harness. On the other hand, these are the high dollar value accounts that providers want to keep; but if there's no reason to avoid switching, this market really will feel like a utility market (i.e. it'll be like switching ISPs or cell phone providers - annoying but fungible).
The AI companies want to differentiate and become something more than a commodity, even if it's as critical as a utility is.
Enterprises tend to be your largest customers. Especially when current stock values for tech companies are majority predicated on AI becoming a staple in everyday life.
My gut is that the market for "ai as a tool" aka Claude Code/Codex/computer use/etc is a significantly bigger one than "models behind the scenes of some service". I've seen people use evals a lot in the latter case and very little in the former case (outside of people's whose job is basically to review the new releases). I haven't personally met anyone with something like "here are a bunch of tickets + a snapshot repo checkout, please try to solve them all" eval approach.
Though honestly I'm also surprised by the hype around Jev from a POV of "wait, are so many people just building on these by using them for classification tasks vs something more multi-step or generative?"
Agree. Switching models with a keypress is for developer coding.
Jev and this decisions api are mostly useful for inference at scale in a workload where cost and latency matter… and that’s where evals become crucial. Could coding tools use it? Sure, but that’s probably a special case.
For every production use case I build a eval suite which I use for prompt tuning and model evaluation and config. How else do you establish your model and prompt combination works? How do you upgrade to a newer model or decide on a fallback?
This same suite can simply be run very handoff to switch to a new prod model.
If you are a business dealing with with anything remotely sensitive then this is not so easy and you are basically forced to do business with a big player.
Not only can you switch models easily, but their competition can easily duplicate their product. I'm not sure stickiness has been found yet, but my guess is being more of G-Suite for "intelligence" than being a token hawker.
well, it's a function with the string(string) type signature, so it's bound to be weakly coupled, it's literally stringly typed.
If there's efforts to build vendor lock in, it's going to be in the surrounding api, whether it's streaming responses, or this weird prediction thing, or temperature settings, seeds, stuff like that.
And even then, competitors can copy the interface because interfaces are not copyrightable.
The only way they can get lock in is with the data. Right now it’s like a return to the early Web 2.0 days where everyone had an API. You hook the harness up to Slack, Gmail, GitHub, etc. so it can do work.
And like Web 2.0 I’m sure the day will come where the walled gardens return. All the old SaaS companies are starting to charge for agentic access. Proportionally more of the AI budget will go to them instead of the model providers, who are still stuck in this price war
That shorthand rule of thumb is often applicable, but can lead to risky / incorrect assumptions.
The central distinction isn't simply interface versus implementation. It's functional systems and constraints versus protectable expression - a distinction that can cut through both an interface and its implementation.
copyrighting an interface would break anti-trust, as it would mean no competitors can provide their alternative interface implementing products, on either side of the interface.
It would be like patenting a philips screwdriver or screw.
Idk why Jev was even hyped in the first place. Mostly by social media people who are the type to write inspirational LinkedIn articles, Medium posts and make longwinded Youtube videos about subjects they don't know or care to know much about.
It's always been obvious that small models with a specific purpose will outplay the larger more general models. It becomes a question of "do I want to be able to toss _anything_ at this frontier model, have it handle it all but pay the price" or "do I want to toss _specific things_ at this micro model, have it handle it but not be flexible".
It's the same with agentic RAG/search, things are moving towards much smaller search-specific models rather than tossing RAG chunks at a large frontier LLM. It's like how I can ask a multimodal frontier model to identify bounding boxes for objects in an image...but if I want to do that faster/cheaper/at 60fps then I should be using yolo or similar.
I think that technical people tend to underestimate the importance of convenience. If it were up to hacker news, everyone would be on Linux, everyone would assemble their own laptop and nobody would own an iPhone.
Even for technical problems sometimes having something fast and convenient that doesn't require a ton of fine tuning opens up a lot of doors. Those door might've easily been openable previously with a few days of effort. But the difference between a few days and something that you can set up with a quick account and a prompt is massive
I completely agree. First, there's the split between the ones using AI to write coder and there's the Ai that gets used in software.
Writing code is _hard_ because it's by definition accurate logic math will tons of abstractions that build on each other. Our brains are great at this. Frontier models with lotsa context are too. Let's ignore those though.
There are a lot of problems that are simple. Accuracy is important (e.g. is it nudity?), but it's still simple. The difference between Jev and the general idea of small models you described is that Jev (and a million copycats, Jev being a copycat itself) only answers multiple choice. That's clever; why wax poetic when you can ask a question and just get back the multiple choice, standardized tests love them for a reason.
So really it's:
a) "Big Know it All" Models. Slow and pricey, but they'll handle anything you through at them.
b) "Small Domain-Specific" model that still speak answers. They need some amount of "can form eloquent answers" in there with the specialized knowledge so still have a bit of a floor, but totally make sense.
c) "Memorized multiple choice tests, and can read but not write." Sometimes open-ended answers really are a downside when the answer space is sufficiently constrained, and read-side literacy really
Not nit-picking, but (c) really is more different than just a specialized model. And you're right, lots of problems fit that shape — hell, before AI blue up the world that's _all_ computers could do — and many times ambiguity and hallucinations are drawbacks of open-ended answers. You just need to ensure that "N/A" is always a valid answer, or at least that you get a confidence score.
Try making more measured statements, you exaggerate your point and end up being wrong. Of course this new barely used product feature is not all people want. Of course this minor product feature is not the nail in the coffin of whatever argument, they are just responding fast to a smaller competitor by providing that feature themselves, something that happens thousands of times in business, did Instagram prove it was a commodity when it implemented reels by copying tik tok? Did Uber prove it was a commodity when it started offering food delivery? Doesn't make much sense.
> If I were OpenAI or Anthropic I’d be racing to make their products as sticky as possible bc ppl will flock to what’s cheapest otherwise.
Hopefully people will flock to whatever product is making its mission to be commodity and the easiest to replace. Really don't want another free ingress, 100$/TB egress Cloud situation.
> If I were OpenAI or Anthropic I’d be racing to make their products as sticky as possible bc ppl will flock to what’s cheapest otherwise.
My approach would be per-user (or per-project) "memory".
I don't mean a tack-on like a RAG / document store with "notes" added to it in the background, but an actual medium- and long-term memory. Something like an extension of the KV cache stored in High Bandwidth Flash (HBF), or a subset of the weights trained "online", similar to LoRA.
This would not be transferable to any other base model, so would be excellent "lock in".
The downside is that the memories likely wouldn't be transferable to new models either, but I can imagine solutions to that too. I.e.: Train an MLP to "translate" from the old memory weight space to the new one.
It’s interesting that both companies have fully embraced agentic development with unlimited token budgets and yet still can’t expand beyond the original core products
All of those companies that naive HNers used to say they could code in a weekend literally can be coded in a weekend now, so why haven’t they?
If enterprise is the goal they’re really at the whims of the old school software companies because they own no data of their own. Imagine Google decides to push Gemini one day and now ChatGPT can’t write docs anymore.
> If I were OpenAI or Anthropic I’d be racing to make their products as sticky as possible bc ppl will flock to what’s cheapest otherwise.
That's what I have been saying: OpenAI or Anthropic's moat will not be the models, but how they integrated into enterprise clients and make it super hard for them to switch to a different provider. Kind of like Microsoft Office does it. Or Slack. Or Google Work. None of these products are better in any sense, they just cater to the needs of big enterprise clients in another way (customer relationship, sales, maybe compliance) and make it hard to switch
But OpenAI and Ant can be in more than just the commodity part of the business. They can both make frontier models and sticky products on top of those that have network or other effects that make competition tough.
The question isn't whether the market is big enough. It's whether at a race to the bottom on prices is sustainable and worth what investors expected it to be. The original story of OpenAI and Anthropic was that it was a race to super intelligence and whoever gets there first basically captures all the money in the economy. That kind of investment pitch means missing out not just on big returns but perhaps infinite return on investment. It seemed like it'd be true when the market was young and the cost to compete was prohibitive, but now anyone can compete with open weight models that are cheaper and maybe not the best, but are good enough.
You raise $200B to be a high margin low capex business, not an industrial commodity producer with high capex and margins being set by competitors who can duplicate your product and undercut you on price.
If somebody from OpenAI or Anthropic is reading this, one way to make the products stickier would certainly be to not make the desktop apps a complete shitshow.
Maybe they could for example point their amazing models to their GitHub issues, and use their power to actually fix the bugs and myriad of papercuts before solving cancer and world war/peace/whathever the stockholders believe they want.
All pros and cons of LLM coding aside, something has really shifted for me with the latest agentic coding. I’ve found my motivation to work slowly slipping away. No matter what role I had I always found something to keep me engaged with the job. Now though there’s no motivation. Every week it feels like my skills, talents, even ideas themselves are less and less relevant. I’m just meat shuffling data and permissions between bots
You’re just describing the exact situation we’re in but substituting a drone for a rifle. If the government is mad they should just partner with a contractor who is less concerned about this, which lo-and-behold they did it’s a company called OpenAI.
Anthropic isn’t a supply chain risk since they easy found someone else
I think part of the big push to "slow down AI development" is to add some sort of regulatory pressure that will give them sort cover to train less models and slow their burn rates
Are you implying that OpenAI using someones unpublished research without their permission to solve an career defining math problem with their latest model in order to publish first is a problem with the mathematicians?
My read on this document is that people's work isn't being fairly cited more than what does it mean to be a mathematician in this age.
I didn't know this article was about that issue at all. Yeah, if the issue is properly citing work then yes, OpenAI needs to do that. But the article read like it was tackling a completely different issue.
That's one of several issues, obviously a big one, and they do touch on it:
> Often these solutions are announced in a rush, leaving no time for a proper writeup, the isolation of new methods and ideas, and citing relevant previous work of others. As in all creative professions, this raises severe attribution and plagiarism questions.
Foldable gimmicks aside the new materials and components sound really cool. How they have the screen layers that move freely seems innovative. The new ceramic they’re using for the outer shell/screen sounds great and I hope we see it in more of their devices:
“Ceramic Shield 2 on the outer display reduces glare and provides 3x better scratch resistance than the previous generation”
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