4 ms·

All this demonstrates how non-sticky all this tech really is. When your product is basically just an API call it’s trivial to just swap you out for someone else

by cmiles8 8mo ago

All this demonstrates how non-sticky all this tech really is. When your product is basically just an API call it’s trivial to just swap you out for someone else. As such it’s unclear what the prize at the end of the present race to the bottom is. We swapped OpenAI out for Claude and it required updating about 15 lines of code. All these guys are just commodity to us. If next week there’s a better supplier of commodity AI we’ll spend an hour and swap to something else again. There’s zero loyalty here.

pinkmuffinere 8mo ago
Ya, agreed. This makes me think that (long term) the ai race won’t be won on the merits of individual models, but on pricing — I think Google has a some strong advantages here because they know how to provide cheap compute, and they already have a ton of engineers doing similar things, so it’s a marginal cost for them instead of having to hire and maintain whole devoted teams.
- rblatz 8mo ago
  AI consumes entire data centers of compute. You aren’t tucking a few racks into a corner of a data center, you are building entirely new ones. There will be whole devoted teams.
  - pinkmuffinere 8mo ago
    But Google already builds data centers. Will there really be devoted AI-datacenter teams? Or will they just expand the normal datacenter teams, and ask them to use GPUs/TPUs instead of CPUs?
HotHotLava 8mo ago
It's an ironic situation; logically what should be the moat are the models, costing hundreds of millions of investment cost to train and operate so it would make sense if we see different provider focusing in different directions. But right now we have 3-5 top contenders that are so evenly matched that the de-facto sticking point is mostly the harness, ie. the collection of proven plugins/commands/tools/agent features that are tuned to the users personal workflow.
- wonnage 8mo ago
  Cost is never a good moat.
  - dd82 8mo ago
    the companies migrating off vmware due to broadcom shittiness would disagree with you https://arstechnica.com/information-technology/2026/02/most-vmware-users-still-actively-reducing-their-vmware-footprint-survey-finds/ https://arstechnica.com/information-technology/2026/02/most-... CloudBolt’s survey also examined how respondents are migrating workloads off of VMware. Currently, 36 percent of participants said they migrated 1–24 percent of their environment off of VMware. Another 32 percent said that they have migrated 25–49 percent; 10 percent said that they’ve migrated 50–74 percent of workloads; and 2 percent have migrated 75 percent or more of workloads. Five percent of respondents said that they have not migrated from VMware at all. Among migrated workloads, 72 percent moved to public cloud infrastructure as a service, followed by Microsoft’s Hyper-V/Azure stack (43 percent of respondents). Overall, 86 percent of respondents “are actively reducing their VMware footprint,” CloudBolt’s report said.
    - latchkey 8mo ago
      It is easier to do in the cloud than it is to do with actual hardware though, because you'll need enough hardware to do the migration. There is a capital moat around that. I feel like the company that can figure out how to 100% safely live migrate any VMWare workload to another "cheaper" solution, will do quite well.
- groestl 8mo ago
  > are so evenly matched It's because the real value of the models is in what we (humanity) fed them, and all of them have eaten the same thing for free.
  - nradov 8mo ago
    That's why the frontier LLM companies are now spending a lot more to license exclusive proprietary training data from private sources in order to gain a quality edge in certain business domains.
seydor 8mo ago
This is the new web hosting. All the valuations are absurd
- gruez 8mo ago
  Doesn't "web hosting" print money for Amazon?
spiffytech 8mo ago
Unfortunately this is why Anthropic is so aggressive about preventing Claude subscriptions from being used with other tools.
- latchkey 8mo ago
  According to this article, they can't even service the amount of paying customers that they have.
  - tonyedgecombe 8mo ago
    They should put their prices up then.
oytis 8mo ago
> someone else We have basically 4 companies in the world one can seriously consider, and they all seem to heavily subsidise usage, so under normal market conditions not all of them are going to survive.
- dghlsakjg 8mo ago
  Open Router shows that commodity api providers have figured out how to do this unsubsidized. The training runs aren’t priced in, but the cost of inference is clearly pretty cheap.
timcobb 8mo ago
> As such it’s unclear what the prize at the end of the present race to the bottom is. is it ever clear? pretty much everything seems to be a senseless race to bottom.
remus 8mo ago
> As such it’s unclear what the prize at the end of the present race to the bottom is. It's a market worth many billions so the prize is a slice of that market. Perhaps it is just a commodity, but you can build a big company if you can take a big slice of that commodity e.g. by building a good product (claude code) on top of your commodity model.
- adammarples 8mo ago
  The entire point of a race to the bottom is that your competitors keep reducing their prices until those billions disappear
- cmiles8 8mo ago
  The revenue slice is there, problem is though in a race to the bottom like we’re in now there isn’t much profit at the bottom. And these companies desperately need profit to justify the gigantic capital spend and depreciation title wave that’s on the horizon. There’s no clear way now things don’t just get really pretty quickly.
ghywertelling 8mo ago
Let me explain a possible moat with an example. I have curated my youtube recommendations over the years. It knows my likes and dislikes very well. It knows about me a lot. The same moat exists in interactions with Claude. Claude remembers so many of preferences. It knows that I work in Python and Pandas and starts writing code for that combination. It knows about what type of person I am and what kind of toys I want my nephews and nieces to play. These "facts" about the person are the moat now. Stackoverflow was a repository of "facts" about what worked and what didn't. Those facts or user chat sessions are now Anthropic's moat.
- dghlsakjg 8mo ago
  “Hey Claude, write out a markdown file of all of my preferences so any AI agent can pick up where you left off”
  - politelemon 8mo ago
    In fact, here, I'll do it myself.
    - ghywertelling 8mo ago
      You are missing the correlations that Claude can derive across all these user sessions across all users. In Google analytics, when I visit a page and navigate around till I find what I was looking for or didn't find it, that session data is important for website owners how to optimize. Even in Google search results, when I think on 6th link and not the first, it sends a signal how to rearrange the results next time or even personalize. That same paradigm will be applicable here. This is network effects and personalization and ranking coming togther beautifully. Once Anthropic builds that moat, it will be irreplaceable. If not, ask all users to jump from Whatsapp to Telegram or Signal and see how difficult it is. When anthropic gives you the best answer without asking too much, the experience is 100x better.
      - dghlsakjg 8mo ago
        The underlying technology is a thin layer of queryable knowledge/“memories” in between you and the llm, that in turn gets added to the context of your message to the llm. Likely RAG. It can be as simple as a agents.md that you give it permission to modify as needed. I really don’t think that they are correlating your “memories” with other people’s conversations. There is no way for the LLM to know what is or isn’t appropriate to share between sessions, at the moment. That functionality may exist in the future, but if you just export your preferences, it still works. The moat - at this point in time - is really not as deep and wide as you are making it out to be. What you are imagining doesn’t exist yet. Indexing prior conversations is trivially easy at this point, you can do it locally using an api client right this moment. Besides all that, you will be shocked at how quickly a new service can reconstruct your preferences. I started a new YouTube account, and it was basically the same feed within a few days. In any case, my feeling is that we should have learned at this point not to keep our data in someone else’s walled garden.
gdilla 8mo ago
just having strict control over context management in session is a nice differentiator. Shared tooling between desktop and cli and is nice too. they've differentiated enough.
Balgair 8mo ago
→All these guys are just commodity to us. Just want to note something there: Okay, premise that AI really is 'intelligent' up to the point of business decisions. So, this all then implies that 'intelligence' is then a commodity too? Like, I'm trying to drive at that your's, mine, all of our 'intelligence' is now no longer a trait that I hold, but a thing to be used, at least as far as the economy is concerned. We did this with muscles and memory previously. We invented writing and so those with really good memories became just like everyone else. Then we did it with muscles and the industrial revolution, and so really strong or endurant people became just like everyone else. Yes, many exceptions here, but they mostly prove the rule, I think. Now it seems that really smart people we've made AI and so they're going to be like everyone else?
- EQmWgw87pw 8mo ago
  Well as of right now, mathematically and scientifically, the way an LLM works has nothing to do with how the human brain works.
  - coldtea 8mo ago
    The way this thing "looks like a duck, swims like a duck, and quacks like a duck" has nothing to do with the way a real duck "looks like a duck, swims like a duck, and quacks like a duck". Who cares, as long as the end results are close (or close enough for the uses they are put to)? Besides, "has nothing to do with how the human brain works" is an overstatement. "The term “predictive brain” depicts one of the most relevant concepts in cognitive neuroscience which emphasizes the importance of “looking into the future”, namely prediction, preparation, anticipation, prospection or expectations in various cognitive domains. Analogously, it has been suggested that predictive processing represents one of the fundamental principles of neural computations and that errors of prediction may be crucial for driving neural and cognitive processes as well as behavior." https://pmc.ncbi.nlm.nih.gov/articles/PMC2904053/ https://pmc.ncbi.nlm.nih.gov/articles/PMC2904053/ https://maxplanckneuroscience.org/our-brain-is-a-prediction-machine-that-is-always-active/ https://maxplanckneuroscience.org/our-brain-is-a-prediction-...
    - 8mo ago
credit_guy 8mo ago
The moat is compute. In my case, I always use Opus 4.6 in my work, but quite often I get a 504 error, and that's quite annoying. I get errors like that with Gemini too. I can't estimate if I'd get a similar number of errors with ChatGPT, since I use it very infrequently. But imagine that at some point one of the big 3 (OpenAI, Anthropic, Google) gets very high availability, while the others have very poor availability. Then people would switch to them, even if their models were a bit worse. Now, OpenAI has been building like crazy, and contracting for future builds like crazy too. Google has very deep pockets, so they'll probably have enough compute to stay in the game. But I fear that Anthropic will not be able to match OpenAI and Google in terms of datacenter build, so it's only a matter of time (and not a lot of time) until they'll be in a pretty tight spot.
deleted 8mo ago
[deleted]

by cmiles8 8mo ago

pinkmuffinere 8mo ago

Ya, agreed. This makes me think that (long term) the ai race won’t be won on the merits of individual models, but on pricing — I think Google has a some strong advantages here because they know how to provide cheap compute, and they already have a ton of engineers doing similar things, so it’s a marginal cost for them instead of having to hire and maintain whole devoted teams.

rblatz 8mo ago
AI consumes entire data centers of compute. You aren’t tucking a few racks into a corner of a data center, you are building entirely new ones. There will be whole devoted teams.
- pinkmuffinere 8mo ago
  But Google already builds data centers. Will there really be devoted AI-datacenter teams? Or will they just expand the normal datacenter teams, and ask them to use GPUs/TPUs instead of CPUs?

HotHotLava 8mo ago

It's an ironic situation; logically what should be the moat are the models, costing hundreds of millions of investment cost to train and operate so it would make sense if we see different provider focusing in different directions. But right now we have 3-5 top contenders that are so evenly matched that the de-facto sticking point is mostly the harness, ie. the collection of proven plugins/commands/tools/agent features that are tuned to the users personal workflow.

wonnage 8mo ago
Cost is never a good moat.
- dd82 8mo ago
  the companies migrating off vmware due to broadcom shittiness would disagree with you https://arstechnica.com/information-technology/2026/02/most-vmware-users-still-actively-reducing-their-vmware-footprint-survey-finds/ https://arstechnica.com/information-technology/2026/02/most-... CloudBolt’s survey also examined how respondents are migrating workloads off of VMware. Currently, 36 percent of participants said they migrated 1–24 percent of their environment off of VMware. Another 32 percent said that they have migrated 25–49 percent; 10 percent said that they’ve migrated 50–74 percent of workloads; and 2 percent have migrated 75 percent or more of workloads. Five percent of respondents said that they have not migrated from VMware at all. Among migrated workloads, 72 percent moved to public cloud infrastructure as a service, followed by Microsoft’s Hyper-V/Azure stack (43 percent of respondents). Overall, 86 percent of respondents “are actively reducing their VMware footprint,” CloudBolt’s report said.
  - latchkey 8mo ago
    It is easier to do in the cloud than it is to do with actual hardware though, because you'll need enough hardware to do the migration. There is a capital moat around that. I feel like the company that can figure out how to 100% safely live migrate any VMWare workload to another "cheaper" solution, will do quite well.
groestl 8mo ago
> are so evenly matched It's because the real value of the models is in what we (humanity) fed them, and all of them have eaten the same thing for free.
- nradov 8mo ago
  That's why the frontier LLM companies are now spending a lot more to license exclusive proprietary training data from private sources in order to gain a quality edge in certain business domains.

seydor 8mo ago

This is the new web hosting. All the valuations are absurd

gruez 8mo ago
Doesn't "web hosting" print money for Amazon?

spiffytech 8mo ago

Unfortunately this is why Anthropic is so aggressive about preventing Claude subscriptions from being used with other tools.

latchkey 8mo ago
According to this article, they can't even service the amount of paying customers that they have.
- tonyedgecombe 8mo ago
  They should put their prices up then.

oytis 8mo ago

> someone else We have basically 4 companies in the world one can seriously consider, and they all seem to heavily subsidise usage, so under normal market conditions not all of them are going to survive.

dghlsakjg 8mo ago
Open Router shows that commodity api providers have figured out how to do this unsubsidized. The training runs aren’t priced in, but the cost of inference is clearly pretty cheap.

timcobb 8mo ago

> As such it’s unclear what the prize at the end of the present race to the bottom is. is it ever clear? pretty much everything seems to be a senseless race to bottom.

remus 8mo ago

> As such it’s unclear what the prize at the end of the present race to the bottom is. It's a market worth many billions so the prize is a slice of that market. Perhaps it is just a commodity, but you can build a big company if you can take a big slice of that commodity e.g. by building a good product (claude code) on top of your commodity model.

adammarples 8mo ago
The entire point of a race to the bottom is that your competitors keep reducing their prices until those billions disappear
cmiles8 8mo ago
The revenue slice is there, problem is though in a race to the bottom like we’re in now there isn’t much profit at the bottom. And these companies desperately need profit to justify the gigantic capital spend and depreciation title wave that’s on the horizon. There’s no clear way now things don’t just get really pretty quickly.

ghywertelling 8mo ago

Let me explain a possible moat with an example. I have curated my youtube recommendations over the years. It knows my likes and dislikes very well. It knows about me a lot. The same moat exists in interactions with Claude. Claude remembers so many of preferences. It knows that I work in Python and Pandas and starts writing code for that combination. It knows about what type of person I am and what kind of toys I want my nephews and nieces to play. These "facts" about the person are the moat now. Stackoverflow was a repository of "facts" about what worked and what didn't. Those facts or user chat sessions are now Anthropic's moat.

dghlsakjg 8mo ago
“Hey Claude, write out a markdown file of all of my preferences so any AI agent can pick up where you left off”
- politelemon 8mo ago
  In fact, here, I'll do it myself.
  - ghywertelling 8mo ago
    You are missing the correlations that Claude can derive across all these user sessions across all users. In Google analytics, when I visit a page and navigate around till I find what I was looking for or didn't find it, that session data is important for website owners how to optimize. Even in Google search results, when I think on 6th link and not the first, it sends a signal how to rearrange the results next time or even personalize. That same paradigm will be applicable here. This is network effects and personalization and ranking coming togther beautifully. Once Anthropic builds that moat, it will be irreplaceable. If not, ask all users to jump from Whatsapp to Telegram or Signal and see how difficult it is. When anthropic gives you the best answer without asking too much, the experience is 100x better.
    - dghlsakjg 8mo ago
      The underlying technology is a thin layer of queryable knowledge/“memories” in between you and the llm, that in turn gets added to the context of your message to the llm. Likely RAG. It can be as simple as a agents.md that you give it permission to modify as needed. I really don’t think that they are correlating your “memories” with other people’s conversations. There is no way for the LLM to know what is or isn’t appropriate to share between sessions, at the moment. That functionality may exist in the future, but if you just export your preferences, it still works. The moat - at this point in time - is really not as deep and wide as you are making it out to be. What you are imagining doesn’t exist yet. Indexing prior conversations is trivially easy at this point, you can do it locally using an api client right this moment. Besides all that, you will be shocked at how quickly a new service can reconstruct your preferences. I started a new YouTube account, and it was basically the same feed within a few days. In any case, my feeling is that we should have learned at this point not to keep our data in someone else’s walled garden.

gdilla 8mo ago

just having strict control over context management in session is a nice differentiator. Shared tooling between desktop and cli and is nice too. they've differentiated enough.

Balgair 8mo ago

→All these guys are just commodity to us. Just want to note something there: Okay, premise that AI really is 'intelligent' up to the point of business decisions. So, this all then implies that 'intelligence' is then a commodity too? Like, I'm trying to drive at that your's, mine, all of our 'intelligence' is now no longer a trait that I hold, but a thing to be used, at least as far as the economy is concerned. We did this with muscles and memory previously. We invented writing and so those with really good memories became just like everyone else. Then we did it with muscles and the industrial revolution, and so really strong or endurant people became just like everyone else. Yes, many exceptions here, but they mostly prove the rule, I think. Now it seems that really smart people we've made AI and so they're going to be like everyone else?

EQmWgw87pw 8mo ago
Well as of right now, mathematically and scientifically, the way an LLM works has nothing to do with how the human brain works.
- coldtea 8mo ago
  The way this thing "looks like a duck, swims like a duck, and quacks like a duck" has nothing to do with the way a real duck "looks like a duck, swims like a duck, and quacks like a duck". Who cares, as long as the end results are close (or close enough for the uses they are put to)? Besides, "has nothing to do with how the human brain works" is an overstatement. "The term “predictive brain” depicts one of the most relevant concepts in cognitive neuroscience which emphasizes the importance of “looking into the future”, namely prediction, preparation, anticipation, prospection or expectations in various cognitive domains. Analogously, it has been suggested that predictive processing represents one of the fundamental principles of neural computations and that errors of prediction may be crucial for driving neural and cognitive processes as well as behavior." https://pmc.ncbi.nlm.nih.gov/articles/PMC2904053/ https://pmc.ncbi.nlm.nih.gov/articles/PMC2904053/ https://maxplanckneuroscience.org/our-brain-is-a-prediction-machine-that-is-always-active/ https://maxplanckneuroscience.org/our-brain-is-a-prediction-...
  - 8mo ago

credit_guy 8mo ago

The moat is compute. In my case, I always use Opus 4.6 in my work, but quite often I get a 504 error, and that's quite annoying. I get errors like that with Gemini too. I can't estimate if I'd get a similar number of errors with ChatGPT, since I use it very infrequently. But imagine that at some point one of the big 3 (OpenAI, Anthropic, Google) gets very high availability, while the others have very poor availability. Then people would switch to them, even if their models were a bit worse. Now, OpenAI has been building like crazy, and contracting for future builds like crazy too. Google has very deep pockets, so they'll probably have enough compute to stay in the game. But I fear that Anthropic will not be able to match OpenAI and Google in terms of datacenter build, so it's only a matter of time (and not a lot of time) until they'll be in a pretty tight spot.

deleted 8mo ago

[deleted]