Is it me or do none of the AI companies have a "moat" in the Ben Grahmm sense.<p>I use their services, but I frankly don't care who provides it. I'll chase the chepest/best and have no issue switching from one to another.<p>The only moat I can see is Microsoft providing its services to companies in its Azure system. Nervous IT departments probably like that it's not leaving their control if Bob in the SAP team spins up some AI crap.
AWS and Google at least own their own hardware (Trainium and TPUs, respectively). It's a moat in the sense that designing, building, and deploying your own chips at scale is quite a feat and not easily replicated. The vertical integration will allow them to continue to be profitable once the models get good enough and competitors' prices race to the bottom. Google has Gemini; AWS may not deploy its own models (yet?), but that's not necessarily a losing position, as long as the market is able to run models sourced elsewhere on Trainium and the price is right.
I think anthropic with its enterprise strategy and google
with its integration in everything have a bit of a moat.<p>But I switched from ChatGPT to Claude 3 months ago because my account was down for like 6 hours. I haven’t used it since. It’s too easy to switch away from chatbots on a whim. There is no moat for that.
> I think anthropic with its enterprise strategy and google with its integration in everything have a bit of a moat.<p>But... Anthropic doesn't <i>have</i> a moat. It's clear at this point that SOTA models are not a moat, and Opus 4.6-level (or GLM 5.2) is sufficient.<p>Google, though... they own the entire vertical, from the semiconductors to the end-user software. They may have a moat.
I guess I’m thinking a lot of companies seem to be getting Claude code subscriptions. It usually takes some time and effort for an org to switch away from one solution. In the meantime a lot of workflows get more and more tied to Claude in particular.<p>It’s not much of a moat, but it’s more than a lot of orgs have.
I've been thinking for a while, there's not real winners here except the incumbent technology providers. Hear me out: all models are converging towards the same level, gains are getting smaller and harder to come by. The models are commodities nothing more.<p>This is the leap, nobody really wants to front a model for someone else. If i build an agent, or a service that requires a model, I'd prefer to push the model onto someone else, preferably at no cost. This is a leap as I'm sure right now, most people / businesses are thinking actually i do want to own / front the model.<p>However, if you accept the leap the easiest way to do this is to make the model the users problem.<p>From a business point of view that makes things really easy, from a customer point of view, they simply have to accept whatever their vendor of choice is pushing down their throats.<p>So as a business I build for whatever model Google makes available to android, and whatever model windows bundles, and whatever model Apple bundles, and, excluding the long tail of Chinese vendors and Linux (sorry, its always left out) and that's it, problem solved, and the customer picks up the tab for the tokens
The moat is shifting from technology to access to proprietary training data. It doesn't matter how good your LLM platform is if you don't have good data to feed the training run. Public Internet data and published media is already mined out. Now the frontier LLM vendors have shifted to licensing proprietary data that's locked up behind corporate firewalls, and even hiring human domain experts specifically to create new training content in target verticals. You'll see the effects of this next year, although it might not be obvious to those who mostly only use LLMs for coding tasks in popular programming languages for which there was already a lot of training data.
> Now the frontier LLM vendors have shifted to licensing proprietary data that's locked up behind corporate firewalls, and even hiring human domain experts specifically to create new training content in target verticals.<p>That's a losing proposition for any token provider - it's expensive and slow, and when you're done everyone with money to rent a last-gen H100 is going to distill your "closed" model anyway.
The adoption of standards like skills and agent setup helps a ton. Nobody wants to be locked into an AI vendor like with cloud systems in general. And companies can't hold on to the #1 spot across multiple areas for very long, so users are even more motivated to move their process and stack between coding tools and AI companies behind them like Claude code.<p>Vendor lock in cannot happen, or you're bankrupt.
Google has a bit of a Network Effect going... my vehicle got an OTA update to use Gemini. Between that, search, storage, and the YT Premium bundle it was enough to convince me to float a subscription.
Amazon Bedrock is probably middlemanning an insane amount of token consumption these days for the same reasons.
Is Bedrock a "middleman?" I believe that they run all inference inside of AWS data centers, on their own infrastructure.<p>Their new endpoint even promises zero operator access [0]<p>[0] <a href="https://aws.amazon.com/blogs/machine-learning/exploring-the-zero-operator-access-design-of-mantle/" rel="nofollow">https://aws.amazon.com/blogs/machine-learning/exploring-the-...</a>
Sure, but fundamentally they’re acting as a distributor of someone else’s product in the form of the frontier models. That’s a classic middle-man.<p>No value judgement. I think this is a fantastic strategy.
Weights are worth far more than data centers.
> I use their services, but I frankly don't care who provides it. I'll chase the chepest/best and have no issue switching from one to another.<p>For the hyperscalers, there is an ease of remaining in the Azure/AWS/GCP fabric from a data provenance perspective, particularly for regulated industries or large, risk-averse enterprises. There's also, of course, a certain network egress tax in most cases.
Uhh. I actively and vocally avoid all things Microsoft. I see Microsoft and I immediately think buggy software with zero security.
Nvidia has a moat. Hardware is hard. No one really competes with them for general compute
AMD Instinct is their direct competitor for compute and they are better per dollar, better per watt, and out competing on raw performance.<p>Only thing holding them back is fab capacity which nVidia keeps buying in bulk to keep them small.
Have you ever actually had anyone work with these chips? Developer ux on amd is terrible.
AMD is held back by their interconnect and firmware disadvantage compared to nvidia. They’ve been trying really hard to create their own cuda, but rocM and HIP still aren’t very popular especially for research.
I thought that Nvidia's moat was more in CUDA? Hardware is hard but we've already seen other companies like Google design neural processors with compute efficiency close to Nvidia.
General compute is also the worst solution to the problem.<p>Nvidia's entire business is dependent on Google not being able to make TPUs fast enough.
Oh great, good to know the shovel seller has the market cornered.<p>Now back to the conversation, do any of the gold miners have a moat? Or is this a race to the bottom?