AI enables new tools & features but in itself is not a product.
There's a good essay from Andrew Chen on this topic: Revenge of the GPT Wrappers: Defensibility in a world of commoditized AI models
"Network effects are what defended consumer products, in particular, but we will also see moats develop from the same places they came from the past decades: B2B-specific moats (workflow, compliance, security, etc), brand/UX, growth/distribution advantages, proprietary data, etc etc." [1]
Also check out the podcast with the team at Cursor/Anysphere for details into how they integrate models into workflows [2]
An enterprise using RAG, fine tuning etc. to leverage their data and rethinking how RL and vector DBs etc. can improve existing ops ... is likely going to make some existing moats much better moats
If your visibility on current state of AI is limited to hallucinogenic LLM prompts -- it's worth digging a bit deeper, there is a lot going on right now
Well “AI” is a lot more than just generic text generators. ML (read: AI that makes money) is the bread and butter of all of the largest internet companies. There’s no LLM that can accurately predict user behavior.
And even if there was, the fast follower to the Bitter Lesson is the Lottery Ticket Hypothesis: if you build a huge, general model to perform my task, I can quickly distill and fine tune it to be better, cheaper, and faster.
> In short, unless you’re building or hosting foundation models, saying that AI is a differentiating factor is sort of like saying that your choice of database is what differentiated your SaaS product: No one cares.
I still think of AI as the analogy of databases is perfect. No database is set up for the necessary applications where they get deployed. The same is true for LLMs except for some very broad chatbot stuff where the big players already own everything.
And if AI is just these chat bots, the technology is going to be pretty minor in comparison to database technology.
The choice of AI does matter, and older models can potentially do your task much better than newer ones.
In the course of building my side project (storytveller), I’ve found that newer models tend to do worse at storytelling. I’ve tested basically every model under the sun that is available for use in production, and one stands out - now it may be that others will come along and not do the research that I’ve done to choose the same model, and thus my application will be better than theirs in part because of the AI model I choose.
Having “AI” as part of your application will not matter as much, that I agree with, but having “the right AI” will.
Of course,the user experience will definitely matter as well, as will the marketing and other criteria - another point of agreement with the article. But that does not diminish the fact that, if your application does not involve testing benchmarks, there is a good chance that a model that may not be the newest could still be the best for your particular use case, so you should not just blindly choose the latest shiniest model as this article sort of implies.
I now see AI as part 2 of the CPU evolution. I think there are lots of correlations we can draw on looking at it that way:
1) Lots of players enter at the start because there are no giant walled gardens yet.
2) Being best in class will require greater and greater capex (like new process nodes) as things progress.
3) New classes of products will be enabled over time depending on how performance improves.
There is more there, but, with regard to this post, what I want to point out is that CPUs were pretty basic commodities in the beginning, and it was only as their complexity and scale exploded that margins improved and moats were possible. I think it will play out similarly with AI. The field will narrow as efficiency and performance improves, and moats will become possible at that point.
When will people realize that the use of AI art in any piece of content is almost as bad as bad typo or ads. It devalues the content and even adds a barrier to acceptance and produces the feeling that the creator does not value my time and attention.
That's something these companies don't seem to understand. Any model that is smart enough to be considered a true AI is also smart enough to teach what it knows to other AI models. So the process of creating a complex AI is commoditized. It just takes another group with access to the original AI to train other models with similar knowledge.
I also believe that, just like humans, AI models will be specialized so we'll have companies creating all kinds of special purpose models that have been trained with all knowledge from particular domains and are much better in particular fields. Generic AI models cannot compete there either.
This article didn't really say all too much, essentialy you can't differentiate your product with prompts alone, and you need deeper integrations with workflows, ok thats pretty clear - what else?
Deepseek showed us very well that openAI at the very least does not have a significant moat. Also, that the ridiculous valuations dreamt up for AI companies are make-believe at best.
If - and that's a big if - LLM-Tech turns out to be the path to true (not the OpenAI-definition) AGI, everybody will be able to get there in time, the tech is well known and the research teams notoriously leaky. If not, Another AI winter is going to follow. In either case, the only ones who are going to make a major profit there are the ones selling shovels during the gold rush - nvidia. Well, and the owners who promised investors all kinds of ridiculous nonsense.
Anyway, the most important point, in my opinion, is that it's a bad idea to believe people financially incentivized to hype their AIs into unrealistic heights.
People who say this are forgetting what it was like in the 2000s, when patent suits were flying back and forth like WWI gas shells. Once people realized that they could patent almost any old idea simply by adding "with a computer" or "on the Internet," the floodgates opened.
Rest assured, right now people are filing claims to the same old stuff, only now "with AI" tacked on. And rest assured, the rubber-stamping machine in the USPTO's basement is running 24/7 approving them.
> In many ways, the whole point of AI applications is that it should feel like magic because something that you previously had to do by hand is now fully automated with believable intelligence. If you’re thinking about taking traditional forms of UX and adding AI to them, that’s an okay starting point but not a defensible moat.
No. Stop! Please! I want my UX in an app to do the damn thing I precisely intend it to do, not what it believes I intend to do - which is increasingly common in UX design - and I hate it. It's a completely opaque black box and when the "magic" doesn't work (which it frequently does not, especially if you fall outside of "normal" range) - the UX is abysmal to the point of hilarity.
What we call today "AI" will replace human thought about as much as the TI-99/4A Speech Synthesizer replaced the human voice. Despite not putting talented voice actors out of work by a long shot, artificial voices have found many uses: automated announcements, weather information for aviation and maritime applications, assistive software for the blind, etc. So it will be with machine learning. Use it as a tool to augment your ability to find trends in seas of unstructured data, but the hard intellectual work you'll still need to do yourself. I wish more people would get this.
It’s something that everyone has to implement because their products will be inferior without it. But it’s not something you can use to build a monopoly easily, and since everyone has to do it there will be many people racing to the bottom pushing the price down.
You can’t build a moat with AI (redux)
(frontierai.substack.com)129 points by cgwu 23 hours ago | 99 comments
Comments
There's a good essay from Andrew Chen on this topic: Revenge of the GPT Wrappers: Defensibility in a world of commoditized AI models
"Network effects are what defended consumer products, in particular, but we will also see moats develop from the same places they came from the past decades: B2B-specific moats (workflow, compliance, security, etc), brand/UX, growth/distribution advantages, proprietary data, etc etc." [1]
Also check out the podcast with the team at Cursor/Anysphere for details into how they integrate models into workflows [2]
[1] https://andrewchen.substack.com/p/revenge-of-the-gpt-wrapper...
[2] https://www.youtube.com/watch?v=oFfVt3S51T4&t=1398s
If your visibility on current state of AI is limited to hallucinogenic LLM prompts -- it's worth digging a bit deeper, there is a lot going on right now
And even if there was, the fast follower to the Bitter Lesson is the Lottery Ticket Hypothesis: if you build a huge, general model to perform my task, I can quickly distill and fine tune it to be better, cheaper, and faster.
I still think of AI as the analogy of databases is perfect. No database is set up for the necessary applications where they get deployed. The same is true for LLMs except for some very broad chatbot stuff where the big players already own everything.
And if AI is just these chat bots, the technology is going to be pretty minor in comparison to database technology.
In the course of building my side project (storytveller), I’ve found that newer models tend to do worse at storytelling. I’ve tested basically every model under the sun that is available for use in production, and one stands out - now it may be that others will come along and not do the research that I’ve done to choose the same model, and thus my application will be better than theirs in part because of the AI model I choose.
Having “AI” as part of your application will not matter as much, that I agree with, but having “the right AI” will.
Of course,the user experience will definitely matter as well, as will the marketing and other criteria - another point of agreement with the article. But that does not diminish the fact that, if your application does not involve testing benchmarks, there is a good chance that a model that may not be the newest could still be the best for your particular use case, so you should not just blindly choose the latest shiniest model as this article sort of implies.
The hammer does matter.
Once an algorithm/technique is discovered, it becomes a free library to install.
Data and user-base are still the moat. The traffic that ChatGPT stole from Google is the valuable part.
1) Lots of players enter at the start because there are no giant walled gardens yet. 2) Being best in class will require greater and greater capex (like new process nodes) as things progress. 3) New classes of products will be enabled over time depending on how performance improves.
There is more there, but, with regard to this post, what I want to point out is that CPUs were pretty basic commodities in the beginning, and it was only as their complexity and scale exploded that margins improved and moats were possible. I think it will play out similarly with AI. The field will narrow as efficiency and performance improves, and moats will become possible at that point.
I also believe that, just like humans, AI models will be specialized so we'll have companies creating all kinds of special purpose models that have been trained with all knowledge from particular domains and are much better in particular fields. Generic AI models cannot compete there either.
If - and that's a big if - LLM-Tech turns out to be the path to true (not the OpenAI-definition) AGI, everybody will be able to get there in time, the tech is well known and the research teams notoriously leaky. If not, Another AI winter is going to follow. In either case, the only ones who are going to make a major profit there are the ones selling shovels during the gold rush - nvidia. Well, and the owners who promised investors all kinds of ridiculous nonsense.
Anyway, the most important point, in my opinion, is that it's a bad idea to believe people financially incentivized to hype their AIs into unrealistic heights.
I read the first one when it was posted here too and I don't get their point. It's a lot of words, but what are you trying to say?
Rest assured, right now people are filing claims to the same old stuff, only now "with AI" tacked on. And rest assured, the rubber-stamping machine in the USPTO's basement is running 24/7 approving them.
Start there, no matter the tool.
No. Stop! Please! I want my UX in an app to do the damn thing I precisely intend it to do, not what it believes I intend to do - which is increasingly common in UX design - and I hate it. It's a completely opaque black box and when the "magic" doesn't work (which it frequently does not, especially if you fall outside of "normal" range) - the UX is abysmal to the point of hilarity.
First: AI requires an awful lot of resources, which in itself is a moat.
Second: having a moat doesn’t prevent your service to be attacked. See Tesla.
Third: not having a moat doesn’t prevent your service from dominating. See TikTok.
It’s something that everyone has to implement because their products will be inferior without it. But it’s not something you can use to build a monopoly easily, and since everyone has to do it there will be many people racing to the bottom pushing the price down.
superbowl salesforce Ad that friend shared with me to get my comments. I still have no idea wtf this is or what AI has to do with it.
https://www.youtube.com/watch?v=rcLAeURXvHY