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For people who are in AI companies or have heard their pitches: What's the typical response to "What makes your AI special that can't be replicated by a dozen c

For people who are in AI companies or have heard their pitches: What's the typical response to "What makes your AI special that can't be replicated by a dozen competitors?"

paxys 3y ago
This isn't unique to AI. If you are hesitant to invest in startups because their products could be duplicated by competitors/big tech then you should not be a VC at all.
version_five 3y ago
I've sold various "AI" consulting projects, I tell people that all the AI hard- tech is open source and that there's nothing that differentiates it. What is different is implementation experience and industry customization. For example everyone has datasets scraped from the internet, but there are not deep application specific datasets publicly available. Likewise experience with the workflows in an industry. It's just software, there's little "secret sauce" in the engineering, it's the knowledge of the customer problem that's the differentiator.
- PaulHoule 3y ago
  I worked at a place where we thought there was value into putting it together in one neat package with a bow. That is, a lot of people are thinking at the level of "let's build a model" but for a business you will need to build a model and then update it repeatedly with new data as the world changes and your requirement changes. There would be a lot to say for a solution that includes tools for managing training sets, foundation models, training and evaluation, packages stuff up for inference in a repeatable way, etc. One trouble though is that you have to make about 20 decisions or so about how you do those things and developing that kind of framework people get some of them wrong and it will drive you crazy because other people will make different wrong decisions than you will. (To take an example, look at the model selection tools in scikit-learn and huggingface. Both of these are pretty good for certain things but they don't work together and both have serious flaws... And don't get me started with all the people who are hung up on F1 when they really should be using AUC...) So given the choice of (a) building out something half baked vs (b) fighting with various deficiencies in a packaged system, you can't blame people for picking (a) and "Just doing it". (Funny enough I always told people at that startup that we'd get bought by one of our customers, I thought it was going to be a big four accounting firm, a big telecom, or an international aerospace firm but... it turned out to be a famous shoe and clothing brand.)
PaulHoule 3y ago
(1) In the current environment things are moving so fast that the model of "get VC funding", "hire up a team", "talk to customers", "find product market fit" is just not fast enough. Contrast that how quickly Adobe rolled out Generative Fill, a product that will keep people subscribed to Photoshop. (e.g. it changed my photography practice in that now I can quickly remove power lines, draw an extra row of bricks, etc. I don't do "AI art" but I now have a buddy that helps retouch photos while keeping it real) If they went and screwed around with some startup they'd add six months to a project like that unless it was absolutely in the place where they needed to be. (2) If you were like Pinecone and working on this stuff before it was cool you might be a somebody but if you just got into A.I. because it was hot, or if you pivoted from "blockchain" or if you've ever said both of those things in one sentence I am sorry but you are a nobody, you are somebody behind the curve not ahead of the curve. (3) I've worked for startups and done business development in this area years before it was cool and I can say it is tough.
- lolinder 3y ago
  #1 is a really interesting point. Traditionally startups have a velocity advantage over the big companies because they don't have all the red tape, but in AI the big companies seem to have the advantage. The amount of data you need for training and the compute resources required means that startups are stuck with APIs that someone else provides, but a giant company like Adobe can train their own very quickly just based on the research papers that are out there and their own data.
  - PaulHoule 3y ago
    Some of it is that a VC-based company that just got funding today is not going to have any product at all for at least six months or a year if not longer. Somebody who needs a system built for their business right now gains very little talking to them. If a startup is a year or two post funding it might really have something to offer, but the huge crop of A.I. startups funded in the last six months have missed the bus. Big co's can frequently move very fast when there is a lot on the line.
dgb23 3y ago
A lot of it is UX and molding things to specific domains and use cases. Just like forms over SQL, there seems to be a never ending demand.
- claytonjy 3y ago
  Is Jasper a counterexample? Good UX, domain-specific, but still a standalone ChatGPT wrapper forced to do layoffs because they have no moat.
  - alex1212 3y ago
    From memory, they raised a huge round just before chat gpt went viral. Not sure if they would have been able to do so well if they were raising now. Very much doubt it.
alex1212 3y ago
We focus on "selling" the market size, customer problem-solution fit and not so much the AI part. AI is just the means to an end, a better way to solve the problem that we are solving. I saw some interesting stats the other day that the majority of investments in AI focus on infrastructure (databases etc) and foundational models.
chriskanan 3y ago
Having large amounts of curated data that is hard to procure, e.g., medical imaging data. If one can scrape the data from the web, I can't imagine having much of a moat or selling point.
- alex1212 3y ago
  Its tricky because by definition the more use-case specific the data the harder to obtain at scale, with some exceptions.
claytonjy 3y ago
1. research talent. There's not actually that many people in the world that can adequately fine-tune a large cutting-edge model, and far fewer that can explore less mainstream paths to produce value from models. Only way to get good researchers is to have name-brand leaders, like a top ML professor. 2. data. Can't do anything custom without good training data! How to get this varies widely across industry. Partnerships with established non-tech companies are a common path, which tend to rely on the network and background of founders. Even with both those things it's not easy to outcompete a large, motivated company in the same space, like a FAANG. They have the researchers, they have the data and partnerships, so the way to beat them is to move quickly and hope their A- and B-teams are working on something else.
- alex1212 3y ago
  Research / talent is how Mistral was able to justify its valuation at 3 weeks old. Pre product, pre anything.
  - claytonjy 3y ago
    Yes, totally. Not something anyone reading this is going to replicate!
    - alex1212 3y ago
      Interestingly enough I think there is lack of talent on the investment side of things too. Very few investors have the right skillsets in their teams to be able to do deep technical due diligence required for true AI solutions.
    - Palmik 3y ago
      > Not something anyone reading this is going to replicate! They were L7/L6 ML researchers/eng at FAANG, I'd bet there are quite a few people like that lurking here.
      - claytonjy 3y ago
rvz 3y ago
> "What makes your AI special that can't be replicated by a dozen competitors?" As you can see with all the responses here, they have failed to realize that this is a trick question. The real answer is that none are special and can be replicated by tons of competitors.
- rvz 3y ago
  So after waiting for more responses falling for that trick question, no-one here has still realized that companies with the hardware are the ones that have the 'moat' which CANNOT be replicated by a dozen competitors. So all these AI startup companies depending on cloud AI services or even open source models have no moat. Only the same big tech incumbents.
paulddraper 3y ago
Everything can be replicated with time and money. All the usual things. First mover Features Integrations Platform synergies
yujian 3y ago
I work on Milvus at Zilliz and we encounter people working on LLM companies or frameworks often, I don't ask this question a lot a lot, but it looks like at the moment many companies don't have a real moat, they are just building as fast as they can and using talent/execution/funding as their moat I've also heard some companies that build the LLMs say that those LLMs are their moat, the time, money, and research that goes into them is high

For people who are in AI companies or have heard their pitches: What's the typical response to "What makes your AI special that can't be replicated by a dozen competitors?"

paxys 3y ago

This isn't unique to AI. If you are hesitant to invest in startups because their products could be duplicated by competitors/big tech then you should not be a VC at all.

version_five 3y ago

I've sold various "AI" consulting projects, I tell people that all the AI hard- tech is open source and that there's nothing that differentiates it. What is different is implementation experience and industry customization. For example everyone has datasets scraped from the internet, but there are not deep application specific datasets publicly available. Likewise experience with the workflows in an industry. It's just software, there's little "secret sauce" in the engineering, it's the knowledge of the customer problem that's the differentiator.

PaulHoule 3y ago
I worked at a place where we thought there was value into putting it together in one neat package with a bow. That is, a lot of people are thinking at the level of "let's build a model" but for a business you will need to build a model and then update it repeatedly with new data as the world changes and your requirement changes. There would be a lot to say for a solution that includes tools for managing training sets, foundation models, training and evaluation, packages stuff up for inference in a repeatable way, etc. One trouble though is that you have to make about 20 decisions or so about how you do those things and developing that kind of framework people get some of them wrong and it will drive you crazy because other people will make different wrong decisions than you will. (To take an example, look at the model selection tools in scikit-learn and huggingface. Both of these are pretty good for certain things but they don't work together and both have serious flaws... And don't get me started with all the people who are hung up on F1 when they really should be using AUC...) So given the choice of (a) building out something half baked vs (b) fighting with various deficiencies in a packaged system, you can't blame people for picking (a) and "Just doing it". (Funny enough I always told people at that startup that we'd get bought by one of our customers, I thought it was going to be a big four accounting firm, a big telecom, or an international aerospace firm but... it turned out to be a famous shoe and clothing brand.)

PaulHoule 3y ago

(1) In the current environment things are moving so fast that the model of "get VC funding", "hire up a team", "talk to customers", "find product market fit" is just not fast enough. Contrast that how quickly Adobe rolled out Generative Fill, a product that will keep people subscribed to Photoshop. (e.g. it changed my photography practice in that now I can quickly remove power lines, draw an extra row of bricks, etc. I don't do "AI art" but I now have a buddy that helps retouch photos while keeping it real) If they went and screwed around with some startup they'd add six months to a project like that unless it was absolutely in the place where they needed to be. (2) If you were like Pinecone and working on this stuff before it was cool you might be a somebody but if you just got into A.I. because it was hot, or if you pivoted from "blockchain" or if you've ever said both of those things in one sentence I am sorry but you are a nobody, you are somebody behind the curve not ahead of the curve. (3) I've worked for startups and done business development in this area years before it was cool and I can say it is tough.

lolinder 3y ago
#1 is a really interesting point. Traditionally startups have a velocity advantage over the big companies because they don't have all the red tape, but in AI the big companies seem to have the advantage. The amount of data you need for training and the compute resources required means that startups are stuck with APIs that someone else provides, but a giant company like Adobe can train their own very quickly just based on the research papers that are out there and their own data.
- PaulHoule 3y ago
  Some of it is that a VC-based company that just got funding today is not going to have any product at all for at least six months or a year if not longer. Somebody who needs a system built for their business right now gains very little talking to them. If a startup is a year or two post funding it might really have something to offer, but the huge crop of A.I. startups funded in the last six months have missed the bus. Big co's can frequently move very fast when there is a lot on the line.

dgb23 3y ago

A lot of it is UX and molding things to specific domains and use cases. Just like forms over SQL, there seems to be a never ending demand.

claytonjy 3y ago
Is Jasper a counterexample? Good UX, domain-specific, but still a standalone ChatGPT wrapper forced to do layoffs because they have no moat.
- alex1212 3y ago
  From memory, they raised a huge round just before chat gpt went viral. Not sure if they would have been able to do so well if they were raising now. Very much doubt it.

alex1212 3y ago

We focus on "selling" the market size, customer problem-solution fit and not so much the AI part. AI is just the means to an end, a better way to solve the problem that we are solving. I saw some interesting stats the other day that the majority of investments in AI focus on infrastructure (databases etc) and foundational models.

chriskanan 3y ago

Having large amounts of curated data that is hard to procure, e.g., medical imaging data. If one can scrape the data from the web, I can't imagine having much of a moat or selling point.

alex1212 3y ago
Its tricky because by definition the more use-case specific the data the harder to obtain at scale, with some exceptions.

claytonjy 3y ago

1. research talent. There's not actually that many people in the world that can adequately fine-tune a large cutting-edge model, and far fewer that can explore less mainstream paths to produce value from models. Only way to get good researchers is to have name-brand leaders, like a top ML professor. 2. data. Can't do anything custom without good training data! How to get this varies widely across industry. Partnerships with established non-tech companies are a common path, which tend to rely on the network and background of founders. Even with both those things it's not easy to outcompete a large, motivated company in the same space, like a FAANG. They have the researchers, they have the data and partnerships, so the way to beat them is to move quickly and hope their A- and B-teams are working on something else.

alex1212 3y ago
Research / talent is how Mistral was able to justify its valuation at 3 weeks old. Pre product, pre anything.
- claytonjy 3y ago
  Yes, totally. Not something anyone reading this is going to replicate!
  - alex1212 3y ago
    Interestingly enough I think there is lack of talent on the investment side of things too. Very few investors have the right skillsets in their teams to be able to do deep technical due diligence required for true AI solutions.
  - Palmik 3y ago
    > Not something anyone reading this is going to replicate! They were L7/L6 ML researchers/eng at FAANG, I'd bet there are quite a few people like that lurking here.
    - claytonjy 3y ago

rvz 3y ago

> "What makes your AI special that can't be replicated by a dozen competitors?" As you can see with all the responses here, they have failed to realize that this is a trick question. The real answer is that none are special and can be replicated by tons of competitors.

rvz 3y ago
So after waiting for more responses falling for that trick question, no-one here has still realized that companies with the hardware are the ones that have the 'moat' which CANNOT be replicated by a dozen competitors. So all these AI startup companies depending on cloud AI services or even open source models have no moat. Only the same big tech incumbents.

paulddraper 3y ago

Everything can be replicated with time and money. All the usual things. First mover Features Integrations Platform synergies

yujian 3y ago

I work on Milvus at Zilliz and we encounter people working on LLM companies or frameworks often, I don't ask this question a lot a lot, but it looks like at the moment many companies don't have a real moat, they are just building as fast as they can and using talent/execution/funding as their moat I've also heard some companies that build the LLMs say that those LLMs are their moat, the time, money, and research that goes into them is high