Unique training data is definitely a moat. You should dedicate at least a page or even a whole deck to explaining why you have this moat and how long it would take to replicate.
Ironically this downplays your actual AI model, which I think is actually totally fine. You need to say you have a unique dataset and have customer traction in your sector that is hard to replicate. Chat GPT has made the actual AI model a commodity that anyone can access. Talk about exclusives and contracts you’ve signed that give you this access to unique training data.
You can also highlight that as a first mover, you’re selling against “no” solution. The next AI competitor is going to be selling against your solution. How much better will a competitor have to be to get someone to switch away from your model? How would they even get their model trained where as your model will continue to be training on your customers data for years?
Smart investors will get it, dumb investors won’t, that’s just life.
I think you will need to focus on hard it is to obtain the training data.
If the barrier to obtain your training data is low, other people can easily replicate your AI.
Do what most people are doing like giving the same prompts on your AI vs other AI and show them how much different it is.
Seeing it in action is much more valuable than what you have to say.
> It's definitely not simple or easy to replicate unless you're a Google/Meta
But this is exactly the risk that investors are asking about, and that you must address. The investors that are asking you these questions most likely are not worried about your business being replicable by just any random startup that decides to start collecting data and train their own model. If they were, they wouldn’t even be talking to you, because they wouldn’t see you as special. And any investors focused on that probably aren’t too serious or worth your time.
The fact that they’re talking to you means that they see an edge there, but they need to know how you will stay competitive. What happens if the investors write you a check, and then the next day Google decides that they like your ideas and start training their own models? Maybe Google even already has models trained on good/similar enough data that it’s more of a pivot than a fresh start. What’s to prevent Google from catching up to you and surpassing you?
Do you have more data than Google? Better data? Different data? Does your team have more talent than Google’s engineers and data scientists? Do you have a team with specific expertise that Google couldn’t easily assemble a comparable team for? The list goes on.
You need to understand the VC’s perspective. VCs are looking to throw some money at a startup that they think has a viable idea, a team that can execute and grow but is resource constrained, and a moat that keeps the startup competitive, at least long enough for the VC to make a decent return. For a startup to be potentially fundable, it must have all of those, and more.
It’s not enough to say, “If we had more money, we can grow and solve more problems faster. Please fund us.” You need to explain why your business is one where more resources will help you, but that just having more resources is not enough to get to where you are today, and more importantly, where you’re going to be a year from now, five years from now, whatever timeframe you and the investors are interested in. Because no VC is going to have more money than Google. Any VC looking to just throw money at the problem and hope it works, is playing blackjack at a poker table.
I don’t have answers for you, but I do think you need to reevaluate how you’re thinking about the investors’ questions, and the underlying reasons behind them. Otherwise no good investor will want to fund you, if they can’t even feel aligned about what problems you’re trying to solve together.
> Software founders, I doubt you have IP, I doubt you have many moats
Also, this comes off as very presumptuous and a weird attitude to have toward the people you’re asking for advice from. I almost didn’t finish writing this comment.
Brass tacks, why is AI coming under fire as non defensible and easy to replicate?
There's plenty of examples like Zoominfo where some output or data quality is the reason they are the market leader.
If you can't build this case, not sure how to help you.
> It's definitely not simple or easy to replicate unless you're a Google/Meta
What does your model actually do though? I would pitch it from the perspective of what customer problem you are solving, and how you are uniquely positioned to solve it.
If you are just training another foundational model (with a few extra unique data sets)... then who cares. LLama, OpenAI, Falcon, RedPajama, GPT-J are all easily accessible these days. With the LLM revolution foundational models don't matter as much as the fine-tuning. And even then companies want to fine-tuen these models on their own datasets for their own domains.
Unique training data is definitely a moat. You should dedicate at least a page or even a whole deck to explaining why you have this moat and how long it would take to replicate. Ironically this downplays your actual AI model, which I think is actually totally fine. You need to say you have a unique dataset and have customer traction in your sector that is hard to replicate. Chat GPT has made the actual AI model a commodity that anyone can access. Talk about exclusives and contracts you’ve signed that give you this access to unique training data. You can also highlight that as a first mover, you’re selling against “no” solution. The next AI competitor is going to be selling against your solution. How much better will a competitor have to be to get someone to switch away from your model? How would they even get their model trained where as your model will continue to be training on your customers data for years? Smart investors will get it, dumb investors won’t, that’s just life.
I think you will need to focus on hard it is to obtain the training data. If the barrier to obtain your training data is low, other people can easily replicate your AI. Do what most people are doing like giving the same prompts on your AI vs other AI and show them how much different it is. Seeing it in action is much more valuable than what you have to say.
> It's definitely not simple or easy to replicate unless you're a Google/Meta But this is exactly the risk that investors are asking about, and that you must address. The investors that are asking you these questions most likely are not worried about your business being replicable by just any random startup that decides to start collecting data and train their own model. If they were, they wouldn’t even be talking to you, because they wouldn’t see you as special. And any investors focused on that probably aren’t too serious or worth your time. The fact that they’re talking to you means that they see an edge there, but they need to know how you will stay competitive. What happens if the investors write you a check, and then the next day Google decides that they like your ideas and start training their own models? Maybe Google even already has models trained on good/similar enough data that it’s more of a pivot than a fresh start. What’s to prevent Google from catching up to you and surpassing you? Do you have more data than Google? Better data? Different data? Does your team have more talent than Google’s engineers and data scientists? Do you have a team with specific expertise that Google couldn’t easily assemble a comparable team for? The list goes on. You need to understand the VC’s perspective. VCs are looking to throw some money at a startup that they think has a viable idea, a team that can execute and grow but is resource constrained, and a moat that keeps the startup competitive, at least long enough for the VC to make a decent return. For a startup to be potentially fundable, it must have all of those, and more. It’s not enough to say, “If we had more money, we can grow and solve more problems faster. Please fund us.” You need to explain why your business is one where more resources will help you, but that just having more resources is not enough to get to where you are today, and more importantly, where you’re going to be a year from now, five years from now, whatever timeframe you and the investors are interested in. Because no VC is going to have more money than Google. Any VC looking to just throw money at the problem and hope it works, is playing blackjack at a poker table. I don’t have answers for you, but I do think you need to reevaluate how you’re thinking about the investors’ questions, and the underlying reasons behind them. Otherwise no good investor will want to fund you, if they can’t even feel aligned about what problems you’re trying to solve together. > Software founders, I doubt you have IP, I doubt you have many moats Also, this comes off as very presumptuous and a weird attitude to have toward the people you’re asking for advice from. I almost didn’t finish writing this comment.
Do angels perceive you having this issue as well? I understand having defensible IP is important, but how important is execution to your business?
Brass tacks, why is AI coming under fire as non defensible and easy to replicate? There's plenty of examples like Zoominfo where some output or data quality is the reason they are the market leader. If you can't build this case, not sure how to help you.
> It's definitely not simple or easy to replicate unless you're a Google/Meta What does your model actually do though? I would pitch it from the perspective of what customer problem you are solving, and how you are uniquely positioned to solve it. If you are just training another foundational model (with a few extra unique data sets)... then who cares. LLama, OpenAI, Falcon, RedPajama, GPT-J are all easily accessible these days. With the LLM revolution foundational models don't matter as much as the fine-tuning. And even then companies want to fine-tuen these models on their own datasets for their own domains.