Matt Turck

Some simple math: if a great sales person can do 6 deals a year (in a category creation context) and they’re paid $400k a year OTE, to have reasonable margins, you need their quota to be 3x OTE, so that’s $1.2M… so that basically means you need to have $200k * The reality is that for every one dollar a customer spends on software, they spend $2 to $3 on services (contract engineering, integration, etc), it’s a large TAM Except for a couple of examples like Atlassian and Dropbox, however, all successful enterprise startups at some point overlay a sales team on top of organic growth (see Slack, Github, etc.) * Having those two sales motions (bottom’s up and sales team) makes things way more complicated than building a consumer company… for example, you can have a user you acquired through an organic sales motion, but they’re not the buyer for your product and they don’t have budget for it

In part because the entire hedge fund industry has been performing generally poorly recently (years of performance trailing the stock market), there’s been mounting pressure on hedge funds to evolve rapidly, particularly fundamental ones. * Our approach is what type of information can help us better understand a specific company…  like how sales are going to look like next quarter… so we ask ourselves, if we were that company’s data science team, what data would we want to see? * The multi-million dollar data sale is something you saw maybe 3 or 5 years ago… now people understand what data is actually worth, and they’re not trying to get exclusive access to just one data set, they know it’s part of a bigger mosaic” [our investors] want to understand what the model actually does and can explain the factors… it’s more about delivering a list of names that they can look at, based of the machines, and then there’s a level of due diligence”

There are basically three categories of AI startups: * “AI-first” startups are startups whose product simply could not function without AI at its core, whether they serve consumers or enterprises. This post is mostly about “AI-first” startups, although some lessons may apply to other categories, and perhaps to “deep tech” (or “frontier tech”) startups in general. For example, you’ll see startups building a V1 of their product that doesn’t have any actual AI in it, and functions with software and humans. I have also learned that it’s really helpful to start building the Product function early into AI-first startups, and they’ll provide a nice counterbalance to the R&D teams.

We’ll chat today about how blockchain can help AI, but it is worth noting that there is a number of ways AI can help blockchain – another interesting discussion for another day. To explore how to build such a decentralized marketplace, let’s chat about how to decentralize the three key building blocks of AI: data, models and computing power. Sometimes you need to create your own data for purposes of AI training – either because you don’t have access to the right data set, or because the use case you are training the AI is too new that the data simply does not exist. Putting it altogether, you can imagine a fully decentralized AI marketplace where people provide their data, developers compete to provide the best machine learning models, and the whole system works as a self-reinforcing network that attracts more and more participants and creates better and better AI.