At Point Nine, we have been focused on investing in SaaS companies and have been fortunate to work with several generations of successful businesses in this segment over the years. Since joining the firm 24 months ago, I’ve spent a significant share of my time looking at SaaS companies using machine learning to change industries.
From my vantage point, it appears that we’re now at a turning point when it comes to the use of AI in B2B applications. I published this post to that effect 18 months ago and I thought it was time to dive deeper into the topic with the benefit of hindsight and data. To that end, I’ve attempted to consolidate our thoughts on investing in AI-first SaaS businesses by creating a customary Point Nine napkin! Why? Because this is the format we typically use at Point Nine to consolidate our thoughts (see our SaaS and Marketplace funding napkin here and there) ;)
This post consists of 3 parts:
First, I will share a quick historical perspective on the broader SaaS industry and use it to explain some of the key success factors of each of these generations at a (very) high level,
Second, I define what I call an “AI-first SaaS business” and outline a work-in-progress investment thesis for seed stage startups in this category,
Third, I try to explain why AI-first SaaS is an exciting category based on the analysis of some of their intrinsic characteristics.
AI-first SaaS startupsFirst,
let’s start with a definition:“AI-first SaaS companies use data and machine learning algorithms at their core to build disruptive product experiences.
While most AI companies at the Series A or B stage start showcasing revenues that are not so different from a typical SaaS startup, they tend to look very different at the seed stage. This is in part because building a ML-powered software today is more complex and takes more time than building a typical SaaS startup. On top of all the challenges that comes with building a SaaS company, AI-first SaaS businesses need to:
Identify problems and experiences well suited to a data-driven solution
Collect and label data
Extract value from the data by building models with or without feature engineering
Find early customers to validate and contribute to the (future) value of their learning system.
The good news it that the expectations of VCs when it comes to AI-first SaaS startups also differ from those for other types of SaaS startups. Hence, if we were to wait to have the same revenue metrics for an AI-first SaaS startup than for a typical SaaS startups, we would probably miss all the seed-stage AI deals.
Now, without further ado, here is a WIP investment thesis for AI-first SaaS businesses at the seed stage. Consider this a .9 version (it has not yet made it until v1.0) and it’ll likely evolve many times as we keep on investing in AI-first businesses. Our SaaS funding napkin also evolved a bunch of times already (see the 2016, 2017, 2018 versions here).
Revenue
The company has a few paid pilots running.
Some early indications that customers will be ready to pay a significant amount of money once the product will be running in production.
No, MRR is not a requirement!
Valuation / Round size
$2–3M round at pre-money valuation between $6 and 10M pre-money.
As mentioned earlier, AI-first businesses tend to raise a little more money at a higher valuation currently than other traditional SaaS businesses.
The round size and valuations are impacted by lots of different factors but to put a range here, round size and valuation are typically 20–50% larger than traditional software companies (consistent with what MMC found in their research surveying UK based startups).
Product
A Minimum Viable Product that demonstrates what an AI model can do (a.k.a a MVP and a first model has been built).
Customer validation
It addresses an important pain point (can be read as “in the top 3 priorities of the buyer”) for a well defined target audience.
Customers can validate that the product has a chance to provide a large enough ROI for them to try it or to switch from competing non AI-powered SaaS products. The challenge here is that even if the ROI is not massively superior to “dumb” products, it still needs to be sufficiently superior for customers to switch product, contribute their data and eventually get a much better product than they used to. Don’t ask me to define sufficiently superior, only customers can :)
A good indication that, longer term, the ROI driven by the usage of the product could be 5–10x higher than the expected price for which it will be sold and will increase with time as the product ingests more data.
Market
One unvalidated assumption that we have here is that smaller markets (in the few hundred millions) could still be large enough because AI-first SaaS companies have winner takes all dynamics (check out the last part of this article for that) that typical SaaS companies do not have.
AI specifics
Data
Predictive power: the initial dataset has predictive power according to the problem the company is trying to solve.
Shared learnings: the data from each single customer can be pooled into a larger learning dataset.
Size: the number of features (dimensionality) and the size of the dataset (breadth) is (or will be) unique compared to other datasets owned by competitors.
Performance of the model
The team has already reached or has precise ideas on how much data, time or money is required to get to an accuracy level that is significant enough for customers to use the product (a.k.a reach the “Minimum Algorithmic Performance” = the minimum performance to justify adoption). This term that was initially coined by our friends at Zetta (if you want to learn more, check this post out).
The team knows what performance benchmarks are relevant to the customer judging their product.
Feedback loop(s)
The user feedback is closely integrated in the product. It is used in order to generate a superior model performance and, more broadly, a superior product experience.
Bonus point if the User Experience is adapting depending on what kind of data is required to improve the algorithm’s performance (experts call this Interactive Machine Learning).
Distribution
Some ideas or early signals that there will be an efficient way distribute the product (a.k.a. a way to maintain a good ACV vs. CAC ratio at scale). Not sure what this means, check Christoph’s “5 Ways to build a 100M business”, it works for all generations of SaaS companies including AI-first businesses.
Defensibility
It’s neither obvious, nor fast nor cheap to access enough data to reach the MAP.
“Data Collection Edge”: the company has an edge (e.g. a first mover advantage or unique access to proprietary data), which will prevent other players with more resources to gather more data faster and eventually have better algorithmic performances. I wrote a whole post a year ago on the topic: check it out!
Want a napkin version and compare it with other SaaS startups? Here it is!