Clouded Judgement 9.27.24 - The Foundation of Foundation Models

Every week I’ll provide updates on the latest trends in cloud software companies. Follow along to stay up to date!

Foundation Models Are to AI what S3 was to the Public Cloud

Many people look at 2006 as the birth of the public cloud - the year Amazon launched AWS. Microsoft launched Azure in 2010, and Google launched GCP to the public in 2011 (they launched a preview of Google App Engine in 2008, but made it publicly available in 2011). If we rewind the clock back 15+ years, the product portfolios of the public cloud players were quite limited - often times just simple storage and compute products (like S3 and EC2). There was a consensus at the time that these services would one day become a race to the bottom commodity. However, a couple things happened. The cloud turned out to be a much bigger opportunity than anyone ever imagined. At the same time, the public cloud providers diversified their product portfolio significantly, many products built on top of the initial simple storage and compute offerings. AWS now has 200+ distinct products across compute, storage, databases, networking, monitoring, security, analytics, infrastructure monitoring, application development, etc, and more recently AI. On top of that- we HAVE seen significant pricing pressure. S3 has dropped nearly 97% in price, while EC2 has fallen nearly 90%! Despite that, the public cloud providers built behemoth businesses (with high margins).

Why am I mentioning all this? I see many parallels between today’s AI development and the attitudes toward model providers, similar to what we saw during the early days of the public cloud buildout. It’s easy to say things like “the models will all commoditize!” or “the cost structure doesn’t make sense! No one can implement these with high margins!” But just like the criticism of the public cloud, I think these concerns have some truth to them (the pricing pressure), but also will be overblown.

Below is a graphic that OpenAI has shared. In the last 18 months GPT4 has dropped ~90% in price! If you take the GPT4o-mini pricing, the cost reductions are even more dramatic (but GPT4o-mini is not apples to apples with the GPT4 starting point).

Just like the evolution of the public cloud, we should expect these trends to continue! There will be significant pricing compression from here. And those price compressions will lead to significant market expansion (just like it did in the public cloud). Why? As we make something cheaper, TONS of latent demand will be unlocked, and new use cases will become possible that historically never were.

On top of that - we should expect models to be act one - just like S3 and EC2 were act one for the public cloud players. It is just the foundation for the model providers. In 10 years I expect a suite of hundreds of products to be built around models - whether those are agentic capabilities / related functionalities, consumer apps, or something else we can’t even imagine now. Many of those products will be built by independent companies, with model providers offering similar capabilities. Models are simply the foundation for one of the largest tech platform shifts we’ll see.

This is not meant to be an unequivocal endorsement of the leading model providers today. I do believe, however, that they have put themselves in a position to be special, and it now all comes down to execution.

Top 10 EV / NTM Revenue Multiples

Top 10 Weekly Share Price Movement

Update on Multiples

SaaS businesses are generally valued on a multiple of their revenue - in most cases the projected revenue for the next 12 months. Revenue multiples are a shorthand valuation framework. Given most software companies are not profitable, or not generating meaningful FCF, it’s the only metric to compare the entire industry against. Even a DCF is riddled with long term assumptions. The promise of SaaS is that growth in the early years leads to profits in the mature years. Multiples shown below are calculated by taking the Enterprise Value (market cap + debt - cash) / NTM revenue.

Overall Stats:

• Overall Median: 5.3x

• Top 5 Median: 15.4x

• 10Y: 3.8%

Bucketed by Growth. In the buckets below I consider high growth >27% projected NTM growth (I had to update this, as there’s only 1 company projected to grow >30% after this quarter’s earnings), mid growth 15%-27% and low growth <15%

• High Growth Median: 9.8x

• Mid Growth Median: 8.7x

• Low Growth Median: 4.1x

EV / NTM Rev / NTM Growth

The below chart shows the EV / NTM revenue multiple divided by NTM consensus growth expectations. So a company trading at 20x NTM revenue that is projected to grow 100% would be trading at 0.2x. The goal of this graph is to show how relatively cheap / expensive each stock is relative to their growth expectations

EV / NTM FCF

The line chart shows the median of all companies with a FCF multiple >0x and <100x. I created this subset to show companies where FCF is a relevant valuation metric.

Companies with negative NTM FCF are not listed on the chart

Scatter Plot of EV / NTM Rev Multiple vs NTM Rev Growth

How correlated is growth to valuation multiple?

Operating Metrics

• Median NTM growth rate: 12%

• Median LTM growth rate: 16%

• Median Gross Margin: 75%

• Median Operating Margin (9%)

• Median FCF Margin: 16%

• Median Net Retention: 110%

• Median CAC Payback: 41 months

• Median S&M % Revenue: 40%

• Median R&D % Revenue: 24%

• Median G&A % Revenue: 17%

Comps Output

Rule of 40 shows rev growth + FCF margin (both LTM and NTM for growth + margins). FCF calculated as Cash Flow from Operations - Capital Expenditures

GM Adjusted Payback is calculated as: (Previous Q S&M) / (Net New ARR in Q x Gross Margin) x 12 . It shows the number of months it takes for a SaaS business to payback their fully burdened CAC on a gross profit basis. Most public companies don’t report net new ARR, so I’m taking an implied ARR metric (quarterly subscription revenue x 4). Net new ARR is simply the ARR of the current quarter, minus the ARR of the previous quarter. Companies that do not disclose subscription rev have been left out of the analysis and are listed as NA.

Sources used in this post include Bloomberg, Pitchbook and company filings

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