Clouded Judgement 3.20.26 - Digital Twins

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

Digital Twins

Every week I meet with founders building in the agent space. And lately, I keep hearing the same concept come up over and over - digital twins (or some version of this). When a concept starts showing up as frequently as this one, my ears generally perk up. Digital twins are the thing perking up my ears! And I think they’re about to become one of the most important concepts in AI. I think they could become a layer that helps scales AI to the masses (and consumption of AI).

So what actually is a digital twin? The term originally comes from manufacturing. You’d build a digital replica of a physical asset (a jet engine, a factory floor) to simulate and monitor it. With AI it’s the same core concept, but with a totally new application. In the AI era, a digital twin is just representing knowledge (from any source, in any form) digitally, so an agent can act on it. That knowledge could live in a person’s head, across a dozen siloed systems, in years of company history, or in the collective behavior of your customers. The twin is just the bridge between that knowledge and the agent that needs it to do work.

There are a couple main flavors of digital twins that I wanted to highlight here. If you’re building anything in the digital twin space, I’d love to talk to you!

1. Knowledge capture (workflows)

2. Institutional memory (knowledge retention)

3. The expert twin (scaling your best performers)

4. The customer twin (queryable customer knowledge, anytime)

5. Knowledge multiplication (1-to-many)

6. Personal monetization

The most obvious one is workflow knowledge capture. Think about something as simple as quote-to-cash, or sending a contract, or onboarding a new customer. Someone on your team knows exactly how that process works (and more importantly what to do in edge cases, when to ask for approval when something doesn’t look right, etc). They know which systems to pull from, whose approval you need, what order things happen in. But nowhere is it written down. It lives entirely in that person’s head because they’ve done it a thousand times. Until you can represent that knowledge digitally and hand it to an agent, the agent is going to keep getting stuck. Digital twins are how you fix that. I saw a cool launch from a company called Edra this week that reminded me of this concept. To automate workflows, we first need to understand what the workflow is!

Closely related is institutional memory. When a key employee leaves, their knowledge walks out the door with them. We’ve all felt that, and it can be quite painful depending on the employee. Digital twins give you a way to preserve not just the process documentation, but the actual judgment and pattern recognition that made that person valuable. You’re capturing the what, why and“when it gets weird, here’s how to handle it” knowledge.

Then there’s what I’d call the expert twin. Every company has a power law distribution of talent. There’s the sales rep who always finds a way to close, the SOC analyst who knows how to triage alerts in their sleep, the on-call SRE who’s seen every outage pattern and knows exactly what to pull. These people are incredible, but are also massive bottlenecks. There’s only one of them. A digital twin of your best performer will help you build better agents and raise the floor for everyone else. The new hire gets trained by the twin. The average rep gets real-time coaching from the twin. You stop hoping everyone eventually gets as good as your best person, and you just... give everyone access to that expertise directly.

The customer twin is another one I’m seeing more of. Instead of running a one-off survey or scheduling a round of user interviews every time you want to test a hypothesis, you build a persistent digital representation of your customer base that you can query anytime. What would our ICP think of this new feature? How would our mid-market segment respond to this pricing change? Ask the twin. Companies like Simile and Aru are already building in this direction (more for market research then b2b customer research) - the idea being you do the research once, build the baseline, and then run as many studies as you want against the digital version rather than constantly recruiting new participants.

That’s a good example of a broader pattern I’d call knowledge multiplication. Taking something that used to be 1-to-1 and making it 1-to-many. One survey becomes unlimited studies. One expert becomes a resource available to the entire company. Which brings me to the most interesting flavor of all of this.

What if you’re the expert?

If you’re a graphic designer with a distinct style and aesthetic, you used to be capped by time. You could only take on so many projects. You had to turn clients away. Now imagine you build a digital twin of yourself (your taste, your process, your design sensibility) and others can hire “you” without needing calendar time with the real you. Same thing for an executive coach. You’ve spent years developing a methodology and a point of view that genuinely helps people. Historically, you could only serve so many clients. A digital twin changes that entirely. You go from a time-constrained practice to an infinitely scalable one.

This is where I think the job displacement narrative gets it wrong. Everyone asks “will AI take my job?” But the better question is “can I build a digital twin of myself before someone else does it for me?” The people who win in this world are generally the ones who move fastest to adopt new technologies. With AI, this could mean they’re the ones who figure out how to package and distribute their own knowledge and taste at scale. I’ll always remember my couple friends in high school who learned how to program iPhone apps before the app store got bloated. Their early apps printed money! (at least for a high schoolers standard).

The throughline across all of these is the same: the bottleneck to the agentic era isn’t model intelligence. The models are already good enough. The bottleneck is knowledge representation. And specifically, how do we represent that knowledge digitally. Agents can only act on knowledge they have access to. And right now, most of the world’s most valuable knowledge is locked in people’s heads, scattered across systems, or sitting undocumented in the institutional memory of companies. Digital twins are how you unlock it. That’s why I keep hearing about this concept in meeting after meeting. And it’s why I think we’re just getting started.

Quarterly Reports Summary

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: 3.3x

• Top 5 Median: 17.7x

• 10Y: 4.3%

Bucketed by Growth. In the buckets below I consider high growth >22% projected NTM growth, mid growth 15%-22% and low growth <15%. I had to adjusted the cut off for “high growth.” If 22% feels a bit arbitrary, it’s because it is…I just picked a cutoff where there were ~10 companies that fit into the high growth bucket so the sample size was more statistically significant

• High Growth Median: 10.4x

• Mid Growth Median: 5.9x

• Low Growth Median: 2.7x

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 its 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: 13%

• Median LTM growth rate: 15%

• Median Gross Margin: 76%

• Median Operating Margin (0%)

• Median FCF Margin: 20%

• Median Net Retention: 109%

• Median CAC Payback: 33 months

• Median S&M % Revenue: 35%

• Median R&D % Revenue: 23%

• Median G&A % Revenue: 15%

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 pay back its 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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