Systems of Record Won the SaaS Era - Clearinghouses Will Win the Agents Era

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Systems of Record Won the SaaS Era - Clearinghouses Will Win the Agents Era

Back in December I wrote about the fight to become the front door to the systems of record. In that post I wrote about distribution, who sits between the user and the data (and why sitting there is strategic). This post is an expansion of that post (and the 1 or 2 I wrote after about similar topics). What really should AI companies be racing towards? If systems of record won in the SaaS era (ie they had the durable moats), what’s the equivalent in the AI era? Of course the answer is still partially “the system of record”, where maybe you swap out “record” with something like “agents” or “work".” But let’s come up with something new :)

Let’s start by looking at the SaaS era, and what qualities created durable successful companies. In SaaS, one main goal just about every company aspired towards was to become a system of record. Store and govern critical data. Own the place where workflows kicked off (or where workflows touched). If you got there (Salesforce for customer data, Workday for employee data, NetSuite for financial data) you ended up with an incredibly deep moat. Everything integrated with the system of record. And when you had hundreds of independent workflows and solutions built on top of the system of record, even thinking about ripping out that foundational layer was impossible. Ripping out a system of record was so painful that customers would really avoid it at all costs... The change management was almost never worth it. If you think upgrading a piece of software to a new version breaks things, imaging ripping out the layer all your workflows touched.. Price increases, mediocre product velocity, difficult upgrade cycles... didn’t matter. The data was in there, the workflows were built around it, and the switching costs got harder and harder with every year that went by.

So what’s the equivalent prize in AI? I think it’s becoming The Clearinghouse for agents.

In financial markets, the clearinghouse sits between different parties that aren’t able to fully trust each other. The clearinghouse verifies / authorizes / settles trades, and ultimately keeps the receipt. Nobody really loves the clearinghouse, but it’s clear it has to exist for the ecosystem to transact.

Now think about where enterprise software is heading. Agents from tons of different vendors, acting autonomously, touching your most critical data, and even in the future spending real money. Some company has to sit in the middle of all that and decide: which agent is cleared to act? On what data? With what limits? And can you prove what happened after the fact? Whoever holds that seat holds incredibly “strategic real estate.” (and every founder I’ve worked with has probably heard me discuss strategic real estate over and over). That’s the clearinghouse.

This may sound counterintuitive, but owning the clearinghouse for agents (given agent companies themselves will want to be the clearinghouse) may create a deeper moat than the one systems of record had. A system of record controlled your data. It kind of controlled your workflows (but not always, oftentimes someone else controlled the workflows, but the data in the system of record was a critical part of the path). The Clearinghouse controls four things: memory (what your agents know), context (what they see and how it’s served), execution (what they’re allowed to do), and governance (who’s allowed to do what, plus the audit trail behind all of it). If migrating off a system of record was painful, migrating off the thing that holds your policies, your permissions, and your entire audit history is probably harder (especially when the agents start to handle more and more of the work). AND - I think these agent companies that become The Clearinghouse will start to look more and more like systems of record in their own right. Data in systems of record were oftentimes transactional data. Data in agent systems of records (ie Clearinghouses) will be agent traces, agent evals, agent telemetry data, agent A/B data, etc

And governance is obviously a really important part of the story here. Governance used to be more of a compliance checkbox at the end of the sales cycle. It was the thing the security team made you sit through after the deal was basically done and agreed to by the business folks. Now it’s what CIOs will focus on at the forefront of a deal (or at a minimum are thinking about from meeting #1). Once agents act autonomously, the buying question changes. It’s no longer “is the model good?” Every model is good (or good enough). The question becomes “can I see what every agent did, set policy on what it can touch, and prove it to my auditors?” The biggest companies are already showing the way... Databricks leads with evaluation and governance. KPMG just announced they’re wrapping Microsoft’s Agent 365 in their “Trusted AI” framework. These companies are selling clearance. Governance is some very strategic real estate on how you earn the clearinghouse seat (and like systems of record, what makes it nearly impossible to lose once you have it).

This is why everyone is racing. Microsoft (Agent 365 + Copilot woven through Windows and Office), Salesforce, Snowflake, Databricks aren’t fighting over model quality anymore (not that they ever really were…). They all see the same prize: the clearinghouse is the toll booth every agent action passes through.

And lots of people going after this! The data players (Snowflake, Databricks) believe they win from below (data gravity becomes clearing gravity, because the agents have to come to where the data lives). The OS and productivity players (Microsoft) believe they win from above (own the surface where employees actually kick off agents, and the clearing happens wherever the user is). The new crop of Agent native companies believe an entirely new layer emerges! All arguments are right about one thing: whoever wins gets to define the knowledge graph, the governance frameworks, and which workflows get automated first.

Which brings me to the founder question. If you’re building in this market, ask yourself honestly: am I on a path to becoming a clearinghouse, or am I building a feature that clears through someone else’s?

The good news - there are real paths to the clearinghouse seat, even for startups. The first is vertical: earn clearinghouse status in an industry the horizontal players won’t go deep on. Things like owning proprietary data, simplifying regulatory complexity, or workflow depth they can’t absorb from the outside. The second is a bit harder, but more impactful. Become the clearinghouse across clearinghouses. No enterprise is going to run only Microsoft’s agents. Or only Anthropic models. Someone has to govern the multi-vendor mess (the parallel was multi-cloud). Set policy across all of it, clear actions across all of it, hold the audit trail across all of it, etc. Become the “single pane of glass.” The incumbents can’t credibly be neutral here (Microsoft governing Salesforce’s agents? Good luck). That neutral seat is a super strategic position in software right now.

I’ll end with this. Becoming a system of record got you the moat in SaaS. Becoming The Clearinghouse for agents gets you that same moat in AI. The lock in just moves from your data to your permissions. The source-of-truth era is transitioning into the source-of-permission era. Founders should pick their path to the clearinghouse now, because in 18 months there won’t be room to be undecided.

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: 3.1x

• Top 5 Median: 26.8x

• 10Y: 4.5%

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: 16.7x

• Mid Growth Median: 4.8x

• Low Growth Median: 2.6x

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: 16%

• Median Gross Margin: 76%

• Median Operating Margin 2%

• Median FCF Margin: 21%

• Median Net Retention: 110%

• Median CAC Payback: 44 months

• Median S&M % Revenue: 34%

• Median R&D % Revenue: 23%

• Median G&A % Revenue: 13%

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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