Clouded Judgement 3.6.26 - Get in the Token Path

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Get in the Token Path

As always, these posts are more of a brain dump of “what I’m thinking” about…And lately I’ve been thinking about a pattern that keeps showing up when I study the biggest infrastructure winners of the cloud era, and what it means for AI companies today.

Here’s the general idea: the biggest infrastructure winners of the cloud era monetized the core consumption primitive of the platform. In the cloud era, that primitive was compute, storage, and network I/O. In the AI era, it increasingly looks like tokens.

Let’s unpack.

When cloud computing first started taking off, the core primitive of the platform was very clear: compute. Everything that happened in the cloud ultimately boiled down to compute cycles running somewhere inside a data center. Storage, networking, and databases all mattered of course, but the engine driving the system was compute.

That had an interesting downstream effect. The companies that ultimately became the biggest infrastructure businesses of the cloud era found ways to align their revenue directly with compute activity (or they charged directly for compute). They owned the meter.

AWS and the other hyperscalers obviously did this, their business’ are literally selling compute hours. The more workloads move to the cloud, the more compute gets consumed, the more AWS and the hyperscalers made. But it wasn’t just the cloud providers.

Let’s look at some of the infra leaders of the cloud buildout.

Databricks monetizes job compute. Every time a customer runs a data pipeline, trains a model, or processes a workload, Databricks' revenue grows automatically. Snowflake monetizes query compute, similar story. Every new query, every new dataset, every new workload meant more revenue without having to sell a single new seat. Datadog monetizes telemetry generated by compute workloads. Every new microservice, every new container, every new cloud instance generates incremental Datadog revenue. More cloud compute = more Datadog revenue. Cloudflare monetizes requests generated by applications running on compute. MongoDB charges based on storage and compute consumed through Atlas.

The details vary, but the pattern is remarkably consistent. The biggest companies ended up sitting directly in the execution path of workloads, with pricing models that scaled automatically as compute activity increased (and compute was one of the core primitives of the cloud buildout).

And this is the key insight - these companies didn’t just have “consumption pricing.” Lots of companies have consumption pricing and grow slowly. What made these companies special is that their consumption unit was the same unit the entire ecosystem was scaling. When the world spun up more compute, these companies grew without doing anything. Their revenue was structurally coupled to the platform’s growth vector.

That might seem obvious today, but during the early years of the cloud buildout many infrastructure companies were still trying to monetize software the old way. Perpetual licenses, term licenses, maintenance contracts, support subscriptions layered on top of open source software. These models worked well in the on-premise world where infrastructure growth was slow, predictable, and tightly controlled.

But cloud computing fundamentally changed the underlying economics. Workloads could scale instantly. Compute consumption could grow by orders of magnitude. The companies that adapted to this new world quickly built the biggest outcomes. The ones that didn’t…well, the contrast is striking.

Docker might be the most instructive example. Docker was containerization. They were one in the same. It was the technology that made cloud-native development possible. Millions of developers used it. Arguably the most important developer tool of the cloud era. But Docker never figured out how to monetize the primitive. They couldn’t connect their massive developer adoption to the underlying compute spend that containers enabled. Kubernetes (open sourced by Google) ate their orchestration business, and every hyperscaler ended up monetizing Docker’s own innovation through managed container services. Docker enabled billions of dollars in compute spend…they just never captured any of it (they have been doing a much better job over the last few years, however. This commentary is more geared towards their origins).

The common thread across Docker and others who gained massive adoption but ran into some sort of business model wall was similar. Most were deeply embedded in the cloud infrastructure stack and were critical tooling. But when they failed, it was often because they didn’t figure out how to make their revenue a derivative of the core consumption primitive. They monetized adjacently, through seats, support contracts, consulting, and the market rewarded them accordingly. Or rather, didn’t.

Compare these outcomes to Snowflake, Datadog, and Cloudflare. Same era. Same underlying platform. But radically different business models, and radically different outcomes. The difference? The winners owned the meter. They put themselves directly in the path of the underlying compute primitive.

Now Map this to AI

If cloud infrastructure was built on the primitive of compute, AI infrastructure is being built on a different primitive: tokens.

Every AI workload ultimately boils down to tokens being generated, processed, and consumed by models. Prompts become tokens. Context becomes tokens. Responses become tokens. Agents running multi-step workflows can generate enormous volumes of tokens as they reason through tasks. Tokens are the atomic unit of work in modern AI systems.

And once you start looking at the ecosystem through this lens, a very familiar pattern emerges.

The model providers like OpenAI and Anthropic - they literally are the token primitive (like the hyperscalers were the compute / storage primitive for the cloud buildout). They charge per token in, per token out.

But it’s not just the model providers. The fastest-ramping AI companies today are the ones sitting directly in the token path.

Coding agents are the standout. Cursor reportedly hit $2B ARR recently according to online reports. Every keystroke, every code completion, every agent action triggers inference, and their business model has evolved from simply charging per seats (these seats now come with usage limits!). Their revenue is structurally coupled to token consumption.

Companies in the business of inference more directly (Inferact, Baseten, Fireworks, Together, etc) are essentially selling the raw primitive.

The companies that sit closest to the generation and consumption of tokens are the ones whose revenue naturally scales with AI activity. Meanwhile, other parts of the AI ecosystem are experimenting with more traditional SaaS pricing models, seat-based developer tools, platform subscriptions, enterprise licenses layered on top of open source frameworks.

Those businesses may still succeed. Many developer tools companies built valuable franchises during the cloud era. But if history is any guide, the biggest infrastructure companies tend to emerge where the core unit of platform activity is measured and monetized. And we already have a pretty clear set of counterexamples from the cloud era showing what happens when you don’t.

Now, being in the token path is necessary but not sufficient. This is an important nuance.

The pure-play CDN companies of the cloud era were technically “in the compute path.” They charged based on bandwidth and requests. Traffic was exploding. They should have been massive winners. But bandwidth turned out to be a commodity. Prices compressed relentlessly. Limelight Networks had record traffic during the streaming boom of 2020-2021 and declining revenue at the same time. They eventually rebranded (to Edgio) and went bankrupt. Meanwhile Cloudflare, which started in a similar spot, layered on security, developer tools, and edge compute, building real differentiation and switching costs on top of the primitive. Same starting point, radically different outcomes.

The lesson for AI founders: get in the token path, but build something differentiated on top of it. Don’t just be a pipe that tokens flow through. Be the layer that makes those tokens more valuable, whether that’s through better developer experience (Cursor), specialized vertical models, security and compliance tooling, or proprietary data moats.

There’s also a timing dimension. In the cloud era, the companies that established themselves as defaults early in the compute path captured the most value. Datadog, Snowflake, and Cloudflare all got to scale before the primitives became fully commoditized. The implication: the window to get into the token path is now. Inference costs are dropping rapidly (which is great, it means more tokens consumed, but it also means per-unit economics compress over time). You need to be in the path and build a moat before that happens.

The biggest infrastructure winners of the cloud era monetized the core consumption primitive. The ones that didn’t, even the ones with massive adoption, beloved products, and deep integration into the stack, produced dramatically smaller outcomes.

The primitive has changed. It’s no longer compute cycles. It’s tokens. And if the pattern holds, the most valuable AI infrastructure companies of the next decade will be the ones that find a way to get themselves directly into the token path.

If you own the meter, growth takes care of itself.

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: 3.5x

• Top 5 Median: 17.7x

• 10Y: 4.1%

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: 6.5x

• 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: 75%

• Median Operating Margin (1%)

• Median FCF Margin: 20%

• Median Net Retention: 109%

• Median CAC Payback: 34 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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