To The Moon

The AI Factory

If I zoom out for a moment and look at the current trajectory of AI infrastructure, it’s hard not to see an entirely new pattern forming. Inference keeps getting faster. Inference engines keep getting smarter. And the ecosystem around them keeps getting more modular and open. What once felt like specialized machinery locked inside a handful of labs is now drifting into the hands of every company with a GPU budget and a few strong engineers.

Neoclouds like $CoreWeave, Inc.(CRWV)$ and $TOGETHER PHARMA LTD.(TGPHF)$ have rewritten the economics of GPU access. Inference clouds like Fireworks, Baseten and fal have done the same for reliable serving (and we’ve already separated into separate inference engines for text vs multi-modal outputs). Meanwhile, open source engines like vLLM, SGLang and TensorRT have quietly become the new optimization layer, the thing that makes serving models actually usable at scale. And all of this is happening at the same time that open source models keep pushing the frontier. DeepSeek’s R-series, Llama 3.2, Qwen2.5, Kimi K2 - each release not only narrows the gap with frontier models, but expands the surface area of what you can build without relying on a closed ecosystem. An open stack is emerging!

Put these threads together and something interesting happens. For the first time, companies can set up their own “AI Factory.”

The playbook looks roughly the same across industries. Start with a state-of-the-art open source model. Distill it down to something smaller, faster and cheaper to run. Fine tune it on your own workflows and data - the messy institutional knowledge that no lab has access to. Deploy that model onto your own inference stack (built around one of the open source engines listed above) or someone else’s cloud. Monitor how it behaves, evaluate its outputs, stress test it against edge cases, then fold that data back into the next fine-tuning run. Rinse, repeat. Every loop, the model becomes a little more aligned to your business and a little more performant for your users.

Said another way, the AI Factory is a closed feedback system where models are created, optimized, deployed, observed and recreated again- like a production line. And instead of hardware coming off the belt, you get models that become more specialized, more accurate and more economically efficient every cycle.

A year ago this was a fantasy. The infrastructure simply wasn’t there. Serving was brittle. GPUs were scarce (still are…). Open models weren’t good enough to meaningfully fine tune. And most companies lacked the muscle to run an end-to-end stack. But the infrastructure has matured to the point where this loop is now not just possible, but increasingly common. And as it matures, it opens the floodgates to entirely new categories of applications and workflows that were previously out of reach.

The takeaway is simple: whoever builds the platform that owns this entire loop - from model selection to distillation to fine tuning to deployment to monitoring to retraining, will be wildly successful. Because the AI Factory will become the operating system for how modern AI will actually get built.

Quarterly Reports Summary

Top 10 EV / NTM Revenue Multiples

$Palantir Technologies Inc.(PLTR)$ $Cloudflare, Inc.(NET)$ $CrowdStrike Holdings, Inc.(CRWD)$ $Snowflake(SNOW)$ $Datadog(DDOG)$ $Figma(FIG)$ $Zscaler Inc.(ZS)$ $Shopify(SHOP)$ $Guidewire(GWRE)$ $Veeva(VEEV)$

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: 4.8x

• Top 5 Median: 24.4x

• 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: 14.8x

• Mid Growth Median: 6.5x

• Low Growth Median: 3.7x

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: 12%

• Median LTM growth rate: 14%

• Median Gross Margin: 76%

• Median Operating Margin (2%)

• Median FCF Margin: 19%

• Median Net Retention: 108%

• Median CAC Payback: 31 months

• Median S&M % Revenue: 37%

• Median R&D % Revenue: 24%

• 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.

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