To The Moon

Over the last year we have all been trained (at least I have…) to look at model launches through one lens. What is the benchmark score and who sits at the top of the leaderboard. It felt like every product announcement was immediately reduced to a scatter plot and a few social media hot takes about who beat whom by a few points on MMLU or GPQA. But something subtle started to shift with the Gemini 2.0 family and became much clearer with this week’s Gemini 3.0 launch. The story is no longer only about raw model quality. The frontier is getting crowded, the performance gaps are narrowing, and the real competition is moving up the stack into platforms. Said another way, the next decade in AI will be defined not only by model breakthroughs, but also by distribution, integrations, and the shape of the ecosystems that sit on top of these models.

$Alphabet(GOOG)$ $Alphabet(GOOGL)$ made this point in a very loud and very intentional way. When they launched Gemini 3.0, the focus was not only on the fact that Ultra and Flash and Nano continue to close the gap on frontier competitors. Instead the headline was how deeply Gemini is being bundled across the entire Google universe. Android, Chrome, Search, Workspace, YouTube, and now a unified multimodal API that treats text, audio, vision, and actions as native elements. The real message was that Gemini is moving beyond just being a model and into a full operating system level of capability. Once it is inside everything you touch, the model quality almost becomes secondary because the distribution channel becomes the moat. If Android eventually ships with a first class reasoning engine at the OS layer, everything else starts from behind.

OpenAI is taking a similar but different approach. Rather than using an existing distribution surface, they are turning ChatGPT into a superapp that becomes a destination in its own right. The App Store / Apps SDK inside ChatGPT, more memory features, the shift toward persistent agents, the deeper integration with developer tools, all point toward OpenAI trying to pull users into a new gravity well instead of relying on someone else’s platform. (You could argue Sora was slightly different, it was it’s own app). It’s the idea that users will increasingly start their workflows inside ChatGPT and let the agent fan out into the external world. It is a high risk, high reward strategy. If it works, OpenAI becomes the gateway to most digital activity. If the shift stalls, they remain a model company competing in a crowded field. They are also running an enterprise application playbook - it will be interesting to see how their enterprise agents are released (like Aardvark). Will enterprise apps (ie agents) fall under the chatgpt business experiences or something else?

Anthropic is also playing a similar, but different, game. They are not trying to become a consumer destination. They are leaning into the enterprise, safety, reliability, and API layer. Their model releases are impressive, but the bigger story is the positioning. They want to be the safe and predictable choice for companies that need AI infrastructure without the swirl of consumer ambitions. It is a strategy that borrows from AWS in the early cloud days. Win the trust of developers, then grow with them as AI flows deeper into enterprise systems. And they’ve already started moving more up the stack into applications, like Claude Code.

And then there is $Meta Platforms, Inc.(META)$ , which continues to open source aggressively. It almost feels like they are trying to flatten the entire model layer so that differentiation moves into the products and services built on top. Every time Meta ships a stronger open model, it forces the field to move the real competition higher up the stack. If the base capabilities are available to everyone for free, then the platform becomes the only place left to build a moat. They do need to start playing some catch up, though

All of this circles back to the same observation. Model quality is converging faster and faster. It is not that the frontier is slowing down, it is that the gap between the frontier and everyone else is closing with each release. Once multiple labs can deliver competitive large scale models, the battle shifts to where those models live, how you interact with them, what tools surround them, how tightly they integrate with your daily workflows, and how many developers decide to build atop them. In that world the real differentiation will come from platform strategy, not raw numerical superiority. The same thing happened with search. The search engine wars were first about which engine indexed more web pages…Then it evolved!

We are just at the beginning of the AI platform wars. Google is bundling Gemini everywhere. OpenAI is building a superapp. Anthropic is carving out the trusted enterprise API position. Meta is pushing open source deeper into the market. And each approach is starting to look less like a product launch and more like a declaration of platform intent. The next few years will be shaped by how these strategies collide, overlap, and compound. The models will keep improving, but the gravitational forces are moving elsewhere. The new frontier is platform reach.

Quarterly Reports Summary

Top 10 EV / NTM Revenue Multiples

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

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: 4.5x

• Top 5 Median: 23.1x

• 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: 13.3x

• Mid Growth Median: 6.0x

• Low Growth Median: 3.4x

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: 18%

• Median Net Retention: 108%

• Median CAC Payback: 32 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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