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**TL;DR: Demand for the cost intelligence spiking is not priced in but is inevitable.**
Last time I posted about my 40-bagger GOOGL LEAP play, everyone flamed me for not posting until it had already partially materialized. So here I am, right before I make my next massive play. I made that play because of similar macro considerations (the fact that Google's best model was slightly worse than ChatGPT, but 10-20x cheaper), as well as the market mispricing of what businesses AI was going to disrupt (search/ads disruption being overblown). I'd also like to point out that in my previous post I was clowned for telling everyone to buy ADBE puts, and ADBE is down 30% since then.
**Overview:**
The market is seeing massive spend on AI datacenters and are running in fear. At the same time, every time Anthropic announces a new product an entire industry drops 10%. The market is both admitting that AI is going to disrupt several massive industries, but at the same time pretending that demand for AI compute capacity is a massive debt risk and very unlikely to profit. **The market is failing to connect the dots that we are <1 year away from AI datacenters becoming pure profit machines.**
**Market Context:**
Currently, the market has crowned the winners of AI as the companies providing the infrastructure to the hyperscalers: semiconductors, GPU/TPUs, energy companies, electrical utilities, memory, etc.. Every buy side investment report I read emphasizes the "AI trade" as profiting from the absurd CAPEX spending of hyperscalers. Many have explicitly called out that token providers (the model creators/datacenters) are unable to extract margins.
There is also a pervasive (and accurate) narrative depressing valuations for model creators: the lack of a moat. Model creators (Google, OpenAI, Anthropic) will struggle to demand high margins because switching costs for models are low and quality is close. This narrative is likely true, but I think it misses the real shift:
**The value in the AI trade is shifting from going 100% infrastructure players to a 50/50 split between infrastructure and datacenter hyperscalers (the owners of the "Intelligence Factories")**
**Who owns AI capable datacenters today?**
*Source: Epoch AI + 2026 Internal Projections*
|Company|**B200-Equivalents Owned & Installed**|
|:-|:-|
|**Google (Alphabet)**|**\~0.41M–0.67M (Heavy TPU Bias)**|
|**Microsoft (Azure)**|\~0.24M–0.35M|
|**Meta**|\~0.26M (High Uncertainty, likely not AI capable)|
|**Amazon**|\~0.10M–0.16M|
|xAI|\~0.15M|
|CoreWeave|\~0.08M–0.10M|
|Oracle|\~0.03M–0.05M |
**THE DD: The "Token Factory" Thesis**
**Assumptions:**
1. **Economic Value is Here:** The latest generation—**Gemini 3.1 Pro, Opus 4.6, and GPT 5.3 Codex—can provide massive economic value autonomously.** 4% of all public GitHub commits are now created by Claude Code, Mag 7 margins are spiking while laying people off, top engineers, physicists, and mathemeticians are admitting AI is now better at their job than they are, or an essential tool to use to keep up. This is the worst these models will ever be and they are drastically cheaper than humans for the same tasks. I think everyone is putting their head in the sand pretending we're not about to get blown out of the water by AI, but I'm not here to sell you AI: you are free to blindfold yourselves and ignore reality.
2. **Enterprises, small businesses, and individuals are behind in AI use, not because AI is fundamentally unproductive, but because the infrastructure required to leverage AI does not exist today**. It will take time for adoption to happen, use cases to proliferate, and systems to take form to give AI the information it needs to succeed. So much of the tasks of white collar work that demands 60-200k salaries today can already be automated by AI today. It's absurd to think that corporations, the military, etc. will not pay for intelligence that is greater than human capability and cheaper.
3. **Cyclical Intelligence:** Building a datacenter takes 2+ years. Serving intelligence at scale can be thought of as cyclical, like the memory (DRAM) business. **We are entering the "high demand, tight supply" phase of that cycle.** (I personally believe demand for intelligence will **never** surpass supply, but my thesis doesn't require that to be true)
**Thesis: Capturing the Supercycle**
When the utility of current models is realized, the price GPU/datacenter owners charge is no longer relative to *cost*—it’s tied to the **economic value** of the tokens produced.
The unit of value is no longer the GPU; it’s **tokens of Intelligence.** As enterprises realize that an agentic workflow (like Claude Code or Gemini Agents) can replace a more and more *tasks* that humans currently do for cheap, demand for tokens will go vertical.
The price Anthropic charges for API access will spike because they can’t serve demand. But that margin won’t go to them—**it will go to Google, who they rent their TPUs from.** You can think of a datacenter as a "token factory." When demand for intelligence spikes, the margins of these factories do too, until enough new factories come online. This looks like the revenue of every datacenter doubling on flat costs (think about gold mining stocks when gold spikes). I hear a ton of arguments that the cost of an individual token is rapidly declining for the cheapest models so therefore the need for datacenters will crater, but the reality is that cost of serving the highest quality intelligence at any moment has not significantly dropped and in many cases has actually risen.
**Positions:**
I'm holding onto **\~$650k in GOOG LEAPS** (sadly down like \~300k from 2 weeks ago) will roll the March contracts into new 500C 2027-28 expirations when I get Long Term Capital Gains. This GOOGL LEAP position already has \~200k in realized gains, cost basis was $33k total.
* **65 GOOGL 380C Jan 15 '27** (\~$146k)
* **36 GOOGL 300C Jan 15 '27** (\~$157k)
* **50 GOOGL 365C Dec 17 '27** (\~$251k)
* **8 GOOGL 300C Jun 18 '26** (\~$24k)
* **15 GOOG 300C Mar 20 '26** (\~$27k)
**New Token Supercycle play:**
* **NEW:** 21 MSFT 800C Jan 21 '28 (\~$12,500)
* **NEW:** 11 MSFT 660C Mar 19 '27 (\~$4,500)
* **NEW**: 35 AMZN 370C Jan 15 '27 (\~$8200)
* **NEW**: 22 CRWV Shares ($2000)
* **NEW**: 72 IREN Shares ($3000)
* **NEW**: 1408 HIVE Shares ($3000)
I don't buy options when IV is high, as I only like LEAPS and LEAPS + IV = atrocious risk/reward profiles, hence the shares. Additional shoutouts that are likely also supported by the same thesis but I personally didn't invest in: NBIS, ORCL, META (quite behind though). I am almost certain to expand this position over time.
Feel free to disagree with my assumptions, I am not really going to argue with people who think that AI isn't economically valuable or is a bubble about to pop. I'm basing my assumptions on conversations with people at AI labs who all complain that compute is incredibly scarce, my own personal use of AI at work (not going to dox myself), and the very obvious signal we are getting from the capex of the Mag 7. I *am* interested in discussing with anyone who thinks my assumptions don't actually support my thesis though or thinks the margins of AI will go elsewhere.