Probably Way Late to This, But...
Am I seeing this right? Google looks like it's quietly assembling a full vertical AI stack while everyone watches the pieces individually.
TL;DR: Aluminium OS + Gemini Ultra price cuts + compute-metered subscriptions + the DRAM/HBM shortage all converge in a way that makes Google's position unusually clean. It also makes quality/capable local inference more difficult for individuals at the same time. The new "efficient" Gemini 3.5 Flash burns 2x the industry-average tokens to answer benchmarks. The efficiency story is a bald-faced lie. Tell me what I'm missing.
Please let me know where I'm wrong. I'd LOVE to be wrong. It would be worth you laughing at me hysterically if you prove me wrong about the overall picture I'm seeing here.
Aluminium OS / Googlebooks. shipping Q3 2026. Android-based desktop OS replacing ChromeOS on consumer hardware. HP, Lenovo, Acer, ASUS as launch partners. Gemini baked into the OS at the kernel level which means every Googlebook user is a Gemini user by default. No app to download, no competing assistant at the same level of integration. Anyone else have a distribution channel like this? I don't think so.
Ultra price cut. Top tier dropped from $250 to $200, and a new $100 Ultra tier pops up. Gemini User numbers reportedly doubled to 900M. ChatGPT Pro is $200. Claude Max is $100/$200. Google just undercut both while adding features...?
Compute-based metering on Gemini Pro/Ultra (May 17, 2026). Five-hour rolling windows, weekly/monthly caps, and personalization features shrink your usage cap when turned on? Subscribers reporting 50% of a five-hour window burned in five prompts. Google is cutting headline prices while tightening what we actually get. Avg revenue per user climbs either way.
The DRAM/HBM shortage. AI data centers projected to consume 70% of high-end DRAM in 2026. Consumer RAM prices up 110% in Q1. Micron killed Crucial to focus on AI buyers. Most people are essentially priced out of creating local inference rigs at exactly the moment cloud inference is consolidating.
Hedges everywhere. $40B in Anthropic with Claude running on Google TPUs. Planned 1M TPU-8 global cluster. If OpenAI buckles under its $27B 2026 burn and Microsoft starts wanting out, Google will be set to save ChatGPT through some agreement or partnership. Three of the four frontier AI models in the US will all pay Google one way or another. Likely influence over all of it is speculative but the notion holds water.
The verbosity lie. Here's where the marketing collapses. Gemini 3.5 Flash launched with Google's "4x faster, frontier benchmarks at Flash prices" framing. Artificial Analysis actually ran it. Gemini 3.5 Flash generated 73M output tokens to complete the Intelligence Index suite, against a leaderboard average of 36M. Roughly 2x the industry average. Cost $1,552 to run the Intelligence Index, which was 5.5x more than Gemini 3 Flash and 75% more than Gemini 3.1 Pro, from increased token usage and token prices. Verbosity increases 40-100% on complex tasks. TTFT at 'high' thinking level is 17.75 seconds versus sub-5 at low/medium.
So the "cheap, fast Flash that beats Pro" pitch only holds if you ignore that 3.5 Flash runs through tokens and your subscription compute budget like a scalded cat. Output is $9/1M tokens on a Flash model. Pro went from $8 to $12. The per-token efficiency story peters out from the per-task token volume. Net cost to the user goes up while the marketing says it goes down.
Google doesn't need to kill a competitor They need to be the last one with healthy unit economics when the music stops. OS distribution + aggressive headline pricing + verbose models that quietly inflate real cost + TPU inference advantages + a hardware bottleneck punishing every alternative path = a vertical stack assembling itself while regulators are still litigating the last generation of platform consolidation.
The position we're left in stinks for a lot of reasons, certainly in the retail markets for compute. I can't say Google engineered the RAM shortage, but they seem oddly well-positioned compared to everyone of their competitors to benefit from it. The DRAM allocation shift is market-driven. HBM margins are obscene, AI buyers locked multi-year contracts, consumer DRAM became economically irrational. I get all that. Google didn't have to whisper to Samsung or SK Hynix. But Google benefits enormously from the shortage and price hikes on consumer VRAM, no?
As I said, I want to be wrong. So...
Where am I overweighting?
Does the hardware squeeze look as bad from where you sit? New builds price out of reach by the month.
Anyone running token-count comparisons in production: how bad is 3.5 Flash verbosity hitting your bill, if you've looked yet?
Antitrust folks: is anyone positioned to see 'OS + AI assistant + cloud inference + TPU silicon' as a single thing in time to do something about it?
Thoughts? TIA.