---
title: "Speculative Reluctance"
date: 2026-04-15          # required; used for ordering, feed, and display
abstract: >               # optional; shown in the metadata block and link previews
  AI labs are likely deliberately reluctant to scale because they are aware that any imminient shift to locally run models as the norm would render their compute redundant. We take Anthropic as a principal case study to validate this hypothesis. 
tags:                     # optional; see Tags section
  - ai
  - tech
  - speculative
  - open

# Epistemic profile — all optional; the entire section is hidden unless `status` is set
status: "Working model"   # Draft | Working model | Durable | Refined | Superseded | Deprecated
confidence: 55            # 0–100 integer (%)
importance: 3           # 1–5 integer (rendered as filled/empty dots ●●●○○)
evidence: 1              # 1–5 integer (same)
scope: broad            # personal | local | average | broad | civilizational
novelty: moderate         # conventional | moderate | idiosyncratic | innovative
practicality: high    # abstract | low | moderate | high | exceptional
confidence-history:       # list of integers; trend arrow derived from last two entries
---

Running a lab that developers frontier LLMs is somewhat like playing a game that, by all measurable metrics external, you are bound to lose. The amount of compute required to train a frontier LLM is unbelievably expensive. The expense of inference is even more astronomical. OpenAI claims at the time of this writing to have somewhere between 900 Million and 1 Billion active users, all of whom require some amount of inference cost, and some small subset of whom consume an enormous amount of compute - to use their words, this is ["commercial scale."](https://openai.com/index/accelerating-the-next-phase-ai/). This isn't to mention the immense amount of competition - there are many major players in the United States alone contributing models that push the boundaries. OpenAI may have been the first, but Anthropic, Google, Meta, xAI, and, yes, even Amazon and Bytedance are following right along. 

Then there's the news that the stock market doesn't want to hear. Ask yourself: who is deliberately left off the above list? If you're thinking of models like GLM, Qwen, MiniMax, and the notorious Deepseek, then we're on the same page. These models are rapidly approaching the capabilities of the frontier models that remain behind intrusive "competitive moats"^[This phrasing is adopted from Jared James Grogan's 2026 paper ["The End of the Foundation Model Era](https://arxiv.org/abs/2604.06217)] that do little more than violate the rights of their users. The advantages that such models provide are immense, and labs of the first list cannot ignore the likelihood of their precedence increasing in the weeks and months to come. In fact, I hypothesize that we are already seeing the reaction of frontier labs to these increasing capabilities, through the lense of juxtaposition: the jargon has remained constant, as if to negate any possibility of an "AI Bubble" bursting, but the quiet actions of the companies that aren't notoriously announced and decreed have shifted.

## Inference is the Name of the Game
