auto: 2026-04-16T02:18:20Z

content/essays/beyond-comorbidity-indices/index.md

@@ -25,24 +25,16 @@ novelty: moderate
 practicality: moderate
 bibliography: data/bci-paper.bib
 repository: "https://git.levineuwirth.org/neuwirth/beyond_comorbidity_indices"
-history:
+summary: |
-  - date: "2026-03-28"
-    note: Preprint auto-formatted for levineuwirth.org
----
-
-::: {.annotation .annotation--static}
-<div class="annotation-header">
-<span class="annotation-label">Summary</span>
-<span class="annotation-name">Key points</span>
-</div>
-<div class="annotation-body">
   **Question.** Among adult hospitalizations in a national claims database, does a deep learning model using ICD-10-CM diagnosis codes improve prediction of 30-day unplanned readmission and 30-day postdischarge in-hospital mortality compared with benchmark models based on Charlson and Elixhauser comorbidity indices?
 
   **Findings.** In this cohort study of 3,226,831 temporally held-out discharges, the ICD-10-CM--based model showed better discrimination than benchmark comorbidity-index models for both outcomes.
 
   **Meaning.** Using the full set of discharge diagnosis codes may improve short-term claims-based outcome prediction beyond summary comorbidity indices.
-</div>
+history:
-:::
+  - date: "2026-03-28"
+    note: Preprint auto-formatted for levineuwirth.org
+---
 
 ## Introduction (Background and Significance)
 
@@ -178,6 +170,8 @@ The analytic code (including the non-elective readmission implementation, model
 
 The authors report no conflicts of interest related to this work.
 
+::: aftermatter
+
 ## Tables
 
 ### Table 1: Performance metrics for the ICD model vs. CCI and ECI {#table-1}
@@ -369,3 +363,5 @@ Results are shown in eTable 3, Panel C. ICD-only variants had slightly worse AUR
 **(B)**
 
 ![Web calculator interface without demographics.](figures/efig4b-calculator-without-demographics.png)
+
+:::

content/essays/scaling_outage.md

@@ -0,0 +1,27 @@
+---
+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