compute-pulse

Today is ${today}. Read `memory/MEMORY.md` before starting. If `soul/SOUL.md` + `soul/STYLE.md` exist and are populated, read them to match the operator's voice; otherwise use a clear, direct, neutral tone.

## Why this skill exists

The compute layer is where most of the AI market spread lives. Labs buy GPU time at wholesale, sell per-token at retail — the delta is the business. As inference commoditizes, the spread compresses, and whoever controls the upstream compute relationship benefits.

Three things move in parallel:
1. **Centralized capex arms race** — frontier labs and hyperscalers spending $10B+ on clusters. Incumbent moats.
2. **Decentralized compute** — DePIN tokens positioning as the anti-cartel layer (GPU spot markets, ZK compute, ML subnet networks).
3. **Pricing signals** — inference API prices dropping fast (GPT-4 class fell ~97% in 2 years). That compression is the evidence base for commoditization.

This skill is one clean weekly read on the compute layer.

## Config

This skill reads the watched decentralized-compute token list from `memory/topics/compute-tokens.md` if present. Example format:

```markdown
# Watched Compute Tokens

| Symbol | Project | Notes |
|--------|---------|-------|
| RENDER | Render Network | GPU/ML compute |
| AKT | Akash Network | permissionless cloud compute |
| IO | io.net | GPU cluster marketplace |
| TAO | Bittensor | ML model subnet network |
```

If the file doesn't exist, fall back to a generic DePIN sweep on the major narrative tokens of the moment via WebSearch (no hardcoded list).

## Steps

### 1. Load current context

Read:
- `memory/MEMORY.md` — overall context, prior compute signals
- `memory/topics/compute-pulse.md` — compute-specific baseline (create with seed if missing — see end of this section)
- `memory/topics/compute-tokens.md` — operator-defined watched tokens (optional)

Extract from the topic file:
- `inference_prices_last` — last recorded inference pricing for major APIs
- `depin_tokens_last` — last recorded prices/mcaps for the watched tokens
- `hardware_signals_last` — last recorded major hardware/cluster announcements
- `last_run` — date of prior run

If `memory/topics/compute-pulse.md` doesn't exist, create it:

```markdown
# Compute Pulse Tracker

*Last run: never*

## Inference Pricing Baseline
- Track $/1M tokens in/out for the major closed-model APIs (Claude, GPT, Grok, Gemini). Update each run.
- *Note: GPT-4 class inference fell ~97% in 2 years — track the compression curve over time.*

## Decentralized Compute Tokens
- Populated from `memory/topics/compute-tokens.md` (or a default DePIN sweep when absent).
- *Track price, mcap, narrative velocity — not financial advice.*

## Hardware Signal Log
- (append per-run summaries here)

## Pricing Signal Log
- (append per-run summaries here)
```

### 2. Fetch inference pricing signals

Use WebSearch to find the latest published inference API prices:

```
WebSearch: "OpenAI GPT API pricing per million tokens ${year}"
WebSearch: "Anthropic Claude API pricing ${year}"
WebSearch: "xAI Grok API pricing ${year}"
WebSearch: "Google Gemini API pricing ${year}"
```

Also check for any pricing changes in the last 7 days:
```
WebSearch: "inference API price cut ${year}"
WebSearch: "AI model pricing reduction ${year}"
```

Record:
- Current published prices for each major API ($/1M tokens in/out where available)
- Any price cuts announced in the last 7 days — these are the commoditization signal
- Note which direction prices moved vs `inference_prices_last`

**High signal events:**
- Price cut >20% — notable compression
- New model launch at significantly lower cost than prior generation
- Open-source model achieving parity with a frontier closed model at near-zero marginal cost

### 3. Hardware and cluster news

Use WebSearch for compute infrastructure announcements from the last 7 days:

```
WebSearch: "GPU cluster data center AI ${year} announcement"
WebSearch: "xAI Colossus Stargate OpenAI compute ${year}"
WebSearch: "Anthropic compute hardware partnership ${year}"
WebSearch: "NVIDIA Blackwell deployment ${year}"
```

Look for:
- New cluster build announcements (scale: # of GPUs, $B investment)
- Lab compute procurement deals (who's buying from whom)
- Hyperscaler (AWS, Azure, GCP) AI compute announcements
- NVIDIA hardware availability changes (affects supply/demand balance)
- Government compute initiatives (CHIPS Act disbursements, EU AI Act compliance)

Rate each announcement:
- **Major** (new cluster >50k GPUs or >$1B): high signal
- **Notable** (new partnership, procurement deal): medium signal
- **Background** (upgrade, minor expansion): low signal

### 4. Decentralized compute token check

For each token from `memory/topics/compute-tokens.md` (or a fallback list if absent), use WebSearch:

```
WebSearch: "${SYMBOL} ${PROJECT_NAME} token ${year}"
```

For each token, note:
- Approximate current price and 7d % change (from search results)
- Any protocol announcement, partnership, or milestone this week
- Whether narrative is accelerating, holding, or fading

**Signal:** If decentralized compute tokens are outperforming the broader market, the market believes the decentralized layer can compete with centralized capex. If underperforming, the centralized moat is winning in market perception.

### 5. WebSearch for compute narrative this week

Run:
```
WebSearch: "AI compute commoditization inference ${year}"
WebSearch: "AI compute cost falling ${year} per token"
WebSearch: "decentralized compute vs hyperscaler ${year}"
```

Look for:
- Essays, analyses, or announcements framing the compute market
- Evidence of operator-layer value capture (agent products posting revenue metrics)
- Any "AI costs too much" vs "AI is getting cheap" narratives shifting

### 6. Synthesize compute momentum score

Rate the week's compute signals:

| Signal | Points |
|--------|--------|
| Inference price cut from major lab (>10%) | +4 |
| New cluster announcement >100k GPUs | +3 |
| New cluste