0.49
Market implied
How it works
From a question to a decision that compounds.
Cascadian runs one spine — context pinned, effect identified, ripple simulated, recommendation made, human approval, outcome scored. Every pass through the loop leaves the next decision smarter.
How it works
Ask the question. A pipeline answers.
Your analyst asks Claude. Claude calls the Cascadian MCP — one server assembles the context, checks identification, simulates the ripple, and returns a defensible recommendation.
ClaudeOpus 4.8 ⌄CASCADIAN MCP · LIVE
Worked example — illustrative
Where will capital likely flow in the AI value chain next — and when?
Called Cascadian · simulate_intervention("ai value chain") ✓PINNING CONTEXT · CHECKING IDENTIFICATION · SIMULATING THE RIPPLE…
Live odds · resolves Q3 2026417 SOURCES · 1,000 SIMULATIONS
Will AI power constraints force hyperscaler capacity misses?
0.45 → 0.70
The read
The bottleneck has moved from silicon to electrons — and the market hasn’t updated. Transformer lead times at 160 weeks cannot resolve inside a calendar year. Disclosure is forced at Q3 earnings; power infrastructure re-rates 25–45%.
Causal chainPass-through
01$700B capex overwhelms the gridTrigger confirmed — queues already filed1.00
02Grid interconnection queues overflowBuilt for 200–400 GW a year, not 700+0.87
03Equipment shortages extend timelines160-week transformer lead times — physical0.85
04Stranded capacity accumulates — 10+ GWAnnounced phase, no construction underway0.79
05Hyperscalers acknowledge on Q3 earningsThe weakest joint — named on purpose0.71
0.41 CHAIN PRODUCT+ BASE RATE & MECHANISMS → 0.70 CALL
Invalidation armedEmergency fast-track approvals above 5 GW before Q3 revise the call to 0.44— exitable, not a one-way bet.
The wrong shape
Everything else hands an agent the wrong shape of answer.
An agent about to act needs a memory it can trust, a reason it can defend, and a record of what it knew. Nothing else returns all three.
Source
What you get
The gap
Web search
Links ranked by popularity
No provenance, no causal structure, already stale
News API
Headlines the moment they print
Tells you it happened — not what it changes
Generic LLM
A fluent guess from training data
No memory you can trust, no idea what causes what, no record of what you knew
Market data
Prices and the consensus
The consensus — not the gap you can act on
Cascadian
An identified effect, its knock-on ripple, and the context it was decided on — in one call
None. Expectation frozen before the outcome, reconstructable after
The edge
The distance between their number and ours.
Market consensus reads 0.49. The causal model reads 0.68. The distance between consensus and the causal answer is the edge — quantified, time-boxed, and scored after the fact.
0.68
Cascadian · live
+19pts
Not yet priced
Settled historyConsensusCascadian · liveIllustrative
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