What if AI agents autonomously close most real GitHub issues?
A frontier model autonomously closing 80% of real GitHub issues guts seat-based dev-tool SaaS even as it lifts AI-compute demand — so the trade is long the picks-and-shovels (NVDA/AVGO/memory) and short seat-priced software, with a demand-drag drag on the broad index. Rhymes with the May-2023 Nvidia guidance blowout that bid compute while the productivity/displacement narrative pressured incumbent software multiples. Forward angle: if inference, not training, drives the workload, the capex beneficiary mix tilts toward custom silicon and memory over merchant GPUs — roots (ai_capex up, displacement up) are sensible.
Every number ships with its receipt — the odds, the range, the precedents, and a public grade at Reality Check. The statistical machinery that produces it is proprietary.
The butterfly cascade
How this trigger trickles across markets, left → right — the root shock, its first‑order moves, then the ripple effects. Drag any node; tap a market for its real price history.
Resolution timeline — how this probability is moving
Our model's odds (electric blue) over time vs the market's (Polymarket, amber), from the past toward the 1–3 years horizon. Each dot is a real macro event that nudged the probability — green pushed it up, red pushed it down. Tap a dot for the source. Loading the probability audit trail…
What it would mean
If this plays out, it is a risk-off shock. A frontier model autonomously closes 80% of real GitHub issues, gutting enterprise dev-tool and seat-based SaaS valuations. The trigger decomposes into signed root‑shocks — AI capex ▲ · Job displacement ▲ — which propagate through our causal graph to the markets below.