When the markets price AGI.

I. The instrument that did not exist five years ago.

Until recently, the question "what is the probability AGI is achieved by 2028?"had no market answer. It had opinions — many of them — but no instrument. You could read McKinsey, you could read OpenAI's blog, you could read Yudkowsky, and you would assemble a personal posterior somewhere between five percent and ninety percent depending on which authors you trusted.

Kalshi changed that. The market for "Will any company achieve AGI by 2028?" is currently liquid — open interest measured in tens of thousands of contracts, a steady bid-ask, a settled YES price the market refreshes every minute. Whatever you think the probability is, there is now a number with capital behind it that you can compare your view to.

II. Why a CFO should care about this market.

The AGI question is not abstract for the operator. It is the most consequential compound-uncertainty input to every five-year strategic plan written in 2026. If the market-priced probability of AGI by 2028 is twenty percent, the strategic plans that assume zero probability and the plans that assume ninety percent are both wrong — by different amounts, against the same instrument.

Palanor reads the AGI market as a signal. We do not assume the market is right. We use it as one input among many in the AI Margin Compression Index, in the relevant Schemas, and on the Loom Canvas when a steward wants to compose a scenario that includes what does the world believe about AGI as a factor.

III. The wider shift.

Fifteen Kalshi markets currently flow into the Palanor signal lattice as live signals — discovered by Numen on a daily cron, scored against a stewardship relevance rubric, gated by liquidity, refreshed hourly. They sit alongside FRED economic series, equity index thresholds, oil and gold, and the proprietary Custom Indices in the same database row format.

The unification is the point. Until prediction markets are first-class signals on the same lattice as the macroeconomic data series, an operator cannot compose a scenario that asks what happens to our margins if AGI lands by 2028 AND credit spreads have already tightened? Once they sit in the same vocabulary, the question is composable in Loom Canvas like any other graph.

IV. What the AGI market is currently saying.

We do not anchor essays to a specific reading because the reading evolves. What we can say structurally: the market-priced probability of AGI by 2028 has been non-trivial — well above the "basically zero" that the equity-market consensus implicitly assumes when it values software companies at twenty-five times current earnings. The market is not pricing AGI as a tail risk. It is pricing it as a meaningful base-case scenario for the back half of the decade.

That divergence — between what the AGI prediction market prices and what equity valuations imply about AGI probability — is the kind of inconsistency Palanor is built to surface. The instruments do not match. The narrative-implicit probabilities do not match the betting-market-priced probabilities. Stewards deserve to see both.

V. Three implications.

For PE operating partners — Five-year holding-period assumptions for software portfolio companies need an AGI sensitivity. Not because AGI by 2028 is certain — the prediction market is well below fifty percent — but because the assumed-zero-probability scenario embedded in current software multiples is too narrow.

For mid-market software CFOs— Your business model assumes a certain trajectory for inference cost. Palanor's AI Margin Compression Index combined with the AGI prediction market gives you two different reads on that trajectory. They will disagree. The disagreement is the data.

For family offices and institutional LPs — Direct exposure to the software sector deserves a probability-weighted look across multiple AGI-timing scenarios. The Palanor scenario library carries archetypes pre-built; the AGI prediction-market signal flows into all of them.

VI. The argument is older than the technology.

Prediction markets as a signal source are not new. Friedrich von Hayek wrote about price discovery in 1945. Robin Hanson formalized prediction markets in the late 1990s. The Iowa Electronic Markets predicted US elections more accurately than most polls for two decades.

What is new is liquidity. Until 2024, prediction markets were either too thin to read or restricted to small-stakes use cases. Kalshi's CFTC license and Polymarket's growth turned the depth threshold. The markets are now readable signals, not novelty bets. Palanor is built on that turn.

VII. What you can do with it now.

The fifteen Kalshi markets currently in the Palanor signal lattice are live today. They show up in the Signals page under prediction markets, scored by Numen for stewardship relevance, refreshed hourly. Stewards can subscribe to them like any other signal. They can reference them in scenarios. They can weight them in Loom Canvas.

Stewards can also add their own — paste a Kalshi market URL into /signals/markets/add, the platform validates, adds it to your org's signal lattice, subscribes you, pulls the first observation. Two flows, same lattice, same vocabulary.

About this article.

Third article in The Palanor Index Series. The first is at /pov/ai-margin-compression-thesis. The second, on capital tightness, is at /pov/capital-tightness-thesis.