Turn Claude into an Accurate Forecaster

Add the FutureSearch connector if you haven't already. Then ask Claude any forecasting question:

Will Anthropic IPO before July 2027? Forecast the probability.

Or for numeric forecasts:

Forecast the first-day market cap of Anthropic and OpenAI when they IPO. Give me the full probability distribution in billions of USD.

Go to futuresearch.ai/app and enter any forecasting question:

Will Anthropic IPO before July 2027? Forecast the probability.

Or for numeric forecasts:

Forecast the first-day market cap of Anthropic and OpenAI when they IPO. Give me the full probability distribution in billions of USD.

pip install futuresearch
export FUTURESEARCH_API_KEY=your_key_here  # Get one at futuresearch.ai/app/api-key

Binary forecasting — yes/no probabilities:

import asyncio
import pandas as pd
from futuresearch.ops import forecast

questions = pd.DataFrame([
    {
        "question": "Will Anthropic IPO before July 1, 2027?",
        "resolution_criteria": "Resolves YES if Anthropic common shares trade on a public exchange before July 1, 2027.",
    },
    {
        "question": "Will OpenAI IPO before January 1, 2028?",
        "resolution_criteria": "Resolves YES if OpenAI common shares trade on a public exchange before January 1, 2028.",
    },
])

async def main():
    result = await forecast(input=questions, forecast_type="binary")
    print(result.data[["question", "probability", "rationale"]])

asyncio.run(main())

Numeric forecasting — percentile distributions:

valuation_questions = pd.DataFrame([
    {
        "question": "What will Anthropic's first-day market cap be when it IPOs?",
        "resolution_criteria": "Market capitalization at close of first trading day, in billions USD.",
        "background": "Anthropic raised $30B at a $380B valuation in early 2026.",
    },
    {
        "question": "What will OpenAI's first-day market cap be when it IPOs?",
        "resolution_criteria": "Market capitalization at close of first trading day, in billions USD.",
        "background": "OpenAI raised at $852B in March 2026. CFO guided toward 2027 listing.",
    },
])

async def main():
    result = await forecast(
        input=valuation_questions,
        forecast_type="numeric",
        output_field="market_cap",
        units="billions USD",
    )
    print(result.data[[
        "question", "market_cap_p10", "market_cap_p25",
        "market_cap_p50", "market_cap_p75", "market_cap_p90",
    ]])

asyncio.run(main())

Use context when all rows share common framing:

result = await forecast(
    input=geopolitics_questions,
    forecast_type="binary",
    context="Focus on EU regulatory and diplomatic sources. Assume resolution by end of 2027.",
)