Forecast Anthropic and OpenAI IPOs: Dates and Valuations

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

Forecast when Anthropic and OpenAI will IPO. Use date-mode forecasting at high effort. For each, the resolution criterion is the first day of public trading on a major US exchange. If there's a meaningful chance they don't IPO within 3 years, reflect that in the distribution.

Results take about 10 minutes.

Go to futuresearch.ai/app and enter:

Forecast when Anthropic and OpenAI will IPO. Use date-mode forecasting at high effort. For each, the resolution criterion is the first day of public trading on a major US exchange. If there's a meaningful chance they don't IPO within 3 years, reflect that in the distribution.

Date mode returns p10/p50/p90 columns as YYYY-MM-DD strings. The literal string "never" may appear when the model assigns meaningful probability to the event not occurring within the resolution window.

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

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

date_questions = pd.DataFrame([
    {
        "company": "Anthropic",
        "question": "When will Anthropic IPO?",
        "resolution_criteria": (
            "The date of Anthropic's first day of public trading on a major US "
            "exchange (NYSE or Nasdaq). Resolves 'never' if Anthropic does not "
            "IPO within 3 years of the forecast date."
        ),
    },
    {
        "company": "OpenAI",
        "question": "When will OpenAI IPO?",
        "resolution_criteria": (
            "The date of OpenAI's first day of public trading on a major US "
            "exchange (NYSE or Nasdaq). Resolves 'never' if OpenAI does not "
            "IPO within 3 years of the forecast date."
        ),
    },
])

async def main():
    async with create_session(name="AI Lab IPO Date Forecasts") as session:
        result = await forecast(
            session=session,
            input=date_questions,
            forecast_type="date",
            output_field="ipo_date",
            effort_level="HIGH",
        )
        return result.data

results = asyncio.run(main())
print(results[["company", "ipo_date_p10", "ipo_date_p50", "ipo_date_p90"]])

Ask Claude:

Forecast the first-day public market cap (in billions USD) for Anthropic and OpenAI when they IPO. Use a high-effort numeric forecast and return the full percentile distribution. Anthropic raised $30B at a $380B valuation in early 2026; OpenAI raised at $852B in March 2026.

Results take about 10 minutes.

Go to futuresearch.ai/app and enter:

Forecast the first-day public market cap (in billions USD) for Anthropic and OpenAI when they IPO. Use a high-effort numeric forecast and return the full percentile distribution. Anthropic raised $30B at a $380B valuation in early 2026; OpenAI raised at $852B in March 2026.

Set forecast type to numeric and effort to high.

The SDK's forecast() with forecast_type="numeric" returns p10 through p90 percentiles for the requested field. effort_level="HIGH" increases accuracy at a higher per-row cost.

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

valuation_questions = pd.DataFrame([
    {
        "company": "Anthropic",
        "question": "What will Anthropic's first-day public market cap be on its IPO?",
        "resolution_criteria": "First-day closing market cap in billions USD on the day Anthropic lists on a public exchange.",
        "background": "Anthropic raised \$30B at a \$380B valuation in early 2026, on roughly \$19B ARR. Engaged Wilson Sonsini in late 2025; talks with JPMorgan, Goldman, Morgan Stanley.",
    },
    {
        "company": "OpenAI",
        "question": "What will OpenAI's first-day public market cap be on its IPO?",
        "resolution_criteria": "First-day closing market cap in billions USD on the day OpenAI lists on a public exchange.",
        "background": "OpenAI raised at a \$852B valuation in March 2026. CFO Sarah Friar has guided toward a 2027 listing. Recently completed transition to a Public Benefit Corporation.",
    },
])

async def main():
    async with create_session(name="IPO Market Cap Forecasts") as session:
        result = await forecast(
            session=session,
            input=valuation_questions,
            forecast_type="numeric",
            output_field="market_cap",
            units="billions USD",
            effort_level="HIGH",
        )
        return result.data

results = asyncio.run(main())
print(results[["company", "market_cap_p10", "market_cap_p50", "market_cap_p90"]])

Output columns: market_cap_p10 through market_cap_p90 (float, monotonically non-decreasing), units (string), rationale (string).