AI Researcher Startup Valuations
Forecast seed valuations for 116 AI researchers if they left to found a startup.
116 researchers forecasted
Forecast
Forecast outcomes, timelines, valuations
Forecasting agents research sets of questions to produce probabilities, dates, or numbers about the future. Accuracy backed by our forecasting evals.
▶ 2-min demo video coming soon
Case studies
Polymarket Profit Screening
Forecast 100 prediction market questions to find mispriced contracts, then sort by expected ROI.
31 profitable questions identified
Anthropic & OpenAI IPO Forecasts
Forecast IPO timelines and first-day valuations for the two leading AI labs.
Date + valuation + reasoning
Live case study
Evaluating our forecasting on real stock returns
On August 5, 2025 we ran our forecast methodology against every S&P 500 company, producing 5 and 10 year projections of revenue, margins, and shareholder payouts. We built an industry-neutral, dollar-neutral paper portfolio from the results: long the most undervalued names, short the most overvalued. From August 2025 to May 2026 the portfolio returned +26.6% against the S&P 500's +16.9%, at a Sharpe ratio of 3.1 and 21% annualized alpha. It updates as the market moves.
Give your AI a team of forecasters
Claude Code
claude mcp add futuresearch --scope project --transport http https://mcp.futuresearch.ai/mcp Then ask Claude to forecast your data.
Python SDK
pip install futuresearch
from futuresearch.ops import forecast
result = await forecast(
task="Will this person leave
to found an AI startup
by end of 2027?",
input=researchers_df,
)Pricing
Start with $20 in free credits. No credit card required. Costs vary based on the complexity of research needed to produce each forecast.
| Case study | Rows | Forecast type | Cost |
|---|---|---|---|
| AI researcher startup valuations | 116 | Numeric (percentiles) | $9 |
| Polymarket profit screening | 55 | Binary probabilities | $5 |
| Anthropic & OpenAI IPO forecasts | 2 | Date + valuation | $1 |
Why costs vary
You only pay for the intelligence each row needs. Simple binary questions cost less; numeric forecasts with full percentile distributions require more research. Default effort resolves automatically: HIGH for a single forecast, LOW for many. Accuracy is independently measured on BTF-2, described in the Strategic Reasoning paper, with the dataset on Hugging Face. For a real-world test of the same forecasting methodology, see how a paper portfolio built from these forecasts has tracked the S&P 500.
Date mode returns a full distribution over timing as {output_field}_p10 … _p90 YYYY-MM-DD strings, with the literal string "never" reserved for percentiles in the indefinite future.
Resources
Guides
Start forecasting your data
Start with $20 free credit. No credit card required.