Good Judgment’s 2025 in Review

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A Record Year and What We Learned About AI, Markets, and the Future of Forecasting

It’s been a challenging year. Public imagination has been captured by prediction markets and AI alike as potential oracles for, well, everything. And yet, here we are at Good Judgment Inc, not just standing but setting records.

This year, Good Judgment launched an unprecedented 1,140 forecasting questions across our public and private platforms, with a void rate of exactly zero. That’s a benchmark other forecasting platforms cannot claim.

Our top-line developments in 2025:

  • Our Superforecasters have continued to outperform the markets, as featured in the Financial Times, and provide precise probabilities in our 11th annual collaboration with The Economist.
  • Good Judgment won an Honourable Mention in the 2025 IF Awards from the Association of Professional Futurists (APF) together with our UK partners ForgeFront for our joint Future.Ctrl methodology. This is a much-coveted professional award in the foresight industry.
  • Good Judgment’s CEO Dr. Warren Hatch delivered a keynote address at UN OCHA’s Global Humanitarian Policy Forum. We find it especially heartwarming that global leaders at the top level are paying attention to Superforecasting as a way to improve decision-making.
  • We have added an executive education program to our Superforecasting Workshops menu. It’s designed for decision-makers who want to incorporate probability forecasts into their process. So far, our client list includes a major technology company, an oil multinational, and investment funds, among others.
  • We now offer in-person workshops as part of a leadership development program with our Canadian partner, Kingbridge Centre.

But beyond the big names and numbers, we’ve learned something important about where human forecasting fits in an increasingly automated world.

The Two-Front Challenge

On one side, prediction markets like Polymarket have drawn enormous attention. On the other, large language models (LLMs) have shown remarkable ability to synthesize information and generate plausible-sounding forecasts. So are Superforecasters still the best in the field? Are we still needed?

Our answer, backed by data, is yes.

Outperforming the Markets

For the third year in a row, our US Federal Reserve forecasts beat the CME’s FedWatch tool, a result we’ve been documenting throughout the year and that was featured in the Financial Times. Three years is a pattern.

What about Polymarket? On questions like Fed rate decisions, we find it essentially duplicates FedWatch, volatility included. In other words, the prediction market hype hasn’t translated into better forecasts on questions like these, and these are the type of questions that matter most to our clients.

The AI Question

Forecasting Research Institute, our sibling organization, runs the only forecasting competition we know that directly pits humans against AI models. According to their latest results, the best-performing LLM still lags the best human forecasters by 40%.

Why the gap? It comes down to a fundamental difference in what forecasters do best versus what AI does best.

AI synthesizes existing information. If the answer to a question is somewhere on the internet, a well-trained model will surface it quickly. But for questions marked with greater volatility (who wins the next election, where markets are heading, what happens next in a geopolitical crisis), the answer isn’t sitting in a database. It’s contingent on human behavior, a much harder variable to predict than mere extrapolation from data.

The best human forecasters go a step or two beyond the retrieval and synthesis of information. They weigh evidence, model uncertainty, update their thinking as conditions change, and produce nuanced judgments. That’s a capability AI could tap into only by accessing Superforecasters’ aggregated forecasts and their detailed reasoning. As we’ve written elsewhere, “What our clients value are not only the numbers but also the rationales that Superforecasters provide with their numerical forecasts. By examining their chain of reasoning—something that black-box systems cannot provide reliably—leaders are able to scrutinize assumptions, trace causal links, and stress-test scenarios by noting hidden risks.” For the types of questions we see in our client work, Superforecasters are still the best.

Looking Ahead

None of this means we’re ignoring AI developments. Quite the opposite. We’ve been actively experimenting with how to integrate AI into the Superforecasting process. It is Good Judgment’s opinion that a hybrid approach is the path forward. Not AI replacing Superforecasters, but AI amplifying what Superforecasters already do well.

As we head into the new year, we are seeing momentum picking up once again on the business side. FutureFirst™, our subscription-based forecast monitoring tool, has seen all Q4 renewals go through. Once organizations experience what our structured forecasting provides and build it into their workflows, they tend to stay.

On the training side, we are now offering Advanced Judgment & Modeling, the next-level Superforecasting training to graduates of our two-day workshop. As a Texas National Security Review study found, decision-makers tend to be vasty overconfident but can improve calibration even with brief training. Our analysis supports these findings.

Our public workshop continued to receive stellar ratings from participants in 2025. Here’s an excerpt from one of our favorite reviews:

“The content was excellent and incredibly practical, diving deep into the art and science of forecasting. The unexpected highlight was the group itself. It was one of the most uniquely thoughtful, globally diverse rooms I’ve been part of in a long time. … Grateful for the experience and the brilliant people I met. Highly recommend it to anyone serious about sharpening their judgment or improving decision quality.”
— Jeff Trueman, Eisengard AI, November 2025

Although 2025 marked ten years since the publication of Tetlock and Gardner’s Superforecasting: The Art and Science of Prediction, forecasting as a discipline is still a novel way of thinking for many organizations. It feels risky to put a number on a prediction, because with numbers comes accountability. But accountability leads to better forecasting and hence better decisions. This case is getting easier to make, especially when we can point to years of Superforecasters’ documented outperformance of the competition, the markets and, now, the machines.

To our clients, staff, and forecasters: thank you. We wouldn’t be here without your energy, rigor, and recognition. Here’s to another year of proving what human judgment can do with the ever-evolving tools that we have.

Human vs AI Forecasts

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Human vs AI Forecasts: What Leaders Need to Know

In October 2025, our colleagues at the Forecasting Research Institute released new ForecastBench results comparing large language models (LLMs) and human forecasters on real-world questions. Superforecasters still lead with a difficulty-adjusted Brier score of 0.081, while the best LLM to date, GPT-4.5, scores 0.101.

In other words, Superforecasters have a roughly 20% edge over the best model (lower scores are better).

Meanwhile, LLMs have surpassed the median public forecaster and continue to improve. FRI notes that a simple linear extrapolation would suggest LLM-Superforecaster parity by November 2026.

While Good Judgment Inc helped recruit Superforecasters for this study, we were not involved in its design or execution.

Good Judgment’s Take

We track AI benchmark results closely. Our client work points to a slower timeline than that suggested by the FRI. For the kinds of problems leaders bring to us, we doubt the Superforecaster-AI gap will close within the next year, or, for questions with limited or fuzzy data—which tends to be the case with most high-impact real-world questions—even in the next several years, if ever. The reasons for our take are threefold.

First, ForecastBench tested binary (yes/no) questions. In our client work, more than two thirds of questions are multinomial or continuous. Leaders often need a probability distribution and point estimates, not just a yes/no threshold. Asking whether US GDP, for instance, will exceed 3% next year is less informative than estimating the full distribution so organisations can plan for a range of outcomes.

Second, as the original GJP research has shown, teaming and aggregation raise accuracy by 10% to 25%. Structured teaming and advanced aggregation (beyond simple medians) reduce noise, ensure a broader spectrum of viewpoints and data points, and improve calibration, especially on complex questions that require subjective judgment. The factors of teaming, advanced aggregation, and question types merit further study, in our judgment.

Third, the AI research tournaments typically collect forecasts at a single point in time. However, new information is always coming in, which makes updated forecasts more accurate and more useful for decision makers.

It is also important to note that a 0.081 vs 0.101 result translates roughly to a 20% edge in accuracy. For decision makers, a 20% improvement can change both the choice and the outcome.

What This Means for Decision Makers

For organisations that need timely, reliable, high-stakes decisions, this is the key takeaway: AI progress is real, but disciplined human judgment still sets the bar. For the best results today, our view is human + AI. Use Superforecasters and Superforecasting methods with capable, secure models for faster, better forecasts.

What our clients value are not only the numbers but also the rationales that Superforecasters provide with their numerical forecasts. By examining their chain of reasoning—something that black-box systems cannot provide reliably—leaders are able to scrutinize assumptions, trace causal links, and stress-test scenarios by noting hidden risks. This transparency makes the decision process more deliberate, accountable after fact, and explainable to stakeholders.

As CEO Dr. Warren Hatch noted in a recent Guardian interview, “We expect AI will excel in certain categories of questions, like monthly inflation rates. For categories with sparse data that require more judgment, humans retain the edge. The main point for us is that the answer is not human or AI, but human and AI to get the best forecast possible as quickly as possible.”

Learn more about FutureFirst and see ahead of the crowd.

Four Steps to Integrate Probabilities into Decisions

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Four Steps to Integrate Probabilities into Decisions

Picnic scene and wedding scene side by side with a weather icon
The true yes/no boundary for action is rarely 50%.

Decision makers often want a simple “yes” or “no.” This creates a challenge when you’re in the business of providing probabilities. If you tell them there’s a 76% chance of Event X happening by Date Y, you might get this response: “So that’s a yes!” When you point out the remaining 24% chance it won’t happen, you might get: “So that’s a no?”

Busy leaders need to get straight to action. They often simplify the process by treating anything above 50% as a “yes” and anything below as a “no.” However, it’s rarely the case that the yes/no boundary is 50% in the real world. The true yes/no boundary, which we call the decision threshold, depends on the nature of the decision, the cost, and the stakes involved.

Say there’s a 28% chance of rain. For a casual picnic, you might accept the risk and go anyway. But for an outdoor wedding reception, your threshold for action is probably much lower. You may set up tents just in case.

The decision threshold is fundamentally about cost-benefit analysis: How many false positives are you willing to accept in order to avoid missing a real threat or opportunity?

For example, if you risk losing $100 and the cost of mitigation is $35, it may be worth taking action (mitigating the risk) if the probability of loss exceeds 35%.

The Four-Step Framework

Instead of simply delivering a forecast for the decision maker to interpret, we reverse the process by defining the yes/no boundary first. Here’s our four-step process for decision makers to use forecasts with clear thresholds, leading to better, faster judgments.

1. Identify the core decision. Begin by clearly stating the decision the organization faces. Here are a few examples:

  • Committing to an innovation cycle to mitigate the risk of a future regulatory ban on a key product.
  • Preparing for a possible increase in US tariffs on a critical base metal import.
  • Validating the underlying assumptions of a new investment thesis before allocating capital.

2. Set the decision threshold. The yes/no boundary can only be set by the decision maker. Will they act at 20%? 40%? 60%? By focusing on the cost and stakes, they avoid defaulting to the simple (and often misguided) 50% threshold.

3. Pose the question to the forecasters. Ideally, they should be unaware of the specific decision or the decision maker’s identity. This firewall ensures the probability estimate remains independent and unbiased. But if anonymity can’t be provided, treat the forecasting as a separate process as much as possible. Remember: Forecasts focus on how the world will be while decisions often reflect what we want the world to be.

4. Deliver the actionable forecast. With the yes/no boundary already in place, the decision maker can use the probability estimate immediately to make a call. With this robust framework, decision makers are able to use probabilities effectively to arrive at better decisions faster.

Learn more about FutureFirst and see ahead of the crowd.