Human vs AI Forecasts

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

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.

What’s a month?

What’s a month?

Why question wording must be exact in forecasting

Superforecaster Ryan Adler turns a live CNBC disagreement about Tesla shares into a quick guide on clarity. Good forecasting starts with shared definitions.

On Monday morning (4 August 2025), I was pounding away on my keyboard with CNBC playing in the background. Living in the Mountain time zone, morning meant the Halftime Report, hosted by Scott “The Judge” Wapner. I was loosely listening in when it became clear that Wapner and “Investment Committee” member Joe Terranova were having a disagreement over whether Tesla shares were up or down over the past month. The exchange was cordial but awkward, as Wapner insisted that Tesla shares were down in the past month based on where the stock was trading that morning, but Terranova was very confident that it was up in the past month. They eventually went to commercial and came back having discovered the source of discrepancy. The problem wasn’t that one was right and the other wrong. The problem was that they were each defining “month” differently.

A month before 4 August 2025 would have been 4 July 2025, a market holiday. The chart CNBC showed related back to the closing price of Tesla on 3 July (about $315). Terranova, on the other hand, was using the opening price as of the opening bell on 7 July 2025, four weeks previous, when the price was a bit under $300. The two talked past each other for a bit until the reason for the difference was identified.

Ambiguity Kills Forecasts

What does this have to do with forecasting? Everything!

Among the many lessons that came out of the Good Judgment Project, it was clear that the fight against ambiguity is essential and never-ending. While others may give this fight a lower priority, it is front-and-center on our minds at Good Judgment with every question drafted and reviewed.

If a term or clause could be interpreted reasonably in different ways, we define that term and include examples as needed. And even if someone interprets something in an arguably unreasonable way, such as asserting that the death of a country’s president doesn’t mean that the person stops being that country’s president (it’s happened repeatedly, for some reason), we clarify.

We aren’t perfect, and the world sometimes creates situations that weren’t on anyone’s radar when a germane question was launched beforehand. That said, we know that everybody must be contemplating the same elements of an event they are asked to forecast. Leaning on Potter Stewart’s concurrence in Jacobellis v. Ohio, where he said, “I know it when I see it,” may work when deciding that a movie is not obscene, but it is no way to set a threshold for a forecasting question. Otherwise, we would invite static from the crowd instead of signal.

Bottom line: The CNBC confusion shows how ambiguity kills forecasts. Define upfront what counts, when it counts, and who decides, and leave as little as possible to interpretation. Good forecasting starts with good question writing.

Do you have what it takes to be a Superforecaster? Find out on GJ Open!

* Ryan Adler is a Superforecaster, GJ managing director, and leader of Good Judgment’s question team