Ben & I started Mantic with the mission to solve forecasting. This summer, Mantic out-predicted all 676 humans in the preeminent forecasting tournament. We’ve raised $25M to scale. We’re serving the brave decision-makers who want to grapple with uncertainty. Join the mission.

We ran a $25,000 contest to uncover: where do the best AI forecasting systems disagree about the future? The contest attracted superforecasters and hedge fund quants, who spent months probing for wedge questions. What were the winning tactics?

Mantic now makes *conditional* forecasts! This is a capability @itsmebenday and I have been excited about for a long time. What's conditional forecasting? You're looking at a Mantic forecast — e.g. of the oil price — and you want to see how it would vary under different scenarios, e.g. the Strait of Hormuz reopening. This is one of our most asked-for features, and it's taken some research to get right. We're currently treating it as an explainability technique. We give some examples in today's Friday Forecasts post (link in reply).

Inkling is our first open model from @thinkymachines and is now available on Tinker! Check out these quotes from Tinker customers on their experience with Inkling: @_Mantic_AI: "Not only does Inkling outperform Kimi K2.6 on our forecasting evals, it does so with half the output tokens." @trajectorylabs: "We’ve been impressed by how sharp and efficient the model is. Its reasoning is concise, its tool calling is consistently strong, and it holds up well on complex, long-horizon agentic tasks. It feels like a meaningful unlock for what teams can build with open-source models designed for customization." @lightningrodai: "We came away impressed by the model’s underlying reasoning ability. It’s thoughtful, original, and refreshingly unsycophantic.”

My ICML 2026 invited talk. Towards superhuman forecasting.

5 forecasts, every Friday. Follow along on Substack: blog.mantic.com/p/friday-for…

We sent Mantic's Fed rate path forecasts to the FT who compared us against the market. Mantic matched the market's accuracy overall: - In the first half of 2025, markets priced cuts that didn't happen, while Mantic was considerably less dovish across the entire term structure. - This flipped in the second half of 2025 w/ the Sep-Dec cuts where the market scored better. These results were using our general-purpose forecasting engine, without dedicated work. @gabrielpfritsch is now working on some upgrades -- so I'm looking forward to beating the market!

It’s day 2 at ICML and I’ve already met loads of people interested in forecasting, RL and @_Mantic_AI. If that’s you and we haven’t met yet, hmu on here or on the ICML app. Would love to chat! blog.mantic.com/p/mantic-at-…

RSVP: luma.com/3sc37zpa

Humans + LLMs has been standard at the top level of forecasting for a while, virtually everyone uses them. Trusting them beyond search and simulations was another story. With @_Mantic_AI, I've experienced a step change that might be as big as the move from manual Google to LLMs

Monitoring the Iran war on Polymarket and Kalshi has been a big upgrade, but it still feels like drinking the future through a narrow straw. We flew top forecaster @DrTournesol to London to ask Mantic any question he wanted about Iran. This exercise is a peak into future, not just of the war but of forecasting itself. Yann was one of the top ranked forecasters and question-askers on Metaculus last year. The best forecasters love collaborating with each other, but Yann has never had such a responsive forecasting partner as Mantic. This is like a "Claude Code moment" for understanding the future. Here are Yann's 28 key questions, most of which aren't on the prediction markets (link in reply).

We're trialling a new kind of forecasting tournament. The challenge: submit forecasting questions that trigger divergent predictions from the top AI forecasting systems. There's a $25k prize pool for the question writers, allocated by how much disagreement you can elicit. Motivation: - AI forecasters are becoming competitive with human pros. - Many questions are "solved", e.g. if I ask "Will a nuclear bomb go off in Europe this month?" all the models know it's <1%. - Still, other questions are intractable, because of aleatoric uncertainty. "What will be NVIDIA stock price in 1 year?" Again, the models will agree (this time by being very uncertain), and there's not much to learn. - If you can make the AIs disagree, you've found something interesting: a place where the AIs have divergent models of how the world works or differences in what information sources they're relying on. - Identifying these wedge questions will help the field develop AI forecasters that can tackle genuinely challenging problems. This is exactly what we'll need them for, as we navigate the uncertain world ahead. Please apply! Link in reply.

We're launching a new kind of forecasting tournament at @_Mantic_AI. There's $25k in prizes for writing questions, see post below to read more and apply.

We're undergoing a two-sided "prediction revolution" : (1) The rise of prediction markets (Polymarket, Kalshi) (2) AI's getting much better at predicting world events (Mantic) Iran is the first major geopolitical crisis where we can benefit from both. Gabriel offers a peak into the new paradigm.

Models that are great at calibrated predictions will be transformative for decision making. Excited about Mantic's work and proud they're using Tinker. Their new blog post digs into their methodology and findings.

I always dreamed of AGI as a wise advisor for humanity. Although LLMs are great for coding & knowledge work, I wouldn’t trust them to give me advice on my career, business strategy, or policy preferences. How can we build AI systems optimized for wisdom? At Mantic we believe the unlock is prediction: predicting world events as accurately as possible, and hill-climbing this single metric. Today we share some recent progress on the Thinking Machines website, having found Tinker a great platform for our RL experiments. TL;DR: We RL-tune gpt-oss-120b to become a better forecaster than any other model. Having good scaffolding is a prerequisite. A fun result: our tuned model + Grok are decorrelated from the other best models, and so are the most indispensable when picking a team.