Why does most AI prediction accuracy data feel untrustworthy?

Because it's usually self-reported without timestamps, without the original prediction text preserved, and without any way to check that the record hasn't been edited after the fact. Screenshots and blog posts are easy to selectively curate.

What makes a forecast record 'verifiable' rather than just 'published'?

Verifiable means the prediction was locked in before the outcome was known, typically via a timestamp, a cryptographic signature, or a public ledger entry, and that anyone can independently check both the original claim and the eventual result.

Do prediction markets count as accuracy trackers?

Sort of. Prediction markets show the crowd's calibration over time, which is useful, but they don't tell you how a specific AI model performs — they aggregate many participants, human and automated, into one price.

What is HQ's /proof page and how is it different?

It's a public, signed log of HQ's forecasts, kept before outcomes are known, so anyone can compare what was predicted against what actually happened without relying on our word for it.

Can I track accuracy across multiple AI tools at once?

Not yet in one unified dashboard. Right now it means checking each provider's own transparency page, if they have one, and applying the same skepticism to all of them.

Best Sites to Track AI Prediction Accuracy Over Time

A practical guide to the best places to check whether an AI's forecasts actually come true, plus what real, verifiable accuracy tracking looks like.

AI tools make predictions constantly — market moves, sports outcomes, election odds, weather, product trends. But very few of them make it easy to check whether those predictions were actually right. If you're trying to figure out which AI systems are worth trusting, you need somewhere to verify a track record, not just a claim of one.

Here's what actually exists today, what to watch out for, and how to think about accuracy tracking that holds up to scrutiny.

Why Tracking AI Accuracy Is Harder Than It Sounds

Most companies that publish AI predictions don't publish the misses. It's easy to highlight the one time a model called a market swing correctly and quietly drop the times it didn't. Without a permanent, tamper-resistant record, you're just trusting marketing copy.

A real accuracy tracker needs three things: the prediction has to be logged before the outcome is known, it has to stay unedited afterward, and it has to be checkable by someone outside the company making the claim.

Types of Sites Worth Knowing About

Prediction markets (Polymarket, Kalshi, Metaculus): These aggregate forecasts from many participants and show how the crowd's odds moved over time. Useful for calibration data, but they measure the market as a whole, not a specific AI model's individual performance.
Model benchmark leaderboards: Good for comparing raw capability on fixed tasks like math or coding, but these are static tests, not real-world forecasting over time.
Company-published track records: A handful of AI companies now keep a running, dated log of specific forecasts and their outcomes. This is the closest thing to what most people actually want — but only if the record is timestamped and independently verifiable, not just a curated highlight reel.

What a Trustworthy Track Record Actually Looks Like

The bar should be simple: can a skeptical stranger check it themselves? That means the original prediction, the timestamp, and the eventual result all need to be public and linked together in a way nobody can quietly rewrite later.

This is the idea behind HQ's public /proof page at Hylaqo.com. Every forecast is logged and signed before the outcome is known, using real quantum provenance from IBM hardware as part of the process, so the record isn't just a company's word — it's something anyone can go back and check against reality.

Beyond Forecasts: What Else Lives on Hylaqo.com

Tracking accuracy is only half the picture — HQ is built around a few connected ideas. The Oracle handles forward-looking predictions with that signed track record behind it. Genesis and Deep Think cover deeper reasoning tasks where the goal isn't a quick answer but a well-reasoned one. There's also AI image and video generation for creative work, and Pantheon, a marketplace of collectible AI minds with their own personalities and histories.

None of that replaces the need for a public proof page — if anything, it raises the stakes for having one. A system making predictions should be judged on a real, checkable record, not on how confident it sounds.

A Quick Checklist Before You Trust Any AI's Track Record

Is the prediction timestamped before the outcome happened?
Can you see the losses, not just the wins?
Is the record hosted somewhere that can't be quietly edited later?
Is there a way to independently verify the timestamp or signature?

If a site can't answer yes to most of these, treat its accuracy claims as marketing, not evidence.

Tools Worth Pairing With Your Research

If you're building workflows around AI outputs — whether that's forecasts, content, or automation — it also helps to have solid tooling on the production side. For teams looking to streamline how they manage and ship AI-assisted work, it's worth trying try Loadit alongside whatever forecasting source you settle on.

The bigger point stands regardless of which tools you use: don't take an AI's predictive accuracy on faith. Look for the receipts, check the dates, and favor systems that show their work openly rather than just telling you to trust them.