Saad Ullah
What happens when you ask over 158,000 people to predict how good an unreleased AI model will be? That's exactly what RecallNet did with GPT-5, creating one of the largest crowd-prediction experiments in AI history. The results offer fascinating insights into how public expectations match up with reality in our rapidly evolving AI landscape.
The Experiment That Changed AI Forecasting
AI commentator Emrys recently highlighted this groundbreaking experiment through RecallNet Predict. More than 158,000 participants submitted over 7.8 million forecasts, comparing GPT-5's expected performance against 50 leading AI models, including Elon Musk's Grok 4 and Google's Gemini 2.5. Once GPT-5 launched, its real performance was tested head-to-head in the Recall Model Arena, showing just how accurate (or off) public expectations really were.
This wasn't just random guessing. Many participants were developers, researchers, and AI enthusiasts who work with large language models daily. Their collective predictions gave us a snapshot of what the AI community genuinely believed GPT-5 would deliver.
Crowdsourced forecasting isn't new - it's been used successfully in politics, economics, and sports. But bringing this approach to AI benchmarking creates something unique: a way to measure both sentiment and expectations in a field that moves at lightning speed.
The approach reveals two crucial insights:
- Expectations shape narratives - when thousands predict GPT-5 will dominate, it influences investors, companies, and developers to prepare for major changes
- Reality checks matter - once the model hits real testing, hard data cuts through the hype
When Expectations Met Reality
The results were eye-opening. While most forecasts suggested GPT-5 would crush almost every competitor, the actual head-to-head matches told a more complex story.
GPT-5 did excel where people expected - particularly in reasoning-heavy tasks and handling long contexts. The community's optimism was largely justified. But other models didn't just roll over. Google's Gemini 2.5 and Anthropic's Claude showed real competitive strength, especially in areas like real-time responsiveness and handling multiple types of content.
The crowd forecasts got the direction right - GPT-5 did land near the top - but they sometimes missed how much progress other models had made. This shows us something important: the AI world isn't a one-horse race anymore, even when everyone's betting on OpenAI.
Why This Matters for AI's Future
This kind of large-scale, open evaluation is exactly what the AI world needs. Traditional model testing is often closed off, unclear, or limited to academic labs. RecallNet's approach changes that by allowing mass participation from experts worldwide, transparent results that everyone can see, and dynamic benchmarking that shows how models actually perform against each other in real use.
For businesses, researchers, and policymakers, this offers something invaluable: a clear view of what AI models can actually do, beyond the marketing hype.
The GPT-5 experiment might mark the start of a new era in AI evaluation - one that combines collective wisdom with transparent, competitive testing. The takeaway? Expectations matter for setting the stage, but reality is what ultimately counts. By comparing predictions with actual results, we get both a feel for community sentiment and a grounded view of real technological progress.
As competition heats up between OpenAI, Google, Anthropic, and new players entering the field, these crowd-powered testing arenas could become the gold standard for evaluating the next generation of AI breakthroughs.
Saad Ullah
