Study Shows Reasoning Models Have an "Overthinking" Problem 🧠

Welcome to another edition of Horizon AI,

A new study shows that, just like with humans, reasoning models perform better when they don’t overthink things.

Let’s jump right in!

Read Time: 4.5’ min

Here's what's new today in the Horizon AI

• Chart of the week: Office workers hide AI advantages from employers

• Shorter Reasoning Boosts AI Accuracy by 34%, Study Finds

• Free Resources

• AI tools to check out

• Video of the week

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Chart of the week

Office workers hide AI advantages from employers

• The data was obtained from security software company Ivanti’s 2025 Technology at Work report, based on a survey of 1,116 employees conducted in February 2025.

• Many employees use AI tools like ChatGPT without employer approval, with 46% of office workers and 38% of IT professionals using unauthorized tools.

• Workers often hide their AI use due to concerns about increased workload, reduced perceived value of their skills, and fears of being judged or replaced.

Source

AI News

META

Shorter Reasoning Boosts AI Accuracy by 34%, Study Finds

A recent study by Meta's FAIR team and The Hebrew University of Jerusalem challenges the common belief that longer thinking chains lead to better reasoning, and in fact, shows they can counterintuitively lead to worse results.

Details:

• Researchers found that forcing LLMs to “think” less actually improves their performance on complex reasoning tasks, meaning that shorter reasoning processes not only lead to more accurate results but also significantly reduce computational costs.

• For the same reasoning task, shorter reasoning chains are up to 34.5% more likely to yield correct answers than the longest chains sampled for the same question.

• Based on these results, the researchers developed “short-m@k,” a new approach that runs multiple reasoning attempts in parallel but stops computation once the first few finish. They found it can reduce computational resources by up to 40% while maintaining the same level of performance.

• They also found that training AI models on shorter reasoning examples improved performance, once again challenging a fundamental assumption in AI development.

• This research challenges previous studies, such as OpenAI’s work on “chain-of-thought” prompting and “self-consistency” methods, which promoted longer reasoning chains to improve model performance.

As millions of dollars are poured into developing ever more powerful models, these findings could help tech giants save a significant amount in computational costs by revising current methods and adopting a more “don’t overthink it” approach.

Source

Resources

👀 I lost my job to AI, but then used it to supercharge my search for a new role

🧠 The Psychology of AI Persuasion

💡 Is AI making it harder for new college grads to get hired in tech?

🚀 It’s not your imagination: AI is speeding up the pace of change

👉 The best use cases for each ChatGPT model

AI Tools to check out

✅ Piny: Visual editor for Astro, React, Next.js & Tailwind that runs in your IDE.

🌐 AssistLoop: Build custom AI chatbots for your website in minutes.

⚡ Reelup: A video-commerce platform that turns your product videos into interactive, shoppable experiences.

✍ Humanize AI: Turn AI content into natural, human-like, undetectable text.

👉 OpusClip Thumbnail: Create viral YouTube thumbnails in 1 click.

Video of the week

AI 2027: A Realistic Scenario of AI Takeover

A video that discusses a researched scenario, AI 2027, that explores how AI could lead to human extinction.

It presents two possible outcomes: a happy ending and a nightmare ending.

That’s a wrap!

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Gina 👩🏻‍💻