The Laude Institute Slingshots AI grants have officially been announced — and they’re already making waves in the artificial intelligence research community.

Unveiled on Thursday, the first round of Slingshots AI grants will support 15 pioneering projects that aim to “advance the science and practice of artificial intelligence.”

Designed as a hybrid between a research grant and a startup accelerator, the Laude Institute Slingshots AI grants program gives researchers access to resources that traditional academic institutions often can’t provide — such as funding, compute infrastructure, and dedicated engineering support.

What Makes the Slingshots Program Unique?

What sets the Laude Institute Slingshots AI grants apart from conventional funding programs is their results-oriented structure.

In return for this support, grant recipients are expected to deliver a tangible product at the end of their project — whether it’s a startup prototype, an open-source codebase, or a novel AI model that contributes to the broader ecosystem.

This approach ensures that the projects move beyond theory and into real-world impact, encouraging a blend of research and applied innovation.

Meet the First 15 Slingshots Grant Recipients

The inaugural batch of Laude Institute Slingshots AI grants includes 15 projects, many of which tackle one of AI’s most complex challenges — evaluation.

Among the selected projects are some familiar names for those following cutting-edge AI research, including:

• Terminal Bench – a command-line coding benchmark that tests AI coding proficiency.
• ARC-AGI – a long-standing project focused on artificial general intelligence benchmarks.
• Formula Code – a Caltech and UT Austin collaboration assessing AI agents’ code optimization capabilities.
• BizBench – developed at Columbia, it’s a proposed benchmark for evaluating “white-collar AI agents.”

Other projects in the Slingshots AI grants lineup explore topics like reinforcement learning structures, model compression, and AI-driven software evaluation frameworks.

A Spotlight on CodeClash — A New Way to Evaluate AI

One of the standout projects in this first cohort of Laude Institute Slingshots AI grants is CodeClash, led by John Boda Yang, co-founder of SWE-Bench.

Building on the success of SWE-Bench, CodeClash introduces a competition-based evaluation system for assessing how AI agents write and improve code.

“I do think people continuing to evaluate on core third-party benchmarks drives progress,” Yang told TechCrunch. “I’m a little bit worried about a future where benchmarks just become specific to companies.”

His sentiment underscores the importance of open, independent AI evaluation frameworks, something the Laude Institute Slingshots AI grants aim to promote.

Why the Slingshots AI Grants Matter

The launch of the Laude Institute Slingshots AI grants represents a major step toward bridging the gap between academic AI research and real-world applications.

By encouraging collaboration, transparency, and open benchmarking, the initiative hopes to accelerate innovation in AI development — from reinforcement learning to code generation and beyond.

Moreover, the program’s emphasis on measurable outputs ensures that each grant contributes directly to the broader AI ecosystem, not just theoretical exploration.

The Future of AI Research with Laude Institute

The Laude Institute Slingshots AI grants are poised to become a cornerstone in global AI research funding. With this first batch of 15 diverse projects, the institute is signaling a long-term commitment to fostering breakthroughs in AI evaluation, optimization, and applied intelligence.

As future rounds of grants are announced, researchers and startups alike will be watching closely — eager to see how Slingshots reshapes the next generation of AI innovation.