16 Concrete, Ambitious AI Project Proposals for Science and Security

By Alejandro Acelas 🔸 @ 2025-08-11T20:28 (+5)

This is a linkpost to https://ifp.org/the-launch-sequence/

The Institute for Progress released a collection of essays by guest authors on how AI could be used to accelerate progress in science and defend against its major risks. You might find a few familiar names among the authors, including Miles Brundage, Ruxandra Teslo, and Sella Nevo.  

Here's the list of essays:

Introduction

• Preparing for Launch — An introduction to The Launch Sequence: Why shaping AI progress matters, and how to go about it.

AI for Security

• Scaling Pathogen Detection with Metagenomics — How to generate the data necessary to reliably detect new pathogen outbreaks with AI. By Simon Grimm
• Operation Patchlight — How to leverage advanced AI to give defenders an asymmetric advantage in cybersecurity.
• The Great Refactor — How to secure critical open-source code against memory safety exploits by automating code hardening at scale
• A Sprint Toward Security Level 5 — How to protect American AI from nation-state level threats
• The Infinity Project — How to use AI and mathematics to prove and improve science and security
• Preventing AI Sleeper Agents — How to ensure American AI models are robust and reliable via a DOD-led red- and blue-teaming effort
• Faster AI Diffusion Through Hardware-Based Verification — How to use privacy-preserving verification in the AI hardware stack to build trust and limit misuse

AI for Science

• Benchmarking for Breakthroughs — How to incentivize AI for national priorities through a strategic challenge and evaluations program
• Using X-Labs to Unleash AI-Driven Scientific Breakthroughs — How to adapt our science funding mechanisms to the unique infrastructure needs of large-scale AI projects
• The Replication Engine — How to build automated replication infrastructure for better, faster science
• Teaching AI How Science Actually Works — How block-grant labs can generate the real-world data AI needs to do science
• A Million-Peptide Database to Defeat Antibiotic Resistance — How to build a large peptides database to train the AlphaFold for new antibiotics
• Biotech’s Lost Archive — How to fuel AI by unlocking the FDA’s knowledge of biotech failures
• Scaling Materials Discovery with Self-Driving Labs — How to close the gap between AI-guided material design and real-world validation
• Mapping the Brain for Alignment — How to map the mammalian brain’s connectome to solve fundamental problems in neuroscience, psychology, and AI robustness