Yoshua Bengio: Public Investment in AI Risk Research Is "The Most Important Project for Humanity"
Yoshua Bengio isn't raising a red flag for effect. He's saying out loud what many in government and engineering already feel: state-backed research into AI risks should be treated as a top-tier national mission - "the most important project for the future of humanity."
His message in London was direct. Governments need to force transparency on how AI developers manage risk, and they need to reward companies that take it seriously. Coordination between countries and companies isn't optional - it's a safety net. As a starting point, he pointed to the EU AI Act as a workable model for process, oversight, and accountability.
Why public money matters
Market forces prioritize speed and features. Safety gets deprioritized unless it's funded and enforced. Public investment can cover the gaps private incentives ignore: capability evaluations, red-teaming, interpretability, incident reporting, and sociotechnical impact research.
This isn't about slowing progress. It's about reducing downside risk while keeping useful systems online. Think aviation-grade safety, but for general-purpose models.
Policy moves governments can act on now
- Create a national AI Safety Research Mission with predictable, multi-year funding for evaluations, interpretability, and alignment research.
- Mandate a risk management system for high-impact AI, aligned to established frameworks such as the NIST AI Risk Management Framework.
- Require pre-deployment safety cases for high-risk and frontier systems: model cards/system cards, adversarial testing results, and third-party audits.
- Set incident reporting rules (e.g., 72-hour disclosure) and fund a national AI incident database to share lessons across sectors.
- Adopt clear transparency obligations: training data provenance summaries, evaluation coverage, dangerous capability testing, energy/compute declarations, and post-deployment monitoring plans.
- Use procurement as leverage: no contract without a documented AI risk program and evidence of continuous testing.
- Incentivize safety R&D with tax credits and safe harbors for companies that meet stringent reporting and audit requirements.
- Coordinate internationally on thresholds for large training runs, red-team protocols, and cross-border incident sharing. The EU AI Act is a strong reference point.
- Back independent testbeds and evaluation labs so results aren't solely controlled by model vendors.
- Fund workforce training for regulators, auditors, and public-sector engineering teams to close the skills gap.
What this means for CIOs and heads of development
Waiting for regulation is a risk in itself. Build the muscle now and you'll ship safer systems faster when rules tighten. Here's a practical baseline you can implement within a quarter.
- Adopt an AI risk register with owners, mitigations, and clear acceptance criteria. Review it at every release gate.
- Stand up an evaluation pipeline: red-team tests, prompt injection and jailbreak checks, privacy leakage tests, bias/fairness screens, and stress testing for reliability.
- Define dangerous capability evaluations (autonomy, cyber-enabled misuse, bio, persuasion) and block releases that fail thresholds.
- Implement kill switches, rate limiting, and traceable inference logs. Monitor for drift and abuse post-deployment.
- Publish system cards: intended use, known limits, testing coverage, and contact paths for responsible disclosure.
- Use independent audits for the highest-impact systems and keep audit artifacts ready for regulators and procurement.
Coordination that actually works
- Set up joint gov-industry red-team exercises with shared playbooks and anonymized findings.
- Create a sector AI-ISAC for rapid incident sharing and standardized severity tiers.
- Pool compute and datasets for public-interest evaluations so smaller teams can meet the same bar as big labs.
Metrics that keep you honest
- Evaluation coverage (%) across risk categories and model versions
- Time-to-mitigate critical findings
- Post-deployment incident rate and severity
- False positive/negative rates on safety filters under attack
- Alignment drift across updates and fine-tunes
Bottom line
Bengio's point is simple: without public investment and clear rules, AI risk management stays optional - until it isn't. Governments set the floor. Companies that build above it will move faster with fewer surprises.
If your team needs structured upskilling on safety, governance, and AI tooling, explore curated learning paths by job at Complete AI Training.
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