Real Consequences

Enterprises cannot deploy generative AI agents in production today because they cannot trust LLMs to behave reliably in the real world. LLMs are prone to errors, easy to attack, and slow to recover. Even if they were originally aligned to be honest and helpful, they can be easily compromised. Broken free from guardrails, they can diverge from developer goals, degrade user experience, and damage enterprise reputation and revenue.

Winter is Coming

The transformative potential of generative AI will remain unrealized if enterprises cannot trust foundation models:

If automakers cannot trust self-driving cars to recognize traffic signs altered by stickers

If attorneys cannot trust legal assistants to cite real cases

If investment banks cannot trust search engines to extract facts from earnings reports without memorizing personal identifiable information

If call centers cannot trust chatbots to protect confidential customer and corporate information

If retailers cannot trust recommendation engines to recognize fake product reviews

If software developers cannot trust code-generation tools to produce safe code

Incidents of Harm

Visit the AI Incident Database to see real world examples of harms caused by the deployment of flawed AI systems.