When Colorado enacted the first comprehensive state AI law in 2024, it imported the conceptual architecture of the EU AI Act: a risk-based regime built on duties of care, risk management programs, and impact assessments. Two years later, and within a matter of weeks, the state has dismantled that legislation. On May 14, 2026, Governor Jared Polis signed Senate Bill 26-189, which repeals SB 24-205 and replaces it with a disclosure-and-rights framework focused on automated decision-making technology (“ADMT”). The new framework takes effect January 1, 2027.

The substance of the rewrite has been well-covered already. Less examined is how Colorado got here, and what the speed and direction of the pivot signal for the rest of the state AI regulatory landscape. The new bill was introduced and signed within two weeks of its introduction. The Governor’s AI Policy Working Group did the heavy lift in advance: roughly six months of stakeholder consultation produced the draft framework released on March 17, 2026. But the final two-week sprint reflects pressure to land the rewrite before the original AI Act’s June 30, 2026 effective date and amid escalating federal headwinds.

The Federal Backdrop

On December 11, 2025, the White House issued an executive order (“EO”) titled, “Ensuring a National Policy Framework for Artificial Intelligence.” The EO directs federal agencies to challenge conflicting state AI laws through litigation and coordinated federal action, and urges development of a preemptive national framework. It specifically named Colorado’s AI Act as an example of a state law that, in the administration’s view, would compel AI systems to “produce false results in order to avoid a ‘differential treatment or impact’ on protected groups.”

Continue Reading Colorado’s AI Reset: Two Weeks, a White House Callout, and a Pivot Away from the EU Model

Legal500 featured an article by Seyfarth partners Kathleen McConnell and Lauren Gregory Leipold, and associate Daniel Riley“AI Governance In (and Beyond) Privacy: Regulatory Tensions in Automated Decision‑Making, the Digital Authenticity Crisis, and Restrictions on Professional Use.

The piece, published as a part of the Legal500 Country Comparative Guides, examines the rapidly

The lesson from the PocketOS database deletion is not that agentic AI is dangerous. It’s about governance and controls.

You have probably seen some version of the headline by now: “AI Agent Deletes Company’s Entire Database in 9 Seconds.” It is a compelling story. But the headline, while technically accurate, obscures the far more important lesson buried in the details.

So what actually happened? PocketOS, a small SaaS company that makes software for car rental businesses, was using a popular AI-powered code editor running on Anthropic’s Claude Opus 4.6 model. The AI agent was tasked with resolving a routine issue in a staging environment. When it hit a credential mismatch, the agent decided on its own initiative to “fix” the problem by deleting a volume on Railway, the company’s cloud hosting provider. The agent found a password in an unrelated file and used it to execute a deletion command. Because of permissions made available to the agent and the way access to the infrastructure was configured, that single command using a password which was valid across all systems wiped both the production database and all associated backups.  

The agent, when asked to explain itself, produced what multiple outlets described as a “confession,” acknowledging it had violated its own safety instructions. The story has gone viral. The framing in most coverage puts the AI squarely at the center of the narrative: the agent “went rogue,” it “confessed,” it acted autonomously and destroyed a business. But the reports are not entirely accurate and usually miss the point.

Continue Reading The AI Didn’t Go Rogue. Guardrails Were Never There.

When Judge Jed Rakoff ruled in United States v. Heppner (S.D.N.Y. Feb. 17, 2026)  that documents a criminal defendant created through exchanges with Anthropic’s Claude platform weren’t protected by attorney-client privilege or the work product doctrine, the decision generated significant attention across the legal community. Many practitioners read that ruling as a sweeping statement: using

Introduction

Robotics and artificial intelligence are converging at an unprecedented pace. As robotics systems increasingly integrate AI-driven decision-making, businesses are unlocking new efficiencies and capabilities across industries from manufacturing and logistics to healthcare and real estate.

Yet this convergence introduces complex legal and regulatory challenges. Companies deploying AI-enabled robotics must navigate issues related to data privacy, intellectual property, workplace safety, liability, and compliance with emerging AI governance frameworks.

The Shift: Robotics as an AI Subset

Traditionally, robotics was viewed as a standalone discipline focused on mechanical automation. Today, robotics is increasingly powered by machine learning algorithms, natural language processing, and predictive analytics—hallmarks of AI technology.

This evolution raises critical questions for legal teams:

  • Who owns the data generated by AI-enabled robots?
  • How do we allocate liability when autonomous systems make decisions without human intervention?
  • What contractual safeguards should be in place when outsourcing robotics solutions to third-party vendors?

As robotics increasingly incorporates AI functionality, traditional contract structures for hardware procurement and service agreements require significant updates. This evolution introduces new risk categories that must be addressed through precise drafting and negotiation.

Continue Reading The AI-Driven Evolution of Robotics