Protester shot dead at anti-Trump 'No Kings' rally
A demonstrator was shot dead at Salt Lake City's "No Kings" protest when a man believed to be part of the event's peacekeeping team fired at another man allegedly aiming a rifle at protesters.
Police took the alleged rifleman, Arturo Gamboa, 24, into custody on Saturday evening on a murder charge, Salt Lake City Police Chief Brian Redd said at a Sunday news conference.
The bystander, Arthur Folasa Ah Loo, 39, died at the hospital.
Detectives don't yet know why Gamboa pulled out a rifle or ran from the peacekeepers, but they accused him of creating the dangerous situation that led to Ah Loo's death.
Redd said a man dressed in a brightly coloured vest fired three shots from a handgun at Gamboa, inflicting a relatively minor injury but fatally shooting Ah Loo.
The gunshots sent hundreds of protesters running, some hiding behind barriers and fleeing into parking garages and nearby businesses, police said in a statement.
"That's a gun. Come on, come on, get out," someone can be heard saying in a video posted to social media that appears to show the events.
"No Kings" protests swept across the country Saturday, and organisers said millions rallied against what they described as President Donald Trump's authoritarian excesses. Confrontations were largely isolated.
The Utah chapter of the 50501 Movement, which helped organise the protests, said in a statement on Instagram that they condemned the violence.
The Utah chapter did not immediately respond to AP questions about the peacekeeping team. It was unclear who hired them, whether they were volunteers or what their training was prior to the event. Redd said that the peacekeepers' actions are also part of the investigation.
The shooter and another person in a vest allegedly saw Gamboa separate from the crowd of marchers in downtown Salt Lake City, move behind a wall and withdraw a rifle around 8pm, Redd said.
When the two men in vests confronted Gamboa with their handguns drawn, witnesses said Gamboa raised his rifle into a firing position and ran toward the crowd, said Redd.
That's when one of the men dressed in the bright vests shot three rounds, hitting Gamboa and Ah Loo, said Redd. Gamboa, who police said didn't have a criminal history, was wounded and treated before being booked into jail.
Police said they recovered an AR-15 style rifle, a gas mask and a backpack at the scene.
Hashtags

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Washington Post
26 minutes ago
- Washington Post
A vibe shift in San Francisco — and its new centrist mayor
While Democrats may seem in retreat around the country, the new mayor of San Francisco, Daniel Lurie, is off to a promising start in a way that holds hope for others in his beleaguered party. It is early days still, but Lurie is governing as a pragmatic, pro-business centrist.


CBS News
36 minutes ago
- CBS News
One dead, three injured in Little Haiti shooting
One person was killed and three others were injured in an early morning shooting in Miami's Little Haiti neighborhood. According to Miami police, just after midnight they received a ShotSpotter alert in the area of NW 62 Street and NW 2 Avenue. When officers arrived they found a male who had been shot. He was rushed to Jackson Memorial Hospital's Ryder Trauma Center. The body of a second person who had been shot was found at NW 2 Avenue and NW 56 Street. Police said two other people who had been shot arrived at the Ryder Trauma Center. Several blocks of the neighborhood have been cordoned off with crime scene tape. Investigators focused on a blue sedan stopped in the middle of NW 62 Street near the intersection of NW 2 Avenue. Dozens of evidence markers dotted the ground near the car. The glass of a bus stop next to the car was shattered. Investigators also walked the grounds of nearby Miami Edison Senior High School and its basketball courts. Police are trying to determine what led to the shooting.


Fast Company
an hour ago
- Fast Company
Why government's AI dreams keep turning into digital nightmares—and how to fix that
Government leaders worldwide are talking big about AI transformation. In the U.S., Canada, and the U.K., officials are pushing for AI-first agencies that will revolutionize public services. The vision is compelling: streamlined operations, enhanced citizen services, and unprecedented efficiency gains. But here's the uncomfortable truth—most government AI projects are destined to fail spectacularly. The numbers tell a sobering story. A recent McKinsey analysis of nearly 3,000 public sector IT projects found that over 80% exceeded their timelines, with nearly half blowing past their budgets. The average cost overrun hit 108%, or three times worse than private sector projects. These aren't just spreadsheet problems; they're systemic failures that erode public trust and waste taxpayer dollars. When AI projects go wrong in government, the consequences extend far beyond budget overruns. Arkansas's Department of Human Services faced legal challenges when its automated disability care system caused 'irreparable harm' to vulnerable citizens. The Dutch government collapsed in 2021 after an AI system falsely accused thousands of families of welfare fraud. These aren't edge cases—they're warnings about what happens when complex AI systems meet unprepared institutions. The Maturity Trap The core problem isn't AI technology itself—it's the mismatch between ambitious goals and organizational readiness. Government agencies consistently attempt AI implementations that far exceed their technological maturity, like trying to run a marathon without first learning to walk. Our research across 500 publicly traded companies for a previous book revealed a clear pattern: organizations that implement technologies appropriate to their maturity level achieve significant efficiency gains, while those that overreach typically fail. Combining this insight with our practical work implementing digital solutions in the public sector led to the development of a five-stage AI maturity model specifically designed for government agencies. Stage 1: Initial/Ad Hoc. Organizations at this stage operate with isolated AI experiments and no systematic strategy. Stage 2: Developing/Reactive. Agencies begin showing basic capabilities, typically through simple chatbots or vendor-supplied solutions. Stage 3: Defined/Proactive. Organizations develop comprehensive AI strategies aligned with strategic goals. Stage 4: Managed/Integrated. Agencies achieve full operational integration of AI with quantitative performance measures. Stage 5: Optimized/Innovative. Organizations reach full agility and influence how others use AI. Most government agencies today operate at stages 1 or 2, but AI-first initiatives require stage 4 or 5 maturity. This fundamental mismatch explains why so many initiatives fail. Without the right cultural frameworks, technological expertise, and technical infrastructure, organization-wide transformation based around AI capabilities stand little chance of success. Start Where You Are, Not Where You Want to Be The path to AI success begins with brutal honesty about current capabilities. A national security agency we studied exemplifies this approach. Despite seeing enormous opportunities in large language models, they recognized serious risks around data drift, model drift, and information security. Rather than rushing into advanced implementations, they are pursuing incremental development grounded in institutional knowledge and cultural readiness. This measured approach doesn't mean abandoning ambitious goals—it means building toward them systematically. Organizations must select projects that are appropriate to their maturity level while ensuring each initiative serves dual purposes: delivering immediate value and advancing foundational capabilities for future growth. Three Immediate Opportunities For agencies at early maturity stages, three implementation areas offer immediate value creation opportunities while building toward transformation: 1. Information Technology Operations IT represents the most accessible entry point for government AI adoption. The private sector offers a road map — 88% of companies now leverage AI in IT service management, with 70% implementing structured automation operations by 2025, up from 20% in 2021. AI can transform government IT through chatbots handling common user issues, intelligent anomaly detection identifying network problems in real-time, and dynamic resource optimization automatically adjusting allocations during peak periods. These capabilities deliver immediate efficiency gains while building the technical expertise and collaborative patterns needed for higher maturity levels. The challenge lies in government's unique constraints. Stringent security requirements along with legacy systems at agencies like Social Security and NASA create implementation hurdles that private sector organizations rarely face. Success requires careful navigation of these constraints while building foundational capabilities. 2. Predictive Analytics Predictive analytics represents perhaps the highest-value opportunity for early-stage agencies. Government organizations possess vast data resources, complex operational environments, and urgent needs for better decision-making—perfect conditions for predictive AI success. The U.S. military is already demonstrating this potential, using predictive modeling for command and control simulators and live battlefield decision-making. The Department of Veterans Affairs has trialed suicide prevention programs using risk prediction algorithms to identify veterans needing intervention. Beyond specialized applications, predictive analytics can improve incident management, enable predictive maintenance, and forecast resource needs across virtually any government function. These implementations advance AI maturity by building essential data management practices and analytical capabilities while delivering immediate operational benefits. Unlike complex generative AI systems, predictive analytics can be implemented successfully at any maturity stage using well-established machine learning techniques. 3. Cybersecurity Enhancement Cybersecurity offers critical immediate value, with AI applications spanning digital and physical protection domains. Modern AI security platforms process vast amounts of data across networks, endpoints, and physical spaces to identify threats that traditional systems miss—a capability that is particularly valuable given increasing attack sophistication. Current implementations demonstrate proven value. The Cybersecurity and Infrastructure Security Agency's Automated Indicator Sharing program enables real-time threat intelligence exchange. U.S. Customs and Border Protection deploys AI-enabled autonomous surveillance towers for border situational awareness. The Transportation Security Administration uses AI-driven facial recognition for streamlined security screening. While national security agencies implement the most advanced applications, these capabilities offer immediate value for all government entities with security responsibilities, from facility protection to data privacy assurance. Building Systematic Success Creating sustainable AI capabilities requires following five key principles: Build on existing foundations. Leverage current processes and infrastructure while controlling implementation risks rather than starting from scratch. Develop mission-driven capabilities. Create implementation teams that mix technological and operational expertise to ensure AI solutions address real operational needs rather than pursuing technology for its own sake. Prioritize data quality and governance. AI systems only perform as well as their underlying data. Implementing robust data management practices, establishing clear ownership, and ensuring accuracy are essential prerequisites for success. Learn through limited trials. Choose use cases where failure won't disrupt critical operations, creating space for learning and adjustment without catastrophic consequences. Scale what works. Document implementation lessons and use early wins to build organizational support, creating momentum for broader transformation. The Path Forward Government agencies don't need to choose between ambitious AI goals and practical implementation. The key is recognizing that most transformation happens through systematic progression. While 'strategic leapfrogging' is possible in some situations, it is the exception rather than the norm. By starting with appropriate projects, building foundational capabilities, and scaling successes, agencies can begin realizing concrete AI benefits today while developing toward their longer-term transformation vision. The stakes are too high for continued failure. With 48% of Americans already distrusting AI development and 77% wanting regulation, government agencies must demonstrate that AI can deliver responsible, effective, and efficient outcomes. Success requires abandoning the fantasy of overnight transformation in favor of disciplined, systematic implementation that builds lasting capabilities. The future of government services may indeed be AI-first, but getting there requires being reality-first about where agencies stand today and what it takes to build toward tomorrow. (This article draws on the cross-disciplinary expertise and applied research of Faisal Hoque, Erik Nelson, Professor Thomas Davenport, Dr. Paul Scade, Albert Lulushi, and Dr. Pranay Sanklecha.)