A New York jury in a high-stakes antitrust trial has found that Live Nation and its subsidiary, Ticketmaster, illegally maintained monopoly power in the ticketing market.
The complaint, initially brought by the Department of Justice and the dozens of state attorneys general in 2024, alleged that the company engaged in “anticompetitive conduct,” resulting in fans paying higher fees, artists having fewer options for touring, and coercing venues to use Ticketmaster.
It also claimed that Live Nation monopolized the industry by controlling:
• ticketing
• concert booking
• venues
• promotions
Live Nation has vehemently denied acting as a monopoly.
In closing arguments, an attorney for the states addressed the jury. He said, “You’re New Yorkers. I trust that you know when someone is blowing smoke or being straight with you.”
29% of workers in the U.S., U.K., and Europe admit to sabotaging their company’s AI strategy. Not for the reasons you may think.
The Fast Company report shows this is not about skill or technical readiness. Many employees understand the tools. The issue is rooted beneath the surface. Employees are avoiding AI, feeding it low quality inputs, or working around it because they do not trust how it will be used against them. The concerns are straightforward. Job displacement. No clarity on how outputs are evaluated. Tools introduced without context or training. People feel acted on, not included.
That creates friction you will not see in a dashboard, but you will feel it in outcomes.
From a leadership standpoint, this is a clear signal. When people resist, incentives and expectations are not aligned. If AI is framed as a cost reduction effort, employees will protect themselves. If success metrics are unclear, they fall back to familiar ways of working. If leaders are not explicit about how AI informs decisions, trust erodes quickly.
There is also a security implication many teams are underestimating. When employees do not have clear guidance and practical education, they will find ways around the system. Shadow AI is a growing problem in most organizations, whether acknowledged or not. In agent-driven environments, this goes beyond inefficiency and into tangible security risk.
What should leaders be doing about this? Here is where I’d start:
1. Remove ambiguity. Do not rely on static documents. Build clarity into how work is executed. Define where AI is used, where it is not, and which decisions remain human.
2. Make incentives explicit. If employees believe AI adoption leads to headcount reduction, resistance is a rational response. Align AI usage with growth and better outcomes, not replacement.
3. Invest in real enablement. Move beyond general training. Provide role-specific guidance that shows how AI improves the work in front of people.
4. Measure behavior, not rollout. Look at where AI is ignored, overridden, or bypassed. That is where the strategy is not landing.
What are your thoughts? Anything else I didn’t think of?