Not long into his new documentary, Oobah Butler tells the cofounder of his newly minted company, Drops, that they should create a piece of luxury luggage that “looks like a bomb” and will sell for $200,000.

Immediately, I’m thinking his quest to get £1 million in 90 days might have come to an early end.

But I’m wrong.

Butler is a British prankster documentarian who is known for his stunts, like managing to get Amazon to sell its drivers’ urine as energy drinks or creating a fake restaurant called the Shed and gaming TripAdvisor to make it the top-rated London restaurant on the platform. His latest documentary, made for the UK’s Channel 4, is called How I Made £1 Million in 90 Days. Set in London and New York, it takes on the worlds of startups, venture capital, crypto, and what ultimately comes across as a lot of bullshitting, in the name of striking it rich quick.

Butler opens the film by saying, as someone who didn’t grow up with money and isn’t particularly motivated by it, he’s fascinated by the fact that people “idolize” wealthy entrepreneurs.

“It came from a place of wanting to understand why … everyone is so obsessed with money in this way,” he tells WIRED. “And I’m not talking about survival. I’m not talking about affording to exist. I’m talking about … being addicted to the making of money.”

His only rules for getting £1 million ($1.3 million USD) are that he’s not allowed to break the law and whatever costs he incurs trying to make it are his to bear. He employs several strategies to rack up the cash, including simply asking rich people for it (this doesn’t go well) and creating hype for crypto company UNFK by doing things like tricking bankers into committing crimes on camera. He also creates Drops, a company that makes news for its controversial stunts and then tries to capitalize on the attention by selling “very overpriced” items.

Butler seeks the advice of Venmo cofounder Iqram Magdon-Ismail, who quickly declares himself Butler’s cofounder on Drops and seems very enthusiastic at first, musing that the company is already “worth at least $10 million” just because the two of them are attached to it, and that they might be able sell out Madison Square Garden in a year’s time to tell their story. Their brainstorming session includes schemes for buying the first piece of land on Mars and selling the opportunity to name the “first branded species.” But after Butler suggests the bomb-like suitcase and a pair of “real life ad blocking sunglasses” that remove the wearer’s vision entirely, Magdon-Ismail temporarily ghosts him.

Butler then embarks on a memecoin adventure that goes south, before coming back to Drops and launching the “first legal child sweatshop in Britain in over a century.” He finds a loophole to avoid paying the child workers, reasoning that because he is filming the kids for the documentary, they are technically performers. His underage staff help him come up with marketing ideas to sell bespoke soccer jerseys featuring a fake religious cigarette brand called Holy Smokes. Though the clothing line gets coverage in GQ, Butler doesn’t sell anything close to £1 million worth of jerseys.

Artificial intelligence is now part of everyday life. It’s in our phones, schools and homes. For young people, AI shapes how they learn, connect and express themselves. But it also raises real concerns about privacy, fairness and control.

AI systems often promise personalization and convenience. But behind the scenes, they collect vast amounts of personal data, make predictions and influence behavior, without clear rules or consent.

This is especially troubling for youth, who are often left out of conversations about how AI systems are built and governed.

Concerns about privacy

My research team conducted national research and heard from youth aged 16 to 19 who use AI daily—on social media, in classrooms and in online games.

They told us they want the benefits of AI, but not at the cost of their privacy. While they value tailored content and smart recommendations, they feel uneasy about what happens to their data.

Many expressed concern about who owns their information, how it is used and whether they can ever take it back. They are frustrated by long privacy policies, hidden settings and the sense that you need to be a tech expert just to protect yourself.

As one participant said, “I am mainly concerned about what data is being taken and how it is used. We often aren’t informed clearly.”

Uncomfortable sharing their data

Young people were the most uncomfortable group when it came to sharing personal data with AI. Even when they got something in return, like convenience or customization, they didn’t trust what would happen next. Many worried about being watched, tracked or categorized in ways they can’t see.

This goes beyond technical risks. It’s about how it feels to be constantly analyzed and predicted by systems you can’t question or understand.

AI doesn’t just collect data, it draws conclusions, shapes online experiences, and influences choices. That can feel like manipulation.

Parents and teachers are concerned

Adults (educators and parents) in our study shared similar concerns. They want better safeguards and stronger rules.

But many admitted they struggle to keep up with how fast AI is moving. They often don’t feel confident helping youth make smart choices about data and privacy.

Some saw this as a gap in digital education. Others pointed to the need for plain-language explanations and more transparency from the tech companies that build and deploy AI systems.

Professionals focus on tools, not people

The study found AI professionals approach these challenges differently. They think about privacy in technical terms such as encryption, data minimization and compliance.

While these are important, they don’t always align with what youth and educators care about: trust, control and the right to understand what’s going on.

Companies often see privacy as a trade-off for innovation. They value efficiency and performance and tend to trust technical solutions over user input. That can leave out key concerns from the people most affected, especially young users.

Power and control lie elsewhere

AI professionals, parents and educators influence how AI is used. But the biggest decisions happen elsewhere. Powerful tech companies design most digital platforms and decide what data is collected, how systems work and what choices users see.

Even when professionals push for safer practices, they work within systems they did not build. Weak privacy laws and limited enforcement mean that control over data and design stays with a few companies.

This makes transparency and holding platforms accountable even more difficult.

What’s missing? A shared understanding

Right now, youth, parents, educators and tech companies are not on the same page. Young people want control, parents want protection and professionals want scalability.

These goals often clash, and without a shared vision, privacy rules are inconsistent, hard to enforce or simply ignored.

Our research shows that ethical AI governance can’t be solved by one group alone. We need to bring youth, families, educators and experts together to shape the future of AI.

The PEA-AI model

To guide this process, we developed a framework called PEA-AI: Privacy–Ethics Alignment in Artificial Intelligence. It helps identify where values collide and how to move forward. The model highlights four key tensions:

1. Control versus trust: Youth want autonomy. Developers want reliability. We need systems that support both.
2. Transparency versus perception: What counts as “clear” to experts often feels confusing to users.
3. Parental oversight versus youth voice: Policies must balance protection with respect for youth agency.
4. Education versus awareness gaps: We can’t expect youth to make informed choices without better tools and support.

What can be done?

Our research points to six practical steps:

• Simplify consent. Use short, visual, plain-language forms. Let youth update settings regularly.
• Design for privacy. Minimize data collection. Make dashboards that show users what’s being stored.
• Explain the systems. Provide clear, non-technical explanations of how AI works, especially when used in schools.
• Hold systems accountable. Run audits, allow feedback and create ways for users to report harm.
• Teach privacy. Bring AI literacy into classrooms. Train teachers and involve parents.
• Share power. Include youth in tech policy decisions. Build systems with them, not just for them.

AI can be a powerful tool for learning and connection, but it must be built with care. Right now, our research suggests young people don’t feel in control of how AI sees them, uses their data or shapes their world.

Ethical AI starts with listening. If we want digital systems to be fair, safe and trusted, we must give youth a seat at the table and treat their voices as essential, not optional.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

It might seem counterintuitive in a sport built around speed, but the world governing body for competitive cycling wants to slow elite riders down.

Worried about high-speed crashes during pro-racing events, the Union Cycliste Internationale (UCI) has proposed a cap on the gear size riders can use. The idea is to lower the possible top speed bikes can achieve.

The risks are real, too. At the recent Tour Down Under Men’s Classic in Australia, a high-speed multi-rider crash on the final corner sent bikes into the barriers and into the crowd, badly injuring a spectator.

In August this year, champion British rider Chris Froome crashed while training in France, suffering a collapsed lung, broken ribs and a spinal fracture.

But would restricting gear size prevent these kinds of high-speed crashes? Certainly, not everyone thinks so.

Earlier this month, a Belgian court paused the rule change after teams and a major cycle component maker argued the safety case was not proven. While slower bikes might sound safer, they argue, the evidence tells a different story.

What the evidence tells us

The proposed rule would limit the largest gear size to 54 teeth on the front chainring and 11 on the rear sprocket. The idea is simple: lower the top gear to reduce top speed and, in theory, cut risk.

But while speed clearly matters when it comes to crashes, it is only one part of how they happen in a tightly packed peloton (the main pack of riders in a road race).

Our recent review of 18 studies of race speed and crash risk found two clear patterns:

• higher speed makes injuries worse once a crash occurs
• but the link between speed and the chance of crashing is weaker and depends on context.

Injury rates in the UCI WorldTour have climbed even though average race speeds have been steady. So, something else is at work.

We also examined the proposed gear cap itself. Based on our analysis, we argue any rule change should be evidence-based rather than simply a reaction to pressure after high-profile incidents.

Understanding why crashes occur is central to this. Essentially, they are about people and space, and happen for a number of reasons:

• when riders fight for position as they enter a narrowing corner
• when sprint “trains” (riders in the same team lining up for aerodynamic efficiency) cross wheels
• or when road “furniture” appears too late to be avoided.

In this year’s Paris–Nice race, for example, Mattias Skjelmose struck a traffic island at speed and abandoned the race. Reports described it as a poorly marked obstacle.

Course design, peloton density and inconsistent rule enforcement often play a bigger role than a few extra kilometers per hour.

Why a gear limit won’t help much

On hill descents, where many serious injuries occur, riders freewheel in a tucked body position. Gravity and aerodynamics set the speed—gearing does not.

When riders are actually pedaling in a sprint, a 54×11 gear at high “cadence” (around 110–120 revolutions per minute) gives a speed of roughly 65 kilometers per hour (km/h). The very fastest finishes in elite men’s races reach about 75 km/h—the absolute peak speed.

A cap on gearing would trim roughly 5–10 km/h from the top-end, bringing the fastest sprints down to around 65–70 km/h. But most sprint pileups start below those speeds and are triggered by contact or line changes.

Lowering everyone’s top speed could even bunch the field more tightly and raise the risk of contact. The pro-cycling world already knows what helps:

These steps match what other high-speed sports have done to reduce injuries. Motor sports redesign the environment rather than just limit speed, with NASCAR and IndyCar having adopted energy-absorbing barriers to cut wall-impact forces.

And alpine skiing manages risk with course design, as well as nets and airbag protection to control speed and crash severity.

Similar approaches to safety are used in aviation, mining and health care. The aim is to focus on the environment and behavior, measure exposure, fix the hotspots and share what works to keep improving safety.

This article is republished from The Conversation under a Creative Commons license. Read the original article.