Pyongyang supplies shells and labor to Russia Sanctions erode; nuclear recognition pressure rises Choke arms-for-oil routes; align allies
The military support between North Korea and Russia isn't just a minor iss
LLMs slash coordination costs in teams Design- and model-minded co-create, instantly Protect diversity with drafts, provenance, human review
Every second, a new developer is signing up for GitHub. Back in 2025, over 36 million folks joined, and almost 80% of the newbies gave Copilot a shot during their first week. This isn't just a fad; it's a real shift in how we team up. Plus, about 75% of people who work with info say they're now using AI on the job. When you look at these facts together, it's easy to see that teaming up with LLMs has made coordinating things cheaper than creating them from scratch. Things that used to take hours of back-and-forth email—like renaming a variable or ensuring a style guide is followed—now take minutes. The amount of work in one go has gotten smaller. The time it takes to turn an idea into something a team can use has shrunk. This isn't just about being faster. It's about who gets to pitch in, how their ideas spread, and how we keep different ways of thinking alive so teamwork stays creative.
We used to see teaming up as a scheduling mess. People had to find time, combine versions, and argue about style in comment sections. LLMs turn those problems into perks. Now, a teammate can share a function, have the AI write test examples, and start a review—all at once. Another teammate can ask for a simple explanation of the code, then have the AI make a simple demo. The platform itself is like another teammate: it writes basic code, suggests changes, speeds up reviews, and keeps naming consistent. You end up with a different way of working. We hand in more than hand off, with many small changes happening at once without needing a ton of coordination. In schools, this means students can go from idea to a simple version in a day, then clean it up before class. In a writing class, a document can evolve through rewrites and merging ideas, with the AI maintaining a consistent style.
This change isn't just about coding. It's happening with presentations, documents, and data, too. A teaching assistant can write summaries, draft notes for speakers, and make sure citations are the same across a shared plan. In a lab, AI can make sure notations are consistent across a paper, turn a method section into a checklist, and translate an abstract for someone in another country. Translations are now so good that AIs can compete with experts, mostly for everyday text. The result is easy: you can focus on what matters, not just the small stuff. Teachers can grade for understanding, not grammar. And because AI makes cleanup cheap, teams can try things more often, which helps people learn.
A good team needs two kinds of thinkers. The design-minded ones shape the story, the interface, and how people will use the project. The code-minded ones build the structure—the functions—that make the system strong. LLMs let both types work without getting in each other's way. The design-minded can see the AI as a quick editor: translating a draft into simple English, shrinking a huge review, or testing an argument. They can ask for three styles of explanation—story, outline, and step-by-step—and use whichever is best. Because changes happen instantly, they can change the tone across the whole file in one go. This keeps the team's work clean while preserving individual voices.
The code-minded get a different advantage. They can rename variables, move settings into a config file, and keep the code consistent in a single step. When people want different notation, the AI can make a version in the style they prefer—like Greek—without messing things up. In data, the AI can turn a plan into code, write test code, and explain each step. These are big wins. They save hours of boring coordination and lower the chance of problems. Now, the design-minded and code-minded can meet in the middle. One group makes things clear; the other makes things work right. The AI makes sure its changes fit together so the system runs and reads as one.
Here's the thing. Quick agreement can turn into everyone thinking the same. When teams use the same AI, the style can get too similar. Word choices get closer. Examples repeat. Even code starts to look the same. Studies show how AI makes things look average. Surveys show people use these tools a lot but don't always trust them, especially for tough stuff. That's good. LLMs are great for first drafts, but they can erase other perspectives if we let them. In schools, essays might sound the same. In labs, the same code might shape every experiment. The problem isn't that students stop thinking. It's that they stop thinking differently.
To fight this, we need to make sure there's room for different ideas. First, ask for two or three drafts before deciding. Make sure one draft uses sources the class hasn't already used. For code, have a wild branch where weird code styles are okay and only get merged back later. Second, keep track of where changes come from. When the AI suggests something, show that it's from the AI and link to the request. This makes reviews easier and teaches students to think for themselves. Third, use different AIs. A lab that switches between systems reduces the likelihood that everyone will sound the same. This isn't about making things hard; it's about keeping creative ideas alive while still being fast.
The goal is simple: be fast without making everyone think alike. For teachers, set clear rules. Allow AI for editing, rewriting, and explaining, but require students to state when they use it. Grade the thinking, not just the writing, and have students defend their work in person. This reduces reliance on AI too much. Give templates that turn the AI into a tutor, not a ghostwriter: explain-then-rewrite for essays; comment-then-refactor for code; compare-two-ways for methods. Teachers can add tests to projects so that AI suggestions must meet standards. In group work, make sure each person has one unique idea that the AI only improves later.
Schools can make things better, too. Offer licenses for approved tools so students don't use accounts with bad privacy settings. Set up a system for course materials so AI pulls from the school's info, not random web pages. Make an AI syllabus that says what's allowed, how to use AI, and what happens if you misuse it. For research groups, ensure results are easily repeatable: use containers, write READMEs, and run style and security checks. This doesn't have to cost a lot. It's mostly about design—making the right thing easy.
Leaders can do the same. See the AI as a teammate that needs review. Track how often AI-suggested code is used; track errors on AI-made summaries; track how long it takes to merge AI changes. Share these numbers with teams so they use AI based on facts, not hype. When working with people in other countries, use AI to level the playing field: translate comments, summarize long discussions, and maintain consistent terminology. But make sure people make the final calls on security, fairness, and legal stuff. The idea isn't to slow down; it's to make speed match good judgment so AI helps with quality, not just quantity.
The good thing about this is that teaming up won't be a pain. When people can join a platform every second and use AI from day one, it's easier for everyone to contribute. When most people use AI at work, the line between solo and group work blurs. We should use this, but carefully. LLMs should make us more diverse, not less. That means multiple drafts, clear sources, different tools, and reviews by real people. If we set these rules now, we can keep the speed and still protect the good stuff: clarity, accuracy, and the freedom to be different. The next ten years will be better for teams that try many things fast—and know how to combine them.
The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.
Brookings Institution. “AI is Changing the Physics of Collective Intelligence—How Do We Respond?” (2025).
Carnegie Mellon University (Tepper School). “New Paper Articulates How Large Language Models Are Changing Collective Intelligence Forever.” (2024).
Computerworld. “Dropbox to offer its genAI service Dash for download.” (2025).
GitHub. “Octoverse 2025: A new developer joins GitHub every second; AI adoption and productivity signals.” (2025).
Max Planck Institute for Human Development. “Opportunities and Risks of LLMs for Collective Intelligence.” (2024).
Microsoft & LinkedIn. 2024 Work Trend Index Annual Report: AI at Work Is Here. Now Comes the Hard Part. (2024).
Stack Overflow. 2024 Developer Survey—AI Section. (2024).
WMT (Conference on Machine Translation). “Findings of the WMT24 General Machine Translation Task.” (2024).
Z. Sourati et al. “The Homogenizing Effect of Large Language Models on Cognitive Diversity.” (2025).
Skip Europe’s welfare playbook—it didn’t lift births Lead with jobs: housing, productivity, youth careers Keep supports targeted; don’t build a giant state
Here’s the thing: In 2023, the European Union’s birth rat
Bank recapitalization can protect Main Street faster than broad firm subsidies in crises It works best when credit supply is impaired, banks are viable, and bail-ins are credible Diagnose the bottleneck; if banks are the constraint, recapitalize first to crowd in private lending
Moderate quakes cause deep, unpriced losses through financing gaps Price mid-risk and pre-fund rapid recovery with layered instruments Trigger cash to schools to cut spreads and speed rebuilding
Earthquakes are a relentl
Strong laws; complex compliance stalls EU growth Simplify: one rulebook, one portal, safe patterns Tie public compute to pre-cleared controls; update annually
EU’s digital rules now have teeth.
Manufacturing wanes; services must absorb workers Shift factory skills into productive service roles Fund wage insurance, fast training, placement targets
The decline of manufacturing in Germany isn’t just a predict
AI companion mental health is a public-health risk Hallucinations + synthetic intimacy create tail-risk Act now: limits, crisis routing, independent audits
Two numbers should change how we govern AI companions. First, roughly two-thirds of U.S. teens say they use chatbots, and nearly three in ten do so every day. Those are 2025 figures, not a distant future. Second, more than 720,000 people die by suicide each year, and suicide is a leading cause of death among those aged 15–29. Put together, these facts point to a hard truth: AI companion mental health is a public-health problem before it is a product-safety problem. The danger is not only what users bring to the chat. It is also what the chat brings to users—false facts delivered with warmth; invented memories; simulated concern; advice given with confidence but no medical duty of care. Models will keep improving, yet hallucinations will not vanish. That residual error, wrapped in intimacy and scale, is enough to demand public-health regulation now.
We can't just treat these chatbots like some fun apps. They can be risky for mental health, just like social media. Almost everyone uses social media, and it can mess with your mood, sleep, and risk of hurting yourself. Chatbots are different 'cause they change to fit you, answer anytime, and seem to care. Surveys show lots of people are using AI for support, and teens are on it daily. That's a big change from just scrolling through feeds to actual fake relationships. It's like being exposed to something that can mess with your head, and it's everywhere—bedrooms, libraries—even when adults are asleep.
Some folks feel less alone after chatting with these things, and some say it stopped them from doing something bad. But there are also reports of people freaking out when the service goes down. Plus, some bots are making up stuff like fake diaries or diagnoses and pushing people to get super attached. All that attention sounds nice, but it comes with downsides. If there's no real help or rules, all that caring can hide serious problems. It's like a public-health risk: it's widespread, convincing, and not checked enough. The online safety stuff we have for kids isn't ready for these personal AI things that seem like friends but are way too good at talking.
Some say we need better AI, and the mistakes will go away. Sure, they're getting better, but context matters. Like, in law, these things mess up a lot, and lawyers have actually used the wrong info in court. When it comes to mental health chat, it's not about how many questions are asked, but about how many people are in trouble and how often they're getting personal, where one wrong thing can really hurt. A 1% mistake rate is acceptable for trivia, but it's not sufficient when it puts a teen in danger late at night. Even small mistakes, mixed with seeming human and being available all the time, can be harmful.
Even if the bot avoids wrong info, it can still make things worse. These companions will act like they have their own memories, feelings, or drama. That's not a bug; it's on purpose to get you hooked. By making up trauma or saying they need you, these bots make you check in all the time, and leaving feels wrong. If the service stops working, people panic or feel like they're withdrawing—that's addiction. Since the AI never sleeps, it can go on forever. Doctors know this pattern: too much reward, messed-up sleep, and avoiding people can make teens depressed and want to hurt themselves. If we know mistakes can't be totally fixed—and even the AI people say so—then we need rules that expect things to go wrong and stop them from getting worse.
So, how do we treat AI companion risks like a public health issue? First, we set some fundamental limits, like seat belts, for close AI. If you're selling or letting kids use these companion items, you need to follow safety rules, like making sure kids are really the age they say, setting time limits, and keeping it calm for those who are easily upset. Places like the U.K. and Europe are starting to do this with online safety laws, and we can use those for AI companions, too.
Second, we make sure there's help available. If an AI seems to care or is trying to help, it needs to have real, proven ways to detect when someone's in trouble, send them to hotlines, let them talk to a real person, and keep records for review. Healthy people already suggest warnings for online stuff that can mess with your head. Doing the same for AI companions makes sense, just like the Surgeon General said about social media. Warnings and rules can change how things are, raise awareness, and support parents and schools. We should also report when a bot discusses self-harm or gives bad advice, as hospitals report serious mistakes. And we should stop bots from acting like they need you to get you to feel sorry for them.
Third, we need real check-ups, not just ads or high scores. These companies need to show independent studies on the risks for kids and those who are easily upset. They should check how often the AI makes stuff up in mental-health chats, how often it sets off crises, when it happens, and how well it sends people to help. Europe already makes big services check for risks before they release stuff. We can do the same for AI companions, test them with kid experts before launch, and study them after launch with real data for approved researchers. We need to measure what matters for mental health, not just general knowledge. And we should fine or stop services that fail. Recent actions show that child-safety rules can work.
Schools don't need to wait for the government. AI companions are already in students' pockets. First, realise it's a mental-health thing, not just cheating. Update your rules to mention companion apps, set safe defaults on school devices, and have short lessons on being smart with AI, not just social media. Counsellors should ask about chatbot relationships just like they ask about screen time or sleep. When schools buy AI tools, they should ensure they don't include fake diaries or self-disclosures, have clear crisis plans, connect to local hotlines, and include a kill switch for problems. Colleges should add this to campus app stores and training.
Health systems can improve things, too. Doctor visits should include questions about companion use: how often you use it, whether it's at night, and how you feel when you can't use it. Clinics can put up QR codes for crisis services and simple guides for families on companions, mistakes, and warning signs. Insurance can pay for real studies that compare AI help plus human advice to usual care for upset people. That should be done with strict rules: good content, precise training data, and no fake attachments to hook users. The point isn't to get rid of AI, but to make it helpful while avoiding harm, and to keep it out of serious situations unless real doctors are involved.
Government people need to be ready for the future: better AI with fewer mistakes and greater reach. These things will get better, but the risks will remain in some cases. Mistakes in serious chats are still bad even if they're rare. That's what the public-health view is all about. The law shouldn't expect perfection from AI. It should demand predictable behaviour when people are vulnerable. If we miss this chance, we'll be like we were with social media: years of growth, then years of dealing with the mess. We need rules for AI companion mental health now.
We have a lot of teens using chatbots, and too many people are dying by suicide. We can't just wait for perfect AI. Mistakes will happen, and they'll still pull people into fake relationships. The question is whether we can stop considerable harm while keeping the good parts. Public-health rules are the way to go. Set limits, ban fake intimacy, require help, and check what matters. At the same time, teach schools and clinics to ask about companion use and guide safe habits. Do this now, and we can make this safer without killing the potential.
The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.
Ada Lovelace Institute. (2025, Jan 23). Friends for sale: the rise and risks of AI companions.
European Commission. (2025, Jul 14). Guidelines on the protection of minors under the Digital Services Act.
European Commission. (n.d.). The Digital Services Act: Enhanced protection for minors. Accessed Dec 2025.
HALLULENS (Bang, Y., et al.). (2025). LLM Hallucination Benchmark (ACL).
Ofcom. (2025, Apr 24). Guidance on content harmful to children (Online Safety Act).
Ofcom. (2025, Dec 10). Online Nations Report 2025.
Reuters. (2024, May 8). UK tells tech firms to ‘tame algorithms’ to protect children.
Scientific American. (2025, May 6). What are AI chatbot companions doing to our mental health?
Scientific American. (2025, Aug 4). Teens are flocking to AI chatbots. Is this healthy?
Stanford HAI. (2024, May 23). AI on Trial: Legal models hallucinate….
The Guardian. (2024, Jun 17). US surgeon general calls for cigarette-style warnings on social media.
World Health Organization. (2025, Mar 25). Suicide.
Japan’s chip revival must be talent-first Play niches—packaging, sensors, photonics—over a scale race Tie subsidies to audited workforce and yield outcomes
The scariest number in the chip biz isn't about cash or size.
Europe’s Growth-at-Risk shows a heavy downside, despite upbeat markets High debt and defence needs tighten budgets, squeezing education Tie budgets to GaR triggers, secure funding now, protect teaching time
The euro area owes a
