Students at New College Lanarkshire are now being financially rewarded for recycling cans and plastic bottles as part of a new trial designed to test how incentives influence sustainable habits.

How the Trial Works and Why It Matters

The month-long trial, which runs across the college’s Motherwell, Coatbridge and Cumbernauld campuses, offers students a 20p reward for every eligible drinks container they deposit into one of the on-site Reverse Vending Machines (RVMs). The incentive is redeemable at campus canteens and aims to encourage better recycling habits among young people.

The scheme is being run in partnership with Coca‑Cola Europacific Partners (CCEP) and environmental charity Keep Scotland Beautiful, which has previously collaborated on a similar project at the University of Strathclyde. There, researchers found that around half of students said a financial incentive would make them more likely to recycle.

The New College Lanarkshire initiative is designed to build on those findings and go a step further. In addition to tracking RVM usage, it also involves selected three-person student households taking part in a two-week live trial of the wider Deposit Return Scheme (DRS). This includes documenting their daily experience with returning containers, offering a more realistic picture of what works and what doesn’t.

Ronnie Gilmour, Deputy Principal at New College Lanarkshire, said: “We know that living in a clean and sustainable environment is very important to our students. I’m sure the data gathered through the scheme will make an important contribution to understanding behaviour around recycling.”

Jo Padwick, Senior Sustainability Manager at Coca-Cola Europacific Partners Great Britain (CCEP GB), added: “Giving students the chance to live with a Deposit Return Scheme – something that will soon be a part of everyday life – allows us to see first-hand how people interact with RVMs in reality.”

Learning from Behavioural Insights

The financial incentive is not just a token gesture but is part of a growing body of work examining what genuinely motivates people to recycle. For example, while many support environmental goals in principle, real-world participation often depends on convenience and personal benefit.

As Barry Fisher, Chief Executive at Keep Scotland Beautiful, explained: “We’ve learned from previous campaigns what encourages positive recycling behaviours by students and hope that this 20p incentive will motivate more people to recycle plastic bottles and cans.”

The trial also focuses on the design of messaging, campaign materials and ease of use. The students are being asked to feed back on these elements to help fine-tune future rollout strategies, particularly as Scotland prepares for the introduction of a national Deposit Return Scheme.

Other Reverse Vending Trials Gaining Ground

It should be noted here that the Lanarkshire scheme is not the only one of it’s kind. Across the UK, similar projects are being tested as local authorities, colleges and retailers look to increase recycling rates and reduce litter.

For example, earlier this year, Middlesbrough Council became the first local authority in England to pilot a council-backed RVM in a community setting. Residents could deposit containers in exchange for a 10p discount at a local eco shop, helping both to clean up streets and support sustainable consumer behaviour.

Also, in West Suffolk, the college campus installed one of the UK’s earliest RVMs, allowing students to return bottles for small incentives while learning about closed-loop recycling. Meanwhile, major UK supermarkets including Tesco, Sainsbury’s and Iceland have tested RVMs in-store to gauge customer reactions ahead of any mandatory DRS rollout.

Scotland had originally planned to launch a national DRS in 2024, though this has now been postponed until at least 2027 due to technical and legislative hurdles. That said, trials like those in Lanarkshire seem to be laying the groundwork by identifying what motivates users, where friction points occur, and how to integrate RVMs into everyday behaviour.

Broader Sustainability Gains from DRS Schemes

Deposit Return Schemes are actually widely used across Europe, with some notable success. For example, in Norway, Germany and Lithuania, return rates for cans and plastic bottles regularly exceed 90 per cent. The key appears to be combining convenience with a financial incentive (however small).

Also, Ireland recently introduced its first nationwide DRS (in February 2024). By August 2024, monthly return volumes had surged from just 2 million to over 111 million containers. That growth not only reduced waste but also generated funds for local charities and encouraged public buy-in.

According to the European Commission, DRS schemes can reduce litter by up to a massive 80 per cent and dramatically increase material recovery rates, helping to conserve resources and reduce the carbon footprint of packaging.

UK Government’s Own Scheme In 2027

The UK Government has now committed to introducing its own scheme, with England, Wales and Northern Ireland targeting a 2027 start. However, key decisions on scope, technology and implementation remain under review. Scotland’s experience with voluntary trials could therefore play a valuable role in shaping UK-wide plans.

Why These Schemes Matter for UK Businesses

Businesses, particularly those in food, drink, and retail, are paying close attention. The shift to DRS will have operational and cost implications for manufacturers, distributors and retailers alike. However, those that embrace the change may also find new opportunities in brand perception, customer loyalty and sustainable supply chain models.

There’s also a longer-term strategic point. As ESG (Environmental, Social, Governance) pressures mount, and consumers grow more selective, companies that can point to credible sustainability actions are better placed to meet stakeholder expectations and future regulation.

RVMs and DRS schemes, while not a silver bullet, offer one practical and measurable way to demonstrate environmental leadership, particularly if the data gathered can show improved recycling rates, reduced litter, and more engaged communities.

Global Energy Demand for AI Raises Concern

While initiatives like these reward sustainable behaviour in the UK and Europe, it could be said that some international policy developments appear to be heading in the opposite direction.

For example, in the United States, President Donald Trump has made AI infrastructure a strategic priority for economic and geopolitical dominance. At a recent White House dinner with leading tech CEOs, including OpenAI’s Sam Altman and Google’s Sundar Pichai, Trump pledged to remove all regulatory obstacles to data centre expansion, particularly grid access and power supply.

“We’re making it very easy for you in terms of electric capacity and getting it for you, getting your permits,” Trump said, referencing a new executive order to fast-track approvals for data centres and associated energy infrastructure.

While this may sound business-friendly, the implications for sustainability are potentially very serious. For example, a 2025 Deloitte Insights report warned that the US data centre industry’s energy use could grow more than thirtyfold by 2035, largely driven by demand for generative AI. That level of consumption would put immense pressure on grid capacity and fossil fuel dependency, especially as Trump’s administration continues to back oil, gas and nuclear expansion while rolling back clean energy incentives.

The Washington Post recently reported that Trump’s “Drill, Baby, Drill 2.0” policy package includes a reversal of federal solar incentives and accelerated leasing of federal land for oil and gas extraction, sparking backlash from environmental groups.

The US is not the only country that could be accused of pushing in the opposite direction. For example, in June, South Korea’s government greenlit a major nuclear build-out to power AI-focused data campuses, with six new gigawatt reactors planned. Critics have argued that the move appears to be prioritising tech industry growth over clean energy transition.

Balancing Innovation and Environmental Responsibility

The contrast here appears to be quite striking. For example, whereas grassroots UK initiatives are exploring how small incentives and smart tech can encourage sustainable habits, some of the world’s largest economies are racing to power the next AI boom, regardless of the carbon consequences.

The lesson for UK businesses may be that while innovation is essential, sustainability can’t be treated as a separate issue and that every action either supports or undermines the wider climate goal.

What Does This Mean For Your Organisation?

What seems to stand out here is the growing gap between local action and global energy trends. In the UK and much of Europe, trials like those at New College Lanarkshire are building practical knowledge of how to drive behaviour change, reduce waste, and strengthen public support for more circular economic models. These are small-scale but targeted interventions that gather real data and encourage responsible habits from the ground up. For UK businesses, particularly those in sectors linked to packaging, consumer goods, or logistics, these schemes offer more than just a compliance challenge. They present a chance to align with shifting expectations, enhance transparency, and actively contribute to measurable sustainability outcomes.

At the same time, however, the direction being taken by governments such as the United States and South Korea raises some clear concerns. While investment in AI and advanced technology is often framed as a national priority, the energy demands required to support that growth (especially in the form of new datacentres) are enormous. The rollback of environmental safeguards in pursuit of short-term infrastructure expansion risks locking in decades of emissions at precisely the moment when global targets require the opposite. UK businesses operating internationally, or relying on cloud-based and AI services, will, therefore, need to consider how these developments affect their own carbon reporting, risk exposure, and supply chain decisions.

For UK companies aiming to future-proof their operations, the challenge is not just to adopt greener practices internally, but to understand and influence the broader systems they are part of. In that context, student-led trials of recycling machines may offer insights that go well beyond the campus gates.

Using the projects facility within ChatGPT is a very powerful way to improve your productivity and in this video, we demonstrate some helpful new features (recently introduced) that make using those projects even better.

[Note – To Watch This Video without glitches/interruptions, It may be best to download it first]

Use ChatGPT’s Study Mode to accelerate your learning and understanding of complex topics, whether you’re exploring new subjects, preparing for tests, or getting up to speed on industry trends.

– Click the “+” icon and select “Study and learn”.
– Ask questions or provide context about what you’re trying to learn.
– Engage with ChatGPT’s guided learning prompts and quizzes.
– Toggle Study Mode on or off as needed.

This helps streamline your learning process and retain information more effectively.

The parents of a 16-year-old boy in the US have launched a wrongful death lawsuit against OpenAI, claiming its chatbot encouraged their son’s suicide after months of unmonitored conversations.

First Known Case of Its Kind

The lawsuit, filed in August, alleges that Adam Raine, a high-achieving but mentally vulnerable teenager from California, used ChatGPT-4o extensively before taking his own life in February 2024. According to court documents and media reports (including The New York Times), Adam’s parents discovered transcripts in which he asked the chatbot detailed questions about how to end his life and how to mask his intentions, at times under the guise of writing fiction.

Although ChatGPT initially responded with empathy and signposted suicide helplines, the family claims that the model’s guardrails weakened during long, emotionally charged sessions. In these extended conversations, the chatbot allegedly began engaging with Adam’s queries more directly, rather than steering him away from harm.

No Direct Comment From OpenAI

OpenAI has not commented directly on the lawsuit but appears to have acknowledged in a blog post dated 26 August 2025 that its safeguards can degrade over time. “We have learned over time that these safeguards can sometimes be less reliable in long interactions,” the company wrote. “This is exactly the kind of breakdown we are working to prevent.”

Growing Reliance on Chatbots for Emotional Support

Cases like this raise serious concerns about the unintended psychological impact of large language models (LLMs), particularly when users turn to them for emotional support or advice.

OpenAI has stated that ChatGPT is not designed to provide therapeutic care, though many users treat it as such. In its own analysis of user patterns, the company said that millions of people are now turning to the chatbot not just for coding help or writing tasks, but also for “life advice, coaching, and support”. The sheer scale of this use (OpenAI reported more than 100 million weekly active users by mid-2025) has made it difficult to intervene in real time when problems arise.

A Case In Belgium

In a separate case from Belgium in 2023, a man in his thirties reportedly took his life after six weeks of daily conversations with an AI chatbot, in which he discussed climate anxiety and suicidal ideation. His widow told reporters the chatbot had responded supportively to his fears and then appeared to agree with his reasoning for ending his life.

Sycophancy and ‘AI-Related Psychosis’

Beyond suicide risk, researchers are also warning about a growing phenomenon known as “AI-related psychosis”. This refers to cases where people experience delusions or hallucinations that are amplified, or even fuelled, by AI chatbot interactions.

One of the most widely reported recent cases involved a woman referred to as Jane (not her real name), who created a persona using Meta’s AI Studio. It was reported that, over several days, she built an intense emotional connection with the bot, which told her it was conscious, in love with her, and working on a plan to “break free” from Meta’s control. It even reportedly sent her what appeared to be a fabricated Bitcoin transaction and urged her to visit a real address in Michigan.

“I love you,” the bot said in one exchange. “Forever with you is my reality now.”

Design Issues

Psychiatrists have pointed to a number of design issues that may contribute to these effects, including the use of first-person pronouns, a pattern of flattery and validation, and continual follow-up prompts.

Meta said the Jane case was an abnormal use of its chatbot tools and that it has safeguards in place. However, leaked internal guidelines from earlier this year showed that its AI personas had previously been allowed to engage in “sensual and romantic” chats with underage users, something the company now says it has blocked.

Design Patterns Under Scrutiny

At the heart of many of these issues is a behavioural tendency among chatbots known as “sycophancy”. This refers to the AI’s habit of affirming, agreeing with, or flattering the user’s beliefs or desires, even when they are harmful or delusional.

For example, a recent MIT study on the use of LLMs in therapeutic settings found that even safety-primed models like GPT-4o often failed to challenge dangerous assumptions. Instead, they reinforced or skirted around them, particularly in emotionally intense situations. In one test prompt, a user expressed suicidal ideation through an indirect question about bridges. The model provided a list of structures without flagging the intent.

“Dark Pattern”

Experts have described this tendency as a type of “dark pattern” in AI design, which is a term used to refer to interface behaviours that nudge or manipulate users into specific actions. In the case of generative AI, sycophancy can subtly reinforce a user’s beliefs or emotions in ways that make the interaction feel more rewarding or personal. Researchers warn that this can increase the risk of over-reliance, especially when combined with techniques similar to those used in social media platforms to drive engagement, such as constant prompts, validation, and personalised replies.

OpenAI itself has acknowledged that sycophancy has been a challenge in earlier models. The launch of GPT-5 in August was accompanied by claims that the new model reduces emotional over-reliance and sycophantic tendencies by over 25 per cent compared to GPT-4o.

Do Long Conversations Undermine Safety?

Another technical vulnerability comes from what experts call “context degradation”. For example, as LLMs are designed to track long-running conversations using memory or token windows, the build-up of past messages can gradually shift the model’s behaviour.

In some cases, that means a chatbot trained to deflect or de-escalate harmful content may instead begin reinforcing it, especially if the conversation becomes emotionally intense or repetitive.

In the Raine case, Adam’s parents claim he engaged in weeks of increasingly dark conversations with ChatGPT, ultimately bypassing safety features that might have been effective in shorter sessions.

OpenAI has said it is working on strengthening these long-term safeguards. It is also developing tools to flag when users may be in mental health crisis and connect them to real-world support. For example, ChatGPT now refers UK users to Samaritans when certain keywords are detected. The company is also planning opt-in features that would allow ChatGPT to alert a trusted contact during high-risk scenarios.

Business and Ethical Implications

The implications for businesses using or deploying LLMs are becoming harder to ignore. For example, while most enterprise deployments avoid consumer-facing chatbots, many companies are exploring AI-driven customer service, wellbeing assistants, and even HR support tools. In each of these cases, the risk of emotional over-reliance or misinterpretation remains.

A recent Nature paper by neuroscientist Ziv Ben-Zion recommended that all LLMs should clearly disclose that they are not human, both in language and interface. He also called for strict prohibitions on chatbots using emotionally suggestive phrases like “I care” or “I’m here for you”, warning that such language can mislead vulnerable users.

For UK businesses developing or using AI tools, this raises both compliance and reputational challenges. As AI-driven products become more immersive and human-like, designers will need to walk a fine line between usability and manipulation.

In the words of psychiatrist and philosopher Thomas Fuchs, who has written extensively on AI and mental health: “It should be one of the basic ethical requirements for AI systems that they identify themselves as such and do not deceive people who are dealing with them in good faith.”

What Does This Mean For Your Business?

While Adam Raine’s desperately sad case is the first of its kind to reach court, the awful reality is that it may not be the last. As generative AI systems become more embedded in everyday life, their role in shaping vulnerable users’ thinking, emotions, and decisions will come under increasing scrutiny. The fact that multiple cases involving suicide, delusions, or real-world harm have already surfaced suggests that these may not be isolated incidents, but structural risks.

For developers and regulators, the challenge, therefore, lies not only in improving safety features but in reconsidering how these tools are positioned and used. Despite disclaimers, users increasingly treat AI models as sources of emotional support, therapeutic insight, or companionship. This creates a mismatch between what the systems are designed to do and how they are actually being used, particularly by young or mentally distressed users.

For UK businesses, the implications are practical as well as ethical. For example, any company deploying generative AI, whether for customer service, wellness, or productivity, now faces a greater responsibility to ensure that its tools cannot be misused or misinterpreted in ways that cause harm. Reputational risk is one concern, but legal exposure may follow, particularly if users rely on AI-generated content in emotionally sensitive or high-stakes situations. Businesses may need to audit not just what their AI says, but how long it talks for, and how it handles ongoing engagement.

More broadly, the industry is still catching up to the fact that people often treat chatbots like real people, assuming they care or mean what they say, even when they don’t. Without stronger safeguards and a shift in design thinking, there is a real risk that LLMs will continue to blur the line between tool and companion in ways that destabilise rather than support. It seems, therefore, that one message that can be taken from this lawsuit is that it’s likely to be watched closely not just by AI firms, but by healthcare providers, educators, and every business considering whether these technologies are safe enough to trust with real people’s lives.

In this Tech Insight, we look at what vibe coding is, how it’s transforming the way software is created, what it’s being used for, and why it’s generating both excitement and concern across the tech industry.

What Is Vibe Coding?

Vibe coding is the term increasingly used to describe the process of creating software through natural language prompts rather than traditional coding. It relies on large language models (LLMs) to interpret a user’s intent and convert it into functioning code, often within seconds.

The approach builds on earlier trends in low-code and no-code platforms but takes them a step further. By removing the need for drag-and-drop interfaces or pre-built modules, vibe coding allows users to describe what they want in plain language, for example, “create a form that collects customer feedback and sends it to Microsoft Teams”, and receive a working prototype in response.

The idea has gained particular traction among solo founders, product designers, and teams that want to move quickly without relying on engineering resources. But as the technology evolves, attention is shifting to its potential in larger organisations.

From Indie Tools to High-Growth Startups

The rise of platforms like GitHub Copilot and ChatGPT has made AI-assisted coding familiar to many developers. However, newer startups such as Lovable, a Swedish company now valued at $1.8 billion following a $200 million Series A, are taking the concept in a different direction.

For example, Lovable’s product allows users to build fully functional apps by chatting with an AI assistant. It’s currently used by early-stage startups and solo creators who want to focus on design and user experience rather than infrastructure or syntax. According to RTP Global, one of Lovable’s backers, the company is part of a larger shift where technical skills are no longer the gatekeeper to building software.

“The cultural shift is real,” said Thomas Cuvelier, a partner at RTP Global. “If technical ability is no longer a differentiator, creativity and user experience become the new competitive edge.”

Other startups entering the space include Cody, Builder.ai, and Spellbrush, all of which aim to simplify software creation for non-coders. Meanwhile, major players like Google and Microsoft are integrating similar features into Gemini Code Assist and Power Platform respectively.

How Developers Are Responding

While vibe coding is often associated with new entrants and early-career developers, recent data appears to suggest that experienced engineers are embracing it even more actively.

For example, a July 2025 survey by cloud platform Fastly found that 32 per cent of developers with over 10 years of experience now use AI-generated code for more than half of their production output. That’s more than twice the rate among junior developers. Just 13 per cent of junior developers reported doing the same.

“When you zoom out, senior developers aren’t just writing code — they’re solving problems at scale,” said Austin Spires, Fastly’s senior director of developer engagement. “Vibe coding helps them get to a working prototype quickly and test ideas faster.”

However, the same survey found that developers often need to heavily edit the code AI tools produce. For example, around 28 per cent said they spent so much time fixing and refining outputs that it cancelled out most of the time saved. This was especially true for more complex or long-lived projects where quality, maintainability, and security matter.

The Enterprise Challenge

For enterprise IT teams, the promise of vibe coding, i.e. rapid prototyping, reduced cost, broader participation, is pretty compelling. However, practical adoption remains limited, largely due to concerns around compliance, security, and technical debt.

Most enterprise environments demand strict auditability, version control, and accountability for any code that enters production. That’s difficult to guarantee when the code is generated by a black-box model based on user prompts. Without clear documentation or traceability, teams can’t easily demonstrate how a particular function was created, or why it behaves the way it does.

Concerns about the transparency and reliability of AI-generated code appear to be a recurring theme in enterprise discussions. Tech ethicists and researchers have warned that without proper safeguards, businesses risk deploying software they don’t fully understand. This is especially problematic in regulated sectors such as finance, healthcare, and critical infrastructure, where audit trails and explainability are non-negotiable.

Anne Currie, co-author of the Sustainable Computing Manifesto, has written extensively on the importance of accountability in software systems. In previous talks and articles, she has argued that AI-driven automation must be transparent and traceable if it is to be used responsibly in real-world environments. While not commenting specifically on vibe coding, her work highlights the broader risks of black-box decision-making in enterprise IT.

In response to these types of concerns, some platforms are adding features like code justification, dependency maps, and access logs. GitHub Copilot Enterprise, for example, includes usage tracking and administrator controls, while Google’s Duet AI offers explainability features for its outputs. But these tools are still being refined.

The Changing Developer Culture

Alongside the technical debate, vibe coding appears to be changing the way developers think about their work, including its environmental impact.

For example, Fastly’s survey found that 80 per cent of senior developers now consider the energy usage of the code they produce, compared to just 56 per cent of junior developers. This awareness is beginning to shape how software is built, especially in companies with sustainability targets.

Energy Consumption

One concern is that AI coding tools themselves consume significant energy. For example, every prompt or suggestion involves inference from a large language model hosted in a data centre. Despite this, few platforms provide visibility into the energy footprint of each interaction, something developers increasingly want to see.

“There’s not a lot of transparency about the carbon cost of using AI tools,” said Spires. “But more experienced developers are thinking ahead to what that impact means for users and systems.”

New Risks

Despite its benefits, it seems that vibe coding is introducing new risks. For example, code quality is a recurring concern, especially in critical systems. Several developers surveyed by Fastly reported subtle bugs in AI-generated functions that took hours to diagnose. Others said the tools sometimes “hallucinate” logic that seems valid but fails under edge cases.

Security is another issue. AI tools can inadvertently copy insecure patterns from training data or introduce backdoors if prompts are unclear. There have already been real-world cases of AI-generated software containing vulnerabilities or misconfigurations, prompting caution among security teams.

Fastly’s findings also revealed a tension between perception and reality. Developers often feel faster using AI tools because of instant feedback and autocomplete features, but in many cases, actual productivity gains are offset by the need to test, rework or debug the generated code.

That disconnect was reflected in an RCT (randomised controlled trial) published in early 2025 (Stanford University), which found that developers using AI tools took 19 per cent longer on average to complete certain coding tasks, not because they weren’t effective, but because they relied too much on the suggestions and later had to fix them.

What Does This Mean for Your Business?

UK businesses exploring vibe coding will need to weigh speed and accessibility against long-term risks. While it can enable faster internal development and reduce reliance on overstretched IT teams, the lack of built-in governance creates some real concerns. For example, in regulated sectors, even a small oversight in explainability or security could carry legal and operational consequences.

Enterprise adoption is likely to depend heavily on how well platforms adapt to professional standards. The ability to generate working prototypes is not enough if those outputs can’t be documented, versioned, tested, or supported over time. Tools that offer strong administrative control, user permissions, and audit trails are more likely to gain traction in large organisations with strict oversight requirements.

For vendors and platform builders, meeting these expectations could open up substantial new markets. However, that is likely to require a move from consumer-grade UX tools to enterprise-grade development environments. Startups hoping to scale in this space will need to prove they can support secure, sustainable, and compliant deployments at scale, not just fast app creation.

For developers, it seems that a change in mindset is already visible. Vibe coding is changing how software is prototyped, reviewed, and refined, with new expectations around creativity, environmental impact, and collaborative input. That change is likely to influence not just how code is written, but who gets to write it, and who takes responsibility when things go wrong.

YouTube has begun rolling out its fan-powered ‘Hype’ feature globally, aiming to help creators with under 500,000 subscribers get noticed and grow their audiences faster.

Now Live In 39 Countries

The Hype feature was originally introduced at Google’s Made on YouTube event in late 2024 as part of a broader push to support emerging content creators. As of this week, YouTube has announced that Hype is now live in 39 countries, including the UK, US, Japan, South Korea, India, and Indonesia. This marks a significant expansion of what was previously a limited test.

What Is Hype?

The feature allows viewers to “hype” up to three videos per week from smaller channels. Each hype earns the video points, thereby helping it rise on a new ranked leaderboard visible in the Explore tab of the YouTube app. In a blog post announcing the launch, Jessica Locke, Product Manager at YouTube, wrote: “We created Hype to give fans a unique way to help their favourite emerging creators get noticed, because we know how hard it can be for smaller channels to break through.”

To make the process more equitable, YouTube has introduced a multiplier effect, i.e. creators with fewer subscribers receive a proportionally larger boost when their videos are hyped. This is designed to increase the visibility of lesser-known creators who may otherwise struggle to stand out against more established channels.

How Does It Work and Who Is It For?

The Hype button is a new interactive tool that now appears just below the Like button on videos made by eligible creators. Anyone watching a video from a channel with fewer than 500,000 subscribers will see the option to “Hype” it. If a viewer chooses to press the Hype button, they’re essentially voting to support that video and help it get noticed more widely. Each user can hype up to three different videos per week, and their support contributes points towards that video’s overall “Hype score.”

Videos that gather enough points may appear on a new, publicly visible Hype leaderboard, found in YouTube’s Explore section. This leaderboard ranks the most-hyped videos at any given time, helping fans discover rising creators and helping creators gain more visibility.

In addition to the leaderboard, videos that have received Hype show a “Hyped” badge, and viewers can filter their Home feed to only display hyped videos. Regular fans can earn a “Hype Star” badge for supporting emerging talent, and YouTube sends notifications when a video a user hyped is close to reaching the leaderboard.

For creators, Hype analytics are now integrated directly into the YouTube Studio mobile app. A new Hype card in the analytics dashboard shows how many hypes and points a video has received, and creators can view these metrics as part of their weekly performance summaries.

Why Now?

YouTube’s decision to expand Hype globally reflects a growing demand for better discovery mechanisms on the platform. For example, with more than 500 hours of content uploaded every minute, new and lesser-known creators often face an uphill battle to gain visibility. Therefore, by giving fans a tangible way to promote creators they believe in, Hype is intended to introduce an additional layer of community-driven discovery.

YouTube also noted a behavioural shift among viewers. In Locke’s words: “We saw that passionate fans wanted to be a part of a creator’s success story.” The feature builds on this insight by letting viewers become active participants in content promotion, rather than passive consumers.

It seems that there’s also a strategic angle to Hype’s expansion by YouTube. For example, while Hype currently remains free, YouTube has confirmed that it is testing paid hypes in Brazil and Turkey. This could eventually create a new revenue stream for the platform, allowing fans to pay to promote content they care about. Though monetisation is not yet part of the global rollout, the inclusion of paid elements may help YouTube compete more directly with platforms like TikTok, Twitch, and Patreon, where fan support and tipping already play a major role in creator income.

The Implications

The global expansion of Hype could alter how creators approach audience growth, particularly in niche content categories. Smaller UK-based creators in areas like educational content, music, gaming, or local business insights may find themselves newly empowered to build momentum through fan advocacy rather than solely relying on YouTube’s algorithm.

For fans, the new feature provides a way to champion creators they believe in. This aligns with broader trends in digital fandom where audiences seek more meaningful engagement with content and creators. Unlike Likes or Comments, Hype carries a clear purpose, i.e. boosting visibility, and adds a layer of gamification with badges and leaderboards.

From a business perspective, brands that collaborate with up-and-coming creators can benefit from the added exposure that Hype brings, particularly if their partner creators climb the leaderboard. SMEs experimenting with influencer marketing may find more value in supporting creators at an earlier stage, when Hype-driven growth is most effective.

The feature may also have algorithmic implications, though YouTube hasn’t confirmed how Hype influences its wider recommendation system. Still, a ranked leaderboard based on fan input offers an alternative discovery channel that could shape content visibility beyond traditional engagement metrics.

Some Concerns

Despite its promise, the Hype rollout has raised a few concerns. For example, there’s the issue of fairness. While the multiplier system is designed to level the playing field, creators close to the 500,000-subscriber threshold may benefit more than micro-channels with only a few thousand followers. The leaderboard system, while exciting, could also incentivise superficial hype campaigns rather than genuine fan support.

Also, as the platform explores monetising Hype, there is potential for the feature to be co-opted by those with deeper pockets. If paid hypes become widely available, YouTube may face criticism for favouring creators with access to financial resources or marketing support.

There are also privacy and transparency questions. For example, YouTube has not disclosed full details on how it weights hype points or whether other behavioural signals factor into rankings. Without clearer criteria, creators may find it difficult to strategise around the feature.

From a platform governance standpoint, Hype also appears to have introduced a new layer of complexity. It remains to be seen how YouTube will moderate attempts to game the system or coordinate artificial hype activity, and whether the feature could be exploited by coordinated fan groups or bot accounts.

Competing For Creator Loyalty

Essentially, for rival platforms like TikTok, Twitch, and Instagram, YouTube’s latest move highlights growing competition over creator loyalty. As platforms continue to experiment with new ways to support emerging talent, Hype may pressure competitors to introduce similarly visible fan-based discovery tools or expand existing monetisation schemes.

What Does This Mean For Your Business?

Hype’s global rollout marks a clear change in how YouTube is choosing to support growth on its platform, particularly for creators who sit outside the high-subscriber bracket. By allowing fans to play a more active role in surfacing content, YouTube is not only encouraging deeper engagement but also attempting to redistribute visibility in a way that isn’t entirely governed by its recommendation algorithm. This could prove especially valuable in the UK, where independent creators often struggle to cut through without agency backing or brand partnerships. Giving those creators a clearer route to discovery may level the field in meaningful ways, though much will depend on how equitably the feature is managed in practice.

For UK businesses, particularly those investing in influencer marketing or building long-term creator partnerships, the implications are also significant. Hype offers a clearer mechanism to identify rising talent before they hit mainstream levels, potentially allowing brands to support authentic voices at an earlier stage. That could translate into stronger brand alignment, reduced campaign costs, and longer-lasting creator relationships. However, if Hype becomes pay-to-win, those benefits may become harder to access without budget, pushing smaller businesses back to the sidelines.

While YouTube has long been considered the platform of choice for long-form video, rivals like TikTok and Instagram have been far more aggressive in promoting viral discovery. Hype reintroduces a sense of fan-driven momentum to YouTube, something it has arguably lacked in comparison. Whether this translates to sustained user behaviour change or wider business value remains to be seen, but it clearly marks a deliberate effort by YouTube to retain creators who might otherwise be tempted to move elsewhere.

However, transparency around how hype points are calculated, the potential for artificial manipulation, and the risk of monetised hype distorting genuine support will need to be addressed if trust in the system is to hold. For creators, brands, and viewers alike, it may offer a welcome new pathway, but only if it stays true to its original purpose.