It shows up in the AI results box, takes up valuable answer space, and often appears ahead of websites that have done the actual research.

This is not making search better. It is making search worse.

Reddit is a community platform. It is built around user comments, opinions, arguments, personal experiences and anonymous posts.

Some of that content can be useful, but much of it is unverified. It can be inaccurate, outdated, misleading or completely wrong.

That should immediately disqualify it from being treated as a trusted source for AI-generated answers.

Google’s AI results are being presented to users as helpful summaries of the web. But when those summaries rely heavily on Reddit, Google is giving community comments the appearance of authority.

A Reddit thread should not be treated the same way as a researched article, an industry report, a technical guide, a specialist publication or original journalism.

That is the core problem.

Google’s algorithm appears fixated on Reddit. Instead of directing users to credible niche websites, industry experts and publishers who produce real-world insights, it keeps pushing people toward community-driven answers.

In many cases, Reddit is not adding value; it is taking attention away from stronger sources and pushing original publishers into the background while their insights are republished on Reddit.

Industry websites research topics, check facts, speak to experts and review data, yet Google’s AI can still leave a wrong Reddit answer in place even when a specialist publisher has provided the correct one after the fact.

They build knowledge around specific subjects. They create the material that makes the open web useful yet Google’s AI search often places Reddit in the spotlight instead.

That is not fair to publishers, and it is not good for users.

A random Reddit comment should not outrank a properly researched article just because Google’s AI systems find forum discussions easy to summarise.

Community posts may be useful for opinions, complaints or personal experiences, but they should not be treated as reliable answers to factual questions.

Google has blurred that line.

When users see Reddit inside an AI answer, many will assume the information has been checked or validated. But that is not how Reddit works.

Reddit does not operate like a newsroom. It does not verify every claim. It does not require expertise. It does not guarantee accuracy.

Anyone can post. Anyone can comment. Anyone can sound confident while being wrong.

That is a serious weakness when Google is using the platform to help answer public search queries.

The problem becomes even bigger when Reddit takes up the limited space inside AI search results. Google’s AI answer box is not a normal search page with ten blue links. It is a compressed result that tells users what to think, where to look and which sources matter.

If Reddit keeps appearing there, it effectively hogs the answer space.

That means specialist websites, independent publishers and original researchers lose visibility. Their work is pushed lower, while Reddit threads receive the traffic, the authority and the attention.

That is how AI search damages the open web.

Google cannot claim to improve search while steering users away from accurate, original information and toward anonymous community discussions. It cannot keep rewarding a handful of major platforms and pretend that its search engine is becoming more useful.

A small group of websites cannot be right on every question being asked online.

Reddit is not the answer to everything. Wikipedia is not the answer to everything. YouTube is not the answer to everything. Major platforms cannot replace the depth and accuracy of specialist websites across every industry, niche and subject.

The web is too broad for that.

AI search should be finding the best source for each question, not repeatedly leaning on the same familiar platforms. If the question requires verified information, the answer should come from credible publishers, official sources, industry experts, research bodies or specialist websites.

Reddit should only appear when the user is clearly asking for community opinion.

For example, Reddit may be relevant if someone searches for personal experiences with a product, complaints about a service, or what users think about a particular issue.

But it should not be treated as a primary source for technical, health, legal, financial, scientific, business or news-related answers.

That is where Google needs to draw the line.

Right now, AI search is massively flawed. It is too easily impressed by large platforms. It gives too much space to community-driven content.

It risks pushing users away from true and accurate information. And it is taking visibility away from the hard-working websites that actually produce original insight.

Google must fix this.

Reddit doesn’t need to be removed from the internet, and it does not need to disappear from search entirely. But it should be removed as a major source for AI-generated answers and heavily limited in search results where accuracy matters.

Google needs to stop treating community comments as authority.

If AI search is going to shape the future of how people find information, then Google has a responsibility to make sure the answers are based on the best available sources — not the loudest platforms, the biggest forums, or the easiest content for AI to summarise.

Until that happens, Google’s AI search will not be improving the web.

It will be narrowing it, weakening it and steering users away from the people and publishers doing the real work.

The post Why Google Must Stop Using Reddit As A Source For AI Search Answers appeared first on Tech Business News.

The open internet appears to be getting smaller as users move away from independent websites and into closed platform ecosystems controlled by a handful of major companies. However, the internet isn’t physically shrinking, but it is drastically centralising

Where the web once allowed anyone to publish and reach a global audience directly, platforms such as Facebook and TikTok now act as the main gateways to online content, using algorithms to decide what people see, share and engage with.

This year in 2026, more than 6.04 billion people were online, yet much of their activity was being funnelled through a narrow group of platforms, including Facebook, TikTok, Instagram and AI chatbots including ChatGPT.

10 Apps The Internet Is Shirking Into

• 1. Facebook
• 2. YouTube
• 3. Instagram
• 4. WhatsApp
• 5. TikTok,
• 6. Messenger
• 7. X
• 8. Reddit
• 9. Snapchat
• 10. Discord

Facebook groups grew into sprawling public forums.

Subreddits became massive discussion boards. Twitter became the default arena for news, politics, business and culture. Everyone was trying to stand in the same few crowded rooms.

That model is now under pressure. A review of recent platform trends, news consumption data and online community behaviour points to a clear shift: users are still online, but many are moving away from giant public forums and into smaller, more controlled spaces.

Private membership websites, Slack groups, WhatsApp chats, Discord servers, paid communities, niche Facebook groups and smaller subreddits are becoming the new gathering points.

The internet has not become smaller in a technical sense. It has become smaller socially. The public layer is noisier, less trusted and increasingly flooded with content that looks polished but often carries little human weight.

In Australia, the shift is already visible in how people consume information. The 2026 Digital News Report found television remains the most used news source overall, but social media is now almost level with it and ahead of online news.

It also found nearly one in 10 Australians are using generative AI tools such as ChatGPT and Gemini for news.

Globally, social and video platforms have overtaken traditional news websites and television as major news pathways, even as more people say they are tuning out or losing interest.

That is the contradiction now shaping the web. People are more connected than ever, but many are less willing to trust the open feed.

They still want information, but they increasingly want it filtered through people they recognise, communities they understand and spaces where conversation does not vanish inside an algorithm within minutes.

AI has accelerated the break. A year or two ago, content still carried some scarcity. Writing a credible article, producing images, creating video, building a social media campaign or publishing a useful guide took time and some level of skill.

Today, the internet can manufacture an endless stream of blog posts, graphics, videos, captions, comments and fake expertise in minutes.

That abundance has changed the value equation. Information is no longer scarce. Trust is. Human connection is. Proof of identity is.

A small community where members know who is speaking can now feel more valuable than a public platform filled with strangers, bots, recycled opinions and AI-generated noise.

Regulation and policy changes

Regulation is also reshaping the open web. Governments are tightening rules around online content, privacy and platform conduct, but the cost of compliance can hit smaller publishers and creators harder than major technology companies.

While many of these laws are aimed at protecting users, they may also strengthen the dominance of large platforms that have the legal, technical and financial resources to absorb new regulatory demands.

The cultural shift in how people use the internet

The shift is also cultural. For many users, particularly younger audiences, browsing the open web has been replaced by scrolling through algorithm-driven feeds inside apps.

The old habit of following links, exploring independent sites and discovering content organically is giving way to a constant stream of posts selected to maximise engagement.

Efforts to preserve and expand the open internet

Efforts to protect the open internet are still underway. Open-source tools, decentralised platforms and alternative publishing models are trying to preserve the web’s independence and accessibility.

These projects face a major challenge in competing with the scale and convenience of dominant platforms, but they remain an important counterweight to a more centralised internet.

The problem is not just volume. It is confidence.

Reuters reported in 2025 that research by the European Broadcasting Union and the BBC found leading AI assistants misrepresented news content in nearly half of tested responses, with sourcing and accuracy problems appearing across major tools.

Pew polling found 63% of Americans believed AI was advancing too quickly, while 66% were worried it could spread inaccurate information.

For ordinary users, the result is exhaustion. Large platforms remain useful for discovery, but they are increasingly poor places for belonging. In a group of 500,000 people, most users are invisible.

Conversations move too quickly. Trolls and opportunists arrive easily. Attention becomes a competition. In a group of 500 people, names become familiar. Threads continue over days or weeks. Reputation starts to matter. Bad behaviour is harder to hide.

This is why smaller online communities are gaining strength. They offer something large platforms struggle to provide: continuity. People are not just looking for more posts. They are looking for context.

They want to know whether the person giving advice has lived the problem, solved it before or at least shown up consistently enough to be taken seriously.

That does not mean every member needs to post.

One of the biggest mistakes in judging online communities is assuming silence equals failure. Jakob Nielsen’s widely cited 90-9-1 rule describes a common pattern in online communities: about 90% of users mainly observe, 9% contribute occasionally and 1% account for most activity.

That pattern is often misunderstood. A quiet member is not necessarily an inactive member. Many people join a community to read, learn, compare experiences and feel connected without needing to speak every day.

Nobody expects every Netflix viewer to make films. Communities work in a similar way. Most people consume the value created by a smaller number of active contributors.

The stronger communities are also becoming more specific. Broad categories such as fitness, business, dogs or retirement are too general to create real belonging.

A group for marathon runners over 40 is clearer. A community for first-time golden retriever owners is more useful.

A private forum for people planning to retire within five years has a sharper purpose than a general retirement page. The narrower the community, the easier it is for members to recognise themselves in it.

That specificity is now a competitive advantage. It gives people a reason to return. It also creates cleaner discussion, because members are not fighting to define what the group is about. The purpose is already understood.

For publishers, brands, creators and businesses, this presents a serious challenge. The old growth model was built around reach: more traffic, more followers, more impressions, more pageviews.

The new model is increasingly built around depth: fewer people, but stronger relationships.

A massive audience that does not trust you may be worth less than a small audience that listens, replies, buys, shares and stays.

This is also a public-interest issue. As conversation moves into closed or semi-closed spaces, more of the internet becomes harder to observe, moderate and report on.

Public debate may become less visible. Misinformation can spread inside private channels where outsiders, journalists and researchers have limited access.

At the same time, many users are moving into those spaces because the open platforms have failed to provide trust and safety at scale.

The internet’s next fight will not simply be over who has the biggest audience. It will be over who has the most credible room. The winners may not be the people with millions of followers or hundreds of thousands of subscribers.

They may be the people running smaller, sharper communities where members feel known, understood and protected from the noise outside.

The Problem With a Smaller Internet

The main problems include:

• Fewer voices are heard when most online traffic flows through a small number of major platforms.
  
  
• Smaller publishers struggle to grow because they are less visible in search results, feeds and AI-generated answers.
  
  
• Independent writers lose reach as audiences are pulled toward large platforms and closed ecosystems.
  
  
• Original perspectives become harder to find because the same dominant sources are repeatedly surfaced.
  
  
• Online information becomes less diverse when users are shown similar articles, summaries and opinions across multiple platforms.
  
  
• Discovery becomes weaker because people are less likely to stumble across unfamiliar websites, niche experts or alternative viewpoints.
  
  
• Major platforms gain more control over what people see, click, trust and share.

At first glance, a smaller internet can feel more convenient. Information is easier to find, search takes less effort, and users can get answers quickly without moving across dozens of websites.

But that convenience comes with a cost.

When most online traffic is controlled by a small number of platforms, the web becomes less open. Smaller publishers struggle to reach readers, independent writers lose visibility, and original perspectives are pushed further to the edges.

Over time, this narrows what people see. Instead of discovering new sources, different viewpoints and unfamiliar ideas, users are more likely to encounter the same information recycled across the same major platforms.

The internet may become faster, but it also becomes less diverse.

The shift leaves the web at a crossroads.

The open internet is not disappearing overnight. It is being narrowed slowly by platform control, commercial pressure, regulation and changing user habits.

That shift leaves the web at a crossroads. Closed platforms offer speed and convenience, but they also reduce independence, visibility and diversity online.

The open web may no longer dominate the way it once did, but it remains essential to innovation, public expression and access to information. Keeping it alive will require deliberate action from publishers, developers, policymakers and users.

However, the internet is not dying. It is retreating from the open feed into thousands of smaller villages. In many cases, that may be healthier. It also marks the end of an assumption that shaped the web for more than a decade: that bigger was always better.

By early 2026, more than 6.04 billion people were online, representing around 73% of the global population after a single-year increase of more than 240 million users. But about 2.2 billion people still remain offline.

The post The Open Internet Is Getting Smaller As Users Move Into Smaller Online Communities appeared first on Tech Business News.

Distributed in Australia by Ozdare, Kevin Murphy partnered with ecommerce accelerator Pattern after facing strong consumer demand on Amazon Australia despite having no official marketplace presence.

The brand entered the platform in November 2025, just before the Black Friday Cyber Monday sales period, and quickly turned the channel into one of its fastest-growing retail outlets.

Within four months of launch, Kevin Murphy increased units sold by 115% quarter-on-quarter, while average order value rose 8.4%.

The growth suggests shoppers were not simply chasing discounts but actively looking for premium salon-grade haircare through Amazon Australia.

“Given the growing influence of marketplaces in Australia, it was important for Kevin Murphy to establish a stronger presence where consumers are increasingly searching for and purchasing products,” said George Leighton, Head of Retail (Consumer) for Ozdare/Kevin Murphy.

“At the same time, maintaining the balance between our professional salon channel and consumer retail presence remained a key priority throughout the process.” Leighton said.

The move also highlights a growing tension for premium beauty and haircare brands. Avoiding Amazon can leave room for grey market sellers, inconsistent pricing and a weakened customer experience.

But entering the platform without the right controls can risk damaging a brand’s salon relationships and premium positioning.

Pattern’s ANZ Managing Director, Merline McGregor said the results reflected a broader shift occurring across the Australian retail landscape as premium brands increasingly embrace marketplaces as strategic growth channels rather than viewing them as discount environments.

“Many premium beauty and haircare brands have historically approached Amazon cautiously because of concerns around pricing control, unauthorised sellers and protecting brand equity,” McGregor said.

“What Kevin Murphy has demonstrated is that with the right retail media, marketplace and brand protection strategy, Amazon can become a highly effective growth channel that complements existing retail and salon partnerships rather than competing against them.” he said.

Since launch, Kevin Murphy has also increased its Amazon Buy Box ownership from 65% to 91%, while multiple unauthorised sellers have been removed from the platform.

Pattern also built and managed Kevin Murphy’s Amazon Australia storefront, optimised product listings and rolled out advertising across branded search, category discovery and competitor targeting.

By the end of the first quarter, about 80% of ad-driven sales were coming from first-time Kevin Murphy customers on Amazon Australia, suggesting the channel is not only capturing existing demand but also introducing the brand to new shoppers.

“The reality is consumers are already searching for premium brands like Kevin Murphy on marketplaces, regardless of whether those brands officially sell there or not,”

“What Kevin Murphy has demonstrated is that when brands take ownership of that customer experience with the right marketplace, retail media and brand protection strategy, Amazon can become a powerful channel for both growth and new customer acquisition,” concluded McGregor.

The post Kevin Murphy Turns Grey-Market Challenge Into 141% Amazon Revenue Growth appeared first on Tech Business News.

While more than half of surveyed employees report using AI within their organisation, only 10% say it is embedded into core business processes, where work is executed, decisions are made, and outcomes are measured.

That gap points to a growing problem for Australian companies. AI may be appearing in offices, inboxes and standalone productivity tools, but it is still rarely built into the workflows where decisions are made, work is completed and business outcomes are measured.

Nearly one in three employees said their organisation still relies on standalone AI tools, rather than integrating the technology into end-to-end processes.

That means AI is often being used as an add-on, rather than as part of the systems that drive operations, approvals, customer service, compliance or productivity.

The findings suggest many organisations are still in the early stages of AI adoption, despite the speed at which the technology has entered the workplace.

Staff may be experimenting with AI, but businesses are not yet consistently connecting it to the processes that determine whether the technology improves performance at scale.

Luke Thomas, Vice President, Asia Pacific and Japan at Appian, said the findings highlight a fundamental gap in how organisations are approaching AI adoption.

“AI is being introduced across organisations, but often not in the processes that actually run the business. Just 10% of Australian workers say AI is embedded into core business processes, which lags behind global findings from a recent Harvard Business Review Analytic Services study, sponsored by Appian, in which 18% of respondents reported that AI is primarily integrated within workflows,” Thomas said.

“Process is how organisations structure work, make decisions and serve customers. When AI isn’t connected to that, it lacks the context, control and visibility needed to deliver meaningful outcomes, and importantly, to measure its impact.” he said.

AI Is Showing Up In Australian Workplaces, But Not Deeply Embedded

Australian workers are seeing artificial intelligence appear across the workplace, but new research suggests many organisations are still keeping it at the edges of the business rather than embedding it into the systems that matter most.

The Appian research found employees most commonly report AI being used in customer service, at 20%, followed by marketing, at 17%.

Adoption is far lower in core operational areas, including finance, at 8%, and HR, at 6%. The pattern suggests many Australian organisations are choosing the easier and lower-risk path.

AI is being tested in visible, customer-facing or content-heavy parts of the business, while the more complex workflows that shape cost control, staffing, compliance and business performance remain largely untouched.

“Adoption alone isn’t the issue when it comes to delivering value from AI, it’s application. Many organisations have introduced AI, but until it is embedded into core processes, where work actually happens, it becomes difficult to drive consistent improvements,” said Thomas.

Expectations Remain High, But Outcomes Are Mixed

While AI adoption is accelerating, many organisations remain in the early stages of translating that momentum into value, with expectations often outpacing what employees are seeing in practice.

More than 46% of employees describe their organisation’s expectations of AI as optimistic or overly optimistic, yet only 23% report significant improvements. A further 11% say they have seen no measurable impact, while 15% say it is too early to tell.

“Organisations are understandably optimistic about what AI can deliver, but the results are still varied. In many cases, employees are seeing isolated improvements rather than consistent gains across day-to-day work, which makes it harder to build long-term confidence,” observed Thomas.

Integration and skills gaps remain key barriers

Employees identify a range of technical and organisational barriers to scaling AI.

Skills and talent gaps were cited as the biggest challenge (27%), followed by integration with existing systems (20%). Governance concerns (14%) and lack of clear strategy (13%) also remain significant factors.

These findings reinforce the need for a more structured approach to AI adoption, where AI is fully integrated with existing systems, data, processes and guardrails.

Focus shifts to full AI integration

Encouragingly, organisations are beginning to prioritise deeper AI integration.

The most common focus over the next 12–24 months is integrating AI into core systems and processes (32%), ahead of expanding existing use cases (19%) or continuing experimentation (17%).

“AI has enormous potential, but its value becomes clear when it’s integrated into the processes that run the business and applied to real operational challenges,”

Thomas says this is the case for the National Injury Insurance Scheme Queensland (NIISQ), which supports individuals seriously injured in motor vehicle accidents on Queensland roads.

“NIISQ uses Appian’s gen AI capabilities to extract and identify fields for invoice processing with over 80% data extraction accuracy,” said Thomas

“AI has delivered strong results for NIISQ with a 12x ROI and reduced manual effort by 50%,”

“Australian organisations risk limiting AI’s impact if they adopt it in a piecemeal way. Adding tools to solve isolated tactical problems won’t translate into meaningful performance gains”

“That requires applying AI across core business activities.”

“Australian organisations risk limiting AI’s impact if they adopt it in a piecemeal way. Adding tools to solve isolated tactical problems won’t translate into meaningful performance gains. That requires applying AI across core business activities.” he said.

The post AI Widely Used In Australian Workplaces, But Only 10% Have It Embedded Into Core Processes appeared first on Tech Business News.

The company recently announced the launch of its Cloudflare One Design Partner Designation on June 18, positioning the program around one of the biggest pressure points now facing enterprise technology teams.

The designation focuses on Cloudflare One, the company’s secure access service edge platform, commonly known as SASE.

The first group of named partners includes Arctiq, Consortium, CMT, Presidio and The Missing Link, alongside other selected global partners.

For large organisations, moving away from legacy security architectures is rarely simple, requiring existing environments to be audited, new systems to be mapped and vendor migrations to be carefully managed.

Poorly managed transitions can also expose businesses to configuration mistakes, fragmented controls and security blind spots.

Cloudflare is pitching the new partner designation as a way to reduce that risk by giving selected partners deeper technical training, stronger commercial backing and access to new deployment resources.

“Cloudflare One has evolved into a partner-led engine and our new Design Partner Designation is built to propel long-term growth,” said Tom Evans, Chief Partner Officer at Cloudflare.

“This new framework represents our deepest channel co-investment yet. We are equipping our elite partners with the financial runway and technical mastery they want to scale the Cloudflare One platform,”

“By blending our unified SASE architecture with partner expertise, we are turning complex network migrations into high-margin, high-value consulting opportunities for the AI era.”

The announcement comes as businesses face rising pressure to secure AI adoption across cloud systems, private applications and distributed workforces.

As AI tools become more embedded in daily operations, companies are being forced to rethink how employees, data, applications and automated agents are protected.

To support the program, Cloudflare is also introducing the Cloudflare One Stack, described as a library of AI skills designed to help security teams evaluate, deploy and manage Cloudflare One.

According to Cloudflare, the framework includes structured knowledge, decision trees, tool definitions, blueprint configurations and automated workflows that can be used by AI agents.

For Australian customers, the inclusion of The Missing Link gives the announcement a local angle. The company will work with Cloudflare’s platform to support customers pursuing Zero Trust and SASE strategies.

“Organisations are increasingly looking for ways to reduce complexity by bringing networking and security together within a single, modern architecture”, said Aaron Bailey, CISO and Director of The Missing Link.

“As a Cloudflare One Design Partner, The Missing Link can help customers accelerate their Zero Trust and SASE strategies while improving security, performance, and operational efficiency,”

“Combining Cloudflare’s platform with our cyber security and consulting expertise enables us to help organisations navigate transformation with greater confidence and resilience.” he said.

Other named partners also framed the designation around the need to modernise enterprise security as AI reshapes business operations.

“Organisations are under increasing pressure to modernise legacy architectures, secure AI adoption, and simplify increasingly complex environments,” said Wes Brown, CTO at Arctiq

“By combining the Cloudflare One platform with Arctiq’s expertise in cybersecurity, networking, cloud, and managed services, we help clients accelerate Zero Trust and SASE initiatives while reducing complexity and improving resilience,” Brown said

“Together, we’re helping organisations build secure, connected, and AI-ready environments that can adapt to an evolving threat landscape and support the future of business.” he said.

The broader message from Cloudflare is clear: AI adoption is no longer just a software issue. It is now tied directly to network architecture, identity, access control, data protection and operational resilience.

For businesses still relying on legacy systems, the challenge is not simply whether to adopt AI, but whether their existing security stack can cope with it.

The post Cloudflare Names Australia’s The Missing Link as Global Design Partner For Secure AI appeared first on Tech Business News.

In practice, too many outputs arrive with obvious mistakes, forcing users to spend more credits fixing problems the tool should not have created in the first place.

It can feel like paying to supervise a machine that keeps making the same basic mistake.

The most frustrating problem is text.

Ask Gemini to produce a scene with a sign, product label, news graphic, shopfront or on-screen words, and the result can quickly collapse into warped lettering, nonsense fragments and broken words that look like they were written by someone dreaming in another alphabet.

That might be funny once. It is less funny when each attempt eats into a limited credit balance.

Google’s own documentation makes clear that its video tools are tied to paid plans and AI credits. Google says AI plans provide access to video generation in Gemini and Google Flow, while Flow credit costs are charged per generation, not necessarily per request.

Some requests can create multiple generations, meaning users can burn through credits faster than expected. Veo 3.1 Lite, Fast and Quality generations cost different amounts, with Veo 3.1 Quality listed at 100 credits per generation and Gemini Omni Flash edits listed at 40 credits

That is where the consumer problem begins. If a generation technically completes, but the words are unusable, the user is left with the bill.

Google says that if an AI tool fails, credits should not be affected, although they may take time to reappear.

But a bad video is not the same as a failed video. A clip full of mangled text may still count as a successful generation in the system, even if it is useless to the person who paid for it.

This is not just one user being impatient with new technology. Research on text-to-video systems has found that these models still struggle to generate legible and coherent text, including short words and phrases.

That matters because text is not a decorative extra in many real-world videos. It is often the whole point: a headline, a brand name, a call-to-action, a price, a warning sign, or a location marke

The issue is not that AI video is imperfect. Everyone understands the technology is still developing. The issue is the pricing model.

When a tool repeatedly produces unusable output and then charges users again to fix it, the product starts to feel less like a creative assistant and more like a poker machine with better branding.

Independent testing has also raised similar concerns. The Verge described Google’s newer Omni video model as a “mixed bag”, noting that some results were strong while others produced strange errors.

The review also pointed out the cost of repeated edits, with one round of edits costing credits and a user on a paid plan burning through most of a monthly allowance after roughly 20 clips and a few edits

That is the part Google needs to fix.

A fairer system would treat obvious text corruption as a quality failure, not a completed job. If a user asks for a sign that says “Tech Business News” and Gemini returns something closer to “Teech Bzsnuss Nuws”, that should not cost the same as a usable video.

At the very least many say Google should offer automatic low-cost regeneration for broken text, clearer warnings before text-heavy prompts are generated, or a separate text-rendering layer that lets users edit words after the video is created.

Because right now, Gemini’s video maker can be visually impressive and commercially irritating at the same time.

It can produce cinematic movement, believable lighting and decent sound, then ruin the entire clip with one mangled word in the background.

For casual users, that may be tolerable. For publishers, advertisers, small businesses and creators trying to produce clean work, it is a serious flaw.

Google does not need to pretend AI video is perfect. But if it is going to charge people every time they ask the machine to try again, it should not make them pay full price for fixing mistakes the model should never have made in the first place.

The post Google’s Gemini Video Maker Has A Credit Problem, Not Just A Quality Problem appeared first on Tech Business News.

IDeaS says adoption of its broader commercial strategy products is accelerating as hotel operators look beyond traditional revenue management and pricing optimisation.

The shift reflects a wider change in how hotels are managing revenue, marketing, sales and operations. Rather than relying on separate teams and isolated data, operators are increasingly seeking a single view of business performance, market conditions and growth opportunities.

According to IDeaS, more than 1,600 hotels adopted Rate Data Advantage within the first 100 days of its launch.

The AI-powered market intelligence tool gives hotels visibility into market pricing dynamics and is designed to support faster commercial decision-making.

The company also says nearly 40% of independent hotels, small groups and regional chains within its client community now use Optix, a performance insights platform designed to help teams identify trends, risks and commercial opportunities more quickly.

Independent hotels, smaller groups and regional chains have been among the fastest adopters, with many using the technology to sharpen commercial strategies without adding major organisational complexity.

“Optix has really allowed me to look at the data in an easier and more effective way. I’m no longer spending time collating data I need to review—instead I have it readily available and can put strategies into play a lot faster,” said Ruth Eddy, Revenue Manager, Bavarian Inn Lodge

“Without IDeaS, we might not have been prepared and could have missed potential revenue that we didn’t anticipate,” said Eddy

IDeaS is continuously expanding its portfolio of commercial strategy solutions to support these objectives through capabilities including:

• Market intelligence with Rate Data Advantage (RDA), providing deeper visibility into competitive pricing activity and market dynamics.
  
  
• Performance insights with Optix, helping teams quickly identify trends, opportunities and areas requiring attention.
  
  
• Budgeting and forecasting with RevPlan, creating a centralized view of performance expectations and business plans.
  
  
• Group and event space strategies with Function Space, helping hotels maximize the value of high-demand function and event inventory.

Marketing optimisation with Spotlight, enabling hotel marketing teams to align campaigns and spend with forecasted demand opportunities

Hotels Push Beyond Traditional Revenue Management

The growing uptake comes as hotels face more complex trading conditions, including changing traveller demand patterns, tighter staffing conditions and greater pressure to align pricing, marketing, sales and operations.

IDeaS says its expanding commercial strategy portfolio now includes Rate Data Advantage for market intelligence, Optix for performance insights, RevPlan for budgeting and forecasting, Function Space for group and event space strategy, and Spotlight for marketing optimisation.

Together, the tools are designed to connect data across functions that have traditionally operated in silos, helping hotel teams make faster decisions and respond more effectively to shifting market conditions.

“We’re witnessing a fundamental shift in hospitality. Revenue management is evolving into commercial strategy, and hotel organisations are looking for technology that helps connect decisions across revenue, marketing, sales and operations, “said Dr. Ravi Mehrotra, IDeaS. President, Founder & Chief Scientist

“The rapid adoption of solutions like Rate Data Advantage and Optix demonstrates that hoteliers want more than insights—they want the ability to act on them,”

“The future belongs to organisations that can bring together data, intelligence and execution across the entire commercial function, “he said.

With more than 31,000 installations globally, IDeaS continues to innovate and set the standard for growth, performance, and value in the next era of hospitality.

The post IDeaS Says Hotels Are Moving Beyond Revenue Management Amid Volatile Market Conditions appeared first on Tech Business News.

The matter will now be remitted to the Full Court of the Federal Court to determine whether Block Earner should be liable to pay a penalty and, if so, what penalty should apply.

The Earner product was closed in 2022, before proceedings commenced. The case does not relate to Block Earner’s current or future products, including its crypto-backed lending activities under its recently granted Australian Credit Licence.

It acknowledged the High Court’s decision, which concerns the statutory interpretation of financial product definitions under the Corporations Act.

Charlie Karaboga, Co-founder and CEO of Block Earner, said, “We acknowledge the High Court’s decision and will continue to engage constructively with ASIC and the regulatory process.”

“We continue to believe that legal clarity for Australia’s digital asset sector should come through proper legislative reform, not retrospective litigation,” Karaboga said.

“It is unfortunate that such significant questions about the application of financial services law to digital assets have had to be tested through enforcement against a small, innovative Australian startup,”

“We’re excited about the future and remain committed to ongoing regulatory engagement and to contributing to the development of fair, forward-looking financial services laws in Australia.” said Karaboga

Case Background

In April 2025, the Full Federal Court ruled that Earner, a fixed-yield product that Block Earner voluntarily closed in 2022, was not a financial product, including a financial investment facility or derivative. 

The High Court has now overturned that decision.

There has been no finding of customer loss, dishonesty, or misconduct.

Block Earner’s Regulated Lending Pathway

As an AUSTRAC-registered provider and recently a Australian Credit Licence holder, Block Earner has built its platform on a foundation of regulatory alignment, transparency and user empowerment, delivering innovative financial solutions powered by digital assets and blockchain technology.

In May 2026, ASIC granted Block Earner an Australian Credit Licence, making it the first digital asset platform in Australia to be regulated to provide credit products under its own licence.

The milestone marked a significant step in the evolution of digital assets within Australia’s financial system, creating a regulated pathway for eligible customers to use digital assets as security for credit.

Block Earner now conducts its lending activities under its own Australian Credit Licence, enabling it to originate, underwrite and offer regulated credit products directly.

Previously, Block Earner operated as a credit representative of existing Australian Credit Licence holder, Mortgage Direct, under ASIC’s regulatory framework.

The licence includes the appointment of Charlie Karaboga and James Coombes as Responsible Managers.

Separate from these legal proceedings, Block Earner will continue to progress its application for an Australian Financial Services Licence as part of its broader regulatory roadmap, ahead of the implementation of Australia’s Digital Assets Framework.

The reforms are expected to extend the financial services licensing framework to parts of the digital asset sector, consistent with Block Earner’s focus on operating within Australia’s regulated financial system.

The post Block Earner Focused On Constructive Engagement With ASIC, Following High Court Decision appeared first on Tech Business News.

Many are being asked to manage one of the biggest shifts in modern education without enough training, policy direction or institutional support.

The findings point to a growing public-interest problem for universities: AI is already reshaping teaching, assessment, student support and academic integrity, but the systems meant to govern it are still catching up.

The review, published in the Australasian Journal of Educational Technology, examined 29 empirical studies published between 2018 and 2023, covering 4,341 university teaching academics across multiple countries and disciplines.

Of those studies, 19 focused on traditional AI and 10 examined generative AI tools such as ChatGPT.

The conclusion is not that academics are rejecting AI. Far from it.

Most studies found university educators held somewhat or largely favourable views towards AI and GenAI, particularly where the tools could reduce repetitive work, support lesson planning, generate teaching materials, personalise learning or provide faster feedback to students.

But the optimism comes with a heavy qualification.

Academics are worried about cheating, unreliable outputs, fabricated information, privacy, bias, student overdependence and the erosion of core skills such as critical thinking and independent writing.

In other words, universities are not dealing with a simple technology adoption issue. They are dealing with a structural education problem.

The AI Ban Days Are Already Over

When ChatGPT burst into public use in late 2022, many universities treated it as an academic integrity emergency.

Some institutions moved quickly to restrict or ban generative AI tools, fearing students would use them to complete essays, exams and assignments with little or no original work.

That fear was not imaginary. The review notes that GenAI’s ability to pass assessments helped trigger serious concern among educators about widespread cheating. But the research also shows that the debate has moved well beyond plagiarism.

Teaching academics are now weighing whether AI can be used to improve education rather than simply undermine it.

The review also found GenAI is being considered for course planning, assignment design, research support, writing assistance, translation, feedback, learning materials and student guidance.

That shift matters because blanket bans are becoming less practical. AI tools are now embedded into search engines, workplace software, writing platforms, coding tools and learning systems.

Students are not stepping into an AI-free labour market after graduation. Universities know it, even if their policies haven’t caught up.

Staff See The Benefits — But Not The Guardrails

One of the clearest findings is that academics see AI’s practical value.

Traditional AI was viewed as useful for administrative and system-level tasks, including decision-making, student tracking, learning analytics and automated support.

GenAI, by contrast, was valued for its ability to create content: ideas, outlines, lesson plans, assessment material, writing prompts, summaries and teaching resources.

The review found customisation and personalisation were among the strongest perceived benefits across both AI and GenAI.

In theory, universities could use these tools to deliver more tailored support to students at scale — something higher education has promised for years but struggled to achieve in practice.

For overloaded academics, that is a serious attraction. AI can help with the routine, repetitive and time-consuming parts of teaching. But the research makes clear that efficiency alone is not enough.

The problem is not whether AI can save time. The problem is whether universities can prove it is being used responsibly, accurately and fairly.

The Hallucination Problem Is Now An Education Problem

The review draws an important distinction between traditional AI and generative AI.

Traditional AI tends to classify, predict or automate based on existing data. GenAI creates new material. That generative power is what makes it useful — and dangerous.

Tools such as ChatGPT can produce fluent, confident and persuasive answers that may be wrong, incomplete or entirely fabricated. In education, that creates a serious risk.

A student may not know when an AI answer is false. A staff member under pressure may not have time to check every claim. A poorly designed assessment may reward polished output rather than genuine understanding.

The review identifies accuracy and reliability as major concerns, especially because GenAI can “hallucinate” false answers.

But it also points to a more constructive possibility: universities could teach students to identify and challenge AI errors as part of learning.

That may become one of the defining assessment shifts of the next decade. The question may no longer be, “Did the student use AI?” It may become, “Can the student evaluate what AI produced?”

The Real Gap Is Training

The most damaging finding for universities is not that academics are sceptical. It is that many are under-supported.

Across the studies reviewed, academics repeatedly reported a lack of formal training, clear policy, institutional guidance and practical support. This was not a minor side issue. It was one of the main barriers to adoption.

The review argues that institutions need comprehensive training programs, dedicated AI support structures, ethical guidelines, pilot programs and stronger policy frameworks.

Without those supports, universities risk leaving individual lecturers to make high-stakes decisions on their own.

That is not sustainable.

A lecturer deciding whether a student’s AI use is acceptable should not have to rely on instinct. A course coordinator redesigning assessments should not be guessing. A faculty adopting AI feedback tools should not be operating without privacy, bias and transparency safeguards.

The Public Interest Stakes Are Larger Than Cheating

The temptation is to frame AI in universities as a student cheating story. That is too narrow.

This is about the credibility of degrees, the future of assessment, the workload of academic staff, the skills students take into the workforce, and whether universities can adapt quickly enough without lowering standards.

The review points to three skills that may become more valuable as AI becomes more common: strong evaluative judgement, the ability to find original solutions, and the ability to use AI efficiently and responsibly.

That is a useful warning. If AI can generate a passable essay, basic summary, code sample or research outline, universities will need to place more value on judgement, verification, originality and applied reasoning.

Universities Need To Move From Reaction To Strategy

The research ultimately presents a picture of cautious optimism.

Academics are not anti-AI. Many can see its value. They believe it can improve productivity, support teaching and personalise learning.

But they are also alert to the risks: academic integrity, unreliable outputs, dehumanised learning, privacy concerns and weakened student capability.

That tension is now the centre of the higher education debate.

Universities can no longer treat AI as a temporary disruption or a misconduct problem to be policed at the edges. It is becoming part of the operating environment of higher education.

The institutions that handle it well will not be the ones that simply buy new tools or issue vague policy statements.

They will be the ones that train staff properly, redesign assessment intelligently, protect academic standards and teach students how to work with AI without surrendering their own judgement.

The research makes the direction clear: AI is already inside the university system. The question now is whether universities are prepared to lead it — or whether they will keep asking academics to improvise while the technology moves ahead without them.

The post Universities Are Losing The AI Fight Because Staff Are Being Left To Guess appeared first on Tech Business News.

Agencies that spent years selling page-one rankings, keyword maps, backlink packages and monthly “visibility reports” are now staring at a search environment where the answer may appear before the website.

The citation may matter more than the click, and the customer journey can begin and end inside a machine-generated summary.

It has turned a once-familiar game into something stranger, faster and far less forgiving.

For years, the rules were relatively simple. Build a website. Optimise the pages. Chase rankings. Publish enough content to look alive. Win links. Report traffic. Repeat until the client either renewed the contract or lost patience.

That model is now under serious pressure.

Google’s AI Overviews, AI Mode, ChatGPT, Perplexity, Gemini and other answer engines are not just sending users to websites. Increasingly, they are reading the web, summarising it, selecting sources, and deciding which brands appear credible enough to mention.

For marketers, that changes almost everything.

• The old question was: “Where do we rank?”
• The new question is: “Are we being cited at all?”

The Click Is No Longer Guaranteed

The first shock has been traffic.

Pew Research Center analysed tens of thousands of Google searches and found that users were less likely to click through to websites when an AI summary appeared in the results. In searches with an AI summary, users clicked a traditional search result in 8% of visits.

Without an AI summary, that figure was 15%

That is not a minor wobble. That is a commercial warning light.

Even more uncomfortable for publishers, brands and agencies: users clicked links inside the AI summary itself in only 1% of visits.

In other words, being included is useful, but it does not automatically mean traffic will arrive.

This is the part many marketing decks still dance around. AI search can deliver brand exposure, credibility and influence, but it can also remove the click that made the old reporting model easy to sell.

For agencies built around organic traffic graphs, that is a problem. A big one.

The New Authority Game

The second shock is that AI systems do not appear to treat every type of content equally.

Muck Rack’s May 2026 Generative Pulse study found that earned media accounted for 84% of AI citations across ChatGPT, Claude and Gemini responses. Journalism alone made up 27% of cited sources. Paid and advertorial content accounted for just 0.3%

That finding should be pinned to the wall of every agency still telling clients that a bundle of sponsored blog posts on low-grade “high authority” websites will solve their AI visibility problem.

The machines appear to be sniffing out credibility.

Not perfectly. Not always. But enough to make the old link-selling economy look increasingly tired.

This is where the digital marketing industry starts to split into two camps.

One camp is doing the hard work: proper research, original reporting, technical SEO, schema, brand building, expert commentary, digital PR, content quality, entity optimisation and credible third-party coverage.

The other camp has discovered a new menu of buzzwords.

GEO. AEO. LLMO. AI SEO. AIO optimisation. Search everywhere optimisation. Answer engine optimisation. Citation engineering.

Some of it is useful. Much of it is traditional SEO wearing a metallic jacket and pretending it has just stepped out of a venture-capital pitch meeting.

Agencies Are Being Forced Back To School

The better agencies know the ground has shifted.

They are learning how AI systems select citations, how brand entities are understood, how publisher mentions influence answer engines, how technical accessibility affects AI crawlers, and how to measure visibility when the referral traffic is incomplete or hidden.

They are also learning a harder lesson: content volume is no longer a moat.

The internet is now drowning in cheap articles, synthetic “expert” quotes, recycled research, fake thought leadership and mass-produced LinkedIn sludge. Publishing more words is not a strategy if the words say nothing.

HubSpot’s 2026 State of Marketing report found that 61 per cent of marketers believe AI has created the industry’s biggest disruption in 20 years.

It also found that 80 per cent of marketers use AI for content creation and 75 per cent use it for media production.

That explains the noise.

Everyone has the tools. Far fewer have the judgement.

The agencies that will survive the shift are not the ones producing the most content.

They are the ones producing the clearest evidence of trust: original data, named experts, editorial mentions, accurate technical pages, consistent brand signals and content that answers real questions better than the next 20 results.

The Buzzword Milk Run

Of course, no industry can experience disruption without someone immediately building a three-tier pricing package around it.

Across the market, agencies are now selling “AI search optimisation” audits that look suspiciously like SEO audits with three extra slides.

Some are charging for “LLM visibility strategies” that amount to little more than asking ChatGPT whether a brand appears in a generated answer. Others are repackaging digital PR as “citation acquisition” and acting as though journalists were invented last Tuesday.

There is a useful service underneath some of this. Brands do need to understand how they appear in AI-generated answers. They do need to monitor mentions across AI systems. They do need stronger editorial authority. They do need technically clean websites that machines can understand.

But the hype is running ahead of the discipline.

The uncomfortable truth is that much of this “new” work still depends on old fundamentals: authority, trust, clarity, relevance, crawlability and proof.

A weak brand with thin content will not magically become a trusted AI citation because an agency has renamed its SEO package “Generative Engine Optimisation Pro Max.”

The Measurement Problem

The other challenge is measurement.

Traditional SEO reporting was never perfect, but it had familiar numbers: rankings, impressions, clicks, sessions, conversions, backlinks and domain metrics.

AI search muddies the water.

A brand may be cited inside an AI answer without receiving a click. A customer may ask ChatGPT for a shortlist, compare products inside an answer engine, then arrive later through direct traffic, branded search or even a sales call.

The influence happened, but the attribution trail is broken.

Semrush has estimated that AI search traffic in digital marketing and SEO-related topics may overtake traditional search traffic by early 2028.

It also reported that AI search visitors, where tracked, were worth 4.4 times the average traditional organic search visitor based on conversion rate.

That is the paradox.

There may be fewer casual clicks, but the clicks that do arrive can be more qualified. Users who come from AI systems may already be further through the decision process.

They have asked the question, received the shortlist, compared options and clicked only when they are closer to action.

For clients, this means the old obsession with raw traffic needs to calm down. Visibility, citation share, branded demand, assisted conversions, direct enquiries and source credibility are becoming more important measures of performance.

Google Has Not Left The Building

Despite the excitement around ChatGPT and Perplexity, Google remains central to the story.

Google said in May 2026 that AI Mode had surpassed one billion monthly users, with queries more than doubling every quarter since launch. The company also described its new AI-powered Search box as the biggest upgrade to Search in more than 25 years.

That is not a side experiment. That is the main stage.

Recent academic research has also found that AI Overviews can appear frequently, especially for question-based searches.

One study of trending queries found AI Overview activation at 13.7 per cent overall, rising to 64.7% for question-form queries. Another study using real-user representative queries found AI Overviews generated for 51.5% of queries.

The direction is clear. Search is becoming less like a directory and more like a front-page editor with an algorithmic brain.

That raises enormous questions for publishers, businesses and the agencies advising them:

Who gets cited? Who gets ignored? What happens when the answer is wrong? How does a small business compete when a machine selects the sources before the user sees the open web?

Nobody has fully solved those questions. Anyone claiming otherwise is probably selling a course.

The Winners Will Look More Like Editors Than Hackers

The next phase of digital marketing will reward a different kind of operator.

• Less spammer. More editor.
• Less keyword stuffing. More evidence.
• Less fake authority. More genuine expertise.
• Less “we can get you 100 placements.” More “we can make your business worth citing.”

That is the major shift. AI search has not killed SEO. It has exposed the weakest parts of it.

The shortcuts are becoming more obvious. Thin content looks thinner. Fake experts look faker. Paid link farms look like what they always were: artificial credibility machines built for an older version of Google.

The agencies that understand this are already changing their language and their methods. They are combining technical SEO with digital PR, newsroom-style content, data-led reporting, authority building and AI visibility tracking. They are not abandoning search. They are broadening it.

The agencies that do not understand it are still sending cold emails offering “high DA guest posts” as though the market has not moved on.

It has.

AI has not ended digital marketing. It has made it harder to fake.

And for an industry that has spent years rewarding volume, shortcuts and jargon, that might be the most entertaining development of all.

The post Digital Marketing’s AI Reckoning: The Old SEO Playbook Has Been Thrown Across The Room appeared first on Tech Business News.