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.

The latest reports centre on Compulsion Games, the Montreal studio behind South of Midnight, We Happy Few and Contrast.

Kotaku reported that Microsoft planned to close the studio, while later updates indicated negotiations were continuing between the parties.

The situation has raised speculation that Compulsion may be seeking a path back to independence rather than disappearing entirely.

Concerns now extend beyond one studio.

Separate reports have claimed Ninja Theory, best known for the Hellblade series, is also facing closure, while Compulsion Games and Double Fine are among Microsoft-owned studios said to be in active discussions about potential spin-offs.

Microsoft has not publicly confirmed the full position of each studio.

The public-interest issue is larger than another round of games-industry restructuring. Microsoft spent years buying creative studios to strengthen Xbox, Game Pass and its exclusive content pipeline.

Compulsion Games was acquired in 2018 during a major Xbox expansion that also included Ninja Theory, Playground Games and Undead Labs.

Now, several of those bets appear to be under review.

The timing is particularly notable because South of Midnight was not a failed creative project. The game won Games for Impact at The Game Awards and later claimed Best New Intellectual Property at the BAFTA Games Awards, giving Compulsion one of Xbox’s more distinctive recent critical successes.

The reported cuts follow a recent Xbox memo from Asha Sharma and Matt Booty, which said Xbox had become “over extended” across its studio system and needed to reassess investment priorities over the next five years.

The memo also cited weaker margins, rising hardware costs and heavy investment across content, platform and hardware subsidies.

For players and developers, the question is whether Microsoft’s gaming strategy is moving away from a broad slate of smaller, creative titles and back toward fewer, larger franchises.

For the wider industry, the concern is what happens when major platform holders acquire independent studios, reshape their business around subscription economics, and then decide those studios no longer fit the model.

Meanwhile, Xbox Game Studios boss Craig Duncan and chief of staff Louise O’Connor have both confirmed they are leaving their roles at the company.

The future of Compulsion Games, Ninja Theory and Double Fine may now become a test of whether Xbox’s reset is simply a financial correction, or a deeper retreat from the creative diversity it once used to sell the promise of Game Pass.

The post Xbox Studio Shake-Up Puts Microsoft’s Games Strategy Under Fresh Scrutiny appeared first on Tech Business News.

The email raises clear concerns about mass-distributed backlink outreach, with the sender asking for publishing guidelines and other basic information that is already publicly available.

In the case of Tech Business News, those guidelines are clearly published on the website, including requirements around editorial standards, acceptable submissions and content quality.

The request suggests the sender had not properly reviewed the publication before making contact, raising further questions about whether the outreach was based on genuine editorial engagement or simply part of a broader paid link-building email spam campaign.

The issue is no longer limited to one-off guest post pitches.

Publishers say they are being contacted with requests to buy links, place sponsored anchors, submit thin articles or insert backlinks into existing stories with little connection to the original content.

One of the more concerning practices involves outreach asking how much a news platform or blog would charge to randomly insert links into pre-published articles.

In the SEO industry, this is often described as a “link insertion” or “niche edit”. In other words, a black hat marketing tactic

For publishers, it raises a clear editorial concern: articles that were originally published for readers can later be altered to carry unrelated commercial backlinks.

Respona markets itself as a link-building service used to increase AI visibility and organic traffic from Google, with its website promoting “done-for-you placements” on sites “people actually read”.

The company also promotes software designed to help users find outreach prospects, locate contact information and send personalised pitches.

For website owners, that scale is the problem.

Many publishers are already dealing with heavy inbox volumes from PR agencies, marketing firms and SEO freelancers.

Paid link-building outreach adds another layer of noise, particularly when emails are sent repeatedly, ignore publishing guidelines, or ask for do-follow links that appear designed to influence search rankings rather than inform readers.

Respona’s own material frames link building as a numbers game. In one guide, the company says cold outreach reply rates hover around 10%, meaning 100 prospects may produce about 10 replies and only two to three placements.

The same guide says backlink outreach is “fairly easy to automate” with outreach tools and later suggests 100 to 200 personalised emails may be needed in a campaign to land five to 10 quality backlinks.

That is where the public-interest concern begins. When the business model accepts that only a small percentage of publishers will respond, the incentive is to increase volume.

For publishers, that means more cold emails, more follow-ups, more irrelevant pitches and more time spent filtering commercial backlink requests that have no clear editorial value.

The lower end of the market has become especially aggressive. Website owners are being offered content that is often generic, poorly researched or clearly written around a backlink (Hyperlink) rather than a story.

In some cases, links are pushed into awkward anchor text, unrelated paragraphs or old articles where the connection to the linked business is thin or non-existent.

That practice can damage both reader trust and site quality. A news website or blog risks becoming a dumping ground for paid placements when links are inserted into weak content, old articles or unrelated stories simply because someone is willing to pay.

Google’s spam policies warn that link spam includes creating links primarily to manipulate search rankings. Its examples include buying or selling links for ranking purposes, excessive link exchanges and using automated programs or services to create links to a site.

There are also email compliance concerns. In Australia, the ACMA says commercial electronic messages require consent, must identify the sender and must make it easy to unsubscribe.

The regulator also says businesses remain responsible even if another company sends marketing messages on their behalf.

The distinction between legitimate PR and link spam is important. A relevant pitch from a real company, sent to the right editor, can lead to useful coverage.

A paid request to insert a backlink into an unrelated article is different. It treats publishers not as editorial platforms, but as link inventory.

Respona is not the only company operating in this market. The broader SEO industry includes agencies, freelancers and outreach platforms built around backlink acquisition, guest posting, digital PR and authority-building campaigns.

But Respona’s visibility in the sector has made it a clear example of a larger problem: the normalisation of paid link outreach at scale.

For readers, the impact is less visible but still serious. When weak articles are published or old stories are edited mainly to carry paid backlinks, the information ecosystem becomes more polluted.

Search results can be shaped by commercial link placement rather than editorial judgement.

For website owners, the message is becoming harder to ignore. Paid link-building spam has moved well beyond casual guest post requests.

It has become an industrialised outreach machine, with publishers increasingly asked to sell access to their authority, their archives and their readers.

The post Respona Faces Scrutiny As Website Owners Report Repeated Link-Building Spam Emails appeared first on Tech Business News.

The aerial inspections form part of Powercor’s year-round bushfire mitigation and vegetation management program, with scanning to begin this week around Charlton, Donald and Wychproof before moving to Swan Hill, Mildura, Ouyen and surrounding districts.

Residents may see or hear helicopters flying about 300 metres above the ground. The aircraft use Light Detection and Ranging, known as LiDAR, to measure the distance between tree branches and powerlines.

The data is then used to create detailed 3D models of powerlines and nearby vegetation. Cutting crews use those models to identify where pruning is needed to keep lines clear and reduce the risk of vegetation coming into contact with electrical infrastructure.

Powercor’s Head of Vegetation Management, Ayce Cordy, said annual inspections were an important part of keeping the network safe and reliable.

“Inspecting and managing vegetation growing near powerlines is an essential part of how we keep power safe and reliable for our customers,” Mr Cordy said.

“By capturing data every year, we can track growth rates of vegetation across our network, helping us plan where to cut right now and when we may need to conduct cutting in the future.”

Powercor uses a fleet of three helicopters each year to inspect 100 per cent of more than 77,000 kilometres of powerlines across western Victoria.

The program is particularly important in areas with higher bushfire risk, where larger clearance zones are required under rules regulated by Energy Safe Victoria.

Powercor said its trained cutting crews can enter private property to prune trees back from powerlines, as well as carry out work on public land across the network.

For private properties, customers are usually notified when cutting is required at their address. Crews then complete the pruning work and return to remove debris.

However, Powercor said advance notice may not always be possible when urgent cutting is identified.

The inspections are part of broader efforts to manage fire risk, protect power supply and ensure vegetation is kept clear of electrical assets before dangerous summer conditions return.

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The “RFP to PoC builder” includes human review, governance and traceability features to support responsible use.

Designed and built on the Cloudera platform as part of the partner competition, the solution showcased a  secure enterprise AI use case designed to accelerate solution development without compromising control,  governance, or auditability. 

The solution helps teams translate customer requirements into a structured, reviewable workflow, reducing the time and effort required to move from bid response to a functional proof of concept. 

• Upload RFP documents 

• Extract and structure requirements 

• Review outputs through human-in-the-loop governance stages 

• Maintain end-to-end traceability 

• Generate a working proof of concept aligned to the RFP 

Members of the Enterprise Solutions team spearheaded the challenge under the team name Citadel of  Inference: 

• Damien Rider – Solutions Architect 

• Robert Prather – Data Architect 

• Gerard Maguire – Cloud Engineer 

• Scott Thomson, Doug Whip & Todd Trevillion – Project Sponsors 

According to the judging panel, the entry stood out for both the strength of the use case and the team’s  execution.

Despite limited prior hands-on experience with the Cloudera platform, the team delivered a practical, well-governed prototype at speed. 

“What the Cloudera Hackathon reinforced is that organisations aren’t struggling to adopt AI – they’re struggling to operationalise it in a secure, governed way that delivers real business outcomes.”

“Our ‘RFP to PoC builder’ shows how you can bring AI directly to trusted data, automate complex workflows, and still maintain human oversight and traceability.”

“That’s the shift the market is demanding – moving beyond experimentation to production-ready AI that reduces cost, accelerates delivery, and builds confidence in every output.”

Todd Trevillion, Executive General Manager, Strategic Partnerships and Relationships, Citadel Edge said,” Citadel Edge is now evolving the prototype beyond an RFP-to-PoC solution into a broader internal AI development platform for new greenfield builds.”

“The next phase will focus on hardening and securing the environment to support Citadel Edge’s requirements for a private, sovereign AI development capability.” he said.

The post Citadel Edge Wins Cloudera AI Hackathon With Secure “RFP to PoC Builder” Prototype appeared first on Tech Business News.

Reporters in the UK have reported receiving dozens, and in some cases hundreds, of dubious media pitches each week.

Some are sent by people who are difficult to verify, do not respond to follow-up questions, or move to new email addresses after being blocked.

The issue has raised fresh scrutiny over the way search-engine optimisation campaigns are being pushed through mainstream media, with commercial operators seeking backlinks and brand mentions from trusted news websites.

A Press Gazette investigation has identified a series of cases where fake or questionable experts appear to have been used in press releases later picked up by British media outlets.

The investigation found that some campaigns used polished expert commentary, professional-looking biographies and case studies that were difficult, or impossible, to verify.

The practice appears to be driven largely by SEO value. Links and brand mentions from established news organisations can help commercial websites improve their visibility in search results.

That has created an incentive for some operators to produce media-ready content designed less around public interest and more around gaining coverage.

Press Gazette reported that British businesses including Plumbworld, Ski Vertigo and Edit Suits appeared to have been connected to campaigns involving questionable SEO-led PR tactics.

There is no suggestion the companies necessarily knew the full methods used by third-party agencies or operators acting on their behalf.

One of the most prominent cases involved “Barbara Santini”, who was presented in media coverage as a psychiatrist or psychologist connected to the sex toy retailer Peaches and Screams.

According to Press Gazette, Santini appeared in British media dozens of times but could not be verified as the professional she was claimed to be.

The investigation also examined three related companies — MyJobQuote, PriceYourJob and HomeHow — which were reported to have featured more than 20 fake or questionable experts across topics including gardening, interior design and home improvement.

Many of those experts had little visible professional footprint online. Press Gazette reported that several had no clear LinkedIn profile, no meaningful social media presence and no obvious way for members of the public to book or verify their services.

The publisher later released a dossier identifying more than 500 stories in major news brands that were based on press releases from MyJobQuote and contained fake or misleading expert comment. Some of the articles have since been deleted or amended by publishers.

Other cases involved human-interest stories supplied to the media by linked agencies. Press Gazette reported that three agencies — SignalTheNews, RelayTheUpdate and InformTheAudience — sent UK media questionable case studies, including a widely published story about lottery winners who had lost winning tickets.

The people featured in that story did not appear to exist, according to the report.

The three agencies were also reported to have near-identical website designs and links to Romanian companies. Their websites appeared unfinished.

The findings have added to concern among journalists and editors about how easily AI-generated material can now be used to create convincing media pitches.

A press release can arrive with a professional headshot, a detailed biography, a short quote and a ready-made news angle, even when the person or case study behind it has not been properly verified.

That creates a practical problem for newsrooms already working with fewer staff and tighter publishing deadlines. Media releases are a routine part of daily reporting, but the rise of AI-generated profiles and SEO-driven PR has made basic verification more important.

The issue is not confined to the UK.

In Australia, editors and publishers are also seeing PR campaigns where older public statistics are repackaged and presented as fresh research by agencies or their clients.

In some cases, the data itself may be accurate. The concern is how it is framed. Figures taken from older government reports, regulator publications or industry studies can be reworked into a media release that gives the impression a company has produced new research.

That can mislead both journalists and readers if the original source, date and context of the data are not made clear.

Public data is often useful in journalism and PR, but it needs to be properly attributed.

If a campaign relies on figures from the Australian Bureau of Statistics, a government agency, a regulator or a third-party report, the release should make that clear rather than presenting the material as proprietary research.

The spread of AI-generated PR has also created problems for legitimate agencies, which now face greater scrutiny over the experts, statistics and case studies they put forward..

Case studies also need closer examination, particularly where people are offered through an agency but cannot be interviewed or independently verified.

The Press Gazette findings show how easily commercial material can move from an inbox into published news when it arrives in a polished and plausible format.

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