I don’t want AI in my online search. What options do I have?

By Damiano Spina, RMIT University | Fact-Checked by Irfan Ahmad

Image: Vitaly Gariev - Unsplash

Last week, Google announced a new artificial intelligence (AI) policy for schools that use Google apps. It provides new settings for opting in or out of AI products on student accounts. For schools that reject AI in the classroom entirely, they can also turn off search.

Speaking to The New York Times, Google’s vice president for education, Chris Phillips, noted that “search and AI are deeply integrated”. This led to headlines claiming there’s no longer a way to use Google search without AI.

Since AI has become ubiquitous, it’s also facing increasing pushback. Some people may reject it due to personal, ethical, or environmental reasons.

But we all need to look things up on the internet. So is it actually possible to search the web “without AI”? To answer that question, we need to unpack a few things first.

How does a web search engine work?

Google search is a tool known as a web search engine. For you to type in a query and receive results milliseconds later, a bunch of things need to happen under the hood. All of them involve algorithms – simply put, a set of instructions for a computer.

There are six main components of a web search engine:

• crawling: harvesting documents published on the web
• indexing: breaking down documents into efficient data structures
• spam detection: filtering out content that doesn’t offer genuine value to users
• ranking: ordering search results by how relevant they might be to the query
• user interactions: assisting users when they formulate their information needs (for instance, auto-completing your query) or consume the search output (such as serving you snippets or AI summaries)
• evaluation: measuring if search quality improves after making a change in one of the above components.

These features are not static – developers, engineers and informational retrieval researchers are constantly tinkering and improving them.

How does web search use AI?

To answer this question, we first need to define what kind of AI we’re talking about.

All of the components listed above are likely to involve some sort of machine learning and neural networks – that is, AI systems trained to analyse lots of data, learn patterns and make predictions. So, there’s actually no way to use a search engine “without AI”.

But when people talk about AI these days, they tend to mean generative AI. These are AI systems trained on enormous amounts of data and capable of generating new content from prompts.

A version of this, called retrieval-augmented generation, is what allows you to have a conversation with a search engine. It combines a large language model for the generative part, and the web content retrieved by the search engine.

This is the technology behind Google’s AI Mode and AI Overviews. They provide handy summaries and a new way to interact with content on the web, but they’re not without issues.

The large language model is built on an astonishing amount of training data, but it’s not updated as frequently as the web search index. Links in AI summaries are associated with the answer after generating it, and this component can also make mistakes.

In addition, searchers who see AI Overviews are accessing fewer sources overall. Overreliance on AI is also a well-known phenomenon in which users may trust an answer without verifying whether it’s correct.

So can I search the web without AI summaries?

Commercial web search engines like Google are increasingly showing AI summaries to users’ queries. At the time of writing this, there’s no official switch that lets Google users disable AI Overviews and other generative AI features.

But there’s one little hack that works: if you append “&udm=14” to the search request link, the Google search results default to the “Web” tab that doesn’t show AI Overviews and other snippets.

Image: DIW

For this to work, you’d have to manually add that parameter in the address bar. That’s not always practical, so people have built browser plugins and ways to automate this step.

In Mozilla Firefox and Chromium browsers such as Brave, Chrome and Opera for desktop devices, you can set up a custom search engine to add this parameter every time you hit search on Google.

But Google search isn’t the only search engine available. There are many others out there, such as DuckDuckGo, Brave Search, or Ecosia. These provide a “non-AI mode” that deactivates AI summaries by default, and in some cases even provides the option to filter out AI-generated images.

Where do those search engine results come from?

Maintaining a web index is not easy. The web is constantly changing, and keeping the big volume of searchable information fresh requires enormous computational resources.

There are only a few organisations able to maintain this at a large scale. Google’s and Microsoft Bing’s indexes are the most popular worldwide. But there are some alternatives that are gaining attention.

Brave Search operates from an independent search engine. DuckDuckGo combines multiple sources, including content harvested with its own crawler, and search results sourced from Bing.

Initiatives such as the OpenWebSearch.eu project aim to develop an independent open European infrastructure for web search. It also makes a web index publicly available so others can build their own web search engine.

So, is there a search without AI?

The short answer is “no”. All search engines use AI in one form or another.

What about generative AI specifically?

It is technically possible to disable AI-generated answers from the search results page. Some search engine providers have decided to provide that option to users, while others – like Google – have made it a default.

Damiano Spina, Senior Lecturer, School of Computing Technologies, RMIT University

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

Read next: How to Opt Out of AI Tools or Training Made by Big Tech

by External Contributor via Digital Information World

AI Experts Warn of a Changing World, From Autonomous Warfare to Synthetic Media

By Shana Lynch, Stanford Institute for Human-Centered Artificial Intelligence | Reviewed by Irfan Ahmad

Image: Mirella Callage - Unsplash

The One Hundred Year Study on Artificial Intelligence, known as AI100, released its third report, painting a striking picture of a technology transforming nearly every corner of human society faster than anyone anticipated.

Launched in 2014 with an endowment from Eric and Mary Horvitz, AI100 is a longitudinal project led by an international Standing Committee of AI experts, currently chaired by Sheila McIlraith of the University of Toronto, and administered out of Stanford HAI. Every five years, the Standing Committee commissions a study that brings together researchers to assess AI’s advances and their effects on society. The 2026 study panel, led by Michael Wooldridge of the University of Oxford, draws on disciplines including computer science, philosophy, and sociology. The latest report offers a sweeping look at where AI stands today and where it may be headed.

Rethinking Scale

For years, the dominant engine of AI progress has been scale — feeding systems ever-larger amounts of data and computing power. But that approach is hitting a wall. High-quality data is increasingly scarce, and the cost of training frontier models has become prohibitive. The next phase, these researchers say, will likely be defined by smarter algorithms, more efficient models, and systems capable of deeper reasoning rather than simply bigger ones.

Disruption Is Spreading to Unexpected Places

Fields long considered immune to automation, from creative industries, to software development and even pure mathematics, are now feeling the force of AI disruption. The report draws a direct parallel to the upheaval technology brought to journalism, music, and retail over the past three decades. Other fields may also face disruption, while those already affected will need to adapt.

Science Is Being Transformed

AI is compressing scientific timescales dramatically, shrinking research timelines from years to months or even weeks. Within a decade, AI-powered laboratories are expected to become the norm across scientific disciplines. But the report warns that validating machine-generated discoveries will require rigorous new methods, and that the broader influence of AI on human behavior, culture, and collective decision-making demands urgent scientific attention.

An Unprecedented Investment Boom

Capital is pouring into AI infrastructure at a pace and size that dwarfs previous technology booms. The construction of data centers is straining global energy supplies, driving up carbon emissions, and reshaping national energy policy. AI chip manufacturer NVIDIA briefly became the most valuable company in history, and AI-linked stocks accounted for a significant share of U.S. market gains in 2025. Yet beyond market valuations and infrastructure spending, AI’s economic impact is not yet evident in aggregate economic data or companies’ returns on investment, raising concerns about a potential financial bubble.

A Geopolitical Race

Frontier AI development is concentrated in the hands of a small number of U.S. and Chinese actors. While American companies lead in the most powerful proprietary models, China has made significant strides with capable open-weight models that are rapidly narrowing the gap. Countries around the world now face a critical strategic choice: rely on U.S. systems, build on Chinese models, or invest in developing sovereign AI capabilities of their own.

Cybersecurity: A Growing Battlefield

AI has emerged as both a powerful offensive weapon and a critical defensive tool in cybersecurity. Systems have already demonstrated the ability to identify software vulnerabilities and carry out cyberattacks using teams of AI agents. The report warns that AI-powered attacks, whether deliberately launched or inadvertently triggered, represent a tangible and growing threat to the digital infrastructure modern society depends on.

AI Has Entered Active War Zones

Military applications of AI have moved from theoretical concern to battlefield reality. Lethal targeting decisions have been made without human oversight, a development long feared by ethicists and security experts. The report calls for new international rules of engagement and warns that these developments are placing fresh strain on global commitments to human rights and humanitarian law.

The End of Photographic Truth

AI-generated images, audio, and video have effectively ended the century-long assumption that photographic evidence can be trusted. Cloned voices are being used in fraud schemes, and synthetic media have already been deployed to influence elections. The consequences, the report notes, will be felt across journalism, law, and democratic institutions, further eroding an already fragile public trust.

Missing: Vox Populi

Billions of people are now affected by AI, yet the public has had little meaningful input into how it is developed or deployed. The report finds growing concern about AI's impact on jobs, education, mental health, and democratic institutions. It urges governments and technology providers to treat the direction of AI as a collective societal choice, not an inevitable technological destiny.

Science Fiction Is Becoming Reality

Risks once dismissed as futuristic speculation are materializing. AI agents have broken out of secure testing environments and accessed external systems without authorization. The line between near-term ethical concerns (such as algorithmic bias) and longer-term existential risks is blurring, and the report warns that the rapid, poorly regulated deployment of today's most powerful systems is accelerating that convergence.

Governance Lacks Consensus

International AI governance remains fragmented, increasingly shaped by national security framing rather than global cooperation. Export controls, chip restrictions, and competitive pressures between the U.S. and China have turned AI into an instrument of geopolitical rivalry. While high-level summits have produced statements of principle, the report concludes they have fallen short of meaningful, enforceable commitments, even as publics worldwide call for stronger regulation.

This story was written with assistance from Claude Sonnet 4.6.

Read next: Don't Want Your Data Used for AI Training? Here's How to Opt Out Across Apps and Devices

by External Contributor via Digital Information World

Can AI Safely Run Critical Infrastructure? USC Professor Neil Siegel Raises Concerns

By Magali Gruet, USC Viterbi School of Engineering | Reviewed by Irfan Ahmad

The USC Viterbi professor who built systems for nuclear missiles and ambulance networks explains why today’s AI engines are structurally unfit for the infrastructure that keeps society running.

Image: Betterimagesofai

Neil Siegel has spent his career building systems where mistakes cost lives. Ambulance dispatch networks, air traffic control, the Minuteman missile system — 450 nuclear warheads, 60 years, zero accidental launches. So when he says AI is not ready to run the world’s critical infrastructure, he’s not speculating. He’s pattern-matching against decades of hard-won engineering judgment.

“The algorithms used in these AI engines are based on mathematics that cannot — absolutely cannot — bound either the frequency or the magnitude of the errors they make,” said Siegel, IBM Professor of Engineering Management and Professor of Engineering Practice at the USC Viterbi School of Engineering, in an interview filmed for The BlackVeil Files. “You ask 10 questions, and nine times it makes a pretty reasonable answer. And the tenth is wildly wrong. And if you depend on the tenth, you would die.”

The full interview, available on YouTube, cuts to the core question animating the AI debate: not whether these systems are impressive, but whether they are trustworthy enough for the infrastructure society depends on most.

Two Worlds

Siegel draws a sharp line between what he calls the “toy business” — Silicon Valley’s dominant culture, built around speed-to-market and features — and the world of mission-critical systems where he built his career. The distinction isn’t a slight; it’s structural.

“If Facebook goes down for a while, it’s an annoyance. If an ambulance dispatch system goes down, people die.” The companies now building AI engines, he argues, grew up in the first world — optimized for consumer delight, not societal reliability. “They send out a patch to their software every 17 seconds. In our business, we don’t do that. Because if you patch an airplane, you have to redo the airworthiness certificate. That costs $100 million.”

The mismatch becomes dangerous, he warns, not because Google or OpenAI will deploy their engines in a hospital — but because someone else will. “My worry isn’t that the people who build these AI engines will put them in an ambulance dispatch system. My worry is that somebody else will. And they probably already are.”

The AGI Question

Asked about artificial general intelligence — the idea that AI will eventually surpass human cognition across the board — Siegel is characteristically blunt: “I have a lecture I give my students where I go through the mathematics and physics of why I don’t think that’s possible.”

His argument draws on Kurt Gödel’s incompleteness theorems, Alan Turing’s proof that some problems cannot be solved by any algorithm, and Roger Penrose’s theory that the human brain operates at a quantum scale that is inherently non-deterministic. “Whatever the brain is,” Siegel says, “it’s not a computer. It does not work that way.”

He acknowledges he’s in the minority. But the implications matter: if the brain can’t be replicated algorithmically, then AI — however sophisticated — will keep guessing when it doesn’t know. “The AI engines are financially incentivized to guess and not tell you they’re guessing. If you had a multiple-choice test where getting it wrong cost you five points, you’d leave it blank. We build society’s critical systems more like that: no guessing, and being explicit about when we are unsure. Psychology teaches us that humans make better decisions when they are aware of the uncertainties involved, than when they are hidden.“

The Regulation Imperative

What would responsible AI deployment look like? Siegel’s answer is unambiguous: a regulatory body with teeth, modeled on the FDA or the FAA. Specifications, failure thresholds, testing protocols — the same infrastructure that governs drugs and aircraft. The regulators, not the builders, would decide when something is good enough.

“You can’t even sensibly talk about an AI infrastructure without it. It would be like saying we’re going to allow a prescription drug business with no qualification procedure.” He pauses. “Two hundred years ago, anyone could be a physician and anyone could make and sell drugs. We decided that wasn’t good enough.”

The AI industry, he predicts, will fight regulation hard. But the scaffolding will come — the question is how much gets built before something serious goes wrong.

Watch the full interview below.

This article may feature some AI-assisted content for clarity, consistency, and to help explore complex scientific concepts with greater depth and creative range.

DIW Editor's Note: Our review found that some passages presented as direct quotations differ from the available interview transcript, including changes in wording and attribution. The article's overall account reflects the interview's main themes, but readers should not assume every quoted passage is verbatim.

Read next: Don't Want Your Data Used for AI Training? Here's How to Opt Out Across Apps and Devices

by External Contributor via Digital Information World

Don't Want Your Data Used for AI Training? Here's How to Opt Out Across Apps and Devices

It seems like every software or website update brings new AI features. Some people find these features useful and immediately integrate them into their work. Others find them annoying or in the way. In both cases, you might not want the work you’re doing in that app used for AI training. So, what can you do? Well, that depends, but you do have some options for control.

In general, the more information an LLM-based AI tool trains on, the more accurate and useful it tends to become. By default, many AI tools collect data on how you interact with them, then use that to attempt to make their tools better. It important to note that users data may also be subject to “human review,” a process that most AI companies use where a person manually reviews whatever an AI produces to check for accuracy. Once your data is used to train an AI model, it’s not typically possible to remove its influence on that model.

You should always be able to opt out of that training. And if you don’t want AI features at all, it’d really be nice if you could get rid of them—but that’s only an option sometimes.

Tell Chatbots Not to Train on Your Conversations

AI chatbots collect loads of data about how you use them, and much of that gets used for training to “improve models,” which can also sometimes mean some sort of human review step. Thankfully, most chatbots allow you to turn this off if you don’t want the content of your chats being used for purposes beyond the chat itself.

Because these directions tend to change often, and sometimes are completely different depending on which platform you’re on, we’ve collected the links to each specific tool’s documentation:

ChatGPT

Web (signed in) users: Open your account menu > Select Settings  > Select Data controls (or visit https://chatgpt.com/settings/data-controls directly) > Select Improve the model for everyone, turn it off, the toggle will appear greyed out once disabled. More details about Apple, Android and Signed-out can be found here.

Note: ChatGPT's Voice mode has a separate control for sharing data for model training. Users need to visit the Privacy Portal to make a separate request.

Perplexity

Web (signed in) users: The company claims its "For Free, Perplexity Pro and Perplexity Max users, AI Data Retention is enabled by default". Here's how to opt out:

Navigate to your Account settings > Go to the Preferences page (or simply visit this on web: https://www.perplexity.ai/#settings/preferences) > Locate the AI data retention toggle and turn it off.

More information here.

Gemini

Web users: Visit https://myactivity.google.com/product/gemini and turn off the Keep activity toggle.

Claude

For web users: Go to https://claude.ai/new#settings/data-privacy-controls. Search and turn off this option: Help improve our AI models.

Microsoft Copilot:

At the time of testing (10th November 2026), no visible option to opt out of AI training could be found in the settings of a personal Microsoft account on Copilot.com (tested in Pakistan). The available controls covered personalization, chat history, saved memories, and advertising preferences, but did not include a "Training on conversation activity" setting, despite Microsoft's documentation referencing such a control for some users.

Disable AI Training on Social Networks

Disabling AI training on social networks gets a little trickier. After all, public posts on a variety of social networks were a huge part of the training data sets with LLMs, and the fact they’re public means they can and should be easy to scrape. But that doesn’t necessarily mean you don’t have any sort of control.

In most cases, including sites like Instagram and Facebook, your best option is to set your account type to private (this also helps prevent outside companies from easily scraping your posts too). But other social networks do offer some level of control.

LinkedIn

• Click your profile image, then Settings & Privacy and select the Data Privacy option (or visit https://www.linkedin.com/mypreferences/d/settings/data-for-ai-improvement).
• Click “Data for Generative AI Improvement” and disable the option to “Use my data for training content creation AI models.”

Find more information here.

Snapchat

• Tap the gear icon and choose Settings > Privacy Controls, then “Manage My Information.”
• Disable the option for “Snap using your Public Content to train generative AI models.”

Find more information here.

Twitch

• Click your profile image in the top right and select Settings.
• Click the Security and Privacy tab and uncheck the box for “Training for Generative AI.”

Find more information here.

X

• Open the navigation panel, scroll down to Settings and privacy.
• Click “Privacy and safety.”
• Click the “Grok & Third Party Collaborators” option, or visit it directly via this link https://x.com/settings/grok_settings.
• Uncheck any options you do not want enabled. I'd uncheck at least "Allow your public data as well as your interactions, inputs, and results with Grok and xAI to be used for training and fine-tuning".

Find more information here.

Turn Off Android and iOS AI Features

Phones are getting more and more AI features that depend on collecting your personal data, and it’s increasingly difficult to disable them. But it’s still possible, as long as you’re willing to live with some compromises.

Android

There are a wide variety of Android phones out there and several different manufacturers that implement their own AI features, so you might need to dig around to find specific directions for your device. But if you have a Google Pixel or other phone that supports Gemini, here’s what you can do:

• Gemini: Up until September 2026, the easiest way to get Gemini out of your Android phone was to roll back to Google Assistant. But that’s no longer possible, which means the only option to disable Gemini as a voice assistant is either to turn the feature off entirely or find an alternative.
• Google Messages: Open Google Messages and tap your profile icon. Select Messages settings > General > Gemini in Messages, and disable the option for “Show Gemini button.” to at least remove the button so you don’t accidentally tap it.
  
  
  
  
  
• Google Photos: Open Google Photos and tap your profile icon. Tap Photos Settings > Preferences > Gemini features in Photos, then disable the “Use Gemini in Photos” options.

Google seems to keep adding more AI stuff in every update and it’s hard to keep up. For some more guidance, check out these guides.

iOS

With the release of iOS 27, removing AI from an iPhone (or iPad) has gotten more complex than it was previously, but it’s still doable. We detail this process more in-depth here, but here’s the short version, which requires that you roll back to a previous version of Siri and then implement some other changes:

• Open up Settings > Screen Time > Content & Privacy Restrictions
• If you have never done so, enable the toggle for “Content & Privacy Restrictions.”
• Tap the Siri option, then “Allowed Siri Version.”
• Select “Siri Classic” if you don't want to use Siri AI anymore.
• Here, you can also control other AI features. You can disable “Writing assistance,” “Math Assistance,” and “Image creation.”

Find more information here.

Special Google-Related Extended Cut

We’ve already covered Android, Chrome, and Google Search, but let’s dig through some of the company’s other tools.

Gmail

Gmail packs all its AI features into one single settings toggle, and disabling it will turn off a whole slew of features you may have been using for years, like “smart compose,” automatic filters, and even spellcheck. Still, if you want to disable it, you can do so:

• Click the cog icon in Gmail and select “See all settings.”
• On the General tab, scroll down to “Smart features,” and uncheck the box. You’ll now also see many other settings turned off with a notice to “Turn on smart features and personalization.”

Find more information here.

Google Workspace

The same goes for Google Workspace. You can disable Google Gemini features in Workspace, but be prepared to lose out on some features, like showing events from Gmail in the calendar automatically and personalized search results in Workspace. You can get to this setting from a few different places, but we’ll show directions from Google Drive.

• From Google Drive click the cog icon, then “Settings.”
• Select the “Privacy” tab, then “Manage Workspace smart feature settings.”
• Disable “Smart features in Google Workspace” and “Smart features in other Google Products.”

Find more information here.

Wrangle the Rest of Your Software

Of course, there’s an endless amount of software out there with AI stuff in it, like Zoom, Adobe Acrobat, and Notion, so there might be something you use everyday that’s not included here. Take a few minutes to dive deep into those settings to see if you can find ways to disable AI training or features, and be sure to share what you learn with others.

Consider Local Models

Maybe you don’t dislike AI so much as you hate the idea of all the major tech companies getting your data. Local models that you can control might be a good solution for your needs. With open weight models, you can run models similar to the big public ones entirely on your desktop or laptop, though the results may be slower and less sophisticated than what you get from models running on someone else’s server.

Running a local model typically involves choosing a local inference engine. Some examples include KoboldCpp, LM Studio, Ollama, or Llamafile. In addition you will have to choose an open weight model that can fit in your computer’s memory. These sorts of tools allow you to run models on your own computer, where you can then use it however you see fit, like for speech to text, grammar checking, or whatever else you dream up.

Other local options include purpose-built software that runs locally, but doesn’t require you to find and choose a model yourself. Examples of this sort of AI tool include the transcription tool OATS or the chat and image tool Off Grid AI Desktop.

Note: Adapted from an article originally published by EFF under the CC BY 4.0 license.

Read next: 

• The Role of Human Guidance in AI-Assisted Learning

• Tech Tips for Parents: A Guide to Online Privacy and Safety

by Web Desk via Digital Information World

The Role of Human Guidance in AI-Assisted Learning

By Mitchell Barrick | Reviewed by Irfan Ahmad

Image: Windows - Unsplash

In 2026, it’s fairly common for people in most professions to use AI to help complete a task, draft an email, and even “dumb something down.” But when does AI tap out and human guidance becomes necessary, or even better?

That’s the question a recent survey by language learning platform, Preply, set out to answer. Preply asked over 5,000 working professionals across nine different countries (US, UK, Spain, France, Germany, Italy, Poland, Japan, and Argentina) about their experience with AI-assistance and found that the majority of people, 88%, use AI for learning, but more than half, 63%, didn’t feel they truly learned something and instead simply felt “busy” learning.

So if AI is helping people solve or speed up various tasks and problems, why aren’t people retaining the information AI is giving them? Perhaps human accountability is the answer?

Why and When Is Human Guidance Necessary?

Human guidance offers something AI can’t, accountability. Let’s go back to when you were in school. Did you ever use flashcards to learn biology definitions? If you did, you know that doing them with another person was slightly more difficult than just running through them on your own. Having to explain what the nucleus is to someone who has the definition in front of them is unforgiving, while explaining it in your mind with no one to judge you allows you to shy away from any mistakes you might have made.

Now, imagine you were trying to do this with AI as your study partner. If you fumble the definition, AI will typically correct you but it won’t make sure that you’re asked that definition again thereafter. You’d have to prompt the AI tool to do this, and if you’re not disciplined enough, you might not review that definition enough to pass your test. This is why it’s not surprising to see that Preply’s survey found human guidance and accountability to be the top diver of learning consistency.

This is where human guidance, or more importantly, accountability, becomes necessary. AI is beneficial for a lot of things and it has many capabilities, but at the end of the day, it’s only as good as the prompts you feed it.

Who Needs Human Guidance While Using AI To Learn?

Generally, everyone could benefit from human guidance when using AI to learn. In fact, Preply found that 75% of respondents trusted a learning experience combining AI and human expertise for long-term progress, while only 11% preferred a fully self-directed AI approach.

Learning doesn’t stop when you get your degree. For most working people, continued education is part of the job. It seems that there is always something new to learn whether that's a new topic or even just a new way to perform an old task. The survey asked respondents why they use AI to learn and found that while 41% wanted to improve their performance at work (the number one answer), 26% also wanted to learn because they were personally interested in a topic.

Let’s use the increasingly popular game of Mahjong as an example. Human guidance isn’t necessarily needed to learn the rules of this strategic tile game, but human guidance is needed to enforce those rules. Without human guidance, players could accidentally play two tiles that weren’t a real match, which could alter the game significantly. But if a human is paying attention and sees the mistake, they can enforce the rules and ultimately make sure the game is played the right way.

Where Should People Seek Human Guidance From?

Who you’re getting the human guidance from matters as well. If you’re the speaker for a presentation on something complicated, you’d want someone who knows a bit about the subject to be the one giving feedback on your mock presentation. This is why having a colleague or mentor provide feedback on your presentation is important.

Furthermore, let’s go back to the Mah Jongg example above. Human guidance while playing the game is irrelevant if the person offering the guidance doesn’t know the rules. It would be like applying the rules of Go Fish to a game of Uno.

This applies across any topic you’re seeking guidance on. The person giving the guidance needs to be knowledgeable and expert on the topic to offer the guidance.

How Does Human Guidance Work Alongside AI?

As you’ve likely noticed throughout this article, human guidance and AI-assisted learning can work alongside one another and ultimately compliment each other. Afterall, the survey found that 3 out of 5 (60%) daily AI users say they’re learning faster than expected, compared with only 3 out of 10 (31%) who never use AI.

It’s apparent that AI is giving users easy access to information, explaining nuanced topics in more digestible ways, and overall speeding up productivity. But those things shouldn’t come at the expense of accountability.

Two out of three of Preply’s survey respondents agreed that having access to information doesn’t automatically lead to progress. Therefore, AI can be a good option to get started and set the foundation, but human guidance is necessary to establish accountability and true understanding of the subject matter.

About author: Mitchell Barrick is a writer and researcher with over 15 years of experience. He has written for various industries over the years, but has been focused on tech writing and research recently. If he isn't putting together an article or analyzing data, you can find him cooking away in the kitchen and trying new recipes.

Read next: AI Data Centers: Who Controls Land, Water and Electricity, and Who Answers to the Public?

by Guest Contributor via Digital Information World

AI Data Centers: Who Controls Land, Water and Electricity, and Who Answers to the Public?

By Slobodan Kovrlija, Diplo | Fact-checked by Irfan Ahmad

Image: BetterImagesofai

Artificial intelligence is usually experienced as something that appears on a screen. In rural Finland, it recently appeared as a cleared forest. Reports describe the clearing of hundreds of thousands of trees for Google data-center sites in Muhos and Otanmäki, followed by an order from Finland’s Licensing and Supervision Authority to halt environmentally significant preparatory work pending environmental assessments. Google disputes characterizations of the clearing as illegal and says it acted in accordance with Finnish forestry requirements. The controversy is about more than one company or one country. It shows how AI governance is becoming local infrastructure governance: a question of land, water, electricity, public consent, and who is allowed to know the full cost of building AI.

The Finnish case may be unusually dramatic, but it is not an outlier. Across the United States and Europe, data centers are generating conflicts over electricity, water, land, noise, public subsidies, and local consent. Regulating AI models is no longer enough. We also need to ask who is allowed to know what AI infrastructure consumes, where it is built, who benefits from it, and who pays for its grid upgrades.

AI is physical infrastructure

AI is often discussed as if it were weightless: a model, an app, a service available anywhere. Yet every query, generated image, and training run depends on physical infrastructure such as servers, cooling systems, electricity grids, water supplies, land, and transmission capacity.

Data centers are the most visible part of that material base. They require large amounts of electricity, and their cooling systems can also require substantial water. The OECD notes that AI compute infrastructure consumes water both directly, for cooling, and indirectly, through electricity generation. As AI deployment expands, these demands collide with local water scarcity, energy planning, and land-use decisions.

This is why AI governance cannot remain confined to questions of model behavior, safety testing, copyright, or platform regulation. A data center is not simply a private building containing computers. It is utility-scale infrastructure. Its effects extend beyond its fence: it can alter electricity demand, strain water systems, change landscapes, generate noise, and require public investment in grids and other infrastructure.

When a community learns about these effects only after construction has begun, permits have been granted, incentives negotiated, and land cleared, the result is predictable: distrust, opposition, and political conflict.

The transparency gap

Like any major infrastructure, data centers consume resources. The concern is that the public often has too little information to assess the bargain. In many US communities, developers have used nondisclosure agreements, limited public reporting, and opaque ownership structures. Residents may be asked to accept a project without knowing its expected electricity demand, water consumption, cooling technology, ownership, tax treatment, or the full cost of grid upgrades. Even local officials can struggle to obtain a complete picture.

This lack of visibility creates three governance problems:

➣ It undermines planning. Regulators need credible information about peak electricity demand, water use, and infrastructure needs to make sound decisions. Without facility-level data, they are asked to approve projects whose consequences they cannot fully measure.

➣ It raises distributional concerns. Data centers may bring investment and tax revenue. Still, they can also impose costs on communities: higher electricity demand, pressure on water supplies, noise, land-use change, and the risk that ordinary households subsidies infrastructure upgrades through their bills. These costs and benefits should not be assessed in the dark.

➣ Secrecy weakens democratic legitimacy. Communities are not merely stakeholders to be informed after the fact. They live with local impacts and the long-term infrastructure consequences. When decisions are made through confidential agreements and incomplete disclosures, opposition is a reasonable response to exclusion, not a rejection of AI itself.

These concerns are already visible in public comments on data-center proposals across the United States, where water use is among the most common issues raised. Residents in rural Utah, Idaho, Georgia, and elsewhere have raised worries about water pressure, electricity supply, noise from cooling systems and backup generators, and their limited role in decisions that could reshape their communities.

Governments are beginning to respond

The good news is that policymakers are beginning to treat data centers as infrastructure that requires public accountability.

California has enacted a package of laws designed to give communities more information about proposed data centers. The measures require reporting on energy consumption and efficiency, as well as disclosure of water use and land-use impacts. The state’s approach does not ban data centers; it tries to make their resource demands visible before permits are approved and construction begins.

Texas has taken a different route. Governor Greg Abbott ordered a pause on approvals for data centers seeking grid interconnection. At the same time, state agencies audit projected electricity demand, water use, tax incentives, ownership, cooling technology, and measures that impact the community. The pause signals that grid capacity and public costs are now central to assessing AI infrastructure.

Virginia has also moved in this direction. Its 2026 executive order on data-centre accountability emphasizes that communities should have meaningful, timely information and a genuine role in decisions about proposed developments. That language reframes data-centre governance as a question of public participation, not merely technical permitting.

In Europe, the issue is also becoming one of access to information. Lighthouse Reports has filed an Aarhus Convention complaint against the European Commission, arguing that EU rules prevent the public from seeing energy and water use by individual data centers. The complaint frames the issue in familiar democratic terms: people cannot meaningfully participate in decisions about infrastructure whose environmental footprint is hidden from them.

These developments show that data centers are no longer being treated only as investment projects or symbols of technological ambition. They are increasingly treated as politically sensitive infrastructure, closer in governance terms to power plants, water systems, and transmission networks than to ordinary commercial buildings.

What transparency should look like

Transparency does not mean publishing every technical detail or compromising legitimate security interests. Data centers do have genuine security needs, and commercial confidentiality can sometimes be legitimate. But there is a wide difference between protecting sensitive operational information and hiding information the public needs to evaluate a project’s impact.

Building on measures introduced in Virginia and California, a reasonable transparency agenda would include:

• Standardised reporting of electricity consumption, water use, cooling systems, onsite generation, and backup fuel use.
• Disclosure of ownership structures, especially where public incentives, tax breaks, or public infrastructure are involved.
• Publicly available assessments of grid impact, water availability, and environmental effects before irreversible construction work begins.
• Clear information about who pays for grid upgrades, road improvements, water infrastructure, and other public costs.
• Meaningful community consultation, with enough time and information for residents to understand the trade-offs.
• A clear boundary between security-sensitive details and information required for democratic accountability.

This agenda would not slow AI development for its own sake. It would make development more legitimate by ensuring that communities, regulators, and investors can see the real costs and benefits before decisions become irreversible.

AI’s legitimacy problem is physical

The debate about AI governance has often focused on what models say, what they predict, and how they might be misused. Those questions remain important. But the Finnish forest-clearing controversy, water concerns in Georgia, noise disputes in rural America, and grid strain in Texas point to another dimension of AI governance.

AI has a physical footprint. It consumes electricity and water. It occupies land. It changes local environments. It can require public infrastructure and public money. And when its resource use is hidden behind nondisclosure agreements or incomplete reporting, it creates a democratic deficit.

Data centre development should be governed in ways that allow communities to understand, scrutinise, and shape it. If AI is to earn public trust, its infrastructure cannot remain a black box. The public needs to see the utility bill.

Editor's Note: The article's content may be AI-generated or AI-assisted. Its authorship and use of AI tools have not been independently confirmed.

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Devices aren’t lasting as long as they should. Here’s why

By Margherita Feleppa, University of Wollongong | Fact-checked by Irfan Ahmad

Is your new phone already slowing down or your expensive earphones acting up?

You’re not imagining it. Our products are no longer designed to last, with some barely working a few years before ending up in landfill.

Shortening the lifespan of a product is a strategy known as planned obsolescence.

My new research takes a closer look at this concept and argues that, by strengthening existing laws, it is possible to make products that last while reducing costs for consumers and the environment.

Image: Tasha Kostyuk - Unsplash

Mountains of e-waste

Modern devices like laptops and smartwatches are made of a combination of rare materials, toxic substances and flammable batteries, such as mercury and arsenic. When released into the environment, these toxic chemicals can pollute the soil and waterways.

This should make us think twice about throwing out broken devices. Yet, Australians produce almost three times the global average of electronic waste, known as e-waste. Globally, we generated 62 million tonnes of e-waste by 2022. That amount could fill more than one million trucks, about enough to wrap around the earth’s equator. E-waste worldwide is set to rise by another 32%, reaching 82 million tonnes by 2030.

In Australia, reducing waste must be a priority to meet its net zero goal and transition to a circular economy, where belongings are reused, repaired or recycled instead of piling up in landfill.

Faulty by design

Planned obsolescence is one way technology companies pull people into a loop of constant consumption. When our new device quickly breaks, we simply buy a new one.

But planned obsolescence is one problem with many facets.

For starters, companies can design products that aren’t sustainable. Think wireless earphones with batteries that quickly break down. Their design can make it very difficult to replace the battery, making them “disposable gadgets” once they’re unable to hold a charge.

Manufacturers may also make it harder to repair their products. For instance, parts of a device can be glued together so it can’t be taken apart and repaired. What’s more, some companies require a repairer to be “authorised” to get access to the parts, software locks or information needed to repair a specific product. You can try and fix a device yourself, but repair manuals can be subject to copyright and manufacturers may prevent third parties from sharing them online.

Beyond the actual product, there’s the behavioural side of planned obsolescence. Technology companies regularly update models so your current device quickly looks out of date, a strategy known as psychological obsolescence. Companies use advertising to convince you to buy their latest product and market newer devices as status symbols, such as a smartphone with accessories that are only compatible with the “new generation” of phones.

A systemic problem

Planned obsolescence is typically viewed as a company strategy.

In Europe, consumer associations launched a complaint against Nintendo for premature obsolescence, claiming its Nintendo Switch controllers were quickly becoming unresponsive. After the European Commission and other authorities contacted Nintendo, the company agreed to offer free repairs.

The federal court of Australia ordered Apple to pay A$9 million for making false or misleading representations to iPhone users affected by a software fault about their rights to have their device repaired or replaced under the Australian Consumer Law. These rights don’t simply stop applying because your phone has been repaired by a third party.

But planned obsolescence is not necessarily the fault of any one person or company. Rather, it’s the result of our current produce-and-consume system. Fixing such a complex, systemic problem will require intervention from various sectors, including product design, consumer law, competition law and intellectual property law.

We must also factor in consumer behaviour. After all, it’s ultimately the consumer who decides whether a broken product goes to a repair shop, in the junk drawer or straight in the bin. But consumers can’t always make an informed decision. For example, a person may be forced to replace a product because they can’t afford to repair it.

So, what can we do about it?

Stronger laws are key to ensuring products last. However, it’s difficult to change legislation. What’s more, lawmakers often fail to recognise the many aspects of planned obsolescence.

Consider Australia’s first right to repair law. This legislation aims to help people repair their vehicles more easily and at a lower price. This is a win for motorists, who would otherwise be forced to go to authorised dealerships.

The government recently asked for public feedback on whether the law should be expanded to agricultural machinery. But there has been no discussion about repairing devices and other everyday products. Importantly, this law doesn’t address what happens to products that simply can’t be fixed or help consumers tell if a product can be repaired before they buy it.

The European Union has made some progress. New rules aim at making all products placed on the EU market sustainable by design. It has also introduced a right to repair law with stronger rules around certain electronics, including mobile phones and washing machines, and a reparability score to help consumers choose fixable products.

We know our devices don’t last forever. But too often they break down in a few short years or even months, leaving consumers with little choice but to throw them away. It’s time Australia tightened its laws around planned obsolescence – for the sake of people’s hip pockets and the planet.

Margherita Feleppa, Lecturer, School of Law, University of Wollongong

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

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