Unless you have been living off-grid for the past few years — and frankly, who could blame you — you have heard about artificial intelligence. It is in the news, in your inbox, and almost certainly in the software running quietly in the background of your daily life. From the search results Google serves you, to the chatbot answering your customer support query, to the recommendation algorithm deciding what you watch next on YouTube, AI has embedded itself into virtually everything we do.
The world has embraced this technology at astonishing speed. The question we need to be asking — and that far too few people are asking loudly enough — is whether that is actually a good thing. Not whether AI is impressive. Not whether it can write a legal brief or generate a portrait from a text prompt. But whether the unchecked, unregulated acceleration of AI into every corner of our lives is something we should be welcoming with open arms, or approaching with serious caution.
The honest answer is: both. AI is genuinely transformative in ways that can benefit society. It is also, if left without governance or human oversight, capable of doing serious and lasting damage to our privacy, our autonomy, and the reliability of the information systems we all depend on. This article walks through both sides — the real promise and the very real dangers — and explains why the question of AI regulation is not a technocratic debate for experts. It is a question for all of us.
At its core, artificial intelligence is the blending of computing power with large information databases to recognise patterns and solve problems. It does not think the way humans do. It does not have intuition, values, or judgement in any meaningful sense. What it has is processing speed and pattern recognition at a scale no human brain can match.
The most prominent example right now is ChatGPT, built by OpenAI. It is a natural language processing tool trained on an enormous corpus of internet text, fine-tuned to respond to questions and generate written content — anything from a social media post to a legal brief to a medical summary. When it works well, it is genuinely remarkable. When it does not, the consequences can be quietly catastrophic.
There is an instructive parallel with earlier technology disruptions. When spreadsheet software arrived in the early 1980s, accountants feared mass redundancy. Instead, Excel and its successors created entirely new categories of work and expanded the accounting industry enormously. Many AI optimists point to this pattern and argue we should relax — that AI will do the same, eliminating drudge work while creating better opportunities. That may be true for some categories. But AI is different from a spreadsheet in one critical way: a spreadsheet does exactly what you tell it. AI generates outputs you did not specify and may not be able to verify. That distinction matters enormously.
The most striking early demonstration of AI’s capacity for harm came not from a cyberattack or a data breach, but from a New York courtroom. In 2023, a lawyer submitted a legal brief to a federal judge that cited multiple case precedents as supporting authority. The judge asked questions. The lawyer checked. Every single cited case was fabricated — invented wholesale by ChatGPT, complete with plausible-sounding case names, parties, and holdings that had never existed.
This is what AI researchers call hallucination — the tendency of large language models to generate confident, fluent, authoritative-sounding text that is simply not true. The model does not know it is lying. It has no mechanism for knowing the difference between accurate and fabricated content. It produces outputs that look correct based on the patterns it has learned. In a courtroom, where legal precedent is the entire foundation of argumentation, this is not a minor glitch. It is a fundamental integrity failure.
The lawyers involved faced sanctions and public humiliation. But the deeper lesson is this: if a trained legal professional could not detect fabricated case citations in a domain they work in every day, what happens when AI-generated misinformation moves into areas where the average person has even less ability to verify it — medical advice, financial guidance, news reporting, government communications?
The hallucination problem is troubling. But for most people in their daily lives, the more immediate threat from AI is what it does with their personal data.
AI systems are voracious consumers of personal information. Every search query, every purchase, every app interaction, every location ping contributes to the datasets that train and refine these systems. The Office of the Victorian Information Commissioner has noted that established notions of privacy — built around the assumption that humans are the primary handlers of personal information — were simply not designed for the computational capability of modern AI. The way we currently think about consent, notice, and control over personal data has never been so fundamentally challenged.
This has concrete, practical consequences. AI systems can be used to build detailed profiles on individuals based on their purchasing patterns, browsing behaviour, social connections, and location history — all drawn from big tech databases that users signed away access to in terms-of-service agreements nobody reads.
These profiles are then used to serve targeted advertising, influence purchasing decisions, and — increasingly — inform decisions about credit, insurance, employment, and housing.
This is just one dimension of how Big Tech quietly shapes culture and power — and AI is rapidly becoming its most potent tool for doing so
Biometric data adds another dimension to this problem. Facial recognition technology, now embedded in law enforcement tools, airport systems, and retail security infrastructure, captures and processes your physical identity without your consent. When biometric databases are breached — and they have been — the consequences are severe and permanent. You can change a password. You cannot change your face.
The combination of AI with synthetic media generation has produced a category of threat that barely existed five years ago: the deepfake. These are AI-generated audio, images, and video that depict real people saying or doing things they never said or did. The technology is now accessible to anyone with a consumer-grade computer and a free software download.
In the political arena, deepfakes have already been deployed as attack tools — part of the same manipulation arsenal now threatening democratic participation. A fabricated audio recording of a candidate saying something inflammatory. A video of a political leader appearing to make a damaging admission. Even when these are subsequently debunked, the damage is frequently done — the retraction never travels as far as the original falsehood. Research consistently shows that corrections are less emotionally resonant and less widely shared than the initial misleading content.
Australia is not immune. In November 2025, the eSafety Commissioner announced enforcement action resulting in several AI-powered ‘nudify’ services withdrawing access to Australian users — tools that generate non-consensual synthetic intimate images. It is a small step in what will be a long regulatory battle. The technology to create such content is advancing faster than the legal frameworks designed to prohibit it.
One of the most insidious risks of AI is not that it will deliberately discriminate — it is that it will do so without anyone intending it, and without anyone noticing until real harm has been done.
AI systems trained on historical data inherit the biases embedded in that data. If past hiring decisions at a company disproportionately favoured certain demographics, an AI trained on that hiring history will learn to replicate those patterns — not because a programmer told it to, but because the data encoded that preference. The same mechanism applies to credit scoring, bail decisions, healthcare triage, and mortgage approvals.
The concern is not hypothetical. There is documented evidence of facial recognition systems performing significantly less accurately on darker-skinned individuals, leading to false identifications with real legal consequences. Hiring algorithms trained on male-dominated tech workforces have been shown to down-rank applications from women. These are not bugs waiting to be fixed — they are the predictable outputs of systems trained on imperfect human history, and they sit alongside the broader arsenal of Big Tech’s AI surveillance tools that are reshaping how power operates in the digital age. Harvard Business School has documented how bias remains one of the biggest ethical challenges for AI systems, with most business leaders still underestimating its implications.
According to a 2025 study by the University of Melbourne and KPMG, only 30 per cent of Australians believe the benefits of AI outweigh its risks, and just 36 per cent of citizens trust AI systems broadly. Approximately 78 per cent of respondents expressed concern about negative outcomes from AI. That is not technophobia — it is a rational response to real evidence.
There is a useful analogy for understanding the pace of AI development: compound interest. Compound interest does not feel dramatic in the early stages. The numbers are small. The changes are incremental. But the underlying mathematics mean that growth accelerates exponentially — and by the time the scale of what is happening becomes visible, it can feel impossible to reverse.
That is where AI is right now. The capabilities of these systems are doubling on timeframes that would have seemed implausible to researchers just a decade ago. In 2022, AI was writing competent marketing copy. By 2024, it was passing medical licensing examinations. By 2025, it was being integrated into legal systems, defence applications, and critical infrastructure. Each individual step seems manageable. The cumulative trajectory is extraordinary.
Even OpenAI’s own CEO, Sam Altman, has testified before the United States Congress and spoken at international forums calling for AI regulation. The argument from inside the industry is not that AI is harmless — it is that if these systems are not brought under meaningful oversight before they are embedded in every critical system, the window for doing so will close. This is a technology that its own architects are warning the world about. That should carry some weight.
Governments around the world are grappling with how to govern AI. The European Union has moved furthest, with its AI Act — the world’s first comprehensive AI law — coming into effect in 2024. It categorises AI systems by risk level and imposes binding requirements on high-risk applications, particularly those dealing with sensitive personal data, employment decisions, and critical infrastructure.
Australia’s approach has been more cautious. The country does not yet have dedicated AI-specific legislation. Instead, the government released its National AI Plan in December 2025, opting to rely on existing technology-neutral laws — primarily the Privacy Act 1988 — supplemented by voluntary guidance frameworks. A new AI Safety Institute, funded with AUD 29.9 million, is scheduled to become operational in early 2026. Its role will be to monitor, test, and advise on emerging AI risks rather than to enforce binding rules.
There have been some concrete steps. The Privacy and Other Legislation Amendment Act, passed in 2024, introduced new requirements for organisations to disclose in their privacy policies when automated decision-making is used in ways that could significantly affect individuals’ rights or interests. These obligations come into effect in December 2026. It is a meaningful transparency requirement — but transparency is not the same as prevention.
Critics have noted the gap. The Australian Competition and Consumer Commission Senior Investigator Rosie Evans wrote in March 2025 that without an enforceable regime specifically for AI, Australia may struggle to achieve the regulatory coherence and effectiveness it is aspiring to. The concern is not that voluntary guidance is worthless — it is that voluntary guidance does not stop actors who choose not to follow it.
The government’s own data is illuminating on the public mood. The same 2025 Melbourne/KPMG study found that only 30 per cent of Australians believe current laws and safeguards are adequate to manage AI risks. The public is ahead of the policy.
It would be dishonest to write about AI’s risks without acknowledging what it can genuinely do well. AI is being used to accelerate cancer screening by identifying tumour markers in medical imaging that human radiologists miss. It is being used to monitor crop health at scale, detect structural defects in infrastructure, and speed up drug discovery. In disaster response, AI systems can process satellite imagery to identify areas of damage faster than any human team.
For everyday users, the productivity benefits are real. Well-used AI tools can compress hours of research into minutes, assist people with disabilities in ways that were previously impossible, and provide access to information that was once gatekept by expensive professional consultations. These are not trivial gains.
The argument here is not that AI is inherently bad. It is that the technology is moving faster than the guardrails, and that the people most enthusiastic about deployment are not always the same people bearing the costs when it goes wrong. Over 44 per cent of private sector companies plan to invest in AI systems, and more than 35 per cent are already using these tools in production environments. The integration is already happening. The question is whether it happens with adequate oversight or without it.
Most people interact with AI-driven systems dozens of times a day without realising it. The live chat on a retailer’s website. The spam filter on your email. The face unlock on your phone. The news feed ranking on your social media platform. These are not neutral tools — they are systems making decisions about what you see, who contacts you, and how you are categorised.
There are practical steps worth taking right now:
Treat AI-generated content with the same scepticism you would apply to any anonymous source. If an AI gives you medical, legal, or financial information, verify it independently before acting on it.
Audit the apps on your devices and review their permissions. Many are feeding your data into AI training pipelines you never consented to.
Consider privacy-first alternatives for your devices. A degoogled phone running GrapheneOS eliminates many of the passive data collection vectors that Big Tech AI systems depend on. Your phone is the single biggest source of continuous personal data in most people’s lives.
Use a privacy-focused Linux computer for sensitive tasks. Linux-based systems eliminate many of the telemetry feeds baked into mainstream operating systems.
Stay engaged with the regulatory conversation. Australia’s AI Safety Institute is launching in 2026. Public pressure matters. Governments respond to constituencies that are paying attention.
The genie is well out of the bottle. Nobody serious is arguing that AI can or should be uninvented. What is being argued — by researchers, by regulators, and increasingly by the technologists building these systems themselves — is that the window for establishing meaningful governance frameworks is narrowing. Once AI is irreversibly embedded in legal systems, financial infrastructure, healthcare delivery, and national defence, the ability to course-correct becomes dramatically more limited.
Australia’s ethical principles for AI — published back in 2019 and updated through successive guidance frameworks — include commitments to privacy protection, transparency, accountability, and human oversight. These are the right principles. The question is whether they will ever be backed by enforceable law, or whether they will remain aspirational statements that organisations can safely ignore when compliance is commercially inconvenient.
The calmer, more experienced minds that need to shape this technology’s future are not just policymakers in Canberra or Brussels. They are the ordinary people who will live with the consequences of the decisions being made right now — often without being consulted, and often without fully understanding what is being decided on their behalf.
Friend or foe? The honest answer is that AI is both, simultaneously, and which one it becomes for any individual or society depends almost entirely on the governance choices we make in the next few years. That is not a reason for paralysis. It is a reason to pay very close attention.
If this article has prompted you to think differently about the devices in your pocket and on your desk, that is a good start. The most direct action most people can take is to reduce the data they feed into Big Tech AI systems in the first place — because data they do not have is data they cannot misuse.
FreedomTech can help with that in two specific ways:
Our degoogled phones and tablets run privacy-first operating systems like GrapheneOS and CalyxOS, stripping out the data collection infrastructure that Google’s Android is built on. Your phone stops being an AI data feed.
Our Linux privacy computers are built without the telemetry baked into Windows and macOS — no background reporting to Microsoft or Apple servers, no data sharing with advertising ecosystems.
You cannot opt out of AI. But you can significantly reduce how much of your personal life fuels it.