Tech Explained: What should we do when AI starts believing its own fiction? in Simple Terms

Large language models are known to hallucinate—or confidently invent facts that can mislead unsuspecting users. While casual internet users are vulnerable, even experts can be caught unawares when AI-generated content strays beyond their core areas of knowledge.

The problem, though, runs deeper. LLMs are trained on vast troves of internet data, books, code repositories, and research papers, some of which already contain AI-generated material. As synthetic content feeds back into training pipelines, the risk is no longer restricted to just hallucination and deepfakes, but extends to amplification.

Now, before we dive deep into what is essentially AI hallucination dialled to an eleven, here’s a quick look at what’s in this week’s edition:

• Elon Musk warns AI could outsmart humanity in a decade
• AI tool of the week: How to use ChatGPT Translate
• Amazon layoffs, and Yahoo’s AI search

AI hallucination, dialled to an 11

Consider this. A senior lawyer in Australia had to apologise to a judge for filing submissions in a murder case that included fake quotes and non-existent case judgments generated by AI, according to a 27 January Associated Press article.

So, when AI begins to recycle and reinforce its own outputs, it becomes increasingly harder to tell how much an LLM produces is grounded in reality, and how much is machine-made myth.

Sample this. GPT 5.2, OpenAI Inc.’s latest LLM, cited Grokipedia nine times while responding to over a dozen queries, according to a recent article by The Guardian.

Launched in October, Elon Musk’s Grokipedia seeks to challenge Wikipedia’s model. But unlike Wikipedia’s community-driven editing system, Grokipedia relies entirely on an AI model to generate articles and process proposed changes, with no direct human editing. Users may submit suggested corrections through a feedback form but cannot make edits themselves.

The platform has also drawn scrutiny for reflecting right-wing viewpoints on topics such as same-sex marriage and the 6 January attack on the US Capitol.

Need zero-trust policies

As organisations accelerate both adoption and investment in AI initiatives, the volume of AI-generated data will continue to rise, according to a 21 January note by Gartner. According to the 2026 Gartner CIO and Technology Executive Survey, 84% of respondents expect their enterprise to increase funding for Gen AI in 2026.

This means future generations of LLMs will increasingly be trained on outputs from previous models, heightening the risk of “model collapse”, where AI tools’ responses may no longer accurately reflect reality.

This will push enterprises and governments to implement zero-trust data governance policies, where no AI system is trusted by default. In another two years, 50% firms will implement a zero-trust posture for data governance following the spread of unverified AI-generated data, Gartner predicts.

Active metadata management practices will become a key differentiator, enabling organisations to analyse, alert and automate decision-making across their data assets, according to the research firm. This practice enables real-time alerts when data is stale or requires recertification, helping organisations quickly identify when business-critical systems may become exposed to inaccurate or biased data.

What action is needed?

According to Gartner, organisations should consider several strategic actions to manage the risks of unverified data, including appointing an AI governance leader to establish zero-trust policies, AI risk management and compliance operations, and work closely with data and analytics (D&A) teams to ensure both AI-ready data and systems capable of handling AI-generated content.

This trend is already panning out. A 6 October study by the IBM Institute for Business Value suggests that Indian firms are building strong momentum in AI leadership, with chief AI officers (CAIOs) emerging as a key driver of strategy and execution. The study found that 77% of CAIOs in India reported strong C-Suite support, reflecting strong organisational alignment to scale AI effectively. Further, while 25% of the Indian enterprises surveyed had a CAIO, 67% of them aimed to have one within the next two years, demonstrating India’s growing appetite for aligning a leader to direct AI strategy that drives measurable outcomes.

A joint study by Amazon Web Services and Access Partnership, titled ‘Generative AI Adoption Index’, corroborated that 60% organisations had appointed CAIOs and another 26% were planning to by 2026.

Other measures

The Gartner note also suggests setting up cross-functional teams that include cyber-security, D&A and other relevant stakeholders to conduct comprehensive data risk assessments to identify business risks related to AI-generated data and determine which are addressed by existing data security policies and which need new strategies.

Meanwhile, governments too are adopting a tough stand. The European Union’s AI Act, often cited as the most stringent regulatory framework to date, illustrates this approach. Instead of treating all AI systems equally, it categorises applications by risk. High-risk uses such as biometric identification or credit scoring face strict audits and transparency obligations, while lower-risk applications operate under lighter requirements. The aim is to focus regulatory pressure where potential harm is greatest.

• The United States has taken an even more flexible route. Rather than binding legislation, it has leaned on the NIST AI Risk Management Framework, which encourages continuous evaluation and monitoring without mandating pre-deployment approval. The emphasis is on “trust but verify”, allowing AI systems to be deployed but requiring developers and users to monitor outcomes, mitigate bias, and respond quickly to failures.
• In the United Kingdom, zero-trust principles are being channeled through sector-specific regulators rather than a single AI law. Financial services, healthcare, and critical infrastructure face tailored oversight, supported by regulatory sandboxes that allow companies to test AI systems in controlled environments.
• Singapore has followed a similar path. Its Model AI Governance Framework focuses on human oversight, explainability, and ongoing testing, while avoiding hard bans or heavy upfront approvals.

Across these jurisdictions, a common pattern is emerging. Zero-trust governance is being framed less as a gatekeeping mechanism and more as a lifecycle obligation. Approval is no longer a one-time event but an ongoing process, with AI systems expected to adapt as risks evolve. This marks a deeper shift in how innovation is understood. Speed to market, once the dominant metric, is giving way to resilience and accountability. Governments appear to be betting that AI capable of surviving audits, public scrutiny, and real-world failures will ultimately scale more sustainably.

But will this stifle innovation?

On the flip side, as governments adopt zero-trust governance for AI, could constant scrutiny, audits, and controls end up slowing innovation in one of the world’s fastest-moving sectors? The reason: applied rigidly, zero-trust approaches can increase compliance costs, delay deployment, and disproportionately burden startups and researchers. Extensive documentation requirements and continuous monitoring frameworks risk favouring large technology firms with deep legal and compliance resources, potentially narrowing the innovation pipeline.

In this context, the Indian government’s techno-legal approach contrasts sharply with rigid regulatory models of the West. The country’s AI Governance Guidelines aim to strike a balance between innovation and safety. The four-part framework outlines seven principles—trust, fairness, human-centred design, responsible innovation, accountability, equity, and safety—and six pillars: infrastructure, capacity building, policy, regulation, institutions, and risk mitigation. The action plan defines short-, medium-, and long-term outcomes.

That said, while the danger of overregulation remains, the greater risk for policy makers may now lie in deploying powerful AI systems without safeguards. Hence, zero-trust governance may ensure that innovation can endure.

By AI & Beyond, with Jaspreet Bindra & Anuj Magazine

The AI hack we unlocked today is based on a tool — ChatGPT Translate

What problem does ChatGPT Translate solve? Traditional translation tools often fall short when context and tone matter. Whether you’re translating a business email, academic paper, or customer communication, getting the words right is only half the battle. The real challenge? Ensuring your message resonates with your audience while maintaining the appropriate tone and cultural nuance.

ChatGPT Translate addresses these pain points by going beyond word-for-word translation. It helps professionals adapt their communications for different contexts, whether you need a formal business tone, simplified language for broader audiences, or academic precision. This is particularly valuable for global teams, customer support operations, and international business communications where tone and context can make or break relationships.

How to access: https://chatgpt.com/translate/

ChatGPT Translate can help you —

Adapt tone and context: Transform translations to match your audience—formal for executives, simplified for customers, or academic for research papers

Multi-input flexibility: Type, speak, or upload images containing text for instant translation across 50+ languages

Refine with AI prompts: Use one-tap customisation options like “make it sound more fluent”, “make it more business formal”, “explain it to a child”, or “translate for an academic audience”.

Example: Imagine you are responding to a complaint from a Spanish-speaking customer about a delayed shipment. Here’s how ChatGPT Translate helps:

Translate your response: Paste your English explanation into ChatGPT Translate and select Spanish.

Choose your tone: Click “Translate this and make it sound more fluent” for natural phrasing.

Add empathy: Select “Translate this as if you’re explaining it to a child” to ensure simple, compassionate language.

Review in ChatGPT: Each prompt option redirects you to the full ChatGPT interface for deeper customisation.

What makes ChatGPT Translate special?

Context-aware translation: Unlike traditional tools, it considers tone, audience, and cultural nuance not just literal meaning.

One-tap tone adjustment: Four built-in prompts instantly reshape your translation—fluent, business formal, simplified, or academic.

Seamless AI integration: All customisation options flow directly into ChatGPT’s interface for unlimited refinement possibilities.

Note: The tools and analysis featured in this section demonstrated clear value based on our internal testing. Our recommendations are entirely independent and not influenced by the tool creators.

AI BITS AND BYTES

Second round of Amazon layoffs, courtesy AI

Amazon.com Inc. has confirmed 16,000 job cuts worldwide over the next three months amid its restructuring and expansion plans in AI.

Amazon will provide US-based employees 90 days to find a new role internally, along with severance and additional support during their transition, Beth Galetti, senior vice president of people experience and technology at Amazon, announced in a blog post on Wednesday.

Earlier, the second largest employer in the US mistakenly sent a notice to some employees, confirming a wave of upcoming layoffs at the company.

Employees in the company’s cloud division received an internal email acknowledging “organisational changes” at Amazon, CNBC reported. The notice appeared to have been sent prematurely to Amazon Web Services (AWS) employees, a day before the next round of Amazon layoffs was scheduled to begin.

Yahoo enters the chat with AI search rival

Yahoo!’s search engine was a force to reckon with in the late 1990s. However, instead of building a world-class search tool in-house, it outsourced the application to other—ironically to Google from 2000 to 2004. It’s decline is history.

Now, Yahoo is re-entering the search wars with its new AI-powered “answer engine” called Yahoo Scout. Available in beta for users in the US, the new AI tool is designed to compete directly with the likes of Google’s AI Mode, Perplexity, and ChatGPT’s real-time search feature.

Yahoo has partnered with Anthropic to use Claude as the primary Foundational AI model for Scout. It is also leveraging its long-standing relationship with Microsoft Corp. by using Bing’s API to provide real-time answers, backed by authoritative sources. Will it help revive its mojo? We will be closely watching this space.

SoftBank in talks to invest $30 billion more in OpenAI

SoftBank Group Corp. is in talks to invest as much as $30 billion more in Sam Altman’s OpenAI, The Wall Street Journal reports, citing people familiar with the knowledge of the matter. A separate report by Reuters quoted people as saying that SoftBank is in deliberations to commit more capital to the Google Gemini rival.

EU steps in to make sure Google gives rivals access to its AI

The EU said it’s stepping in to make sure Google gives rivals access to Gemini AI services and data as required by the bloc’s flagship digital rulebook. European Union regulators have also opened a formal investigation into Elon Musk’s social media platform X after his AI chatbot Grok started spewing non-consensual sexualized deepfake images on the platform.

Elon Musk says AI could outsmart humanity within a decade

Elon Musk has renewed his warnings about AI, predicting that these systems could surpass the combined intelligence of humanity as early as the end of this year, and almost certainly by 2031 at the latest. Meanwhile, The Doomsday Clock—a symbolic gauge of how close humanity is to self-destruction—has been moved to 85 seconds (from 89) to midnight, its closest point ever, as global risks from nuclear weapons, climate change and AI intensify.

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