In the modern age, artificial intelligence (AI) is revolutionizing how we live, work, and think – sometimes in ways we don’t fully understand or anticipate. In newsrooms, classrooms, boardrooms, and even bedrooms, tools like ChatGPT and other large language models (LLMs) are rapidly becoming standard companions for generating text, conducting research, summarizing content, and assisting in communication. But as we embrace these tools for convenience and productivity, there is growing concern among educators, journalists, editors, and cognitive scientists that we are trading long-term intellectual development for short-term efficiency.

As a news editor, one of the most distressing observations has been the normalization of copying and pasting AI-generated content by young journalists and writers. Attempts to explain the dangers of this trend – especially how it undermines the craft of writing, critical thinking, and authentic reporting – often fall on deaf ears. The allure of AI is simply too strong: its speed, its polish, and its apparent coherence often overshadow the deeper value of struggling through a thought or refining an idea through personal reflection and effort.

This concern is not isolated to journalism. A growing body of research across educational and corporate environments points to an overreliance on writing tools as a silent threat to cognitive growth and intellectual independence. The fear is not that AI tools are inherently bad, but that their habitual use in place of human thinking – rather than in support of it – is setting the stage for diminished creativity, shallow learning, and a weakening of our core mental faculties.

One recent study by researchers at the Massachusetts Institute of Technology (MIT) captures this danger with sobering clarity. In an experiment involving 54 students, three groups were asked to write essays within a 20-minute timeframe: one used ChatGPT, another used a search engine, and the last relied on no tools at all. The researchers monitored brain activity throughout the process and later had teachers assess the resulting essays.

The findings were stark. The group using ChatGPT not only scored lower in terms of originality, depth, and insight, but also displayed significantly less interconnectivity between brain regions involved in complex thinking. Worse still, over 80% of students in the AI-assisted group couldn’t recall details from their own essays when asked afterward. The machine had done the writing, but the humans had not done the thinking. The results reinforced what many teachers and editors already suspect: that AI-generated text, while grammatically sound, often lacks soul, depth, and true understanding.

These “soulless” outputs are not just a matter of style – they are indicative of a broader problem. Critical thinking, information synthesis, and knowledge retention are skills that require effort, engagement, and practice. Outsourcing these tasks to a machine means they are no longer being exercised. Over time, this leads to a form of intellectual atrophy. Like muscles that weaken when unused, the mind becomes less agile, less curious, and less capable of generating original insights.

The implications for journalism are especially dire. A journalist’s role is not simply to reproduce what already exists but to analyze, contextualize, and interpret information in meaningful ways. Journalism relies on curiosity, skepticism, empathy, and narrative skill – qualities that no machine can replicate. When young reporters default to AI tools for their stories, they lose the chance to develop these essential capacities. They become content recyclers rather than truth seekers.

Educators and researchers are sounding the alarm. Nataliya Kosmyna, lead author of the MIT study, emphasized the urgency of developing best practices for integrating AI into learning environments. She noted that while AI can be a powerful aid when used carefully, its misuse has already led to a deluge of complaints from over 3,000 educators – a sign of the disillusionment many teachers feel watching their students abandon independent thinking for machine assistance.

Moreover, these concerns go beyond the classroom or newsroom. The gradual shift from active information-seeking to passive consumption of AI-generated content threatens the very way we interact with knowledge. AI tools deliver answers with the right keywords, but they often bypass the deep analytical processes that come with questioning, exploring, and challenging assumptions. This “fast food” approach to learning may fill informational gaps, but it starves intellectual growth.

There is also a darker undercurrent to this shift. As AI systems increasingly generate content based on existing data – which itself may be riddled with bias, inaccuracies, or propaganda – the distinction between fact and fabrication becomes harder to discern. If AI tools begin to echo errors or misrepresentations without context or correction, the result could be an erosion of trust in information itself. In such a future, fact-checking will be not just important but near-impossible as original sources become buried under layers of machine-generated mimicry.

Ultimately, the overuse of AI writing tools threatens something deeper than skill: it undermines the human drive to learn, to question, and to grow. Our intellectual autonomy – our ability to think for ourselves – is at stake. If we are not careful, we may soon find ourselves in a world where information is abundant, but understanding is scarce.

To be clear, AI is not the enemy. When used responsibly, it can help streamline tasks, illuminate complex ideas, and even inspire new ways of thinking. But it must be positioned as a partner, not a replacement. Writers, students, and journalists must be encouraged – and in some cases required – to engage deeply with their work before turning to AI for support. Writing must remain a process of discovery, not merely of delivery.

As a society, we must treat this issue with the seriousness it deserves. Schools, universities, media organizations, and governments must craft clear guidelines and pedagogies for AI usage that promote learning, not laziness. There must be incentives for original thinking and penalties for mindless replication. We need a cultural shift that re-centers the value of human insight in an age increasingly dominated by digital automation.

If we fail to take these steps, we risk more than poor essays or formulaic articles. We risk raising a generation that cannot think critically, write meaningfully, or distinguish truth from fiction. And that, in any age, is a far greater danger than any machine.

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In a livestream on X, Musk both bragged about the model yet simultaneously fretted about the impact on humanity if the AI turns evil. 

“This is the smartest AI in the world,” said Musk while surrounded by members of his xAI team. “In some ways, it’s terrifying.”

He compared Grok 4 to a “super-genius child” in which the “right values” of truthfulness and a sense of honor must be instilled so society can benefit from its advances. 

Musk admitted to being “worried,” saying that “it’s somewhat unnerving to have intelligence created that is far greater than our own, and will this be bad or good for humanity?”

The xAI owner concluded that “most likely, it’ll be good.”

Musk said Grok 4 is designed to perform at the “post-graduate level” in many topics simultaneously, which no person can do. It can also handle images, generate realistic visuals and tackle complex analytical tasks.

Musk claims that Grok 4 would score perfectly on SAT and graduate-level exams like GRE even without seeing the questions beforehand.

Alongside the model release, xAI introduced SuperGrok Heavy, a subscription tier priced at $300 per month. A standard Grok 4 tier is available for $30 monthly, and the basic tier is free.

OpenAI, Google, Anthropic and Perplexity have unveiled higher-priced tiers as well: ChatGPT Pro, at $200 a month; Gemini Ultra, at $249.99 a month; Claude Max, at $200 a month; and Perplexity Max, for $200 a month.

See also: Elon Musk Startup xAI Launches App Offering Access to Grok Chatbot

Turbulent Week for Grok and X

Grok 4’s launch follows a turbulent week marked by antisemitic content generated by Grok 3 and the resignation of Linda Yaccarino, the CEO of X.

Grok 4 is being released in two configurations: the standard Grok 4 and the premium “Heavy” version.

The Heavy model features a multi-agent architecture capable of collaborative reasoning on challenging problems.

The model demonstrates advances in multimodal processing, faster reasoning and an upgraded user interface. According to xAI, Grok 4 can solve complex math problems, interpret images — including scientific visuals such as black hole collisions — and perform predictive analytics, such as estimating a team’s odds of winning a championship. 

Benchmark data shared by xAI shows that Grok 4 Heavy outperformed previous models on tests such as Humanity’s Last Exam.

xAI outlined an aggressive roadmap for the remainder of 2025: launching a coding‑specific AI in August, a multimodal agent in September and a model capable of generating full video by October.

Grok 4’s release intensifies the competition among leading AI firms. OpenAI is expected to roll out GPT‑5 later this summer, while Google continues to develop its Gemini series.

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A small but growing number of cyber insurers are incorporating language into their policies that specifically addresses risks from artificial intelligence (AI). The June 2025 issue of The Betterley Report’s Cyber/Privacy Market Survey identifies at least three insurers that are incorporating specific definitions or terms for AI. This raises an important question for policyholders: Does including specific language for AI in a coverage grant (or exclusion) change the terms of coverage offered?

To be sure, at present few cyber policies expressly address AI. Most insurers appear to be maintaining a “wait and see” approach; they are monitoring the risks posed by AI, but they have not revised their policies. Nevertheless, a few insurers have sought to reassure customers that coverage is available for AI-related events. One insurer has gone so far as to state that its policy “provides affirmative cover for cyber attacks that utilize AI, ensuring that the business is covered for any of the losses associated with such attacks.” To the extent that AI is simply one vector for a data breach or other cyber incident that would otherwise be an insured event, however, it is unclear whether adding AI-specific language expands coverage. On the other side of the coin, some insurers have sought to limit exposure by incorporating exclusions for certain AI events.   

To assess the impact of these changes, it is critical to ask: What does artificial intelligence even mean?

This is a difficult question to answer. The field of AI is vast and constantly evolving. AI can curate social media feeds, recommend shows and products to consumers, generate email auto-responses, and more. Banks use AI to detect fraud. Driving apps use it to predict traffic. Search engines use it to rank and recommend search results. AI pervades daily life and extends far beyond the chatbots and other generative AI tools that have been the focus of recent news and popular attention.

At a more technical level, AI also encompasses numerous nesting and overlapping subfields.  One major subfield, machine learning, encompasses techniques ranging from linear regression to decision trees. It also includes neural networks, which, when layered together, can be used to power the subfield of deep learning. Deep learning, in turn, is used by the subfield of generative AI. And generative AI itself can take different forms, such as large language models, diffusion models, generative adversarial networks, and neural radiance fields.

That may be why most insurers have been reluctant to define artificial intelligence. A policy could name certain concrete examples of AI applications, but it would likely miss many others, and it would risk falling behind as AI was adapted for other uses. The policy could provide a technical definition, but that could be similarly underinclusive and inflexible. Even referring to subsets such as “generative AI” could run into similar issues, given the complex techniques and applications for the technology.

The risk, of course, is that by not clearly defining artificial intelligence, a policy that grants or excludes coverage for AI could have different coverage consequences than either the insurer or insured expected. Policyholders should pay particular attention to provisions purporting to exclude loss or liability from AI risks, and consider what technologies are in use that could offer a basis to deny coverage for the loss. We will watch with interest cyber insurers’ approach to AI — will most continue to omit references to AI, or will more insurers expressly address AI in their policies?

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This article was co-authored by Anna Hamel