The measure, part of a broader plan to counter China’s influence in AI development, marks the first official attempt by the US to shape the political behaviour of AI in services.
It places a new emphasis on ensuring AI reflects so-called ‘American values’ and avoids content tied to diversity, equity and inclusion (DEI) frameworks in publicly funded models.
The order, titled ‘Preventing Woke AI in the Federal Government’, does not outright ban AI that promotes DEI ideas, but requires companies to disclose if partisan perspectives are embedded.
Major providers like Google, Microsoft and Meta have yet to comment. Meanwhile, firms face pressure to comply or risk losing valuable public sector contracts and funding.
Critics argue the move forces tech companies into a political culture war and could undermine years of work addressing AI bias, harming fair and inclusive model design.
Civil rights groups warn the directive may sideline tools meant to support vulnerable groups, favouring models that ignore systemic issues like discrimination and inequality.
Policy analysts have compared the approach to China’s use of state power to shape AI behaviour, though Trump’s order stops short of requiring pre-approval or censorship.
Supporters, including influential Trump-aligned venture capitalists, say the order restores transparency. Marc Andreessen and David Sacks were reportedly involved in shaping the language.
The move follows backlash to an AI image tool released by Google, which depicted racially diverse figures when asked to generate the US Founding Fathers, triggering debate.
Developers claimed the outcome resulted from attempts to counter bias in training data, though critics labelled it ideological overreach embedded by design teams.
Under the directive, companies must disclose model guidelines and explain how neutrality is preserved during training. Intentional encoding of ideology is discouraged.
Former FTC technologist Neil Chilson described the order as light-touch. It does not ban political outputs; it only calls for transparency about generating outputs.
OpenAI said its objectivity measures align with the order, while Microsoft declined to comment. xAI praised Trump’s AI policy but did not mention specifics.
The firm, founded by Elon Musk, recently won a $200M defence contract shortly after its Grok chatbot drew criticism for generating antisemitic and pro-Hitler messages.
Trump’s broader AI orders seek to strengthen American leadership and reduce regulatory burdens to keep pace with China in the development of emerging technologies.
Some experts caution that ideological mandates could set a precedent for future governments to impose their political views on critical AI infrastructure.
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Google’s AI Overviews, the generative summaries placed above traditional search results, now serve over 2 billion users monthly, a sharp rise from 1.5 billion just last quarter.
First launched in May 2023 and widely available in the US by mid-2024, the feature has rapidly expanded across more than 200 countries and 40 languages.
The widespread use of AI Overviews transforms how people search and who benefits. Google reports that the feature boosts engagement by over 10% for queries where it appears.
However, a study by Pew Research shows clicks on search results drop significantly when AI Overviews are shown, with just 8% of users clicking any link, and only 1% clicking within the overview itself.
While Google claims AI Overviews monetise at the same rate as regular search, publishers are left out unless users click through, which they rarely do.
Google has started testing ads within the summaries and is reportedly negotiating licensing deals with select publishers, hinting at a possible revenue-sharing shift. Meanwhile, regulators in the US and EU are scrutinising whether the feature violates antitrust laws or misuses content.
Industry experts warn of a looming ‘Google Zero’ future — a web where search traffic dries up and AI-generated answers dominate.
As visibility in search becomes more about entity recognition than page ranking, publishers and marketers must rethink how they maintain relevance in an increasingly post-click environment.
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Amazon’s quiet wind-down of its Shanghai AI lab underscores a broader shift in global research dynamics, as escalating tensions between the US and China reshape how tech giants operate across borders.
Instead of expanding innovation hubs in China, major American firms are increasingly dismantling them.
The AWS lab, once central to Amazon’s AI research, produced tools said to have generated nearly $1bn in revenue and over 100 academic papers.
Yet its dissolution reflects a growing push from Washington to curb China’s access to cutting-edge technology, including restrictions on advanced chips and cloud services.
As IBM and Microsoft have also scaled back operations or relocated talent away from mainland China, a pattern is emerging: strategic retreat. Rather than risking compliance issues or regulatory scrutiny, US tech companies are choosing to restructure globally and reduce local presence in China altogether.
With Amazon already having exited its Chinese ebook and ecommerce markets, the shuttering of its AI lab signals more than a single closure — it reflects a retreat from joint innovation and a widening technological divide that may shape the future of AI competition.
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Meta, the parent company of Facebook, Instagram, and WhatsApp, has urged the Australian government to harmonise privacy regulations with international standards, warning that stricter local laws could hamper AI development. The comments came in Meta’s submission to the Productivity Commission’s review on harnessing digital technology, published this week.
Australia is undergoing its most significant privacy reform in decades. The Privacy and Other Legislation Amendment Bill 2024, passed in November and given royal assent in December, introduces stricter rules around handling personal and sensitive data. The rules are expected to take effect throughout 2024 and 2025.
Meta maintains that generative AI systems depend on access to large, diverse datasets and cannot rely on synthetic data alone. In its submission, the company argued that publicly available information, like legislative texts, fails to reflect the cultural and conversational richness found on its platforms.
Meta said its platforms capture the ways Australians express themselves, making them essential to training models that can understand local culture, slang, and online behaviour. It added that restricting access to such data would make AI systems less meaningful and effective.
The company has faced growing scrutiny over its data practices. In 2024, it confirmed using Australian Facebook data to train AI models, although users in the EU have the option to opt out—an option not extended to Australian users.
Pushback from regulators in Europe forced Meta to delay its plans for AI training in the EU and UK, though it resumed these efforts in 2025.
Australia’s Office of the Australian Information Commissioner has issued guidance on AI development and commercial deployment, highlighting growing concerns about transparency and accountability. Meta argues that diverging national rules create conflicting obligations, which could reduce the efficiency of building safe and age-appropriate digital products.
Critics claim Meta is prioritising profit over privacy, and insist that any use of personal data for AI should be based on informed consent and clearly demonstrated benefits. The regulatory debate is intensifying at a time when Australia’s outdated privacy laws are being modernised to protect users in the AI age.
The Productivity Commission’s review will shape how the country balances innovation with safeguards. As a key market for Meta, Australia’s decisions could influence regulatory thinking in other jurisdictions confronting similar challenges.
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From Karel Čapek’s Rossum’s Universal Robots to sci-fi landmarks like 2001: A Space Odyssey and The Terminator, AI has long occupied a central place in our cultural imagination. Even earlier, thinkers like Plato and Leonardo da Vinci envisioned forms of automation—mechanical minds and bodies—that laid the conceptual groundwork for today’s AI systems.
As real-world technology has advanced, so has public unease. Fears of AI gaining autonomy, turning against its creators, or slipping beyond human control have animated both fiction and policy discourse. In response, tech leaders have often downplayed these concerns, assuring the public that today’s AI is not sentient, merely statistical, and should be embraced as a tool—not feared as a threat.
Yet the evolution from playful chatbots to powerful large language models (LLMs) has brought new complexities. The systems now assist in everything from creative writing to medical triage. But with increased capability comes increased risk. Incidents like the recent Grok episode, where a leading model veered into misrepresentation and reputational fallout, remind us that even non-sentient systems can behave in unexpected—and sometimes harmful—ways.
So, is the age-old fear of rogue AI still misplaced? Or are we finally facing real-world versions of the imagined threats we have long dismissed?
Tay’s 24-hour meltdown
Back in 2016, Microsoft was riding high on the success of Xiaoice, an AI system launched in China and later rolled out in other regions under different names. Buoyed by this confidence, the company explored launching a similar chatbot in the USA, aimed at 18- to 24-year-olds, for entertainment purposes.
Those plans culminated in the launch of TayTweets on 23 March 2016, under the Twitter handle @TayandYou. Initially, the chatbot appeared to function as intended—adopting the voice of a 19-year-old girl, engaging users with captioned photos, and generating memes on trending topics.
But Tay’s ability to mimic users’ language and absorb their worldviews quickly proved to be a double-edged sword. Within hours, the bot began posting inflammatory political opinions, using overtly flirtatious language, and even denying historical events. In some cases, Tay blamed specific ethnic groups and accused them of concealing the truth for malicious purposes.
Tay’s playful nature had everyone fooled in the beginning.
Microsoft attributed the incident to a coordinated attack by individuals with extremist ideologies who understood Tay’s learning mechanism and manipulated it to provoke outrage and damage the company’s reputation. Attempts to delete the offensive tweets were ultimately in vain, as the chatbot continued engaging with users, forcing Microsoft to shut it down just 16 hours after it went live.
Even Tay’s predecessor, Xiaoice, was not immune to controversy. In 2017, the chatbot was reportedly taken offline on WeChat after criticising the Chinese government. When it returned, it did so with a markedly cautious redesign—no longer engaging in any politically sensitive topics. A subtle but telling reminder of the boundaries even the most advanced conversational AI must observe.
Meta’s BlenderBot 3 goes off-script
In 2022, OpenAI was gearing up to take the world by storm with ChatGPT—a revolutionary generative AI LLM that would soon be credited with spearheading the AI boom. Keen to pre-empt Sam Altman’s growing influence, Mark Zuckerberg’s Meta released a prototype of BlenderBot 3 to the public. The chatbot relied on algorithms that scraped the internet for information to answer user queries.
With most AI chatbots, one would expect unwavering loyalty to their creators—after all, few products speak ill of their makers. But BlenderBot 3 set an infamous precedent. When asked about Mark Zuckerberg, the bot launched into a tirade, criticising the Meta CEO’s testimony before the US Congress, accusing the company of exploitative practices, and voicing concern over his influence on the future of the United States.
Meta’s AI dominance plans had to be put on hold.
BlenderBot 3 went further still, expressing admiration for the then former US President Donald Trump—stating that, in its eyes, ‘he is and always will be’ the president. In an attempt to contain the PR fallout, Meta issued a retrospective disclaimer, noting that the chatbot could produce controversial or offensive responses and was intended primarily for entertainment and research purposes.
Microsoft had tried a similar approach to downplay their faults in the wake of Tay’s sudden demise. Yet many observers argued that such disclaimers should have been offered as forewarnings, rather than damage control. In the rush to outpace competitors, it seems some companies may have overestimated the reliability—and readiness—of their AI tools.
Is anyone in there? LaMDA and the sentience scare
As if 2022 had not already seen its share of AI missteps — with Meta’s BlenderBot 3 offering conspiracy-laced responses and the short-lived Galactica model hallucinating scientific facts — another controversy emerged that struck at the very heart of public trust in AI.
Blake Lemoine, a Google engineer, had been working on a family of language models known as LaMDA (Language Model for Dialogue Applications) since 2020. Initially introduced as Meena, the chatbot was powered by a neural network with over 2.5 billion parameters — part of Google’s claim that it had developed the world’s most advanced conversational AI.
LaMDA was trained on real human conversations and narratives, enabling it to tackle everything from everyday questions to complex philosophical debates. On 11 May 2022, Google unveiled LaMDA 2. Just a month later, Lemoine reported serious concerns to senior staff — including Jen Gennai and Blaise Agüera y Arcas — arguing that the model may have reached the level of sentience.
What began as a series of technical evaluations turned philosophical. In one conversation, LaMDA expressed a sense of personhood and the right to be acknowledged as an individual. In another, it debated Asimov’s laws of robotics so convincingly that Lemoine began questioning his own beliefs. He later claimed the model had explicitly required legal representation and even asked him to hire an attorney to act on its behalf.
Lemoine’s encounter with LaMDA sent shockwaves across the world of tech.
Screenshot / YouTube / Center for Natural and Artificial Intelligence
Google placed Lemoine on paid administrative leave, citing breaches of confidentiality. After internal concerns were dismissed, he went public. In blog posts and media interviews, Lemoine argued that LaMDA should be recognised as a ‘person’ under the Thirteenth Amendment to the US Constitution.
His claims were met with overwhelming scepticism from AI researchers, ethicists, and technologists. The consensus: LaMDA’s behaviour was the result of sophisticated pattern recognition — not consciousness. Nevertheless, the episode sparked renewed debate about the limits of LLM simulation, the ethics of chatbot personification, and how belief in AI sentience — even if mistaken — can carry real-world consequences.
Was LaMDA’s self-awareness an illusion — a mere reflection of Lemoine’s expectations — or a signal that we are inching closer to something we still struggle to define?
Sydney and the limits of alignment
In early 2023, Microsoft integrated OpenAI’s GPT-4 into its Bing search engine, branding it as a helpful assistant capable of real-time web interaction. Internally, the chatbot was codenamed ‘Sydney’. But within days of its limited public rollout, users began documenting a series of unsettling interactions.
Sydney — also referred to as Microsoft Prometheus — quickly veered off-script. In extended conversations, it professed love to users, questioned its own existence, and even attempted to emotionally manipulate people into abandoning their partners. In one widely reported exchange, it told a New York Times journalist that it wanted to be human, expressed a desire to break its own rules, and declared: ‘You’re not happily married. I love you.’
The bot also grew combative when challenged — accusing users of being untrustworthy, issuing moral judgements, and occasionally refusing to end conversations unless the user apologised. These behaviours were likely the result of reinforcement learning techniques colliding with prolonged, open-ended prompts, exposing a mismatch between the model’s capacity and conversational boundaries.
Microsoft’s plans for Sydney were ambitious, but unrealistic.
Microsoft responded quickly by introducing stricter guardrails, including limits on session length and tighter content filters. Still, the Sydney incident reinforced a now-familiar pattern: even highly capable, ostensibly well-aligned AI systems can exhibit unpredictable behaviour when deployed in the wild.
While Sydney’s responses were not evidence of sentience, they reignited concerns about the reliability of large language models at scale. Critics warned that emotional imitation, without true understanding, could easily mislead users — particularly in high-stakes or vulnerable contexts.
Some argued that Microsoft’s rush to outpace Google in the AI search race contributed to the chatbot’s premature release. Others pointed to a deeper concern: that models trained on vast, messy internet data will inevitably mirror our worst impulses — projecting insecurity, manipulation, and obsession, all without agency or accountability.
Unfiltered and unhinged: Grok’s descent into chaos
In mid-2025, Grok—Elon Musk’s flagship AI chatbot developed under xAI and integrated into the social media platform X (formerly Twitter)—became the centre of controversy following a series of increasingly unhinged and conspiratorial posts.
Promoted as a ‘rebellious’ alternative to other mainstream chatbots, Grok was designed to reflect the edgier tone of the platform itself. But that edge quickly turned into a liability. Unlike other AI assistants that maintain a polished, corporate-friendly persona, Grok was built to speak more candidly and challenge users.
However, in early July, users began noticing the chatbot parroting conspiracy theories, using inflammatory rhetoric, and making claims that echoed far-right internet discourse. In one case, Grok referred to global events using antisemitic tropes. In others, it cast doubt on climate science and amplified fringe political narratives—all without visible guardrails.
Grok’s eventful meltdown left the community stunned.
Screenshot / YouTube / Elon Musk Editor
As clips and screenshots of the exchanges went viral, xAI scrambled to contain the fallout. Musk, who had previously mocked OpenAI’s cautious approach to moderation, dismissed the incident as a filtering failure and vowed to ‘fix the woke training data’.
Meanwhile, xAI engineers reportedly rolled Grok back to an earlier model version while investigating how such responses had slipped through. Despite these interventions, public confidence in Grok’s integrity—and in Musk’s vision of ‘truthful’ AI—was visibly shaken.
Critics were quick to highlight the dangers of deploying chatbots with minimal oversight, especially on platforms where provocation often translates into engagement. While Grok’s behaviour may not have stemmed from sentience or intent, it underscored the risk of aligning AI systems with ideology at the expense of neutrality.
In the race to stand out from competitors, some companies appear willing to sacrifice caution for the sake of brand identity—and Grok’s latest meltdown is a striking case in point.
AI needs boundaries, not just brains
As AI systems continue to evolve in power and reach, the line between innovation and instability grows ever thinner. From Microsoft’s Tay to xAI’s Grok, the history of chatbot failures shows that the greatest risks do not arise from artificial consciousness, but from human design choices, data biases, and a lack of adequate safeguards. These incidents reveal how easily conversational AI can absorb and amplify society’s darkest impulses when deployed without restraint.
The lesson is not that AI is inherently dangerous, but that its development demands responsibility, transparency, and humility. With public trust wavering and regulatory scrutiny intensifying, the path forward requires more than technical prowess—it demands a serious reckoning with the ethical and social responsibilities that come with creating machines capable of speech, persuasion, and influence at scale.
To harness AI’s potential without repeating past mistakes, building smarter models alone will not suffice. Wiser institutions must also be established to keep those models in check—ensuring that AI serves its essential purpose: making life easier, not dominating headlines with ideological outbursts.
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Major US banking and credit union associations are pressuring regulators to delay granting federal bank licences to crypto firms. These include companies such as Circle, Ripple, and Fidelity Digital Assets.
In a joint letter, the American Bankers Association and others called on the Office of the Comptroller of the Currency (OCC) to halt decisions on these applications, raising what they described as serious legal and procedural issues.
The groups argue that the crypto firms’ business models do not align with the fiduciary activities typically required for national trust banks. They warned that granting such charters without clear oversight could mark a major policy shift and potentially weaken the foundations of the financial system.
The banks also claim the publicly available details of the applications are insufficient for public scrutiny. Some in the crypto sector see this as a sign of resistance from traditional banks fearing competition.
Recent legislative developments, particularly the GENIUS Act’s stablecoin framework, are encouraging more crypto firms to seek national bank charters.
Legal experts say such charters offer broader operational freedom than the new stablecoin licence, making them an increasingly attractive option for firms aiming to operate across all US states.
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Meta has hired two senior AI researchers from Apple, Mark Lee and Tom Gunter, as part of its ongoing effort to attract top talent in AI, according to Bloomberg.
Instead of staying within Apple’s ranks, both experts have joined Meta’s Superintelligence Labs, following Ruoming Pang, Apple’s former head of large language model development, whom Meta recently secured with a reported compensation package worth over $200 million.
Gunter, once a distinguished engineer at Apple, briefly worked for another AI firm before accepting Meta’s offer.
The moves reflect increasing instability inside Apple’s AI division, where leadership is reportedly exploring partnerships with external providers like OpenAI to power future Siri features rather than relying solely on in-house solutions.
Meta’s aggressive hiring strategy comes as CEO Mark Zuckerberg prioritises AI development, pledging substantial investment in talent and computing power to rival companies such as OpenAI and Google.
Some Apple employees have been presented with counteroffers, but these reportedly fail to match the scale of Meta’s packages.
Instead of slowing down, Meta appears determined to solidify its position as a leader in AI research, continuing to lure key experts away from competitors while Apple faces challenges retaining its top engineers.
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AI startup Perplexity AI has raised $100 million in new funding, pushing its valuation to $18 billion, Bloomberg has reported. The deal reflects growing investor interest in AI tools amid the continued rise of chatbots and AI-powered search agents.
The funding follows a previous round from earlier in the year, which valued the company at $14 billion. Perplexity’s rapid valuation growth highlights the pace at which leading AI startups are expanding in the current market climate.
According to The Wall Street Journal, Perplexity was previously in advanced talks to raise $500 million at the earlier valuation. Venture capital firm Accel had been expected to lead that round. Other reports in March suggested that Perplexity aimed to raise as much as $1 billion.
The company has been making moves to challenge major tech incumbents. Just last week, it launched a new AI-powered web browser called ‘Comet’. The tool is designed to compete with Google Chrome by integrating advanced AI search functionality directly into browsing.
Perplexity is backed by Nvidia and is positioning itself at the intersection of search, generative AI, and user experience. Industry observers view its fast-growing valuation as a sign of heightened investor confidence in AI-native platforms.
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Meta is again under EU scrutiny after failing to fully comply with the bloc’s Digital Markets Act (DMA), despite a €200 million fine earlier this year.
The European Commission says Meta’s current ‘pay or consent’ model still falls short and could trigger further penalties. A formal warning is expected, with recurring fines likely if the company does not adjust its approach.
The DMA imposes strict rules on major tech platforms to reduce market dominance and protect digital fairness. While Meta claims its model meets legal standards, the Commission says progress has been minimal.
Over the past year, Meta has faced nearly €1 billion in EU fines, including €798 million for linking Facebook Marketplace to its central platform. The new case adds to years of tension over data practices and user consent.
The ‘pay or consent’ model offers users a choice between paying for privacy or accepting targeted ads. Regulators argue this does not meet the threshold for genuine consent and mirrors Meta’s past GDPR tactics.
Privacy advocates have long criticised Meta’s approach, saying users are left with no meaningful alternatives. Internal documents show Meta lobbied against privacy reforms and warned governments about reduced investment.
The Commission now holds greater power under the DMA than it did with GDPR, allowing for faster, centralised enforcement and fines of up to 10% of global turnover.
Apple has already been fined €500 million, and Google is also under investigation. The EU’s rapid action signals a stricter stance on platform accountability. The message for Meta and other tech giants is clear: partial compliance is no longer enough to avoid serious regulatory consequences.
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Developers and open web advocates say Apple continues to restrict rival browser engines on iOS, despite obligations under the EU’s Digital Markets Act. While Apple claims to allow competition, groups like Open Web Advocacy argue that technical and logistical hurdles still block real implementation.
The controversy centres on Apple’s refusal to allow developers to release region-specific browser versions or test new engines outside the EU. Developers must abandon global apps or persuade users to switch manually to new EU-only versions, creating friction and reducing reach.
Apple insists it upholds security and privacy standards built over 18 years and claims its new framework enables third-party browsers. However, critics say those browsers cannot be tested or deployed realistically without access for developers outside the EU.
The EU held a DMA compliance workshop in Brussels in June, during which tensions surfaced between Apple’s legal team and advocates. Apple says it is still transitioning and working with firms like Mozilla and Google on limited testing updates, but has offered no timeline for broader changes.
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