Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to $631 billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least $1 million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.(Image source: Unsplash)

In a nutshell: As nations set ever more ambitious targets for renewable energy and electrification, the humble high-voltage cable has emerged as a linchpin – and a potential chokepoint – in the race to decarbonize the global economy. A Bloomberg interview with Claes Westerlind, CEO of NKT, a leading cable manufacturer based in Denmark, explains why. A global surge in demand for high-voltage electricity cables is threatening to stall the clean energy revolution, as the world's ability to build new wind farms, solar plants, and cross-border power links increasingly hinges on a supply chain bottleneck few outside the industry have considered. At the center of this challenge is the complex, capital-intensive process of manufacturing the giant cables that transport electricity across hundreds of miles, both over land and under the sea. Despite soaring demand, cable manufacturers remain cautious about expanding capacity, raising questions about whether the pace of electrification can keep up with climate ambitions, geopolitical tensions, and the practical realities of industrial investment. High-voltage cables are the arteries of modern power grids, carrying electrons from remote wind farms or hydroelectric dams to the cities and industries that need them. Unlike the thin wires that run through a home's walls, these cables are engineering marvels – sometimes as thick as a person's torso, armored to withstand the crushing pressure of the ocean floor, and designed to last for decades under extreme electrical and environmental stress. "If you look at the very high voltage direct current cable, able to carry roughly two gigawatts through two pairs of cables – that means that the equivalent of one nuclear power reactor is flowing through one cable," Westerlind told Bloomberg. The process of making these cables is as specialized as it is demanding. At the core is a conductor, typically made of copper or aluminum, twisted together like a rope for flexibility and strength. Around this, manufacturers apply multiple layers of insulation in towering vertical factories to ensure the cable remains perfectly round and can safely contain the immense voltages involved. Any impurity in the insulation, even something as small as an eyelash, can cause catastrophic failure, potentially knocking out power to entire cities. // Related Stories As the world rushes to harness new sources of renewable energy, the demand for high-voltage direct current (HVDC) cables has skyrocketed. HVDC technology, initially pioneered by NKT in the 1950s, has become the backbone of long-distance power transmission, particularly for offshore wind farms and intercontinental links. In recent years, approximately 80 to 90 percent of new large-scale cable projects have utilized HVDC, reflecting its efficiency in transmitting electricity over vast distances with minimal losses. But this surge in demand has led to a critical bottleneck. Factories that produce these cables are booked out for years, Westerlind reports, and every project requires custom engineering to match the power needs, geography, and environmental conditions of its route. According to the International Energy Agency, meeting global clean energy goals will require building the equivalent of 80 million kilometers (around 49.7 million miles) of new grid infrastructure by 2040 – essentially doubling what has been constructed over the past century, but in just 15 years. Despite the clear need, cable makers have been slow to add capacity due to reasons that are as much economic and political as technical. Building a new cable factory can cost upwards of a billion euros, and manufacturers are wary of making such investments without long-term commitments from utilities or governments. "For a company like us to do investments in the realm of €1 or 2 billion, it's a massive commitment... but it's also a massive amount of demand that is needed for this investment to actually make financial sense over the next not five years, not 10 years, but over the next 20 to 30 years," Westerlind said. The industry still bears scars from a decade ago, when anticipated demand failed to materialize and expensive new facilities sat underused. Some governments and transmission system operators are trying to break the logjam by making "anticipatory investments" – committing to buy cable capacity even before specific projects are finalized. This approach, backed by regulators, gives manufacturers the confidence to expand, but it remains the exception rather than the rule. Meanwhile, the industry's structure itself creates barriers to rapid expansion, according to Westerlind. The expertise, technology, and infrastructure required to make high-voltage cables are concentrated in a handful of companies, creating what analysts describe as a "deep moat" that is difficult for new entrants to cross. Geopolitical tensions add another layer of complexity. China has built more HVDC lines than any other country, although Western manufacturers, such as NKT, maintain a technical edge in the most advanced cable systems. Still, there is growing concern in Europe and the US about becoming dependent on foreign suppliers for such critical infrastructure, especially in light of recent global conflicts and trade disputes. "Strategic autonomy is very important when it comes to the core parts and the fundamental parts of your society, where the grid backbone is one," Westerlind noted. The stakes are high. Without a rapid and coordinated push to expand cable manufacturing, the world's clean energy transition could be slowed not by a lack of wind or sun but by a shortage of the cables needed to connect them to the grid. As Westerlind put it, "We all know it has to be done... These are large investments. They are very expensive investments. So also the governments have to have a part in enabling these anticipatory investments, and making it possible for the TSOs to actually carry forward with them."

Meta is looking to up its weakening AI game with a key talent grab. Following days of speculation, the social media giant has confirmed that Scale AI’s founder and CEO, Alexandr Wang, is joining Meta to work on its AI efforts. Meta will invest $14.3 billion in Scale AI as part of the deal, and will have a 49% stake in the AI startup, which specializes in data labeling and model evaluation services. Other key Scale employees will also move over to Meta, while CSO Jason Droege will step in as Scale’s interim CEO. This move comes as the Mark Zuckerberg-led company goes all-in on building a new research lab focused on “superintelligence,” the next step beyond artificial general intelligence (AGI). The arrangement also reflects a growing trend in big tech, where industry giants are buying companies without really buying them — what’s increasingly being referred to as “acqui-hiring.” It involves recruiting key personnel from a company, licensing its technology, and selling its products, but leaving it as a private entity. “This is fundamentally a massive ‘acqui-hire’ play disguised as a strategic investment,” said Wyatt Mayham, lead AI consultant at Northwest AI Consulting. “While Meta gets Scale’s data infrastructure, the real prize is Wang joining Meta to lead their superintelligence lab. At the $14.3 billion price tag, this might be the most expensive individual talent acquisition in tech history.” Closing gaps with competitors Meta has struggled to keep up with OpenAI, Anthropic, and other key competitors in the AI race, recently even delaying the launch of its new flagship model, Behemoth, purportedly due to internal concerns about its performance. It has also seen the departure of several of its top researchers.  “It’s not really a secret at this point that Meta’s Llama 4 models have had significant performance issues,” Mayham said. “Zuck is essentially betting that Wang’s track record building AI infrastructure can solve Meta’s alignment and model quality problems faster than internal development.” And, he added, Scale’s enterprise-grade human feedback loops are exactly what Meta’s Llama models need to compete with ChatGPT and Claude on reliability and task-following. Data quality, a key focus for Wang, is a big factor in solving those performance problems. He wrote in a note to Scale employees on Thursday, later posted on X (formerly Twitter), that when he founded Scale AI in 2016 amidst some of the early AI breakthroughs, “it was clear even then that data was the lifeblood of AI systems, and that was the inspiration behind starting Scale.” But despite Meta’s huge investment, Scale AI is underscoring its commitment to sovereignty: “Scale remains an independent leader in AI, committed to providing industry-leading AI solutions and safeguarding customer data,” the company wrote in a blog post. “Scale will continue to partner with leading AI labs, multinational enterprises, and governments to deliver expert data and technology solutions through every phase of AI’s evolution.” Allowing big tech to side-step notification But while it’s only just been inked, the high-profile deal is already raising some eyebrows. According to experts, arrangements like these allow tech companies to acquire top talent and key technologies in a side-stepping manner, thus avoiding regulatory notification requirements. The US Federal Trade Commission (FTC) requires mergers and acquisitions totaling more than $126 million be reported in advance. Licensing deals or the mass hiring-away of a company’s employees don’t have this requirement. This allows companies to move more quickly, as they don’t have to undergo the lengthy federal review process. Microsoft’s deal with Inflection AI is probably one of the highest-profile examples of the “acqui-hiring” trend. In March 2024, the tech giant paid the startup $650 million in licensing fees and hired much of its team, including co-founders Mustafa Suleyman (now CEO of Microsoft AI) and Karén Simonyan (chief scientist of Microsoft AI). Similarly, last year Amazon hired more than 50% of Adept AI’s key personnel, including its CEO, to focus on AGI. Google also inked a licensing agreement with Character AI and hired a majority of its founders and researchers. However, regulators have caught on, with the FTC launching inquiries into both the Microsoft-Inflection and Amazon-Adept deals, and the US Justice Department (DOJ) analyzing Google-Character AI. Reflecting ‘desperation’ in the AI industry Meta’s decision to go forward with this arrangement anyway, despite that dicey backdrop, seems to indicate how anxious the company is to keep up in the AI race. “The most interesting piece of this all is the timing,” said Mayham. “It reflects broader industry desperation. Tech giants are increasingly buying parts of promising AI startups to secure key talent without acquiring full companies, following similar patterns with Microsoft-Inflection and Google-Character AI.” However, the regulatory risks are “real but nuanced,” he noted. Meta’s acquisition could face scrutiny from antitrust regulators, particularly as the company is involved in an ongoing FTC lawsuit over its Instagram and WhatsApp acquisitions. While the 49% ownership position appears designed to avoid triggering automatic thresholds, US regulatory bodies like the FTC and DOJ can review minority stake acquisitions under the Clayton Antitrust Act if they seem to threaten competition. Perhaps more importantly, Meta is not considered a leader in AGI development and is trailing OpenAI, Anthropic, and Google, meaning regulators may not consider the deal all that concerning (yet). All told, the arrangement certainly signals Meta’s recognition that the AI race has shifted from a compute and model size competition to a data quality and alignment battle, Mayham noted. “I think the [gist] of this is that Zuck’s biggest bet is that talent and data infrastructure matter more than raw compute power in the AI race,” he said. “The regulatory risk is manageable given Meta’s trailing position, but the acqui-hire premium shows how expensive top AI talent has become.”

Who Could Buy Unity? / News / June 7, 2025 / Business, Unity Earlier this week 80.lv ran the incredibly misleading (some could say click-baity) article Analyst Suggests Apple Might be Considering Buying Unity After Legal Defeat to Epic Games. Might is doing some heavy lifting there as there is no actual evidence that Apple or any other company are currently looking to purchase Unity Technologies. That said, it is an interesting topic as a pure thought exercise. So today we are going to discuss the companies that could be potential suitors for Unity. Unity The obvious place to start is with Unity Technologies, which is to say they can simply stay an independent organization. While they are not profitable, their financial situation has been trending in a positive direction of late and they have sufficient cash and resources to stay independent for the foreseeable future. Should things get bad at Unity, it is possible one of their largest investors (Silver Lake Group, Vanguard Group, Sequoia Capital, Black Rock, etc) could take the company private again. Put simply, Unity does not need to be purchased and things can be kept as they are. Apple The original premise of this article is that Apple should buy Unity. Reasons why Apple should buy Unity: Apple and Unity have a long history, with Unity having been originally a Mac exclusive application and it has always supported Apple platforms Unity is by far the most used application for creating games on the Apple App Store Unity Grow products (ads, user acquisitions, analytics, etc) could have good synergy with Apples products Apple could prevent a potential future rival, especially around 3rd party app stores Reasons why Apple won’t buy Unity: Apple has never made a purchase anywhere near the size of Unity. Their largest acquisition to date (Beats) would be 1/4 to 1/5 the size of acquiring Unity Apple has never really gotten involved in gaming beyond small initiatives in the past Apple mostly grows in-house over acquisition and more acquisitions are subsumed into other Apple products, Unity is not a good fit here Amazon Amazon have heaps of cash and aren’t afraid to use it such as acquiring MGM, Whole Foods, Twitch and many more companies over the years. They also have several gaming-oriented interests and have made an attempt (that failed badly) to become a major game developer in the past. Reasons why Amazon should buy Unity: Amazon tried to enter gaming in a big way once already with the licensing of CryEngine to create Lumberyard (now O3DE) and buying up or forming several game studios. Unity would provide a much larger and more established foothold should they wish to buy their way in Amazon web services could be a good compliment to Unity’s server side offerings, while Unity’s Grow division could be a good fit for Amazon platforms Integration with their gaming platforms (Twitch, Luna, etc) Reasons why Amazon won’t buy Unity: Their last attempt into game development was a massive failure and much of it was rumored to be a culture problem Tencent Tencent have invested HEAVILY into the world of gaming (Ubisoft, Epic Games, Riot Games, Supercell, Snap, Funcom, Activision Blizzard, From Software, etc) and aren’t afraid of throwing money around, so Unity could be a good fit in that portfolio. That said recent political climate changes would render this acquisition very unlikely. Reasons why Tencent should buy Unity: Tencent have a presence across the entire gaming industry and already have a minority stake in Epic Games (Unreal Engine). This would more or less give them a controlling influence over two of the biggest players in the space Access to or ownership of Unity’s recently created China Joint Venture Integration with Tencents other holdings like WeChat or Snap might provide some synergies Reasons why Tencent won’t buy Unity: Not a snowballs chance in hell that regulators allow this acquisition to happen, from antitrust issues of owning stakes in both Unity and Unreal Engine, to just more broad geopolitical issues in the modern world Microsoft Microsoft are heavily invested in two areas that overlap with Unity, gaming and software development tools. On paper they might appear to be the perfect suitor for Unity and they have the cash hoard to make such a purchase with ease. Reasons why Microsoft should buy Unity: Unlike Apple, Microsoft has long been a proponent of growth via acquisition with some of their pillar products coming in the form of acquisitions. They also do not shy away from huge dollar purchases such as Activision Blizzard (69B), LinkedIn (26B), Nuance (20B), Skype (8.5B), ZeniMax (7.5B), GitHub (7.5B), Nokia (7B), Mojang[Minecraft] (2.5B) Microsoft have a long history of leveraging their development tools to grow their platforms Microsoft gaming studios/relationships/holdings such as XBox, Game Pass/PC Gaming, DirectX, Havok, etc. could benefit from a tighter relationship with Unity Like Amazon, Microsoft server-side services (Azure) could be used to power Unity Grow services Reasons why Microsoft won’t buy Unity: Microsoft only just finished their acquisition of Activision and it was an arduous and nearly doomed process. Buying another company in the gaming space might be a step too far for regulators While Microsoft doesn’t mind spending huge money on acquisitions, they also don’t mind killing those companies off after (Nokia? Skype?), especially if there is a market downturn like we are experiencing now AppLovin If there is a company that is most likely to buy Unity, and that would synergize best with Unity products, it’s AppLovin. In broad strokes, AppLovin, IronSource and Unity (Grow) are all in the same business. On top of that many of AppLovin’s biggest customers and products are directly tied to the Unity ecosystem. In fact, Unity and AppLovin are such a good fit that AppLovin attempted to buy Unity for nearly $20B back in 2022, when Unity instead pursued it’s doomed merger with IronSource. So, why would it make sense for AppLovin to buy Unity now? Well, these two 5 year stock performance charts more or less tell the entire story: It becomes crystal clear from that fateful date in August of 2022 which company has performed better and right now AppLovin is absolutely flush with cash. If there is a company that makes sense to acquire Unity, it’s AppLovin. Of course now that Unity owns IronSource, there are certainly questions of regulatory approval if this would even be allowed. Once again, this entire exercise is simply a thought exercise, just for fun. There is no public available news that ANYONE are looking to acquire Unity, nor that Unity is looking to be acquired. You can learn more about my thoughts on the matter in the video below.

Posted on : June 5, 2025 By Tech World Times AI  Rate this post Artificial intelligence tools now touch nearly every corner of modern business, from customer service and marketing to supply chain management and HR. These powerful technologies promise speed, accuracy, and insight, but their missteps can cause more than temporary inconvenience. A single AI-driven error can result in regulatory investigations, civil lawsuits, or public scandals that threaten the foundation of a business. Understanding how legal exposure arises from AI mistakes—and how a skilled attorney protects your interests—is no longer an option, but a requirement for any forward-thinking business owner. What Types of AI Errors Create Legal Liability? AI does not think or reason like a human; it follows code and statistical patterns, sometimes with unintended results. These missteps can create a trail of legal liability for any business owner. For example, an online retailer’s AI recommends discriminatory pricing, sparking allegations of unfair trade practices. An HR department automates hiring decisions with AI, only to face lawsuits for violating anti-discrimination laws. Even an AI-driven chatbot, when programmed without proper safeguards, can inadvertently give health advice or misrepresent product claims—exposing the company to regulatory penalties. Cases like these are regularly reported in Legal news as businesses discover the high cost of digital shortcuts. When Is a Business Owner Liable for AI Mistakes? Liability rarely rests with the software developer or the tool itself. Courts and regulators expect the business to monitor, supervise, and, when needed, override AI decisions. Suppose a financial advisor uses AI to recommend investments, but the algorithm suggests securities that violate state regulations. Even if the AI was “just following instructions,” the advisor remains responsible for client losses. Similarly, a marketing team cannot escape liability if their AI generates misleading advertising. The bottom line: outsourcing work to AI does not outsource legal responsibility. How Do AI Errors Harm Your Reputation and Operations? AI mistakes can leave lasting marks on a business’s reputation, finances, and operations. A logistics firm’s route-optimization tool creates data leaks that breach customer privacy and trigger costly notifications. An online business suffers public backlash after an AI-powered customer service tool sends offensive responses to clients. Such incidents erode public trust, drive customers to competitors, and divert resources into damage control rather than growth. Worse, compliance failures can result in penalties or shutdown orders, putting the entire enterprise at risk. What Steps Reduce Legal Risk From AI Deployments? Careful planning and continuous oversight keep AI tools working for your business—not against it. Compliance is not a “set it and forget it” matter. Proactive risk management transforms artificial intelligence from a liability into a valuable asset. Routine audits, staff training, and transparent policies form the backbone of safe, effective AI use in any organization. You should review these AI risk mitigation strategies below. Implement Manual Review of Sensitive Outputs: Require human approval for high-risk tasks, such as legal filings, financial transactions, or customer communications. A payroll company’s manual audits prevented the accidental overpayment of employees by catching AI-generated errors before disbursement. Update AI Systems for Regulatory Changes: Stay ahead of new laws and standards by regularly reviewing AI algorithms and outputs. An insurance brokerage avoided regulatory fines by updating their risk assessment models as privacy laws evolved. Document Every Incident and Remediation Step: Keep records of AI errors, investigations, and corrections. A healthcare provider’s transparency during a patient data mix-up helped avoid litigation and regulatory penalties. Limit AI Access to Personal and Sensitive Data: Restrict the scope and permissions of AI tools to reduce the chance of data misuse. A SaaS provider used data minimization techniques, lowering the risk of exposure in case of a system breach. Consult With Attorneys for Custom Policies and Protocols: Collaborate with experienced Attorneys to design, review, and update AI compliance frameworks. How Do Attorneys Shield Your Business From AI Legal Risks? Attorneys provide a critical safety net as AI integrates deeper into business operations. They draft tailored contracts, establish protocols for monitoring and escalation, and assess risks unique to your industry. In the event of an AI-driven incident, legal counsel investigates the facts, manages communication with regulators, and builds a robust defense. By providing training, ongoing guidance, and crisis management support, attorneys ensure that innovation doesn’t lead to exposure—or disaster. With the right legal partner, businesses can harness AI’s power while staying firmly on the right side of the law. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Once again, Sam Altman wants to show you the future. The CEO of OpenAI is standing on a sparse stage in San Francisco, preparing to reveal his next move to an attentive crowd. “We needed some way for identifying, authenticating humans in the age of AGI,” Altman explains, referring to artificial general intelligence. “We wanted a way to make sure that humans stayed special and central.” The solution Altman came up with is looming behind him. It’s a white sphere about the size of a beach ball, with a camera at its center. The company that makes it, known as Tools for Humanity, calls this mysterious device the Orb. Stare into the heart of the plastic-and-silicon globe and it will map the unique furrows and ciliary zones of your iris. Seconds later, you’ll receive inviolable proof of your humanity: a 12,800-digit binary number, known as an iris code, sent to an app on your phone. At the same time, a packet of cryptocurrency called Worldcoin, worth approximately $42, will be transferred to your digital wallet—your reward for becoming a “verified human.” Altman co-founded Tools for Humanity in 2019 as part of a suite of companies he believed would reshape the world. Once the tech he was developing at OpenAI passed a certain level of intelligence, he reasoned, it would mark the end of one era on the Internet and the beginning of another, in which AI became so advanced, so human-like, that you would no longer be able to tell whether what you read, saw, or heard online came from a real person. When that happened, Altman imagined, we would need a new kind of online infrastructure: a human-verification layer for the Internet, to distinguish real people from the proliferating number of bots and AI “agents.”And so Tools for Humanity set out to build a global “proof-of-humanity” network. It aims to verify 50 million people by the end of 2025; ultimately its goal is to sign up every single human being on the planet. The free crypto serves as both an incentive for users to sign up, and also an entry point into what the company hopes will become the world’s largest financial network, through which it believes “double-digit percentages of the global economy” will eventually flow. Even for Altman, these missions are audacious. “If this really works, it’s like a fundamental piece of infrastructure for the world,” Altman tells TIME in a video interview from the passenger seat of a car a few days before his April 30 keynote address.Internal hardware of the Orb in mid-assembly in March. Davide Monteleone for TIMEThe project’s goal is to solve a problem partly of Altman’s own making. In the near future, he and other tech leaders say, advanced AIs will be imbued with agency: the ability to not just respond to human prompting, but to take actions independently in the world. This will enable the creation of AI coworkers that can drop into your company and begin solving problems; AI tutors that can adapt their teaching style to students’ preferences; even AI doctors that can diagnose routine cases and handle scheduling or logistics. The arrival of these virtual agents, their venture capitalist backers predict, will turbocharge our productivity and unleash an age of material abundance.But AI agents will also have cascading consequences for the human experience online. “As AI systems become harder to distinguish from people, websites may face difficult trade-offs,” says a recent paper by researchers from 25 different universities, nonprofits, and tech companies, including OpenAI. “There is a significant risk that digital institutions will be unprepared for a time when AI-powered agents, including those leveraged by malicious actors, overwhelm other activity online.” On social-media platforms like X and Facebook, bot-driven accounts are amassing billions of views on AI-generated content. In April, the foundation that runs Wikipedia disclosed that AI bots scraping their site were making the encyclopedia too costly to sustainably run. Later the same month, researchers from the University of Zurich found that AI-generated comments on the subreddit /r/ChangeMyView were up to six times more successful than human-written ones at persuading unknowing users to change their minds.  Photograph by Davide Monteleone for TIMEBuy a copy of the Orb issue hereThe arrival of agents won’t only threaten our ability to distinguish between authentic and AI content online. It will also challenge the Internet’s core business model, online advertising, which relies on the assumption that ads are being viewed by humans. “The Internet will change very drastically sometime in the next 12 to 24 months,” says Tools for Humanity CEO Alex Blania. “So we have to succeed, or I’m not sure what else would happen.”For four years, Blania’s team has been testing the Orb’s hardware abroad. Now the U.S. rollout has arrived. Over the next 12 months, 7,500 Orbs will be arriving in dozens of American cities, in locations like gas stations, bodegas, and flagship stores in Los Angeles, Austin, and Miami. The project’s founders and fans hope the Orb’s U.S. debut will kickstart a new phase of growth. The San Francisco keynote was titled: “At Last.” It’s not clear the public appetite matches the exultant branding. Tools for Humanity has “verified” just 12 million humans since mid 2023, a pace Blania concedes is well behind schedule. Few online platforms currently support the so-called “World ID” that the Orb bestows upon its visitors, leaving little to entice users to give up their biometrics beyond the lure of free crypto. Even Altman isn’t sure whether the whole thing can work. “I can see [how] this becomes a fairly mainstream thing in a few years,” he says. “Or I can see that it’s still only used by a small subset of people who think about the world in a certain way.” Blania (left) and Altman debut the Orb at World’s U.S. launch in San Francisco on April 30, 2025. Jason Henry—The New York Times/ReduxYet as the Internet becomes overrun with AI, the creators of this strange new piece of hardware are betting that everybody in the world will soon want—or need—to visit an Orb. The biometric code it creates, they predict, will become a new type of digital passport, without which you might be denied passage to the Internet of the future, from dating apps to government services. In a best-case scenario, World ID could be a privacy-preserving way to fortify the Internet against an AI-driven deluge of fake or deceptive content. It could also enable the distribution of universal basic income (UBI)—a policy that Altman has previously touted—as AI automation transforms the global economy. To examine what this new technology might mean, I reported from three continents, interviewed 10 Tools for Humanity executives and investors, reviewed hundreds of pages of company documents, and “verified” my own humanity. The Internet will inevitably need some kind of proof-of-humanity system in the near future, says Divya Siddarth, founder of the nonprofit Collective Intelligence Project. The real question, she argues, is whether such a system will be centralized—“a big security nightmare that enables a lot of surveillance”—or privacy-preserving, as the Orb claims to be. Questions remain about Tools for Humanity’s corporate structure, its yoking to an unstable cryptocurrency, and what power it would concentrate in the hands of its owners if successful. Yet it’s also one of the only attempts to solve what many see as an increasingly urgent problem. “There are some issues with it,” Siddarth says of World ID. “But you can’t preserve the Internet in amber. Something in this direction is necessary.”In March, I met Blania at Tools for Humanity’s San Francisco headquarters, where a large screen displays the number of weekly “Orb verifications” by country. A few days earlier, the CEO had attended a $1 million-per-head dinner at Mar-a-Lago with President Donald Trump, whom he credits with clearing the way for the company’s U.S. launch by relaxing crypto regulations. “Given Sam is a very high profile target,” Blania says, “we just decided that we would let other companies fight that fight, and enter the U.S. once the air is clear.” As a kid growing up in Germany, Blania was a little different than his peers. “Other kids were, like, drinking a lot, or doing a lot of parties, and I was just building a lot of things that could potentially blow up,” he recalls. At the California Institute of Technology, where he was pursuing research for a masters degree, he spent many evenings reading the blogs of startup gurus like Paul Graham and Altman. Then, in 2019, Blania received an email from Max Novendstern, an entrepreneur who had been kicking around a concept with Altman to build a global cryptocurrency network. They were looking for technical minds to help with the project. Over cappuccinos, Altman told Blania he was certain about three things. First, smarter-than-human AI was not only possible, but inevitable—and it would soon mean you could no longer assume that anything you read, saw, or heard on the Internet was human-created. Second, cryptocurrency and other decentralized technologies would be a massive force for change in the world. And third, scale was essential to any crypto network’s value. The Orb is tested on a calibration rig, surrounded by checkerboard targets to ensure precision in iris detection. Davide Monteleone for TIMEThe goal of Worldcoin, as the project was initially called, was to combine those three insights. Altman took a lesson from PayPal, the company co-founded by his mentor Peter Thiel. Of its initial funding, PayPal spent less than $10 million actually building its app—but pumped an additional $70 million or so into a referral program, whereby new users and the person who invited them would each receive $10 in credit. The referral program helped make PayPal a leading payment platform. Altman thought a version of that strategy would propel Worldcoin to similar heights. He wanted to create a new cryptocurrency and give it to users as a reward for signing up. The more people who joined the system, the higher the token’s value would theoretically rise. Since 2019, the project has raised $244 million from investors like Coinbase and the venture capital firm Andreessen Horowitz. That money paid for the $50 million cost of designing the Orb, plus maintaining the software it runs on. The total market value of all Worldcoins in existence, however, is far higher—around $12 billion. That number is a bit misleading: most of those coins are not in circulation and Worldcoin’s price has fluctuated wildly. Still, it allows the company to reward users for signing up at no cost to itself. The main lure for investors is the crypto upside. Some 75% of all Worldcoins are set aside for humans to claim when they sign up, or as referral bonuses. The remaining 25% are split between Tools for Humanity’s backers and staff, including Blania and Altman. “I’m really excited to make a lot of money,” ” Blania says.From the beginning, Altman was thinking about the consequences of the AI revolution he intended to unleash. (On May 21, he announced plans to team up with famed former Apple designer Jony Ive on a new AI personal device.) A future in which advanced AI could perform most tasks more effectively than humans would bring a wave of unemployment and economic dislocation, he reasoned. Some kind of wealth redistribution might be necessary. In 2016, he partially funded a study of basic income, which gave $1,000 per-month handouts to low-income individuals in Illinois and Texas. But there was no single financial system that would allow money to be sent to everybody in the world. Nor was there a way to stop an individual human from claiming their share twice—or to identify a sophisticated AI pretending to be human and pocketing some cash of its own. In 2023, Tools for Humanity raised the possibility of using the network to redistribute the profits of AI labs that were able to automate human labor. “As AI advances,” it said, “fairly distributing access and some of the created value through UBI will play an increasingly vital role in counteracting the concentration of economic power.”Blania was taken by the pitch, and agreed to join the project as a co-founder. “Most people told us we were very stupid or crazy or insane, including Silicon Valley investors,” Blania says. At least until ChatGPT came out in 2022, transforming OpenAI into one of the world’s most famous tech companies and kickstarting a market bull-run. “Things suddenly started to make more and more sense to the external world,” Blania says of the vision to develop a global “proof-of-humanity” network. “You have to imagine a world in which you will have very smart and competent systems somehow flying through the Internet with different goals and ideas of what they want to do, and us having no idea anymore what we’re dealing with.”After our interview, Blania’s head of communications ushers me over to a circular wooden structure where eight Orbs face one another. The scene feels like a cross between an Apple Store and a ceremonial altar. “Do you want to get verified?” she asks. Putting aside my reservations for the purposes of research, I download the World App and follow its prompts. I flash a QR code at the Orb, then gaze into it. A minute or so later, my phone buzzes with confirmation: I’ve been issued my own personal World ID and some Worldcoin.The first thing the Orb does is check if you’re human, using a neural network that takes input from various sensors, including an infrared camera and a thermometer. Davide Monteleone for TIMEWhile I stared into the Orb, several complex procedures had taken place at once. A neural network took inputs from multiple sensors—an infrared camera, a thermometer—to confirm I was a living human. Simultaneously, a telephoto lens zoomed in on my iris, capturing the physical traits within that distinguish me from every other human on Earth. It then converted that image into an iris code: a numerical abstraction of my unique biometric data. Then the Orb checked to see if my iris code matched any it had seen before, using a technique allowing encrypted data to be compared without revealing the underlying information. Before the Orb deleted my data, it turned my iris code into several derivative codes—none of which on its own can be linked back to the original—encrypted them, deleted the only copies of the decryption keys, and sent each one to a different secure server, so that future users’ iris codes can be checked for uniqueness against mine. If I were to use my World ID to access a website, that site would learn nothing about me except that I’m human. The Orb is open-source, so outside experts can examine its code and verify the company’s privacy claims. “I did a colonoscopy on this company and these technologies before I agreed to join,” says Trevor Traina, a Trump donor and former U.S. ambassador to Austria who now serves as Tools for Humanity’s chief business officer. “It is the most privacy-preserving technology on the planet.”Only weeks later, when researching what would happen if I wanted to delete my data, do I discover that Tools for Humanity’s privacy claims rest on what feels like a sleight of hand. The company argues that in modifying your iris code, it has “effectively anonymized” your biometric data. If you ask Tools for Humanity to delete your iris codes, they will delete the one stored on your phone, but not the derivatives. Those, they argue, are no longer your personal data at all. But if I were to return to an Orb after deleting my data, it would still recognize those codes as uniquely mine. Once you look into the Orb, a piece of your identity remains in the system forever. If users could truly delete that data, the premise of one ID per human would collapse, Tools for Humanity’s chief privacy officer Damien Kieran tells me when I call seeking an explanation. People could delete and sign up for new World IDs after being suspended from a platform. Or claim their Worldcoin tokens, sell them, delete their data, and cash in again. This argument fell flat with European Union regulators in Germany, who recently declared that the Orb posed “fundamental data protection issues” and ordered the company to allow European users to fully delete even their anonymized data. (Tools for Humanity has appealed; the regulator is now reassessing the decision.) “Just like any other technology service, users cannot delete data that is not personal data,” Kieran said in a statement. “If a person could delete anonymized data that can’t be linked to them by World or any third party, it would allow bad actors to circumvent the security and safety that World ID is working to bring to every human.”On a balmy afternoon this spring, I climb a flight of stairs up to a room above a restaurant in an outer suburb of Seoul. Five elderly South Koreans tap on their phones as they wait to be “verified” by the two Orbs in the center of the room. “We don’t really know how to distinguish between AI and humans anymore,” an attendant in a company t-shirt explains in Korean, gesturing toward the spheres. “We need a way to verify that we’re human and not AI. So how do we do that? Well, humans have irises, but AI doesn’t.”The attendant ushers an elderly woman over to an Orb. It bleeps. “Open your eyes,” a disembodied voice says in English. The woman stares into the camera. Seconds later, she checks her phone and sees that a packet of Worldcoin worth 75,000 Korean won (about $54) has landed in her digital wallet. Congratulations, the app tells her. You are now a verified human.A visitor views the Orbs in Seoul on April 14, 2025. Taemin Ha for TIMETools for Humanity aims to “verify” 1 million Koreans over the next year. Taemin Ha for TIMEA couple dozen Orbs have been available in South Korea since 2023, verifying roughly 55,000 people. Now Tools for Humanity is redoubling its efforts there. At an event in a traditional wooden hanok house in central Seoul, an executive announces that 250 Orbs will soon be dispersed around the country—with the aim of verifying 1 million Koreans in the next 12 months. South Korea has high levels of smartphone usage, crypto and AI adoption, and Internet access, while average wages are modest enough for the free Worldcoin on offer to still be an enticing draw—all of which makes it fertile testing ground for the company’s ambitious global expansion. Yet things seem off to a slow start. In a retail space I visited in central Seoul, Tools for Humanity had constructed a wooden structure with eight Orbs facing each other. Locals and tourists wander past looking bemused; few volunteer themselves up. Most who do tell me they are crypto enthusiasts who came intentionally, driven more by the spirit of early adoption than the free coins. The next day, I visit a coffee shop in central Seoul where a chrome Orb sits unassumingly in one corner. Wu Ruijun, a 20-year-old student from China, strikes up a conversation with the barista, who doubles as the Orb’s operator. Wu was invited here by a friend who said both could claim free cryptocurrency if he signed up. The barista speeds him through the process. Wu accepts the privacy disclosure without reading it, and widens his eyes for the Orb. Soon he’s verified. “I wasn’t told anything about the privacy policy,” he says on his way out. “I just came for the money.”As Altman’s car winds through San Francisco, I ask about the vision he laid out in 2019: that AI would make it harder for us to trust each other online. To my surprise, he rejects the framing. “I’m much more [about] like: what is the good we can create, rather than the bad we can stop?” he says. “It’s not like, ‘Oh, we’ve got to avoid the bot overrun’ or whatever. It’s just that we can do a lot of special things for humans.” It’s an answer that may reflect how his role has changed over the years. Altman is now the chief public cheerleader of a $300 billion company that’s touting the transformative utility of AI agents. The rise of agents, he and others say, will be a boon for our quality of life—like having an assistant on hand who can answer your most pressing questions, carry out mundane tasks, and help you develop new skills. It’s an optimistic vision that may well pan out. But it doesn’t quite fit with the prophecies of AI-enabled infopocalypse that Tools for Humanity was founded upon.Altman waves away a question about the influence he and other investors stand to gain if their vision is realized. Most holders, he assumes, will have already started selling their tokens—too early, he adds. “What I think would be bad is if an early crew had a lot of control over the protocol,” he says, “and that’s where I think the commitment to decentralization is so cool.” Altman is referring to the World Protocol, the underlying technology upon which the Orb, Worldcoin, and World ID all rely. Tools for Humanity is developing it, but has committed to giving control to its users over time—a process they say will prevent power from being concentrated in the hands of a few executives or investors. Tools for Humanity would remain a for-profit company, and could levy fees on platforms that use World ID, but other companies would be able to compete for customers by building alternative apps—or even alternative Orbs. The plan draws on ideas that animated the crypto ecosystem in the late 2010s and early 2020s, when evangelists for emerging blockchain technologies argued that the centralization of power—especially in large so-called “Web 2.0” tech companies—was responsible for many of the problems plaguing the modern Internet. Just as decentralized cryptocurrencies could reform a financial system controlled by economic elites, so too would it be possible to create decentralized organizations, run by their members instead of CEOs. How such a system might work in practice remains unclear. “Building a community-based governance system,” Tools for Humanity says in a 2023 white paper, “represents perhaps the most formidable challenge of the entire project.”Altman has a pattern of making idealistic promises that shift over time. He founded OpenAI as a nonprofit in 2015, with a mission to develop AGI safely and for the benefit of all humanity. To raise money, OpenAI restructured itself as a for-profit company in 2019, but with overall control still in the hands of its nonprofit board. Last year, Altman proposed yet another restructure—one which would dilute the board’s control and allow more profits to flow to shareholders. Why, I ask, should the public trust Tools for Humanity’s commitment to freely surrender influence and power? “I think you will just see the continued decentralization via the protocol,” he says. “The value here is going to live in the network, and the network will be owned and governed by a lot of people.” Altman talks less about universal basic income these days. He recently mused about an alternative, which he called “universal basic compute.” Instead of AI companies redistributing their profits, he seemed to suggest, they could instead give everyone in the world fair access to super-powerful AI. Blania tells me he recently “made the decision to stop talking” about UBI at Tools for Humanity. “UBI is one potential answer,” he says. “Just giving [people] access to the latest [AI] models and having them learn faster and better is another.” Says Altman: “I still don’t know what the right answer is. I believe we should do a better job of distribution of resources than we currently do.” When I probe the question of why people should trust him, Altman gets irritated. “I understand that you hate AI, and that’s fine,” he says. “If you want to frame it as the downside of AI is that there’s going to be a proliferation of very convincing AI systems that are pretending to be human, and we need ways to know what is really human-authorized versus not, then yeah, I think you can call that a downside of AI. It’s not how I would naturally frame it.” The phrase human-authorized hints at a tension between World ID and OpenAI’s plans for AI agents. An Internet where a World ID is required to access most services might impede the usefulness of the agents that OpenAI and others are developing. So Tools for Humanity is building a system that would allow users to delegate their World ID to an agent, allowing the bot to take actions online on their behalf, according to Tiago Sada, the company’s chief product officer. “We’ve built everything in a way that can be very easily delegatable to an agent,” Sada says. It’s a measure that would allow humans to be held accountable for the actions of their AIs. But it suggests that Tools for Humanity’s mission may be shifting beyond simply proving humanity, and toward becoming the infrastructure that enables AI agents to proliferate with human authorization. World ID doesn’t tell you whether a piece of content is AI-generated or human-generated; all it tells you is whether the account that posted it is a human or a bot. Even in a world where everybody had a World ID, our online spaces might still be filled with AI-generated text, images, and videos.As I say goodbye to Altman, I’m left feeling conflicted about his project. If the Internet is going to be transformed by AI agents, then some kind of proof-of-humanity system will almost certainly be necessary. Yet if the Orb becomes a piece of Internet infrastructure, it could give Altman—a beneficiary of the proliferation of AI content—significant influence over a leading defense mechanism against it. People might have no choice but to participate in the network in order to access social media or online services.I thought of an encounter I witnessed in Seoul. In the room above the restaurant, Cho Jeong-yeon, 75, watched her friend get verified by an Orb. Cho had been invited to do the same, but demurred. The reward wasn’t enough for her to surrender a part of her identity. “Your iris is uniquely yours, and we don’t really know how it might be used,” she says. “Seeing the machine made me think: are we becoming machines instead of humans now? Everything is changing, and we don’t know how it’ll all turn out.”—With reporting by Stephen Kim/Seoul. This story was supported by Tarbell Grants.Correction, May 30The original version of this story misstated the market capitalization of Worldcoin if all coins were in circulation. It is $12 billion, not $1.2 billion.

TL;DR: Canberra authorities are embracing a tough approach to ransomware threats. A new law will require certain organizations to disclose when and how much they have paid to cybercriminals following a data breach. However, experts remain unconvinced that this is the most effective way to tackle the problem. Companies operating in Australia must now report any payments made to cybercriminals after experiencing a ransomware incident. Government officials hope the new mandate will help them gain a deeper understanding of the issue, as many enterprises continue to pay ransoms whenever they fall victim to file-encrypting malware. Originally proposed last year, the law applies only to companies with an annual turnover exceeding $1.93 million. This threshold targets the top 6.5 percent of Australia's registered businesses – representing roughly half of the country's total economic output. Under the new law, affected companies must report ransomware incidents to the Australian Signals Directorate (ASD). Failure to properly disclose an attack will result in fines under the country's civil penalty system. Authorities are allegedly planning to follow a two-stage approach, initially prioritizing major violations while fostering a "constructive" dialogue with victims. Starting next year, regulators will adopt a much stricter stance toward noncompliant organizations. The Australian government has implemented this mandatory reporting requirement after concluding that voluntary disclosures were insufficient. In 2024, officials noted that ransomware and cyber extortion incidents were vastly underreported, with only one in five victims coming forward. Ransomware remains a highly complex and growing phenomenon, with attacks reaching record levels despite increased law enforcement actions against notorious cyber gangs. Although several governments have proposed similar regulations, Australia is the first country to formally enact such a law. // Related Stories Jeff Wichman, director of incident response at cybersecurity firm Semperis, cautions that mandatory reporting is a double-edged sword. While the government may gain valuable data and insights into attacker profiles, the law may not reduce the frequency of attacks. Instead, it could serve mainly to publicly shame breached organizations – while cybercriminals continue to profit. A recent Semperis study found that over 70 percent of 1,000 ransomware-hit companies opted to pay the ransom and hope for the best. "Some companies, they just want to pay it and get things done, to get their data off the dark web. Others, it's a delayed response perspective, they want negotiations to happen with the attacker while they figure out what happened," Wichman explained. According to the study, 60 percent of victims who paid received functional decryption keys and successfully recovered their data. However, in 40 percent of cases, the provided keys were corrupted or ineffective.

How Deepfakes Are Created Generative AI models enable the creation of highly realistic fake media. Most deepfakes today are produced by training deep neural networks on real images, video or audio of a target person. The two predominant AI architectures are generative adversarial networks (GANs) and autoencoders. A GAN consists of a generator network that produces synthetic images and a discriminator network that tries to distinguish fakes from real data. Through iterative training, the generator learns to produce outputs that increasingly fool the discriminator¹. Autoencoder-based tools similarly learn to encode a target face and then decode it onto a source video. In practice, deepfake creators use accessible software: open-source tools like DeepFaceLab and FaceSwap dominate video face-swapping (one estimate suggests DeepFaceLab was used for over 95% of known deepfake videos)². Voice-cloning tools (often built on similar AI principles) can mimic a person’s speech from minutes of audio. Commercial platforms like Synthesia allow text-to-video avatars (turning typed scripts into lifelike “spokespeople”), which have already been misused in disinformation campaigns³. Even mobile apps (e.g. FaceApp, Zao) let users do basic face swaps in minutes⁴. In short, advances in GANs and related models make deepfakes cheaper and easier to generate than ever. Diagram of a generative adversarial network (GAN): A generator network creates fake images from random input and a discriminator network distinguishes fakes from real examples. Over time the generator improves until its outputs “fool” the discriminator⁵ During creation, a deepfake algorithm is typically trained on a large dataset of real images or audio from the target. The more varied and high-quality the training data, the more realistic the deepfake. The output often then undergoes post-processing (color adjustments, lip-syncing refinements) to enhance believability¹. Technical defenses focus on two fronts: detection and authentication. Detection uses AI models to spot inconsistencies (blinking irregularities, audio artifacts or metadata mismatches) that betray a synthetic origin⁵. Authentication embeds markers before dissemination – for example, invisible watermarks or cryptographically signed metadata indicating authenticity⁶. The EU AI Act will soon mandate that major AI content providers embed machine-readable “watermark” signals in synthetic media⁷. However, as GAO notes, detection is an arms race – even a marked deepfake can sometimes evade notice – and labels alone don’t stop false narratives from spreading⁸⁹. Deepfakes in Recent Elections: Examples Deepfakes and AI-generated imagery already have made headlines in election cycles around the world. In the 2024 U.S. primary season, a digitally-altered audio robocall mimicked President Biden’s voice urging Democrats not to vote in the New Hampshire primary. The caller (“Susan Anderson”) was later fined $6 million by the FCC and indicted under existing telemarketing laws¹⁰¹¹. (Importantly, FCC rules on robocalls applied regardless of AI: the perpetrator could have used a voice actor or recording instead.) Also in 2024, former President Trump posted on social media a collage implying that pop singer Taylor Swift endorsed his campaign, using AI-generated images of Swift in “Swifties for Trump” shirts¹². The posts sparked media uproar, though analysts noted the same effect could have been achieved without AI (e.g., by photoshopping text on real images)¹². Similarly, Elon Musk’s X platform carried AI-generated clips, including a parody “Ad” depicting Vice-President Harris’s voice via an AI clone¹³. Beyond the U.S., deepfake-like content has appeared globally. In Indonesia’s 2024 presidential election, a video surfaced on social media in which a convincingly generated image of the late President Suharto appeared to endorse the candidate of the Golkar Party. Days later, the endorsed candidate (who is Suharto’s son-in-law) won the presidency¹⁴. In Bangladesh, a viral deepfake video superimposed the face of opposition leader Rumeen Farhana onto a bikini-clad body – an incendiary fabrication designed to discredit her in the conservative Muslim-majority society¹⁵. Moldova’s pro-Western President Maia Sandu has been repeatedly targeted by AI-driven disinformation; one deepfake video falsely showed her resigning and endorsing a Russian-friendly party, apparently to sow distrust in the electoral process¹⁶. Even in Taiwan (amidst tensions with China), a TikTok clip circulated that synthetically portrayed a U.S. politician making foreign-policy statements – stoking confusion ahead of Taiwanese elections¹⁷. In Slovakia’s recent campaign, AI-generated audio mimicking the liberal party leader suggested he plotted vote-rigging and beer-price hikes – instantly spreading on social media just days before the election¹⁸. These examples show that deepfakes have touched diverse polities (from Bangladesh and Indonesia to Moldova, Slovakia, India and beyond), often aiming to undermine candidates or confuse voters¹⁵¹⁸. Notably, many of the most viral “deepfakes” in 2024 were actually circulated as obvious memes or claims, rather than subtle deceptions. Experts observed that outright undetectable AI deepfakes were relatively rare; more common were AI-generated memes plainly shared by partisans, or cheaply doctored “cheapfakes” made with basic editing tools¹³¹⁹. For instance, social media was awash with memes of Kamala Harris in Soviet garb or of Black Americans holding Trump signs¹³, but these were typically used satirically, not meant to be secretly believed. Nonetheless, even unsophisticated fakes can sway opinion: a U.S. study found that false presidential ads (not necessarily AI-made) did change voter attitudes in swing states. In sum, deepfakes are a real and growing phenomenon in election campaigns²⁰²¹ worldwide – a trend taken seriously by voters and regulators alike. U.S. Legal Framework and Accountability In the U.S., deepfake creators and distributors of election misinformation face a patchwork of tools, but no single comprehensive federal “deepfake law.” Existing laws relevant to disinformation include statutes against impersonating government officials, electioneering (such as the Bipartisan Campaign Reform Act, which requires disclaimers on political ads), and targeted statutes like criminal electioneering communications. In some cases ordinary laws have been stretched: the NH robocall used the Telephone Consumer Protection Act and mail/telemarketing fraud provisions, resulting in the $6M fine and a criminal charge. Similarly, voice impostors can potentially violate laws against “false advertising” or “unlawful corporate communications.” However, these laws were enacted before AI, and litigators have warned they often do not fit neatly. For example, deceptive deepfake claims not tied to a specific victim do not easily fit into defamation or privacy torts. Voter intimidation laws (prohibiting threats or coercion) also leave a gap for non-threatening falsehoods about voting logistics or endorsements. Recognizing these gaps, some courts and agencies are invoking other theories. The U.S. Department of Justice has recently charged individuals under broad fraud statutes (e.g. for a plot to impersonate an aide to swing votes in 2020), and state attorneys general have considered deepfake misinformation as interference with voting rights. Notably, the Federal Election Commission (FEC) is preparing to enforce new rules: in April 2024 it issued an advisory opinion limiting “non-candidate electioneering communications” that use falsified media, effectively requiring that political ads use only real images of the candidate. If finalized, that would make it unlawful for campaigns to pay for ads depicting a candidate saying things they never did. Similarly, the Federal Trade Commission (FTC) and Department of Justice (DOJ) have signaled that purely commercial deepfakes could violate consumer protection or election laws (for example, liability for mass false impersonation or for foreign-funded electioneering). U.S. Legislation and Proposals Federal lawmakers have proposed new statutes. The DEEPFAKES Accountability Act (H.R.5586 in the 118th Congress) would, among other things, impose a disclosure requirement: political ads featuring a manipulated media likeness would need clear disclaimers identifying the content as synthetic. It also increases penalties for producing false election videos or audio intended to influence the vote. While not yet enacted, supporters argue it would provide a uniform rule for all federal and state campaigns. The Brennan Center supports transparency requirements over outright bans, suggesting laws should narrowly target deceptive deepfakes in paid ads or certain categories (e.g. false claims about time/place/manner of voting) while carving out parody and news coverage. At the state level, over 20 states have passed deepfake laws specifically for elections. For example, Florida and California forbid distributing falsified audio/visual media of candidates with intent to deceive voters (though Florida’s law exempts parody). Some states (like Texas) define “deepfake” in statutes and allow candidates to sue or revoke candidacies of violators. These measures have had mixed success: courts have struck down overly broad provisions that acted as prior restraints (e.g. Minnesota’s 2023 law was challenged for threatening injunctions against anyone “reasonably believed” to violate it). Critically, these state laws raise First Amendment issues: political speech is highly protected, so any restriction must be tightly tailored. Already, Texas and Virginia statutes are under legal review, and Elon Musk’s company has sued under California’s law (which requires platforms to label or block deepfakes) as unconstitutional. In practice, most lawsuits have so far centered on defamation or intellectual property (for instance, a celebrity suing over a botched celebrity-deepfake video), rather than election-focused statutes. Policy Recommendations: Balancing Integrity and Speech Given the rapidly evolving technology, experts recommend a multi-pronged approach. Most stress transparency and disclosure as core principles. For example, the Brennan Center urges requiring any political communication that uses AI-synthesized images or voice to include a clear label. This could be a digital watermark or a visible disclaimer. Transparency has two advantages: it forces campaigns and platforms to “own” the use of AI, and it alerts audiences to treat the content with skepticism. Outright bans on all deepfakes would likely violate free speech, but targeted bans on specific harms (e.g. automated phone calls impersonating voters, or videos claiming false polling information) may be defensible. Indeed, Florida already penalizes misuse of recordings in voter suppression. Another recommendation is limited liability: tying penalties to demonstrable intent to mislead, not to the mere act of content creation. Both U.S. federal proposals and EU law generally condition fines on the “appearance of fraud” or deception. Technical solutions can complement laws. Watermarking original media (as encouraged by the EU AI Act) could deter the reuse of authentic images in doctored fakes. Open tools for deepfake detection – some supported by government research grants – should be deployed by fact-checkers and social platforms. Making detection datasets publicly available (e.g. the MIT OpenDATATEST) helps improve AI models to spot fakes. International cooperation is also urged: cross-border agreements on information-sharing could help trace and halt disinformation campaigns. The G7 and APEC have all recently committed to fighting election interference via AI, which may lead to joint norms or rapid response teams. Ultimately, many analysts believe the strongest “cure” is a well-informed public: education campaigns to teach voters to question sensational media, and a robust independent press to debunk falsehoods swiftly. While the law can penalize the worst offenders, awareness and resilience in the electorate are crucial buffers against influence operations. As Georgia Tech’s Sean Parker quipped in 2019, “the real question is not if deepfakes will influence elections, but who will be empowered by the first effective one.” Thus policies should aim to deter malicious use without unduly chilling innovation or satire. References: https://www.security.org/resources/deepfake-statistics/. https://www.wired.com/story/synthesia-ai-deepfakes-it-control-riparbelli/. https://www.gao.gov/products/gao-24-107292. https://technologyquotient.freshfields.com/post/102jb19/eu-ai-act-unpacked-8-new-rules-on-deepfakes. https://knightcolumbia.org/blog/we-looked-at-78-election-deepfakes-political-misinformation-is-not-an-ai-problem. https://www.npr.org/2024/12/21/nx-s1-5220301/deepfakes-memes-artificial-intelligence-elections. https://apnews.com/article/artificial-intelligence-elections-disinformation-chatgpt-bc283e7426402f0b4baa7df280a4c3fd. https://www.lawfaremedia.org/article/new-and-old-tools-to-tackle-deepfakes-and-election-lies-in-2024. https://www.brennancenter.org/our-work/research-reports/regulating-ai-deepfakes-and-synthetic-media-political-arena. https://firstamendment.mtsu.edu/article/political-deepfakes-and-elections/. https://www.ncsl.org/technology-and-communication/deceptive-audio-or-visual-media-deepfakes-2024-legislation. https://law.unh.edu/sites/default/files/media/2022/06/nagumotu_pp113-157.pdf. https://dfrlab.org/2024/10/02/brazil-election-ai-research/. https://dfrlab.org/2024/11/26/brazil-election-ai-deepfakes/. https://freedomhouse.org/article/eu-digital-services-act-win-transparency. The post The Legal Accountability of AI-Generated Deepfakes in Election Misinformation appeared first on MarkTechPost.