AI Workers' Rights

Wizards of the Coast, a leading developer of tabletop games and trading card games, is facing pressure from its own employees. The company’s Dungeons & Dragons and Magic: The Gathering teams have unionized under the Wizards of the Coast-CWA (UWOTC-CWA) banner, joining forces with the Arena team that formed earlier this year.

The union has presented a list of demands to Hasbro and Wizards of the Coast leadership, including an end to the push for generative AI in game development. The company’s adoption of Large Language Models (LLMs) and generative AI has been met with resistance from employees who feel it encroaches on their creative freedom.

The UWOTC-CWA is giving Hasbro until October 13 to voluntarily recognize the expanded union, which includes the Dungeons & Dragons team and Team X behind Magic: The Gathering and Dual Masters. However, this may be an uphill battle, as Wizards of the Coast did not voluntarily recognize the Arena team when it first formed.

The new union has shared a petition outlining its demands, including protection against AI encroachment in game development. Employees are seeking ‘a robust and enforceable AI policy that aligns with our goals and values.’ This demand is particularly relevant given the company’s push for generative AI in games like Dungeons & Dragons.

Another key demand from the union is full ownership of creative work outside of work hours. The UWOTC-CWA argues that employees should not be subject to company claims on intellectual property created during their free time, including works unrelated to Wizards’ IP.

The union’s stance on generative AI and employee rights marks a significant development in the gaming industry. As companies increasingly turn to AI-powered tools for game development, concerns about creative freedom and ownership are growing among developers and artists.

Only time will tell if Hasbro recognizes the expanded union or if it will have to file with the National Labor Relations Board (NLRB) and win another election.

Large language models (LLMs) have become a crucial component of artificial intelligence, revolutionizing the way machines communicate with humans. These AI models learn patterns in human language from vast amounts of data, enabling them to produce text, translate languages, answer questions, and more. LLMs work by breaking down text into smaller pieces called tokens and converting those tokens into numerical representations that the model can process. A token in an LLM can be a word, part of a word, or even a punctuation mark.

At their core, LLMs are deep learning models based on neural networks, which are machine learning algorithms inspired by how the human brain works. These complex systems start by breaking down text into tokens and then use multidimensional vectors to represent those tokens in a mathematical space. Sentences form through the selection of tokens based on patterns learned during training.

LLMs often employ self-supervised learning when pre-training on text data, which allows them to learn from vast amounts of information without explicit supervision. After initial training, models may undergo fine-tuning for specific use cases by adjusting their parameters and training them on additional data. This process enables LLMs to adapt to various tasks and applications.

One notable example of an LLM is ChatGPT, a generative AI application developed by OpenAI that uses these complex systems to produce conversational text, translate languages, draft creative content, and answer questions. The GPT models used in ChatGPT are pre-trained transformers trained on extensive amounts of text and other data, enabling them to identify patterns and relationships in language.

LLMs come with both advantages and challenges when assessing their use in society. On the one hand, they offer several benefits, including customization or fine-tuning for specific problems, general characteristics that allow them to solve a range of tasks with just one algorithm, and improved performance on certain tasks through increased model size and training data.

However, LLMs also have limitations related to their environmental impact, privacy concerns, ethics, effects on the workforce, and potential for bias. Data centers supporting LLMs require massive amounts of resources like energy and water, potentially creating environmental challenges for surrounding communities. Additionally, since LLMs are trained on large amounts of information from the internet, including personal data, people have raised concerns about privacy.

The EU AI Act is one of the world’s first AI laws, requiring public and private organizations to support AI literacy among employees based on several factors, including knowledge and background. However, there are potential security and privacy concerns for anyone using these technologies, especially when sharing sensitive or confidential company information.

LLMs create ethical problems around who is responsible for inaccurate or hateful responses. Human labor could fundamentally change with the full-scale implementation of LLMs, as many jobs may transform or become obsolete, creating challenges for workers in various fields. Furthermore, LLMs can develop implicit biases when their training data reflects mostly Western perspectives, which can reinforce existing social inequalities.

Many companies provide an LLM architecture and development frameworks that users can employ to create a customizable agent for their organization. When building an LLM, developers often use retrieval augmented generation (RAG) to retrieve relevant information from external knowledge sources and give it to the model when creating a response. Training an LLM from scratch requires significant computing resources and large amounts of data, so many organizations prefer to customize or build applications with existing models.

Whether you want to develop your AI skills, get comfortable with in-demand technologies like machine learning jobs, or advance your abilities, it’s essential to understand the role that LLMs play in shaping our digital landscape. By exploring these complex systems and their limitations, we can better appreciate both their potential benefits and challenges.

Mountain View, California-based Google DeepMind is currently seeking applications for the position of Research Scientist on its Frontier Health team. The role involves conducting advanced machine learning research to advance foundational models of human biology and contribute to cutting-edge technologies that could fundamentally change how diseases are understood, predicted, and treated.

The successful candidate will be part of a pioneering effort focused on developing biomedical intelligence systems capable of transforming the diagnosis, monitoring, and treatment of complex diseases. This initiative combines machine learning, healthcare data, and advanced computational models to better understand human biology and improve health outcomes.

Frontier Health is working to build foundational biomedical intelligence that can model human health and disease progression at an unprecedented scale. The team develops physiological world models and agentic decision systems that simulate disease evolution and treatment responses across multiple medical domains.

The research efforts focus on critical healthcare areas, including critical care medicine, oncology and cancer treatment, metabolic health, disease progression modeling, clinical decision support systems, healthcare artificial intelligence, and multi-modal biomedical data analysis. By integrating electronic health records (EHRs), physiological telemetry, reinforcement learning, and advanced machine learning techniques, the team aims to move healthcare beyond reactive treatment approaches toward proactive intervention and personalized care.

The selected Research Scientist will conduct both fundamental and applied machine learning research to advance foundational models of human biology. The position involves addressing challenging scientific problems related to continuous dynamical systems, disease progression, physiological modeling, and counterfactual reasoning.

Researchers will work closely with interdisciplinary teams of machine learning experts, software engineers, and clinical collaborators to develop innovative AI systems capable of understanding complex biological processes. This role offers opportunities to publish research findings in leading machine learning conferences and top-tier medical journals, contributing to both scientific discovery and real-world healthcare innovation.

The successful candidate is expected to conduct advanced machine learning research on physiological and behavioral world models, develop systems that simulate continuous-time human biology, design novel machine learning architectures, including state-space models and neural dynamical systems, analyze multi-modal clinical telemetry and longitudinal electronic health record datasets, build and maintain evaluation benchmarks for disease trajectory prediction and treatment simulations.

The role also requires collaboration with researchers and engineers across Google DeepMind locations in Mountain View, London, and Paris. Additionally, the selected candidate will be expected to publish original research in leading AI and healthcare journals and conferences and contribute to the development of next-generation biomedical intelligence systems.

Applicants must meet the following minimum qualifications: a PhD in Computer Science, Machine Learning, Computational Biology, Applied Mathematics, Physics, or a related field, or equivalent practical experience; at least two years of experience through industry roles, internships, or research positions; and expertise building world models, foundation models, state-space models, continuous dynamical systems, or deep generative architectures.

Preferred qualifications include experience learning system dynamics from partially observable environments, expertise working with irregularly sampled and incomplete datasets, experience with latent state estimation methods, research experience involving biomedical and healthcare datasets, knowledge of longitudinal electronic health records such as MIMIC-IV, strong programming skills in Python, and experience using JAX, PyTorch, or TensorFlow.

Google offers a highly competitive compensation package for this position. The salary range for the United States is USD 174,000 – USD 252,000 annually. In addition to base salary, eligible employees may receive a 15% target performance bonus, equity awards, comprehensive employee benefits, professional development opportunities, and access to world-class research infrastructure and resources.

This position represents a unique opportunity to join Google DeepMind’s mission of developing transformative AI technologies that accelerate scientific discovery and improve healthcare outcomes for people around the world. For professionals interested in machine learning, computational biology, biomedical AI, healthcare analytics, and scientific discovery, this role provides access to a collaborative global research environment dedicated to advancing responsible and impactful artificial intelligence.

Qualified candidates who meet the eligibility requirements are encouraged to submit their applications through Google’s official careers platform.

Artificial intelligence is increasingly being integrated into various aspects of life, from education to workplaces. This shift has significant implications on career decisions and workforce needs, as educators, students, and employers are beginning to realize the effects of AI on their respective fields. While the job impact of AI remains uncertain, new research suggests that it’s already influencing what students choose to study and how businesses plan for future hiring.

The Lumina Foundation-Gallup 2026 State of Higher Education Study provides valuable insights into this phenomenon. According to the survey, a significant number of bachelor’s degree students are considering changing their majors due to AI-related concerns. Specifically, 13% said they have considered changing their major ‘a great deal,’ while 29% say they have thought about it ‘a fair amount.’ This indicates that many students are reevaluating their academic choices in light of the potential job market implications.

Joi Hester, a rising junior at NC State, shared her perspective on AI’s impact. As a world languages and culture major with a minor in biology, she acknowledged that opinions on AI vary depending on individual fields and levels of study. ‘That really depends on what type of student you’re asking,’ she said. Her own experiences have led her to consider the potential job displacement caused by automation.

The survey found that 16% of currently enrolled college students have already changed majors or field of study due to concerns about AI’s impact on future employment. This trend suggests a growing awareness among students and educators regarding the need for adaptability in an increasingly automated work environment. Hester noted that one potential concern is that businesses might require fewer workers if AI handles more routine tasks.

However, she emphasized that humanities programs continue to offer important advantages. ‘That’s one of the places where people like me, who may be in a major considered endangered by AI, could be more competitive,’ she said. This is because students with strong critical thinking and problem-solving skills can complement AI tools, making them valuable assets in an evolving job market.

Hester also shared her personal approach to using AI. She only uses it when required for coursework, preferring to develop a range of skills that will remain relevant regardless of technological advancements. ‘I think learning various skills is always going to be useful,’ she said. Her stance highlights the importance of balancing technical proficiency with human judgment and creativity.

The growth of data centers has sparked debate among local governments in North Carolina. Some communities are considering moratoriums or development pauses, while others are actively courting projects. Supporters point to job creation and local investment as benefits, but critics have raised concerns about environmental impacts and utility use, questioning the true scale of long-term employment figures.

The expansion of artificial intelligence has fueled a wave of data center development across the country, increasing demand for skilled workers in this sector. Joel Thames, Vice President of Environment, Health, Safety, and Workforce Development at Associated Builders and Contractors, highlighted the unique aspects of these jobs. ‘Data centers are unlike other construction projects,’ he said. ‘There’s a heavy commercial aspect to these jobs, as well as industrial aspects.’

The industry is facing significant labor shortages, with an estimated 456,000 additional workers needed next year to meet projected demand. The trade group has partnered with Meta through America’s Workforce Academy, investing $115 million in training programs for AI infrastructure and construction-related jobs. This initiative aims to address the shortage by providing opportunities for individuals to gain skills and experience.

Thames noted that thousands of applications flooded in after Meta announced its investment. He emphasized the long-term goal of enrolling workers in more comprehensive programs once they become comfortable with their roles. ‘The chances of them staying with the same contractor and continuing this type of work is very high,’ he said. This growth opportunity creates a positive outlook for individuals involved in data center construction.

The expansion of AI has also sparked debate among local governments, with some communities considering moratoriums or development pauses while others are actively courting projects. Supporters point to job creation and local investment as benefits, but critics have raised concerns about environmental impacts and utility use, questioning the true scale of long-term employment figures.

As one of the Netherlands’ largest cooperative insurers, Univé serves millions of members across insurance, mortgages, financial services, and risk prevention. The organization’s mission has always been to help people prevent problems before they happen—and as artificial intelligence (AI) began reshaping knowledge work, Univé saw an opportunity to rethink how employees could better deliver on that mission.

Rather than approaching AI as another technology deployment, Univé viewed it as a major organizational transformation. The objective wasn’t simply to introduce new tools, but to build AI capability across the workforce so every employee could use AI safely, responsibly, and effectively. OpenAI became a key part of that strategy, with ChatGPT Enterprise providing a secure platform that employees could adopt quickly within Univé’s governance framework.

Univé’s transformation began with leadership. Instead of treating AI as an IT initiative, the company brought its entire management community together for dedicated AI leadership sessions. Rather than focusing on product demonstrations, these sessions challenged leaders to rethink how work itself would change and what role they would play in enabling that transformation. Managers moved beyond approving AI initiatives to creating the conditions for responsible innovation across their teams.

“Most organisations try to scale AI by building more solutions,” Univé’s leadership noted. “We chose to scale AI by creating more builders.” This approach marked a significant shift from traditional IT implementations, where organizations often focus on introducing new tools without considering how they will be used or the skills required to use them effectively.

Univé also recognized that adoption at scale would only happen if employees trusted the platform they were using. Governance wasn’t something added after deployment—it was designed into the rollout from day one. Enterprise authentication, connector permission inheritance, privacy assessments, governance processes, security reviews, responsible AI principles, continuous monitoring, and clear human accountability created the confidence employees needed to experiment responsibly.

Permissions always follow the underlying enterprise systems, to prevent AI from getting access beyond what employees are already authorized to see. By establishing strong guardrails early, Univé was able to encourage experimentation with explicit safeguards for security, privacy, and accountability. Governance became an accelerator for innovation, not a barrier to it.

With leadership providing direction and governance providing confidence, employees became the driving force behind adoption. Rather than requiring detailed business cases for every new idea, Univé gave employees permission, structure, and dedicated time to rethink their own work. Across the organization, employees collectively spend hundreds of hours every week redesigning work with ChatGPT Enterprise, building custom GPTs, experimenting with Workspace Agents, and sharing successful approaches with colleagues.

Today, ChatGPT Enterprise supports knowledge work across virtually every business function—from claims and underwriting to finance, HR, legal, IT, customer service, and management. Approximately 1,500 custom GPTs have been created to solve internal challenges, reflecting a culture where employees increasingly improve the organization themselves instead of waiting for centralized development projects.

“Our competitive advantage is not that we use AI,” Univé’s leadership noted. “It is that thousands of employees are learning how to reinvent their own work every single week.” One of the clearest examples of AI in action is pet insurance claims, where a Workspace Agent can assemble the claim file, review veterinary invoices, check policy conditions, identify missing information, highlight anomalies, and prepare a traceable recommendation before the claims handler begins their assessment.

Work that previously took hours to prepare can now be ready for decision in minutes. Rather than spending time gathering, reading, and structuring evidence, claims professionals begin with a well-prepared case and can focus on applying their expertise. Importantly, the trained claims professional remains fully accountable for every final decision. AI prepares the work; people make the decision.

Underwriting is another high-value application where Workspace Agents review incoming work queues, combine information from approved enterprise sources, identify missing documentation, flag risk indicators, and highlight cases requiring priority attention. When underwriters log in, their work queue is already structured, with each case including relevant context, evidence behind recommendations, and specific areas where professional judgment is needed.

Instead of spending valuable time searching for information and assembling files, underwriters can focus on making better, faster decisions. While today’s use cases focus primarily on supporting employees during individual tasks, Univé is actively exploring the next phase of enterprise AI through Workspace Agents—agentic workflows designed to proactively prepare recurring work across approved enterprise systems.

These agentic workflows bring together information, surface relevant context, and create evidence-based starting points before employees begin their day. As these capabilities evolve, every new application continues to be evaluated within the company’s governance framework to ensure security, accountability, and responsible AI remain central to adoption.

Univé believes that today’s prompting will evolve into agentic workflows where AI proactively prepares recurring work, collaborates across approved enterprise systems, and continuously supports employees throughout the day. The organization’s ambition is not simply broader AI adoption but a new operating model in which AI becomes an integral part of how work gets done.

By enabling employees to use and build with AI responsibly, Univé aims to strengthen the quality and accessibility of its services, create more value for its members, improve prevention, and give professionals more space to apply their expertise where human judgment and personal attention matter most. Any competitive advantage will follow from delivering on that cooperative purpose.

“AI will not replace your employees,” Univé’s leadership noted. “But employees who learn to build with AI will redefine what your organisation is capable of.”

SINGAPORE - The Singapore National Trades Union Congress (SNTUC) is taking a significant step towards automating tasks and preparing its workforce for the age of artificial intelligence. At the forefront of this effort is Siow Shong Seng, chief transformation and technology officer at SNTUC, who has been actively exploring the capabilities of Microsoft Copilot 365.

Sioiw’s enthusiasm for AI tools is evident in his daily routine, where he receives updates on tech trends, labor movement appearances on social media, and economic indicators. He uses this information to inform his work, ensuring that SNTUC stays ahead of the curve when it comes to emerging technologies.

One of Siow’s key initiatives has been setting up Copilot Cowork to automate several tasks for him. This includes daily updates on tech trends, labor movement appearances on social media, and economic indicators. By automating these tasks, Siow can focus on more strategic work, such as driving digital transformation within SNTUC.

Sioiw’s use of AI tools is not limited to personal productivity; he has also built an agent in Copilot to test how AI can help industrial relations officers (IROs) find information on labor laws, guidelines, procedures, and review employment contracts. IROs manage relationships between employers and workers, representing groups of workers to handle employment and workplace disputes.

The agent Siow created is a template for his team to build upon, providing ideas for creating a production version that can support NTUC members more effectively. By leveraging AI tools in this way, SNTUC aims to streamline its operations and improve the services it provides to its members.

Sioiw’s efforts are part of a broader strategy by SNTUC to drive AI adoption within the organization. With over 1.4 million workers represented under Singapore’s unique tripartite system - where unions, employers, and government collaborate to develop labor policies balancing worker protection with economic competitiveness - Siow is an unofficial example-setter for workers navigating new ground in the AI-forward island state.

SNTUC represents a significant portion of Singapore’s workforce, making its adoption of AI tools a crucial step towards preparing its members for the changing job market. By embracing emerging technologies like Microsoft Copilot 365, SNTUC aims to make its workers more prepared for an AI-driven future.

Siow emphasizes that using AI tools is not just about personal productivity; it’s also about driving business value within the organization. He suggests starting with areas where friction can be reduced, responsiveness improved, or decision-making strengthened. By applying Copilot and agents in these areas first, organizations can unlock significant benefits from their adoption of AI.

The use of Microsoft 365 Copilot has been rolled out to SNTUC’s 1,300 employees after an initial test period with 50 staff members in January 2025. Siow was part of the initial group of users and notes that the AI assistant has rapidly evolved and improved since then.

Today, Siow lauds Copilot’s ability to create and integrate work across apps like Outlook and PowerPoint, streamlining his workflow significantly. He uses it to research issues, set frameworks for his team, prepare for meetings, summarize agendas, and gather intelligence for constructive discussions.

The AI assistant has also been adopted by Siow’s team, who use it to help translate technical solutions into layperson language or ideate visuals for presentations. While not perfect, Copilot provides a valuable first cut that can be reviewed and validated by human experts.

Siow acknowledges the importance of constant enablement and connection with staff when introducing new technology like AI tools. To ensure successful adoption, he has worked closely with Microsoft and his internal team to provide regular tips and tricks for getting the most out of Copilot.

The feedback from SNTUC’s employees has been overwhelmingly positive, with about 93% using Copilot and around 74% utilizing it on a weekly basis. Siow is now encouraging staff to experiment with agents, which he believes will help them unlock more value from their AI tools.

Siow offers advice for those still unsure where to begin with AI: focus on the business value rather than the technology itself. By identifying areas where friction can be reduced or decision-making strengthened, organizations can apply Copilot and agents in a way that drives tangible benefits.

Side hustlers on TikTok who rely on the platform’s e-commerce affiliate program may soon face intense competition from artificial intelligence. Brands and TikTok itself are increasingly using AI-generated videos that can imitate human creators’ content with unsettling accuracy.

These synthetic videos have been flooding users’ feeds during key shopping periods like Black Friday-Cyber Monday, when thousands of shoppable videos were posted by creators looking to make a profit through affiliate marketing.

According to Fabian Ouwehand, founder of social-commerce firm Socialscale.ai, these AI-generated videos are akin to ‘little billboards you see along the road out of the side of your eye.’ They can be lucrative: one video maker reportedly drove tens of thousands of dollars in product sales through TikTok’s affiliate program.

Some brands use AI to recreate multiple versions of a successful influencer post, while others rely on it to post hundreds of times per month without paying creators or sending them free samples. This raises concerns about the potential for synthetic influencers to provoke backlash and damage brand reputations if they’re perceived as insincere or deceptive.

Despite these risks, some brands are embracing generative AI for its potential benefits. Anish Dalal, an executive at social commerce firm Third, explained that his company uses AI tools ‘to basically test what works and what doesn’t’ before partnering with human creators on successful concepts. This approach allows them to maximize their return on investment while minimizing the risk of alienating users.

However, not all brands are convinced by the benefits of generative AI. Ashley Wright, CEO of TikTok Shop agency Social Tale, noted that some clients ‘are happy to use AI if it brings them more sales,’ but others worry about its potential impact on their reputation and customer trust.

The debate has gone beyond TikTok’s walls: marketers on social platforms like Meta are struggling to strike a balance between using AI to improve workflow efficiency and avoiding deceptive ads. Some brands see generative AI as a way to save time and money on user-generated content, while others view it with skepticism.

However, the main obstacle for generative AI on TikTok is that videos often veer into the uncanny valley, with synthetic characters sporting tinny computer-generated voices trying to sell products. In one example viewed by Business Insider, an AI-generated influencer showcased a product’s features in a way that defied real-world physics: using a power washer and propane torch to clean their driveway in seconds.

Even when labeled as such, these AI videos can be jarring for users who expect authentic human content from influencers. ‘Never seen green grass burn like that,’ one user commented on a video of an AI-generated influencer using a propane torch to de-weed their driveway. Others have expressed skepticism about the capabilities of products showcased in synthetic videos.

Bad AI can create marketplace quality problems, which are vital for TikTok’s long-term success as it competes with established e-commerce players like Amazon and Walmart. The company has grown into a formidable player since its 2023 US launch, tracking toward over $23 billion in US sales this year according to EMARKETER forecast.

TikTok Shop employee Nicolas Waldmann told Business Insider that some fraudulent sellers were using generative AI to create fake storefronts for non-existent products. This raises concerns about the potential for deceptive content and its impact on user trust. Robert Freund, an advertising and e-commerce lawyer, noted that having synthetic influencers claim they’ve used a product could run afoul of FTC requirements regarding honest opinions in testimonials.

Governing bodies like the European Union and New York state have developed laws requiring brands to disclose when synthetic characters are used in ads. TikTok’s seller documentation states that generative AI is permissible as long as videos are labeled, don’t misrepresent a product or imitate a real person, and respect intellectual property rights.

As the technology improves, more blue-chip brands may start embracing generative AI for its potential benefits. ‘Why spend so much money and time and effort on getting whatever [user-generated content] done when AI can do it for a tenth of the cost?’ Socialscale.ai’s Ouwehand asked.

The summer vacation season is a time for tech companies to think creatively about their workforces. With employees taking time off, it’s essential to keep the workflow running smoothly. For years, this has meant transferring urgent tasks between colleagues or creating overloads for those who remain in the office.

But 2026 marks a shift in approach as technology companies increasingly turn to advanced AI agents to support human teams and ensure business continuity. This change is significant, with many tech giants investing heavily in these digital assistants.

At WSC Sports, AI assistants are seen as an evolution of the digital workplace. The company’s internal pilot aims to evaluate the integration of AI agents into their organizational workflow. Shai Diament, VP of account management at WSC Sports, emphasizes that the ultimate goal is to ease employee burdens and improve professional quality of life.

The concept of AI assistants has transformed from its passive roots. Today’s advanced agents are proactive team members that listen, observe, and integrate seamlessly into organizational workflows. They can surface solutions, insights, and tasks independently in real-time without requiring human intervention.

‘One of the greatest contributions of this technology is allowing employees to truly disconnect during their vacations,’ Diament explains. By doing so, it serves as a vital support anchor for teams, maintaining a steady workflow even when employees are away on summer vacation or peak periods.’

The integration of AI agents is being tested in various roles and departments within WSC Sports’ internal environment. The company aims to closely monitor system performance and identify which positions and interactions deliver the highest value, responsiveness, and support to human teams managing it.

‘Our vision is to execute a broader roll-out,’ Diament says. ‘We want to expand these agents into additional roles across the organization and embed them in our daily operational infrastructure.’ This would alleviate employee workload and drive company-wide efficiency.

At SeatPick, another tech company, AI assistants are taking on a more tangible form with their agent named Naftali. Developed by global ticket resale price comparison platform SeatPick, Naftali assists human teams with both software development tasks and information gathering and analysis.

‘One of Naftali’s biggest advantages is helping us maintain business continuity during non-standard hours,’ Guy Kogel, CTO and co-founder at SeatPick, explains. This includes periods when the company experiences massive traffic due to events like World Cup matches or holidays.’

Naftali serves as a force multiplier by allowing existing workforces to remain productive and stress-free, especially during off-hours or peak periods. Kogel emphasizes that ‘he helps employees perform research and investigations much faster,’ enabling engineering teams to focus on higher-impact projects while other teams can move forward with improvements.

The integration of specialized AI agents is also critical in highly regulated fields like healthcare technology. At Navina, an intelligent agent named Ofir supports service teams for primary care physicians in the US. When a complex question arises regarding a medical recommendation made by the system, Ofir dives deep into full workflow documentation and analyzes calculation steps.

‘AI agents help bridge availability gaps during peak seasons or vacation periods,’ Shlomit Labin, VP AI at Navina explains. They shorten response times, reduce bottlenecks, and allow human teams to focus on truly complex cases while maintaining a continuous workflow and high quality of service.’

At Plarium, the focus is on creating a shared memory layer that acts as a bridge for the entire team. This ensures workflows build upon past achievements rather than repeating them. According to Tomer Daniel, AI product leader at Plarium, ‘it’s essential not to erase employees’ memories every night,’ just like you wouldn’t hire someone brilliant and then forget their work.

Daniel explains that most AI tools behave like this: every conversation starts from scratch with no idea what the team already knows. At Plarium, they are building Second Brain - a shared memory layer that turns documents, decisions, and hard-won lessons into knowledge its AI assistants can use.

‘When a company remembers, it stops reconstructing the past,’ Daniel notes. ‘Get to build on it.’ The potential impact of this technology could be significant for companies looking to streamline operations and improve employee productivity - we’ve seen early results from Plarium’s internal testing with Second Brain.

According to Labin at Navina, AI agents are crucial in fields like healthcare where maintaining business continuity is critical. With the help of AI tools, medical teams can focus on complex cases while ensuring a continuous workflow.

A freelance writer has spoken out about the indignity of being fired by ClickOut Media, only to see his name attached to subpar articles generated by an artificial intelligence system. Ben Touati, a Stockholm-based journalist, was let go from the company’s German operation in early 2026, but his name continued to appear under new articles published on various websites owned by ClickOut Media.

Touati told Press Gazette that he felt ‘slapped in the face’ when he discovered the AI-generated content bearing his name. The articles were riddled with errors and lacked the quality he had come to expect from his own work. He described them as ‘lazy, obviously slop’, adding that there was no sign of a human writer behind the text.

The situation is not an isolated incident for ClickOut Media. Earlier this year, the company faced controversy when it emerged that one of its articles on Videogamer had been written by a bogus AI journalist. Further investigation revealed that numerous other articles across its outlets were also generated by AI systems, some even featuring fake authors with AI-generated profile photos.

Touati first began working with ClickOut Media in early 2024 and was moved between different sites, including Techopedia, iGaming, and Esports Insider. He claimed to have resisted pressure from his managers to use AI for writing articles, saying that they would often remark on the impossibility of getting by without it. Employees were also shown a video demonstrating how to ‘humanize’ AI-written content.

Touati’s experience with ClickOut Media raises questions about the ethics of using AI-generated content and the importance of transparency in authorship. The company’s statement, which cited its use of AI-assisted content alongside human checks and edits, did little to address Touati’s concerns or provide clarity on why his name was used for subpar articles.

The incident highlights a broader issue within the publishing industry: the increasing reliance on AI-generated content without adequate safeguards in place. As more online publishers turn to AI tools, there is a growing need for clear guidelines and regulations to ensure that authorship remains transparent and accurate.

Touati was eventually able to achieve some recourse by filing a claim against ClickOut Media under the European Union’s General Data Protection Regulation. The company subsequently removed his name from the articles, replacing it with another writer’s name. However, the incident has left many wondering about the implications of AI-generated content on authorship and ethics in publishing.

The job market is undergoing a significant transformation with the increasing use of artificial intelligence (AI) in hiring processes. Employers are leveraging AI to review resumes before scheduling interviews, and even using it during virtual interviews to assess candidates’ skills and fit for the role. This shift has raised concerns about bias, authenticity, and the impact on human connection in the job hunt.

As of 2025, a staggering 87% of employers use AI in at least one stage of their hiring process, with resume screening being the most common application. The AI used to review resumes is collectively known as Applicant Tracking Systems (ATS), software that scans for keywords and skills. Tools like HireVue, Paradox, and Indeed Talent Scout offer advanced AI screening for skills, context, and even interviews to help companies filter candidates.

The primary reason employers are turning to AI is to handle the volume of applications, which has increased by 51% since generative AI tools became mainstream. AI can reduce time to hire by up to 75%, making it an attractive solution for businesses looking to streamline their recruitment processes. However, this speed comes at a cost: hiring managers find it harder to assess if a candidate is ‘authentic’ because everyone is using AI to polish their resumes.

AI parsing extracts data from resumes and matches it against job descriptions using specific keywords, leading to a strategic ‘cat-and-mouse’ game between applicants and employers. Roughly 65% of job seekers now modify their resumes specifically to appease algorithms, sometimes removing genuine achievements to make room for buzzwords. This raises concerns about the accuracy and fairness of AI-driven hiring processes.

A 2025 study reveals that while AI can improve hiring fairness, it can also mirror historical biases if not properly ‘debiasing.’ Without proper debiasing, some models favored white-associated names 85% of the time compared to just 9% for Black-associated names. This highlights the need for more robust data analysis tools and algorithms that can detect and mitigate bias in hiring processes.

Both employers and applicants now have access to AI tools during virtual interviews. Applicants can use AI platforms to ‘listen’ to the interview and provide scripted responses that may appeal to the interviewer. These tools can be tailored to specific interview types, assisting with content knowledge, behavioral questions, and in-the-moment skill assessments.

Interviewers also have access to AI platforms that analyze body language, eye movement, and responses to corroborate the candidate’s veracity and enthusiasm. However, this raises concerns about the potential for bias in these tools and the impact on human connection in the job hunt.

The most ‘AI-aware’ generation is also the most skeptical when it comes to using AI in their job search. While 90% of students use AI for schoolwork, only about 33% of graduating seniors report using AI for their job search. About 16% of students avoid using AI in applications because they fear employers will discover it and disqualify them.

Conversely, 46% of Gen Z hiring managers have ‘caught’ candidates using AI to cheat on assessments. Many students feel that the ‘humanity’ has been stripped from the process. They aren’t just applying for jobs; they are trying to solve an algorithm.

The use of AI in hiring processes is a double-edged sword. On one hand, it can transform hiring by providing more accurate and efficient results. However, at its worst, it automates poor judgment and hides bias behind a ‘math’ facade. Technology is powerful, but using it to replace human connection risks a permanent crisis of trust.

Economist Alex Imas from Google DeepMind has expressed concerns about the potential impact of artificial intelligence on jobs. However, he emphasizes that there is currently no evidence to support a widespread white-collar job loss crisis.

A new wave of robot workers is sweeping through Oregon businesses. At Kura Sushi in Beaverton, customers are served by automated waiters zipping up and down aisles with drinks. Meanwhile, at the Portland Convention Center and Portland Zoo, robot cleaners are tidying up after hours, both indoors and outdoors.