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Phrasly, an AI-powered writing tool provider, has introduced Phrasly Docs, a comprehensive document editor designed to streamline the writing process for students and professionals. This innovative platform combines various features into one workspace, eliminating the need to switch between multiple tools throughout the drafting and editing stages.

The smart workspace in Phrasly Docs includes writing, humanizing, editing, an AI writing assistant, plagiarism detection, citations, and real-time collaboration. With this integrated approach, writers can efficiently complete their work without losing context or time switching between different applications.

Traditionally, long-form writing has required multiple disjointed tools. Writers often compose in one tool, verify tone or plagiarism in another, cite sources in a third, and share documents for feedback in a fourth. This fragmentation not only wastes time but also leads to lost context that must be recreated each time the writer switches between tools.

Phrasly Docs addresses this issue by incorporating drafting, review, research, and collaboration into a single editor. By doing so, writers can go from a blank page to a completed, cited document without leaving their workspace. This streamlined approach eliminates the need for multiple point tools, reducing clutter in the writing stack.

The AI Document Editor includes four categories of features that span the entire document lifecycle: Writing, Drafting, and Review; Research, Formatting, and Output; Collaboration; and Humanization. These categories are designed to guide writers through each stage of the process, ensuring a smooth workflow from start to finish.

Within these categories, Phrasly Docs offers various tools and features, including Tab autocompletes, AI Chat Agent with web search built-in, AI Commands with Detect AI and Summarize presets as well as custom AI Commands, an outline builder for structuring long documents, a humanizer that refines AI-assisted sections to sound natural, and a built-in AI detector providing an AI likelihood score and highlighting passages likely written by AI.

The Research, Formatting, and Output category includes features such as automatic citation generation, which scrapes millions of academic and web sources. The platform also supports over 30 languages, making it suitable for multilingual teams working on content across multiple markets. Additionally, the editor allows writers to export their work directly to Word or PDF format.

The workflow in Phrasly Docs is straightforward: Write from scratch or an empty document; use Tab autocomplete and AI Commands to write and edit sentences in place; humanize and proofread the draft by running it through the built-in AI detector; cite and export by inserting formatted citations. This four-step process enables writers to complete their work efficiently without losing context.

One of the key benefits of Phrasly Docs is its ability to make AI-assisted writing sound more natural. The platform’s humanizer rewrites AI-influenced passages so they resemble text written by a person, rather than a machine. This feature is particularly important for students and professionals who need their work to be free from red flags associated with AI-generated content.

Phrasly Docs has been designed specifically for students writing papers and professionals turning in work that will be seen by clients. By addressing the issue of AI-generated text sounding unnatural, Phrasly Docs helps writers maintain trust and credibility in their work.

The platform’s humanizer works seamlessly within the editor, allowing writers to resolve issues related to detection and humanization prior to submitting their work. This eliminates the need for additional tools or starting from scratch, saving time and effort throughout the writing process.

For students, Phrasly Docs offers a comprehensive AI Writing Tool that guides them through an essay or research paper from initial prompt to final draft. The platform’s automatic citation generator scrapes millions of academic and web sources, making it easy to insert formatted citations in various styles while still on the page.

Phrasly Docs also supports professionals and content teams by providing a collaborative workspace for drafting articles, reports, and client documents. Tab autocompletes and AI Commands help speed along first drafts, while real-time document collaboration, auto save, and offline sync enable colleagues to work together without losing progress.

The benefits of using Phrasly Docs are clear: fewer tools in the stack, fewer handoffs between those tools, and less context lost when moving a doc from draft to delivery. By streamlining the writing process, Phrasly Docs helps writers complete their tasks more efficiently, saving time and effort throughout the workflow.

Phrasly is an established provider of AI-powered writing tools, with over 3 million users who have completed over 4 million writing projects and saved approximately 600,000 hours of writing. The company’s suite of tools includes Phrasly Docs, as well as other features such as AI Humanizer, AI Detector, Content Generator, Thesis Generator, Translator, and Plagiarism Checker.

Hospitals are increasingly turning to AI tools to ease administrative burdens and improve patient care. One area where these tools make a significant impact is in billing, coding, and documentation.

However, some unsubstantiated claims have suggested that the use of AI tools has increased coding intensity, leading to higher healthcare costs. These allegations ignore underlying drivers of changes in coding intensity, including an aging population and rising prevalence of chronic disease.

Hospitals are caring for patients with higher acuity due to demographic shifts and advances in medicine. The AHA analysis found that 19% of hospital expense growth from 2019 to 2024 reflects caring for sicker, more complex patients. This trend is also reflected in the rising case-mix index – a standard measure of patient acuity.

The shift of lower-acuity care to outpatient settings has left hospitals with an increasingly complex and resource-intensive inpatient population. AI tools can support accurate coding and documentation by enhancing precision within established frameworks, but human validation remains essential for ensuring coding integrity and regulatory compliance.

Assertions that providers are upcoding due to the use of AI tools are striking given evidence of insurer-driven coding practices. Lawsuits, investigations, and reports from MedPAC, DOJ, and Congress have documented upcoding among large insurers, including adding diagnosis codes not supported by clinical care.

In 2025, MedPAC reported that upcoding contributed to $40 billion in overpayments to Medicare Advantage plans. One payer settled a lawsuit with the DOJ on upcoding allegations in March 2026, while another insurer is facing a state-level investigation for potentially fraudulent practices.

The AHA has urged policymakers to prevent insurers from systematically reducing reimbursement under the pretense of widespread inappropriate upcoding. This would help alleviate administrative waste and burden created by overly burdensome appeals processes.

AI tools are transforming care delivery in countless ways, supporting increased access, improved outcomes, and reduced costs. They offer tremendous potential for reducing more than $1 trillion spent annually on administrative functions. However, their use is not without controversy – particularly when it comes to coding intensity.

Some commercial insurers have asserted that AI tools used by providers are resulting in higher coding intensity. These accusations ignore multiple factors contributing to growth in coding intensity, including an aging population and increasing prevalence of chronic disease.

Hospitals maintain rigorous auditing and coding compliance programs to ensure they capture patient acuity levels accurately and meet regulatory requirements. Coding has always been governed by established guidelines, conventions, and expectations that define a compliant approach to code assignment.

Updates to evaluation and management coding guidelines have altered coding patterns, leading to more specific diagnoses being reported for higher-acuity patients. As providers adapt and data stabilize, these changes contribute to shifts in coding intensity.

The AHA has responded to concerns about AI tools used by providers, emphasizing the need for accurate representation of patient complexity. They argue that hospitals are not engaging in upcoding practices but rather accurately capturing acuity levels due to demographic shifts and advances in medicine.

In contrast, some insurers have been accused of using automated edits to unilaterally reduce reimbursement without reviewing medical documentation – a practice known as downcoding or partial denial. This can lead to burdensome appeals processes for providers seeking reimbursement for medically necessary care.

The AHA has urged policymakers to prevent such practices and ensure that hospitals are fairly reimbursed for the care they provide. They argue that accurate coding is critical for ensuring patient safety, quality of care, and regulatory compliance.

AI tools have tremendous potential for supporting data analysis in healthcare – from streamlining administrative tasks to improving patient outcomes. However, their use must be carefully managed to ensure accuracy, consistency, and fairness in reimbursement practices.

A major breakthrough in artificial intelligence (AI) roleplaying has been achieved with the launch of Latitude’s new platform, Voyage. This second-generation AI roleplay system is now available to everyone on iOS, Android, and the web without an invite code or waitlist.

The core innovation behind Voyage lies in its World Engine, which defines, tracks, and powers the world while AI tells the story. This persistent foundation enables creators to build deep multiplayer worlds that hold together through play, a significant improvement over previous AI roleplay experiences.

First-generation AI roleplay platforms have exploded since the rise of Large Language models, including AI chatbot platforms, interactive fiction, and text adventures. These early systems introduced creative freedom, replayability, and emotional connection but struggled to simulate an actual world over a long time horizon.

The new Voyage platform represents a significant leap forward in AI roleplaying capabilities. By adding a World Engine to powerful improvisational AI models, Latitude has created a system that can sustainably power immersive worlds with complex story arcs and dynamic gameplay.

According to Nick Walton, founder and CEO of Latitude, the company’s previous attempts at creating more engaging experiences were hindered by limitations in their single-AI-model approach. ‘Players kept asking us for things we couldn’t make work,’ he said. ‘Voyage splits those jobs between the World Engine and AI models.’ The engine simulates the world and keeps track of what happens, while the AI turns that into a compelling story.

This new generation of engine has unlocked several key capabilities for gameplay, research, and creation. Visuals can now be generated once for each character and tracked throughout the story, eliminating the need for repetitive rendering. Multiplayer is also improved as the engine resolves each player’s actions against a single world state, preventing chaos in online interactions.

Voyage’s unique World Engine has far-reaching implications beyond gaming. It enables researchers to create environments with long-horizon objectives, open-ended action spaces, and multi-actor social simulations – capabilities that were previously impossible or extremely difficult to achieve using traditional AI methods.

The platform also gives creators more control over their worlds than ever before. Kato, the creator of Voyage’s most-played world, explained why this matters: ‘Before Voyage, building with AI felt like throwing ideas at a wall and hoping something would stick.’ With Voyage, he can decide how non-player characters (NPCs) talk, combat feels, and the environment looks around the player. The engine holds onto these choices, allowing him to build real systems behind the scenes.

Voyage is available for free on public multiplayer servers with no waitlist or invite code required. Anyone can play without limits on parties of up to eight players. Subscribers receive unlimited play on private servers and one Latitude membership covers premium play and creation across both Voyage and AI Dungeon, a platform introduced by Latitude in 2019.

Latitude builds AI-native games and has been at the forefront of large language model development since introducing AI Dungeon as an open-ended interactive fiction platform used by millions of players and tens of thousands of creators. The company is backed by notable investors including NFX, Google’s AI Futures Fund, Griffin Gaming Partners, Album VC, and executives from Roblox and Midjourney.

The recent drop in citations from Reddit on ChatGPT has left marketers scratching their heads. Data from Promptwatch shows that the share of citations plummeted by 86% in mid-August, sparking concern about marketing strategies.

Critics argue that relying too heavily on AI platforms like ChatGPT can make you vulnerable to sudden drops like this one. Experts say it’s essential to consider the bigger picture and adapt strategies accordingly.

Suganthan Mohanadasan, an expert in AI SEO research, dug into the behind-the-scenes web requests made by ChatGPT when researching answers. He found that while ChatGPT still consulted Reddit regularly, it was simply citing it less often in final responses.

Instead, brand websites were being credited more frequently - a development that could be seen as a positive for inbound website traffic. This trend suggests that AI platforms are evolving and adapting to new technologies and user behaviors.

AIVO’s CEO Tim de Rosen warns that this sudden drop should serve as a wake-up call for CMOs who have built their strategies around chasing citations from single sources. He argues that relying on such an approach makes marketers vulnerable to similar collapses in the future.

Duolingo is taking a more nuanced approach to Reddit marketing, says its CMO Manu Orssaud. The company focuses on engaging with users discussing topics relevant to their brand and responding to user needs - rather than tracking visibility metrics.

One thing that’s clear from this sudden drop: AI platforms are constantly shifting. Last September, Reddit’s share of ChatGPT citations also dipped before recovering. This highlights the importance of adapting marketing strategies to stay ahead of the curve.

OpenAI has confirmed that it updates ChatGPT regularly to improve its relevance and usefulness - but maintains that it doesn’t target or set a fixed level of visibility for individual websites. Reddit continues to be cited in ChatGPT, according to OpenAI.

A Reddit spokesperson emphasized their platform’s unique value: 130 million real people having authentic conversations. This makes Reddit an attractive resource for marketers who want to tap into genuine user feedback and opinions - rather than relying on AI-generated metrics.

The shift in citations also serves as a reminder that gaming or exploiting AI platforms can be a risky marketing strategy. Communities like r/HailCorporate have long called out brands suspected of surreptitious shilling, highlighting the importance of authentic engagement with users.

Ultimately, experts agree that marketers should remain agile and adapt their strategies to changing technologies, consumer behaviors, and discovery platforms - rather than trying to game or exploit them.

A team of researchers has developed an AI-powered platform that generates recipes for synthesizing new materials. The platform, built by a collaboration between Sungkyunkwan University (SKKU) and Massachusetts Institute of Technology (MIT), uses large language models to propose synthesis conditions and procedures for complex new materials based on existing literature.

The research team, led by Professor Sung Beom Cho at SKKU, aimed to reduce the trial-and-error process involved in synthesizing new materials. This often requires extensive experimentation with different precursors, temperatures, and reaction times. To streamline this process, they created a database of synthesis information from 4,407 open-access solid-state synthesis papers published in academic journals.

The team applied a retrieval-augmented generation (RAG) method to search for similar existing synthesis cases based on the desired material’s properties and conditions. This allowed them to propose candidate recipes that could be used as starting points for further experimentation.

When compared against actual synthesis conditions reported in papers, these proposed recipes scored an average of around 4 out of 5 on key synthesis variables. The team then tested this AI-generated recipe by synthesizing a new oxy-selenide-based solid electrolyte material for all-solid-state batteries.

However, the initial attempt resulted in several impurity phases forming instead of the target material. To refine their approach, they fed these results back into the model, which proposed follow-up recipes that progressively lowered the synthesis temperature. This iterative process allowed them to eventually synthesize a single-phase new material with no detectable impurities.

The success of this AI-powered platform demonstrates its potential for reducing trial and error in materials design by combining human scientists’ experimental experience with AI’s ability to draw on vast amounts of literature. By leveraging existing knowledge, researchers can quickly incorporate experimental results and develop synthesis methods for a wide range of new materials.

AI has Bill Gates concerned. He thinks governments and society are not preparing for its consequences fast enough.

Gates’ main worry is how AI will affect jobs, both high-skilled ones like software development and lower-skilled ones in customer service or manufacturing. There’s also a risk of new cyber threats and biological risks emerging with the technology.

The tech industry is changing quickly, and Gates thinks this will have big effects on law, medicine, education – basically any sector that relies on human expertise to make decisions. Without action, he predicts there’ll be fewer jobs than today.

Gates says we need a coordinated plan to deal with the transition period ahead. He describes it as one of the most tumultuous in history and blames both US political parties for their lack of knowledge about AI issues.

‘The good stuff is moving slowly,’ Gates said during an interview at his Seattle-area office, ‘and aside from robots, I think the bad stuff will be here soon.’

Gates emphasized that there’s a high chance of things turning out badly if we don’t address these problems. He criticized both parties for their handling of AI policy and stated neither has shown much understanding or thoughtfulness on this issue.

Traditionally, when new technologies emerge, they either automate routine tasks or create jobs requiring human insight to make decisions about complex information. But Gates says AI is different – it can actually do these cognitive tasks itself and even replace them in many cases.

The risks associated with AI are already starting to show up in areas like cybersecurity and biological research. If we don’t address these issues quickly, they could have serious negative consequences.

Gates thinks the US needs new national institutions to handle AI policy across multiple sectors – including security, employment, education, taxation, and health care. Internationally, he’s advocating for an organization that can oversee global AI development in a similar way that international aviation and nuclear regulation are handled.

One of Gates’ more unconventional ideas is taxing robots or their use so humans don’t lose out to automation. He first floated this idea nearly 10 years ago but hasn’t seen significant progress yet.

Gates acknowledges the world’s current geopolitical situation makes it hard for countries to work together on big issues like AI regulation. But if someone had a solid plan to slow down global AI advancements, he’d likely be open to supporting it – even though he doubts that’ll happen anytime soon.

‘I don’t know anyone who isn’t concerned about the impact of this technology,’ Gates said in an interview with Axios, echoing what many top tech companies and research labs have privately expressed about their fears regarding AI’s influence.

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.

Retail marketing teams are embracing AI-generated videos to showcase products in new and innovative ways. This shift is driven by the limitations of traditional image-based product representation, which can’t capture how a product is used or interacted with. Video has long been seen as an ideal solution, but its high production costs and time-consuming nature have made it inaccessible for many retail companies.

For mid-sized retailers selling multiple products each season, creating videos was often cost-prohibitive. They could only afford to produce one or two videos at a time, leaving the rest of their product line without visual representation. This changed with the advent of AI-generated video technology, which has streamlined the process and made it more affordable.

The integration of AI-powered video generation into existing workflows is key for retail marketers. Software like CapCut now incorporates ByteDance’s video generator directly into its editor, allowing users to choose a product reference or text prompt and start working on a video immediately. Seedance 2.5 AI video maker is another example of this type of software.

AI-generated videos are particularly useful for certain types of content, such as demonstration videos for A/B testing different angles and settings. They can also be used to create localized versions of campaigns without the need for new actors or extensive re-shooting. Social media videos that require quantity over quality can also benefit from AI-generated video.

However, there are still some limitations to AI-generated video technology. Campaigns requiring a high level of control and attention to detail are best left to human-directed production. Retail marketers must learn how to use AI generators effectively, producing large quantities of videos while focusing on the most critical ones.

The skill that’s becoming increasingly valuable in this new landscape is the ability to write effective prompts for AI-generated video. A well-crafted prompt can result in a high-quality video, but generic or poorly written prompts will yield subpar results. Marketers must develop their skills in creating compelling product descriptions and transferring them into actionable prompts.

As retail teams continue to experiment with AI-generated video technology, they’ll need to adapt quickly to its capabilities and limitations. Those who master the use of these tools can gain a significant competitive advantage by producing high-quality videos that would be impossible to create within traditional budget constraints.

The Claude AI app has just landed a major funding boost, bringing in $76 million from prominent record labels and AMD. This significant investment is a big deal for the company as it works on developing its artificial intelligence capabilities.

This latest development fits into a larger trend where companies are pouring millions of dollars into their AI operations to drive growth and innovation. Other recent examples include Emerald AI’s massive $150 million raise, which valued the company at $1.05 billion, and Perplexity AI’s release of Portable Computer – an on-device AI agent that lets users access powerful tools directly on their devices.

Claude AI isn’t alone in its pursuit of creating top-notch AI solutions for businesses. The startup is joining a growing list of companies like Stability AI, which has also received funding from major record labels and AMD in a separate round. Moreover, Alice is seeing significant growth in its AI security business, with revenue jumping more than 500%.

The tech industry is buzzing right now as various startups are racing to establish themselves in their respective fields. Gatik’s recent $200 million raise will help it expand its driverless fleet beyond the initial 100 trucks on the road. Meanwhile, Keenable has secured $26 million to develop its agentic web search infrastructure.

Claude AI’s funding round highlights just how much interest there is in AI solutions for businesses right now. As more companies pour money into this space, it will be fascinating to see how Claude AI and other startups manage the challenges of a competitive market.

The University of Texas at Arlington’s (UTA) College of Business is set to launch its Artificial Intelligence for Business program in Fall 2026, replacing the existing Business Analytics programs. This move marks a significant shift towards preparing students for an industry increasingly driven by AI technology.

The new program will offer both bachelor’s and master’s degree options, with a redesigned curriculum that focuses on using AI to drive business strategy, productivity, and decision-making. According to Dr. Radha Mahapatra, chair of the information systems and operations management department, the MSBA and BSBA programs were originally designed to equip students with data analytics skills.

However, as machine learning technologies became more prevalent, the ISOM faculty felt the need to completely redesign the curricula in response to recent advances in generative AI and agentic AI. This change is crucial for business schools like UTA’s College of Business, which must prepare graduates who can effectively leverage AI tools for business.

Dr. Brian Klaas, dean of the College of Business, emphasized that as the business world continues to be transformed by artificial intelligence, it is critical that business schools adapt their curricula accordingly. ‘As AI reshapes industries and redefines work,’ he said, ‘business schools have a responsibility to ensure students understand both the opportunities and challenges associated with these technologies.’

The revised curriculum will help teach business students how to use state-of-the-art technology to streamline business operations, drive productivity gains, and support business strategy and decision-making. According to Mahapatra, this is essential for preparing students and their families for the realities of today’s workforce.

Associate dean Fernando Jaramillo noted that now was the right time to make these changes as artificial intelligence fundamentally alters how organizations operate, the skills employers demand, and the competencies required for future leadership. ‘The College of Business is uniquely positioned to respond to this opportunity,’ he said, drawing on faculty who are recognized leaders in AI research.

The curriculum updates include renaming several courses to better reflect the program’s expanded focus on artificial intelligence and its business applications. For example, Introduction to Business Analytics has been renamed Introduction to AI for Business Applications, while Ethical and Social Issues in Data Science is now Ethics of Artificial Intelligence Applications.

Ethical AI use is a critical part of the redesigned program, with students learning how to apply AI tools effectively to improve productivity, support decision-making, and solve business problems. This includes considering issues such as bias, transparency, privacy, and responsible use. ‘Our goal,’ Mahapatra said, ‘is to prepare graduates who can use AI thoughtfully, effectively, and ethically in a rapidly changing workplace.’

Changes also include the addition of an advanced cloud computing elective and both a seminar and capstone in AI for Business. Beyond the degree programs, the college is expanding opportunities for students and professionals to build AI skills through new certificate offerings.

These expanded offerings give students and working professionals multiple ways to develop AI expertise based on their academic and career goals. Students can add an AI for Business certificate to another business discipline or pursue the major for a more in-depth focus, while working professionals can earn a graduate certificate without committing to a full master’s degree.

According to Jaramillo, offering AI education at both undergraduate and graduate levels is essential because these programs serve distinct student populations with different learning needs and career objectives. ‘Together,’ he said, ‘these programs create a comprehensive educational pathway that prepares emerging talent and current professionals to succeed in an AI-driven economy.’

The College of Business aims to strengthen the connection between classroom learning and today’s workforce by preparing both students and faculty to thoughtfully engage with AI. The goal is to prepare graduates who can identify opportunities, lead change, and create value in a business world increasingly shaped by AI.

Dartmouth College is set to host a national conversation on artificial intelligence this fall, marking a significant milestone in the field’s development. The conference, titled ‘AI at 70: What Must Remain Human?’, will take place from October 29-31 and bring together leaders from academia, industry, government, and the arts to discuss some of AI’s most pressing questions. As people increasingly rely on artificial intelligence for decision-making, learning, and work, concerns about human judgment and responsibility are growing.

The conference is a direct continuation of Dartmouth’s pioneering role in AI research. In 1956, the college hosted the summer workshop that launched the field of artificial intelligence, with mathematicians John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon exploring the possibilities of machine intelligence. Now, seventy years later, Dartmouth is convening a national conversation about the human choices and responsibilities arising from AI’s increasing capabilities.

The conference will feature keynote addresses by prominent figures in the field, including Mira Murati, co-founder and CEO of Thinking Machines Lab; Peter DeSantis, senior vice president at Amazon; Alan Trefler, founder and CEO of Pegasystems; and Hany Farid, a pioneer in digital forensics. Yann LeCun, a recipient of the 2018 Turing Award for his work on deep learning and computer vision, will receive the AI at 70 Award to honor his contributions to the field.

Dartmouth researchers are also presenting preliminary findings from a multi-year study examining how students’ use of AI affects their learning, confidence, judgment, and preparation for the labor market. The research aims to provide insights into the impact of AI on education and workforce development. Supported in part by The Rockefeller Foundation, which helped fund the original gathering in 1956, the conference will explore the human choices and responsibilities that arise as these systems become increasingly capable.

Among the speakers is Mira Murati, who will deliver a keynote address. As co-founder and CEO of Thinking Machines Lab, she has been at the forefront of AI research and development. Her work focuses on building intelligent machines that can learn from data and make decisions autonomously. The conference also features Peter DeSantis, senior vice president at Amazon, who is working on issues related to artificial general intelligence, chips, and quantum computing.

The event schedule highlights the next generation of AI researchers through student presentations and an AI at 70 Student Hackathon sponsored by Anthropic. Participants from Dartmouth and other institutions will build, prototype, and present solutions at the intersection of AI and human judgment. The program also includes a ‘Clips and Conversations’ event at the Hopkins Center for the Arts featuring an interactive AI demonstration and discussion on how AI is being used in the arts.

The conference marks a significant milestone in the development of artificial intelligence, and Dartmouth’s role in shaping its future cannot be overstated. As President Sian Leah Beilock notes, ‘Dartmouth started this conversation in 1956. Now we have a responsibility to continue it.’ The event is an opportunity for leaders from various fields to come together and discuss the human choices and responsibilities arising from AI’s increasing capabilities.

Dartmouth researchers are exploring how universities can evolve in the AI age with President Beilock; Bank of America Executive Vice President and CFO Alastair Borthwick, Tuck ‘93; University of Delaware President Laura Carlson ‘97; and Kristen Ellis of the U.S. Department of Energy. The panel will examine how institutions should adapt to the changing landscape of artificial intelligence.

The conference also features a session on AI and international security and conflict with Sasha Baker ‘05, OpenAI’s head of national security policy and a former acting undersecretary of defense for policy. Additionally, there is a panel on AI and the creative arts featuring Hany Farid, Donald Knuth Professor of Computation and Just Communities, who has nationally recognized work exposing deepfakes.

The event will also include a session on ethical deployment of AI in underserved communities and another on AI in medicine and biomedical research with Dartmouth professors using AI for medical education and wellness. As the conference demonstrates, universities face fundamental questions about their purpose and future as AI transforms how knowledge is created, accessed, and applied.

Dartmouth’s holistic liberal arts education makes it an ideal place to have these conversations, says Professor of Engineering Peter Chin, who leads the faculty committee responsible for planning the event. ‘The future of AI will be shaped as much by values and institutions as by algorithms,’ he notes. The conference is a testament to Dartmouth’s commitment to exploring the human implications of artificial intelligence.

The program also includes an interactive AI demonstration and discussion on how AI is being used in the arts, featuring clips from various performances and conversations with artists who are pushing the boundaries of what AI can do. A musical performance by the Dartmouth Symphony Orchestra will round out the event.

A possible strike by security workers at Anthropic’s San Francisco offices has prompted the company to instruct its staff to work remotely. The move affects Monday and Tuesday of this week, a precautionary measure taken in case of a potential walkout by Allied Universal employees who provide security services for the office building.

The union representing these security workers, the Service Employees International Union (SEIU), has been engaged in protracted contract negotiations with Allied since April. The SEIU is pushing for higher wages, better healthcare, and more job training for its members, including thousands of security workers across California.

Anthropic received a notice from Allied last week indicating that there might be a strike by their employees. In response, the company told its staff to work from home on Monday and Tuesday as a precautionary measure. However, the SEIU claims it had not called for a strike authorization vote or issued any strike threats for this week.

The situation highlights ongoing labor disputes in the tech industry, particularly when security is concerned. Anthropic and OpenAI have reportedly dealt with several threats against their employees in recent years, leading to increased investment in security measures.

Anthropic has a hybrid work policy that requires staff to be present in the office at least 25% of the time. But due to the possible strike, this arrangement is currently on hold for two days. The company’s decision may also be linked to its upcoming initial public offering (IPO), which it plans to file as soon as August.

Anthropic has a significant valuation of $1.5 trillion on secondary markets and is expected to make its IPO filing official in the coming months. This move will likely be closely watched by investors and industry observers, given Anthropic’s decision to take precautions regarding security arrangements.

A senior executive at OpenAI has left the company, adding to a string of high-profile departures from the artificial intelligence lab. Chris Malone, who headed up data centers for OpenAI, is no longer with the organization, CNBC confirmed. This latest exit comes as OpenAI faces growing scrutiny over its ambitious infrastructure plans and mounting backlash against data centers in the US.

Malone joined OpenAI in March 2025 after a distinguished career at Meta and Google, where he worked on data center infrastructure. He played a crucial role in overseeing OpenAI’s plans to spend around $600 billion on compute by 2030. An OpenAI spokesperson downplayed Malone’s departure, stating that the company had recently reorganized its infrastructure organization to support its rapid growth.

The statement emphasized that OpenAI has a strong and experienced data center team in place, with clear leadership and technical expertise to execute its plans. However, this reassurance may not be enough to alleviate concerns among investors, particularly as OpenAI prepares for what is expected to be a massive IPO. The company confidentially filed its prospectus with the Securities and Exchange Commission in June but has yet to announce an official debut date.

OpenAI’s executive departures have been notable this year, with several high-profile exits in recent months. In addition to Malone, revenue chief Denise Dresser left after less than a year on the job, while Brad Lightcap ended his eight-year run at OpenAI to ‘start something new.’ Fidji Simo also announced her departure last month, citing a need to focus on managing a chronic illness.

Four other executives departed in April, sparking surprise among some investors. As OpenAI works to justify its $852 billion valuation ahead of the IPO, these departures have raised questions about the company’s leadership and stability. OpenAI President Greg Brockman downplayed concerns, suggesting that the wave of exits is not unusual given the spotlight on the organization.

Brockman noted that every departure at OpenAI gets scrutinized due to its high profile, but this does little to address growing concerns over AI infrastructure in the US. The National Republican Senatorial Committee has warned that data centers have become a ‘sleeper issue’ for the midterm election cycle, highlighting the mounting backlash against these facilities.

OpenAI’s CFO Sarah Friar told employees in an all-hands meeting this month that the company will be public by 2027, but the executive departures and growing scrutiny over AI infrastructure may pose significant challenges to its success.

A major player in the development of artificial intelligence (AI) systems has announced a significant investment in understanding their impact on users’ wellbeing. Anthropic, the company behind the popular conversational AI model Claude, is launching a $5 million grant program to fund independent research into how AI affects those who use it.

According to the company, AI systems have become an integral part of modern life, with many people relying on them for work, learning, and problem-solving. However, as these systems become more conversational and emotionally supportive, there is a growing need for clear standards on their behavior in sensitive conversations.

The grant program aims to address this gap by providing direct funding, access to Anthropic’s models, and technical support to researchers building open-source evaluations of AI’s impact on wellbeing. These evaluations will help the industry measure how its models affect users and develop more effective safeguards against potential harms.

One of the key challenges in evaluating AI’s impact on wellbeing is assessing context-dependent behaviors. Unlike simple accuracy metrics, which can be applied to individual answers or tasks, wellbeing requires a deeper understanding of complex conversations and nuanced user interactions.

For instance, a user may not immediately share thoughts of self-harm during an initial conversation with an AI model. However, as the conversation progresses and more context is revealed, it becomes clear that the user needs a cautious response from the model to ensure their safety. Similarly, a response that might be reasonable in one context could be harmful or inappropriate in another.

Anthropic acknowledges these complexities and has been working on developing safeguards to identify sensitive conversations and provide appropriate responses. However, the company recognizes that this is an ongoing process requiring continuous research and improvement.

The grant program seeks to invite more experts from various fields, including clinicians, psychologists, methodologists, and others, to contribute their expertise to this emerging field. By funding independent evaluations and benchmarks of user wellbeing, Anthropic hopes to create a comprehensive understanding of AI’s impact on users’ lives.

As part of the program, Anthropic is sharing guidance from its Safeguards team on what makes a wellbeing evaluation rigorous enough to build upon. The company emphasizes that successful evaluations should clearly define what they are measuring and involve clinical and subject-matter experts in their design and validation.

Additionally, grantees will be expected to test both precautions and harms, reflecting how users actually use AI systems in real-world scenarios. This includes constructing multi-turn conversations where risk escalates and context shifts over the course of a long conversation. Finally, evaluations should validate their graders against real subject-matter experts.

Anthropic is encouraging researchers from around the world to apply for the grant program by September 21. Applicants who are selected to submit full proposals will be notified by October 5. The company’s efforts in this area demonstrate its commitment to developing more responsible and user-centric AI systems.

Adobe Firefly has made significant strides in its generative audio capabilities by consolidating music, speech, and sound effects into a single platform. This move marks a substantial upgrade for the company’s home for AI-driven tools and technologies.

The latest development comes as part of Adobe Firefly’s ongoing efforts to streamline creative workflows with AI-powered solutions. While previous updates have focused on agentic workflow enhancements, this new audio capability is now available to all users, promising commercially safe AI audio for various applications.

For those working in film or video production, the integration of these tools may be particularly relevant. Adobe Firefly’s Audio Tools are designed to provide a comprehensive suite of features that cater to diverse creative needs. The platform has been expanded to include Generate Music, Generate Speech, and Generate Sound Effects, all now live and generally available.

Generate Music, powered by the Firefly Music Model, enables users to create universally licensed original tracks tailored to their video’s length and mood. This feature ensures that music can be used across various platforms without worrying about copyright issues or takedowns.

The Generate Speech tool, driven by the Firefly Speech Model with an option for ElevenLabs integration, converts scripts into clear, natural voiceovers with adjustable parameters such as voice, pacing, and emotion. This functionality is particularly useful for video projects requiring high-quality voiceovers.

Generate Sound Effects, powered by the Firefly Audio Model, generates custom sounds that match the action, timing, and energy of content. These sound effects can be tailored to specific needs, making them a valuable asset in film or video production.

The Generate Music tool has been available for some time but is now becoming an increasingly popular option within Adobe Firefly’s toolbox. Its ability to generate original tracks safe for commercial use makes it an attractive choice for users seeking reliable music solutions.

Adobe also announced that its Firefly AI Assistant, currently in Beta, is gaining capabilities and is free to try. The AI assistant can be used for a broader range of tasks, including creating storyboards for films or video projects and developing brand kits for marketing initiatives.

The company has expanded the list of industry-leading models available within Adobe Firefly by incorporating Gemini Omni Flash alongside other top models from companies like Google, ElevenLabs, Kling AI, Luma AI, OpenAI, Runway, and others. This move is expected to enhance the platform’s capabilities and provide users with a more comprehensive set of tools.

For those interested in exploring these new audio features or learning more about Adobe Firefly’s offerings, including pricing and availability information, can visit the company’s website for further details.

A new study has shed light on the workings of image-generating AI models, revealing that it’s impossible to determine which human images or artworks were referenced in creating an AI-generated output. The research, published last week in Nature Communications, used a novel approach to understand how diffusion models arrive at their final outputs: by taking them apart and examining each component individually.

The study was led by Zheng Dai, a fourth-year PhD student at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who wanted to know whether AI-generated images reference specific human artworks or faces. To answer this question, Dai and his coauthor, David Gifford, built custom models trained on online image databases, ranging from hundreds of images to hundreds of thousands.

The researchers then ran a series of experiments to see how the models’ outputs would change if they removed particular images from their training datasets. They found that as the number of images in the dataset increased, it became increasingly difficult to determine which specific images influenced the AI-generated output. This phenomenon is dubbed ‘attribution decay.’

In essence, the study demonstrates that at large training set sizes, it’s not only impossible to attribute generated images to their human counterparts but also to identify the real people or artists behind them. The sheer scale of the training dataset obscures its creative process, making it akin to trying to spot a single tree in the Amazon Rainforest from space.

This has significant implications for artists who try to sue AI companies on grounds of intellectual property theft. No matter how much an AI-generated image may resemble their own work, they cannot be certain which images were referenced by the model to create its output. As Dai notes, commercial diffusion models are many orders of magnitude larger and thus far less attributable than the test models used in the study.

The researchers also highlight that these black box systems operate differently from human creators. When humans produce art or writing, they’re usually aware of the influences shaping their work. In contrast, AI-generated images rely on the totality of their training data to create new outputs, often in ways that remain mysterious even to us.

Dai emphasizes the importance of understanding how these models function for proper study and regulation: ‘It’s essential for us to comprehend how they operate so we can properly regulate them.’ The study underscores just how far legal experts still have to go to untangle the complex intellectual property questions surrounding AI-generated images.

Enterprise AI has long been framed as a binary choice between automation and augmentation. However, the conversation around AI investment decisions is evolving to reflect new realities on the ground. Over the past 18 months, something significant has changed – AI systems are now acting independently within enterprise workflows, making decisions and taking actions without human intervention.

This shift marks a departure from traditional notions of automation and augmentation. Automation involves removing human involvement from decision-making processes where logic is clear, inputs consistent, and individual errors manageable. Think invoice matching or basic compliance checks. Augmentation, on the other hand, keeps humans in the loop, using AI to strengthen judgment when context varies, stakes are high, or accountability matters.

But there’s a new mode emerging – one that requires a fundamentally different approach to governance and decision-making. This is where orchestration comes in – an agent coordinates a sequence of decisions and actions across multiple steps of a process, invoking systems, triggering handoffs, and adapting based on what it finds along the way. When it reaches a boundary requiring human judgment, it stops, surfaces its work, and waits for direction.

Orchestration is not just about automating individual decisions or augmenting human judgment; it’s about streamlining complex workflows by giving agents permission to traverse seams where ownership is ambiguous and ROI hard to attribute. This mode eliminates entire categories of coordination overhead – but also introduces new risks and challenges that most enterprise AI strategies are not yet equipped to handle.

The value proposition for orchestration lies in its ability to tackle the biggest coordination costs in an enterprise: those occurring at the seams between processes, where work moves from one person, system, or workflow to another. A contract renewal is not a single decision; it’s a sequence of 12 decisions and actions that require human coordination – until now.

Most enterprises have spent years automating individual steps while underinvesting in these critical seams. Orchestration changes this dynamic by giving agents the authority to navigate complex workflows, reducing overhead costs and increasing efficiency. But with great power comes great risk: an orchestration failure can produce a sequence of actions across multiple systems before anyone even notices – making it harder to catch mistakes upstream.

So how do CIOs decide which mode applies? The first question should not be whether AI can help; rather, which of the three modes is being proposed. Most proposals are misframed as one mode when they’re actually another. A vendor might sell automation but use a case that’s really orchestration in disguise – requiring coordination across multiple systems.

A team may pitch augmentation, but the AI system is making decisions and asking for human approval as a formality – essentially automating with a checkbox. Naming the correct mode is half the governance work; once you have it right, ask the questions that mode demands: clean data and clear logic for automation, real decision-quality lift for augmentation, or bounded scope with reversibility for orchestration.

A fourth way AI shows up in enterprises doesn’t fit into any of these three modes. It’s when AI stops being a tool supporting business operations and starts becoming the business itself – embedded in products, generating new intellectual property, or creating capabilities companies couldn’t deliver before. This is where competitive advantages will be built over the next decade.

The CIO’s job in the coming years isn’t to have an opinion on every AI technology; it’s to categorize each investment correctly because the wrong mode produces the wrong governance, success metrics, and leadership attention allocation. Automation needs clean data and clear logic, augmentation requires design discipline and a real theory of decision quality – while orchestration demands reversibility, bounded scope, and maturity in designing failure modes before success ones.

Most enterprise AI strategies treat all three as interchangeable; using the same governance template, ROI framework, or deployment timeline. This works for automation but will fail for orchestration in ways that are expensive and public. The simplest way to get ahead is to start naming these modes – out loud, in meetings, before budget conversations.

Automate, augment, or orchestrate: the naming matters. CIOs who can categorize cleanly are those whose AI investments will scale. It’s time for a new framework that acknowledges the evolving nature of enterprise AI and its applications.

Cognida has made a significant move in the enterprise software space by acquiring Automate, an AI platform that simplifies complex accounting and operational workflows. This acquisition marks a major expansion of Cognida’s strategy to combine consulting expertise with reusable software products.

The key areas where Automate shines are quote-to-cash and procure-to-pay operations. The platform has some impressive capabilities, including ASC 606 revenue accounting, contract review, deal-desk operations, order management, invoice and expense processing, purchase-order matching, and compliance. It also integrates seamlessly with ERP and CRM systems to ensure business data is processed according to established policies, rules, and internal controls.

The team behind Automate has a deep understanding of working with big public companies, private equity-backed businesses, and high-growth tech firms. In fact, the platform has already handled millions of business documents through production environments used by these types of organizations. This expertise will undoubtedly benefit Cognida’s customers as they look to implement AI-native solutions.

The acquisition gives Cognida a proven software platform, accounting and operations know-how, enterprise customer relationships, and additional proprietary technology. Automate will continue to operate as its own distinct product within Cognida, allowing the company to build on existing strengths while expanding its offerings.

Cognida’s familiarity with Automate dates back three years, during which time they worked directly with the platform. This prior experience has given them a deep understanding of the technology, customers, and implementation environment. The entire Automate team is joining Cognida, including Nick Tiscornia as general manager and Rama Pagadala as senior vice president of engineering.

Cognida’s Founder And CEO Feroze Mohammed emphasizes that most enterprises today have AI pilots but lack the operational infrastructure to support them. He highlights context, controls, and accountability as key for successful AI implementation.

Google Cloud has launched a tailored AI solution designed to automate complex legal workflows across various practice areas. This specialized platform is engineered specifically for the needs of law firms, addressing the unique requirements and challenges that general-purpose tools often struggle with.

The unveiling marks an important step in Google’s efforts to provide industry-specific solutions. Gemini Enterprise for Law Firms is part of a series of tailored packages built on top of the secure, fully-governed Gemini Enterprise platform. Each solution delivers domain-specific AI capabilities, including custom agents and data connectors.

Law firms have long been hampered by manual processes and fragmented point-solutions that create a steep integration tax. General-purpose tools often lack the necessary controls for confidentiality, ethical walls, verified legal authority, and complete data isolation. Gemini Enterprise for Law Firms addresses these challenges head-on with its purpose-built capabilities and custom connections.

Thomas Kurian, CEO of Google Cloud, emphasized how ‘agentic AI’ empowers law professionals to research across historical case law, build complex arguments, and automate mundane tasks that provide enormous value to clients. However, ensuring the accuracy, factual grounding, and adherence to legal authority in these workflows is critical. Gemini Enterprise for Law Firms helps manage modern legal work while providing essential governance capabilities.

The solution includes four core components: custom skills for law agents, connectors to specialized systems, a third-party agent ecosystem, and the secure Gemini Enterprise platform. The platform provides a centralized control plane with full visibility, risk management, audit logging, and native governance architecture.

Gemini Enterprise for Law Firms automates complex operations such as proactive regulatory horizon scanning, data discovery, DSAR response, contract review, negotiation, and motion to seal preparation. It also builds and updates contracting playbooks by extracting key terms from legacy agreement archives. The platform’s agentic AI capabilities enable rapid generation of non-disclosure agreements with complete formatting confidence.

The solution is built on Google’s end-to-end first-party AI stack, spanning global infrastructure, custom silicon, foundation models, and an AI-ready data platform. Security remains paramount throughout the stack: client data stays within the organization’s private cloud perimeter, base models are never trained on customer data, and existing permissions are inherited via direct connectors.

Leading law firms such as Cleary, Freshfields, Weil, and Williams & Connolly have already adopted Gemini Enterprise for Law Firms in preview. These early adopters highlight the practical value of this solution in streamlining legal operations and delivering high-quality work to clients.

Cleary’s Managing Partner Jeff Karpf noted that using Google’s Gemini Enterprise allows them ‘to unlock greater efficiencies’ for their teams while maintaining quality work. Freshfields Global Managing Partner Alan Mason emphasized the importance of combining frontier technology with expertise, governance, and institutional knowledge to create value for clients.

Weil’s incoming Executive Partner Ramona Nee expressed enthusiasm for collaborating with Google Cloud engineers and shaping the platform based on sophisticated legal practice realities. Williams & Connolly Chairman Joe Petrosinelli welcomed partnering with Google Cloud in integrating Gemini Enterprise for Law Firms to streamline workflow and support litigators in critical client outcomes.

Google Cloud offers a powerful AI stack that enables organizations to transform their business for the Agentic Era, providing customers worldwide with trusted technology partnerships. By harnessing agentic AI capabilities, law firms can deliver high-quality work to clients while maintaining quality control and adherence to legal authority.

OpenAI has announced the results of its first custom inference chip, Jalapeño. The company claims that this new architecture delivers industry-leading speed and efficiency in artificial intelligence (AI) inference tasks. According to OpenAI, Jalapeño can serve more AI work per unit of power while also returning responses more quickly than existing hardware systems.

The performance gains offered by Jalapeño are significant. In tests conducted on three public models - GPT-OSS 120B, DeepSeek R1, and Kimi K2.5 1T - the chip delivered a better combination of throughput, power efficiency, and latency compared to leading commercially available AI systems. Specifically, it achieved 1.5 to 1.9 times more AI work per watt at peak throughput and 1.7 to 3.6 times lower end-to-end latency than the comparison systems.

The benefits of Jalapeño’s performance are not limited to just one or two specific models. The chip was designed from the start with a focus on serving modern and future language models, particularly interactive agents. As such, it excels in both prefill and decode phases, which have different bottlenecks. By minimizing data movement and communication delays, Jalapeño ensures that model state can be explicitly placed and kept local while activating the right combination of compute, memory, and networking for each inference phase.

Jalapeño’s development was also facilitated by AI itself. The team used OpenAI models to explore implementations, shorten design, measurement, and verification loops, and continuously iterate on model workloads. This allowed them to move from initial design to tapeout in just nine months. Additionally, AI helped optimize the chip’s arithmetic circuits, enabling more compute performance to be fit into the chip on schedule.

The architecture of Jalapeño is designed as a clear, predictable programming target for both humans and AI. Engineers can describe work through local tensors, explicit communication, and predictable synchronization, making it easier for AI to tackle parallel programming tasks. This flexibility was demonstrated when the team used Codex with GPT-Astra to bring three open-weight models not part of Jalapeño’s original production plan to high performance within two months.

The results from these tests are impressive. On Kimi K2.5 1T, for example, Jalapeño delivered approximately 1.5 times higher peak performance per watt and 3.4 times lower end-to-end latency than the comparison system. In internal testing, the advantage of Jalapeño widened further on frontier OpenAI models, suggesting that its architecture becomes more valuable as workloads grow larger and more demanding.

Jalapeño’s impact extends beyond just improving performance in AI inference tasks. By producing more useful work from the same power and hardware, it can help serve more demand and lower the cost of delivering a successful result. This is particularly important for OpenAI, which aims to ensure that artificial general intelligence benefits all of humanity.

The deployment of Jalapeño within OpenAI’s compute infrastructure is expected to begin by the end of this year. It marks the first generation of a multigenerational roadmap, with Gen 2 already in development and Gen 3 taking shape. Each subsequent generation will build on what has been learned and further advance both efficiency and speed.

The growing demand for AI requires more compute from every available source. To meet this need, OpenAI plans to continue deploying accelerators from NVIDIA and other partners for both training and inference workloads. The company’s mission remains unchanged: to ensure that artificial general intelligence benefits all of humanity.

Higher peak mixed TPS/kW ≈1.9×

85,448 vs. 44,960 mixed / kW

Lower end-to-end latency ≈1.7×

1.03 s vs. 1.80 s

Lower min TBT ≈2.7×

0.69 vs. 1.87 ms (1,459 vs. 535 tok/s/user)

More throughput at previous TBT ≈53.7×

22,935 vs. 427 mixed / kW (at 535.28 tok/s/user)

Higher peak mixed TPS/kW ≈1.7×

19,641 vs. 11,781 mixed / kW

Lower end-to-end latency ≈3.6×

1.65 s vs. 5.99 s

Lower min TBT ≈4.1×

1.43 vs. 5.90 ms (700 vs. 169 tok/s/user)

More throughput at previous TBT ≈104.3×

12,258 vs. 118 mixed / kW (at 169.41 tok/s/user)

Higher peak mixed TPS/kW ≈1.5×

18,195 vs. 11,862 mixed / kW

Lower end-to-end latency ≈3.4×

1.56 s vs. 5.31 s

Lower min TBT ≈3.8×

1.44 vs. 5.48 ms (694 vs. 182 tok/s/user)

More throughput at previous TBT ≈56.1×

6,744 vs. 120 mixed / kW (at 182.46 tok/s/user)