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DoorDash sees limited AI orders, but explores new tools
DoorDash CEO Tony Xu discusses AI agents like Meta's Muse, revealing low order numbers but plans for enhancing DoorDash's AI tools.
AI tools create new openings for hackers as businesses adopt them, report warns
The Comcast Business AI cybersecurity report warns that hackers are using AI to refine attacks while employee adoption of AI tools creates new security risks.
A Dead Salmon's Fake Brain Activity Still Haunts Science, and AI Tools Can Fall for the Same Trap
A dead salmon once showed fake brain activity on an fMRI scan. Now AI researchers say tools that explain AI models face the same statistical trap.
AI in policing risks deepening discrimination, research warns
AI tools learn from biased records and may deepen discrimination against minorities without oversight, researchers say.
Best AI Tools for Small Business Marketing (2026): CapCut Featured for Creating Professional Video and Visual Content by Expert Consumers
Expert Consumers has recognized CapCut in its 2026 coverage of the best AI tools for small business marketing, highlighting the platform's collection of AI-powered features for creating and editing video, images, audio, and other visual content. The recognition reflects growing interest in tools that support practical marketing activities, including campaign development, promotional content production, and adapting creative assets for different digital channels.
ClicData Launches MCP Server to Bring Trusted Business Data to AI Assistants for Midmarket Teams
Every query runs inside existing ClicData permissions, security protocols, and audit trails, so AI tools reach only data a user is authorized to see.
Science of Learning Should Guide AI Use Decisions (opinion)
AI tools may be evolving quickly, but we have a deep research base we can draw on to determine how they help meet—or undermine—any particular learning goal.
OpenAI acquired biotech startup Orchestra Bio to expand its life sciences AI tools
OpenAI will fold Orchestra's team and technology into Rosalind, its AI models and tools for life sciences researchers
AI facial health recognition tools help physicians assess age and survival risk to guide personalized treatment
Researchers are using AI tools to analyze simple facial photographs to determine key indicators of a person's health. Mass General Brigham researchers built FAHR-Face (Foundation Artificial Intelligence ...
Your Company Doesn’t Need More AI Tools. It Needs These 6 Operating Changes
Giving employees another AI tool won’t transform a business by itself. Leaders from Homebase, Tiny Health, and Zoom explain the operating changes that turn access into measurable work.
AI facial health recognition tools help physicians assess age and survival risk to guide personalized treatment
Mass General Brigham researchers trained a new AI foundation model on more than 40 million facial images, and used it to develop AI tools to estimate biological age and directly predict survival risk in patients with cancer
South Brunswick BOE Approves High School Computer Science Research Study Involving ChatGPT
South Brunswick High School students will participate in research exploring how ChatGPT and AI tools can support computer science education.
Huntington Bank Introduces The 1866 Studio, an AI-Powered Small Business Operating System Built with Google Cloud
New connected experience designed to help small business owners build and scale their businesses with access to financial, operational and business management AI tools and expertise COLUMBUS,...
Consumer apprehension grows as US retailers adopt AI-assisted pricing tools
U.S. consumers are uneasy about the increasingly high-tech ways that restaurant chains and major retailers are setting prices. Digital price tags are replacing paper ones in grocery aisles. AI tools
Is Teladoc Health (TDOC) Cheap Following Its New AI Tools?
Teladoc Health (TDOC) just rolled out two AI tools inside its Solo platform, SoloVitals and SoloScribe, aimed at contactless vital sign tracking and streamlined clinical note taking for hospital and health system clients. Despite Teladoc Health unveiling AI tools aimed at making virtual visits smoother for hospitals, the 30-day share price return is down 10% and the 90-day move has fallen 41.24%. The 5-year total shareholder return has declined 95.88%, pointing to longer term momentum that...
A new UNC initiative aims to bring AI tools to rural and low-resource health care clinics
From transcription tools to customer service, many small clinics don't have the bandwidth to navigate AI.
Can AI solve our mental health access problem?
Last week I asked you what worries you most about AI in health. It was close, but your top answer was “AI giving bad health advice.” Last week’s poll results: You’re in good company. This concern was top of mind at a conference I joined at Stanford last week, where researchers, clinicians, and people who build AI systems spent a day debating trust and safety in health AI. Stepping back, I’m also hearing your emerging concerns about AI’s impact on our well-being generally. I see this as a matter of public health, and we’ll take it in pieces as we learn together. Today we’ll focus on AI chatbots used for therapy for mental health. Early this month, Governor Newsom vetoed Senate Bill 903, California’s most ambitious attempt to regulate AI use for mental health. It’s at the center of active debate, and raises the questions patients, family members, citizens, and voters will face as AI moves deeper into health care. Let’s work through it. What is “AI-based therapy”? It’s really two things: purpose-built mental health apps designed to deliver structured support, and general chatbots like ChatGPT, Claude, and Gemini that people increasingly use for emotional support—even if they weren’t built for it. Spoiler alert: I don’t know whether the shift toward AI therapy is good or bad. The evidence isn’t there yet. But I do know it’s already happening, and I have some ideas about how we can roll it out with safety in mind. The therapy dilemma, in a nutshell Mental health challenges are common, and strain health and well-being across populations. Therapy (interactive dialogue with a trained, empathetic practitioner) helps many people heal. The demand for licensed therapists far exceeds the supply in California, and AI-based therapy can narrow that gap. AI-based therapy remains novel, unproven, and not well tested. Right off the bat, we can see we’re dealing with trade-offs here. Since the main driver of demand for therapy from a robot is lack of access to therapy from a human, we’ll start there. It’s too hard to find and afford a human therapist California is short roughly 55,000 licensed, non-prescribing therapists and counselors, about 40% of what we need. Psychiatrists, who rely on talk therapy as a complement to prescribed medications, are even more scarce. Eight counties have none, and density ranges from 1.7 per 100,000 residents in San Benito County, to 68.1 in Marin County. My wife is a psychiatrist in Marin, and even here access is tight. If it’s hard in the best-supplied county, imagine rural Sierra County, or the San Joaquin Valley. In 2024, more than half of Californians who tried to make a mental health appointment struggled to find a provider who took their insurance, compared with 14% for physical health care. When Senate staff posed as “secret shopper” patients and called 120 mental health providers in Medicare Advantage networks, they landed an appointment less than once for every five tries. Insurers pay behavioral health clinicians about 22% less than medical and surgical clinicians for comparable office visits. On top of that, billing denials and prior authorizations hit hardest at the solo and small practices common in mental health. Psychiatrists opt out of Medicare at the highest rate of any specialty. In many places, a cash practice for therapy fills quickly with patients paying $250 or more an hour, with no insurance paperwork at all. Families who can pay cash find providers more easily. Those who rely on insurance, including Medi-Cal enrollees, older adults, rural residents, and non-English speakers, compete for what’s left. So we can’t pretend the human system works, and we can’t blame people for turning to AI for support when they’re in distress. AI is filling the gap It’s estimated that roughly a quarter of AI users use it for mental health support. If you have internet, it’s available 24/7, it’s cheap, and it speaks your language. Uninsured adults are about twice as likely as insured adults to do it (30% vs. 14%). It feels anonymous in ways that may enable people to ask things they’re unsure or embarrassed about. The largely hidden, personal use makes it hard to study whether it’s actually helping people with their mental health issues, but researchers are trying. Does AI therapy work? A 2026 analysis of nearly 40 randomized trials found chatbots showed a real but small reduction in depression symptoms. But most of these trials were compared to an alternative of no therapy, not a human therapist. So the benefit of AI therapy is probably real, but modest, and mostly untested against the alternative of human contact. We need more studies. Serious concerns OpenAI’s own estimate is that more than a million ChatGPT users each week show signs of suicidal planning or intent. These are high stakes encounters, and AI doesn’t always get it right. That’s partly because a chatbot can sound like a therapist, but it has no license, no duty of care, and it’s not clear who to hold accountable when it gets things wrong. AI is also designed to help users in whatever task they’re seeking to accomplish. People may feel like they’re getting therapy, but they’re mainly getting a convincing conversation. It’s easy for an AI companion to wander into gray areas, like playing along with “creative writing” that may be about the user’s own suicidal intent. Courts are driving safety conversations A wave of lawsuits have been filed on behalf of families claiming that chatbots pushed people they loved toward self-harm, including teens who died by suicide. The first settlements came this year. OpenAI is headquartered in San Francisco, and dozens of cases alleging that ChatGPT drove users to suicide or self-harm are now before a San Francisco judge. Character.ai has filed for bankruptcy after several states brought lawsuits accusing the chatbot of pushing dangerous content to kids. But the technology seems to be improving. Last month, an independent evaluation found that newer AI models “almost never endorse suicide.” That’s progress, but our societal appetite for even one preventable death is very low. So how do we move forward? Finding a middle ground Earlier this month the governor vetoed Senate Bill 903, a bill designed to place guardrails on the use of AI for therapy. The bill would have prohibited any AI-based platform from being advertised as psychotherapy, and would have required a licensed professional to review and approve each encounter where AI was used to support therapeutic decisions. Newsom called it overly broad, since even routine screening questions would have needed clinician sign-off. Knowing we’re already short on mental health providers, requiring human review of AI encounters may not relieve our access problems. Still, the answer isn’t no oversight. Here’s one feasible short term solution. Rather than reviewing each encounter, a licensed clinician can design the guardrails: what counts as a crisis, what triggers an immediate handoff to a human, and what gets sampled for audit each month. The AI runs inside that structure. Conversations get checked, failures get reported, and a real clinician owns the framework. Medicine already has models to learn from. The FDA process for rolling out newly approved medications is an example. After a product reaches patients, we keep watching for adverse events. Patient-facing AI needs the same “digital pharmacovigilance”: systematic tracking of harms, near misses, and the types of groups that get hurt and why. It’s also critical that users know that they can reach a human if they’re in crisis. Most chatbots now include messages like these: Bottom line The rush to use unproven AI tools for mental health support is mostly a workaround, a sign of a health system people can’t get into. We need better access to proven care from licensed clinicians. That takes reimbursement that makes accepting insurance viable, and a bigger workforce. But, built and tested well, AI could be part of the answer. It could be the 2 a.m. check-in when no one else is awake, or a way for patients to practice skills their therapist taught them. It could be the first step to get someone the right level of care, and take paperwork off clinicians’ plates so they spend more time with patients. None of this is simple. The SB 903 veto doesn’t end the debate, and the legislature will be back. In the meantime, those of us using these tools need to stay informed, ask hard questions, and hold the companies building them accountable. I’ll try to do my part, and we’ll keep learning together. Love,Matt P.S. If you or someone you know is struggling with mental health challenges, emotional distress, substance use problems, or just needs to talk, call or text 988- you’ll reach a real person— or chat at 988lifeline.org 24/7. Dr. Matt Willis is the author of Your Local Epidemiologist in California. A California native, he’s served as a primary care physician, CDC epidemiologist, and public health officer for Marin County, where he guided the pandemic response. He lives in Marin with his family and their dogs, Teddy and Ramona. Thank you Cynthia He, MD, PhD, and Parnika Saxena, MD, for their guidance in preparing this article. Cynthia works in an outpatient mental health clinic, treating patients who have Medi-Cal or, sometimes, no insurance. Parnika works in an inpatient psychiatric unit, treating patients with public or private insurance or self-pay ability. All opinions are their own and do not represent their employers.
Companies pledge free software and computing for AI in science
The European Commission has announced that a group of companies and foundations will offer free services, such as software or access to supercomputers, to European scientists using AI in their research.Unveiled on October 15 at a conference in Dublin, the initiative is the latest step by the Commission to try to help researchers use the technology. Scientists have used forms of AI, such as machine learning, for decades. But advances in deep learning in the past decade, and the wider interest in large language models since the release of ChatGPT in 2022, have spurred governments to invest in new AI tools that could help the scientific process.In July, the US announced it is spending $5 billion on the Genesis Mission, which it describes as a “national effort to harness AI for science,” funding 278 projects across areas such as autonomous labs or new AI tools in astronomy. The Commission has also identified AI in science as a priority, but the €107 million it pledged from Horizon Europe in 2025 does not come close to US spending levels. That said, the vast majority of public research spending in the EU comes from national funds. Today’s announcement at the European AI in Science Summit doesn’t commit any new public money. Instead, it launches the AI in Science Enablers Initiative, a set of voluntary contributions from companies and foundations. The idea is that the companies also benefit by getting access to scientific ecosystems, and feedback on how their tools work. Google, for example, has put up €3 million of cloud computing credits to European researchers. The Novo Nordisk Foundation intends to make its supercomputer, Gefion, available to European researchers, according to the Commission statement. The chipmaker Nvidia said it would provide access to tools, compute, training and collaborative projects.The publisher Elsevier also offered complementary licenses to some of its AI tools, plus AI training for PhD, postdoctoral and master’s students. The French AI firm Mistral will provide the European research community with access to its open-weight models, Vibe seats and application programming interface credits, according to the Commission’s statement. Vibe is one of the firm’s chatbot models. The idea is that more firms and foundations will come on board in the future as the initiative grows.
Ubisoft plans cuts at German studios, adding to wave of workforce reductions
Ubisoft is planning to reduce its workforce in Germany, where it operates a trio of studios, by about 100 workers, Game File has learned. The company wants to offer employees buyouts to, should enough people accept, avoid having to lay anyone off. “In Germany, we initiated discussions with the Works Council last week regarding a proposed voluntary departure plan across Ubisoft Dusseldorf, Ubisoft Berlin & Ubisoft Mainz,” a company rep told Game File. A “works council” is a union-like group representing employees. “The voluntary plan is the primary measure being proposed to support the intended organizational changes,” the Ubisoft rep added. The company declined to confirm the scale of the potential headcount cut. Of the impacted studios, Mainz might be the best known to the public. It leads development of the Anno series and shipped the well-reviewed strategy game Anno 117: Pax Romana in November 2025. Berlin and Dusseldorf operate as support studios within Ubisoft’s creative network. Berlin recently supported the development of Rainbow Six Siege and the Rayman Legends Retold remake. Dusseldorf also contributed to those projects, as well as The Settlers and Ubisoft-branded escape rooms. Dusseldorf is the biggest of the three studios, with some 400+ workers, compared to just over 100 at the other two, a source familiar with the reductions and the teams told Game File. More than half of these new reductions of about 100 people are expected to happen at Dusseldorf. The planned cuts were announced at a town hall for the German studios on Tuesday of last week. In an email follow-up, the studio group’s managing director, Benedikt Grindel, said the cuts were intended to “best position our German studios for the future... with a clearer focus on their role, capabilities and organizational setup needed for the work ahead.” The email was an echo of the past. In January 2025, Grindel had emailed the Ubisoft developers in his region (or studio “zone”) to explain that a round of 185 cuts at the time, including about 65 at Dusseldorf, were “essential to improve our operational efficiency and ensure the long-term sustainability of our studios, the zone and the company.” A half-decade of continued job cuts Ubisoft has been aggressively cutting its headcount for years. At the end of September 2022, it had 20,729 workers. By the end of March 2026, that total was down to 16,590. Studio management is trying to trim costs as it grapples with a changing industry and a drought of releases, let alone hits. The company announced earlier this year that it had reduced its costs since 2022 by “nearly €325 million” ($362m) and planned to lower them through 2028 by another €180 million ($200m). In June the company said it was closing down its Winnipeg and Belgrade studios and cutting roles in its Barcelona team. Even commercial and creative success hasn’t spared studios from this ongoing company-wide contraction. As Game File was nailing down details around the proposed cuts in Germany, Ubisoft management was informing workers of cuts in its Singapore studio, which shipped a hit remake of Assassin’s Creed IV: Black Flag this past July. Some 70 positions in Singapore are being eliminated “to align the studio with upcoming production needs,” per a company rep’s statement to Game File. In early 2024, a large team in the Singapore studio launched the multiplayer pirate game Skull & Bones. It performed poorly but has been supported with regular content updates since. Many of Ubisoft’s studios around the world are heavily subsidized via regional grants and tax breaks. Singapore had been among them for years, but received no such local assistance in the 12 months ending this past March. News of the Singapore cuts broke today alongside word that Ubisoft is also proposing the closure of RedLynx, pending negotiations with officials. The Finnish studio behind the motorbike series Trials was founded in 2000 and sold to Ubisoft in 2011. As reported by VGC (and corroborated by Game File), Ubisoft management told employees it could not come up with a new, sustainable assignment for RedLynx. The 2025 workforce at Ubisoft Dusseldorf were straight-up cuts. This time, Ubisoft is hoping the reductions at its trio of German studios will largely come from workers taking a payout to leave. The company offered such voluntary dismissals at its Massive Entertainment studio (The Division) a year ago. For the German studios’ buyouts this time around, Ubisoft and the Works Council had discussed a formula that’d pay a worker’s monthly salary multiplied by 1.5 and by the number of years they’ve worked at the company, according to Game File sources. The formula is still subject to negotiations with the Works Council and has not yet been part of an official offer to workers. It could be adjusted up or down, and it could be supplemented with additional bonuses. Asked about the floated payout formula, a Ubisoft rep told Game File, “The terms of a potential voluntary departure program remain subject to negotiation with employee representatives. No final agreement has been reached, and we won’t comment on specific proposals while discussions are ongoing.” Game File is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe Item 2: Epic’s AI adventures Epic Games (Fortnite, the Unreal engine) is all-in on using AI to try to transform the way it makes video games and game-making tools. It’s leaders appear to be undeterred by any resistance among gamers and some game developers around generative AI—and instead emboldened by advances, particularly in AI-assisted programming. A recap of the last three days: On Monday, Bloomberg reported that Epic management told workers last week that development of an upcoming game combining Star Wars and Marvel characters would be “AI first.” Employees would be expected to use AI models such as Claude to code the game. A manager told Epic’s developers there would be “[n]o more hand-written code” and that art for the game would be “generated with full AI workflows,” per the outlet. Just ahead of that story running, Epic founder Tim Sweeney tweeted: “We have a huge pivot underway here at Epic, moving developers around to retool for maximum AI acceleration. The aim isn’t to do the same stuff with fewer people, but to enable everyone here to build MORE and BETTER stuff, and to bring the benefits to all teams using Unreal.” In a follow-up, Sweeney predicted that most game development processes would be at least three times faster a year from now than they were in January 2026, thanks to AI. To a person on X who told him he was failing to read the room, Sweeney said: “Much of the room wants it to be 2016 again, but it’s not. Our job, as the provider of tools and technology to much of the industry, is to design for the present that is and the future that will soon be.” On Wednesday, Epic announced it was releasing a faster launcher for its Epic Games Store marketplace. It did so by stating in a blog post as clear as could be that the launcher is an AI-enabled, vibe-coded project: Programming is hard. So, instead of doing that, we vibe coded a new Epic Games Launcher that’s way faster and more responsive than the current one, and has a new look. Programming is hard? That was unexpected phrasing from a company founded by a legendarily great programmer. Back in 2011, Sweeney told me that he’d picked up programming quickly as a kid and rejoiced at the power it gave him. “I could look at any program that anybody else had written and, with enough effort, build something cooler.” Item 3: The Xbox Labs experiment (and one of the people behind it) Microsoft’s Xbox division, which is flooding the zone with announcements lately, yesterday revealed a new internal tech-tinkering team called Xbox Labs. In the words of Xbox boss Asha Sharma Labs is “a small group of game makers, engineers and scientists whose job is to turn emerging ideas into things our players and developers can experience.” Conventional wisdom coming out of the announcement is that Labs is a team that’ll focus on ideas driven by generative AI. In her announcement, Sharma cited some of the dizzying achievements in tech and gaming over the past month as AI agents and other AI tools have been deployed to swiftly figure out how popular games work, then rebuild them and mix them up. She mentioned seeing ”games rapidly decompiled and rebuilt to run on hardware they were never originally designed to support” and said “[p]erhaps more surprisingly, we’ve seen many of the biggest games in the world have been remixed into the source code of another.” (Minecraft plus Elden Ring, for example, or Fortnite + Super Monkey Ball + Overwatch + Diablo + others.) Sharma also cited the work of Windows PC games being ported to run in browsers, a seeming reference to a recent AI-assisted fan port of Halo. Or perhaps Call of Duty or GTA. Sharma didn’t use the specific term “AI” in her memo, but she came close, citing AI “agents” and their potential for game-making tools that Labs could help craft: I think XBOX has an important role to play here. Coding became the first major workload for agents because they can write, run, and test software. Games share many of those same properties, and people spend more than 72 billion hours a year playing XBOX games, roughly five times the estimated time spent writing code worldwide. We’ve spent 25 years learning how to make great games, and I want our game makers to help shape what these new technologies make possible. As we learn, we’ll share the tools we build with developers across the industry, just as we always have. When I asked an Xbox rep if Labs was an AI-centric team, they said genAI was not the focus, describing it as having a broader mandate for using new tech in new ways in gaming. As an example, they pointed to Xbox’s recently launched disc-to-digital gaming usage rights program. Microsoft didn’t specifically name anyone involved full-time in Xbox Labs yesterday, but on social media and LinkedIn, I spotted Adrian Mato IDing himself as leading the creatio of tools at Labs and talking up the launch. Mato first caught my attention in late September, when he said on X that he was “starting to build a new team at Xbox focused on products and tools for how games get built and distributed as agentic tools become more capable.” At the time, he framed AI-based tools as a supplement to traditional game dev tools. “I don’t think we need to replace them,” he said. Since then, he’s publicly shown interest in AI and in games. He’s recently spent some of his X feed marveling at the power of AI agents and praising the lighting in the latest Gears of War. Following the Labs announcement yesterday, he enthused about the Xbox Labs’ stylish landing page. He wrote: “We built some fun tools to art-direct shaders in real time, experimented with interactive ASCII art, and sprinkled in lots of tiny details. “Move your mouse to leave a colorful trail that reveals the original footage, or interact with the video to trigger bursts of particles. “A lot of love went into making it all feel just right. “More to come”
'I won't trust AI': Readers share mixed reaction to AI tools that calculate people's biological ages
Scientists have developed AI tools to measure a person's true "biological age." Live Science asked readers whether they would be comfortable using these tools.
Unlock free AI tools for your small business through UH Maui College webinar
Small business owners and sole proprietors across Hawaiʻi are invited to learn how autonomous AI tools can streamline operations, save time and stretch limited resources in an upcoming free webinar offered by University of Hawaiʻi Maui College.
Lightsprint secures funding to enhance AI coding for non-techies
Lightsprint, a San Francisco startup, raises $6.1M to help non-engineers like designers and product managers edit code using AI tools.
LABJ Stock Index: October 12
AI Tools and Your Privacy: What You Need to Know Easy-to-use artificial intelligence tools may soon change our work and school lives – but they come with
How parenting expert Emily Oster says teens should be using AI
Emily Oster, the founder of ParentData, shared her tips to parents navigating their kids using AI tools like ChatGPT for Teens.
AI with Intention: The TRAPP Framework
While artificial intelligence can streamline and support many aspects of public health communication, deciding when and how to use AI tools requires careful judgment.
The AI tools helping meet demand for home healthcare
Home healthcare companies are increasing investment in AI tools that aid in documentation, referrals and fall prevention.
Computer Science Instructor Has Students on (and in) the Right Course
The final projects in Anurag Tiwari’s upper-level AI course at the College of Charleston turn student-built AI tools into hiring assets.
Would you trust AI to calculate your true 'biological age'?
Scientists have developed various AI tools to measure people's biological ages. Would you use one?
Google's AI Detection Website Is Now Available
SynthID Detector will flag content created with AI tools from OpenAI, Google, Apple and other companies.
CAPCOM Wants REX Engine to Become an Engine for the “AI Generation,” Where AI Could Write Code and Hunt Bugs
CAPCOM has detailed its plans to integrate AI tools into the REX Engine, so that it's ready for the "AI Generation".
Bishop’s computer science chair says AI is eliminating ‘grunt work’ and reshaping digital jobs
Dr. Russell Butler, chair of Bishop’s University’s Department of Computer Science, says artificial intelligence is increasingly taking over routine digital “grunt work,” changing the skills students and workers may need in the years ahead. Photo courtesy of Bishop’s University. Click here to read our platform! Get local news like this every morning. Subscribe free below. Subscribe Pulse Newsroom Artificial intelligence is already taking over some of the routine digital work once performed by entry-level employees, according to Bishop’s University Computer Science chair Dr. Russell Butler — a shift he believes will make expertise, judgement and the ability to evaluate AI-generated work increasingly important. Butler described the change as automation reaching what he calls the “bottom of the cognitive pyramid” during an interview with The Pulse about AI, university education and the future of digital work. His argument is not that humans are disappearing from the workplace. Instead, he sees their role changing as AI becomes capable of handling more of the repetitive tasks involved in computer-based work. “Everything’s converging on just AI basically doing all the grunt work,” Butler said. The bottom of the cognitive pyramid Butler is from Lennoxville, completed his undergraduate studies at Bishop’s and later earned his PhD at the Université de Sherbrooke. He joined Bishop’s in 2019 and now chairs its Department of Computer Science. He uses AI in research, teaching and other creative work, and said the technology is increasingly capable of handling routine digital tasks that once required substantial human time. “You’ve got the grunt work, like moving things around in Excel, doing basic programming tasks,” Butler said. “That’s like the bottom grunt work that the entry-level worker would have done two or three years ago.” Butler went further, saying in his view that much of this type of work has already been absorbed by AI tools. He connected that change to a shift he sees taking place in computer science, where entry-level roles have declined while experienced workers can use AI to increase what they can produce. “A senior developer now has access to essentially an unlimited amount of junior developers on demand,” Butler said. He was describing AI systems metaphorically, not literal employees. His point was that one experienced developer can now ask AI tools to produce code or other technical work that might previously have been assigned to more junior workers. That raises a broader question for universities and employers. If some of the simpler work traditionally given to newcomers can now be done by machines, how do people gain the experience needed to become senior workers themselves? Students doing work that once required years of programming Butler said he is already seeing the change inside his classroom. He created a Bishop’s course called CS208, Coding in Plain English, to show students — including those without traditional programming backgrounds — what they can accomplish using AI. Some come from disciplines such as psychology or neuroscience with no previous programming experience. “What I’m finding is that students who have zero programming experience, they come in from another discipline like psychology or neuroscience or something, they are now able to get up and running with real data sets,” Butler said. Butler’s own doctoral work involved neuroimaging, and he said he has assigned students work he considers comparable to the kinds of analysis he performed during his PhD. He described giving students a real dataset, an expected result and instructions to use AI to complete the analysis. Butler said those students have been able to carry out work at a level he associates with tasks he once performed after years of programming experience. That does not mean a student using AI instantly acquires the same knowledge as an experienced computer scientist. But Butler sees it as evidence that AI can dramatically lower the technical barrier between having a problem to solve and producing a working result. The role of the person using the system then changes. They need to know what to ask for, understand enough about the subject to judge the answer and take responsibility for what is ultimately produced. What about AI hallucinations? One persistent concern surrounding generative AI is hallucination — when a system produces information that sounds convincing but is false or unsupported. Butler said that problem has improved substantially in newer systems and is not as severe as it was with earlier models. He pointed in particular to newer AI systems’ ability to search the web, consult scientific databases and return source links rather than simply generating a citation from their internal model. Butler nevertheless said users should verify what AI produces. “You should verify,” he said while discussing citations generated with AI. His position is that hallucinations have become less common in the systems he uses, not that incorrect AI output is impossible. Human checking remains part of the process. Hours of work compressed Butler offered another example from his administrative work at Bishop’s. The university has been comparing CEGEP courses with first-year Bishop’s courses to determine where students may qualify for advanced standing. Traditionally, Butler said, that means reviewing two sets of course descriptions and manually matching one institution’s courses with another’s. As department chair, that could require hours of reading and comparison. He said AI can now be given both sets of material and asked to generate an initial mapping automatically. “It saves me hours and hours of work,” Butler said. Butler said the AI-generated mapping can also produce a better result than he would produce himself. He sees similar opportunities in businesses. A company may receive specifications, drawings, emails or other digital material from customers and then have employees convert that information into an internal format before anything can be manufactured or delivered. AI can increasingly perform parts of that intermediate processing. In Butler’s view, the employee does not necessarily disappear. Instead, the job becomes more about checking the result. “The human is still there,” he said. Judgement could become more valuable That change is central to Butler’s view of where digital work may be headed. If AI can produce a first draft, write basic code, compare documents or process large amounts of information, he argues that knowledge and judgement become increasingly important. “The real scarce resource going forward is going to be domain knowledge, expertise, and… credentials, authority, the ability to work with other people,” Butler said. He is therefore encouraging students not only to learn AI tools but also to broaden their education. He mentioned acting, fine art and photography as examples of courses that can help develop what he described as taste and intuition — the ability to recognize what is good, what is not and what should be changed. “And that’s the skill that they need now because the grunt work has been automated and now it’s about evaluating what the AI produces and telling it how to improve or expand the output,” Butler said. That does not remove the need for technical knowledge. Butler’s argument is that the emphasis may shift away from manually completing every step of a task and toward directing, checking and improving work produced with AI assistance. He also stressed that humans remain responsible for final decisions. “At the end of the day, the human has to make the final call,” Butler said, pointing to the question of accountability. Physical work may be different Butler draws a distinction between digital work and jobs requiring people to manipulate the physical world. He expects AI to continue advancing quickly in tasks performed almost entirely on computers. He is more cautious about claims that humanoid robots will soon be able to replace workers such as plumbers or electricians. Butler said those trades would not be replaced “anytime soon” in his view and described some current robotics demonstrations as overhyped. He described the situation as counterintuitive. “It’s almost like we solved the highest level first,” he said, contrasting advances in digital AI with the harder problem of building robots capable of operating reliably in the physical world. Butler’s argument is that some digital occupations could face automation sooner than trades requiring complex physical work. The wider AI shift in the Townships Butler’s comments come as local institutions are increasingly grappling with how AI should be used. The federal government recently announced up to $162 million over five years for 10,000 AI-related placements through Mitacs. The Université de Sherbrooke told The Pulse that the number of its collaborative research and innovation projects supported through Mitacs has approximately doubled since 2018-19, while the number of placements has approximately tripled. UdeS also said it already has projects involving both AI adoption and the application of AI to other fields. At Bishop’s, Butler said part of his role involves building the Computer Science department’s local presence, connecting students with regional partners and strengthening relationships with the Sherbrooke technology ecosystem. Those developments put universities in a difficult position. They have to prepare students to use the tools employers increasingly have available while also making sure graduates understand enough to recognize when those tools are wrong, incomplete or being applied to the wrong problem. The risk of losing human skills Butler does not see AI as risk-free. Later in the interview, he raised a separate concern: as AI becomes better at reading, writing, programming and other cognitive tasks, people could lose some of the underlying skills that historically helped produce new ideas. “The other danger is that we’re going to lose the human skills,” Butler said. He specifically mentioned reading and writing and argued that people could lose some of the lower-level understanding that contributes to breakthroughs. Butler said AI systems are based on existing human knowledge and argued that they become much more powerful when paired with a capable person. That tension runs through his view of the technology. AI can remove hours of routine work. It can allow beginners to perform tasks that once required substantial technical training. It can give experienced workers significant leverage. But the more capable the technology becomes, the more important it may be for people to develop the judgement needed to decide what should be done in the first place. For Butler, that is where education may have to change. Students still need to understand their field. They still need to know when something is wrong. And they still need to be able to make the final decision. But if AI increasingly handles the bottom of the cognitive pyramid, the value of a worker may depend less on how quickly they can perform the grunt work and more on whether they understand what the work is supposed to accomplish. Don’t miss the next story that affects your community. Join thousands of readers getting The Pulse every morning—plus breaking news alerts when it matters most. If you value this kind of local journalism, consider supporting it. Subscribe, share and like! Subscribe Share
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The controversy started on September 8, when OpenAI announced it had cracked a mathematical problem that had gone unsolved for 90 years. Using a swarm of AI agents, millions of dollars’ worth of computing power and a new internal model, the company said it had finally proved that a long-used formula for modelling fluid dynamics breaks down under certain conditions. However, Tristan Buckmaster, a New York University mathematician who had been independently working with collaborators on the same problem using OpenAI’s Codex tool, hit back with a statement questioning whether the company had snooped on his Codex sessions to gain an advantage. OpenAI has always denied it looked up the “specific user data” of Buckmaster and colleagues. However, the company initially admitted that “while unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models,” although its proofs “differ significantly” from Buckmaster’s. The firm later said that, following an investigation, it concluded that Buckmaster’s Codex prompts “could not have influenced the [OpenAI] system in any way, including through training.”Funder concernYet OpenAI’s initial admission that its model might have ingested Buckmaster’s ideas, then spat them out to its own mathematicians, has scientistsworriedthat their work could leak out if shared with AI. Some AI tools give users an opt-out option to stop their sessions from training the model. If such guarantees can be trusted, this gives scientists some confidence they can use an LLM to develop an idea without the risk of it leaking out.But there’s a bigger potential vulnerability. To win money to investigate ideas, scientists have to share them with funding bodies. These grant proposals are normally kept strictly confidential, to prevent other scientists from scooping them, or to protect commercially valuable intellectual property. To preserve this confidentiality, when the NSF uses AI models, “we have to be sure that it's not making server calls out to an external party, leaking that data,” said Gallo to delegates in Paris. Leaks through reviewersHowever, funders normally use external academic reviewers to assess proposals. And there are growing worries that these reviewers are feeding grant ideas into AI models to help them make a judgement. Research funders have noticed that peer review reports and funding recommendations have “suddenly become much longer, more detailed and complex,” said Diogo Machado, head of the data science unit at Technopolis, a science and technology-focused consultancy. “The sense is that reviewers are increasingly using generative AI in their review work, even where guidelines sometimes forbid it,” he told Science|Business. “This is very difficult for funders to control or prove.” Happening at scaleThis sharing of confidential proposals with AI models is “happening at scale already,” said James Wilson, executive director of the Research on Research Institute, based at University College London, during the debate in Paris. In response, Gallo said the NSF had “strict guidance” around the uploading of proposals to AI models. But he added the NSF was mulling new types of assessment to make sure applicants were truly the originators of their ideas, such as following a short-form proposal with an oral presentation, which would allow the foundation to “really interrogate the investigator on their intellectual contribution.” Related articlesHow can Europe cope with a flood of AI-assisted research grant proposals?The risks and benefits of AI in screening research grant proposals Viewpoint: AI could make scientific overload worse“For us as funders, a lot of the ways to ensure that the application remains the applicant's own intellectual contribution, those ways are quite regressive,” Peter Clifford, head of strategy at Research Ireland, told delegates. “They're maybe ten or 15 or 20 years old, and they don't scale particularly well.” Disputes over the origin of ideas are as old as modern science. Isaac Newton and Gottfried Wilhelm Leibniz spent more than a decade arguing over who invented calculus, for example. 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A column by Sascha Lobo Is AI making us dumb? So asked DER SPIEGEL on its cover a while ago. After a few weeks of recovery and a therapeutic intervention with the help of my AI, I feel able to respond. That’s a bit unfair, of course; the article itself is more nuanced than the loud cover, the image of a schoolkid with dull prompt questions, or the subtitle: “AI is conquering the schools, and educators fear a disaster.” The short answer to “Is AI making us dumb?” is: no. The long answer is more interesting, and even carries a preliminary glimmer of the spectacular. Unfortunately, it requires a detour into the sphere of artificial intelligence, into the mogul-field terrain of technology assessment, the attention economy, and the destructive virus of cultural pessimism. Along with narrowing the scope to AI’s effects on education and work rather than on intelligence. Because the honest version of the question would be: Does AI help us learn and work, or not? Generative AI, which set off the still-ongoing hype with ChatGPT, has only existed in relevant distribution since late 2022, early 2023. For a technology-driven transformation of people and society, that is extremely short. For that reason alone, research is still in its infancy. And technology assessment is not a fast discipline anyway. Prominent voices from Elizabeth Eisenstein to RAND to Tom Wheeler say we still cannot conclusively judge the consequences of the printing press. Its invention is a mere 580 years back, and some scholars would rather not circulate assessments that might have to be retracted in two or three hundred years. We’re Going Through Growing Pains A phenomenon that already caused upheaval in social media research makes serious assessment of AI’s consequences even harder: the “moving target” problem of LLM-ology, as the linguist Sean Trott called it in 2025. AI develops so quickly and so unpredictably that researchers often end up studying snapshots of the past. There are concepts, from morphological analysis to anticipatory governance, for keeping research, planning, and regulation halfway meaningful anyway. But even with those, universally valid statements are rare. DER SPIEGEL - The German View is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. Subscribe We should therefore treat findings about the interplay between humans and AI as far more procedural and situational. Unfortunately, this challenge comes at the worst possible time. We are going through growing pains: AI’s everyday impact has grown very large very fast, and with it the longing for guidance. Annoyingly, the body of solid knowledge has not exploded along with it. Experts cannot be moved to broad consensus on even banal statements about AI. Sascha Lobo, born in 1975, is an author and strategy consultant focusing on the internet and digital technologies. Together with Jule Lobo, he explores the debates of the day in the podcast “Feel the News – Was Deutschland bewegt” (”What Moves Germany”). Nvidia CEO Jensen Huang says software developers are dying out. Bill Gates considers coders one of only three professions that will survive the AI tsunami. Top tech economists find virtually irrefutable arguments both for and against an AI bubble. The same naturally applies to the structurally youth-hostile debate over whether AI is good or bad for young people. The gap between the demand for insight and the supply of solid knowledge creates an orientation vacuum. This vacuum of AI cluelessness is filled, as the attention economy described by Georg Franck in 1998 would have it, with half-knowledge (funnily enough, often AI-generated), marketing, and self-marketing. After all, infinite money is at stake, along with academia’s best current shot at global fame. But this iridescent froth of interpretation operates more by the algorithmic principles of the digital public sphere than by seriously earned insight. That is why dramatized, fear-feeding claims about AI are so promising. It tempts parts of academia into distorted drivel, and parts of the media then sensationalize the published drivel once more. Was the Research Aimed at a Desired Result? How badly this can influence academia and public perception is demonstrated by one of the most-discussed studies on AI’s effects: “Your Brain on ChatGPT.” The MIT study by Nataliya Kosmyna also gets plenty of space in the SPIEGEL cover story. What was measured and interpreted: lower brain activity in people who write with AI assistance, compared to people who only google or write without any tools. As is common with preprints, the study has not yet been peer-reviewed, and it contains substantial weaknesses that experts have sharply criticized. The first round had 54 participants in three groups, the second only 18, who reportedly also came from MIT’s own orbit. There was no preregistration (committing in advance to what you want to find out with which data). In their analysis, Stanković et al. even discovered an opposite effect that the study does not further explain. Some measurement data has not (yet) been published. And the EEG analysis method used is considered too imprecise to support such far-reaching conclusions. The biggest criticism, though, is the suspicion of an activism-driven study. In an interview with Time, lead author Kosmyna said she published the study immediately, rather than after full peer review, because she feared politicians might otherwise decide to put ChatGPT in kindergartens. She considers that absolutely harmful. It is legitimate to hold such a view. It just seems unscientific to approach a study with it, and then to design the setup so that the chance of a fitting desired result remains as high as possible. Ideologies Collide It is not easy to separate genuine AI insights from studies that trivialize and weaken well-founded AI criticism. The same goes for the opposing, AI-enthusiastic narratives, because the pro-AI side works against it with billions in resources. Ideologies collide, fed by egos, PR strategies, moral posturing, and once-in-a-lifetime opportunities. Doomsday marketing makes the search even harder: AI companies can profit from warnings about the dangers of AI, whether through the notoriety of apocalypse narratives or because, in some target groups, danger translates into power. Some seemingly negative findings are therefore quite welcome in the AI industry. If you try to get at the findings presumably less influenced by interest groups, the picture in education seems relatively clear: AI can improve educational outcomes when the right AI is used the right way, namely pedagogically. Simply dumping an AI chatbot into a classroom, by contrast, accomplishes little. Huge surprise. But something else is also obvious: whether they are allowed to or not, kids use AI for school, which is why the controlled integration of AI tools into education is, in the medium term, entirely without alternative. In the world of work, the landscape of findings is more complex and requires more differentiation. A series of serious studies points to an AI effect that suits neither the alarmists, regulation fetishists, and AI opponents, nor the trillion-dollar corporations with their armada of paid scientists, PR professionals, and ecosystems: working with AI helps the inexperienced (at least for now) considerably more than it helps the pros, in certain fields of work, and provided they know how to use it correctly. Missing Domain Knowledge Among the experienced, the professionals, and the high flyers, by contrast, despite frequent time savings, partially negative effects and new problems are emerging. Take the jagged frontier problem: AI solves task A brilliantly and fails completely at task B, even though both look similar even to professionals. Or the verification-cost problem, in which AI’s greater productivity gets eaten up by the enormous effort of checking its output. Or the illusion of competence, because AI makes new domains seem easily penetrable, until you painfully discover that deep domain knowledge is missing. Taken as a whole, the effects of working with AI do lean positive. But the promise that AI works as a turbocharger for everyone and boosts practically everything simply refuses to materialize. The AI magic the world has been feeling since ChatGPT arrived in late 2022 has so far barely shown up in most companies’ numbers. The consulting firm McKinsey has coined a term for this, the “gen AI paradox,” and believes that work processes and corporate structures would have to be rebuilt quite fundamentally to realize AI’s benefits at scale. Reuters just reported that only 3.3 percent of Microsoft’s customers have subscribed to the AI package Copilot. That may be down to the product; in other areas we continue to see soaring successes and new AI triumphs. But after the first quarter of 2026, disillusionment is the word for all too many AI projects at traditional companies: because the promised revolution of AI agents has so far failed to arrive or been postponed, and because of the leveling effect that many of the studies cited here suggest. Against Inequality The societal effects, on the other hand, appear rather encouraging. Many previous technologies operated on the Matthew principle, named for the Gospel verse about giving to those who already have: they tended to reward the more competent and more privileged. AI, however, seems more often to have an equalizing effect once a threshold of access and education is crossed. Some anecdotal insights point in this direction, for example, how an unemployed man in Leipzig successfully defended himself in court in 2025 against fraud accusations from Germany’s federal employment agency — without a lawyer, armed only with ChatGPT. The AI made mistakes and cited invented court rulings, but in the end the man walked away unpunished. And a key capability of artificial intelligence is its capacity to learn: set up correctly and fed with data, it can keep getting better. It is a question of time, or more precisely of AI innovations and training data, until even free AI chatbots write better legal briefs than law graduates with perfect exam scores. Whether that will make anyone dumb is something the world’s press will surely inform us of in due time. Subscribe Leave a comment
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As AI increasingly mediates how customers research, evaluate, and choose suppliers, competitive advantage is shifting from direct customer understanding to managing AI-shaped interactions. Drawing on the experiences of three small-to-medium sized businesses they worked with, the authors show how firms can adapt. A manufacturer improved sales efficiency by screening AI-generated inquiries before investing engineering time in quotations. A boutique hotel learned to monitor how AI tools described its business and revised public content to improve accuracy and differentiation. A B2B software company replaced periodic customer reviews with continuous monitoring of customer feedback and AI-generated market perceptions. Across all three cases, the lesson is that firms must treat AI as a new intermediary in the customer relationship. Winning companies will not necessarily spend the most on technology; they will build systems for continuously listening, interpreting signals, and adapting strategy in markets where both customers and the information they rely on are increasingly AI-mediated.
For one small business, AI was key to a quick start and expansion
Here Now Health isn't an AI-based company, but its quick path from an idea in founder Michelle Turner's mind to an operating mental health platform for foster children relied deeply on the technology now restructuring the U.S. economy. Working from her Virginia Beach home, she used AI tools to school herself in startup culture, create a business plan and fine-tune a presentation for early-stage investors. Funding began to flow, and the company, launched in January 2025, now has 16 employees and is certified in three states to provide Medicaid-funded mental health counseling for children entering the foster system, a gap in care she identified through her own experience as a foster parent.
How I Went From Side Hustle to 7 Figures in 12 Months Using 4 AI Tools (No Employees, No Investors)
Most solopreneurs aren't failing because they need more AI tools —they're failing because they're using AI to patch outdated systems instead of rebuilding their business around an AI-first workflow that scales.