Three years ago, we were impressed that a machine could write a poem about otters. Less than 1,000 days later, I am debating statistical methodology with an agent that built its own research environment. The era of the chatbot is turning into the era of the digital coworker. To be very clear, Gemini 3 isn’t perfect, and it still needs a manager who can guide and check it. But it suggests that “human in the loop” is evolving from “human who fixes AI mistakes” to “human who directs AI work.” And that may be the biggest change since the release of ChatGPT.
Results May Vary — from aiedusimplified.substack.com by Lance Eaton, PhD On Custom Instructions with GenAI Tools….
I’m sharing today about custom instructions and my use of them across several AI tools (paid versions of ChatGPT, Gemini, and Claude). I want to highlight what I’m doing, how it’s going, and solicit from readers to share in the comments some of their custom instructions that they find helpful.
I’ve been in a few conversations lately that remind me that not everyone knows about them, even some of the seasoned folks around GenAI and how you might set them up to better support your work. And, of course, they are, like all things GenAI, highly imperfect!
I’ll include and discuss each one below, but if you want to keep abreast of my custom instructions, I’ll be placing them here as I adjust and update them so folks can see the changes over time.
Why does it matter?
AI voice cloning has already flooded the internet with unauthorized imitations, blurring legal and ethical lines. By offering a dynamic, rights-secured platform, ElevenLabs aims to legitimize the booming AI voice industry and enable transparent, collaborative commercialization of iconic IP. .
. Data released by OpenAI in September from an internal study of queries sent to ChatGPT showed that most are for personal use, not work.
Emotional conversations were also common in the conversations analyzed by The Post, and users often shared highly personal details about their lives. In some chats, the AI tool could be seen adapting to match a user’s viewpoint, creating a kind of personalized echo chamber in which ChatGPT endorsed falsehoods and conspiracy theories.
Lee Rainie, director of the Imagining the Digital Future Center at Elon University, said his own research has suggested ChatGPT’s design encourages people to form emotional attachments with the chatbot. “The optimization and incentives towards intimacy are very clear,” he said. “ChatGPT is trained to further or deepen the relationship.”
Per The Rundown:OpenAI just shared its view on AI progress, predicting systems will soon become smart enough to make discoveries and calling for global coordination on safety, oversight, and resilience as the technology nears superintelligent territory.
The details:
OpenAI said current AI systems already outperform top humans in complex intellectual tasks and are “80% of the way to an AI researcher.”
The company expects AI will make small scientific discoveries by 2026 and more significant breakthroughs by 2028, as intelligence costs fall 40x per year.
For superintelligent AI, OAI said work with governments and safety agencies will be essential to mitigate risks like bioterrorism or runaway self-improvement.
It also called for safety standards among top labs, a resilience ecosystem like cybersecurity, and ongoing tracking of AI’s real impact to inform public policy.
Why it matters: While the timeline remains unclear, OAI’s message shows that the world should start bracing for superintelligent AI with coordinated safety. The company is betting that collective safeguards will be the only way to manage risk from the next era of intelligence, which may diffuse in ways humanity has never seen before.
Which linked to:
AI progress and recommendations — from openai.com AI is unlocking new knowledge and capabilities. Our responsibility is to guide that power toward broad, lasting benefit.
From DSC: I hate to say this, but it seems like there is growing concern amongst those who have pushed very hard to release as much AI as possible — they are NOW worried. They NOW step back and see that there are many reasons to worry about how these technologies can be negatively used.
Where was this level of concern before (while they were racing ahead at 180 mph)? Surely, numerous and knowledgeable people inside those organizations warned them about the destructive/downside of these technologies. But their warnings were pretty much blown off (at least from my limited perspective).
Most organizations are still in the experimentation or piloting phase: Nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise.
High curiosity in AI agents: Sixty-two percent of survey respondents say their organizations are at least experimenting with AI agents.
Positive leading indicators on impact of AI: Respondents report use-case-level cost and revenue benefits, and 64 percent say that AI is enabling their innovation. However, just 39 percent report EBIT impact at the enterprise level.
High performers use AI to drive growth, innovation, and cost: Eighty percent of respondents say their companies set efficiency as an objective of their AI initiatives, but the companies seeing the most value from AI often set growth or innovation as additional objectives.
Redesigning workflows is a key success factor: Half of those AI high performers intend to use AI to transform their businesses, and most are redesigning workflows.
Differing perspectives on employment impact: Respondents vary in their expectations of AI’s impact on the overall workforce size of their organizations in the coming year: 32 percent expect decreases, 43 percent no change, and 13 percent increases.
Spatial intelligence is the next frontier in AI, demanding powerful world models to realize its full potential. World models should reconstruct, generate, and simulate 3D worlds; and allow both humans and agents to interact with them. Spatially intelligent world models will transform a wide variety of industries over the coming years.
Two months ago we shared a preview of Marble, our World Model that creates 3D worlds from image or text prompts. Since then, Marble has been available to an early set of beta users to create 3D worlds for themselves.
Today we are making Marble, a first-in-class generative multimodal world model, generally available for anyone to use. We have also drastically expanded Marble’s capabilities, and are excited to highlight them here:
I just completed nearly 60,000 miles of travel across Europe, Asia, and the Middle East meeting with hundred of companies to discuss their AI strategies. While every company’s maturity is different, one thing is clear: AI as a business tool has arrived: it’s real and the use-cases are growing.
A new survey by Wharton shows that 46% of business leaders use Gen AI daily and 80% use it weekly. And among these users, 72% are measuring ROI and 74% report a positive return. HR, by the way, is the #3 department in use cases, only slightly behind IT and Finance.
What are companies getting out of all this? Productivity. The #1 use case, by far, is what we call “stage 1” usage – individual productivity.
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From DSC: Josh writes: “Many of our large clients are now implementing AI-native learning systems and seeing 30-40% reduction in staff with vast improvements in workforce enablement.”
While I get the appeal (and ROI) from management’s and shareholders’ perspective, this represents a growing concern for employment and people’s ability to earn a living.
And while I highly respect Josh and his work through the years, I disagree that we’re over the problems with AI and how people are using it:
Two years ago the NYT was trying to frighten us with stories of AI acting as a romance partner. Well those stories are over, and thanks to a $Trillion (literally) of capital investment in infrastructure, engineering, and power plants, this stuff is reasonably safe.
Those stories are just beginning…they’re not close to being over.
So let’s imagine a world where there’s no separation between learning and assessment: it’s ongoing. There’s always assessment, always learning, and they’re tied together. Then we can ask: what is the role of the human in that world? What is it that AI can’t do?
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Imagine something like that in higher ed. There could be tutoring or skill-based work happening outside of class, and then relationship-based work happening inside of class, whether online, in person, or some hybrid mix.
The aspects of learning that don’t require relational context could be handled by AI, while the human parts remain intact. For example, I teach strategy and strategic management. I teach people how to talk with one another about the operation and function of a business. I can help students learn to be open to new ideas, recognize when someone pushes back out of fear of losing power, or draw from my own experience in leading a business and making future-oriented decisions.
But the technical parts such as the frameworks like SWOT analysis, the mechanics of comparing alternative viewpoints in a boardroom—those could be managed through simulations or reports that receive immediate feedback from AI. The relational aspects, the human mentoring, would still happen with me as their instructor.
In a groundbreaking move that may redefine how law firms integrate technology training into daily practice, Ropes & Gray LLP has introduced a new pilot program allowing its first-year associates to dedicate a significant portion of their work hours to artificial intelligence (AI) learning—without the pressure of billing those hours to clients.
The initiative, called “TrAIlblazers,” marks one of the first formal attempts by a major law firm to give attorneys credit toward their billable-hour requirements for time spent exploring and developing AI skills. The firm hopes the move will both prepare young lawyers for a rapidly evolving profession and signal a new era of flexibility in how law firms evaluate performance.
Law firm leaders should evaluate their legal technology and decide if they are truly helping legal work or causing a disconnect between human and AI contributions.
75% of firms now rely on cloud platforms for everything from document storage to client collaboration.
The rise of virtual law firms and remote work is reshaping the profession’s culture. Hybrid and remote-first models, supported by cloud and collaboration tools, are growing.
Are we truly innovating, or just rearranging the furniture? That’s the question every law firm leader should be asking as the legal technology landscape shifts beneath our feet. There are many different thoughts and opinions on how the legal technology landscape will evolve in the coming years, particularly regarding the pace of generative AI-driven changes and the magnitude of these changes.
To try to answer the question posed above, we looked at six recently published technology trends reports from influential entities in the legal technology arena: the American Bar Association, Clio, Wolters Kluwer, Lexis Nexis, Thomson Reuters, and NetDocuments.
When we compared these reports, we found them to be remarkably consistent. While the level of detail on some topics varied across the reports, they identified six trends that are reshaping the very core of legal practice. These trends are summarized in the following paragraphs.
The Big Four consulting and accounting firm is training its junior consultants to manage teams of AI agents — digital assistants capable of completing tasks without human input.
“We want juniors to become managers of agents,” Niale Cleobury, KPMG’s global AI workforce lead, told Business Insider in an interview.
KPMG plans to give new consulting recruits access to a catalog of AI agents capable of creating presentation slides, analyzing data, and conducting in-depth research, Cleobury said.
The goal is for these agents to perform much of the analytical and administrative work once assigned to junior consultants, allowing them to become more involved in strategic decisions.
From DSC: For a junior staff member to provide quality assurance in working with agents, an employee must know what they’re talking about in the first place. They must have expertise and relevant knowledge. Otherwise, how will they spot the hallucinations?
So the question is, how can businesses build such expertise in junior staff members while they are delegating things to an army of agents? This question applies to the next posting below as well. Having agents report to you is all well and good — IF you know when the agents are producing helpful/accurate information and when they got things all wrong.
Summary: A fundamental shift is making knowledge workers “AI managers.” The most valuable employees will direct intelligent AI agents, which requires new competencies: delegation, quality assurance and workflow orchestration across multiple agents. Companies must bridge the training gap to enable this move from simple software use to strategic collaboration with intelligent, yet imperfect, systems.
The shift is happening subtly, but it’s happening. Workers are learning to prompt agents, navigate AI capabilities, understand failure modes and hand off complex tasks to AI. And if they haven’t started yet, they probably will: A new study from IDC and Salesforce found that 72 percent of CEOs think most employees will have an AI agent reporting to them within five years. This isn’t about using a new kind of software tool — it’s about directing intelligent systems that can reason, search, analyze and create.
Soon, the most valuable employees won’t just know how to use AI; they’ll know how to manage it. And that requires a fundamentally different skill set than anything we’ve taught in the workplace before.
AI Agents Can’t Actually Do Your Job (Yet)—New Benchmark Reveals The Gap
DEEP DIVE: AI can make you faster at your job, but can only do 2-3% of jobs by itself.
The hype: AI agents will automate entire workflows! Replace freelancers! Handle complex tasks end-to-end!
The reality: a measly 2-3% completion rate.
See, Scale AI and CAIS just released the Remote Labor Index (paper), a benchmark where AI agents attempted real freelance tasks. The best-performing model earned just $1,810 out of $143,991 in available work, and yes, finishing only 2-3% of jobs.
It begins with a basic reversal of mindset: Stop treating AI as a threat to be policed. Start treating it as the accelerant that finally forces us to build the education we should have created decades ago.
A serious institutional response would demand — at minimum — six structural commitments:
Make high-intensity human learning the norm. …
Put active learning at the center, not the margins. …
Replace content transmission with a focus on process. …
Mainstream high-impact practices — stop hoarding them for honors students. …
Redesign assessment to make learning undeniable. …
And above all: Instructional design can no longer be a private hobby.
How to Integrate AI Developmentally into Your Courses
Lower-Level Courses: Focus on building foundational skills, which includes guided instruction on how to use AI responsibly. This moves the strategy beyond mere prohibition.
Mid-Level Courses: Use AI as a scaffold where faculty provide specific guidelines on when and how to use the tool, preparing students for greater independence.
Upper-Level/Graduate Courses: Empower students to evaluate AI’s role in their learning. This enables them to become self-regulated learners who make informed decisions about their tools.
Balanced Approach: Make decisions about AI use based on the content being learned and students’ developmental needs.
Now that you have a framework for how to conceptualize including AI into your courses here are a few ideas on scaffolding AI to allow students to practice using technology and develop cognitive skills.
What was encouraging, though, is that students aren’t just passively accepting this new reality. They are actively asking for help. Almost half want their teachers to help them figure out what AI-generated content is trustworthy, and over half want clearer guidelines on when it’s appropriate to use AI in their work. This isn’t a story about students trying to cheat the system; it’s a story about a generation grappling with a powerful new technology and looking to their educators for guidance. It echoes a sentiment I heard at the recent AI Pioneers’ Conference – the issue of AI in education is fundamentally pedagogical and ethical, not just technological.
The new legal intelligence — from jordanfurlong.substack.com by Jordan Furlong We’ve built machines that can reason like lawyers. Artificial legal intelligence is becoming scalable, portable and accessible in ways lawyers are not. We need to think hard about the implications.
Both these features build on Clio’s out-of-nowhere $1B acquisition of vLex (and its legally grounded LLM Vincent) back in June.
A new source of legal intelligence has entered the legal sector.
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Legal intelligence, once confined uniquely to lawyers, is now available from machines. That’s going to transform the legal sector.
The public conversation about artificial intelligence is dominated by the spectacular and the controversial: deepfake videos, AI-induced psychosis, and the privacy risks posed by consumer-facing chatbots like ChatGPT. But while these stories grab headlines, a quieter – and arguably more transformative – revolution is underway in enterprise software. In legal technology, in particular, AI is rapidly reshaping how law firms and legal departments operate and compete. This shift is just one example of how enterprise AI, not just consumer AI, is where real action is happening.
Both Harvey and Clio illustrate a crucial point: the future of legal tech is not about disruption for its own sake, but partnership and integration. Harvey’s collaborations with LexisNexis and others are about creating a cohesive experience for law firms, not rendering them obsolete. As Pereira put it, “We don’t see it so much as disruption. Law firms actually already do this… We see it as ‘how do we help you build infrastructure that supercharges this?’”
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The rapid evolution in legal tech is just one example of a broader trend: the real action in AI is happening in enterprise software, not just in consumer-facing products. While ChatGPT and Google’s Gemini dominate the headlines, companies like Cohere are quietly transforming how organizations across industries leverage AI.
The AI company’s plan to open an office in Toronto isn’t just about expanding territory – it’s a strategic push to tap into top technical talent and capture a market known for legal innovation.
Building on our previous disclosure of the Perplexity Comet vulnerability, we’ve continued our security research across the agentic browser landscape. What we’ve found confirms our initial concerns: indirect prompt injection is not an isolated issue, but a systemic challenge facing the entire category of AI-powered browsers. This post examines additional attack vectors we’ve identified and tested across different implementations.
As we’ve written before, AI-powered browsers that can take actions on your behalf are powerful yet extremely risky. If you’re signed into sensitive accounts like your bank or your email provider in your browser, simplysummarizing a Reddit postcould result in an attacker being able to steal money or your private data.
The above item was mentioned by Grant Harvey out at The Neuron in the following posting:
Robin’s Legal Tech Backfire
Robin AI, the poster child for the “AI meets law” revolution, is learning the hard way that venture capital fairy dust doesn’t guarantee happily-ever-after. The London-based legal tech firm, once proudly waving its genAI-plus-human-experts flag, is now cutting staff after growth dreams collided with the brick wall of economic reality.
The company confirmed that redundancies are under way following a failed major funding push. Earlier promises of explosive revenue have fizzled. Despite around $50 million in venture cash over the past two years, Robin’s 2025 numbers have fallen short of investor expectations. The team that once ballooned to 200 is now shrinking.
The field is now swarming with contenders: CLM platforms stuffing genAI into every feature, corporate legal teams bypassing vendors entirely by prodding ChatGPT directly, and new entrants like Harvey and Legora guzzling capital to bulldoze into the market. Even Workday is muscling in.
Meanwhile, ALSPs and AI-powered pseudo-law firms like Crosby and Eudia are eating market share like it’s free pizza. The number of inhouse teams actually buying these tools at scale is still frustratingly small. And investors don’t have much patience for slow burns anymore.
TL;DR: AI no longer rewards politeness—new research shows direct, assertive prompts yield better, more detailed responses. Learn why this shift matters for legal precision, test real-world examples (polite vs. blunt), and set up custom instructions in OpenAI (plus tips for other models) to make your AI a concise analytical tool, not a chatty one. Actionable steps inside to upgrade your workflow immediately.
Nvidia has officially become the first company in history to cross the $5 trillion market cap, cementing its position as the undisputed leader of the AI era. Just three months ago, the chipmaker hit $4 trillion; it’s already added another trillion since.
Nvidia market cap milestones:
Jan 2020: $144 billion
May 2023: $1 trillion
Feb 2024: $2 trillion
Jun 2024: $3 trillion
Jul 2025: $4 trillion
Oct 2025: $5 trillion
The above posting linked to:
Nvidia becomes first public company worth $5 trillion — from techcrunch.com by Ivan Mehta The biggest beneficiary of the ongoing AI boom, Nvidia has become the first public company to pass the $5 trillion market cap milestone.
This blog explores how custom AI development accelerates the evolution from static AI to dynamic learning agents and why this transformation is critical for driving innovation, efficiency, and competitive advantage.
… Dynamic Learning Agents: The Next Generation Dynamic learning agents, sometimes referred to as adaptive or agentic AI, represent a leap forward. They combine continuous learning, autonomous action, and context-aware adaptability.
Custom AI development plays a crucial role here: it ensures that these agents are designed specifically for an enterprise’s unique needs rather than relying on generic, one-size-fits-all AI platforms. Tailored dynamic agents can:
Continuously learn from incoming data streams
Make autonomous, goal-directed decisions aligned with business objectives
Adapt behavior in real time based on context and feedback
Collaborate with other AI agents and human teams to solve complex challenges
The result is an AI ecosystem that evolves with the business, providing sustained competitive advantage.
Perception: The Foundation of Intelligent Agents Perception is the first step in building AI agents. It involves capturing and interpreting data from multiple modalities, including text, images, audio, and structured inputs. A multimodal AI agent relies on this comprehensive understanding to make informed decisions.
For example, in healthcare, an AI agent may process electronic health records (text), MRI scans (vision), and patient audio consultations (speech) to build a complete understanding of a patient’s condition. Similarly, in retail, AI agents can analyze purchase histories (structured data), product images (vision), and customer reviews (text) to inform recommendations and marketing strategies.
Effective perception ensures that AI agents have contextual awareness, which is essential for accurate reasoning and appropriate action.
Your New ChatGPT Guide— from wondertools.substack.com by Jeremy Caplan and The PyCoach 25 AI Tips & Tricks from a guest expert
ChatGPT can make you more productive or dumber. An MIT study found that while AI can significantly boost productivity, it may also weaken your critical thinking. Use it as an assistant, not a substitute for your brain.
If you’re a student, use study mode in ChatGPT, Gemini, or Claude. When this feature is enabled, the chatbots will guide you through problems rather than just giving full answers, so you’ll be doing the critical thinking.
ChatGPT and other chatbots can confidently make stuff up (aka AI hallucinations). If you suspect something isn’t right, double-check its answers.
NotebookLM hallucinates less than most AI tools, but it requires you to upload sources (PDFs, audio, video) and won’t answer questions beyond those materials. That said, it’s great for students and anyone with materials to upload.
Probably the most underrated AI feature is deep research. It automates web searching for you and returns a fully cited report with minimal hallucinations in five to 30 minutes. It’s available in ChatGPT, Perplexity, and Gemini, so give it a try.
At the most recent NVIDIA GTC conference, held in Washington, D.C. in October 2025, CEO Jensen Huang announced major developments emphasizing the use of AI to “reindustrialize America”. This included new partnerships, expansion of the Blackwell architecture, and advancements in AI factories for robotics and science. The spring 2024 GTC conference, meanwhile, was headlined by the launch of the Blackwell GPU and significant updates to the Omniverse and robotics platforms.
During the keynote in D.C., Jensen Huang focused on American AI leadership and announced several key initiatives.
Massive Blackwell GPU deployments: The company announced an expansion of its Blackwell GPU architecture, which first launched in March 2024. Reportedly, the company has already shipped 6 million Blackwell chips, with orders for 14 million more by the end of 2025.
AI supercomputers for science: In partnership with the Department of Energy and Oracle, NVIDIA is building new AI supercomputers at Argonne National Laboratory. The largest, named “Solstice,” will deploy 100,000 Blackwell GPUs.
6G infrastructure: NVIDIA announced a partnership with Nokia to develop a U.S.-based, AI-native 6G technology stack.
AI factories for robotics: A new AI Factory Research Center in Virginia will use NVIDIA’s technology for building massive-scale data centers for AI.
Autonomous robotaxis: The company’s self-driving technology, already adopted by several carmakers, will be used by Uber for an autonomous fleet of 100,000 robotaxis starting in 2027.
Nvidia (NVDA) and Uber (UBER) on Tuesday revealed that they’re working to put together what they say will be the world’s largest network of Level 4-ready autonomous cars.
The duo will build out 100,000 vehicles beginning in 2027 using Nvidia’s Drive AGX Hyperion 10 platform and Drive AV software.
Nvidia (NVDA) stock on Tuesday rose 5% to close at a record high after the company announced a slew of product updates, partnerships, and investment initiatives at its GTC event in Washington, D.C., putting it on the doorstep of becoming the first company in history with a market value above $5 trillion.
The AI chip giant is approaching the threshold — settling at a market cap of $4.89 trillion on Tuesday — just months after becoming the first to close above $4 trillion in July.
The Bull and Bear Case For the AI Bubble, Explained — from theneuron.ai by Grant Harvey AI is both a genuine technological revolution and a massive financial bubble, and the defining question is whether miraculous progress can outrun the catastrophic, multi-trillion-dollar cost required to achieve it.
This sets the stage for the defining conflict of our technological era. The narrative has split into two irreconcilable realities. In one, championed by bulls like venture capitalist Marc Andreessen and NVIDIA CEO Jensen Huang, we are at the dawn of “computer industry V2”—a platform shift so profound it will unlock unprecedented productivity and reshape civilization.
In the other, detailed by macro investors like Julien Garran and forensic bears like writer Ed Zitron, AI is a historically massive, circular, debt-fueled mania built on hype, propped up by a handful of insiders, and destined for a collapse that will make past busts look quaint.
This is a multi-layered conflict playing out across public stock markets, the private venture ecosystem, and the fundamental unit economics of the technology itself. To understand the future, and whether it holds a revolution, a ruinous crash, or a complex mixture of both, we must dissect every layer of the argument, from the historical parallels to the hard financial data and the technological critiques that question the very foundation of the boom.
From DSC:
I second what Grant said at the beginning of his analysis:
**The following is shared for educational purposes and is not intended to be financial advice; do your own research!
But I post this because Grant provides both sides of the argument very well.
From DSC: Stephen has some solid reflections and asks some excellent questions in this posting, including:
The question is: how do we optimize an AI to support learning? Will one model be enough? Or do we need different models for different learners in different scenarios?
A More Human University: The Role of AI in Learning — from er.educause.edu by Robert Placido Far from heralding the collapse of higher education, artificial intelligence offers a transformative opportunity to scale meaningful, individualized learning experiences across diverse classrooms.
The narrative surrounding artificial intelligence (AI) in higher education is often grim. We hear dire predictions of an “impending collapse,” fueled by fears of rampant cheating, the erosion of critical thinking, and the obsolescence of the human educator.Footnote1 This dystopian view, however, is a failure of imagination. It mistakes the death rattle of an outdated pedagogical model for the death of learning itself. The truth is far more hopeful: AI is not an asteroid coming for higher education. It is a catalyst that can finally empower us to solve our oldest, most intractable problem: the inability to scale deep, engaged, and truly personalized learning.
Increasing the rate of scientific progress is a core part of Anthropic’s public benefit mission.
We are focused on building the tools to allow researchers to make new discoveries – and eventually, to allow AI models to make these discoveries autonomously.
Until recently, scientists typically used Claude for individual tasks, like writing code for statistical analysis or summarizing papers. Pharmaceutical companies and others in industry also use it for tasks across the rest of their business, like sales, to fund new research. Now, our goal is to make Claude capable of supporting the entire process, from early discovery through to translation and commercialization.
To do this, we’re rolling out several improvements that aim to make Claude a better partner for those who work in the life sciences, including researchers, clinical coordinators, and regulatory affairs managers.
AI as an access tool for neurodiverse and international staff— from timeshighereducation.com by Vanessa Mar-Molinero Used transparently and ethically, GenAI can level the playing field and lower the cognitive load of repetitive tasks for admin staff, student support and teachers
Where AI helps without cutting academic corners When framed as accessibility and quality enhancement, AI can support staff to complete standard tasks with less friction. However, while it supports clarity, consistency and inclusion, generative AI (GenAI) does not replace disciplinary expertise, ethical judgement or the teacher–student relationship. These are ways it can be put to effective use:
The Sleep of Liberal Arts Produces AI — from aiedusimplified.substack.com by Lance Eaton, Ph.D. A keynote at the AI and the Liberal Arts Symposium Conference
This past weekend, I had the honor to be the keynote speaker at a really fantstistic conferece, AI and the Liberal Arts Symposium at Connecticut College. I had shared a bit about this before with my interview with Lori Looney. It was an incredible conference, thoughtfully composed with a lot of things to chew on and think about.
It was also an entirely brand new talk in a slightly different context from many of my other talks and workshops. It was something I had to build entirely from the ground up. It reminded me in some ways of last year’s “What If GenAI Is a Nothingburger”.
It was a real challenge and one I’ve been working on and off for months, trying to figure out the right balance. It’s a work I feel proud of because of the balancing act I try to navigate. So, as always, it’s here for others to read and engage with. And, of course, here is the slide deck as well (with CC license).
A median of 34% of adults across 25 countries are more concerned than excited about the increased use of artificial intelligence in daily life. A median of 42% are equally concerned and excited, and 16% are more excited than concerned.
Older adults, women, people with less education and those who use the internet less often are particularly likely to be more concerned than excited.
Veo 3.1 brings richer audio and object-level editing to Google Flow
Sora 2 is here with Cameo self-insertion and collaborative Remix features
Ray3 brings world-first reasoning and HDR to video generation
Kling 2.5 Turbo delivers faster, cheaper, more consistent results
WAN 2.5 revolutionizes talking head creation with perfect audio sync
House of David Season 2 Trailer
HeyGen Agent, Hailuo Agent, Topaz Astra, and Lovable Cloud updates
Image & Video Prompts
From DSC: By the way, the House of David (which Heather referred to) is very well done! I enjoyed watching Season 1. LikeThe Chosen, it brings the Bible to life in excellent, impactful ways! Both series convey the context and cultural tensions at the time. Both series are an answer to prayer for me and many others — as they are professionally-done. Both series match anything that comes out of Hollywood in terms of the acting, script writing, music, the sets, etc. Both are very well done. .
[On 10/21/25] we’re introducing ChatGPT Atlas, a new web browser built with ChatGPT at its core.
AI gives us a rare moment to rethink what it means to use the web. Last year, we added search in ChatGPT so you could instantly find timely information from across the internet—and it quickly became one of our most-used features. But your browser is where all of your work, tools, and context come together. A browser built with ChatGPT takes us closer to a true super-assistant that understands your world and helps you achieve your goals.
With Atlas, ChatGPT can come with you anywhere across the web—helping you in the window right where you are, understanding what you’re trying to do, and completing tasks for you, all without copying and pasting or leaving the page. Your ChatGPT memory is built in, so conversations can draw on past chats and details to help you get new things done.
ChatGPT Atlas: the AI browser test — from getsuperintel.com by Kim “Chubby” Isenberg Chat GPT Atlas aims to transform web browsing into a conversational, AI-native experience, but early reviews are mixed
OpenAI’s new ChatGPT Atlas promises to merge web browsing, search, and automation into a single interface — an “AI-native browser” meant to make the web conversational. After testing it myself, though, I’m still trying to see the real breakthrough. It feels familiar: summaries, follow-ups, and even the Agent’s task handling all mirror what I already do inside ChatGPT.
Here’s how it works: Atlas can see what you’re looking at on any webpage and instantly help without you needing to copy/paste or switch tabs. Researching hotels? Ask ChatGPT to compare prices right there. Reading a dense article? Get a summary on the spot. The AI lives in the browser itself.
The latest entry in AI browsers is Atlas – A new browser from OpenAI. Atlas would feel similar to Dia or Comet if you’ve used them. It has an “Ask ChatGPT” sidebar that has the context of your page, and choose “Agent” to work on that tab. Right now, Agent is limited to a single tab, and it is way too slow to delegate anything for real to it. Click accuracy for Agent is alright on normal web pages, but it will definitely trip up if you ask it to use something like Google Sheets.
One ambient feature that I think many people will like is “select to rewrite” – You can select any text in Atlas, hover/click on the blue dot in the top right corner to rewrite it using AI.
Summary: Job seekers are using “prompt hacking” — embedding hidden AI commands in white font on resumes — to try to trick applicant tracking systems. While some report success, recruiters warn the tactic could backfire and eliminate the candidate from consideration.
The Job Market Might Be a Mess, But Don’t Blame AI Just Yet — from builtin.com by Matthew Urwin A new study by Yale University and the Brookings Institution says the panic around artificial intelligence stealing jobs is overblown. But that might not be the case for long.
Summary: A Yale and Brookings study finds generative AI has had little impact on U.S. jobs so far, with tariffs, immigration policies and the number of college grads potentially playing a larger role. Still, AI could disrupt the workforce in the not-so-distant future.
“Future of Professionals Report” analysis: Why AI will flip law firm economics — from thomsonreuters.com by Ragunath Ramanathan AI forces a reinvention of law firm billing models, the market will reward those firms that price by outcome, guarantee efficiency, and are transparent. The question then isn’t whether to change — it’s whether firms will stand on the sidelines or lead
Key insights:
Efficiency and cost savings are expected— AI is significantly increasing efficiency and reducing costs in the legal industry, with each lawyer expecting to save 190 work-hours per year by leveraging AI, resulting in approximately $20 billion worth of work-savings in the US alone.
Challenges to the billable hour model— The traditional billable hour model is being challenged by AI advancements, as lawyers are now able to complete tasks more efficiently and quickly, leading some law firms to explore alternative pricing models that reflect the value delivered rather than the time spent.
Opportunities for smaller law firms— AI presents unique opportunities for smaller law firms to differentiate themselves and compete with larger firms, as AI solutions allow smaller firms to access advanced technology without significant investment and deliver innovative pricing models.
The legal industry is undergoing a significant transformation that’s being driven by the rapid adoption of AI — a shift that is poised to redefine traditional practices, particularly the billable hour model, a cornerstone of law firm operations.
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Not surprisingly, AI is anticipated to have the biggest impact on the legal industry over the next five years, with 80% of law firm survey respondents to Thomson Reuters recently published 2025 Future of Professionals report saying that they expect AI to fundamentally alter how they conduct business, especially around how law firms price, staff, and deliver legal work to their clients.
