AI Rounds

Morgan puts on her resume writer and interview coach hat for this one. From applicant tracking systems to AI-conducted video interviews, the job market has been transformed faster than most people realise.They look at a QZ article showing that 74% of companies say AI has improved hiring quality, but also that cover letters have been effectively devalued. Morgan breaks down what actually matters now: networking, in-person interview prep, and how to handle an AI interview without falling apart. RESOURCESHow AI is changing the job search — and how to make it work for you ArticleWelcome to the Era of Relational Intelligence ArticleNúria's website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠nurianegrao.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Morgan's website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠morganleafemd.com⁠⁠

Vanessa Chang advises companies on AI adoption and writes Think with V, a newsletter about what AI is doing to how we think. She joins Núria and Morgan to talk about the half of AI literacy that gets almost no attention, which is not how to use the tools but how to use your own mind well enough to direct them.Most of her consulting work, she says, lands nowhere near the tech stack. It lands in context and knowledge management, and often in something as ordinary as file management. Companies that hand people a tool and wish them luck tend to be solving the wrong problem. The pattern she sees in failed rollouts is the same three things in order: nobody mapped the process, nobody knows where the knowledge lives, and nobody asked where people actually are on trust and appetite for change.The second half of the conversation turns personal. Vanessa makes the case for metacognition, for pauses and walks without a podcast playing, and for forming your own intent before you open a chat window. Her framing of AI as a blanket thrown over dysfunction, giving shape to something that was always there, is the line that stuck with Morgan for the rest of the day.In this episodeSelf-fluency, and why Vanessa treats it as half of AI literacyThe multi-hyphenate career pattern showing up across the AI spaceCognitive operations: knowledge structure, knowledge access, and where knowledge meets processWhy most AI implementation plans fail, and the three things they skipTrust level and appetite for change as the real variables in adoptionWhere AI sits between social media and crypto on the hype-to-utility curveWhy solopreneurs and small operators have the most leverage right nowAI as a lens that brings existing dysfunction into focusMetacognition, and how to start noticing the way you actually thinkThinking while moving: proprioception, walking, talking with your hands, voice dictationBlank space and pause as processing, not as doing nothingForming your intent before you ask, instead of running straight to an answerLearning, unlearning, and knowing when to apply frictionWhat all of this means for how kids are being taught to use AIDon't miss Vanessa speaking at the AI Fluent Professional Summit in November. Join the summit: https://nuria-negrao.thrivecart.com/ai-fluent-professional-summit/?ref=vanessa-changLINKS:Vanessa's LinkedIn: https://www.linkedin.com/in/vanessachang/Website: https://vanessachang.com/Think with V newsletter: https://thinkwithv.beehiiv.comBernie: https://textbernie.meMosaek: https://mosaek.ioHosted by Nuria Negrão (⁠⁠⁠nurianegrao.com⁠⁠⁠) and Morgan Leafe (⁠⁠⁠morganleafemd.com⁠⁠⁠). Sign up for Nuria's newsletter: ⁠⁠⁠https://nurianegrao.kit.com/signup⁠

Anthropic is now watermarking content generated with Claude, and Morgan read the headlines the way a lot of working writers did: Is my client going to see a watermark on my work? Núria explains what is actually happening, starting with the EU AI Act transparency phase that took effect in August 2026, which requires AI-generated content shown to the public to be labeled, with a carve-out for content that has been sufficiently edited and quality-controlled by humans.The watermark itself is not a visible stamp. It is a statistical pattern in word choice, the same approach Google has used with Gemini since 2024, and neither company has released a public tool that can read it. The watermark also cannot tell anyone how much human involvement went into a document. Heavy AI editing of a fully human draft can leave a watermark behind, and light AI use may leave none, so its presence or absence says much less than people assume. OpenAI, meanwhile, tried watermarking, dropped it after user backlash, and is expected to need something like it to comply with the EU rules.From there the hosts get into the AI detector question that comes up at every conference. Most detectors are doing pattern recognition, not watermark reading, and they routinely flag formulaic writing, which includes scientific and medical writing, and prose by people writing in English as a second language. The one exception people keep naming is Pangram, which optimizes against false positives. The bigger question, they argue, is why you want to detect AI at all: catching deepfakes and unreviewed AI advice is a different problem from wanting a guarantee that a human stands behind the quality of the work. That is where client conversations, AI policies, and disclosure come in, including the medical communications companies that now hand contractors a Copilot license so the AI use stays inside their walls.In this episodeWhat triggered the watermarking decision: the EU AI Act’s transparency requirementsThe human-editing carve-out, and who the law is actually trying to protectHow a statistical watermark works, and why nobody can read it yetGoogle’s head start: Gemini has been watermarked since 2024Why a watermark cannot measure human involvement in either directionOpenAI’s abandoned watermark and the user backlash behind itWhy AI detectors flag scientific writing and second-language EnglishPangram, false positives, and optimizing for precision over recallThe real question: what are you trying to find out when you test for AI?Client trust, AI policies, and disclosing your processMedcomms companies issuing Copilot licenses to keep confidential work containedWhat clients actually want: a guarantee of quality at the endLINKS:Hosted by Nuria Negrão (⁠⁠⁠⁠nurianegrao.com⁠⁠⁠⁠) and Morgan Leafe (⁠⁠⁠⁠morganleafemd.com⁠⁠⁠⁠). Sign up for Nuria's newsletter: ⁠⁠⁠⁠https://nurianegrao.kit.com/signup⁠

Heather Duncan started out as an English professor, went back to school for an MPH, and is now a PhD student in epidemiology working on how local health departments can actually use AI. She joins Núria and Morgan to talk about the gap between what the technology can do and what public health is currently doing with it.A 2024 survey from the National Association of County and City Health Officials found that about 8% of local health department staff were using AI at all, while roughly 70% said they were interested. Heather explains what sits in that gap: cost, cybersecurity, unclear legal exposure around sensitive health data, and enterprise tools built for profit-driven organizations being sold to agencies that are not one.She also takes on two arguments that come up in almost every AI conversation. On the environment, she makes the case that the all-or-nothing framing misses the options in between, including locally run open-weight models and retrieval-augmented generation systems that a county health department can host itself. On bias, she walks through the melanoma detection model that performed well until researchers stratified by skin tone, and why the failure was in the training data rather than the algorithm. Her line on this: AI is a mirror, and it amplifies what is already there.In this episodeHeather’s route from literary studies to epidemiology, and the question that carried across bothLearning SAS, R, and Python, and why coding education has not caught up to how coding actually works nowThe three worlds of public health: academic, applied/governmental, and private sector, and where AI adoption actually sits in eachWhere AI is already earning its place in epidemiology: genomics, precision medicine, pharmacoepidemiology, population monitoring, and data quality checksAI as an institutional knowledge resource in departments with heavy staff turnoverWhy enterprise AI tools are a poor fit for public health, and the value misalignment behind thatData centers, environmental mitigation, and the siting of infrastructure in historically redlined neighborhoodsOpen-weight models, local hosting, and RAG as a lower-resource alternativeThe melanoma model that broke down on darker skin, and the structural reasons the training data was thinShaming people for using AI as a class and access issueDon't miss Heather's session at the AI Fluent Professional Summit on implementation and evaluation frameworks for organizations without a profit motive this November. Join the summit: ⁠⁠https://nuria-negrao.thrivecart.com/ai-fluent-professional-summit/?ref=heather-duncanLINKS:Heather's LinkedIn: https://www.linkedin.com/in/heather-duncan-mph-59686694/M&D Science Consulting: https://www.mdscienceconsulting.com/Those Nerdy Girls contributor profile: https://thosenerdygirls.org/nerdygirls/heather-duncan-mph/Hosted by Nuria Negrão (⁠⁠nurianegrao.com⁠⁠) and Morgan Leafe (⁠⁠morganleafemd.com⁠⁠). Sign up for Nuria's newsletter: ⁠⁠https://nurianegrao.kit.com/signup⁠

Anne Murphy spent decades in nonprofit fundraising before founding She Leads AI, where she's trained thousands of women to use AI responsibly and is building a living archive of women using it to change the rules.In this episode, Anne, Nuria, and Morgan get into why women's voices are critical in this moment — not for professional development or celebration, but for impact — and why women's use of AI is almost certainly underreported. Anne makes the case for building your "discernment muscle" before the gray-area moment arrives, shares the story of a She Leads AI member who built an app to help her non-English-speaking neighbors during ICE raids, and reframes disruption as spotting the white spaces and fixing them. Along the way: what fundraising and AI have in common (all you really have is trust), why no use case is too small, and the very real change-management fallout when an AI note-taker walks into a six-person team.Don't miss Anne's session at the AI Fluent Professional Summit this November. Join the summit: ⁠https://nuria-negrao.thrivecart.com/ai-fluent-professional-summit/?ref=anne-murphyLINKS:- Anne Murphy on LinkedIn: https://www.linkedin.com/in/she-leads-ai/- She Leads AI (community + the 1000 Disruptors project): https://sheleadsai.ai/- CREATE 2026 conference: https://sheleadsai.ai/create-2026- Hosted by Nuria Negrão (⁠nurianegrao.com⁠) and Morgan Leafe (⁠morganleafemd.com⁠). - Sign up for Nuria's newsletter: ⁠https://nurianegrao.kit.com/signup⁠