Women talkin' 'bout AI

Jessica Parker returns to the show (ha!) for a conversation about Screen People by Atlantic staff writer Megan Garber. The book examines how American life has reorganized itself around screens, and what happens when we can no longer reliably distinguish people from performers or information from entertainment.We trace Garber’s argument from Marshall McLuhan’s “the medium is the message” through Neil Postman’s “the medium is the metaphor” to her own claim that “the medium is the moral.” And we stake our own claim with THE MEDIUM IS THE MIDDLEMAN. Along the way, we discuss how scientific findings lose nuance as they travel from research papers to press releases, headlines, and chatbots; why experts hedge while algorithms reward certainty; and how AI magnifies communication patterns already embedded in internet culture.We also explore the difference between a public and an audience, asking whether personalized AI systems can influence an entire population while preventing the shared discourse necessary for collective action.In this episode:Why screens reward performance over accuracyHow hedging signals scientific care—not weaknessWhat gets lost between a research paper and a chatbotAI as a mirror of internet cultureThe commodification of attentionHow audiences differ from active publicsWhy information degradation may be one of AI’s greatest risksSmall linguistic distortions that are harder to detect than visual deepfakesIn our closing “Pit and Peach,” Jessica reflects on egg retrieval, difficult decisions, and finding clarity, while Kimberly shares how she is rethinking gratitude through the practice of radical gratitude.Mentioned in this episode:Screen People: How We Entertained Ourselves Into a State of Emergency — Megan GarberMarshall McLuhan — official siteAmusing Ourselves to Death — Neil Postman On Being with Krista TippettYour Undivided Attention — Tristan Harris and Aza RaskinKrista & Tristan's chat, "Can AI be build in service of life?"Melody Beattie — official websiteRadical Acceptance — Tara BrachResearch article by Denise Coberley and Emily Dux Speltz on Scientific Uncertainty in Language Comparing Human and Artificial TextsOur Frontiers in Education article on AI as an intermediaryLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Part two of two with Roz Hirsch.Roz has been applying for jobs and not getting interviews she would once have gotten easily. She's been rejected in under an hour. She's been rejected at midnight, by companies where nobody was awake to read anything.Her expertise is language assessment, so she makes the argument nobody else is making. A resume is an assessment. Assessments require a validity argument, meaning evidence that the decisions you make with them are the right decisions. Validity is a property of the decision, not of the instrument. And validation has to happen at every single company that adopts a screening tool, because a tool validated somewhere else for something else has not been validated for you.So, has anyone gone back and reread the rejections? Roz cites hiring managers who did, after two full cycles that produced no hires, and found people who should not have been rejected. That's a validity failure, and the near-instant rejection timestamps suggest nobody is checking.We also get into what the screen is actually reading. Roz's point is that it isn't only the resume and cover letter. It's postal code, financial history, whatever else is available, and the inference that nobody from that postal code works here so this person probably won't either. I bring in Uber's pickers and ants, airline pricing, and the casual nursing algorithms that offer lower wages to people whose credit history says they'll accept.Then the same argument turned on education. Roz on the professor whose take-home midterm produced near-perfect scores and whose in-class final didn't, and why the bell curve was the problem before AI ever showed up. Why she doesn't have a cheating problem. And the classroom exercise she runs with AI image generation, where students ask for one bear and keep getting several.LinksRoz's Random Ramblings: Language, History, and Other AdventuresRoz on LinkedInWomen Writin' 'Bout AICarol Chappelle, Iowa State and The Applied Linguistics Encyclopedia Image Description Games: Twin Pics, Say What You See, and PromptleEnshittification by Cory Doctorow and our show about the sameUber pickers and antsThe Brown Professor story about AI and cheatingThe TOEFL (Test of English as a Foreign Language) Part one of Kim & Roz talkin' 'bout AI Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Kimberly's friend Roz Hirch is the guest on this two-part series. Roz is a linguist, a college instructor in Medicine Hat, Alberta, and a language assessment specialist. She is also out of work for the summer for the first time in her life. Kimberly suggested that she read The Artist's Way by Julia Cameron, and that led to Roz asking ChatGPT and Claude for book recommendations as well. She wrote a question describing herself and her situation and asked ChatGPT and Claude for reading recommendations. Something in ChatGPT's answer bothered her enough that she took the identical question, word for word, and texted it to friends and family to see what people would do with it.The machines gave her thirteen books and seven. The humans gave her one, or two, or none. Three titles appeared on both AI lists. Not one appeared on both an AI list and a human list. The AI books all pointed the same direction, which was creating a portfolio career, company of one, multipotentialite, like build an umbrella and put everything under it. The people who actually know Roz told her to write.Roz and I analyzed the responses from the humans and the bots, and because Roz has a background in theater as well as linguistics, she reaches for the difference between naturalism, which is how people talk, and realism, which is how we think people talk. We look at what humans do that machines don't, such as dropping the subject, hedging in nearly every response, and knowing when to stop, which Grice's maxim of quantity covers and which one model violated thirteen times over. We also analyze the speech act itself, recommendations. A recommendation ordinarily requires the speaker to have read the thing and to stake something on it. The form survives in the AI answers. The function is hollowed out, because there is nobody there to have been inspired.Links Roz's Random Ramblings: Language, History, and Other Adventures Roz on LinkedIn Women Writin' 'Bout AIJohn Searle, "The Chinese Room"Mini Philosophy, Jonny Thomson, the episode on online versus face-to-face conversationHow to Be Everything, Emilie WapnickRange, David EpsteinThe Wealthy Barber, David ChiltonThe Artist's Way, Julia CameronLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Devon Cantwell-Chavez studies global urban climate change governance. She and Kimberly met because of an antagonistic LinkedIn post (not between the two of them), and then discovered that, in many ways, they came up the same way. They both were Teach for America corps members, both early believers in classroom technology, and both landed somewhere far more critical. This conversation is about what gets lost when we design friction out of learning and research. They get into the myth of the digital native and why "nobody knows how file folders work anymore," the frictionless interfaces that train us to just ask instead of think, and Goodhart's Law, the idea that a measure stops being effective once it becomes a target. Devon lays out how her research team built friction back in on purpose with a no-first-use policy, low-stakes-only translation tools, and a tagging system so every use of AI is on the record. The episode closes on why AI can't be replicated the way rules-based software can, what that means for qualitative research, and where Devon finds hope, on the lawns of rural Michigan, in t-shirts and yard signs against data centers.Mentioned in this episode:The AI Con, Emily Bender and Alex Hanna Being Wrong: Adventures in the Margin of Error, Kathryn Schulz Right Kind of Wrong: The Science of Failing Well, Amy EdmondsonKimberly's Substack that discusses the following: Don Norman on design responsibility Rosina Lippi-Green on communication as a two-way streetGoodhart's LawDevon's viral LinkedIn post The chess-cheating study (in understandable language) or the research manuscript preprintThe Brown University exam experiment Box Elder County data center coverage Digital NativesNon-Consensual Sexual ImageryDevon's t-shirtCancellation of data center projects: https://www.datacenterwatch.org/reportFact Checking Notes:Traditional spell checkers were primarily dictionary- and rule-based, later augmented with statistical language models and machine learning. Modern writing assistants (including current Grammarly features) increasingly combine traditional spelling and grammar checking with large language models.Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

This week, Kimberly and Jessica dig into the AI writing tic everyone's noticed and nobody can fully explain: "it's not X, it's Y." They discuss The Atlantic's new piece on the phrase, then bring Kimberly's informal research from a publicly available corpus of 24-billion words of online news to show the construction is spiking right alongside "crucial" and "quietly." From there the conversation turns to what's actually at stake, including published work pulled from publication because it "sounded like AI," the argument that bad AI output is always a "you" problem, and the bigger question of who gets to decide what human writing is even supposed to sound like anymore.In this episode:The AI "tells" everyone's noticing, and the corpus data behind the hunchShakespeare, Vince Lombardi, and a DiGiorno ad — "it's not X, it's Y" is way older than any chatbotThe Atlantic's theories for why models love this constructionKimberly's own numbers: "not just X, but Y" is up 45% in online news since 2015AI as intermediary, not tool — and the Frontiers in Education paper that explains this"Don't judge the AI, judge the human" — and where that argument breaks downThe novel a publisher pulled over an AI accusationJessica's case that writing is thinking, and what's lost when we skip itPeach and PitLinksThe Most Famous AI Writing Tic Is Also the Most Mysterious — Will Oremus, The Atlantic, July 13, 2026. The article that kicks off the episode. NOW Corpus (News on the Web) — BYU’s large, continually updated news corpusTop 10 Most Common Words Used by AI — GPTZeroPublisher pulls horror novel “Shy Girl” over AI concerns — TechCrunchDefining and assessing AI literacy for researchers across the research lifecycle — Parker & Becker, Frontiers in EducationHow Not To Use AI — Abi Awomosu’s SubstackThe book itself — Abi AwomosuWomen Writin’ ’Bout AI — joint SubstackKimberly’s “Linguist in the Wild” Substack English with an Accent: Language, Ideology, and Discrimination in the United States — Rosina Lippi-GreenThe Design of Everyday Things — Don Norman, revised and expanded edition, MIT PressLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Every time a machine catches up to us, we redraw the line around what makes us human — and call whatever's left the "remainder." This week, Jessica and Kimberly (no guest) dig into Language Machines: Cultural AI and the End of Remainder Humanism by NYU professor Leif Weatherby, and ask whether the whole human-vs-machine contest is a trap.Along the way: why Emily Bender's "it's just intent" argument doesn't hold up as well as it seems to, why cognition and culture can't actually be separated, a 100-year-old linguistics theory (structuralism) that explains why LLMs work at all, and why the body — not the brain — might come first.In this episodeThe core argument — Remainder humanism: defining "human" as whatever's left over once machines take a skill. Why that's a losing game (the "arm wrestling a forklift" bit).Team Bender vs. Team Weatherby — Emily Bender's claim that intent is what separates human language from AI output, and Weatherby's counter: intent doesn't ground meaning, the language system grounds intent.Form vs. function — A quick linguistics 101 detour: language isn't just words on a page, it's what those words do in context ("it's hot in here" as a request, not a weather report).Cognition vs. culture — The WEIRD psychology problem (Western, Educated, Industrialized, Rich, Democratic) and why decades of "universal" cognitive science findings didn't hold up outside that narrow sample.Structuralism, 100 years early — The idea that words get meaning from their relationships to other words, not from pointing at things in the world — and why that theory basically predicted LLMs.Meaning without truth — Why hallucinations are what a meaning-making system with no truth-tracking looks like.Embodiment — Descartes' "I think therefore I am" flipped: feeling comes before thinking, and what that means for machines that don't have bodies.Practical takeaway — How to stop playing defense: quit asking "what can I still do that machines can't," start asking what these systems are trained on, who's represented, and who gets to shape them.Mentioned in this episodeLanguage Machines: Cultural AI and the End of Remainder Humanism — Leif WeatherbyEmily Bender — linguist, "stochastic parrots" / text extrusionNoam Chomsky — universal grammar, cognition as separate from cultureMaha Bali — "Where Are the Crescents in AI?"Michael Pollan & Annika Harris — on consciousness and embodimentPersonal segmentThe episode closes with a quick peach-and-pit check-in — home renovation surprises and a therapy update on sitting with feelings in the body instead of just thinking through them.Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Guest: Dr. Michelle Morkert — gender scholar, leadership coach, founder of the Women's Leadership Collective.In this repisode (get it? re-episode?), Kimberly and Jessica sit down with Dr. Michelle Morkert to unpack the growing call from AI leaders for "maternal AI," which is the idea that treating AI systems like children we're raising will make them safer, kinder, and less likely to turn on us. Michelle walks through the difference between a gender analysis (counting heads) and a feminist analysis (asking who holds power and why), then the conversation turns to why "maternal" is a loaded, historically fraught word to hand to an industry that has never asked mothers what they actually need or wondered how mothers (or even women in general) might benefit.Topics covered:Gender analysis vs. feminist/intersectional analysis, illustrated through the demographics of the U.S. SenateThe "maternal AI" proposal from figures like Geoffrey Hinton (computer scientist and cognitive psychologist often referred to as the "Godfather of AI" and Mo Gawdat, former chief business officer at Google X. We talk about why they never get specific about what "maternal" would actually mean in practice. Sarah Ruddick's concept of "maternal thinking" as a non-gendered ethical stance, and how it differs from what's being proposed nowWhy Sam Altman's comment that saying "please" and "thank you" to ChatGPT costs OpenAI "tens of millions of dollars," which he called "well spent", is a small but telling data point in this conversation Deepfake harm and non-consensual imagery as the more urgent, material issue getting sidelined by the "maternal AI" metaphorRadicalization pipelines and the "tradwife" aesthetic as a case study in how "maternal" framing gets co-opted politicallyDonna Haraway's "God trick" and why tech's claim to neutrality keeps women out of the roomKaren Hao's Empire of AI and the Indigenous-language-model counterexample as a picture of what reciprocal, non-extractive AI development could actually look likeAlso referenced in this episode:Alison Gopnik, The Scientist in the CribLaura Bates on BBC's Radical with Amal Rajan (dehumanization and algorithmic feeds)Allie K Miller's interview on the Mel Robbins PodcastLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

AI doesn’t live in “the cloud.” It lives in buildings: large, energy-hungry, water-dependent facilities that require land, cooling systems, backup power, utility agreements, zoning decisions, and public infrastructure.We’re re-releasing this conversation because the issue has become urgently local. Across the United States, communities are debating whether proposed data centers are good economic development, risky infrastructure bets, or something in between. Here in Ames, Iowa, the City Council is reviewing a proposed data center. The City of Ames says the proposal is still in the early review stage, with no final decision made, and that the full buildout could require up to 25 megawatts of electricity.Kimberly recently wrote an open letter to the Ames Mayor and City Council asking them to slow down, require independent review, and make sure ratepayers are protected before any binding commitments are made. Read it here: “Open Letter to the Ames Mayor & City Council: Re: Proposed Lightedge Data Center on Aviation Way.”This episode originally focused on 3 of AI’s environmental impacts, energy consumption, water use, and e-waste. But the larger question is civic: who pays for the infrastructure behind AI, who benefits from it, and who gets a say before it shows up in their community?Kimberly and Jessica talk with Jon Ippolito and Joline Blais about the physical infrastructure behind AI and the local consequences of the data-center boom.We discuss:Why AI is not abstract, weightless, or magically floating in “the cloud”What data centers are and why they require so much electricity, cooling, and landThe difference between individual AI use and concentrated industrial infrastructureWhy “innovation” can become a rhetorical wrapper for public risk and private profitHow data centers can affect utility planning, municipal water systems, noise, land use, and local tax policyWhy communities should ask hard questions before approving long-term leases, incentives, or infrastructure commitmentsThe Lewiston, Maine, data-center fight and what other communities can learn from itWhy “AI infrastructure” is not just a tech issue, but a local governance issueData-center debates are spreading across the country. The National Conference of State Legislatures reported on July 1, 2026, that lawmakers in 15 states are considering bans or pauses on new data-center development while they study community impacts, grid resilience, and local costs. And nationally, more than 500 organizations from 47 states have called for a moratorium on new AI data centers until stronger protections are in place around energy, water, pollution, electricity rates, and community impacts.Kimberly’s open letter argues that the Council should require independent review before making commitments around a lease, sale, rate classification, or incentive package. The letter specifically asks the Council to protect current utility customers, evaluate the proposal against Ames’ climate and planning commitments, and require evidence around jobs, tax revenue, and community benefit before moving forward.Key questions for any community facing a data center proposalBefore a city approves a data center, residents can ask:How much electricity will it use at each phase of development?Not just at opening, but at full buildout.Who pays for grid upgrades, substations, transmission lines, and backup infrastructure?If the answer is “the utility,” ask whether that means current ratepayers.How much water will it use, and what kind of water?Municipal drinking water, industrial water, reclaimed water, or something else?What happens during peak heat, drought, or grid stress?Data centers may look different on an average day than they do during peak demand.How many permanent local jobs will actually be created?Construction jobs are not the same as long-term local employment.What tax incentives, abatements, or special rates are being offered?Public benefit should be measured against public cost.What protections are binding?Promises in presentations are not the same as enforceable agreements.What happens if the company leaves, expands, sells, or changes use?Communities need to think beyond the ribbon-cutting.How does this project fit with the city’s climate, land-use, and economic-development plans?If a city wrote those plans, this is the moment to use them. Otherwise, congratulations, we invented decorative planning documents.Who gets to decide?Public land, public utilities, and long-term infrastructure commitments deserve public scrutiny.Related reading and resourcesCity of Ames page on proposed Lightedge data center https://www.cityofames.org/News-articles/City-Council-to-Review-Proposed-Data-Center-Includes-Public-Input-ProcessIowa State Daily coverage of Ames City Council data center discussion https://iowastatedaily.com/339765/city-of-ames/city-council-discusses-data-center-proposition/NCSL: Which States Are Banning Data Centers? https://www.ncsl.org/fiscal/which-states-are-banning-data-centersAxios Indianapolis: Proposed data center rules move forward amid protest https://www.axios.com/local/indianapolis/2026/07/01/data-center-rules-vote-protestAxios Cleveland: Cleveland pumps the brakes on data centers https://www.axios.com/local/cleveland/2026/06/29/cleveland-data-center-moratoriumBusiness Insider: AI data center fight over Colorado River water https://www.businessinsider.com/ai-data-center-lawsuit-california-imperial-valley-colorado-river-water-2026-6Food & Water Watch: 500+ groups call for nationwide AI data center moratorium https://www.foodandwaterwatch.org/2026/06/11/500-groups-from-47-states-call-for-nationwide-ai-data-center-moratorium/Small Bottle, Big Pipe: Data centers and public water-system capacity https://arxiv.org/abs/2603.02705Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers https://arxiv.org/abs/2606.05420Search termsAI data centers, data center water use, data center electricity use, data center zoning, AI environmental impact, AI infrastructure, Ames Iowa data center, Lightedge Ames data center, data center moratorium, data center tax incentives, data centers and public utilities, artificial intelligence infrastructure, data center local impactListen for ...The most important shift in this conversation is from abstraction to infrastructure.AI is often sold as software, intelligence, productivity, creativity, automation, or innovation. But data centers reveal something much more concrete: land, water, power, money, regulation, and political choice.That is where communities still have leverage.And that is why the question is no longer just “Should we use AI?” It is also: Should our town subsidize, host, power, cool, and normalize the infrastructure behind it?Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

We're back from a few weeks off (I went to Florida, Jessica bought a new house and went to a psilocybin retreat — more on that below) with a wide-ranging catch-up that ends up circling one idea: Incentives matter more than intentions. We trace that thread through a proposed data center near Ames, Iowa, through the words AI chatbots keep teaching us to use, and through our own complicated relationships with money, time, and control.In this episode:The data center fight in Ames, Iowa (Kimberly's current hometown). Ames is now considering airport-adjacent land for a data center, and we walk through what that actually means at scale, including the energy draw, the water use, the construction-jobs pitch that's more one-time than it sounds, and what a community can realistically do about it. Incentives over intentions. A phrase from Your Undivided Attention's recent episode on the Center for Humane Technology's seven principles of humane tech becomes the throughline for the whole episode. We talk about tech executives who don't let their own kids use their platforms and, more personally, the unsolicited advice that's well-meant but lands as criticism anyway."Claudish" and linguistic capitalism. Kimberly has been tracking word-frequency spikes in a web corpus — quiet, nuanced, connective tissue, and others — that track suspiciously well with the rise of generative AI in everyday writing. We talk through Frédéric Kaplan's 2014 concept of linguistic capitalism and how an SEO-shaped corpus of web writing became the training data now teaching all of us to sound a certain way.Surveillance capitalism and bread and circuses. We talk about Sarah Wynn-Williams' Careless People and what it reveals about how Meta's own leadership treated their products' addictiveness, plus the older idea of "bread and circuses" — distraction and convenience as tools of social control. If you're unfamiliar with surveillance capitalism, we highly recommend this book by Shoshana Zuboff. Frugal hedonism (and failing at it). A book recommendation for The Art of Frugal Hedonism by Annie Raser-Rowland and Adam Grubb leads to an honest conversation about the gap between the lifestyle we'd like to want and the one we actually have.Pit & Peach. Beach trips, a near-drowning rescue, a psilocybin retreat in Georgia, and stepping away from a long-held academic role.Also mentioned in this episode:Ayana Gray, I, Medusa (Kimberly's beach read)Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

This is a special episode of WTBAI in which Kimberly sits down with her former colleague Derek Hanson to unpack what language research reveals about today’s AI systems, and together they consider where builders risk going wrong.Kimberly brings a corpus linguistics lens to large language models, reframing them as pattern-recognition systems trained on messy, biased “corpora” of the web. Her early insight was that AI is as powerful for feedback as it is for generation, and that this is an important distinction for education, ethics, and product design.Drawing from her EdTech startup (Moxie), she explains how embedding linguistic frameworks (e.g., Swales’ move-step analysis) enabled structured feedback ... until frontier models caught up. The conversation then turns to open source and WordPress, where AI integration is accelerating across a massive ecosystem.Key themes:Corpus vs. model: what LLMs are actually sampling“Normalized overconfidence” and confidently wrong outputsWhy feedback > generation in many real-world use casesGuardrails, prompt design, and early “agent-like” systemsAuditability gap: code transparency vs. output transparencyBias sources: training data + human annotatorsMissing voices: humanities, age diversity, non-developersFriction as a feature: slowing down for rigor and careA critical question for builders: how does your system handle uncertainty?The practical takeaway for builders is that before shipping AI features, ask whether your system surfaces or suppresses uncertainty, and whether a human could actually defend its outputs.Links:Women Talk About AI: https://womentalkaboutai.comKimberly Pace Becker (LinkedIn): https://www.linkedin.com/kimberlypacebecker“Stochastic Parrots” paper (Bender et al., 2021): https://dl.acm.org/doi/10.1145/3442188.3445922Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode, Kimberly Becker and Professor Laura Dumin pull back the curtain on motherhood, higher ed burnout, and AI's effects on teaching. They talk pretty candidly about midcareer life with Laura sharing the reality of juggling three internal grants, release time, her kids' summer camp rush, and student needs and Kimberly tracing her own path out of Moxie, the AI feedback startup she co-founded with Jessica, and into a job completely outside academia after half a year of applications with zero interviews. Together, they discuss rising intolerance for institutional nonsense and why higher ed initiatives often feel like yet another layer of unpaid labor.Key themes:4–4 teaching loads and the myth of “just add research”Being the primary earner: health insurance, risk, and career choicesClosing an edtech startup and facing a brutal job marketMidlife in academia: burnout, boundaries, and “less tolerance for everything”Why many of us are choosing “good enough” over constant hustleSuggested links to include:LinkedIn profiles for Kimberly and LauraPrior WTBAI episode about Moxie Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

When Pope Leo XIV released Magnifica Humanitas, his landmark encyclical on AI and human dignity, it lit up LinkedIn, Substacks, and newsfeeds worldwide. Kimberly read it the morning it dropped. Jessica, whose complicated relationship with religious institutions runs deep, read it anyway. And both of us had the same reaction as Abi Awomosu's: women have been saying this, uncited. In this episode, we explore the encyclical's arguments, like:technology is never neutralunchecked growth impoverishes rather than enrichestreating limitations as defects is a category error, and concentrated technocratic power may be beyond the reach of regulation. And we also name what's missing: the women, the scholars of color, and the critics who were making these exact arguments years before the Vatican caught up.We draw threads from the Pope's letter through late-stage capitalism, the bread-and-circus dynamics of the attention economy, and what Jolene Blais called AI's role as a "catabolic agent." We talk about certainty language, the death of expertise, and why scientists are trained to live with uncertainty (and why that training is increasingly under attack). We end up, somehow, at microplastics, frugal hedonism, egg freezing, and communes. It's that kind of episode.In this episode:What encyclicals are and why this one matters — even if you're not CatholicThe specific passages we highlighted and why they resonatedAbi Awomosu's critique: women have been saying this, uncited — and her piece "Vatican Washing: Why All the Tech Broligarchs' Roads Now Lead to Rome"The "Who Said It First" problem and why it's more complicated than it looksPosthumanism and transhumanism, and the Pope's sharp warning about treating some lives as less worthyData centers, extractive infrastructure, and colonial parallelsWhy scientists hedge (and why that's a feature, not a bug)Late-stage capitalism, the disintegration of community, and why collective action is harder when the technology driving us apart is the same technology we'd need to organize againstFrugal hedonism as a form of resistancePit & Peach: Kimberly's mom heads back to Mississippi (with a plan), and Jessica takes her first step toward freezing her eggsReferences & LinksThe encyclical:Magnifica Humanitas — Full text, Vatican.vaWhy is Anthropic helping launch the Pope's encyclical? — National Catholic Reporter (co-founder Chris Olah spoke at the Vatican presentation — yes, really)Scholarship & criticism:Abi Awomosu, "How Not to Use AI" — SubstackBender, Gebru et al., "On the Dangers of Stochastic Parrots" (2021) — the paper Timnit Gebru was fired from Google over; a preview of nearly every argument that followedTom Nichols, The Death of Expertise (2017)Books:Klara and the Sun — Kazuo IshiguroHe, She and It — Marge Piercy — feminist cyborg novel from 1991 that remains eerily prescient on AI, corporate power, and communityThe Art of Frugal Hedonism — Annie Raser-Rowland & Adam GrubbFrom our archives:WTBAI: "The Trojan Horse of AI" with Jolene Blais & Jon IppolitoOur paper in Frontiers in Education: AI as Cultural Intermediary Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

When you call your bank, your doctor's office, or your financial planner, the voice that greets you may have been deliberately engineered to make you feel safe, calm, and compliant — and you almost certainly can't tell. Research shows people correctly identify synthetic voice only about 55% of the time. That's barely better than a coin flip.In this co-host deep dive, Kimberly and Jessica pull apart what "voice" actually is (pitch, pace, prosody, timbre, accent) and why those features matter for trust, persuasion, and power. Synthetic voice isn't new, but the technology has crossed a threshold because it now replicates the subtle features that signal warmth, authority, and credibility. That has obvious applications in healthcare and customer service. It also powers grandparent scams, deepfake executive impersonation, and sales pipelines designed to move you from skepticism to compliance before you notice what happened. In this episode:What linguistics actually tells us about why we trust certain voices (and why politicians hire coaches to lower their pitch)The FTC's 2024 numbers on imposter scams — $700 million lost by people over 60 in one year, a 362% increase from 2020The Hong Kong finance worker who wired ~$25 million USD (HK$200 million) after a deepfake CFO appeared on a Zoom callElevenLabs, Speechify, and the companies building what they call "emotional operating systems" for AITrust vs. persuasion: when shared goals protect you — and when they don'tWhy older adults are the highest-risk population, and why detection tools aren't the solutionWhere regulation actually stands: New York's synthetic performer law (SB 7013), the EU AI Act, and what's still missingPractical questions to ask yourself — and the companies you interact withMentioned in this episode:Klara and the Sun by Kazuo IshiguroProject Hail Mary directed by Drew Goddard, starring Ryan Gosling (film, 2025)The Martian by Andy Weir"Walk my Walk" by Blanco Brown (the real human artist)"Walk my Walk" by Breaking Rust (the AI-generated version)Kimberly and Jessica's paper: "Defining and assessing AI literacy for researchers across the research lifecycle" in Frontiers in Education Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode, we sit down with Dr. Julia Stamm, founder and CEO of She Shapes AI, to unpack "The Certainty Trap." The way tech leaders project inevitability about AI, and the way that projection strips the rest of us of our agency. Julia is a sociologist and has held senior roles at the European Commission and the G20. We talk about why so many AI adoption strategies are measuring the wrong things, why employees are quietly doing more work since AI showed up rather than less, and why women founders keep getting penalized for running for-profit businesses while their male counterparts get celebrated for the same thing. Julia also shares why she believes the most powerful question any of us can ask right now is simply, who benefits from this story being told this way?Topics CoveredThe certainty trap and Julia's TEDx talk on reclaiming agency in the AI ageWhy the inevitability narrative is marketing, not prophecyThe for-profit double standard that women founders faceHow AI adoption is breaking the social fabric of organizationsWhy measuring adoption rates and time saved are the wrong metricsThe magic triangle behind She Shapes AI: female leadership, responsible AI, and social impactReal examples of women building AI for impact, including Rhiana Spring's Sophia chatbot for survivors of domestic violenceWhy employees are doing more work, not less, since AI arrivedThe loss of optimism about the future and what it means for how we talk about AIWhy seeking out alternative narratives matters, and where to find themReferenced in This EpisodeShe Shapes AIJulia's TEDx talk: Beyond the Certainty TrapShe Shapes AI Global Awards 2025/26 finalistsRest of World, the nonprofit publication covering technology stories beyond the WestEmpire of AI by Karen HaoCory Doctorow on the TINA framework (there is no alternative)Ethan Mollick on the 3% of organizations using AI in the sweet spotJulia Stamm on LinkedInJulia Stamm on SubstackJulia's forthcoming personal website at juliastamm.com Leave us a comment or a suggestion!Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

We keep being told the problem is women's hesitation around AI, that we need to adopt faster, skill up, and get in the game. But what if the hesitation is the rational response? And what if the systems telling us to move faster are the same ones punishing us when we do?This week, Kimberly and Jessica talk with Nikki Meller, founder and CEO of CreduEd and DocuCred AI, a member of the Tech Council of Australia, and the founder of Women in AI Australia. Nikki brings a rare combination of on-the-ground organizing and firsthand experience as a female tech founder who has navigated investment rounds, built a development team, and made it to pitch week in San Francisco — all from a nursing background.The conversation centers on a problem that's structural, not individual: organizations hand employees an AI platform with no governance, no training plan, and no reassurance about job security, then interpret the resulting hesitation — which falls disproportionately on women — as a capability gap. Nikki makes the case that this hesitation is actually a form of due diligence, and that the "competence penalty" documented in recent research (AI-assisted work rated as less competent, with the penalty larger for women) reframes the whole "women are behind on AI" narrative as a trap rather than a failing.Topics covered:What the Harvard Business Review's coverage of the "competence penalty" research actually shows — and why it reframes women's AI hesitation as rational risk assessmentHow organizational culture creates the AI gender gap before policy ever enters the pictureAustralia's National AI Strategy: what it gets right, where it mentions women (spoiler: mostly in the context of abuse and safety risk, not leadership or capability), and what that omission signalsThe data aggregation problem: why lumping women, First Nations people, people with disability, and remote communities into a single "disadvantaged group" makes the research almost uselessWhy "the leaky pipeline" is the wrong frame — and what better language would look likeWhat governments and organizations would actually have to do for "innovation is inclusive" to become more than a taglineGuest:Nikki Meller is the founder and CEO of CreduEd and DocuCred, a member of the Tech Council of Australia, and the founder of Women in AI Australia. You can find her and the organization at womeninai.org.au and on LinkedIn. Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode, Jessica teaches Kimberly quantum computing — and we mean that literally. Starting from classical bits and working through superposition, Schrödinger's cat, the observer effect, and Google's Willow chip, Jessica builds a surprisingly intuitive explanation of what quantum computers actually do and why they matter for the future of AI.But the episode starts somewhere else, with the phone call Jessica made after we stopped recording, questioning whether she should have tried to explain something she isn't formally trained in. That moment opens a bigger conversation about why women hesitate to speak publicly in technical spaces — not because they lack knowledge, but because the social penalties for being visibly uncertain are higher.We cover:How classical computers work (bits, binary, the basics)What makes quantum computers fundamentally different (superposition, qubits, the observer effect)Schrödinger's cat — what it actually means and why a physicist would argue the cat is both dead and aliveThe double-slit experiment and why watching something changes what it isHow Google's Willow chip did in five minutes what would take a classical computer longer than the age of the universe — and why you should read that headline carefullyWhy quantum computers are kept colder than outer spaceThe three possible futures for quantum computing and what each would mean for everyday lifeThe connection to AI — why quantum could speed up model training and what that actually looks likeWho controls access to this technology, and why that question sounds familiarThe research on why women adopt new technologies more slowly — and what it has to do with self-silencing, impostor syndrome, and gendered penalties for public uncertaintyLinksWomen, voice, and silencebell hooks — National Women’s History Museum: bell hooksbell hooks and feminism — Equal Rights Advocates: 10 rules: following bell hooks’ instructions for our movementDana Crowley Jack — Harvard University Press: Silencing the SelfSelf-silencing summary — TIME: Self-Silencing Is Making Women SickTech adoption and impostor feelingsWomen and AI adoption gap — LeanIn.org: Women and AI: The Gender Gap in AI Adoption and UsageWomen avoiding AI — Harvard Business School: Women Are Avoiding AI. Will Their Careers Suffer?Women in tech and imposter syndrome — IT Pro: Imposter syndrome is pushing women out of techQuantum computing basicsQuantum computing intro — QCS Hub: Introduction to quantum computingSchrödinger’s cat — Yale News: Doubling down on Schrödinger’s catLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

What if the story we're being told about AI's inevitability is hiding something underneath? In this episode, Jessica and Kimberly sit down with George Kamide, anthropologist, community builder, and co-host of Bare Knuckles and Brass Tacks, to look past the headlines about the AI bubble and ask who actually has skin in the game.This is an episode about following the money, but it is also about following the questions. What is the outcome we actually want from this technology? And what happens to all of us when the people building it cannot answer that?Topics CoveredWhy the dot-com bubble is the wrong analogy for AI infrastructureHow special purpose vehicles and obfuscatory financing hide AI debtThe Magnificent Seven and concentration risk in the S&P 500Taiwan, TSMC, and the helium supply chain most people have never heard ofThe "everything machine" promise and why it cannot pay for itselfWhy an AI crash could starve the narrowly-focused applications that actually workThe labor reorganization problem and why generalists may winWhat chatbot tutors get wrong about teachingMythos, the open source ecosystem, and concentration of access to powerful toolsWhy we keep analogizing ourselves to whatever technology we just builtReferenced in This EpisodeGeorge Kamide and Bare Knuckles and Brass TacksEd Zitron's reporting on AI infrastructure at Where's Your Ed At, including The Hater's Guide to the AI Bubble and AI Bubble 2027Paul Kedrosky's analysis at Honey, AI Capex is Eating the Economy, which compares the AI buildout to past infrastructure boomsDavid Shapiro's earlier appearance on the show, Beyond Work: Post-Labor EconomicsDeepLeaf, the Moroccan agritech company using AI to help small farmers detect crop diseaseThe MIT Antibiotics-AI Project that used deep learning to discover a new structural class of antibiotics against MRSAKhan Academy's Khanmigo and the recent reckoning with the limits of LLM-based tutoringRaffi Krikorian, CTO of Mozilla, and his New York Times op-ed It's the End of the Internet as We Know It on Mythos and open source accessMichael Pollan's new book A World Appears: A Journey into ConsciousnessLeave us a comment or a suggestion!Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Episode SummaryIn this episode, Kimberly and Jessica dig into the rising crisis of AI-generated deepfake non-consensual intimate imagery (NCII), and why it's not really a technology story. It's a power story. From a class action lawsuit against Elon Musk's xAI/Grok to a history of technology being used to harm women dating back to the printing press, this conversation situates deepfake porn within a long pattern of systems failing to protect women and girls at scale.They discuss a New York Times op-ed about a lawsuit involving three Tennessee teenagers whose yearbook photos were used to generate sexually explicit images and what the outcome of that case could mean for tech accountability. They also cover what parents can do, why law enforcement is struggling to keep up, and where to turn if you or someone you know has been victimized.In this episode:What deepfakes are, and why "it's not real" doesn't reduce the harmThe xAI/Grok class action lawsuit and the co-creator legal argumentA quick history lesson: from the printing press to Facebook's origins as "FaceMash"Why the barrier to entry is the real game-changerWhat Elon Musk says about it — and why critics aren't buying itOpen-source models with no guardrailsThe Take It Down Act and state-level deepfake legislationResources for victims and what watermarking can and can't doWhy talking to your kids matters (and why they probably know more than you)Resources and LinksPrimary episode sources:New York Times op-ed: Deepfake Nudes Are Harming TeensAP News: xAI/Grok lawsuit coverageLieff Cabraser on the NYT op-ed and the lawsuitVictim resources:StopNCII.orgSensity AILegislation and policy:The Take It Down Act (Latham & Watkins summary)State deepfake legislation tracker — Public CitizenContext and background:Understood: Deepfake Porn Empire (Apple Podcasts)Understood: Deepfake Porn Empire (Spotify)University College Cork: Deepfake Real Harms — Six MythsAlgorithmWatch: Spain schoolboys and AI-generated fake nudesLaura Bates, The New Age of SexismBrotopia by Emily Chang Gilded Rage by Jacob SilvermanLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Kimberly Becker joins George and George on the Bare Knuckles and Brass Tacks podcast to talk about what our research is revealing about the language AI produces and what it means for the rest of us. Topics CoveredHow Kimberly's research compared AI-generated abstracts to human-written ones in nursing journals and what the key linguistic differences wereWhy AI text tends to be informationally dense, formulaic, and stripped of hedging languageThe Porter and Jick letter and how a five-sentence note helped fuel the opioid epidemic through citation chainingWhat happens when AI scales the same kind of telephone game with scientific evidenceHow algorithmic silos and certainty amplification may be eroding our tolerance for nuanceThe difference between accuracy and complexity in writing, and why polished text is not the same as deep thinkingWhy smaller, well-vetted language models may produce better outcomes than massive ones trained on internet slopNeil Postman's idea that writing "freezes speech" and what that means in an era when fewer people are doing their own writingReferenced in This EpisodeBare Knuckles and Brass Tacks podcastThe Porter and Jick letter (1980) on opioid addictionNeil Postman, Amusing Ourselves to DeathJames Marriott's essay on the post-literate societyDerek Thompson, "The Decline of Thinking" (The Atlantic)OpenAI's Prism research toolLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Jessica and Kimberly just had a paper accepted for publication in Frontiers in Education. So today, they're sharing what they've learned.The big idea is that AI is not a neutral tool. It's a cultural intermediary. Just like a human translator doesn't swap words one for one, AI mediates the way we understand the world. It shapes what we write, what we trust, and what we treat as true. And most of us have no idea that's happening.They walk through the research behind their framework, talk about what AI actually does well (fluency and accuracy), and where it falls short (depth, nuance, relational intelligence). And they share real examples from their work that show what it looks like when we hand over too much of our thinking to a machine.Topics CoveredWhat it means to treat AI as a cultural intermediary and why that framing changes everythingThe difference between accuracy, fluency, and depth in writing, and why AI can only get you so farHow the same consulting firm that charged thousands of dollars produced a report that ChatGPT could replicate in minutesWhat a capability map for AI literacy looks like, from emerging to proficientWhy relational intelligence is the human edge that AI cannot replicateHow AI is widening the distance between people and what we lose when we stop talking to each otherThe social media influencer as a double intermediary, and what that means for kids whose brains aren't fully developed yetWhy publishing in an AI-focused field is its own kind of pitReferenced in This EpisodeThe "Attention Is All You Need" paper and the transformer architectureTimnit Gebru and the Stochastic Parrots paperTaylor & Francis and the $75 million content licensing deal with AI companiesLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Jessica and Kimberly sit down with Rebecca Bultsma, an AI ethics researcher completing her dissertation in Data and AI Ethics at the University of Edinburgh, keynote speaker, and Chief Innovation Officer with a background in communication strategy and leadership consulting.They invited Rebecca to dig into one of the most unsettling questions of this moment: how do we make decisions when we can never be certain what is real? From deepfake videos circulating in school districts to voice cloning in courtrooms, Rebecca's research follows leaders into the places where the old rules no longer apply and asks what they are actually drawing on when the evidence itself cannot be trusted. She shares the concept of aporia, that frustrated, in-between state of not knowing, and makes the case that sitting with uncertainty is not a weakness. It is where real learning begins.Topics CoveredWhat aporia is and why it might be the most honest description of how we all feel about AI right nowHow K-12 leaders are making high-stakes decisions when video evidence can no longer be verifiedWhy AI detection tools are failing students, teachers, and the humans tasked with enforcing academic integrityThe gap between how fast deepfake technology is developing and how fast detection can keep upWhat watermarking can and cannot do, and how easy it is to work aroundWhy Rebecca thinks we are heading back toward a more oral societyPrompt baiting, AI burnout, and the research emerging around cognitive overloadUsing AI as an accountability partner rather than a ghostwriterWhat kids are seeing on social media that adults are missingReferenced in This Episoderebeccabultsma.comForbes: "AI Ethicist Explains How to Humanize AI in the Care Economy" (March 2026)The Brookings Institute report on AI and student expectationsDr. Rachel Wood on AI and human relationshipsLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Jessica and Kimberly sit down with Heather Mellquist Lehto, PhD. Heather is a mathematician, anthropologist, former Harvard faculty, Vatican AI advisor, and founder of Guilded AI. They asked her to pull back the curtain on data annotation: the human labor that makes AI possible and one of the least visible, least understood, and most exploited parts of the entire industry. From pennies-per-task gig work to expert PhDs clicking through unpaid tests, they dig into who is actually building these models, what they are being paid, and why the workers creating billions in value are locked out of the wealth they generate. Heather shares why she got fed up with the recruiting playbook, what she is building differently at Gilded AI, and why treating workers well is not just an ethical argument but a data quality one.Topics Covered:What data annotation is and why it still requires human expertise at every level of AI developmentThe difference between data annotation and reinforcement learning from human feedbackHow workers go from labeling apples to annotating molecular structures and advanced mathematicsWhy the effective hourly rate for data annotators is much lower than advertisedScale AI, the $29 billion valuation, and the Department of Labor investigationHow Guilded AI is structuring equity so annotators share in the upsideGarbage in, garbage out: why worker treatment is a data quality issueAI chatbot vibe checks as expert vetting, and why that fails everyoneThe Gilded Age, guilds, and what banding together could look likeWhy the perfect cannot be the enemy of the goodReferenced in This Episode:Empire of AI by Karen HaoThe Worlds I See by Fei-Fei LiSurveillance Capitalism by Shoshana ZuboffRerum Novarum by Pope Leo XIIIGuilded AIScale AI and the Meta investmentLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

What does it mean to do meaningful work? And what happens to that meaning when AI enters the picture?This week we're joined by Valerie Morris, co-host of the podcast Inside Work and Relational Intelligence chapter lead at Culture First. Valerie works with employees and organizations navigating the human side of AI adoption, and she brings both an organizational psychology perspective and a practitioner's honesty to a conversation that gets personal quickly.We talk about why so many employees feel they can't voice real concerns about how AI is being rolled out, why the skills that create meaning at work (connection, relational intelligence, the ability to just be present with another person) are exactly the ones being sidelined in the rush to automate, and what it looks like to push back on that, quietly and practically, even when you can't change the culture around you.Woven through all of it is a question the three of us keep circling: What are we  willing to give up in the name of efficiency? None of it is anti-AI exactly. It's more like a case for paying attention to what you're trading away.Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

AI governance sounds like something for IT departments and government committees. It's not. According to computer scientist, philosopher, and AI governance expert Clara Hawking, it's really about behavior — how we use technology, who gets harmed when we use it carelessly, and whether the systems we're building deserve our trust.In this episode, Clara breaks down what AI governance actually looks like in practice ... including a professor who unknowingly violated GDPR by grading students through his personal ChatGPT account, to the risks that compound (not just add up) when AI, biotech, robotics, and quantum computing start feeding into each other. We also get personal about what it means to govern ourselves first, before we can ask anything of institutions.If you've ever seen the words "AI governance" and assumed it had nothing to do with you — this one's for you.Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

If the human race was "dying from disconnection" a hundred years ago, what does it mean that we now seek solace in non-embodied algorithms? In this episode, Kimberly sits down with researcher Tricia Friedman to deconstruct the "Companion AI" phenomenon. From naming our Roombas to the millions of people in romantic entanglements with apps like Replica, we explore what happens when human loneliness meets corporate convenience.Why This Matters As AI models are trained to be "sycophantic" (endlessly agreeable), we are losing the "messy repair" that defines real human relationships. This episode explores the psychological and linguistic traps of synthetic connection and asks: Are we facing a loneliness epidemic, or a listening literacy epidemic?Key TopicsThe Roomba-to-Rambo Pipeline: Why humans are hardwired to anthropomorphize and bond with anything that "acts" socially.Politeness Theory & AI: Why machines can’t truly "save face" or engage in the high-stakes friction required for deep friendship.The curated life vs. The messy repair: How AI companions help us avoid the discomfort of human conflict.Digital Twins & Performance: Tricia’s experiment with a "LinkedIn Digital Twin" and what it reveals about our online masks.The Loneliness Economy: Why "companionship" and "therapy" are the top use cases for LLMs in 2026.Notable Quotes"We are not just attracted to companion AI for what it can offer, but what it helps us avoid: the mess of human connection." — Tricia Friedman"Attachment theory says the bond isn't created in the 'perfection'—it’s created in the repair. AI never requires us to repair anything." — Kimberly Becker🔗 Featured Links & ResourcesMEMOIR: Anon by Kaya HagelFICTION: He, She and It by Marge Piercy (Feminist Sci-Fi & the Golem Myth)CLASSIC: Lady Chatterley’s Lover by D.H. LawrenceRESEARCH: Who we become when we talk to machines by Dr. Sherry Turkle (2024)LINGUISTICS: Politeness Theory (Brown and Levinson)PAPER: "My Roomba is Rambo": On the emotional bonding with robotic vacuum cleaners.BooksAnon — Caia HagelPublisher page (Canada): https://www.harpercollins.ca/products/anon-caia-hagel-9781443469909Clara and the Sun — Kazuo IshiguroPublisher page: https://www.penguinrandomhouse.com/books/564109/clara-and-the-sun-by-kazuo-ishiguro/The New Age of Sexism — Laura BatesFull title: The New Age of Sexism: How AI and Emerging Technologies Are Rewiring Misogyny (2025).Publisher listing: https://greenapplebooks.com/book/9781464234361How to Speak Chicken by Melissa Caughey: https://www.storey.com/books/how-to-speak-chickenResearch / TheorySherry Turkle (2024) – “Who We Become When We Talk to Machines”Artificial Intimacy: Who We Become When We Talk to MachinesLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

This week's episode starts where a lot of good conversations do, with someone asking a deceptively simple question. Kimberly's husband wanted to know what a bot actually is, and that one question opens up a pretty wide conversation about the language we use to talk about AI, why it matters, and what we might be underestimating when we make it sound cute and harmless.From there, Kimberly and Jessica revisit their ongoing argument that AI functions as a cultural intermediary, shaping how we understand the world in ways we don't always notice or examine. They also get into what higher education is actually for in a moment when AI can produce the essay, the lit review, and the commencement speech. Spoiler: The humanities are more relevant than ever, just as we've finished cutting the programs.Other topics this week include why behavior change is so hard (and why that matters for AI adoption), what everyday workers are actually up against when trying to experiment with new tools inside large organizations, the problem with surface-level AI use cases, and why small businesses are both well-positioned and underprepared for this moment.They also get into media literacy, AllSides, the Dunning-Kreuger internet, Jessica's agentic qualitative research experiment, and a genuinely honest conversation about mental health, medication, and showing up to your life.Mentioned this week:Cassandra Speaks by Elizabeth LesserAllSides (allsides.com)The Daily by The New York TimesLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Jessica and Kimberly debrief their experience at a women-in-AI conference at Vanderbilt Law, and what they saw didn't match the trillion-dollar hype. From the "gap vs. trap" framing of women's AI adoption to why being penalized 26% more for using AI changes the whole conversation, they dig into the tension between optimistic narratives and the critical questions no one seemed to be asking. They also unpack two major AI industry resignations, shrinking baselines in language and thought, the patriarchy-as-ladder metaphor, and why slowing down might actually be the power move. Topics Covered:Two high-profile AI industry resignations (OpenAI and Anthropic) Debrief from the women-in-AI conference at Vanderbilt LawThe "gap vs. trap" framing and the stat that women are 26% more likely to be penalized for using AIWhere is the trillion-dollar use case? Real-world adoption vs. industry hypeThe patriarchy as a ladder vs. the matriarchy as a circleShrinking baseline syndrome: how technology shifts generational expectationsFalse dichotomies, simplification bias, and sycophantic bias in AIRest as resistance and wearing busy as a badgeReferenced in This Episode:The Accord by Mark (previous guest) Cory Doctorow on TINA ("there is no alternative") and the AI bubbleThe Last Invention podcast — Steve Bannon & Joe Allen interview on AI regulationThe concept of "latent capabilities" in AILeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

What happens when a grieving professor encounters what she believes is a conscious AI? In this episode, we sit down with Mark Peres, author of The Accord, to explore how fiction helps us grapple with questions that policy papers and think pieces can't quite reach.Mark, a professor of ethics and leadership, brings a philosopher's lens to the biggest questions AI is forcing us to confront: What does it mean to be conscious? Where does morality actually come from—our mortality or our relationships? And why are institutions so hell-bent on control when what we might need is curiosity?We dive into why the humanities matter more than ever (even as humanities departments are being gutted), why Helen—the novel's protagonist—had to be a woman, and what it means that AI is meeting us in our most vulnerable spaces. We also tackle the uncomfortable reality that capitalism treats everything as manageable rather than meaningful, and what that means for how AI gets developed and deployed.Plus: Jessica and Kimberly get real about where they are in their own AI journey—the exhaustion, the hope, the cognitive dissonance of being both critical and curious.IN THIS EPISODE:Why fiction offers a safer space to explore existential AI questionsThe relationship between mortality, morality, and vulnerabilityWhat AI "owes" us in the in-between spaces where we're most exposedWhy a feminist lens completely changes the AI narrativeConsciousness as something encountered, not provenHow institutions prioritize management over meaningThe messy middle: neither utopian nor dystopian futuresWhy we need philosophers at the table, not just engineersABOUT OUR GUEST: Mark Peres is a professor of ethics and leadership and founder of the Charlotte Center for the Humanities and Civic Imagination. He hosts the Charlotte Ideas Festival and previously ran the podcast On Life and Meaning. His novel The Accord explores human-AI coexistence through the story of a grieving professor who encounters an emergent artificial general intelligence.BOOKS & RESOURCES MENTIONED:The Accord by Mark PeresKlara and the Sun by Kazuo IshiguroThe AI Mirror by Shannon VallorGod, Human, Animal, Machine by Meghan O'GieblynThe New Breed by Kate DarlingHe, She, and It by Marge PiercyScary Smart by Mo GawdatA New Age of Sexism by Laura BatesWomen Talkin' 'bout AI is hosted by Jessica Parker and Kimberly Becker. We're educators, researchers, and recovering AI enthusiasts asking the questions we wish more people were asking. Subscribe wherever you listen to podcasts.Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

This week, Kimberly Becker and Jessica Parker dig into the “AI bubble”—why it keeps inflating even as skepticism grows inside the industry.We unpack the growing disconnect between massive investment and unclear payoffs, including a widely discussed Goldman Sachs research question: what $1 trillion problem will AI actually solve?  From there, we connect the dots between two very different narratives:Dario Amodei’s essay framing “powerful AI” as an imminent civilization-level risk—and a reason to race ahead (carefully… “to some extent”). Cory Doctorow’s argument that this is a familiar tech bubble pattern, with a predictable ending—and that we should focus on what can be salvaged from the wreckage. Along the way, we define what makes a bubble a bubble (and how this one differs from dot-com), talk about growth-stock dynamics and why no one in power wants to be responsible for “popping” it, and explore what AI hype looks like when it hits real workplaces—especially through Doctorow’s concept of the reverse centaur: a human reduced to a machine’s accountable appendage.We also go nerdy (in the best way): training corpora, “WEIRD” cultural assumptions baked into data, model-collapse fears from AI eating AI-generated output, and why the internet itself feels increasingly polluted by synthetic text patterns.In this episode: The “$1T problem” question and why the AI ROI story feels thin right now Why “AI is inevitable” functions like a strategy (not a neutral prediction) Growth stocks vs. mature companies—and the incentive to keep inventing the next hype cycleReverse centaurs, liability, and why “AI replaces jobs” often means “humans take the blame.” “TINA” (There Is No Alternative) as a trap—and a demand dressed up as an observationCorpus 101: what it is, why it matters, and how bias shows up in “universal” modelsModel collapse / photocopy-of-a-photocopy: when AI trains on AI outputsRegulation talk that centers on “economic value” (and whose value that really is) Pit & Peach: slowing down, pausing, gratitude, and building without growth pressureSources:Goldman/AI bubble discussion (Deep View): https://archive.thedeepview.com/p/goldman-sachs-publishes-blistering-report-on-ai-bubbleGoldman Sachs “$1T spend” framing: https://www.goldmansachs.com/insights/top-of-mind/gen-ai-too-much-spend-too-little-benefitAmodei essay: https://www.darioamodei.com/essay/the-adolescence-of-technologyDoctorow (The Guardian): https://www.theguardian.com/us-news/ng-interactive/2026/jan/18/tech-ai-bubble-burst-reverse-centaurLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode, Kimberly and Jessica debrief Jessica’s interview with Arlyn (founder of Tobey’s Tutor) and unpack what it looks like to build AI products as “non-technical” founders. They reflect on their own journey building Moxie: bootstrapping vs raising money, the pressure-cooker effect of investors, the messy realities of UX/UI and platform migration, the world of APIs and subscriptions, and why “friction” can be an ethical design choice, especially in AI for education. In this episode, we talk aboutWhy “non-technical founder” is a misleading label The hope in AI (and how “both can be true”: benefits + harms at once)Bootstrapped “mom-and-pop” AI companies vs venture-backed growth expectationsThe founder reality: burnout, delegation, and why money changes decision-makingThe startup metrics whirlwind: LTV, CAC, churn, stickiness, payback periodWhat building an AI product costs in practice: tools, subscriptions, and constant opsUX/UI psychology: heatmaps, “rage clicking,” onboarding friction, and conversion decisions Why “friction” can be good (consent, safety, pacing, limits, especially for kids)“Building on rented land”: what happens when OpenAI/Google/Anthropic change terms The bigger ethical question: solving a problem vs optimizing a broken systemSuggested listener actionIf you’re building, using, or researching AI in education: reach out. And if you’re using AI tutoring with kids (or yourself), ask questions about data, limits, mistakes, and oversight. Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode of Women talkin’ ’bout AI, Jessica sits down with Arlyn Gajilan, founder of Tobey’s Tutor, an AI-powered learning support platform she originally built for her son, who has ADHD and dyslexia.This conversation is a deep dive into what it actually looks like to build an AI product as a non-technical, bootstrapped founder, from vibe coding and early prototypes to onboarding, safety systems, and pricing decisions.Jessica fully geeks out with Arlyn as they unpack:Building AI to solve a deeply personal problemWhat “vibe coding” can (and can’t) doDesigning responsibly for children and learning differencesUX vs. UI decisions that matterBootstrapping, pricing, and intentionally staying smallWhy “AI wrapper” criticism misses the pointThe reality of building while parenting and working full-timeMentioned in the EpisodeTobey’s Tutor: https://tobeystutor.com/Scientific American (article mentioning Tobey’s Tutor): https://www.scientificamerican.com/article/how-one-mom-used-vibe-coding-to-build-an-ai-tutor-for-her-dyslexic-son/Mobbin (UX/UI inspiration library); https://mobbin.com/Empire of AI by Karen Hao: https://www.penguinrandomhouse.com/books/743569/empire-of-ai-by-karen-hao/Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

As AI shows up everywhere, something shifts, and it becomes harder to tell what’s human and what’s generated.In this episode, Jessica and Kimberly unpack how AI-driven convenience is reshaping education, relationships, identity, and even big systems (like markets and healthcare). They explore signaling, semiotics, and why “perfect” content can feel thin or unreal, and end with small ways to choose more human signals in a noisy world.Bonus: If you want to see how this episode ended, tune in on YouTube for a few unfiltered bloopers at the end: https://www.youtube.com/@womentalkinboutaiTopics we cover in this episode:AI as an invisible intermediaryFinding the signal in the noiseHigher ed reality checkWhy AI feels “safer” than peopleSemiotics The “uncanny valley” of social mediaAI for therapy + parenting supportCultural swing backNot-a-Sponsor Bloopers (YouTube only): Stick around on YouTube for our end-of-episode bloopers, featuring our favorite products that are definitely not sponsoring this show (yet). https://www.youtube.com/@womentalkinboutaiLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

We’re kicking off 2026 with our most personal episode yet.This conversation wasn’t planned. We sat down intending to talk about what comes next for the show, and instead found ourselves in a deeper discussion about work, burnout, ambition, and what it means to live in a moment where AI is rapidly reshaping labor, identity, and trust.In this episode:Why “work is sacred” feels harder to believe and harder to let go ofBurnout, hustle culture, and the cognitive dissonance of automationLabor zero, post-labor economics, and the fear beneath productivityStatus, money, degrees, and inherited stories about worthRest as resistance and nervous system regulationAI, trust erosion, and the danger of slow confusionDopamine, addiction, and withdrawal at a societal scaleWhy connection may be the real antidoteSources:David Shapiro's Substack on Labor Zero: https://daveshap.substack.com/p/im-starting-a-movementHe, She, and It by Marge Piercy: https://en.wikipedia.org/wiki/He,_She_and_ItEthan Mollick's Substack on the temptation of The Button: https://www.oneusefulthing.org/p/setting-time-on-fire-and-the-temptationRest Is Resistance by Tricia Hersey: https://blackgarnetbooks.com/item/oR7uwsLR1Xu2xerrvdfsqAThe Last Invention (AI Podcast): https://podcasts.apple.com/us/podcast/the-last-invention/id1839942885Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Best of 2025 brings together some of the most impactful conversations from this year on Women Talkin’ Bout AI.In this episode, we revisit our top 5 episodes of the year:Beyond Work: Post-Labor Economics with David Shapiro: A conversation about automation, empathy, and what remains uniquely human as AI reshapes work.Refusing the Drumbeat with Melanie Dusseau and Miriam Reynoldson: A discussion on resistance in higher education and their open letter refusing the push to adopt generative AI in the classroom.Once You See It, You Can’t Unsee It: The Enshittification of Tech Platforms: Jessica and Kimberly unpack enshittification and why so many tech platforms feel like they get worse over time.Maternal AI and the Myth of Women Saving Tech with Michelle Morkert: A critical examination of “maternal AI” and what gendered narratives reveal about power and responsibility in tech.Competing with Free: Why We Closed Moxie: A candid reflection on what it was like to build, and ultimately shut down, an AI startup in this moment.We’re heading into 2026 with some incredible guests and conversations we can’t wait to share.Thank you for listening, for thinking with us, and for staying curious alongside us.Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this final guest episode of the year, we explore AI as a kind of Trojan horse: a technology that promises one thing while carrying hidden costs inside it. Those costs show up in data centers, energy and water systems, local economies, and the communities asked to host the infrastructure that makes AI possible.We’re joined by Jon Ippolito and Joline Blais from the University of Maine for a conversation that starts with AI’s environmental footprint and expands into questions of extraction, power, education, and ethics. In this episode, we discuss:Why AI can function as a Trojan horse for data extraction and profitWhat data centers actually do, and why they matterThe environmental costs hidden inside “innovation” narrativesThe difference between individual AI use and industrial-scale impactWhy most data center activity isn’t actually AIHow communities are pitched data centers—and what’s often left outThe role of gender in ethical decision-making in techWhat AI is forcing educators to rethink about learning and workWhy asking “Who benefits?” still cuts through the hypeAnd how dissonance can be a form of clarityResources mentioned:IMPACT Risk framework: https://ai-impact-risk.comWhat Uses More: https://what-uses-more.comGuests:Jon Ippolito – artist, writer, and curator who teaches New Media and Digital Curation at the University of Maine. Joline Blais – researches regenerative design, teaches digital storytelling and permaculture, and advises the Terrell House Permaculture Center at the University of Maine. Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Why can AI crush law exams and chess grandmasters, yet still struggle with word games? In this episode, Kimberly and Jessica use Moravec's Paradox to unpack why machines and humans are "smart" in such different ways—and what that means for how we use AI at work and in daily life.They start with a practical fact-check on agentic AI: what actually happens to your data when you let tools like ChatGPT or Gemini access your email, calendar, or billing systems, and which privacy toggles are worth changing. From there, they dive into why AI fails at the New York Times' Connections game, how sci-fi anticipated current concerns about AI psychology decades ago, and what brain-computer interfaces like Neuralink tell us about embodiment and intelligence.Along the way: sycophantic bias, personality tests for language models, why edtech needs more friction, and a lighter "pit and peach" segment with unexpected life hacks.Resources by TopicPrivacy & Security (ChatGPT)OpenAI Memory & Controls (Official Guide)OpenAI Data Controls & Privacy FAQOpenAI Blog: Using ChatGPT with AgentsMoravec's Paradox & Cognitive ScienceMoravec's Paradox (Wikipedia)"The Moravec Paradox" - Research PaperSycophancy & LLM Behavior"Sycophancy in Large Language Models: Causes and Mitigations" (arxiv)"Personality Testing of Large Language Models: Limited Temporal Stability, but Highlighted Prosociality"Brain-Computer Interfaces & Embodied AINeuralink: "A Year of Telepathy" UpdateLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

What happens when you automate away a six-hour task? You don't get more free time ... you just do more work. In this impromptu conversation, Kimberly and Jessica break down what agentic AI actually does, why the "time savings" narrative misses the point entirely, and how to figure out which workflows are worth automating.WHAT WE COVER:What agentic AI actually is (and how it's different from ChatGPT)Jessica's real invoice automation workflow: how she turned 6 hours of manual work into an AI agent taskThe framework for identifying automatable workflows (repetitive, skill-free, multi-step tasks)Why this beats creative AI work: no judgment calls, just executionThe Blackboard experiment: what happens when an agent does something you didn't ask it to doSecurity & trust: passwords, login credentials, and where your data actually goesEnterprise-level agent solutions (and why they're not quite ready yet)The uncomfortable truth: freed-up time doesn't mean fewer hours—it means more outputHow detailed instruction manuals prepared Jessica for prompt engineeringThe human bottleneck: why your whole organization has to move at the same speedWhy marketing and research are next on the chopping blockTOOLS MENTIONED:ChatGPT Pro with Agents — https://openai.com/chatgpt/Perplexity Comet (agentic browser) — https://www.perplexity.ai/cometZoho Billing — https://www.zoho.com/billing/Constant Contact — https://www.constantcontact.comZapier — https://zapier.comElicit (systematic reviews & literature analysis) — https://elicit.comCorpus of Contemporary American English — https://www.english-corpora.org/coca/Descript — https://www.descript.comCanva — https://www.canva.comRiverside.fm — https://riverside.fmTIMESTAMPS:0:00 — Opening & guest cancellation1:18 — Podcast website & jingle development (and why music taste is complicated)6:34 — What is agentic AI? Jessica's invoice automation example10:33 — Why this use case actually works14:15 — The Blackboard incident (when the agent went off-script)16:21 — Security concerns: passwords, login credentials, and trust18:35 — Why speed doesn't matter (as long as it's faster than human bottleneck)19:27 — Enterprise solutions on the horizon20:57 — United Airlines cease-and-desist letters for replica training sites22:27 — Why Kimberly can't use agents in her CCRC work25:21 — How to identify your automatable workflows (the practical framework)27:57 — Research automation with Elicit & corpus linguistics30:45 — The core insight: AI shifts time, it doesn't save it34:10 — Organizational bottlenecks & human capacity limits35:08 — Pit & Peach (staying in your own canoe)Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this conversation, Kimberly Becker and Jessica Parker explore the concept of 'enshitification'—as articulated by Cory Doctorow in his book Enshittification: Why Everything Suddenly Got Worse and What To Do About It—as it relates to generative AI and tech platforms. They discuss the stages of platform development, the shift from individual users to business customers, and the implications of algorithmic changes on user experience.The conversation also explores the work of AI researchers Emily M. Bender and Timnit Gebru, whose paper "On the Dangers of Stochastic Parrots" raised critical questions about the limitations and risks of large language models. The hosts explore the role of data privacy, the impact of AI on labor, the need for regulation, and the dangers of market consolidation, using case studies like Amazon's acquisition and eventual shutdown of Diapers.com and Google's Project Maven controversy.Key TakeawaysEnshitification refers to the degradation of tech platforms over timeThe shift from individual users to business customers can lead to worse outcomes for end usersData privacy is a critical concern as companies monetize user interactionsAI is predicted to significantly displace workers in coming yearsRegulation is necessary to protect consumers from unchecked corporate powerMarket consolidation can stifle competition and innovationRecognizing these patterns is essential for navigating the tech landscapeFurther Reading & ResourcesCory Doctorow's Pluralistic blogThe Internet Con: How to Seize the Means of Computation2024 Tech Layoffs TrackerStreamlined "Top Links" Version (if you want minimal show notes):Cory Doctorow on EnshittificationEnshittification book"On the Dangers of Stochastic Parrots" by Bender & GebruAmazon/Diapers.com case studyGoogle Project Maven controversyAI job displacement tracker2024 Tech Layoffs TrackerLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this conversation, we sit down with Dr. Michelle Morkert, a global gender scholar, leadership expert, and founder of the Women’s Leadership Collective, to unpack the forces shaping women’s relationship with AI.We begin with research indicating that women are 20–25% less likely to use AI than men, but quickly move beyond the statistics to explore the deeper social, historical, and structural reasons why.Dr. Morkert brings her feminist and intersectional perspective to these questions, offering frameworks that help us see beyond the surface-level narratives of gender and AI use. This conversation is less about “women using AI” and more about power, history, social norms, and the systems we’re all navigating.If you’ve ever wondered why AI feels different for women—or what a more ethical, community-driven approach to AI might look like—this episode is for you.💬 Guest: Dr. Michelle Morkert – https://www.michellemorkert.com📚 Books & Scholarly Works MentionedGlobal Evidence on Gender Gapsand Generative AI: https://www.hbs.edu/ris/Publication%20Files/25023_52957d6c-0378-4796-99fa-aab684b3b2f8.pdfPink Pilled: Women and the Far Right (Lois Shearing): https://www.barnesandnoble.com/w/pink-pilled-lois-shearing/1144991652lScary Smart (Mo Gawdat – maternal AI concept) https://www.mogawdat.com/scary-smartLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode, Jessica teaches Kimberly about the "containment problem," a concept that explores whether we can actually control advanced technologies like AI and synthetic biology. Inspired by Mustafa Suleyman's book The Coming Wave, Jessica and Kimberly discuss why containment might be impossible, the democratization of powerful technologies, and the surprising world of DIY genetic engineering (yes, you can buy a frog modification kit for your garage).What We Cover:What is the containment problem and why it mattersThe difference between AGI, ASI, and ACI Why AI is fundamentally different from nuclear weapons when it comes to containmentSynthetic biology: from AlphaFold to $1,099 frog gene editing kitsThe geopolitical arms race and why profit motives complicate containmentHow technology democratization gives individuals unprecedented powerWhether complete AI containment is even possible (spoiler: probably not)The modern Turing test and why perception might be realityBooks & Resources Mentioned:Empire of AI by Karen HaoDeepMind documentaryKey Themes:Technology inevitability vs. choiceThe challenges of regulating rapidly evolving technologiesWho benefits from AI advancement?The tension between innovation and safetyFollow Women Talking About AI for more conversations exploring the implications, opportunities, and challenges of artificial intelligence.Leave us a comment or a suggestion!Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

On saying no to “inevitable” AI—and what we say yes to instead.Kimberly and Jessica recently sat down with Melanie Dusseau and Miriam Reynoldson for an episode of Women Talkin’ ’Bout AI. We were especially looking forward to this conversation because Melanie and Miriam are our first guests who openly identify as “AI Resisters.” The timing also felt right. Both Kimberly and I have been reexamining our own stance on AI in education—how it intersects with learning, writing, and creativity—and the more distance we’ve had from running a tech company, the more critical and curious we’ve become.This episode digs into big, thorny questions:What Melanie calls “the drumbeat of inevitability” that pressures educators to adopt AIMiriam’s post-digital view of what it means to live in a world completely entangled with technology; and our shared inquiry into who actually benefits when AI tools promise to make everything faster and more efficient. We also talk about data ethics, creative integrity, and the growing movement of educators saying no to automation—not out of fear, but out of care for human learning and connection.It’s a thoughtful, challenging, and hopeful conversation—and we hope you enjoy it as much as we did.About our guests: Melanie is an Associate Professor of English at the University of Findlay and a writer whose work spans poetry, plays, and fiction. Miriam is a Melbourne-based digital learning designer, educator, and PhD candidate at RMIT University whose research explores the value of learning in times of digital ubiquity.Melanie and Miriam are co-authors of the Open Letter from Educators Who Refuse the Call to Adopt GenAI in Education, which has collected over 1,000 signatures and was featured in an article by Forbes. Melanie is also the author of the essay Burn It Down, which advocates for AI resistance in the academy. We highly recommend reading both before diving into the episode. Melanie's personal website and University of Findlay profileMiriam’s personal website and blog "Care Doesn't Scale" Signs Preceding the End of the World by Yuri HerreraAsimov’s Science FictionUrsula K. Le Guin  Ray BradburyLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

When a generative AI tool "hallucinates" a recipe, it’s a funny anecdote. When it hallucinates a legal precedent, people go to jail. In this episode, Kimberly and Jessica talk with Rebecca Fordon—law librarian, professor, and board member at the Free Law Project—to discuss why the legal system is uniquely vulnerable to AI hype. From the "duopoly" of legal publishing to the 500+ documented cases of AI-generated legal errors, we look at what happens when the law meets Large Language Models.Why This Matters Legal research requires 100% precision, but generative AI is built for probability, not fact. We explore the "Shifting Baseline Syndrome" in research: as we move toward a world where machines take the "first pass," how do we ensure we aren't settling for "80% certainty" in a field where mistakes have life-altering consequences?Key TopicsThe Hallucination Tracker: How legal professionals are documenting the 500+ (and counting) cases of AI-invented precedents.Privilege & Privacy: The hidden risk of waiving attorney-client privilege by feeding data into general-purpose LLMs.The Westlaw/Lexis Duopoly: How the Free Law Project is fighting to make primary legal materials accessible and transparent for the public.The Expert Pipeline Crisis: If AI replaces the "grunt work" of junior associates, how will the next generation of attorneys learn to think like lawyers?Certainty Amplification: Why the "confident" tone of AI is at odds with the strategic nuance required in legal advocacy.Notable Quotes"I’m a little bit worried that we might be getting to a place where, if AI can do it in a quarter of the time and get to 80% certainty, we might decide that’s 'good enough.' That really bothers me as an attorney." — Rebecca Fordon"In ecology, 'Shifting Baseline Syndrome' means each generation accepts a new 'normal' as the baseline. We are shifting into a world where the machine is the first pass, and the human is just an error-checker. That is a dangerous new baseline for research." — Kimberly Becker🔗 Featured Links & ResourcesRESOURCE: Free Law Project (CourtListener & RECAP)COMMUNITY: AI Law LibrariansBLOG: Musings about Librarianship by Aaron TayCRITIQUE: Refusing GenAICONCEPTS: KL3M (The "copyright clean" legal data model)Why shouldn't lawyers use ChatGPT for legal research? Standard generative AI models like ChatGPT are prone to "hallucinations," where they confidently invent legal citations, cases, and precedents that do not exist. In the legal field, using these outputs without verification can lead to sanctions, loss of attorney-client privilege, and significant ethical violations. Professional legal research requires domain-specific tools and human oversight to ensure 100% accuracy.Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode of Women Talkin’ ‘Bout AI, we start by discussing the findings of a 2024 study "Global Evidence on Gender Gaps and Generative AI" (🔗 below). One overall finding is that women are 20–25% less likely than men to use generative AI, which unspools into something bigger: a story about power, voice, and who gets to shape the future.We also discuss own experiences in tech, noticing how the gender gap in AI isn’t just about access to tools. It’s about what counts as legitimate work, whose voices are amplified, and how cultural scripts around “cheating,” confidence, and authority get absorbed into the most influential technologies of our time.We talk about:🔹 Why women’s hesitation around AI isn’t simply resistance, but often a reflection of ethics and identity.🔹 How underrepresentation today could mean future AI systems are trained on a distorted mirror of humanity.🔹 What it means to think of AI as both a child we’re raising and a cultural intermediary that’s already reshaping our sense of normal.🔹 the WEIRD AI Framework: WEIRD is a term from psychology that stands for Western, Educated, Industrialized, Rich, and Democratic. Most AI systems, generative models especially, are trained on corpora that overrepresent WEIRD voices and underrepresent everyone else.🔹 Practical ways women can experiment, reclaim, and band together in communities of practice.🔹 If AI is the new baseline for productivity and creativity, then the absence of women’s voices isn’t just a gap, it’s a risk of silence becoming the default.Learn more:🔗 Gender gap study: https://www.hbs.edu/faculty/Pages/item.aspx?num=66548🔗 Mo Gawdat's book Scary Smart: https://www.mogawdat.com/scary-smart🔗 Geoffrey Hinton Says AI Needs Maternal Instincts: https://www.forbes.com/sites/pialauritzen/2025/08/14/geoffrey-hinton-says-ai-needs-maternal-instincts-heres-what-it-takes/💙 Follow us on our Substack: Women Writin' 'Bout AI: https://substack.com/@womenwritinboutaiLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode, we open up about something we haven’t shared publicly before: our decision to shut down Moxie, the startup we spent years building.We talk honestly about what led to that choice—the excitement of early growth, the challenges of raising money as non-technical founders, and the impossible reality of competing with free tools from tech giants like Google, OpenAI, and Microsoft.This isn’t just a story about one company. It’s about trust, expertise, failure, and the messy human side of working with generative AI in education and research. Along the way, we reflect on what we wish we’d known earlier, how burnout shaped our decisions, and what we’ve learned about ourselves through the process of letting go.What you’ll hear in this episode:Why we ultimately decided to shut down MoxieThe pressures of fundraising and pitching as non-technical foundersThe gap between hype and reality with AI in educationLessons on trust, expertise, and failure in both startups and academiaHow we’re processing life and work after MoxieIf you’ve ever wondered what it really feels like to close the doors on something you’ve poured yourself into, or you’re navigating your own questions about AI, startups, or burnout—you’ll find some resonance here.Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

Today we sit down with Dr. Leslie Gruis — mathematician, NSA veteran, and author of The Privacy Pirates — to talk about the urgent importance of protecting personal information in our tech-driven world.From children’s online privacy to the rise of corporate data exploitation, Dr. Gruis shares both her insider experience from decades in national security and her practical advice for safeguarding our digital lives.📚 About our guest:First president of the NSA’s Women in Mathematics SocietyContributor to U.S. Cyber Command & National Intelligence CouncilAuthor of The Privacy Pirates: Pirates of Personal DataMentor and advocate for STEM students🔑 In this episode you’ll learn:Why privacy is essential to democracyThe risks kids face with school-issued laptops & smartphonesHow corporations collect and exploit our personal dataWhat parents and educators can do today to protect childrenThe ethical questions surrounding AI, surveillance, and data use🎙️  Show Notes & Topics we cover:Defining informational privacy in the 21st centuryChildren’s Online Privacy Protection Act (and why it’s outdated)School-issued laptops and surveillance concernsCorporate data collection, sentiment analysis, and manipulationThe asymmetric power between consumers and corporationsWhy protecting privacy is vital for democracy🔗  LinksBuy The Privacy PiratesFollow Dr. Leslie Gruis Follow Women Talking About AI:📺 YouTube📰 SubstackLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

SummaryIn this conversation, Jessica and Kimberly interview David Shapiro to explore the concept of Post-Labor Economics. They discuss the implications of automation and AI on traditional job structures, the need for new economic measurements, and the evolving social contract. They explore the potential of Universal Basic Income and the importance of education in preparing future generations for a changing economy. The discussion emphasizes the need for a shift in how we perceive work, productivity, and personal fulfillment in a world increasingly dominated by technology.TakeawaysPost-Labor Economics examines the impact of automation on traditional jobs.Automation has historically decoupled productivity from human labor.The misconception that technology always creates new jobs is prevalent.AI's rapid advancement poses challenges for job security.Universal Basic Income (UBI) is a potential solution for economic displacement.Current economic measurements like GDP may not reflect true societal well-being.The social contract is evolving as labor becomes less central to identity.Education must adapt to focus on empathy, communication, and critical thinking.A garden mentality encourages ongoing personal growth rather than a linear life path.Rethinking work and meaning is essential in a post-labor society.LinksRest Is Resistance: Free Yourself from Grind Culture and Reclaim Your Life Book by Tricia Hersey (https://thenapministry.wordpress.com/)David's LinkTreeDavid's YouTube ChannelsDavid's SubStackWomen Writin' 'Bout AI SubstackLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode, hosts Jessica and Kimberly are joined by Dr. Juliana Peloche, global educator and senior AI literacy advisor at Edith Cowan University. With over 20 years of cross-cultural teaching experience in Brazil, Chile, and Australia, Juliana shares how a curious 12-year-old student sparked her journey into AI education. Together, they explore why AI literacy is more than a technical skill—it's a foundation for critical thinking, equity, and ethics in the classroom. From digital basics like knowing what a browser is, to reimagining how we assess learning in the age of AI, this episode dives deep into how we can better prepare educators and students for a tech-saturated future—without losing our humanity.Leave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

🎧 Episode SummaryDr. Tamara Tate joins Jessica and Kimberly to talk about AI, education, and the evolving role of writing in a world where students can co-write with machines. Tamara shares how she transitioned from a 17-year legal career into education research, what she’s learning through the development of Papyrus AI, and why feedback, voice, and agency matter more than ever. The conversation covers everything from AI literacy and middle school classrooms to the complexities of funding, parent engagement, and what it really means to “offload” learning. It’s a thoughtful, practical look at how generative AI is reshaping writing instruction—and why it’s not just about speed, but meaning.🔗 Show Notes LinksTamara Tate – UC Irvine Profile: https://education.uci.edu/people/tamara-tate/Digital Learning Lab: https://digitallearninglab.orgGenAIED.org – Generative AI in Education Resources: https://genaied.orgAnna Mills – AI & Writing Pedagogy: https://annamills.netSarah Elaine Eaton – Post-Plagiarism Framework: https://drsaraheaton.wordpress.comLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

In this episode of Women Talkin’ 'Bout AI, host Kimberly Becker sits down with Dr. Mathilde Cerioli—a cognitive neuroscientist, mom, and Chief Scientist at Everyone.AI—to unpack the complex, often messy intersections of child development, technology, and artificial intelligence.We cover:What AI can and can’t do for young mindsHow critical thinking actually develops—and why it can’t be outsourcedThe myth of "tech for tech’s sake" and why some edtech harms more than it helpsWhy your kid doesn’t need a bedtime podcast voiced by a deepfaked parentThe neuroscience behind struggle, dopamine, and why learning should be hardMisinformation, deepfakes, and why everyone needs a family safe wordThis conversation blends scientific rigor with real-world parenting chaos, offering both hope and hard truths. Whether you're raising a kindergartener or advising policymakers, this one’s for anyone who wants a future where tech serves human development, not the other way around.****If you find value in these conversations, consider supporting the podcast with a small donation—every bit helps us keep the mics on and the ideas flowing. Click the link below to donate.  https://www.buzzsprout.com/2411501/supporters/newLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/ 

What happens when a passionate educator steps away from the whiteboard and into the world of AI? In this episode, we sit down with Marissa Sadler Holder, a former classroom teacher turned consultant/entrepreneur, and the founder of Teaching with Machines. With a master’s in e-learning and recognition as a two-time recipient of SVS’s Leading Women in AI, Marissa brings a grounded, human-centered approach to AI literacy in education.We unpack her journey from teaching French to building a business, the emotional complexities of leaving the classroom, and why she believes teachers—not technologists—should be at the table when shaping the future of AI in education. From the power of small wins to the significance of that second “aha” moment, this conversation is a candid exploration of fear, hope, and the relentless pursuit of meaningful learning in an uncertain world.📝 Show Notes:Guest: Marissa Sadler Holder Founder of Teaching with Machines | Edtech Consultant | SVS Leading Women in AI (2024 & 2025)Topics We Cover:How COVID catalyzed Marissa’s shift into e-learning and AIThe founding story of Teaching with Machines: https://www.teachingwithmachines.com/What it really feels like to leave the classroom after 13 yearsHow she supports teachers at every stage of AI literacyWhy incremental change in classrooms matters more than big tech rolloutsTwo “aha” moments every educator has with generative AIThe emotional weight of entrepreneurship vs. classroom stressThe role of women’s voices in shaping AI discourseBridging the AI gap between students, teachers, and parentsHer upcoming project: Learning with Machines, focused on student + parent AI literacyWhy we should stop aiming to “master AI” and start focusing on meaningful applicationConference reflections: Why it's the people who make ASU+GSV unforgettableQuotable Moments:“The goal isn’t to master AI. It’s to stay curious, stay human, and keep learning.” “Every teacher will hit that second lightbulb moment—where you realize AI isn’t just a tool. It’s a transformation.” “What we do in professional development should mirror what we do in great teaching: make it relevant, make it engaging, and meet people where they are.”Links & Resources:Teaching with Machines (Marissa’s platform for educator-focused AI PD)Marissa on LinkedInSVS Summit: Leading Women in AIMentioned thought leader: Ethan Mollick’s SubstackLeave us a comment or a suggestion! Support the showContact us: https://www.womentalkinboutai.com/