Show notes
We’re revisiting our earlier conversation about Cory Doctorow’s concept of enshittification—the process by which digital platforms begin by serving users, then shift toward serving business customers, and eventually extract value from everyone involved.

When we first recorded this episode, Doctorow’s new book about AI had not yet been published. It is out now: The Reverse Centaur’s Guide to Life After AI: How to Think About Artificial Intelligence — Before It’s Too Late. We’ll be discussing that book in an upcoming episode.

In this re-release, Kimberly has added a short new introduction connecting enshittification to an idea from our more recent discussion of Megan Garber’s Screen People:

A middleman does not simply pass something along. A middleman selects, packages, ranks, translates, recommends, and takes a cut.

With digital platforms, that cut may be financial: advertising revenue, seller fees, subscription fees, or data used to target and price users. With AI systems, it may also be less visible. The cut may be context, uncertainty, minority perspectives, privacy, workers’ bargaining power, or our ability to find and evaluate information without an opaque system deciding what comes first.

In the original conversation, Kimberly and Jessica discuss Doctorow’s three-stage account of enshittification and explore how it shows up across everyday platforms and services.

In this episode

• What Cory Doctorow means by “enshittification”
• Why platforms often begin by serving users well
• How platforms shift from serving users to serving advertisers, sellers, and other business customers
• Why people and businesses become locked into platforms even as those platforms get worse
• Amazon search results, sponsored listings, subscriptions, and shopping friction
• Facebook, Instagram, Google, feeds, ads, and the disappearance of friends’ posts
• Uber, algorithmic wage discrimination, and data-based predictions about worker “desperation”
• Personalized pricing and the possibility of companies charging people based on what they appear willing—or able—to pay
• Private equity, consolidation, strategic acquisition, and why it is difficult for smaller businesses to compete
• Competition and regulation as forms of discipline for corporations
• Interoperability, generic printer ink, app stores, ad blockers, and the right to repair
• Labor power, worker organizing, and why AI-driven workforce reductions may weaken collective bargaining
• Why “the medium is the middleman” helps connect platform economics to AI-mediated knowledge

Related episode and reading

Our discussion of AI, trust, scientific communication, and the “middleman” idea:

How Do AI Chatbots Change Truth, Trust, and Scientific Communication?
Read the Substack post

In that episode and accompanying post, we discuss how AI systems can act as cultural intermediaries: filtering, ranking, summarizing, reframing, and recommending knowledge while making their mediating role difficult to see.

Books mentioned

• Cory Doctorow, Enshittification: Why Everything Suddenly Got Worse and What to Do About It
• Cory Doctorow, The Reverse Centaur’s Guide to Life After AI: How to Think About Artificial Intelligence — Before It’s Too Late
• Megan Garber, Screen People: How We Entertained Ourselves into a State of Emergency

Note

This is a re-release of an earlier conversation with a newly recorded introduction. The main conversation was recorded before the publication of Cory Doctorow’s The Reverse Centaur’s Guide to Life After AI.

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AI-generated deepfakes are getting easier to create, harder to detect, and increasingly being used to target women and children. So who is responsible for stopping the harm: Big Tech, Congress, or the rest of us?

In this episode of Women Talkin’ ’Bout AI, Kimberly Becker talks with Allyson Kapin, founder of Women Who Tech, co-founder of the W Fund, and founder of RAD Campaign, about AI deepfakes, nonconsensual intimate imagery, misinformation, online scams, tech regulation, and political power.

Allyson shares polling showing that Americans are deeply concerned about AI being used to sexually exploit women and children, and explains why she believes both technology companies and lawmakers need to take far greater responsibility.

The conversation also examines a fundamental imbalance in AI regulation -- tech companies possess the technical knowledge, data, money, and control over their platforms, while Congress has the legal power to regulate them but may lack the expertise, time, or political incentives to keep pace.

Kimberly and Allyson discuss:

• How AI deepfakes and nonconsensual sexual imagery are affecting women and children
• Why photos posted online can become raw material for AI-generated sexualized images
• What parents should tell kids about deepfakes, sextortion, and online harassment
• AI-enabled scams targeting older adults
• Why older Americans may also be an important political force in AI regulation
• Whether Congress has enough technical expertise to regulate AI effectively
• The role of Big Tech money and political influence
• How algorithms, platform ownership, and editorial decisions shape what information people see
• Misinformation, media literacy, and the growing burden placed on individuals to determine what is real
• Why asking people to simply “be more media literate” may no longer be enough
• The TAKE IT DOWN Act and other attempts to address nonconsensual intimate deepfakes, including the stalled Defiance Act
• How constituents can pressure members of Congress to pay attention to AI harms
• The historical pattern of new technologies creating harms long before institutions decide who should be accountable

The larger question running through the episode is simple but increasingly urgent:

When technology becomes too sophisticated for ordinary people to reliably protect themselves, where should responsibility sit?

Topics

AI deepfakes, artificial intelligence, AI scams, nonconsensual intimate imagery, NCII, misinformation, disinformation, AI regulation, Congress and AI, Big Tech accountability, women in technology, online safety, child safety, social media algorithms, media literacy, sextortion, deepfake pornography, AI policy, tech policy, older adults and scams, election misinformation

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Kimberly and Jessica are taking a short break from new episodes, so this week we're re-airing one of our favorites, our conversation with Clara Hawking. Clara is a computer scientist, philosopher, and AI governance expert working in K-12 and beyond.

Why now? Since we recorded this, the law Clara champions in this episode has crossed the finish line. On August 2, 2026, the EU AI Act became fully enforceable, making the EU the first jurisdiction to impose comprehensive, binding regulation on AI systems, even as Brussels negotiates a "Digital Omnibus" that would delay the Act's most demanding high-risk provisions until compliance standards are actually ready. And here in the US, the state-by-state patchwork Kimberly and Clara discuss is now itself contested by a House discussion draft called the Great American AI Act, would temporarily preempt state and local laws regulating AI model development for three years. Everything Clara says about trust, risk, and who gets harmed when governance fails has only gotten more current. 

In this episode:

• What AI governance actually is: not IT, not cybersecurity, but behavior and culture
• GDPR and the EU AI Act, explained plainly: rights-based vs. risk-based regulation
• Why parents can't give informed consent about their kids' data, and what an uploaded IEP could cost a child in 20 years
• The convergence phase: AI, biotech, robotics, and quantum computing feeding into each other, and why converged risk compounds instead of adding up
• Trust as the bottleneck for AI adoption, from Jessica's Tesla to recidivism algorithms
• Clara's "me first" governance advice: why are you using this technology, and can you justify the answer?
• Peach and Pit

Links

• EU AI Act resource site — Future of Life Institute's tracker, the most readable overview.
• The EU's official AI Act page — European Commission.
• The Regulatory Tide Goes Out — Jones Walker on the Digital Omnibus and global retrenchment. Good "what's changed since we recorded" companion.
• Great American AI Act discussion draft summary — National Association of Counties on the federal preemption proposal.

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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 accuracy
• How hedging signals scientific care—not weakness
• What gets lost between a research paper and a chatbot
• AI as a mirror of internet culture
• The commodification of attention
• How audiences differ from active publics
• Why information degradation may be one of AI’s greatest risks
• Small linguistic distortions that are harder to detect than visual deepfakes

In 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 Garber
• Marshall McLuhan — official site
• Amusing Ourselves to Death — Neil Postman 
• On Being with Krista Tippett
• Your Undivided Attention — Tristan Harris and Aza Raskin
• Krista & Tristan's chat, "Can AI be build in service of life?"
• Melody Beattie — official website
• Radical Acceptance — Tara Brach
• Research article by Denise Coberley and Emily Dux Speltz on Scientific Uncertainty in Language Comparing Human and Artificial Texts
• Our Frontiers in Education article on AI as an intermediary

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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.

Links

• Roz's Random Ramblings: Language, History, and Other Adventures
• Roz on LinkedIn
• Women Writin' 'Bout AI
• Carol Chappelle, Iowa State and The Applied Linguistics Encyclopedia 
• Image Description Games: Twin Pics, Say What You See, and Promptle
• Enshittification by Cory Doctorow and our show about the same
• Uber pickers and ants
• The Brown Professor story about AI and cheating
• The TOEFL (Test of English as a Foreign Language)
•  Part one of Kim & Roz talkin' 'bout AI 

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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 AI
• John Searle, "The Chinese Room"
• Mini Philosophy, Jonny Thomson, the episode on online versus face-to-face conversation
• How to Be Everything, Emilie Wapnick
• Range, David Epstein
• The Wealthy Barber, David Chilton
• The Artist's Way, Julia Cameron

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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 Edmondson
• Kimberly's Substack that discusses the following: 
  • Don Norman on design responsibility 
  • Rosina Lippi-Green on communication as a two-way street
• Goodhart's Law
• Devon's viral LinkedIn post 
• The chess-cheating study (in understandable language) or the research manuscript preprint
• The Brown University exam experiment 
• Box Elder County data center coverage 
• Digital Natives
• Non-Consensual Sexual Imagery
• Devon's t-shirt
• Cancellation of data center projects: https://www.datacenterwatch.org/report

Fact 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.

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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 hunch
• Shakespeare, Vince Lombardi, and a DiGiorno ad — "it's not X, it's Y" is way older than any chatbot
• The Atlantic's theories for why models love this construction
• Kimberly's own numbers: "not just X, but Y" is up 45% in online news since 2015
• AI 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 down
• The novel a publisher pulled over an AI accusation
• Jessica's case that writing is thinking, and what's lost when we skip it
• Peach and Pit

• The 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 corpus
• Top 10 Most Common Words Used by AI — GPTZero
• Publisher pulls horror novel “Shy Girl” over AI concerns — TechCrunch
• Defining and assessing AI literacy for researchers across the research lifecycle — Parker & Becker, Frontiers in Education
• How Not To Use AI — Abi Awomosu’s Substack
• The book itself — Abi Awomosu
• Women Writin’ ’Bout AI — joint Substack
• Kimberly’s “Linguist in the Wild” Substack 
• English with an Accent: Language, Ideology, and Discrimination in the United States — Rosina Lippi-Green
• The Design of Everyday Things — Don Norman, revised and expanded edition, MIT Press

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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 episode

• The 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 episode

• Language Machines: Cultural AI and the End of Remainder Humanism — Leif Weatherby
• Emily Bender — linguist, "stochastic parrots" / text extrusion
• Noam Chomsky — universal grammar, cognition as separate from culture
• Maha Bali — "Where Are the Crescents in AI?"
• Michael Pollan & Annika Harris — on consciousness and embodiment

Personal segment

The 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.

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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. Senate
• The "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 now
• Why 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" metaphor
• Radicalization pipelines and the "tradwife" aesthetic as a case study in how "maternal" framing gets co-opted politically
• Donna Haraway's "God trick" and why tech's claim to neutrality keeps women out of the room
• Karen Hao's Empire of AI and the Indigenous-language-model counterexample as a picture of what reciprocal, non-extractive AI development could actually look like

Also referenced in this episode:

• Alison Gopnik, The Scientist in the Crib
• Laura Bates on BBC's Radical with Amal Rajan (dehumanization and algorithmic feeds)
• Allie K Miller's interview on the Mel Robbins Podcast

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