Women talkin' 'bout AI

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:
The medium is the middleman.

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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