Yana G.Y. is a product, sales, and marketing leader who specializes in helping creators grow high-converting Substack publications using AI and automation. She is a Mentor, a Member, and a Chartered Marketer at the Chartered Institute of Marketing (CIM), and the author of the newsletter Unplugged by Yana G.Y.
Currently ranked as a top creator in the Education category on Substack, Yana focuses on practical, actionable strategies for monetization and audience growth. Her work often explores the intersection of human creativity and technical efficiency, specifically using tools like Claude and Kit to streamline content production and sales funnels.
Core Expertise & Frameworks
The PPSA Framework: A four-part copywriting method focusing on Pain, Proof, Solution, and Action to drive conversions in paid posts and emails.
AI Implementation: Developing specific prompts and workflows to turn single ideas into multi-format content pieces (e.g., Substack Notes) in minutes.
Monetization Strategy: Challenging common myths about subscriber counts, Yana advocates for early monetization and “outcomes-based” value propositions for paid tiers.
Professional Affiliations
Chartered Marketer: Accredited by the Chartered Institute of Marketing (CIM).
Product & Marketing Leader: Extensive background in leading sales and marketing initiatives across various digital platforms.
Talking Points: The Socioeconomic Impacts of AI on Women
The conversation about artificial intelligence in this country is happening without us. And while the panels argue about whether AI will be a tool or a threat, the data has already answered the question for women, especially for women who have always been the first hit and the last protected.
AI is here. It is already moving. And it is not moving neutrally.
1. Women Are in the Line of Fire: Disproportionately
In the United States, 58.87 million women hold positions highly exposed to AI automation, compared to 48.62 million men (Nartey, 2025). Women also make up the majority of workers in more than half of the 40 occupations most at risk of displacement (Hertz, 2025).
Customer service, administrative roles, data entry, and retail — these are the jobs being eliminated first. They are also the jobs women have built careers in for generations. Customer service representatives face an 80% automation rate by 2025, and 7.5 million data entry positions are projected to be eliminated by 2027 (Nartey, 2025).
This is not a future problem. It is a now problem.
2. The Impact on Black Women Is Already a Crisis
In April 2025 alone, Black women lost 106,000 jobs in a single month, the most severe employment setback of any demographic group, while the national unemployment rate remained stable at 4.2% (Spence, 2025).
Black women experienced a 33% decline in federal employment between January and May 2025, compared with 3.7% for the overall workforce (Spence, 2025). The systematic dismantling of diversity, equity, and inclusion programs removed positions where Black women were heavily concentrated.
This is what the intersection of AI displacement, federal workforce reductions, and DEI rollbacks looks like when you put them on the same chart. It looks like Black women are carrying the cost.
3. We Are Not in the Rooms Where AI Is Being Built
Globally, women make up only 30% of the AI workforce and just 22% of workers in AI, specifically, a figure that has moved by only 4 percentage points since 2016 (International Labor Organization, 2025).
In the United States, women hold 29% of STEM entry-level positions, 24.4% of STEM managerial roles, and only 12.2% of STEM C-suite roles (World Economic Forum, 2025).
The systems making decisions about the rest of our careers are being designed by people who do not share our experience and are trained on data that reflects decades of decisions made about us.
4. The Bias Is Already Built In
Stanford researchers reported in October 2025 that when generative AI was asked to create resumes, it portrayed women as younger and less experienced than men, actively reinforcing age and gender bias in hiring tools (Guilbeault et al., 2025).
AI resume-screening tools have been shown to systematically favor older male candidates over female and younger candidates with identical qualifications, and AI-powered CV screening dismisses language associated with female job seekers (TWF, 2025).
In plain language: the machine is reading our applications through the same bias women have spent careers fighting in human hiring managers, except now it is faster, harder to challenge, and dressed up as objectivity.
5. The Adoption Gap Is Creating a New Ceiling
A Harvard Business School meta-analysis of 143,008 individuals across 25 countries found that women have 22% lower odds of using generative AI than men (Otis et al., 2025).
In the workplace, women are 16 percentage points less likely to use ChatGPT for job tasks even within the same occupation and job responsibilities (Humlum & Westergaard, 2024; Lambert, 2025).
The skeptics will tell you this is a confidence issue. I want to name something else.
Women are not failing to adopt. Women are being cautious about technology that was built without us, trained on data that has historically discounted us, and rolled out in workplaces with a documented history of widening pay gaps with new tools.
That is not timidity. That is pattern recognition.
6. The Competence Penalty
Here is the trap. Research now shows that when women use AI tools in non-routine roles, they face a competence penalty, judged as less capable for using the same tools men get credit for using (TWF, 2025).
So women get punished for not adopting fast enough and punished for adopting at all. That is not a glitch. That is the system working as designed.
7. The Care Economy Will Absorb the Shock
The roles AI is least likely to automate are care roles — nursing, eldercare, childcare, and social work. These are already overwhelmingly held by women, especially women of color, and are underpaid relative to their skills (National Partnership for Women & Families, 2025).
If displaced women are pushed into the care economy without policy intervention, we will see a generational compression of women’s wages even as productivity from AI rises in male-dominated sectors. The global economy already loses more than $7 trillion annually due to gender inequality in the workforce (Stimson Center, 2025).
8. The Policy Landscape Is Just Beginning
New York City requires annual bias audits for automated employment decision tools and public reporting of results. California finalized regulations in October 2025 clarifying how anti-discrimination laws apply to AI tools used in hiring. The Colorado AI Act, effective June 2026, will require developers and users of AI hiring tools to use reasonable care to prevent algorithmic discrimination (Sanford Heisler Sharp McKnight, 2025).
These are starts. They are not enough. Federal protections remain inadequate to the scale of what is coming.
9. What We Do About It
I am not interested in fear. Fear has never moved us forward. Here is what I am interested in.
• Women in the rooms where AI is being built, audited, and deployed, not as advisors, but as decision-makers.
• Learning the tools. Skeptically, on our own terms, with full awareness of their limitations, but learning from them. The women who adapt will lead the women who don’t.
• Policy advocacy that goes beyond bias audits. Real protections. Real recourse. Real consequences for algorithmic discrimination.
• Mentorship that pulls women into STEM and AI pathways early and keeps them there. The pipeline isn’t leaking. It is being drained.
• Organizing. The single most reliable protection women have ever had in the workforce is each other. That has not changed.
The Bottom Line
AI is not coming for women. AI is already here, and the data shows the impacts are landing harder on us, faster on us, and most heavily on the women who have always carried more than their share.
That is the truth.
And the truth is also this. We have been written out of revolutions before. We have written ourselves back in every single time. This one is not different.
References
Guilbeault, D., et al. (2025, October). Researchers uncover AI bias against older working women. Stanford Report. https://news.stanford.edu/stories/2025/10/ai-llms-age-bias-older-working-women-research
Hertz, N. (2025, November 11). The AI labor shock is coming for women. Project Syndicate. https://www.project-syndicate.org/commentary/women-white-collar-workers-will-bear-brunt-of-ai-induced-job-displacement-by-noreena-hertz-2025-11
Humlum, A., & Westergaard, E. (2024). ChatGPT adoption among 18,000 workers in Denmark across 11 occupations [Working paper].
International Labour Organization. (2025, March 5). New ILO data confirm women face higher workplace risks from generative AI than men. https://www.ilo.org/resource/news/new-ilo-data-confirm-women-face-higher-workplace-risks-generative-ai-men
Lambert, L. (2025, May 8). Generative AI like ChatGPT is at risk of creating new gender gap at work. CNBC. https://www.cnbc.com/2025/05/08/ai-risk-chatgpt-gender-gap-jobs-work.html
Nartey, J. (2025). AI job displacement analysis (2025–2030). SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5316265
National Partnership for Women & Families. (2025). AI and emerging risks for women workers. https://nationalpartnership.org/report/ai-emerging-risks-for-women-workers/
Otis, N. G., et al. (2025). Global evidence on gender gaps and generative AI (Harvard Business School Working Paper No. 25-023). https://www.hbs.edu/ris/Publication%20Files/25023_52957d6c-0378-4796-99fa-aab684b3b2f8.pdf
Sanford Heisler Sharp McKnight. (2025, December 16). AI bias in hiring: Algorithmic recruiting and your rights. https://sanfordheisler.com/blog/ai-bias-in-hiring-algorithmic-recruiting-and-your-rights/
Spence, M. (2025). Existential crisis: Black women and the AI reckoning. The Undisruptable Woman. https://margaretspence.substack.com/p/existential-crisis-black-women-and
Stimson Center. (2025). The glass ceiling goes digital: How AI may write women out of work. https://www.stimson.org/2025/the-glass-ceiling-goes-digital-how-ai-may-write-women-out-of-work/
The Women’s Foundation. (2025, September 17). Women at risk: Economic inequalities in the age of AI. https://twfhk.org/women-at-risk-economic-inequalities-in-the-age-of-ai/
World Economic Forum. (2025, May 15). How AI is worsening workplace gender gaps — and how we can course correct. https://www.weforum.org/stories/2025/05/how-ai-is-worsening-workplace-gender-gaps-and-how-we-can-course-correct-7828b8eae9/
Thank you Millie Jones-Cowles, Jan 🇨🇦, Ivelis Reyes, Marlana aka Outtamydamnmind and many others for tuning into my live video with Yana G.Y.! Join me for my next live video in the app.
