Pooja Pillai is an entertainment journalist with Asian Media Group, where she covers cinema, pop culture, internet trends, and the politics of representation. Her work spans interviews, cultural features, and social commentary across digital platforms.
She began her reporting career as a news anchor, scripting and presenting stories for a regional newsroom. With a background in journalism and media studies, she has since built a body of work exploring how entertainment intersects with social and cultural shifts, particularly through a South Indian lens.
She brings both newsroom rigour and narrative curiosity to her work, and believes the best stories don’t just inform — they reveal what we didn’t know we needed to hear.
Jamie Lee Curtis says cosmetic surgery has caused the “disfigurement of generations of women”.
Describes the cosmeceutical industry as “a genocide” against natural human appearance.
Blames AI filters for worsening beauty standards and driving cosmetic procedures.
Opens up about her regrets over getting surgery at 25 and how she now embraces ageing.
Oscar-winner Jamie Lee Curtis has launched a sharp critique of the beauty industry, warning that cosmetic surgery and AI beauty filters have “wiped out” natural human appearance for entire generations of women. In a candid interview with The Guardian, the 66-year-old actress likened the cosmetic procedure boom to a form of “genocide”, a controversial term she insists is deliberate, given the scale and cultural impact of what she sees as industry-driven body modification.
Jamie Lee Curtis says tech-fuelled beauty lies are harming young women’s self-worthGetty Images
Why did Jamie Lee Curtis compare plastic surgery to 'genocide'?
Curtis, who stars in the upcoming Freakier Friday sequel, told The Guardian that the term “genocide” reflects what she views as a mass erasure of natural female beauty. She blames what she calls the “cosmeceutical industrial complex” for encouraging generations of women to pursue artificial enhancements, ranging from fillers to surgical alterations, at the cost of self-acceptance.
“I’ve used that word for a long time because it’s strong,” she said. “I believe we’ve wiped out one or two generations of natural human appearance.”
Although her word choice has raised eyebrows, Curtis stands by it, arguing that society’s obsession with youth and perfection has deformed, not enhanced, women’s lives and appearances.
Jamie Lee Curtis blasts beauty industry for teaching young women to hate themselvesGetty Images
How is AI making beauty standards worse?
Curtis also pointed fingers at AI tools, especially facial filters on social media, for making “fake” the new normal. “Better is fake,” she said, noting how even she finds it hard to ignore the allure of filters once they show a digitally ‘improved’ version of her face.
“The filter face is what people want now,” she added. “It’s impossible to see the before and after and not think, ‘Well, that looks better.’ But what’s better? The lie?”
Her comments highlight growing concern over how AI is shaping unrealistic standards, especially for young women, through apps and image-editing tools that promote an unattainable version of beauty.
Jamie Lee Curtis says young women are chasing a fake version of beauty built by AIGetty Images
What’s Curtis’ personal experience with cosmetic surgery?
Curtis revealed she had a procedure in her 20s after a cinematographer on set commented on her “baggy eyes.” She regretted it immediately and has since become an outspoken advocate against surgical tweaks.
“That’s just not what you want to do at 25,” she said in a previous interview. “And I’ve kind of regretted it ever since.”
Now embracing her natural ageing, grey hair, and wrinkles, Curtis said she’s spent the last 30 years gradually stepping back from the spotlight, unlike her parents, actors Tony Curtis and Janet Leigh, who she says were discarded by Hollywood as they aged.
Jamie Lee Curtis says filtered beauty is damaging how young girls see themselvesGetty Images
Why is Jamie Lee Curtis speaking out now?
Her outspoken stance comes at a time when Curtis is experiencing a powerful late-career resurgence. After winning an Oscar in 2023 for Everything Everywhere All at Once and delivering a critically acclaimed role in The Bear, she’s using her voice to challenge harmful industry standards from within.
“I’ve become a really public advocate to say to women: you’re gorgeous and perfect the way you are,” she said. While she insists she won’t judge others for choosing cosmetic surgery, she calls it a “never-ending cycle” once it begins.
Curtis also warns that young women, often under pressure to fit a filtered ideal, are being sold an illusion of perfection. “Once you start, you can’t stop. But it’s not my job to judge. It’s none of my business.”
AI can make thousands of podcast episodes every week with very few people.
Making an AI podcast episode costs almost nothing and can make money fast.
Small podcasters cannot get noticed. It is hard for them to earn.
Advertisements go to AI shows. Human shows get ignored.
Listeners do not mind AI. Some like it.
A company can now publish thousands of podcasts a week with almost no people. That fact alone should wake up anyone who makes money from talking into a mic.
The company now turns out roughly 3,000 episodes a week with a team of eight. Each episode costs about £0.75 (₹88.64) to make. With as few as 20 listens, an episode can cover its cost. That single line explains why the rest of this story is happening.
When AI takes over podcasts human creators are struggling to keep up iStock
The math that changes the game
Podcasting used to be slow and hands-on. Hosts booked guests, edited interviews, and hunted sponsors. Now, the fixed costs, including writing, voice, and editing, can be automated. Once that system is running, adding another episode barely costs anything; it is just another file pushed through the same machine.
To see how that changes the landscape, look at the scale we are talking about. By September 2025, there were already well over 4.52 million podcasts worldwide. In just three months, close to half a million new shows joined the pile. It has become a crowded marketplace worth roughly £32 billion (₹3.74 trillion), most of it fuelled by advertising money.
That combination of a huge market plus near-zero marginal costs creates a simple incentive: flood the directories with niche shows. Even tiny audiences become profitable.
What mass production looks like
These AI shows are not replacements for every human program. They are different products. Producers use generative models to write scripts, synthesise voice tracks, add music, and publish automatically. Topics are hyper-niche: pollen counts in a mid-sized city, daily stock micro-summaries, or a five-minute briefing on a single plant species. The episodes are short, frequent, and tailored to narrow advertiser categories.
That model works because advertisers can target tiny audiences. If an antihistamine maker can reach fifty people looking up pollen data in one town, that can still be worth paying for. Multiply that by thousands of micro-topics, and the revenue math stacks up.
How mass-produced AI podcasts are drowning out real human voicesiStock
Where human creators lose
Podcasting has always been fragile for independent creators. Most shows never break even. Discoverability is hard. Promotion costs money. Now, add AI fleets pushing volume, and the problem worsens.
Platforms surface content through algorithms. If those algorithms reward frequency, freshness, or sheer inventory, AI producers gain an advantage. Human shows that take weeks to produce with high-quality narrative, interviews, or even investigative pieces get buried.
Advertisers chasing cheap reach will be tempted by mass AI networks. That will push down the effective CPMs (cost per thousand listens) for many categories. Small hosts who relied on a few branded reads or listener donations will see the pool shrink.
What listeners get and what they lose
Not every listener cares if a host is synthetic. Some care only about the utility: a quick sports update, a commute briefing, or a how-to snippet. For those use cases, AI can be fine, or even better, because it is faster, cheaper, and always on.
But the thing is, a lot of podcast value comes from human quirks. The long-form interview, the offbeat joke, the voice that makes you feel known—those are hard to fake. Studies and industry voices already show 52% of consumers feel less engaged with content. The result is a split audience: one side tolerates or prefers automated, functional audio; the other side pays to keep human voices alive.
When cheap AI shows flood the market small creators lose their edgeiStock
Legal and ethical damage control
Mass AI podcasting raises immediate legal and ethical questions.
Copyright — Models trained on protected audio and text can reproduce or riff on copyrighted works.
Impersonation — Synthetic voices can mirror public figures, which risks deception.
Misinformation — Automated scripts without fact-checking can spread errors at scale.
Transparency — Few platforms force disclosure that an episode is AI-generated.
If regulators force tighter rules, the tiny profit margin on each episode could disappear. That would make the mass-production model unprofitable overnight. Alternatively, platforms could impose labelling and remove low-quality feeds. Either outcome would reshape the calculus.
How the industry can respond through practical moves
The ecosystem will not collapse overnight.
Label AI episodes clearly.
Use discovery algorithms that reward engagement, not volume.
Create paywalls, memberships, or time-listened metrics.
Use AI tools to help humans, not replace them.
Industry standards on IP and voice consent are needed to reduce legal exposure. Platforms and advertisers hold most of the cards here. They can choose to favour volume or to protect quality. Their choice will decide many creators’ fates.
Three short scenarios, then the point
Flooded and cheap — Platforms favour volume. Ads chase cheap reach. Many independent shows vanish, and audio becomes a sea of similar, useful, but forgettable feeds.
Regulated and curated — Disclosure rules and smarter discovery reward listener engagement. Human shows survive, and AI fills utility roles.
Hybrid balance — Creators use AI tools to speed up workflows while keeping control over voice and facts. New business models emerge that pay for depth.
All three are plausible. The industry will move towards the one that matches where platforms and advertisers put their money.
Can human podcasters survive the flood of robot-made showsiStock
New rules, old craft
Machines can mass-produce audio faster and cheaper than people. That does not make them better storytellers. It makes them efficient at delivering information. If you are a creator, your defence is simple: make content machines cannot copy easily. Tell stories that require curiosity, risk, restraint, and relationships. Build listeners who will pay for that difference.
If you are a platform or advertiser, your choice is also simple: do you reward noise or signal? Reward signal, and you keep what made podcasting special. Reward noise, and you get scale and a thinner, cheaper industry in return. Either way, the next few years will decide whether podcasting stays a human medium with tools or becomes a tool-driven medium with a few human highlights. The soundscape is changing. If human creators want to survive, they need to focus on the one thing machines do not buy: trust.
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