All Quiet on the Western Front dominated the BAFTA Awards with a record-breaking seven wins including Best Director, Best Film, and Best Film Not in the English Language.
The film sets the record of winning a maximum number of awards being a non-English language film.
The British Academy of Film and Television Arts (BAFTA) hosted its annual Film Awards at the Royal Festival Hall in the Southbank Centre on Sunday.
The ceremony, at the Royal Festival Hall in London, was hosted by Loki actor Richard E. Grant who arrived in a Batmobile wearing a floor-length white cape with a train, reported Variety.
Here's the complete list of winners:
Best Film
All Quiet On The Western Front (winner)
The Banshees Of Inisherin
Elvis
Everything Everywhere All At Once
Tar
Leading Actor
Austin Butler - Elvis (Winner)
Colin Farrell - The Banshees Of Inisherin
Brendan Fraser - The Whale
Daryl Mccormack - Good Luck To You, Leo Grande
Paul Mescal - Aftersun
Bill Nighy - Living
Leading Actress
Cate Blanchett - Tar (Winner)
Viola Davis - The Woman King
Danielle Deadwyler - Till
Ana De Armas - Blonde
Emma Thompson - Good Luck To You, Leo Grande
Michelle Yeoh - Everything Everywhere All At Once
Director
All Quiet On The Western Front - Edward Berger (Winner)
The Banshees Of Inisherin - Martin Mcdonagh
Decision To Leave - Park Chan-wook
Everything Everywhere All At Once - Daniel Kwan, Daniel Scheinert
Tar Todd Field
The Woman King - Gina Prince-bythewood
Supporting Actor
Barry Keoghan - The Banshees Of Inisherin (Winner)
Ke Huy Quan - Everything Everywhere All At Once
Eddie Redmayne - The Good Nurse
Albrecht Schuch - All Quiet On The Western Front
Micheal Ward - Empire Of Light
Brendan Gleeson - The Banshees Of Inisherin
Supporting Actress
Kerry Condon - The Banshees Of Inisherin (Winner)
Dolly De Leon - Triangle Of Sadness
Jamie Lee Curtis - Everything Everywhere All At Once
Carey Mulligan - She Said
Angela Bassett - Black Panther: Wakanda Forever
Hong Chau - The Whale
Adapted Screenplay
All Quiet On The Western Front - Edward Berger, Lesley Paterson, Ian Stokell (Winner)
Living - Kazuo Ishiguro
The Quiet Girl - Colm Bairead
She Said - Rebecca Lenkiewicz
The Whale - Samuel D. Hunter
Editing
Everything Everywhere All At Once - Paul Rogers"All Quiet On The Western Front" - Sven Budelmann (Winner)
The Banshees Of Inisherin - Mikkel E. G. Nielsen
Elvis - Jonathan Redmond, Matt Villa
Top Gun: Maverick - Eddie Hamilton
Cinematography
All Quiet On The Western Front - James Friend (Winner)
The Batman - Greig Fraser
Elvis - Mandy Walker
Empire Of Light - Roger Deakins
Top Gun: Maverick - Claudio Miranda
Animated Film
Guillermo Del Toro's Pinocchio - Guillermo Del Toro, Mark Gustafson, Gary Ungar, Alex Bulkley (Winner)
Marcel The Shell With Shoes On - Dean Fleisher Camp, Andrew Goldman, Elisabeth Holm, Caroline Kaplan, Paul Mezey
Puss In Boots: The Last Wish - Joel Crawford, Mark Swift
Turning Red - Domee Shi, Lindsey Collins
Original Screenplay
The Banshees Of Inisherin - Martin Mcdonagh (Winner)
Everything Everywhere All At Once - Daniel Kwan, Daniel Scheinert
The Fabelmans - Tony Kushner, Steven Spielberg
Tar - Todd Field
Triangle Of Sadness - Ruben Ostlund
Documentary
Navalny - Daniel Roher, Diane Becker, Shane Boris, Melanie Miller, Odessa Rae (Winner)
All That Breathes - Shaunak Sen, Teddy Leifer, Aman Mann
All The Beauty And The Bloodshed - Laura Poitras, Howard Gertler, Nan Goldin, Yoni Golijov, John Lyons
Fire Of Love - Sara Dosa, Shane Boris, Ina Fichman
Moonage Daydream - Brett Morgan
Original Score
All Quiet On The Western Front - Volker Bertelmann (Winner)
Babylon - Justin Hurwitz
The Banshees Of Inisherin - Carter Burwell
Everything Everywhere All At Once - Son Lux
Guillermo Del Toro's Pinocchio - Alexandre Desplat
Makeup and Hair
Elvis - Jason Baird, Mark Coulier, Louise Coulston, Shane Thomas (Winner)
Roald Dahl's Matilda The Musical - Naomi Donne, Barrie Gower, Sharon Martin
The Whale - Anne Marie Bradley, Judy Chin, Adrien Morot
All Quiet On The Western Front - Heike Merker
The Batman - Naomi Donne, Mike Marino, Zoe Tahir (ANI)
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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