Justin Timberlake: I was a child when I first heard Justin Timberlake’s Justified album. The dancing and falsetto captured me, and introduced me to the world of pop music. Since then, watching him grow as an artist and a songwriter has kept me inspired, and always on the search for the next trend.
Damien Rice: In this list, I’ve included artists that have captured my soul in their music and Damien Rice is one of the best examples to do that. His songs like Volcano and It Takes A Lot To Know A Man are perfect for a misty winter day. I urge anyone reading to give his music a go; it’s inspiring and unravels many stories.
Mohammed Rafi: His delicate and deep tone made a huge difference. If you wanted a sad song, he was your man. My parents listened to his music a lot whilst I was growing up. I could never get into it, but as maturity started hitting, it was in sync with my thoughts and emotions.
Imran Khan: For me, seeing a European Pakistani artist making an impact on the British Asian market was one of the most amazing things I’ve witnessed. The frenzy surrounding his rise to the top captured me and so many other artists. Ten years on, people are still trying to hit the industry with the same power.
Ustaad Amanat Ali Khan: Oblivious to the fact he was my grandma’s favourite artist, I was attracted to the passion in his voice. I had heard many singers cover Ae Watan and it was love at first hear, but when I heard the original, the love, the emotion he had was untouchable.
Abida Parveen: The mother of music. If I could sit at anyone’s feet for the sake of music, it’s this woman. The divinity in her sound and presence in her character has had me in a trance. If I could suggest any song of hers, it would be Aaqa alongside Ali Sethi on Coke Studio.
Kishore Kumar: He is the Badshah of Bollywood music. The legendary Kishore Kumar is the rightful owner of the sound that Bollywood music produces today. It’s my belief the likes of Kumar Sanu and Arijit Singh have found their footing in the industry thanks to him. If I could meet anyone from the past, it would be him. A magnificent artist and an even better person.
Taz (Stereo Nation): Taz was the first artist I ever saw perform live, and luckily, I got to meet him right after the event. Crossing over from the UK to the Bollywood industry, I remember there being a mad wave around him. For me he is one of the hidden gems in our culture. Ishq is my all time favourite song by a British Asian artist.
Sami Yusuf: Spiritual music at its finest. Having endured a dark phase in my life, I can easily admit that Sami Yusuf was the beacon of light for me. It was his music that gave me the strength to look within myself and restart this musical journey. Even today, out of all the music I listen to, he is my go-to artist. A blessing for those who follow his music.
My mum: She is the sole reason I make music. Although, many South Asian parents frown upon a musical career, as has my mum, it was still because of her I loved music. Hearing her sing would make me melt, and it spoke to me more than her own words ever could. It’s through that, I learnt that music isn’t only a three-minute piece, it’s a lesson. My list is full of artists who have spoken to my soul and I feel my mum has done that the most.
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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