Vivek Mishra works as an Assistant Editor with Eastern Eye and has over 13 years of experience in journalism. His areas of interest include politics, international affairs, current events, and sports. With a background in newsroom operations and editorial planning, he has reported and edited stories on major national and global developments.
India captain Rohit Sharma eased concerns about an arm injury after scoring 52 and retiring hurt as India defeated Ireland by eight wickets in their T20 World Cup opener in New York on Wednesday.
India needed only 97 runs to win following a strong performance by their bowlers. Left-arm quick Arshdeep Singh struck twice in the third over, reducing Ireland to 9-2 after Rohit won the toss.
Allrounder Hardik Pandya took two wickets in two balls, finishing with 3-27, while Jasprit Bumrah, the player of the match, claimed 2-6.
"Just a little sore," Rohit said at the presentation ceremony. "New ground, new venue, wanted to see what it's like to play on. I don't think the pitch settled down; there was enough there for the bowlers."
Ireland, all out for just 96, needed to capitalize on every chance for a possible upset. Rohit had made just two when he edged Mark Adair through the hands of Andrew Balbirnie at second slip off the last ball of the first over of India's chase.
Adair dismissed Virat Kohli for one, as the star batsman sliced to deep third man, but the damage was done. India began their quest for a major title since their 2013 Champions Trophy win with a commanding Group A victory.
Rohit punished Ireland with a 37-ball innings, including four fours and three sixes, sharing a 54-run stand with Rishabh Pant. The wicketkeeper, returning to international cricket after a car crash in December 2022, finished on 36 not out and ended the match with a reverse-scooped six off Barry McCarthy, securing victory for India with nearly eight overs remaining.
Rohit was not there to finish the match, having been struck on the arm by Josh Little, though he hit the next two balls for sixes. Victory was nearly certain when India dismissed Ireland on a drop-in pitch with variable bounce at a specially built Long Island ground.
India's joy was tempered by the thought they could face similar conditions when they play arch-rivals Pakistan on Sunday.
"I don't know what to expect against Pakistan, we will prepare like the conditions are going to be like that," Rohit said.
Bumrah was unconcerned, saying, "Coming from India, when you see the ball seaming around (here), I would never complain when there's help for the bowlers."
Only four Ireland batsmen reached double figures, with Gareth Delany top-scoring with 26 before he was run out as the innings ended in the 16th over.
"A tough one," said Ireland captain Paul Stirling. "The toss played a really important part in overcast conditions and then the pitch offered all sorts. We weren't quite up to that challenge and India bowled really well to put us under pressure."
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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