INDIA captain Virat Kohli was out for nought first ball in the first Test amid a collapse that saw England great James Anderson take two wickets with successive deliveries on Thursday's (5) second day at Trent Bridge.
Kohli was caught behind off Anderson by wicketkeeper Jos Buttler one ball after Cheteshwar Pujara fell to the same combination for four.
India had been in total command at 97 for 0 in reply to England's meagre first innings 183 but then lost four wickets for 15 runs either side of lunch as they slumped to 112 for 4.
The wickets were welcome news for England after it had been announced earlier on Thursday (5) that Jofra Archer, not playing in this match, would be ruled out for the rest of a year with a recurrence of an elbow injury, meaning the fast bowler would miss both the T20 World Cup and the Ashes tour of Australia.
By the time bad light and then rain combined to bring about an early finish to the day's play, India were 125 for 4 - a deficit of 58 runs.
Rahul shines
One consolation for India was that recalled opener KL Rahul was still there on 57 not out after being reprieved on 52 when Dom Sibley dropped a regulation slip catch. Rishabh Pant was unbeaten on seven.
Star batsman Kohli was undone by a brilliant Anderson delivery that lifted and moved late to take the edge on its way through to Buttler.
The ball before Anderson, previously wicketless in the innings, had number three Pujara caught low down by Buttler for four.
Ajinkya Rahane survived the hat-trick when Anderson strayed down the legside, but England's all-time leading Test wicket-taker still had figures of 2-15 in 13.4 overs at stumps.
Rahane, then the non-striker, was soon out for five in any event when, having been left stranded when Rahul set off and then stopped, he was run out by Jonny Bairstow's direct hit from backward point.
Rahul, having completed a 128-ball fifty including eight fours, ought to have been Anderson's third wicket of the day but Sibley floored a catch off an outside edge going to his left at second slip despite getting both hands to the ball.
Kohli walks back after being dismissed for a first ball duck. (Reuters/Paul Childs)
Anderson started his 14th over but could only manage one ball before the umpires took the players off the field for bad light.
With rain falling, play did not resume again with Anderson then managing one more ball before a fresh shower.
The umpires got play going again with Anderson bowling two more balls only for a fresh burst of rain to halt play again, with two balls left in an over that had started more than two-and-a-half hours earlier.
India had been in command for all but one ball of the morning's play after resuming on 21-0.
Both Rahul, recalled after Mayank Agarwal was ruled out with suspected concussion when hit on the head batting in the nets on Monday - and Rohit Sharma blunted England's attack with disciplined skill.
But on the stroke of lunch, Sharma fell for 36 when, in what appeared to be a lapse in concentration, he pulled a surprise short ball from the Ollie Robinson to Sam Curran at fine leg.
India's four-man pace attack had performed superbly to dismiss England on Wednesday, with Jasprit Bumrah taking 4 for 46.
Only England captain Joe Root, who won the toss, with 64 passed 29 in an innings that featured four ducks - the third time this had happened to England in their last five Tests.
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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