Rohit Shetty says bollywood box office slump is just a phase
Shetty discusses the cyclical nature of the industry and anticipates Singham Again to uplift audiences with its star-studded cast and engaging storyline
Bollywood movies not doing well at the box office this year is just a phase and one should focus on films that have been successful, says filmmaker Rohit Shetty, as he hopes his much-awaited Singham Again makes viewers happy.
Other than small and mid-budget movies like Kiran Rao-directed Laapataa Ladies, Dinesh Vijan’s home production, Munjya, Kareena Kapoor Khan and Tabu-starrer Crew, Ajay Devgn’s Shaitaan, and Rajkummar Rao's Srikanth, the first half of the year has been dismal for the Hindi film industry with the failure of Bade Miyan Chote Miyan, Maidaan, and Yodha.
But Shetty, known for making big-budget, star-studded blockbusters, is not worried.
"It's a phase which comes every five to six years, where a year or two goes bad business-wise. Like, we have had Pathaan, Jawan, Gadar 2, and Oh My God 2, so it's like missing time syndrome. So, we focus on that, we don't look at films that did well," the filmmaker told PTI in an interview.
"For instance, Gadar 2, it's not like they did business of Rs 100 or 200 crore (£9.27 or 18.54 million), it did Rs 500-600 crore plus business. We need to focus on the good part. It happens, you’ve phases. It’s a cycle.”
Following the commercial success of films like “Pathaan,” “Jawan,” and “Gadar 2” last year, many thought the action genre was going to be the flavour of the season but Shetty said such assumptions are wrong.
“We come to a conclusion very soon that only action will work or comedy will not work. Films have to be taken individually, irrespective of the genre. We don’t know which film will work, which won’t and that’s a fact of life,” he said.
The 50-year-old director is optimistic about Singham Again, his next directorial venture with frequent collaborator Ajay Devgn, which opens in theatres on November 1 on the occasion of Diwali.
"It is a big responsibility. I hope when it comes, it will make everyone happy like how Sooryavanshi opened in theatres after Covid-19. I hope this one also makes everyone happy. If that is done, then my job is done,” Shetty added.
The upcoming action film is the third in the Singham series, which started with 2011’s Singham and was followed by Singham Returns in 2014. These movies are tied with the storylines of Sooryavanshi, led by Akshay Kumar and Simmba, starring Ranveer Singh.
Shetty, however, is wary of comparisons between his cop universe and the Avengers movies.
"I can't compare Singham Again with Avengers. They are too big. Ours is a cop universe, we’ve Ajay, Akshay, Ranveer, and Kareena, and then there are new characters this time. We are trying to work in our world and the way it is shaping up, I think people will get what they want from Singham Again.”
The idea, he said, is to create content that appeals to a wide segment of the audience.
"You need to up your game every time because there's a lot of exposure that the audience has, so you need to be on that level. We've to be on that level,” he said.
In Singham Again, Devgn will reprise his fan-favourite cop character of Bajirao Singham, and Arjun Kapoor will be seen as the antagonist. Kareena Kapoor Khan, who played Devgn’s love interest in the second part, will be seen in the third part.
Singham Again is the fifth part in Shetty’s cinematic cop universe, which includes Ranveer Singh’s Simmba (2018) and Akshay Kumar’s Sooryavanshi (2021).
The upcoming film also boasts star-studded appearances including Ranveer Singh, Akshay Kumar, Tiger Shroff, and Deepika Padukone.
Shetty, who recently wrapped the shooting of Singham Again, said CGI in stunts is increasingly becoming an industry standard.
“It depends on what kind of action it is, if it's hand-to-hand combat then CGI is not for me, I try to keep it real. Other than that, all the stunts that we do...CGI is very important because it's safe, easier and it's costlier also. But that's the new thing and you can't be left behind.”
Shetty is returning as the host of Khatron Ke Khiladi season 14 on Colors TV from July 27. (PTI)
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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