Sequels have become a trend in Bollywood. Even if there’s no scope of taking the story forward, filmmakers try to cash on the franchise factor and make the part 2 and sometimes even part 3. While some of the sequels get a good response at the box office due to the franchise factor, the script surely fails to impress.
Today, we are going to look at the list of Bollywood sequels that failed to live up to the expectations.
Once Upon ay Time in Mumbai Dobaara!
Ajay Devgn, Emraan Hashmi, Kangana Ranaut, and Prachi Desai starrer Once Upon a Time in Mumbai was a fantastic film. The movie spoke about the underworld and impressed the audiences. The makers decided to make a sequel to it, where Emraan Hashmi’s character gets an older version in Akshay Kumar. But, the sequel failed to make a mark at the box office.
Welcome Back
2007 release Welcome was a laugh riot, from the first scene itself we were laughing out loud. But that didn’t happen in the sequel. Welcome Back was mounted on a huge scale but unfortunately, the makers didn’t concentrate on the script. Also, John Abraham couldn’t be funny like Akshay Kumar.
Kahaani 2
We are not saying that Vidya Balan starrer Kahaani 2 was not a good film. It was an amazing film and spoke about a very important issue like child abuse. However, as it was titled Kahaani 2, people expected that the sequel will be a suspense-thriller just like the first part. But, unfortunately, that suspense element was missing in it and that’s why audiences didn’t like it much.
Happy Phirr Bhag Jayegi
Mudassar Aziz’s Happy Bhag Jayegi starring Diana Penty, Jimmy Sheirgill, Ali Fazal, and Abhay Deol was a hilarious film. The makers decided to make it a franchise and got Sonakshi Sinha in the sequel. While they upped their game by making the production value better, but script-wise the sequel was strictly average.
Love Aaj Kal 2
Imtiaz Ali made a film titled Love Aaj Kal in 2009 and then in 2020, he made the film once again with the same title and the same concept about how love is different in two eras. The sequel bombed at the box office and it is clearly one of the worst films of 2020.
Race 2 & Race 3
Abbas-Mustan’s Race starring Saif Ali Khan, Akshaye Khanna, Bipasha Basu. Katrina Kaif and Anil Kapoor was an amazing suspense thriller. They decided to make Race 2 but it was quite predictable, and even Saif agreed that the sequel wasn’t as good as the first part. But then, the worst thing happened that producer Ramesh Taurani decided to take the franchise forward with Salman Khan and made Race 3. It was a bad film.
Rock On 2
Last but not the least; we have Rock On 2 on the list. The first instalment was a fantastic film and even the songs of the film are still remembered. However, the sequel, Rock On 2, failed to get good reviews and was a disaster at the box office.
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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