Roti Kapada Aur Makaan: Making of a film masterpiece
The all-time classic, written, produced, directed, and headlined by movie star Manoj Kumar, transcended conventional storytelling, delivering powerful social commentary.
Stills from Roti Kapada Aur Makaan
By Asjad NazirOct 12, 2024
WHEN Roti Kapada Aur Makaan was released in cinemas on October 18, 1974, it strongly resonated with audiences and became the highest-grossing film of the year.
This all-time classic, written, produced, directed, and headlined by movie star Manoj Kumar, transcended conventional storytelling, delivering powerful social commentary on the struggles of the common man. Powered by a strong cast, this social drama about essential human needs explored themes of dignity, survival, economic disparity, and life in a turbulent era.
Eastern Eye decided to celebrate the drama’s golden anniversary by sharing fascinating facts connected to it:
Manoj Kumar visited several unemployment offices and interacted with job seekers to authentically portray his role. He also drew on his early job-hunting struggles for inspiration, with scenes where his character searches for a job reflecting these real-life observations.
Rajesh Khanna was initially considered for the role of Mohan Babu but had to decline due to other commitments. Navin Nischol, approached next, opted out, not wanting to act in a multistarrer. Although Rajendra Kumar was offered the role, he requested improvements that Manoj Kumar declined to make. Shashi Kapoor was ultimately cast, earning great acclaim.
Sharmila Tagore was first offered the role of Sheetal, but she preferred the character played by Moushumi Chatterjee. Kumar, however, ultimately cast Zeenat Aman as Sheetal.
Zeenat Aman initially hesitated to play the morally ambiguous role of a woman leaving her struggling partner for a wealthier man but was eventually persuaded, solidifying her status as a leading lady willing to break norms.
Amitabh Bachchan was relatively unknown when he was cast in the film but had become a star by its release. His presence helped it become a blockbuster. Despite a suddenly packed schedule, Bachchan made special efforts to accommodate the film, shooting some of his most intense scenes between other projects.
Manoj Kumar had offered Smita Patil a role when she was still an unknown, which she declined. Patil would later become one of India’s greatest film actresses.
It was widely reported that Mehmood had been promised a role that did not materialise, leading to public expressions of displeasure by the legendary actor.
Legendary playback singer Mukesh, despite being unwell, completed the popular song Main Na Bhoolunga with Lata Mangeshkar.
Bachchan sustained an injury during an action sequence but continued shooting to avoid disrupting the schedule.
Chatterjee, pregnant during filming, could not shoot for the song Haye Haye Yeh Majboori. The song was shot with Zeenat Aman, though it didn’t suit her character’s story arc. Chatterjee later revealed that Kumar never fully forgave her for her inability to perform in that song.
During the filming of a rape scene, a significant amount of cooking flour fell on Chatterjee. A lot of it went into her mouth, leading to her vomiting the entire night. It also took hours to remove the flour from her hair.
In keeping with his patriotic Bharat persona, Manoj Kumar included no physical romantic scenes with Zeenat Aman in their roles.
This was the first film for which actor Ajay Devgn’s father, legendary stunt director Veeru Devgan, took full charge of the action scenes.
The project was Manoj Kumar’s most ambitious to date, leading him to mortgage his house to fund the film, which became that year’s biggest Bollywood success.
The film employed a lot of symbolism to reflect the socio-political climate of India at the time. Lead characters Bharat (Manoj Kumar) and Vijay (Amitabh Bachchan) symbolised India and victory, respectively, representing hope for a better future amidst struggles.
Indian cultural references were deliberately included in the dialogues to enhance resonance with the audience.
Kumar had filmed an alternate ending in which his character, Bharat, succumbs to life’s pressures and dies. He eventually chose a positive ending to convey a message of resilience and hope.
The initial cut of Roti Kapada Aur Makaan was much longer than the final 160-minute version, with Kumar editing out scenes to keep the narrative focused and concise.
Unused sub-plots that were excluded from the final edit involved a more in-depth exploration of the relationship between Bharat and his younger brother Vijay.
Kumar organised screenings for workers and lower-income groups to gauge the film’s impact on its target audience.
The film’s distributors and many Bollywood insiders initially disliked the title Roti Kapada Aur Makaan, but Kumar maintained it to encapsulate the film’s social message.
Following the film’s success, Kumar organised special screenings for the impoverished in various rural areas and held charity screenings, with proceeds donated to local initiatives aimed at alleviating poverty. This ultimately reflected the film’s core message.
Some sets used in Roti Kapada Aur Makaan were repurposed for other films of the time.
The song Hai Hai Yeh Majboori was later sampled by hip-hop artist Bubba Sparxxx in the song Ugly and remixed by Bally Sagoo on his album Bollywood Flashback 2.
Roti Kapada Aur Makaan was later remade in Telugu as Jeevana Poratam (1986) and continues to inspire Bollywood movies with its themes.
The timeless classic remains relevant today, highlighting the struggles of the poor and working class to secure basic necessities such as food, clothing, and shelter, as the title suggests. Many filmmakers have been inspired by its themes to create movies about the common man’s struggles, using it as a reference point for addressing similar social issues in subsequent decades.
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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