Himesh Patel: ‘Actors of colour are finally being considered for all roles’
Patel’s latest film is Greedy People, a darkly comedic crime thriller from Potsy Ponciroli.
Himesh Patel
By Eastern EyeNov 29, 2024
BRITISH star Himesh Patel drew inspiration from Riz Ahmed and Dev Patel for opening doors for south Asian actors, but said the work of representation is far from done and he has set high standards for himself.
Patel’s latest film is Greedy People, a darkly comedic crime thriller from Potsy Ponciroli.
In the movie, which was premiered on Lionsgate Play last Friday (22), the actor features alongside Joseph Gordon-Levitt and Lily James, his co-star from Yesterday. Set in a small island town, Greedy People follows rookie cop Will Shelley (Patel) and his reckless partner Terry (Gordon-Levitt) after they stumble upon million dollars at a crime scene they inadvertently create.
Their decision to steal the money triggers chaos, drawing in various quirky townsfolk, Will’s pregnant wife (James), a shrimp business owner and a masseur, all driven by greed.
“It leads to a really thrilling climax. I thought the character was really interesting, his sort of contradictions and moral ethical position,” said Patel.
“And then, of course, I knew that Lily James was attached, we’ve worked together before, I had a great time. So I was happy to sort of dive into something very different with her,” he added.
The actor explained his initial reaction after reading the story of Greedy People – why does everyone keep making the worst decision possible at every given opportunity? Patel said, “What made it such a strong script for me was that it made sense. Evryone’s decisions made sense. It wasn’t anything that was done for the sake of escalating the story.
“It was kind of frustrating to read and go, ‘I just made the right decision.’ But at the same time, it made sense to me and it made my life sort of a bit easier as an actor to get into it,” he added.
Patel began his career on EastEnders and then shot to fame with Danny Boyle’s 2019 movie Yesterday. Since then, he has been part of Christopher Nolan’s sci-fi film Tenet, Adam McKay’s Don’t Look Up, dystopian series Station Eleven and now dark comedy Greedy People.
Growing up in the UK to Indian parents, Patel said it was only when he started working as an actor that he was exposed him to the barriers south Asian artistes face and that deepened his understanding of the challenges that lie ahead of him
“The feeling of like wanting to see yourself and not seeing yourself as much, I think I was perhaps only subconsciously aware of it when I was growing up. I was lucky I started acting when I was very young.
“It was when I started professionally working and I was working with other south Asian actors who had struggled to get to where they were, that I realised the battle that we kind of have,” Patel said in an interview.
The actor spoke of his pride in being part of a generation changing the conversation.
He is optimistic about making strides when it comes to inclusivity after seeing the careers of Ahmed and Dev Patel.
“Riz is a great example and Dev for me, he was a spearhead of everything that’s happened over the last many years. I feel Slumdog Millionaire was a real turning point and then the career he’s built off the back of that has been a real sort of calling card. It sets a bar.
“I take a lot of encouragement and I try to follow the example of people like that. I just think the only way I can navigate it is to set my bar high. I think we have to value ourselves highly and then we can get to where we need to get to,” he added.
Patel said another welcome change in the industry is that actors of colour are being roped in for parts that were earlier played by white actors.
Patel has played Jack Malik in Yesterday, Phillip Kaj in Don’t Look Up, had a cameo as Dr John Watson in Enola Holmes 2, and starred in Station Eleven.
“In terms of actors like myself and other actors of colour, being considered for roles that would otherwise have just gone to a white actor, I think that’s a positive thing for the industry,” the actor said.
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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