From wanting to quit tennis a decade ago to making a dream Grand Slam debut against Roger Federer at the US Open last month, Sumit Nagal calls himself a "survivor" and the Indian is hopeful his journey will become less arduous going forward.
The 22-year-old earned a string of new admirers at Flushing Meadows when he came out swinging in his Grand Slam main draw debut to win the opening set against Federer at a packed Arthur Ashe Stadium.
Nagal ultimately went down 4-6 6-1 6-2 6-4 to the owner of 20 Grand Slam singles titles but not before breaking Federer's serve three times in a tie he was praying would be his from the moment he heard the Swiss had been drawn against a qualifier.
"I really wanted that. I was so happy," Nagal told Reuters. "I was getting a massage when my coach messaged saying I am playing Federer. That was the best thing that could've happened.
"I felt a bit nervous while I was walking in. First few minutes were tough, I had never been to a court like that, never seen so many people. But when I finished the match, I enjoyed it, I enjoyed being there.
"It was lot of fun to play on that court ... against Roger. I was happy but I wasn't happy the way I played, I think I could have done better but overall I was satisfied."
After the match Federer predicted a "very solid career" for his opponent, who has never earned a tour-level victory, and said Nagal's game was tailor-made for clay courts.
The Indian agrees and revealed that he has long idolised his near namesake Rafa Nadal, who has won 12 out of his 19 Grand Slams on the French Open clay.
"I like how he plays, how much hunger he brings on the court and the way he fights," Nagal said.
"He does not want to give you any points, you have to work for it."
Nagal showed a similar stomach for a fight against Federer, earning four of his 13 break point opportunities in the final game as the Swiss served for the match in the fourth set.
TENNIS PATH
If it were not for his fighting spirit, Nagal would probably have never made it from New Delhi to New York in any case.
Like most kids growing up in India, he wanted to be a cricketer but it was his teacher father who insisted Nagal swap bat and ball for a racket, setting him on his tennis path.
Two years later, in 2005, Nagal caught the eye of Mahesh Bhupathi, who won India its first Grand Slam title in 1997 when he claimed the French Open mixed doubles, at a trial for his academy and got selected from thousands of youngsters.
But the programme shut down after two years. At the age of 12, Nagal returned home to Delhi and stopped playing for a bit.
"I had thoughts of giving up. I had only played five days in two months," he said.
It was a call from Bhupathi that brought Nagal back to the game but the financial constraints were constant.
"It's been tough. I didn't have a sponsor who would say 'yes I am going to cover your whole year's cost'. It was always someone giving us, funding us for a few thousand, here and there. I was trying to survive," he said.
"Coming from India where you don't earn so much and I was spending through euros or dollars all the time. It wasn't easy. But I did survive those years."
It was not until 2017 that Nagal found some stability when the Virat Kohli Foundation, the philanthropic organisation run by India's cricket captain, offered him a scholarship, while state-owned Indian Oil offered him employment.
Though the match against Federer made him a recognisable name in his homeland, Nagal said he has not been approached by any new sponsors.
"I definitely hope (to get more support)," said Nagal, who is currently ranked 174 in the world. "Nothing has changed for me. I am still playing tournaments I am supposed to play.
"I will try to get my rank as high as possible before the Australian Open."
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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