Gayathri Kallukaran is a Junior Journalist with Eastern Eye. She has a Master’s degree in Journalism and Mass Communication from St. Paul’s College, Bengaluru, and brings over five years of experience in content creation, including two years in digital journalism. She covers stories across culture, lifestyle, travel, health, and technology, with a creative yet fact-driven approach to reporting. Known for her sensitivity towards human interest narratives, Gayathri’s storytelling often aims to inform, inspire, and empower. Her journey began as a layout designer and reporter for her college’s daily newsletter, where she also contributed short films and editorial features. Since then, she has worked with platforms like FWD Media, Pepper Content, and Petrons.com, where several of her interviews and features have gained spotlight recognition. Fluent in English, Malayalam, Tamil, and Hindi, she writes in English and Malayalam, continuing to explore inclusive, people-focused storytelling in the digital space.
SpaceX halted its 10th Starship test flight minutes before liftoff in Texas.
Engineers cited a ground systems issue and will attempt another launch on Monday.
The decision follows multiple explosions in earlier Starship trials.
The rocket is central to Musk’s Mars ambitions and NASA’s lunar mission plans.
Launch halted in Texas
SpaceX called off the 10th test flight of its Starship megarocket on Sunday, marking another delay for Elon Musk’s ambitious space programme. The company announced the halt roughly 30 minutes before liftoff from its Boca Chica launch site, saying the pause was to “allow time to troubleshoot an issue with ground systems.”
A new launch attempt has been scheduled for Monday.
Previous failures
The cancellation is the latest in a string of setbacks for SpaceX’s Starship project. Earlier tests of the rocket’s upper stage in January, March and May ended in mid-flight explosions, while a June “static fire” trial saw the vehicle erupt on the launchpad.
Despite these failures, SpaceX maintains that the fully reusable 403ft (123m) rocket is essential to reducing spaceflight costs and enabling long-duration missions.
Role in Mars and Moon missions
Starship is designed to ferry both people and cargo into space, forming the backbone of Musk’s long-term goal of establishing a settlement on Mars. NASA has also contracted SpaceX to deliver a customised version of Starship for its Artemis programme, which aims to return astronauts to the Moon.
Planned flight objectives
Had Sunday’s launch gone ahead, Starship’s upper stage was expected to separate from its Super Heavy booster at high altitude. The booster, which has previously demonstrated landings using giant mechanical arms, was set to attempt a water landing in the Gulf of Mexico to test a backup engine configuration.
The Starship upper stage was programmed to fire its engines in space, release a batch of mock Starlink satellites, and reignite on a suborbital path around Earth.
Ongoing challenges
Even if the 10th test flight proves successful, SpaceX still faces major engineering challenges. These include making the vehicle rapidly and fully reusable, cutting launch costs significantly, and proving it can refuel using super-cooled propellants in orbit—capabilities seen as vital for both lunar and Martian missions.
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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