On July 10, 2026, Apple filed a lawsuit in a California federal court accusing OpenAI of systematically stealing its trade secrets. The defendants include OpenAI itself, the hardware company io Products (founded by former Apple design chief Jony Ive and later acquired by OpenAI), OpenAI’s Chief Hardware Officer Tang Tan, and former Apple engineer LiuChang.

       Apple is asking for five things: a jury trial, an immediate injunction to stop the alleged infringement, destruction of all confidential materials, redesign of any unreleased hardware products to remove Apple technology, and damages. The most painful demand is the forced redesign — in past cases, Apple has used similar rulings to force deep audits of a company’s entire technical roadmap, exposing secrets and dragging out timelines for years.

       The case quickly grabbed attention across Silicon Valley. Elon Musk jumped in with a jab at his longtime rival Sam Altman: “You stole my open-source charity OpenAI, and now you’re stealing from Apple too?” Altman fired back that Musk’s public company would face SEC trouble if his big promises on compute centers didn’t materialize. Musk replied he would begin “test launches” next year and might even invite Altman — if his parole officer allowed it.

1. The central figure: Tang Tan

       At the heart of the case is Tang Tan. A Malaysian-Chinese engineer who studied at Imperial College London and MIT, he joined Apple around 1999 and spent 25 years there. He rose to vice president of iPhone product design, with his name on over 270 Apple patents. He played a key role in the design of the iPod, multiple iPhone generations, and the Apple Watch.

      In 2024 he left Apple to co-found io with Jony Ive. OpenAI acquired io for roughly $6.4 billion in 2025, and Tang became OpenAI’s Chief Hardware Officer. Apple’s lawsuit treats him as the key link — not just another departing employee, but someone in a senior position who allegedly helped turn knowledge transfer into a repeatable process.

2. The evidence trail

       Apple’s complaint lays out a timeline. In January 2026, LiuChang left Apple after eight years and joined OpenAI. He kept a company-issued laptop that could still access Apple’s internal network. He messaged a colleague still at Apple: “Haha, I can still get into the company storage — so funny.” The finally reply came back: “I’m ready.”

       The two later switched to the Line app to communicate, but Apple’s systems still captured the activity. Liu allegedly downloaded dozens of confidential files covering technical specs, hardware designs, manufacturing details, and testing processes.

       Apple says this alone could be treated as individual misconduct. The bigger problem, according to the lawsuit, is what it claims Tang Tan did:

• During job interviews with Apple employees, he allegedly used internal project codenames that only insiders would know and asked candidates to bring physical parts (batteries, system-in-package chips, motherboards) to the interview for him to examine.
• He reportedly coached people planning to leave Apple on how to “leave gracefully” — advising them not to announce their departure immediately so they could gather more information while they still had access, before devices and permissions were revoked.
• Even before leaving Apple, Tang allegedly emailed sensitive supplier information to his personal account.

      Taken together, Apple argues these actions show a pattern originating from the top of OpenAI’s hardware team rather than isolated employee behavior.

       There is also a separate but damaging claim about the supply chain. Former Apple engineers reportedly approached Apple’s long-time manufacturing partners (the so-called “fruit chain” companies in China) and suggested they could continue working together — sometimes claiming they had “Apple authorization” to use proprietary processes or materials. Some suppliers reportedly agreed to informal arrangements. For Apple, this strikes at the heart of its quality advantage, which has always depended on tight control over specialized manufacturing know-how.

3. Why Apple and OpenAI fell out

       Apple and OpenAI used to cooperate. In 2024 Apple integrated ChatGPT into Siri. Apple expected the usual revenue-sharing model it has with partners: it provides the huge base of iPhone users and the App Store platform, and takes a cut (30% in the first year, dropping to 15% in the second).

       OpenAI saw it differently. It complained that conversion to paid ChatGPT subscriptions ($20/month) was too low and that serving millions of Apple users was expensive because every AI response consumes significant computing power. Unlike Google Search — where adding more users costs almost nothing extra — OpenAI’s marginal cost rises with every additional query.

       OpenAI reportedly pushed back hard and even threatened legal action. Apple responded by switching Siri’s AI partner to Google’s Gemini in the latest iOS versions. The partnership was over.

4. What Apple is really afraid of

       Apple’s premium pricing has long rested on three pillars: superior build quality, distinctive design, and a tightly controlled ecosystem. The lawsuit suggests OpenAI’s talent raid and alleged information gathering threaten all three.

       Quality comes from Apple setting strict standards and working with its supply chain partners to execute them. With reportedly hundreds of Apple hardware engineers now at OpenAI (and supply chain relationships shifting), that knowledge edge is leaking.

       Design appeal is not entirely separate — it depends on the overall experience of quality and software. Without a clear quality or ecosystem advantage, aesthetic differences alone are harder to maintain.

       The biggest long-term threat is the ecosystem. Traditional iOS apps run in “sandboxes” — isolated environments where each app can only access its own data. This protects privacy and security, and has been a core reason Apple’s ecosystem feels more controlled than Android’s.

       AI Agent phones work differently. Instead of opening multiple apps, users speak to a single AI that can pull together data from many sources to complete complex tasks (for example, planning a trip that involves navigation, restaurant booking, food delivery, and hotel reservations). If this model succeeds, the value of Apple’s traditional app sandbox system could shrink dramatically.

       OpenAI is reportedly accelerating its own AI phone plans, aiming for a 2027 launch with annual sales targets in the tens of millions. Other players are exploring similar hardware. For Apple, this is not just another competitor — it is a potential shift in how people interact with their devices.

5. How the case might play out

       Some individuals will likely face consequences, possibly including criminal liability for the most serious leaks. OpenAI is unlikely to accept a court-ordered full audit of its code and hardware designs.

       The more probable path is a negotiated settlement: cutting ties with certain people while offering Apple concessions such as licensing, equity, revenue sharing, or cash. Similar past disputes in Silicon Valley have often ended this way rather than in total victory for either side.

       Whatever the legal outcome, the larger story is the arrival of AI-driven hardware. This lawsuit is Apple’s attempt to slow down a fast-moving competitor during a critical transition. The technology itself — AI agents that can actually get things done across services — is not going to wait for court rulings.

       The real question going forward is not just who wins this case, but what kind of devices we will all be using in a few years.

       But here’s the important clarification: the photo showing a drone carrying two people is AI-generated. It’s simply too dangerous and physically impossible for current drones to safely lift two adults. Real rescues that happened involved lifting just one person, and only in truly life-or-death situations where someone was about to be swept away.

       The real story took place on July 7 in Yunbiao Town. A truck driver was trapped on top of an oil tanker in the middle of the flood. A rescuer in an orange-red suit flew in on a drone, landed on the vehicle, and then both were lifted out together. The whole operation took less than 20 minutes.

       This wasn’t some hobby drone from the park. It was a coordinated effort by ordinary pilots from across China who rushed to the disaster zone on their own.

1. An Old Dam Couldn’t Withstand the Storm

       The disaster started with Typhoon Misaak. Between July 5–6, parts of Nanning and Guigang in Guangxi saw torrential to extremely heavy rain. Binyang County recorded 713 mm in 24 hours, and Shitang Town in Hengzhou saw 565 mm — basically a full year’s rainfall for many northern regions dumped in a single day.

       On the morning of July 6, Liulan Reservoir in Hengzhou experienced overtopping. This 60-plus-year-old homogeneous earth dam (built in 1958–1960 by compacting soil) simply couldn’t hold back the water. Once water flowed over the top, it rapidly eroded the back side of the dam, and the structure gave way.

       By July 9, the floods had caused 39 deaths and 9 people missing. Roads and bridges were destroyed. Even assault boats sometimes couldn’t reach the trapped areas because the current was too strong.

2. Drone Pilots Showed Up on Their Own — Hundreds of Aircraft

       At this critical moment, a special group of people drove in from all over the country.

       Pilots came from Guangxi, Sichuan, Guizhou, Hunan, Guangdong, and even as far as Heilongjiang. They loaded their own drones and generators into their vehicles, paid for their own gas, food, and lodging, and asked for zero payment or subsidies. They just headed straight to Hengzhou.

       Because everyone came spontaneously, exact numbers are hard to track, but reports mentioned WeChat groups with hundreds of members and several hundred drones operating on site.

       By July 8, these drones had delivered more than 1,000 tons of relief supplies. At one distribution point in Dutian Village, five drones worked non-stop around the clock. In Yunbiao Town, single drones covered a 4–5 km radius, reaching multiple trapped villages.

       They couldn’t always pull people out, but they could drop water, instant porridge, and instant noodles — giving trapped residents something to hold onto.

3. DJI Stepped Up: Free Repairs for All Rescue Damage

       One of the most heartwarming details came on July 8. DJI Agriculture issued a notice: any plant-protection or cargo drone used in the Guangxi rescue that got water damage, crashed, or had parts broken during the operation would receive completely free repairs — no labor fees, no parts charges, no inspection costs.

       Normally, water damage or crashes are not covered under warranty. But this time DJI said: just provide your flight logs from the rescue period and report the drone’s serial number, and authorized stores would handle it immediately. Local DJI stores even switched to 24-hour operation to service the pilots overnight.

       For many pilots, a good drone plus accessories and a vehicle can cost 200,000–300,000 RMB. DJI’s policy removed the biggest financial fear — “what if I crash my expensive machine?” — and gave them the confidence to go all in.

4. These Aren’t Toy Drones — They’re Serious Work Machines

       The drones used in the rescue are very different from the small consumer drones people fly in parks for fun.

      These are professional plant-protection drones and cargo drones.

• Plant-protection drones (T series):
  Built for agriculture — spraying pesticides and spreading fertilizer. The common T200 model can carry dozens of kilograms per flight.
• Cargo drones (FlyCart series):
  Designed specifically for lifting and transporting goods. The FC200 can lift up to 200 kg by itself, and multiple units can work together to lift 600 kg.
• Hybrid VTOL / compound-wing drones:
  These take off and land vertically like a multi-rotor drone but fly long distances like a fixed-wing aircraft. Some were used in the disaster zone as temporary flying 4G/5G base stations to restore mobile signals.

      These machines normally work in farmland. This time they went straight into emergency rescue.

5. China’s Drone Industry Grew 100× in Just Ten Years

       Most people don’t realize how big this industry has become.

       Around 2015, DJI’s agricultural drone shipments were only about 2,000 units per year. By 2020 it reached 40,000 units. In 2025 it hit 200,000 units annually. In just ten years, yearly sales grew roughly 100 times.

       By 2025, China had around 500,000 of these agricultural and cargo drones operating nationwide. They covered more than 3.2 billion mu (about 213 million hectares) of farmland in a single year. Roughly one-third of China’s cultivated land now relies on drones for pesticide application.

       DJI holds about 55% of the domestic plant-protection drone market and around 59% globally. These drones are now used in over 100 countries, with especially fast growth in places like Brazil and Mexico.

6. A Brand-New “Atomized” Job: The Agricultural Version of Didi

      This is an entirely new type of work.

       Most pilots are independent individuals or very small teams — no big company, no boss, no formal employment. They connect loosely through WeChat groups and order platforms (one popular one is called “Nongtian Guanjia” or Farmland Steward — basically the agricultural version of Didi). Farmers place orders on an app for spraying or transport, and the platform assigns the job to the nearest qualified pilot.

       Pilots must hold a CAAC drone pilot license issued by China’s Civil Aviation Administration. There are three levels: Visual Line of Sight operator, Beyond Visual Line of Sight Captain (the main one used for real work), and Instructor. Getting certified costs money — roughly 4,000+ RMB for the basic license and 8,000–12,000 RMB for the captain level.

       A complete setup (drone + vehicle + fuel generator + chargers) usually costs 200,000–300,000 RMB. Many pilots took loans to buy their equipment.

       Earnings: Spraying pesticide typically pays about 20 RMB per mu. For cargo lifting, a drone can move around 10 tons in a good day, generating roughly 2,400 RMB in gross revenue before fuel (~300 RMB) and helper labor (~300 RMB). Net daily income can reach over 1,000 RMB under good conditions. It’s real work, though — weather, order volume, and equipment wear all matter. Most pilots say they can break even in about two years, but the market is gradually becoming more competitive, similar to what happened with ride-hailing.

       These pilots are also very good at shooting and editing videos. They post daily on Douyin (Chinese TikTok), which helps them promote their services and sometimes recruit students.

7. Why Did Drones Stand Out So Much This Time?

       Professional rescue teams, government, and military are still the main force in disaster response. So why did the drones get so much attention?

Two main reasons:

       First, the pilots are naturally skilled at content creation. Drone footage has great visual angles, and the videos look dramatic and hopeful — perfect for going viral.

       Second, after a disaster, people need both facts and hope. The drone pilots delivered positive, concrete stories of technology and ordinary people taking action, which felt especially valuable when everything else felt heavy.

8. The Truly Moving Part

       When you connect all the pieces: a group of young people who returned to rural areas to make a living spent 200,000–300,000 RMB (often with loans) on drone equipment. On normal days they post videos, take orders, spray fields, and transport fruit — basically running a small business one job at a time.

       They are scattered, independent operators. Then the flood hit. These strangers, with no central organization, used just their phones and a few WeChat groups to gather into a fleet of over a hundred drones within two or three days.

       No one was directing a grand plan from above. There was no strict hierarchy. It was simply the kindness of ordinary individuals combined with China’s world-leading drone technology and existing digital platforms that allowed them to connect and act so quickly.

       In the past, we often assumed that big things require big, tightly organized systems with top-down commands. What we saw here was something different: countless independent people, normally connected loosely through social media and service platforms, suddenly becoming a capable rescue force when it mattered most.

       The organization was loose. The hearts were kind. Technology did something remarkable — not just lifting a person out of danger, but amplifying and connecting the goodwill of thousands of ordinary people for the first time.

       Life has to go on. Stories like this remind us that even in difficult moments, there are more sources of warmth and possibility than we sometimes realize.

       He said, “Back then, people would walk in with phones to sell all the time. I didn’t even have to ask — you could tell most of them weren’t theirs. These days? I might not see someone like that for months.”

       He described the old customers: hats pulled low, eyes darting around, phone with no box or receipt, straight to the point — “Boss, how much is this worth?” Everyone knew the score, and the boss played along.

       But things have flipped in recent years. It’s not just that fewer people try to sell shady phones. Even when someone finds a lost phone on the street, they usually try to track down the owner and return it.

       Foreigners visiting China often say the same thing surprises them most: packages left at doors stay untouched, takeout left downstairs doesn’t get stolen, and if you drop your phone in a mall, there’s a real chance you’ll get it back.

So they ask: Has China run out of thieves?

       Not exactly. The thieves didn’t suddenly vanish. “Being a thief” simply stopped being a job worth doing.

1. Back Then: Stealing Was a Real “Business”

       Twenty years ago, stealing a wallet could mean thousands in cash you could spend the same day.

       Stealing a phone? Take it somewhere to flash and unlock it, then sell it to a second-hand dealer — instant income.

       The main skill you needed wasn’t technical ability. It was just being fast on your feet. If you didn’t get caught red-handed, the risk afterward was pretty low.

2. Now: Phones Have Become “Hot Potatoes”

       Steal a phone today and the moment it powers on, the owner can track its location.

       Trying to flash or unlock it? Modern phones have much stronger protections, so success rates have dropped sharply.

       Even if you strip it for parts, the money you make often isn’t worth the risk of getting caught — and serving years in prison.

       What used to bring in serious cash now might only be worth a few hundred dollars in parts.

3. Cash Has Disappeared — Stealing a Wallet Barely Pays

       These days a lot of young people leave the house with just their phone. Many don’t even carry a wallet once a year.

        If you do steal one, it probably contains an ID card and a couple of bank cards whose passwords were forgotten long ago.

        After all that effort, you might walk away with only twenty bucks in actual cash.

4. Surveillance Has Closed Almost Every Escape Route

       Back then, a street might have had cameras only outside the bank.

       Now? Cameras are everywhere — apartment entrances, elevators, malls, subways, parking lots, convenience stores, even many residential doorways.

        People joke that there are no more blind spots… because even the blind spots might have an old guy filming with his phone.

        Lose something and it often gets posted in the neighborhood WeChat group within minutes. Neighbors jump in to help look, and recovery rates have gone way up.

5. It’s Not That People Suddenly Got “Better” — They Just Ran the Numbers

       A lot of people assume Chinese society became more moral overnight.

       But most folks aren’t wrestling between good and evil every day. They’re quietly doing the math:

       How much can I actually make from this? What are the chances I get caught? If I do, how many years will it cost me afterward?

       In the past the numbers made sense, so some people did it.

       Now the costs have crept up — dense cameras, accurate tracking, hard-to-sell stolen goods, heavier penalties — while legitimate ways to earn money have multiplied: food delivery, ride-hailing, factory work. The pay might not be amazing, but you can sleep without looking over your shoulder.

       When someone thinks about reaching out, the calculation usually finishes in their head first.

       This one job might not even make that much money.

       But if you get caught, the next several years could be a total loss.

       So the hand naturally stays in the pocket.

6. What Foreigners Are Actually Seeing

       The safety foreigners notice in China isn’t just because there are fewer thieves.

       It’s because an entire society gradually turned “stealing” from a workable way to make a living into a consistently losing proposition.

       Good systems don’t always work by catching more bad guys every day.

        Sometimes they succeed by making the bad choice stop looking attractive to most people.

       When enough individuals quietly decide “Yeah… this isn’t worth it,” the result feels like safety.

       On the evening of July 6, a former Huawei “genius youth” posted online about his second-round interview at DeepSeek. The interviewer stared at his left monitor and said, “You’re copying code, right? If you can’t prove you’re not, the interview is over.” He felt publicly accused of cheating, walked out on the spot, and shared the story. It shot to the top of trending topics the next day. That same afternoon, a well-known investor jumped in and called him “the most untrustworthy founder I’ve ever met.” The former Huawei talent fired back overnight, saying the investor had only wired half the promised money and then stopped.

       Two shiny labels — “Huawei genius youth” and “idealistic, national-level AI model company DeepSeek” — crashed into each other and went viral. But the real story isn’t about who won the argument. It’s a mirror that shows what often happens when AI companies get big funding and try to scale fast: systems break, the wrong people get interviewed for the wrong roles, and everyone ends up frustrated. The core issue isn’t that one interviewer was rude. It’s that DeepSeek’s hiring process hasn’t kept up with its growth, and we keep treating both “genius” individuals and big-name companies as if they’re perfect.

1. What actually happened in that interview

       Li Bojie, born in 1992, is now chief scientist at a startup called Pine AI (he specifically clarified he is not a co-founder). He wanted to do research work at DeepSeek. Instead, the first two rounds were coding tests. When the interview finally happened, the interviewer was late and seemed uninterested in his actual research. The only question that kept coming up was “What engineering challenges have you solved?”

       The most explosive moment was the copying accusation. Because it was a remote interview, he had two monitors on his desk — a completely normal setup for programmers. The interviewer accused him of looking at the left screen to copy code and threatened to end the interview if he couldn’t prove otherwise. Li explained it was just dual monitors. The interviewer didn’t believe him and turned it into a cheating allegation.

       A lot of people’s first reaction was sympathy: even a Huawei genius youth gets treated this way, so how hard must it be for everyone else? But if you look closer, this isn’t mainly about one interviewer’s attitude. It’s about deeper problems in how DeepSeek is building its team right now.

2. Why live coding tests feel outdated

       These days, a lot of code is written with AI assistance. Asking someone to write large chunks of code from scratch on a blank screen, with no tools, has become less useful for judging real-world ability. Turning that directly into a “you’re cheating” accusation makes it worse.

       This kind of mismatch has happened before. In older programming interviews, candidates were sometimes asked to hand-write a keyword that the development environment automatically generates. People who actually wrote code every day never typed it manually, so the test ended up measuring the wrong thing. In the AI era, clinging to old “prove you can code blind” methods often misses the point and can unfairly penalize strong candidates.

3. When interviewers skip the resume, bigger problems follow

       In normal hiring, you read someone’s background first, then talk about what they’ve done and whether it fits the role. In this case, the interviewer clearly hadn’t looked at Li Bojie’s resume. His history as one of the first eight Huawei genius youths, his top-conference papers, his training at a top university — none of it seemed to matter. He was just another person going through the same standardized process.

       Other candidates have reported the same pattern at DeepSeek: multiple HR people adding them on WeChat at once, interviewers arriving late and acting impatient, and questions that have nothing to do with the candidate’s actual experience. When hiring turns into “run everyone through the same script,” you waste time and often end up with poor matches.

4. The “Huawei genius youth” label cuts both ways

       The program started in 2019 when Huawei’s founder personally pushed for it. It offered very high salaries — up to around 200 million RMB a year for the top tier — to attract exceptional young talent. Li Bojie was in that first group of eight. He later left Huawei reportedly turning down a 3 million RMB annual package.

       But a flashy label only proves one thing: strong research ability. It doesn’t automatically mean someone is also great at engineering execution, leading teams, or running a company as CEO. Some former genius youths have stayed and contributed quietly inside big organizations. Others left to chase bigger dreams. The public tends to notice the dramatic exits more than the steady work happening behind the scenes. Treating the label as proof of overall perfection sets everyone up for disappointment.

5. Research and engineering are very different jobs

       Li Bojie wanted a research role. The interview kept pushing toward engineering tasks. This is a common mismatch.

       Think of it this way: research is like working in a lab — exploring new methods, running experiments, publishing papers, and focusing on “can we make this work at all?” Engineering is like running a restaurant that serves customers every day — the food has to be consistent across different kitchens, handle rush hours, fix problems quickly, and keep customers happy long-term. The skills don’t overlap as much as people assume. Code written for a research paper can “run” in a demo, but it often falls apart when you try to make it stable for real users. Putting pure research talent into an engineering-heavy interview without understanding this difference leads to exactly the frustration we saw here.

6. The investment side showed another expectation mismatch

       On the same day, a different story broke. An investor (co-founder of Huobi) publicly accused Li Bojie of taking funding for a project called Metagent and then going silent — not sharing updates or financials. Li replied that the investor had promised $1.5 million but only sent $500,000 and then stopped.

       Both sides seem to have had unrealistic expectations. Investors who back young research stars know the risk is high: great at papers doesn’t automatically mean great at being a CEO or handling investor communications. Smart investors usually release money in stages — give some upfront, then more after clear milestones and transparency. This is called milestone-based funding. It protects the investor while giving the founder a chance to prove progress.

       Li apparently felt the full amount was promised and got frustrated when it didn’t arrive. The investor felt the founder wasn’t communicating properly and chose to limit further risk. Neither side had aligned expectations or built enough trust. The result was public finger-pointing instead of a productive relationship.

7. Why he actually wanted to join DeepSeek

       Li later posted a video clarifying his situation. His current company is running fine — he wasn’t job-hunting because of failure. He wanted to join DeepSeek because almost everyone is now building “harness agents” (AI systems that can use tools, write code, and handle complex tasks on top of large models). Many of the most useful capabilities, however, are locked inside the model companies: how they build training data, how they evaluate performance, and the long system prompts that guide behavior.

       He wanted to go inside and see how things actually work. This isn’t unusual. Even high-profile people in Silicon Valley have left startups to join base model companies just to understand the foundations better. Without an easy external collaboration path at DeepSeek, he tried the normal job route — and ran straight into the hiring issues described above.

8. What happens when a company rushes hiring after funding

       DeepSeek completed its first external funding round on June 16. After that, it clearly wanted to quickly build its own harness agent capabilities. The normal way to grow a new team is to first design the structure, identify the right experienced people, and let them help build the rest of the team layer by layer. Instead, it appears they went straight to headhunters for stacks of resumes and ran everyone through a fast, uniform interview process.

       This “get it done quickly” approach often backfires. You can end up with mismatched roles, interviewers who don’t understand the work, and a team that looks busy but struggles to deliver real results. A famous earlier example is what happened at JD.com during one of its fast-growth periods. They hired a lot of “good enough for now” people to keep the website running. It worked short-term, but later, when they needed to level up and bring in stronger talent, many key positions were already occupied by people who couldn’t handle the next stage. The company had to spend years fixing the gaps.

       DeepSeek risks repeating a version of this story if it keeps prioritizing speed over thoughtful team building.

9. A few final thoughts

This whole episode really comes down to three simple reminders:

• Don’t treat job titles or company names as proof of overall perfection.
• They only show strength in one area. Don’t imagine companies like Huawei or DeepSeek as single, flawless personalities. Every organization has strong parts and weaker parts.
• Nothing in the real world is perfect. Even the most impressive systems have rough edges and rough periods.

       DeepSeek has the money now. Whether it builds a strong team or ends up carrying hidden problems forward depends on how it handles hiring and role matching in the next phase. The world is basically one big makeshift team — some groups know they’re still figuring things out and keep improving, while others act like they’ve already built a perfect temple. The first kind usually travels farther.

      Doubao and Alibaba’s Qwen both announced that their agent features will go offline on July 15. That date happens to coincide with the official start of the “Interim Measures for the Management of Anthropomorphic Interactive AI Services.” Online, many people immediately called it a government ban on AI virtual companions.

      If you look at what is actually being removed, however, it’s not the new generation of agents everyone is excited about. The two are completely different things. It looks more like the big companies are using the new rule as a convenient moment to cut legacy teams and products so they can focus resources on the newer systems.

       In late June 2026, Xiaohongshu quietly filed for a Hong Kong listing. Backed by Goldman Sachs and CICC, the move would mark one of the largest Chinese internet company IPOs in Hong Kong in nearly a decade — with a potential valuation of $30–50 billion. Note the currency: US dollars. Earlier deals by MiniMax and Zhipu were priced in Hong Kong dollars and were significantly smaller in scale.

       But shortly after the filing, a whistleblower letter from a former employee landed at both the Hong Kong Stock Exchange’s listing division and the Securities and Futures Commission. Regulators have confirmed receipt of the email.

       The sender, Chen Hao, previously headed Xiaohongshu’s South China commercial direct-sales team. He holds a court judgment that found the company unlawfully terminated his employment — plus evidence that could directly challenge the integrity of its VIE structure.

1. Xiaohongshu’s Long Road to IPO

       The company had been preparing for years. Back in 2018, a co-founder told Bloomberg it might list within two to three years. In 2021 it hired a former Citi executive as CFO and was reportedly preparing a US roadshow to raise $500–1,000 million.

       Then Didi’s troubled US listing triggered tighter scrutiny on Chinese companies. Xiaohongshu paused its US plans, along with several other high-profile names including Soul, Huolala, Keep, and Himalaya.

       For the next few years, rumors of a Hong Kong listing kept surfacing — and Xiaohongshu repeatedly denied them (the market started calling it the “three denials”). Only in 2025 did it rent office space in Hong Kong, build out a major commercial team, and clean up its financials. The secret filing finally came at the end of June 2026.

2. Why Rush to File Before June 30?

       A listing application needs audited financial statements, and those statements are generally valid for only six months. Xiaohongshu was working with its fully audited 2025 annual results. It had to submit before the end of June — otherwise the audit would expire and the company would need to start over.

       For a company of this size, redoing an audit is expensive and time-consuming. That’s why many firms sprint to file by June 30: it’s not always about market sentiment, but about a hard document deadline.

3. A “Bleeding” Listing?

        In the secondary market, Xiaohongshu shares had been bid up to roughly $50 billion in valuation. Yet the IPO range is being discussed at $30–50 billion.

       The gap exists because today’s Hong Kong market favors AI, robotics, and chip-related stories. Xiaohongshu’s core business — social commerce, livestreaming, and advertising — belongs to an earlier internet era. At this moment, it may have to accept a lower valuation to get listed while the window is still open. Market watchers sometimes call this a “bleeding IPO.”

       It’s not unique to Xiaohongshu. Many companies outside the hottest tech themes face the same pressure right now.

4. ChenHao’s Whistleblower Letter: Three Sharp Questions

       ChenHao joined in June 2022. According to him, he quickly lifted team performance, exceeded targets by 20% in 2023, won a company innovation award, and signed a 30,000-share option agreement with founder and CEO Mao Wenchao (vesting over four years: 50/25/25).

       In December 2023 the company fired him for “incompetence.” Because he hadn’t completed two years, the first 50% of options were cancelled under standard policy. He received statutory severance, but believed the dismissal lacked proper grounds and took the case to arbitration.

       Guangzhou’s Tianhe District Court ruled in his favor in both the first and second instance, finding the termination unlawful. Xiaohongshu was ordered to pay roughly 800,000 RMB (including the option portion). For a company heading toward a multi-billion-dollar valuation, the amount itself is small. The real problem is the unresolved loose end it left behind.

     ChenHao’s letter raises three main issues:

i. VIE structure clarity (the most sensitive)

       ChenHao’s employment contract was with “Shu Yi Shu Er Culture Media,” a domestic Chinese company. To defend itself in the labor case, Xiaohongshu submitted documents stating that this entity had “no direct relationship and no contractual control” with its offshore structure.

That statement touches the heart of the VIE model.

       VIE structures (first popularized by companies like Sohu) let Chinese internet firms list overseas while complying with rules that restrict foreign investment in sensitive sectors. An offshore holding company (usually in the Cayman Islands) lists on the stock exchange. It controls a domestic licensed entity through a series of agreements so that profits can flow upward to foreign shareholders.

       The critical link is that the domestic company’s revenue must be fully captured by those agreements. If one domestic entity is shown to sit outside the control chain, regulators may question whether the structure is watertight — and whether shareholders can actually receive the economic benefits they expect.

       ChenHao’s evidence points straight at this potential weak spot.

ii. Disclosure of the labor dispute

       Since a court has already ruled the termination unlawful, did Xiaohongshu fully disclose the case and its potential impact in the listing documents? Incomplete disclosure would be a straightforward regulatory issue.

iii. ESG and labor compliance

       Public companies are expected to meet labor standards. Systemic employment problems can hurt ESG assessments and investor confidence.               Once shares are sold to the public, any later penalty or scandal over past labor practices would be borne by ordinary shareholders. Regulators therefore examine this area closely.

5. Can It Still Get Through? Time Is the Real Test

       These issues are unlikely to cause an outright rejection. The exchange and regulator will probably send written questions and ask for more information or clarification.

That process carries two costs:

• Money:
  Lawyers, accountants, and sponsors will need to review everything again — potentially several million RMB in extra fees.
• Time:
  The back-and-forth could take two to three months. If Xiaohongshu misses its original window, it may need a fresh audit and could lose the current positive sentiment around tech-related listings in Hong Kong.

       Right now the market is excited about AI-related names like MiniMax and Zhipu. Xiaohongshu hoped to ride that wave. If momentum fades — or if external shocks hit — the valuation or even the ability to complete the deal could suffer.

6. A Lesson Before Listing: Don’t Leave Loose Ends

       Xiaohongshu’s situation sends a clear message to any company planning to go public: once you step into the public markets, historical problems must be cleaned up.

       If you need to let people go, do it properly with full documentation. If you don’t want to do that, settle the matter cleanly. Leaving a “landmine” behind means it can explode at the most vulnerable moment — right when regulators and investors are scrutinizing everything.

       Listing isn’t the finish line. It’s the start of public accountability. Any unfinished business tends to surface under that spotlight. Handling the small things properly is what actually keeps the process on track.

      On June 10, a hacker group called WorldLeaks posted roughly 204,000 files — about 630GB of data — on the dark web. Within hours, the files were freely available for anyone to download.

      The data came from Tata Electronics, one of Apple’s key suppliers in India. The level of detail goes far beyond typical product leaks.

1. What Exactly Was Leaked? More Than Just Looks

The files include:

• Detailed motherboard schematics for the iPhone 18 Pro, created in Siemens engineering software with confidential watermarks — showing layer structures, chip placement, and routing.
• Information on how the foldable iPhone works.
• Technical manuals for the A20 Pro chip (2nm process and packaging).
• Details on Apple’s in-house baseband chip (reportedly codenamed Ganymede).
• Bill of Materials (BOM) lists for hundreds of components, clearly naming suppliers and alternative vendors.
• Factory drop-test photos and videos of the iPhone 18 Pro.
• Copies of some employee passports.

      Because this happened before the iPhone 18 launch, much of the usual September event suspense has already disappeared.

2. Why This Leak Matters More

      Most past Apple leaks focused on design, features, or release timing. While those reduced some surprise, they were often seen as part of the marketing buildup and didn’t seriously harm Apple’s business.

      This leak is different. It reveals how the product is made, who supplies the parts, and at what cost — the foundation of Apple’s supply chain advantage.

      For years, Apple has relied on information asymmetry: each supplier only knows its own piece and pricing, not what others are doing. This gave Apple strong negotiating power to push prices down or switch partners easily.

      The BOM lists and supplier comparison tables have now torn that veil. Suppliers can benchmark each other’s pricing, and competitors get a much clearer view of Apple’s product roadmap and cost structure.

3. Apple’s Secrecy Culture: Jobs Era vs. Cook Era

      Under Steve Jobs, Apple was famous for extreme secrecy. Buildings were divided like apartment blocks — employees’ badges only worked in their own section. Before a launch, staff involved in training were often isolated until after the event.

      Under Tim Cook, product-level leaks became much more common. Renders, mold shots, and analyst reports turned into almost routine parts of new product cycles. Many observers believe Apple sometimes tolerated or even welcomed certain leaks because they generated buzz and acted as free marketing.

      The key difference: earlier leaks mostly revealed “what the product is.” This one exposes “how it’s actually made” — the deeper commercial logic.

4. The Other Side of Supply Chain Power

      Shortly after Apple announced price increases, Micron’s Chief Commercial Officer gave an interview to The Wall Street Journal. He noted that aggressive pricing pressure in the past had not been constructive and had led to canceled capacity investments in 2023.

      As a major customer, Apple has long held strong leverage in price negotiations. This pushed some suppliers to cut back on investment during low-margin periods. When AI demand surged, capacity couldn’t keep up, prices rose, and Apple eventually had to raise its own prices.

      It shows that information asymmetry and tough bargaining can cut both ways. The Tata leak further weakens Apple’s position in this dynamic.

5. Why Tata’s Factory?

      Tata Electronics was only founded in 2020. In just five years, its workforce grew from a few hundred to 75,000 people. Rapid scaling often leaves gaps in security systems and processes.In particular, its iPhone production line combines the factories of Pegatron and Wistron.

      By contrast, Apple’s supply chain in mainland China has had over a decade to mature, with better network separation, access controls, and supplier portal management.

      Historically, Wistron’s Indian factory suffered major losses from labor issues and violence before selling at a steep discount. Pegatron also gradually sold down its stake. Tata inherited and combined elements of these operations, resulting in a less hardened security setup.

6. Will Apple Move Production Back to China?

      Unlikely in any major way. India has become Apple’s second-largest iPhone production base, accounting for roughly a quarter of global output in 2025. Apple views India as a key long-term growth market — not primarily because of lower manufacturing costs, but because of its large population and future consumer potential.

      Unless Indian demand stays disappointing for an extended period, Apple is unlikely to make big shifts in its global production footprint over a single security incident.

7. What’s Really at Stake?

      The leak caused real damage to both Tata and Apple. But the deeper impact is on the information advantage Apple spent decades building in its supply chain.

      As Tim Cook prepares to step down as CEO on September 1 and hand over to hardware chief John Ternus, this incident leaves his successor with a supply chain whose protective information barriers have been partially breached.

      That may be the part Apple needs to take most seriously.

      In these two years, the world was obsessed with one dramatic question: Will AI hurt humanity? Will it replace us? Will it rule us? Elon Musk, Sam Altman, Dario Amodei — everyone was shouting.

      But while the debate raged, China quietly built an advantage in a different contest — the race over algorithms. And it did so in a very Chinese way: not by inventing the best chips or the strongest models, but by creating the world’s most intense competitive arena.

1. China’s internet ecosystem

      People call it the “gu jar.” In an old legend from southwest China, you throw various poisonous insects — centipedes, scorpions, and the like — into a jar. They fight and bite until only the strongest, most toxic one remains: the gu king.

      China’s internet ecosystem works a bit like that jar.

      Inside go hundreds of millions of atomized individuals and countless small merchants. A group of highly compliant platforms runs the jar. Fierce algorithms battle inside. And sitting on the lid is the “jar keeper” — the government. It doesn’t lift the lid, but it controls the temperature and sets the rules that decide who ultimately wins.

      After brutal elimination, the survivors that crawled out include global powerhouses like TikTok, Temu (Pinduoduo’s overseas version), and Shein — plus rising stars like Meituan and Didi that are now expanding worldwide.

2. How does the government manage this jar?

       In February 2025, China’s State Administration for Market Regulation issued the Anti-Monopoly Compliance Guidelines for Internet Platforms. It was followed by waves of meetings with companies, clearly defining eight types of new monopoly risks.

      In plain terms: the insects in the jar were told — these eight ways of “biting” are not allowed.

The eight categories roughly cover:

• Algorithm collusion between platforms:
  Platforms share data and coordinate against users. Search for something on Taobao, and suddenly Douyin recommendations become eerily targeted.
• Helping merchants form price-fixing agreements:
  Platforms assist sellers in setting unified prices or price ranges.
• Unfair high pricing by platforms:
  Using dominant position to raise commission rates that merchants can’t escape (often discussed in the context of Meituan).
• Selling below cost:
  Aggressive subsidy wars, such as Alibaba’s zero-yuan purchase campaigns against Meituan.
• Blocking and shielding:
  Preventing mentions of competitors (e.g., saying “Dou something Yin” instead of Douyin on WeChat).
• Exclusive dealing (“choose one or the other”):
  Merchants can only operate on one platform.
• “Lowest price across the web” (most-favored-nation treatment):
   Influencers sign agreements requiring the absolute lowest price.
• Differential treatment by platforms:
  Big-data price discrimination — new users get better deals than returning ones.

      These rules aim to stop algorithms from being weaponized for monopoly power.

3. Consumers can fight back too — the “add to cart and wait” trick

      Interestingly, consumers sometimes turn the tables. The simplest move: see something you like, add it to your cart, but don’t check out immediately.

      Why does it work? Once the item is in your cart, the algorithm shifts into full persuasion mode — flooding you with coupons, reminders, bundle suggestions, and loyalty points. The goal is simple: get you to complete the purchase.

      Algorithms operate in stages. First they figure out who you are and what you like. Then they bombard you with content. Once you engage, they double down. The moment you add to cart, the mission changes to “close the deal.”

      Platforms would rather give bigger discounts and more coupons than risk an uncompleted transaction. Their core metric is completed orders, not maximizing profit per order.

4. Why can only China grow these kinds of “monsters”?

Three key conditions make this system especially effective in China:

i. Increasing social atomization
      China now has around 320 million flexibly employed people — nearly half of its 700+ million labor force. Many lack stable employers and work in highly individualized, gig-style arrangements. This gives algorithm platforms a massive pool of atomized users and workers. The West hasn’t reached this stage yet, largely due to unions, industry associations, and legal protections. But in the AI era, societies worldwide are likely heading in this direction — exactly the environment these platforms are built to thrive in.

ii. Few traditional brakes or constraints
      Western platforms face unions, lawsuits, and public scrutiny that limit how aggressively they can compete. Chinese platforms operate with fewer external restraints because the system favors atomization for social stability reasons.

iii. Highly concentrated government power
      Western governments that want to regulate algorithms often hit limits from checks and balances. China’s government can set rules, summon companies, and adjust enforcement granularity more directly. This gives the “jar keeper” strong control over the arena.

      Inside this jar, algorithms don’t just target consumers — they also manage delivery riders and small merchants (tiny restaurants, shops, etc.). The government uses frequent guidelines and meetings, mainly targeting big platforms (because they’re large enough and responsive), to keep the whole system stable. High-profile actions, like pulling Didi back from the stock market, serve as strong signals.

5. How powerful are these “monsters” once they go global?

       Once they leave the jar, these companies show remarkable strength. TikTok dominates global short video. Temu disrupts Western e-commerce with ultra-low prices. Shein has taken fast fashion worldwide.

      Meituan and Didi are expanding into Hong Kong, Southeast Asia, Brazil, and the Middle East.

      Local traditional businesses struggle to compete. These companies were forged in China’s most brutal market conditions: they understand human psychology deeply, squeeze efficiency relentlessly, and know exactly how to dance on the edge of rules.

      They often expand using a “scout” approach — small overseas-registered teams or entities test the waters while parent companies provide resources and subsidies. Once they scale, they become hard to dislodge. TikTok, for example, set up a U.S. data-management company, but the core algorithm and operations remain under ByteDance control.

6. The cost — and the open question

      This gu-jar system is undeniably powerful. It has allowed China to lead in adapting algorithms to an atomized society and has produced a batch of globally competitive platforms.

      Yet the jar is fed by hundreds of millions of ordinary people who have been repeatedly “bitten”: delivery workers facing tighter time windows and lower pay, small merchants operating on razor-thin margins, and consumers navigating information asymmetry and algorithmic nudges.

      When a society is largely connected and managed through algorithms, and the main restraining force rests with the government alone, life inside the jar can feel precarious.

      The AI era is only beginning. Atomization will deepen, algorithms will grow stronger. The surviving “monsters” will only become more potent.

      The real question is this: Besides the jar keeper’s hand on the lid, can the people inside also hold onto a rope of their own — some form of meaningful counterbalance?

      There is no clear answer yet. But for anyone who has been shaped by these algorithms — and who has shaped others through them — it’s a question worth keeping in mind.