This as-told-to essay is based on a conversation with Advait Maybhate, a software engineer. The following has been edited for length and clarity. Business Insider has verified his employment and academic history.

When I graduated from the University of Waterloo with my bachelor’s degree in 2023, I had done about a dozen tech internships.

Internships are a big deal at Waterloo, and students usually do six during their time there. I started doing internships before I enrolled and took some gap semesters to squeeze in a couple more stints.

To me, internships meant exploring varied fields, from gaming to fintech. I also got to intern at companies of different scales, from early-stage startups to mature Big Tech companies.

The first summer internship I did at Waterloo was at Google. Interning there was an eye-opening experience. I got to work on Google Search, a product that billions of people, including myself, use every single day.

When I took up the internship, like any freshman, I just thought it would be cool to work at a big company and ship big products. I ended up interning at Google twice, first in the summer of 2019, and then during the following summer in 2020.

During my internships at Google, I learned a lot, particularly about operating as a software engineer on large-scale products. That included learning how to write unit tests and good technical design documents. Big companies are great at that.

That said, I didn’t enjoy the bureaucracy that came with working in a Big Tech company. If you are shipping something on Google Search, you cannot break Google Search. That is just one of the underlying rules.

I understand why things have to be slow at that scale. It’s just that for someone who wants to learn fast and try out different things, it can feel limiting.

Even for my internship projects, it took a few months just for the code to get shipped. Although the projects were technically done, we still had to conduct A/B testing experiments and get sign-offs before the code could be deployed.

Going from Big Tech to startups

That experience eventually set me on the path toward working at startups. I chose to focus on AI because I wanted to be at the edge of what technology can do.

I was initially an AI skeptic. I didn’t buy into the hype of how it could change everything. It was only when I started using AI on a day-to-day basis that I began to appreciate how it could usher in a fundamental shift in the way we work.

It also helps that working on AI is fun and exciting. There are new advancements in space every week, and the frontier of what we can do just keeps going further.

I ended up doing two internships at two AI startups before I graduated. The first one was at Warp, an AI agent platform for developers, and the second one was at Ramp, a fintech startup that uses AI to automate financial operations.

I received full-time offers from both Warp and Ramp and chose to work at Warp. Both were great companies, but I wanted to work at Warp because I wanted to be part of a startup that was in a relatively early stage of development.

Ramp was at a much more mature stage than Warp at the time, and was focused on scaling up. Warp, on the other hand, was still trying to figure things out. On a personal level, I wanted to see how a startup goes through that process. I wanted to grapple with questions like, “How does pricing work? How does the business model work?”

That is harder to see at a mature startup, where all of these things have already been figured out and growth is the priority.

So far, working at Warp for the past two years has lived up to my expectations. We ship code every week. I could be working on something on Tuesday, and it gets shipped out on Thursday. I work maybe 60 to 70 hours a week. It’s a very different kind of velocity and cadence than at Big Tech.

In the near term, I want to continue to work on AI because it’s one of the most rapidly expanding areas in tech. Companies like Warp and its competitors, Cursor and Cognition, are all expanding very rapidly.

I am somewhat tempted to launch my own startup, but I think it’s difficult to gain market share in this hyper-competitive space. That’s something I will give serious thought about in the future.

Do you have a story to share about working at an AI startup? Contact this reporter at ktan@businessinsider.com.

China’s top social media platforms, including ByteDance Ltd.’s TikTok clone Douying and Tencent Holdings’ WeChat, rolled out new features today to try to comply with a new law that mandates all artificial intelligence content is clearly labeled as such.

The new content labeling rules mandate that all AI-generated content posted on social media is tagged with explicit markings visible to users. It applies to AI-generated text, images, videos and audio, and also requires that implicit identifiers, such as digital watermarks, are embedded in the content’s metadata.

The law, which was first announced in March by the Cyberspace Administration of China, reflects Beijing’s increased scrutiny of AI at a time when concerns are rising about misinformation, online fraud and copyright infringement.

According to a report in the South China Morning Post, the law comes amid a broader push by Chinese authorities to increase oversight of AI, as illustrated by the CAC’s 2025 Qinglang campaign, which aims to clean up the Chinese language internet.

WeChat, one of the most popular messaging platforms in China, which boasts more than 1.4 billion monthly active users globally, has said that all creators using its platform must voluntarily declare any AI-generated content they publish. It’s also reminding users to “exercise their own judgement” for any content that has not been flagged as AI generated.

In a post today, WeChat said it “strictly prohibits” any attempts to delete, tamper with, forge or conceal AI labels added by its own automated tools, which are designed to pick up any AI-generated content that’s not flagged by users who upload it. It also reminded users against using AI to spread false information or for any other “illegal activities.”

Meanwhile Douyin, which has around 766 million monthly active users, said in a post today that it’s encouraging users to add clear labels to every AI-generated video they upload to its platform. It will also attempt to flag AI-generated content that isn’t flagged by users by checking its source via its metadata.

Several other popular social media platforms made similar announcements. For instance, the microblogging site Weibo, often known as China’s Twitter, said on Friday it’s adding tools for users to tag their own content, as well as a button for users to report “unlabeled AI content” posted by others.

RedNote, the e-commerce-based social media platform, issued its own statement on Friday, saying that it reserves the right to add explicit and implicit identifiers to any unidentified AI-generated content it detects on its platform.

Many of China’s best known AI tools are also moving to comply with the new law. For instance, Tencent’s AI chatbot Yuanbao said on Sunday it has created a new labeling system for any content it generates on behalf of users, adding explicit and implicit tags to text, videos and images. In its statement, it also advised users that they should not attempt to remove the labels it automatically adds to the content it creates.

When the CAC announced the law earlier this year, it said its main objectives were to implement robust AI content monitoring, enforce mandatory labeling and apply penalties to anyone who disseminates misinformation through AI or uses the technology to manipulate public opinion. It also pledged to crack down on deceptive marketing that uses AI, and strengthen online protections for underage users.

The European Union is set to implement its own AI content labeling requirements in August 2026, as part of the EU AI Act, which mandates that any content “significantly generated” by AI must be labeled to ensure transparency. The U.S. has not yet mandated AI content labels, but a number of social media platforms, such as Meta Platforms Inc., are implementing their own policies regarding the tagging of AI-generated media.

Photo: WeChat

2. Tesla (Nasdaq: TSLA)
Tesla dropped solidly by -3.50%, with the trading volume of 27.32B. Tesla’s CEO Elon Musk states that 80% of Tesla’s value will depend on the Optimus robot. Despite challenges in Europe, including executive resistance and competition, Tesla lowered Model 3 prices in China, marking its long-range version’s debut with a price cut.

3. Alibaba Group Holding Limited (NYSE: BABA)
Alibaba Group Holding Limited surged by 12.90%, with the trading volume of 10.94B. Alibaba plans to invest over 380 billion yuan in the next three years to boost its computing power industry, impacting domestic AI infrastructure. Its Q1 FY 2026 financial report showed a 10% revenue growth and a 76% net profit increase, exceeding expectations.

4. Microsoft (Nasdaq: MSFT)
Microsoft dipped mildly by -0.58%, with the trading volume of 10.63B. UAE AI company G42 is diversifying chip supplies to reduce dependency on Nvidia, engaging with tech giants like Amazon AWS, Google, Meta, Microsoft, and Elon Musk’s xAI for a planned AI park, with Google’s negotiations being the most advanced.

5. Apple (Nasdaq: AAPL)
Apple dipped mildly by -0.18%, with the trading volume of 9.16B. Apple is expanding its retail footprint in India with a new store, Apple Hebbal, set to open in Bangalore on September 2. This follows the openings of Apple BKC in Mumbai and Apple Saket in Delhi. Apple also plans to remove physical SIM card slots in more countries for the iPhone 17 series.

6. Alphabet (Nasdaq: GOOGL)
Alphabet gained mildly by 0.60%, with the trading volume of 8.44B. UAE’s AI company G42 is seeking to diversify its chip suppliers to reduce reliance on Nvidia. They are negotiating with major tech companies including Amazon AWS, Google, Meta, Microsoft, and Elon Musk’s xAI, with Google likely to sign a computing power procurement deal soon.

7. Palantir Technologies (NYSE: PLTR)
Palantir Technologies dipped mildly by -0.89%, with the trading volume of 7.27B. South Korean retail investors showed significant interest in Palantir Technologies, with substantial net purchases over the past week.

8. Meta Platforms (Nasdaq: META)
Meta Platforms dipped mildly by -1.65%, with the trading volume of 6.70B. Meta and Scale AI’s partnership faced challenges as major investment leads to strained relations and data quality concerns. Additionally, Meta plans to release a smart glasses SDK, diverging from trends by opting for LCoS over Micro LED technology.

9. Broadcom (Nasdaq: AVGO)
Broadcom dropped solidly by -3.65%, with the trading volume of 6.42B. Broadcom (AVGO.US) is expected to report a 21% revenue increase to $15.82 billion for Q3, with EPS projected at $1.66. Oppenheimer reaffirmed its “outperform” rating, raising the target price to $325. The AI business could exceed $5 billion in revenue.

10. Marvell Technology (Nasdaq: MRVL)
Marvell Technology plunged by -18.60%, with the trading volume of 6.19B. Company XYZ announced plans for global expansion, focusing on emerging markets and sustainable initiatives. New partnerships aim to enhance technological capabilities, while leadership emphasizes innovation and growth potential.