#数据/训练

We grew from ~0 to $500M ARR, adding $250M last year alone while being EBITDA profitable. 1,300-word post on every growth tactic that worked for us: 1. What got us from 0-$400M ARR in the US works in every country. 2. Retention and Monetisation Hacks. 3. Localization > Translation. 4. Experiment with $1M internal seed checks. 5. Pivoting to only AI content production unlocked 100% ARR growth. Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 1. Acquisition Playbook for 0-$10M ARR in any country in 6 months We run a 90 sec video trailer of an audio drama as an Ad and ask users to download the app if they're interested in the rest of the story. Our core insight after spending >$100M on acquisition is: If the clickthrough rate (CTR) for an ad goes from 2% to 2.25%, our customer acquisition cost (CAC) decreases by ~ 30%. We remodelled our system around this insight and built an AI-first 2.25% CTR ad manufacturing machine that works in every country. For every new country, we take our hit shows -> use LLMs to extract and most intriguing moments -> Write a 5-min script combining all the best parts. The first 60 seconds has to have a hook every 5 seconds and needs to end with a crazy cliffhanger to force a download mid-scroll. If a marketing video is not hitting our benchmarks (2.5% CTR and 55% 3-second through-play), a creative director gets involved to change the hook or cliffhanger to get the numbers there. AI lets us make 1,000 Ads per show, and in total we do ~17.5k Ads per month. When a business creating scales from 1k to 10k Ads, the normal thing is for CAC to skyrocket. But with what I just shared, we 7-8x'd our User Acquisition budget without increasing our CAC materially. It took us 8 months to figure this out, but then the timeline from 0-$10M in every country got shorter and shorter: US revenue grew to $25M in 19 months. (US is now 78% of total) Germany to 21M in 11 months. France to $10M in 3 months. 2. Retention and Monetisation Hacks. We knew we wanted to create an audio entertainment platform, but there was no standard format. For the first 2-3 years, we tried 10 different formats before landing on the winner. After we got it right with audio drama (8-12min chapters, written for mobile fiction, serialized, episodes have strong hooks and end on cliffhangers), avg daily streaming time went from ~25mins to 150+. Audio drama made us realize that Pocket FM was creating a whole new medium. There was no playbook for anything that we were doing. Everything had to be thought & built from scratch. This is what we did for each major bullet: - Monetization: Users have some free daily minutes to listen; then it's pay per episode. We also added the option for users to unlock episodes by watching ads, which is doing extremely well. Ads scaled from zero to a ~$90M run rate in 12 months. - Engagement & production: You don't become obsessed with an app you open once a week. To have users engage daily, we make the next episode free every day. Also helps them build the habit. - Discovery. Huge problem because people consuming Pocket FM enter the app, tap the show, and lock the screen. We fixed it by doing "playlists" of episodes. Once users's free minutes on a show are done, we ask users if they want to pay. If they say no, we play a new show, one where they haven't used their free mins. And we stitch episodes of different shows together that way, creating natural discovery. 3. Localization > translation. 78% of our revenue is still concentrated in the US. Localisation is fixing this: You might write a show for a Spanish audience where language, jokes, folklore, have a certain flavor; if you merely translate the show for, say, a Norwegian audience, that color is lost and hurts the show in the Norway. Listeners would relate more if the show was written by a Norwegian. That's why, instead of translating, we localize shows to different regions. We use AI to take the spine of stories and adapt their whole cultural layer to the other country. The results: a US show that was localized for a German audience had 50% higher retention than the translated version. Localization + our user acquisition playbook led to: - $10M+ ARR in France within 3 months - $21M+ ARR in Germany within 11 months. And this expands writers' addressable market. We get messages of writers thrilled to have revenue coming from the US, India, EU, LatAm, without them doing much incremental work. 4. Experiment. We give $1M checks to new internal initiatives, and the team has 12-18 months to prove their thesis. If they prove it, we double down. This is how Pocket Saga came about, our AI video microdrama app. It's an AI video equivalent of Pocket FM. Same shows and structure, but as a vertical 2-min mobile video series. We launched it two months ago and it's at ~$15M ARR. Pocket's broader thesis is to help creators tell their stories to as many people as possible. Start with audio drama -> multiple languages or localize to diff countries -> microdrama -> more formats like movies, TV shows, and games. Best of all is that writers get revenue streams not only from countries they wouldn't have tapped into, but also from formats they wouldn't have thought possible. 5. Pivoting to only AI content production unlocked 100% ARR growth. In mid-2024, we pivoted to only AI content production. Our growth took a very direct hit. Pocket was already at $200M ARR, growing 50% YoY, and we flatlined for a whole semester. Six months later, the business exploded. After the switch, we went from ~25k hours of content produced per year to over 2.5M hours, which are also higher quality. Because AI orchestrates the writing and more data is fed into it, more blockbusters come out. Over 90 titles have $1M+ in lifetime earnings and 13 crossed $10M. Quality control is done with LLM as a judge, and LLMs are quite tough. Results are very encouraging: - 12-month revenue retention went from 44% to 76%. - In the past year, we added $250M in net new ARR. __________________________________ Pocket Entertainment (Pocket FM + Pocket Saga) has become the largest AI entertainment platform. We have the largest storytelling catalog with 770,000 titles, 550k creators, and 5.5B hours of playtime with minute by minute retention & engagement data. We are using all of this data to improve every aspect of our business. Our Bet: In the next 3 years we will be able to produce Naruto-like series for $1000. Netflix has to spend $17B to find 100 Blockbusters per year. What happens when you can produce 1M high quality shows for $1B? The streaming wars caused a $300B reallocation of market cap. The AI entertainment wars may be a $1T+ reshuffling of market cap. We are at a unique spot. Unlike other AI creator tools like Runway or Midjourney, we own both the supply and demand side of AI content. We've attracted over 550,000 writers who are producing an annualized 2.5M hours of content every year. This pairs with 137B+ minutes streamed. Because of this, growth is accelerating as we scale further. We have multiple S-curves inflecting at the same time: - AI produces better and more ads -> scale faster in new countries - ⁠Writers + AI produce more shows -> more blockbusters - ⁠Localisation -> more reach per blockbuster - ⁠Audio to video format expansion -> larger TAM -> more creators Every aspect of our business is designed to improve another, and everything is aligned so that the number of blockbusters continues to rise. If you're interested in building at the intersection of ai, tech and entertainment, DM me.

In a new allegation, the U.S. government has accused 6 Chinese AI firms of using large-scale distillation to copy American model capabilities. The NSA, CISA and FBI published joint advisory AA26-251A, naming DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun and Z .AI as running industrial-scale distillation campaigns against U.S. frontier models since at least late 2024. The agencies say requests moved through a gray market of API proxies called transfer stations, bulk premium subscriptions shared across developer teams, and third-party aggregators that strip account metadata. Moonshot AI allegedly distilled 17 U.S. models, including Anthropic's Claude Fable 5, to train Kimi-K3. They claim DeepSeek's prompts pushed models to write out hidden chain-of-thought steps, which transfers reasoning method rather than finished answers. So they say that DeepSeek's headline training cost only covers the compute it burned. The advisory argues that number is misleading because it leaves out what the training data actually cost, which DeepSeek allegedly obtained by distilling U.S. models rather than producing it through its own research. So the cheap-training story rests on data someone else paid to create. The mitigation section asks U.S. labs to serve suspected distillers subtly degraded responses without telling them. Its detection indicators are behavioral, covering sustained 24/7 usage, new accounts at immediate maximum throughput, and traffic optimized for cache hits. Those patterns also describe an ordinary enterprise agent fleet, leaving each provider to decide which customers receive an undisclosed downgrade.

🚀 Meet MiniCPM5-2B, a 2B-parameter language model bringing high intelligence density to the edge, now open source! It ranks #1 among open-source models under 4B parameters on the @ArtificialAnlys Intelligence Index, with a score of 23. It also scores 20 on the Agentic Index, bringing an early form of general-purpose agent capability to the edge. Across 34 benchmarks, MiniCPM5-2B achieves an average score of 53.9, covering coding, math, long-context understanding, tool use, and agentic tasks. And this release goes beyond the model itself. We’re opening up the data, training recipes, and RL stack behind MiniCPM5-2B. 🤗 Hugging Face: http://huggingface.co/openbmb/MiniCPM5-2B 💻 GitHub: http://github.com/OpenBMB/MiniCPM Modelscope: http://modelscope.cn/models/OpenBMB/MiniCPM5-2B Web: https://openbmb.cn/

The demand for Chips is so high, that Malaysia is reportedly considering Huawei chips for a $494 million sovereign AI project, despite explicit US warnings against the hardware. Bloomberg reports that the country is evaluating Huawei’s Ascend 910C accelerators, with state-linked Telekom Malaysia selected as the project’s key operator. The priority is keeping sensitive government, military and intelligence data under Malaysian control. Huawei already supplies technology for Telekom Malaysia’s cloud infrastructure, although its current AI computing service uses Nvidia chips. China is no longer just developing its own AI models, but is increasingly becoming a chip producer that, although lagging significantly behind NVIDIA, is able to produce chips well enough itself.