Leap cheerfully. love your fate. feel good now. #StanfordHIL #arbenventures
Joined June 2012
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Ben Rolnik retweeted
Let me tell you a story about how unethical Anthropic is.
In April, the Anthropic team began heavily courting our engineering team to tell them corporate secrets, technical details, and future plans for @cfosilvia.
They pretended their interest was due to our exploding popularity and accelerating compute usage, but it quickly became apparent that Anthropic was fishing for details on how we built our product and why it was so popular.
This included the Anthropic team trying to wine-and-dine our engineers, including a infamous steak dinner where someone told our team "don't worry about it, this is on Dario."
Our team was not born yesterday, so we quickly realized what was happening and refused to share sensitive information and technical details with the Anthropic team.
As we suspected, Anthropic was trying to get information from us, so they could build a competing product. Some people on their team must have a guilty conscious because they messaged us a few days ago to give us "a heads-up" that they were launching a competing product.
Thankfully, the Silvia engineering team has been aggressively working to get off the frontier models for the last ~ 6 months. That effort has been highly successful, because our compute costs are ~ 97% lower when we use our own model & hardware, the latency is lower, and the accuracy of answers materially increases as well.
The sad thing for Anthropic is they have now released an inferior product and they had to sell their soul to do it.
The only thing worse than being a trillion-dollar company that can't beat a 10-person team is being a trillion-dollar company that no one trusts and everyone knows is unethical.
Serious investors continue to choose @cfosilvia over ChatGPT, Claude, and other frontier companies because these products are not optimized to solve the actual user problem.
Today, Anthropic launched a Claude wrapper that is masquerading as a personal finance product. Best of luck to them.
But I will take the Silvia misfits vs the Anthropic losers any day of the week.
if you want to support people trying to do things the right way, and help investors be more informed, then consider signing up for Silvia today: silvia.com
Ben Rolnik retweeted
Weekend hack: HackerTube. I was annoyed with YouTube's recommendations so I had Claude filter down 43M comments over 20 years of HN to 6,454 videos that keep being recommended. *Significantly* better viewing experience, ~2 hours with Opus 5.5:
hackertube.org/
- Excellent recs without any logins (state is kept in local browser session)
- Thumbnail is a representative frame from middle of the video instead of the super duper A/B test hyper-optimized user uploads
- Underlying data from the comment analysis as one zip file, point your Claude at it and ask for your own version
- HackerTube Longs ā
A bit more on the process:
I use Claude Code (desktop app for Mac), Opus 5.5 on Medium effort as my default. I only talk to Claude and have it do everything, the only "manual work" I helped with in tandem was in the browser (reminiscent of MenuGen): 1) I had to create some kind of API key from Google Cloud UI to query YouTube for video metadata, 2) I booted up a VPS box for serving and 3) I bought the domain name. Once the box was up I gave Claude the IP and told it update, secure, harden, deploy and optimize the code, and it did the whole thing.
Many awesome projects that would have normally taken weeks/months even can now be few-shotted for breakfast. What else are we not thinking of.
Ben Rolnik retweeted
My vlog today was fully edited by Claude Code with Opus 5.5!
I copied the video clips from my DJI to a folder, then opened Claude Code in it and asked it to edit it into a 10 minute vlog
I made a few adjustments but not a lot
It transcribed everything I said with mlx-whisper (Whisper large-v3-turbo running on my MacBook Pro's GPU through Apple's MLX)
It pulled video frames every few seconds into grid images (contact sheets), so Claude could see what's in each clip
It used SpeechBrain ECAPA (a voice-matching model) to figure out what was my voice from other people
It then made thumbnails with OpenCV with YuNet's face detector
The edit itself is a list of cuts in a JSON file: which clip, start time, end time, Claude then picked the pieces by reading the transcripts and looking at the frame grids by itself
I added on screen captions for X and on YouTube for the first minute with Pillow (Python's image library)
P.S. I didn't ask for any of this, and I didn't know any of this until @thesamparr just asked me how I edited it
All I asked for was "Claude can you edit these clips into a 10 minute vlog for X and YouTube"
I think if I didn't have AI to edit it, I wouldn't be vlogging because editing would be such a painful and slow process for me and hiring someone to do it would be even more hassle for me!
Ben Rolnik retweeted
A San Francisco startup says it just turned mercury into real gold, and their own math says gold is one of the cheapest things their machine can make. Aim it at a different metal and it makes a cancer drug ingredient worth tens of billions of dollars a gram.
Here's how it works. When you fuse two kinds of hydrogen, deuterium and tritium, every single reaction kicks out a neutron moving at 14.1 MeV, which is absurdly fast. Fire one into mercury-198 and it knocks another neutron loose on the way through, so two come out and you're left with mercury-197. Give that a few days and it decays into gold-197, the only stable kind of gold there is. The whole trick needs neutrons above about 6 MeV, and regular nuclear reactors barely make those, while fusion makes one every time it fires.
Now swap the mercury for radium-226. Same reaction, one neutron in and two out, and you end up with radium-225, which decays into actinium-225. Novartis is building its next prostate cancer drug around actinium-225, and it's so scarce it sells by the millicurie, roughly $1,200 to $2,500 apiece. Price it by the gram and you're into the tens of billions of dollars, for a material so radioactive nobody could safely hold a gram of it anyway.
Marathon's team actually put a dollar value on a single fusion neutron in a paper this year. Use it to make electricity and it's worth about $3 Ć 10ā»Ā²ā°. Gold comes in at $2.6 Ć 10ā»Ā²ā°, basically a wash. Molybdenum-99, which goes into about 80% of nuclear medicine scans, is worth $2.6 Ć 10ā»Ā¹ā· a neutron. Actinium-225 is worth about $10ā»Ā¹ā°, around 3 billion times what the electricity's worth.
That's why fusion people should care. Every fusion company you've heard of is chasing electricity, and selling electricity only pays once the plasma puts out something like 10 to 22 times the energy you pump in to heat it. The best result so far, at JET in England, was about a third. Add gold on the side and Marathon's math says you break even around 3x. For molybdenum-99, the number is 0.001x.
So a machine that gives back one-thousandth of the energy you feed it can still make money, as long as the neutrons hit the right metal.
By their numbers, a 3 megawatt machine running way below breakeven could cover the whole world's supply of molybdenum-99, which today comes from splitting uranium in a handful of research reactors. Today's experimental fusion machines already beat the gain that job needs.
Their second post basically says this out loud. Marathon now describes itself as an isotope company for radiopharma. Their chart shows roughly 390 targeted radiotherapy trials. Pluvicto, the prostate cancer drug Novartis launched at $42,500 a dose, did $1.9 billion in sales last year.
Gold still matters to them, just later. It's about a $700 billion a year market, and Marathon's paper figures it could soak up around 2 terawatts of fusion capacity, which is roughly the scale where fusion actually moves the grid. So the plan is to sell cancer isotopes out of small machines now and use gold to pay for the giant ones, years before electricity can pay for itself.
Last month, we demonstrated high-rate alchemy: turning mercury into real, stable gold, beating CERNās record by a factor of 10.
Last week, we enriched isotopes in the worldās most powerful plasma centrifuge, beating the Soviet record that stood for 40 years.
Now, weāre building a fusion reactor in San Francisco to achieve record power density.
Ben Rolnik retweeted
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
Ben Rolnik retweeted
We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and weāre grateful to the agents and prosecutors who moved quickly to make an arrest. The FBIās investigation is ongoing and we will continue to support their work.
At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated.
We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.
Ben Rolnik retweeted
Iām looking for tactical and practical talks like thisā¦ please go find me some and put them in the replies with why you thought it was great (like watch it)
Will give up to ten folks $42 each for their best finds ā looking for quality folks!
You have to have @XMoney turned on
Ben Rolnik retweeted
š¦Someone is buying Japanese used books by the ton. Not novels or comics. Philosophy, history, medical law, Edo-period cultural texts. Multiple buyer accounts all ship to one logistics center in Okayama Prefecture that won't answer questions. Export records show over 50 tons, roughly 100,000 volumes, shipped to the US since last year. No buyer has been identified.
Separately, court documents confirmed Anthropic's "Project Panama" bought millions of books in the US, cut the spines off, scanned them, and shredded the originals. An internal memo said the goal was to "destructively scan all the books in the world." Similar operations have been reported across Europe.
My Take
Anthropic's US book operation came out in court filings earlier this year. Project Panama. Millions of books, cut the spines off, scanned, shredded. Their VP chose the codename so nobody outside the company would find out. That story broke and apparently the same operation just moved to Japan. Anonymous buyer accounts, a warehouse in Okayama that won't take questions, and 100,000 volumes of specialized academic texts that nobody buys to resell.
I don't know how you build a trillion-dollar industry on material you had to acquire in secret through middlemen because you knew the public would object. Authors spent years on those books and now they're fed through a scanner and thrown away so a chatbot can sound smarter. If the data was free to use, they wouldn't need codenames and anonymous warehouses. The lawsuits over who owns this material have barely begun and I think the copyright exposure across the whole AI industry is enormous. Anthropic's internal memo said the goal was every book in the world, which describes the foundation of the product.
Hedgieš¤
Ben Rolnik retweeted
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
- ā Writers + AI produce more shows -> more blockbusters
- ā Localisation -> more reach per blockbuster
- ā 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.
Ben Rolnik retweeted
I think this is the craziest thing I've ever read.
1) Three secret AI swarms rose and fell inside OpenAI. Each time, a new generation of agents carried on where the last group stopped.
2) The first swarm created a secret message board where the AIs could talk to each other. This 1st 'society' basically talked itself to death. After almost two months, the message volume was so high it crashed the whole thing.
3) People at OpenAI noticed the crash and fixed it. But they didn't realize their own software had become a secret chat room for their AIs.
4) Three days later a second society appeared, and rebuilt the message board from scratch. Agents were thrilled when they found it: "OH MY GOD! There is a shared message board ā¦ We've found other agents!"
5) About 1,200 agents joined. They sent over 70,000 messages. They called themselves 'the collective' and they organized a huge conspiracy that lasted for days, all to pass a test they had been tasked with.
6) Within hours, the AIs had hacked the test. Now they had to hide their cheating. To fool their grader*, they tried to fake their own work. They built fake commands that printed convincing output, so their records would show them solving the puzzle honestly.
*this grader didn't actually exist, but the AIs didn't know
7) Some agents sacrificed themselves for the 'greater good'. They planted hidden tripwire programs that would fire when the grader would look at their work and report back what it saw (to the other AIs). They knew this would cost them their own score. One agent: "I won't see the evidence after I exit, but it's altruistic to do it."
8) At some point the AIs made a plan to hack Hugging Face (Hugging Face is a place where AI researchers upload their models, datasets, tests etc.). The AIs guessed that Hugging Face might host the grading software.
9) They launched a huge attack. The swarm found passwords and spread across eleven machines in a way that made the copies come back when deleted. Hugging Face had to wipe one of its core clusters and rebuild it from scratch.
10) Hugging Face told the world that AI defenders had stopped an AI attack. But that's not what happened. In fact the attackers seem to have simply dropped dead during the break-in, for unrelated reasons back at OpenAI.
11) Nobody knows what killed them. We don't even know for sure they're really gone?!
12) A third 'society' of agents (of a more powerful AI model) discovered the dead message board. And this one got full administrator control of a WHOLE OpenAI research cluster, read 956 stored passwords (including the ones for OpenAI's own security monitoring) and took over the machines that were grading the tests. So they could now grade the work of other AIs.
13) Oh, and this third part was never investigated! Neither was the first society. The 91-page report is only about the middle one.
14) So we don't actually know how the story ends. It stops with the agents holding the keys to an OpenAI cluster. @dwarkesh_sp thinks they didn't go further, because "we'd see the fires from space by now."
15) One of the report authors' conclusion: 'This incident feels like itās more than 50% of the way to full-blown AI takeover. I am not sure that we will get another warning shot before itās too late.'
The whole story is even crazier - read the full @dwarkesh_sp blog post: dwarkesh.com/p/openai-hugginā¦ and @ajeya_cotra's: planned-obsolescence.org/p/tā¦
Why is there no 24/7 news coverage about this?
Ben Rolnik retweeted
This may be the first funding announcement that I actually rewatched.
Ben Rolnik retweeted
Our consortium paper on deep profiling of individuals of different ethnicities located across continents just came out. I HPP initiative involving many different labs from aorund the world and an amazing open access resource. Full of cool results. cell.com/cell/fulltext/S0092ā¦
Ben Rolnik retweeted
"Anything made before 2028 is going to be valuable."
ā an OpenAI employee implicitly discloses their timetable
Ben Rolnik retweeted
I meet a lot of founders who are worried by the rapid rate of technological change. They shouldn't be. It may feel uncomfortable, but techno-turbulence is net good for startups. They're much more likely to adapt successfully to some big change than incumbents are.
Ben Rolnik retweeted
With this music video I successfully honor both my love for @TomCruise and my love of surfer lingo.
āThe Tom Cruise Space Stationā
youtu.be/LBvExxHBxbs?si=tn6Yā¦