went to Taiwan for the first time, not knowing what to expect review: awesome place great people, food, and scene dont really have a complaint tbh, would recommend

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!

“Perps.” Cheney (15 years in M&A and VC, ex-Goldman) picked perps over TRS in the rapid round. Ten minutes earlier he was explaining why paying 100% funding on OpenAI perps makes no sense. The other questions he got: • Bitcoin or ETH • Singapore or Hong Kong • OpenAI or Anthropic • Pre-IPO share or pre-IPO token • Binance or OKX He skipped one of the questions; which one is it? Full episode in the comments 👇 #PreIPO #Anthropic #Tokenization

🎧 RWACast Ep.2: Is AI Pre-IPO Still Worth It at $2 Trillion? “We get in early at a billion or two valuation of a unicorn. When it hits 10 billion, it’s still 10X.” This week on RWACast, we sat down with Cheney (15 years in M&A and VC, ex-Goldman, ran an AI company for banks for a decade) to talk about where pre-IPO deals actually come from, why perp funding rates on OpenAI and Anthropic are running over 100% a year, and the Zoom call where the counterparty showed up as a cartoon. If you’re paying 100% funding to hold a pre-IPO perp, you should probably watch this episode. 🎙 w/ @gdegen 🧵 Episode Timeline: 00:00 Intro 00:17 Cheney’s background: Goldman, M&A, VC, unicorn deals 00:45 Anthropic at $2T: is there still room for retail? 01:21 The AGI bet: $10T of white-collar GDP vs $100B ARR 03:11 Where pre-IPO deals come from: VC pro-rata and the 1.5 market 04:34 The cartoon avatar on Zoom 05:19 Perps vs spot: 100%+ funding rate means paying double 07:31 Would he hedge with perps? Why he holds full exposure 08:16 What perps are actually good for: early exit and price indication 09:03 Only OpenAI and Anthropic? What comes after LLMs 11:06 Cisco then e-commerce: AI is at stage one 12:22 How a deal gets done: the three-party rule and crazy fees 13:37 Using AI for due diligence 14:04 Pre-IPO in five years 14:57 Rapid round: BTC or ETH, SG or HK, TRS or perp, share or token 16:40 AI or biotech 16:59 Binance or OKX Everything mentioned in this podcast is not financial advice. Please do your own research. Full episode in the comments 👇

没参与2049，一直在等有质量的2049小作文。 怎么这次连个好的作文都没有了呢？ 只能说在2049做市场的也是真不容易。 卷到了，累到了。

🎧 RWACast Ep.1: Pre-IPO Onchain: What You're Actually Holding When You Buy a Pre-IPO Token "Some of them buy real shares. Some hold fund units. And some have nothing at all. They just make you a promise." This week on RWACast, we sat down with Robert, Executive Director at Tiantu Asset Management HK (SFC Type 9, ex-ISI), to unpack how the pre-IPO allocation market really works, and why most onchain buyers can't tell which of the three kinds of pre-IPO tokens they own. If you've bought a pre-IPO token, or you're about to, you shouldn't miss this episode. 🎙 w/ @Kristian_cy Mandarin with English subtitles. 🧵 Episode Timeline: 00:00 Welcome to RWACast with Robert, Tiantu Asset Management HK 00:27 Robert's background: ISI, Evercore, Tiantu 04:17 Where pre-IPO returns come from: access first, price second 08:01 How a star deal flows from the cap table down to the market 13:50 When secondary beats primary: $8B round vs $5.5B employee shares 15:31 Where deals break: structure and hidden fees, rarely valuation 22:20 Economic exposure vs legal ownership, and the TRS nobody notices 28:47 Three kinds of pre-IPO tokens: real shares, fund units, or a promise 32:24 Can a company block a token of its own shares? 37:25 What's the fair price: last round, secondary, broker quote, perp 43:40 Conflict of interest: one platform doing everything, no SEC 47:31 The unsolved problem: distribution 50:04 CFIUS, SpaceX excluding China 59:31 Moving tokenization earlier: Anthropic from under $10B to $1T+ 1:07:56 Hong Kong: regulatory innovation and the sandbox window 1:15:34 Final thoughts: platforms have to be the referee Full episode in the comments 👇

Start my day with 🏃 , thanks @Transak for hosting

Singapore, we're coming. 🇸🇬 Alloco is heading to @token2049 Singapore. Our team @gulugggulu, @Kristian_cy, and @GDegen will be there October 5-9. If you'd like to learn more about the future of onchain ETFs & pre-IPO, come say hi.