Garry Tan

notice.. > Delta is blocking AI agents from booking flights > JPMorgan Chase and Charles Schwab block agents from accessing accounts > Yelp requires paid data licensing for agents > Amazon has blocked Grok bot and other shopping agents. the internet is starting to block AI agents that were supposed to do everything for us

Launching GBrain Tools today from Product Hunt. I'm going to use GBrain Tools with Claude Code to block all the emails that spam us immediately after this launch. 🔗👇 in 🧵

I’ll be doing a special event on October 26th in San Francisco about my book & the future of the Democratic Party at the @cwclub. Former NPR political reporter for All Things Considered @sarahmccammon will be interviewing me and all attendees will receive a signed copy of my book. If you’d like to join, buy a ticket commonwealthclub.my.salesforce-sites.com/ticket/#/instances/a0FVb00000J1ZDhMAN

1/ muse spark open is the most capable US open-weight model to date. 816B total params, 42B active. 1M-token context. natively multimodal. built for coding and agentic work like long-horizon tasks, hard software engineering, multi-step reasoning. america! 🇺🇲

This is the person @GrowSF is endorsing 🤦🏻‍♀️

“Don’t tell me the U.S.A. went down the drain because of leftism, apostasy, pornography, polarization, etcetera, etcetera...All these things may have happened, but what finally tore it was that things stopped working and nobody wanted to be a repairman.” city-journal.org/article/walker-percy-novel-love-in-the-ruins

If you wonder why everyone fights over pro rata... Garry Tan's first fund initialized Fund 1 returned 55x. DPI on the first 98 companies added up to about 1x. Instacart was another 1x, but they weren't able to take any of their pro rata. Coinbase, where he kept taking his pro rata through multiple rounds, did the final 53x.

Series A and Series B are the hardest rounds for founders today. Founders launch a vertical AI SaaS company. They reach $1M-$3M ARR in 2 years or $5M-$10M in 3-4 years. They go out to market with their 1x-3x growth. Almost no interest from investors. If it's not the right category or a deep, long-term conviction bet, most investors stay away. Founders are stuck with large pref stacks and a market that doesn't value this growth. They are neither sustainable (at their current burn) nor venture-fundable (without growth). The exit options (if available) don't leave much for the team. In most cases, founders are required to join the acquiring company (but they aren't interested). I talk to many founders who have been misadvised, and they keep grinding without making progress. The Series A or B that didn't happen won't magically happen in 1 year with a little more progress. If the market moves much faster than your company progresses, you will always be behind. If you are told today to hit some metrics and come back, the market will have already moved by the time you return.

Tung endured slings and arrows from detractors, but also praise from supporters, during deliberation and public comment. The meeting ended with no action taken or proposed against Tung and with the resignation of former Supervisor Gordon Mar thevoicesf.org/sf-dems-flip-d8-endorsement-argue-over-fate-of-chair/

Introducing DeepRSI, a new industry standard for measuring recursive self-improvement. On preexisting benchmarks, tasks are contributor-based and short-horizon (~12 hrs). In DeepRSI, we push agents to work 48 hours on ML tasks actually derived from ongoing proprietary research.

Meta Muse's retention is out, and it's almost as good as ChatGPT! It's higher than ChatGPT at D7 (>30%) and just lower at D14 (22%). Not quite in the elite D14 tier of WA (67%) YT (59%) Insta (54%) or TikTok/Snap (37%), though. Muse is one of the highest retaining AI products!

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

The MEDA site behind Jackie Fielder is 2205 Mission Street, slated for 63 condos for teachers. The city used our taxpayer dollars to refinance MEDA’s debt through a zero-interest city loan. Quite a gift from us. Nine years after MEDA acquired the site? No housing. Our local...

We built a new harness using @typesafeai's Jev that cuts the cost of repetitive work by 90%. The harness learns the job as it runs, moving steps from LLM calls to code. Running 100,000 compliance alerts costs >$290K on Opus 5. With agentrun() we got it down to <$26K.