
M&A banker focused on the AI industry. @UofT @carnegiemellon. Not financial advice, DYOR. 🇨🇦 🇺🇸
San Francisco
Joined September 2008
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Jay Wong retweeted
🚨 MUSE ECOSYSTEM ALERT 🚨
today we are announcing Muse Gadgets!
this is an open-source ESP32 firmware and Linux SDK for anyone to make hardware that works with Muse
we are also releasing our own gadget—Muse Home Link—to enable your muse to work with your smart devices (TV, speakers, etc.)
Jay Wong retweeted
David from the Muse team here. I responded to Matt on Threads and sent him a couple of DMs offering to help and look into what happened.
In the past, when we’ve worked with users to investigate similar reports, we’ve consistently learned that Muse was following direct instructions and correctly asked for permission. Would love to help and figure out what’s going on here!
Jay Wong retweeted

Nate Silver@NateSilver538Sep 26
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AI is a big W for 40-somethings. Old enough to have learned how to do things the right way, young enough to learn some new tricks.
Jay Wong retweeted

The Kobeissi Letter@KobeissiLetterSep 24
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BREAKING: Oracle stock, $ORCL, falls -5% after declaring "force majeure" on a massive data center being built in New Mexico.
Oracle has reportedly made the move in order to shield itself from rapidly rising expenses on the project.
Jay Wong retweeted

Nat Friedman@natfriedmanSep 21
United States!
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Replying to @anshnanda
we built muse from scratch, but it is definitely heavily inspired as a product by openclaw. After I used openclaw in january I bought hundreds of mac minis for the MSL team and lots of us fell in love with using openclaw (and other personal agents). @steipete is a genius and his harness was pioneering from the jump. I think a lot of people were inspired by it. our goal with muse was to build something like openclaw that we could make safe and secure and easy to use and scale to billions of people.
Jay Wong retweeted
A few thoughts on the current state of venture capital.
When the Music Is Playing
In July 2007, a few weeks before the credit markets seized up, Chuck Prince, then the CEO of Citigroup, gave an interview to the Financial Times. The line everyone remembers is this one: "As long as the music is playing, you've got to get up and dance." He was mocked for it for years afterward, and he lost his job a few months later. But I have come to think he was saying something honest. He wasn't claiming the music would play forever. He was admitting that he couldn't sit down while it was still going, and neither could anyone else in his seat.
I've been thinking about that quote a lot lately, because right now is the most disorienting period in venture capital I can remember, and I have been doing this for a while.
Here is what makes it disorienting. It's not that things are bad. Some things are spectacular. We have companies in our portfolio growing faster than anything I have seen in my career, and I don't say that lightly. At the same time, we have companies with no revenue, no product, and a founding team you could fit in a conference room raising billions of dollars at valuations of $10 to $50 billion. Both of these things are true at once, and if you try to reason about them with the same framework you will drive yourself crazy.
Two ideas have helped me make sense of it. Neither is mine.
The first is reflexivity, which George Soros has been writing about since the 1980s. In most of life, perception follows reality: the weather is what it is, and your opinion of it changes nothing. In markets, it runs the other way too. Prices change what participants believe, and what participants believe changes the prices. The feedback loop can run for a long time, and while it's running it looks exactly like progress.
Here is how reflexivity is playing out in AI. Full disclosure: Menlo is an investor in Anthropic, so read the following with that in mind. People watched a frontier lab go from a $4 billion valuation to $18 billion, then $60 billion, then $180 billion, then $380 billion, and now something close to a trillion. They drew the obvious conclusion: that is what a neo lab looks like. So the next neo lab gets priced off that path, not off anything it has built. Then it gets marked up in a subsequent round, and the markup itself becomes the proof. Look at Thinking Machines. Look at Reflection. At that point valuation has stopped being an output of the metrics and has become the metric. Nobody is discounting cash flows. They are discounting the last round.
Soros is very clear about one thing, and it's the part people skip: you cannot know when or how a reflexive process ends. You only know that it does. Every one of them has.
The second idea is Chuck Prince's, and it explains why smart people keep dancing even when they can see the loop for what it is. As far as I can tell, there are two groups on the dance floor.
The first group got in early. Firms like ours were in some of these AI companies before the numbers got silly, and the paper gains are enormous. When you are sitting on gains like that, you start to feel like you're playing with house money. I have been around long enough to know that house money is the most dangerous kind, because you don't respect it the way you respect money you had to earn.
The second group missed the early rounds and knows it. Their LPs know it too. So they are trying to make up for lost time by writing very large checks very late, which is the one strategy almost guaranteed to turn a missed opportunity into a real loss.
House money on one side, FOMO on the other, and reflexivity feeding both. That's the whole story. Everyone has a reason to keep dancing, and the reasons are different, which is why nobody can talk anyone else off the floor.
So what do you do? The instinct in our business is to answer with company identification: just pick the right neo lab and you'll be fine. I think that's the trap. When price has become the signal, being right about the company is not enough, because you can be right about the company and still be wrong about the price by a factor of ten. The public-market investors I admire figured this out a long time ago. They spend as much time on how much to own as on what to own.
The winners in venture over the next decade will be the firms that treat portfolio composition and position sizing as seriously as they treat sourcing. How much of the fund is in companies whose valuation rests on the last round rather than on revenue? What happens to the portfolio if the reflexive loop breaks next year instead of in five? Those are not exciting questions. They are the ones that will matter.
The music will stop. It always does. Dance if you must, but know where the chairs are.
Jay Wong retweeted
West Coast: we're approaching an economic transformation unlike anything in modern history.
East Coast: we're looking at three more years of 2.1% GDP growth.
Jay Wong retweeted
My bet is they're launching their Muse competitor and they pushed it back to do it specifically in the middle of Meta Connect
His favorite thing to do is to launch the exact thing someone else is launching during their annual event
Jay Wong retweeted
Spot on. There are many sources of cost for the AV unit economics stack, and hardware is only one part of it. To deliver a viable, scalable product to consumers, operators need best in class AV operations and fleet management, and platform costs to match Uber's costs that dramatically benefit from our global scale.
The other factor that matters: utilization. If you can't keep the vehicles at maximal utilization, you are leaving revenues on the table, further exacerbating the cost problem.
Re-read a great report from two years ago on estimated AV unit economics. A few things stood out...with a very low cost car - mid $30,000 - over 90% of the per mile costs for a scaled ride share service were outside of unit depreciation. $UBER (1/x)
Jay Wong retweeted

Brendan (can/do)@BrendanFoodySep 13
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Mercor now spends 3X as much on LLM inference as we spend on employee salaries.
Our inference spend creates so much ROI that it’s additive to headcount, not replacing it.
It’s becoming increasingly clear how we get to ~10% GDP growth:
1. Within 5 years, as AI diffuses throughout the economy, companies will spend as much on inference as they spend compensating knowledge workers today.
2. Knowledge-worker compensation is roughly $40T/year, so this would eventually mean ~$40T/year of inference spend.
3. If wages remain roughly constant and companies profitably absorb that much inference the way AI-native companies are today, the economy needs on the order of $40T/year of additional final economic output to support it.
4. Producing ~$40T more annual GDP in year 5 than a ~3% growth baseline implies ~9% annual GDP growth on average over the next five years.
Based on the ROI we’re already seeing from inference at Mercor, this feels reasonable.
AI-native companies are a leading indicator for the global economy.
Jay Wong retweeted
Sound like many employees at Anthropic share Jacob's POV, as many posted yesterday that (1) they agree, and (2) said that many more inside the company do also.
It would be useful to name this "belief system" so that we can talk about it in a reasonable way. I suspect "doomer" seems derogatory, and "accelerationist" has others uses.
If there a word we can use so that we can have proper discourse? I'm very serious. I think it would be helpful.
I just went through Anthropic’s threat report & woah!
This is genuinely the craziest article I’ve read all month.
They documented hackers, governments, scammers and Chinese AI labs all using Claude in completely different ways.
& some of the cases are insane.
Here’s a TLDR;
⟣ A suspected Russian state linked group used Claude across phishing, intrusion, data theft and malware development, including rebuilding malware after security products detected it.
According to the article, more than 20 organizations were targeted.
⟣ ShinyHunters-linked hackers used AI agents to scan 1.8M Android apps for exposed secrets and help run breaches across multiple companies.
Anthropic says the agents did nearly all the work in some operations.
⟣ A China-based group built an automated exploit setup that could research vulnerabilities, build offensive tools and keep working against targets with little to no supervision.
⟣ Claude was also being used for government surveillance.
One consultant used it to build Lakana 360 for Mali’s intelligence service, a system designed to monitor roughly 25M SIM cards across the country, including calls, messages, voice interception, watchlists and automated intelligence dossiers.
The finished system runs locally, so even if anthropic bans the account, it won’t shut it down.
⟣ Anthropic found Claude being used across six weapons programs.
One Yemen-based group used multiple Claude Code instances while working on guided rockets, ballistic missiles and a hypersonic-glide project.
They actually tested one of the rockets, it failed, and they returned to Claude afterwards to diagnose the problem.
⟣ A Russia-based team was working on autonomous FPV kamikaze drones capable of identifying target classes, including humans, and approving lethal engagement without a human making the final call.
⟣ One China-linked project built electronic-warfare and air-defense suppression systems, then later changed its scenario to 12 targets in Taiwan, including radar sites, air bases and command infrastructure.
⟣ Anthropic found multiple influence operations too, including fake news networks, fake political accounts, propaganda operations and systems built to copy the writing style of real people.
⟣ Then there’s this fucking dating operation.
More than 20 dating apps.
> 4,700+ AI personas.
> 25,000+ people talking to them.
> Around 2.36M Claude messages in two weeks.
And when someone wanted a video call or social follow, real gig workers could step in to make the fake profile look legit.
⟣ Then Anthropic gets into distillation.
They say they’ve now caught campaigns from Alibaba, Moonshot, DeepSeek, Z.AI, Xiaomi, SenseTime and MiniMax.
Alibaba alone allegedly ran 151M+ Claude exchanges between May and July, peaking at almost 3M per day.
(I just wrote a piece on distillation before this)
> Moonshot: 23M+.
> DeepSeek: 12.1M+ in 14 days.
And this is where it gets messy.
Anthropic says Moonshot and DeepSeek were sometimes routing requests from their own users through Claude without telling them.
Those requests included internal company documents, source code, live credentials, surveillance data and other sensitive information.
⟣ Some labs were also actively trying to extract Claude’s hidden reasoning.
Anthropic says one lab tested 12,000+ different requests, each trying a different method, until it found techniques that worked and scaled them up.
That’s the TLDR.
Got me feeling like 👇
We're publishing our most detailed threat intelligence report to date.
It covers how people tried to misuse Claude—for cyberattacks, influence operations, surveillance, biology, and building weapons—and how we found and stopped them.
We disrupted every operation in the report, and used the lessons from them to strengthen our safeguards. Where appropriate, we also shared what we found with authorities and other AI companies.
These cases are not typical: we’re highlighting some of the most sophisticated misuse we’ve seen. But they’re especially important to discuss, because they show us where AI misuse is headed, where our safeguards work, and where they need to improve.
We’re publishing this report so others can spot the same activity on their own platforms, and so we can give the public a clearer view of how emerging threats develop.
Read the report: anthropic.com/threat-intelli…
Jay Wong retweeted
Muse is out today.
An agent like this is a different kind of system to design. It reads your mail, spends your money, runs your code. Getting that right took real care, and our technical post walks through exactly how. We designed security in.





























