
Founder @freeportmarkets | Prev Semis Trader @ IMC & Jane Street | Field Notes on AI & Macro
New York, Dubai, Hong Kong
Joined February 2022
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Had a great time on the pod with @ethanrkho.
We covered how vol desks actually make bets, why trading firms are the third-largest consumers of AI tokens, and what I'm building @freeportmarkets.
Apparently I also admitted that I was "down an entire Ferrari" on the PA June 22nd on record lmao.
Ex-IMC semiconductor options trader Lihong Wang on seeing the whole market's flow, betting the entire AI stack, & why foundation models are good enough to beat the S&P 500.
Lihong Wang (@Lihong062) | Discretionary semiconductor options trader @ IMC Trading | Now co-founder of Freeport, a YC-backed event-driven perps exchange
"You see what everyone's doing, you trade against the people who are stupid, and you get out of the way or you follow the people who are smart."
We cover:
- How a bet actually gets made on a vol desk: not predicting the future, just selling what's expensive and buying what's cheap
- The edge of the seat: being counterparty to 20-40% of flow in certain options markets, a view "maybe a few dozen people on the entire planet" have
- Knowing who HAS to trade: reverse-engineering banks' structured-product hedging from issuance data
- How desks blow up: overestimating correlation, and why NVDA -17% on DeepSeek didn't mean AMD -12%
- The trader group chats: ~3x beta to the S&P, median 30% drawdown in July, and everyone still bullish
- His portfolio: 50 chip/AI stocks covering the entire stack, up 470% bottom-to-peak, then a 70% drawdown ("I'm literally down an entire Ferrari today")
- Why trading firms may be the third-largest consumers of AI tokens on the planet
- Moats vs. growth: why Cursor got funded with no moat, and what that means for careers in the age of AI
- His hot take: foundation models can already beat the S&P 500 risk-adjusted. The model isn't the hard part, the harness is
Highlights:
00:00 Intro
01:09 How discretionary options bets get made — and blow up
10:23 A message from Onyx
10:53 Warehousing benign flow through July's deleveraging
16:40 Seat leverage and how trading firms allocate talent
22:03 Why quant traders are levered long the AI stack
26:27 Playing the AI hand: leverage, moats, personal brand
36:42 From IMC to Freeport: AI agents for event-driven trading
45:26 Perp DEX endgame: liquidity, fragmentation, and SEC rulemaking
51:21 Sourcing semiconductor alpha from trader dinners
56:05 Narrative edge, DeepSeek puts, and a 70% drawdown
01:04:24 Is AI a bubble? Compounding, path, and Kelly leverage
01:08:39 US, China, and comparative advantage under ASI
01:12:17 Risk-taking when you're post-economic
Holders of bitcoin:native zcash:native monero:native or really anything with a store-of-value thesis should collectively fund research into making gold cheap to manufacture.
With BTC being 6% of gold market cap there potentially enormous positive externality for crypto.
You don’t even need to capture the profits from manufacturing gold.
You just need to make supply elastic enough that the scarcity premium disappears, so digitally enforced scarcity becomes attractive.
Obviously, this is extremely speculative, and nobody has demonstrated commercial-scale gold production this way yet.
But the expected value seems very positive for crypto as major theoretical roadblocks are solved and AI can be used to resolve tractable engineering problems.
There is now a possibility to spend a relatively modest few billion dollars in R&D for some probability of undermining trillions of dollars in gold’s monetary premium.
Last month, we demonstrated high-rate alchemy: turning mercury into real, stable gold, beating CERN’s record by a factor of 10.
Last week, we enriched isotopes in the world’s most powerful plasma centrifuge, beating the Soviet record that stood for 40 years.
Now, we’re building a fusion reactor in San Francisco to achieve record power density.
One of the things AI was supposed to be exceptionally good at, after coding/hacking and biotech, was superhuman persuasion.
I suspect the first major application will be intermediated through humans via political strategy, messaging, narrative formation, coalition building, and the broader machinery of political influence.
Makes me wonder when @elonmusk @DarioAmodei and @sama will turn their enormous compute resources into political advantage.
By the 2028 election, AI will be vastly more intelligent than today.
We are going to witness a level of political scheming and manipulation unlike anything in history.
I’m not sure our institutions, or even our intuitions about how politics works, are remotely prepared for it.
The H-1B program is a great idea in principle that has, in many ways, turned into a grift.
The original idea was that America should be able to attract exceptional foreign talent with specialized skills that strengthen its economy and technological leadership.
But look at the numbers now.
In FY2026, there were ~339,000 eligible beneficiaries competing for a system capped at 85,000 visas.
The six largest H-1B users operating IT outsourcing business accounted for over 25,000 registrations.
We personally know brilliant researchers at AI labs and engineers at companies like @anduriltech @SpaceX @intel @Figure_robot @Tesla who lost the H-1B lottery.
while web designers, IT support workers, and generic tech consultants were competing for the same limited slots.
Surely we can acknowledge that not all occupations face the same scarcity of talent or make the same contribution to advancing the technological frontier.
When America has a limited number of visas to allocate, there is an enormous opportunity cost to treating fundamentally different kinds of work as interchangeable.
I think America should be doing everything possible to attract the world’s best scientists, engineers, researchers, and entrepreneurs.
The problem isn’t immigration, but rather a system that too often failed to distinguish between genuinely scarce expertise and facilitating ordinary labor outsourcing.
Immigration policy must be driven by the present, not the past, and business leaders are too terrified of the left AND the right to acknowledge this.
Recent H1B discourse revolves around a longstanding immigration sleight-of-hand. H1B proponents point to exceptional people who came here decades ago as if they represent the current state of the program in the same way that advocates for low-skill/illegal immigration point to very favorable immigrant crime/welfare/education/etc statistics that do, in truth, have them beating native-born Americans on many fronts.
It is all a sham. The world that created those statistics, people, and businesses no longer exists. Brilliant but desperate Vietnamese fathers who fled communism have nothing in common with young men from Honduras, and the crime rates/economic acccomplishments/integration of the former should not be used to justify mass migration of the latter.
Nor should the accomplishments of people who managed to get an H1B visa in the 90s (when the program was capped to just 65,000 visas) be used to justify staffing agencies and tech companies using the program to replace American jobs for less money. The obvious abuse going on is politically suicidal and something that even the most ardent supporters of the visa should want to crack down on, no immigration program can possibly survive the obvious and widely known reality of what is going on. I am a longtime outspoken advocate for stealing the best people from other countries to make our own better, runaway abuse of existing programs robs the government of the worker trust required to do so.
Lots of people in the tech space know this. Even the dumb people are smart enough to see it. Nobody actually believes these are all incredible people with unique talents performing jobs no American can. Everyone knows the guy in Cubicle Bay 7 doing mediocre but functional web design for half the salary of his American peers is no savant and doesn't meet the statutory requirements for his H-1B visa.
Unfortunately, most people with power or profile are terrified that any public statement or acknowledgement will get them attacked by the right (You guys have been screwing over American workers for years with visa abuse, you damn hypocrite!) or the left (You are racist, sexist, misogynist, and sucking up to this fascist administration!). Or, more likely, both. I know too many tech execs in particular who feel trapped, like the only winning move is to stay silent.
JD is blowing the whole thing wide open by putting facts and stats front and center that nobody wants to engage with. Critics will talk about anything and everything else but these damning facts and this obvious truth, that H1B visas serve neither their original purpose, legal purpose, invented purpose, or the American people.
Lihong | Freeport retweeted
This is crazy.
Short sales have outpaced long buying by a staggering 5:1 month-to-date, pushing short leverage close to five-year highs.
We have 44% of all earnings next week including loads of tech companies.
If the Nasdaq can hold the range into earnings and we see some surprises, that's a monster amount of fuel for a squeeze.
Lihong | Freeport retweeted
The entire term structure of @nvidia H100 GPU rental rates continues to rise.
Lihong | Freeport retweeted
Intense selling in tech stocks over the past 2-weeks
Prior instances marked major market lows.
Chart GS
Lihong | Freeport retweeted
My key takeaways from the $META (Meta Platforms) Q3 2026 earnings call.
1. Muse's cost per user is undisclosed. "We don't have specific numbers to share around the cost structure of Muse today." The free tier runs on the smaller Muse Spark model, and Muse Wonder, the better frontier model, sits behind the paid tier because it costs more to serve. The entire profitability case rests on a 10x improvement in compute cost efficiency over the next year (off-peak capacity, smaller models, inference optimization; inference meaning the cost of running the model each time someone uses it). That 10x is a target with no baseline attached.
2. Free cash flow went negative. Capex was $34.9B in the quarter and free cash flow (cash left after capital spending) was -$2.1B. Cash and securities sit at $82.4B. Full-year 2026 capex is now $135B to $145B, with the low end raised $5B. Total expenses grew 68% to $51.6B, driven by legal costs, infrastructure, third-party cloud, and third-party AI token costs. No 2027 capex or expense numbers until the Q4 call.
3. The new share class is a funding tool. Meta is creating non-voting stock, distributed one-for-one to existing holders (effectively a 2-for-1 split), trading from late January 2027. Stated uses: capital raising, employee compensation, acquisitions. No specific financing plans yet. My read: a company with negative free cash flow just built itself a currency to sell equity or pay for deals without diluting Zuckerberg's voting control.
4. Core ads carried the quarter. Ad revenue $63.1B, up 26%. Impressions up 10%, average price per ad up 14%, with part of the pricing lift credited to easier macro conditions versus Q3 last year. Reported operating income fell 37% to $13B (20% margin) because of a $9.9B multistate Attorneys General settlement charge. Strip that out and operating income grew 11%. Headcount is 66,000, down 12% from Q2 after the May layoffs.
5. Muse demand is strong, and global rollout is gated by compute. 10M+ people in the U.S. and Canada set it up in about six weeks. Roughly 25% of them use it on a given day. Shopping is the top use case at over 25% of weekly actives, average purchase size above $200 (travel included), and Stripe had to raise credit limits in week one. The caveat: these are set-ups and engagement, not paying users, and only two countries have it because serving more people costs too much right now.
6. Monetization starts earlier than the usual Meta playbook. Historically Meta grew a surface first and monetized years later. Muse costs too much to wait. The model: free for most users, a fee on transactions paid by businesses, subscriptions for top models and extra tokens, and paid business tiers. The enterprise side (model API, Muse Code, business agents) is growing "quite quickly" with zero revenue disclosed. New AI businesses begin contributing in 2027, but the meaningful scaling is pushed further out.
7. The quiet story is inside the ad machine. Partnership ads (brands running ads with creators) grew 97% YoY to a $15B+ annual revenue run rate, meaning the current quarter's pace annualized. Moving content-understanding work onto in-house Muse models cut per-unit processing costs by 80%+ in offline tests. That is the cleanest proof so far that owning the models pays: lower cost inside a business already printing money. Family of Apps other revenue grew 59% to $1.1B on WhatsApp paid messaging and subscriptions.
8. Reality Labs is being rationed. Revenue fell 6% to $443M on weaker Quest sales, partly offset by AI glasses growth. New VR devices now ship only on major breakthroughs. Losses guided to $18B to $19B in 2026 and about $15B in 2027, with budget reallocated primarily to AI. A cut, framed as focus.
9. Legal risk is still open. Beyond the $9.9B charge, youth-related trials are scheduled in the U.S. in the coming months, and scrutiny is ongoing in several markets. The quarter's 24% tax rate came in above outlook because part of the legal expense isn't deductible.
Bottom line: Management's story is that Muse is Meta's next billion-user platform and the ad business pays for the buildout. Half of that is proven: ads grew 26% with underlying operating income up 11%, and Muse has real early demand. The other half is not: no cost per user, no Muse revenue number, no 2027 capex, and the 10x efficiency gain is a forecast. Negative free cash flow plus a new non-voting share class moves the question from whether Meta can afford this to how it will pay for it. Owning $META here means betting the cost curve bends before the funding gap forces dilution.
Lihong | Freeport retweeted
So basically, today PHP got two critical bugs that let anyone run code on your server without even logging in, which is bad, and then the details leaked before the patch was even out, which is worse, and it works on WordPress and MediaWiki and Magento and Joomla and Laravel, and Censys says like 1.5 million servers are running a version with no fix, so the NSA was like "take your system offline," and the FBI was like "take your system offline," and CISA and the UK and CERT-EU were all like "take your system offline," and then the Prime Minister of Japan, the actual Prime Minister, posted at 5:55 in the morning telling 48.8 million people to take their system offline, so everyone took their system offline, like the Dutch cybersecurity agency took its own website offline, and the Dutch parliament went offline, and the Port of Rotterdam went offline, and Finland and France and Denmark started pulling government sites, and Wikipedia went down, and Archive.org went down, and Asana and GOG and Something Awful went down, and the University of Vienna's webmail went down for some reason, and that's just PHP. Because GitLab also dropped a CVSS 10.0, which is the highest score they give out, for a bug that's been there since 2014, so that's every self-hosted GitLab for twelve years, and then LiteLLM 1.107.0 got hijacked and shipped a credential stealer and a remote access trojan in every single wheel and the Docker images, including latest, and you didn't even have to import it, just installing it was enough, and it's the second time this year, and then Railway paused every deployment on the entire platform because someone got into the environment variables, so now everyone has to rotate every secret they've ever stored there, and TiDB Cloud said someone was walking around inside with stolen credentials for basically a month and an extortion group says they took 10.6 terabytes, and Zeabur got breached for the second time in two months and is just shutting down, like the whole company, and Node.js patched remote code execution and a TLS bypass, and CISA says Plex is being actively exploited, and someone took over 300 Tor exit relays to steal SSH passwords, and SafePay ransomware has hit over 600 critical infrastructure orgs, and India's biggest bank filed a data loss incident, and Japan has been in an official state of emergency in cyberspace since October 9, which honestly explains the 5:55 AM post. Oh, and eMudhra, a certificate authority that's issued over 60 million certificates, whose entire job is proving websites are who they say they are, was supposed to be using private keys that are random numbers over a hundred digits long, and one of them was 4. Just 4. And the others were like 66 and 237. Anyway, it's Wednesday.
Lihong | Freeport retweeted
We can call it Black October for the security breaches:
Oct 21:
• PHP2Shell
• Railway
• Node.js
• LiteLLM
• GitLab
• SBI
• eMudhra
Oct 20:
• Plex2Shell
• SiteGuard
• Siemens
Oct 19:
• OVHcloud CLI
Oct 17:
• Opencode
Lihong | Freeport retweeted
Lots of Security incidents today, here's some practical tips for those who just wanna protect yourself:
1) UPDATE EVERYTHING - main targets are:
a) Browsers - Chrome, Safari, Firefox
b) OS: : Settings -> General -> software update; Linux: sudo apt update && sudo apt upgrade; Windows: I think there's a button. Do this for any remote home servers & Openclaw/hermes boxes
c) Phones: Update it all
d) Desktop apps (Slack, Cursor, Discord, etc)
2) Limit web browsing this week: Stay within walled gardens (twitter, youtube, etc); don't click random links this week. Just wait until the patches go through. Do step 1 again after a week.
3) Avoid untrusted Wifi networks until your devices are updated
4) Stay vigilant about phishing emails offering "certificate fixes" or fraud updates
5) Have an iPhone 11-16? TURN BLUETOOTH OFF for the time being until apple patches come through.
6) Code/Dev: UPDATE ALL YOUR REPOS - @charliermarsh has already updated uv 0.13.4 which reacts to the eMudra incident for uv (Python), Typesript according to Claude has equivelant. Tell your agents to go and update all your active repos to update package manager across uv, npm, bun, cargo, etc etc
What advice did I miss?
The Logbook (my Substack) recently passed 1,000 subscribers!
Thank you to everyone who’s read, shared, subscribed, or sent me a thoughtful disagreement.
If you’re new, here are the most popular posts: freeportlogbook.substack.com…
I write about a pretty wide range of topics: portfolio construction, market microstructure, volatility & derivatives, macro, corporate finance, DeFi, prediction markets, and the occasional math problem.
I mostly optimize for interestingness: questions worth thinking about, surprising connections between ideas, and explanations that give you a better way to reason about trading and investing.
It’s been particularly rewarding hearing from people in the trading industry who’ve found the microstructure and derivatives pieces useful, and investors who’ve enjoyed the portfolio construction, macro, and corporate finance essays.
The DeFi articles are also a good entry point for people coming from traditional finance who want to understand crypto.
A few starting points from our most-read posts:
• Market Impact — why trade size, liquidity, and volatility are connected through the square-root law.
freeportlogbook.substack.com…
• Options Implied Distributions Are NOT Real-World Distributions — what options prices actually imply, and why a risk-neutral distribution isn’t a forecast.
freeportlogbook.substack.com…
• Market Probabilities Are NOT Real Probabilities — why prediction-market prices can diverge from probabilities even with rational participants.
freeportlogbook.substack.com…
• In Defense of Leveraged ETFs — connecting portfolio theory, CAPM, Kelly, and utility maximization to understand when leverage can make sense.
freeportlogbook.substack.com…
For a more structured way to explore, I’ve organized the essays by topic and suggested reading order:
freeportmarkets.com/research…
Pick whatever catches your curiosity, and subscribe if you find something interesting. There’s plenty more that we will write about in the future!
The answer to the question would typically uses two classic frameworks: (1) Merton’s portfolio problem combined with (2) lifecycle investing.
Consider what you’re maximizing; it is NOT expected terminal wealth but arguably TYPICAL (i.e. median) terminal wealth, which coincides with expected long-run compound growth.
By doing some math, you can show that constant-leverage log growth for leverage L is:
g(L) = r + L(μ − r) − ½L²σ²
The expected-return benefit grows linearly with leverage while the volatility penalty grows quadratically.
Differentiate and we can show that the growth-maximizing exposure is
L* = (μ − r)/σ²
“Volatility drag” is already in the equation but its existence doesn’t establish that 1× is optimal.
For illustration, a 10% expected arithmetic return, 3% financing rate and 18% volatility give an optimum around 2.2× before fees.
Lifecycle investing adds another consideration since a young person’s brokerage account may be a small fraction of their lifetime resources.
With sufficiently reliable future earnings, more equity exposure early can spread market exposure more evenly across a working life.
There is therefore indeed a serious argument for moderate leverage, potentially implemented through a broad-index leveraged ETF.
AI is making traditional quant trading interview questions increasingly useless.
When everyone has access to LLMs, how much can you really learn from asking brainteasers?
A friend at a small independent trading firm shared an interesting interview approach.
Instead of conventional puzzles, they ask deceptively simple questions that lead to long conversations from first principles.
The questions are specifically chosen because there’s so much misleading information online that most AI often gets them wrong.
Three examples:
Are leveraged ETFs bad for long-term investors?
Why do we hedge positions with different assets?
Why can’t we short the VIX?
None has a simple answer. In fact, even the training materials at a major market maker gets it wrong about at least one of these questions
Lihong | Freeport retweeted
"Sports betting is rigged"
It's the dumbest shit you'll ever hear, always from the clueless.. Assume 1 in 10 games are rigged. So WHAT??
Unless you're "in" on the rigging, it doesn't impact you..
50/50 to be rigged in your favor or against you, so no difference long-term 🫠
An emerging bear case for @nvidia is that @Huawei can remain behind on chips while @deepseek_ai helps it become competitive on the cost of running AI.
This could help explain why investors discount ambitious earnings projections across parts of the AI hardware stack, as strong demand over the next few years does not settle how long today’s market shares and margins will last.
1) Huawei and DeepSeek need what the other has.
DeepSeek needs computing capacity it cannot readily obtain. Huawei has chips, but needs a leading model developer to make them work efficiently in production.
Huawei supplies the hardware; DeepSeek supplies demanding workloads and engineers who identify and fix bottlenecks. The result could become a usable alternative to Nvidia’s ecosystem.
2) Huawei already runs some core calculations efficiently.
AI models rely heavily on matrix multiplication. @SemiAnalysis_ tested 1,055 configurations of that calculation, reflecting the different sizes used by open models.
Huawei’s best implementations reached 99.7% of the tested chip’s theoretical compute capacity.
3) DeepSeek is reducing the time hardware spends waiting.
Running a model involves moving data, coordinating chips and scheduling calculations. Those activities can leave expensive computing capacity idle.
DeepSeek is adapting its software to Huawei’s architecture: reducing separate memory transfers, overlapping data movement with calculations, and improving communication between chips.
4) DeepSeek’s revenue disclosure shows why serving efficiency matters.
Separately, DeepSeek reports $38 in revenue per GPU-hour at off-peak API prices. Assume $5–10 of compute cost per hour and that leaves $28–33, or 74–87% of revenue, before other expenses.
The commercial logic is straightforward in that more billable output per hardware-hour can support both low customer prices and attractive economics. The Huawei partnership could help extend efficient serving to an alternative platform.
5) For Nvidia, a credible alternative threatens sales and pricing power.
Initially, that could mean a smaller future opportunity in China. If Huawei becomes competitive more broadly, customers elsewhere could gain bargaining power too.
This is one possible reason investors might assign a modest multiple to strong projected earnings as they can believe near-term demand while questioning how long exceptional margins will persist.
The catalysts to watch are if other customers (aside from DeepSeek) are running models on Huawei at competitive total cost and response speed, without needing DeepSeek’s engineering team.
That would turn a successful collaboration into a competing ecosystem where Nvidia could still make the best chips but face a tougher business.
$NVDA $AVGO $MU
deepseek posted way more alpha on their zhihu page, if you only read twitter you are reading the laowai approved stuff
"Based on this framework, we have implemented a high-throughput, low-latency large-scale inference service for DeepSeek V4.1 Flash. Currently, the service throughput of a single cluster is 155 trillion tokens/day, and the overall revenue is $38 per card per hour (based on off-peak prices)."
it costs roughtly $5-10 per gpu hour so depseek is claiming 80% serving margin at THEIR prices lololololol
Lihong | Freeport retweeted
meta earnings: we're going to spend a bajillion dollars in capex
chips: down 1%
im tired man
A few thoughts from some buy-side analysts we know on cybersecurity following today’s PHP2Shell disclosures.
Cybersecurity spans several distinct functions: identifying vulnerabilities, managing access, detecting and blocking attacks, and recovering when defenses fail. Many vendors operate across these categories, but their exposure to AI differs materially.
The general view is that AI is likely to increase demand for security while putting pressure on the economics of some security products.
1) Frontier labs are already competing in vulnerability discovery.
Researchers using frontier models helped uncover the PHP vulnerabilities. OpenAI’s Codex Security already offers vulnerability identification, validation and remediation capabilities.
That creates a competitive threat to standalone code analysis, routine security reviews, alert triage and parts of vulnerability assessment.
Tenable ($TENB), Qualys ($QLYS) and Rapid7 ($RPD) are names to examine through this lens, although their asset inventories, integrations and broader security capabilities make them more than vulnerability discovery businesses.
2) Enforcement infrastructure appears more defensible.
Cloudflare ($NET) deployed protections ahead of public disclosure as part of the coordinated response.
Zscaler ($ZS) and Netskope ($NTSK) have a related advantage in enterprise access and traffic enforcement.
CrowdStrike ($CRWD) has endpoint telemetry and deployed software capable of containing compromised machines.
These businesses occupy different parts of the security stack, but their value extends beyond producing an accurate diagnosis as they have the deployment footprint and permissions needed to enforce a response.
Better models can improve those products without immediately replacing that infrastructure.
3) Recovery is another relatively defensible function.
Rubrik ($RBRK) addresses what happens when prevention fails: protecting recoverable data and restoring operations.
A more capable model can help coordinate recovery. This makes recovery structurally different from an analytical capability that a frontier lab can reproduce and bundle.
The investment question is where cheaper intelligence strengthens an incumbent’s advantage and where it commoditizes what customers were paying for.
































