In this episode of Professional Punters, we sit down with Noah Kann, one of the youngest hedge fund CEOs in the United States.
Noah is the founder and co-CEO of Venari Asset Management, a multi-strategy hedge fund focused on public markets whose flagship fund has run above-market returns net of fees since inception. Before that, he co-founded Sage Tech Capital, an equities and options fund, and built TradeChoice.io, where he created day-trading indicators for retail investors and grew a community of more than 1,500 members.
This conversation is about how someone builds and runs an actual fund this early, what the strategies look like, from the AI memory trade and Bitcoin miners turned data centers, and what he has learned managing real outside capital along the way.
What’s On The Book
Venari runs long/short equities as the core, which at the time of recording meant AI infrastructure: stocks exposed to the data-center buildout on Earth and now in space with the SpaceX IPO.
The big theme last two year has been memory: LLMs need it, and the Microns, Samsungs, and SKs of the world have enormous amounts of money. However, one ought to not be overly concentrated, so the question becomes how to find other businesses that support the same thesis without the same idiosyncratic risk.
One answer Noah likes: Bitcoin mining companies transitioning into the data-center businesses. The energy problems and the GPU infrastructure a Bitcoin miner has to solve scale pretty proportionately to what data centers need, since mining is constant computation. Hut 8, a previous position, has done very well on exactly this pivot, since miners eventually realize that crypto is cyclical. The general theme Noah keeps coming back to: companies that are foundational experts in one field with the ability to take that expertise somewhere new.
It is not just memory and it is not just data centers. There is a huge energy issue, and it is not only the amount of energy needed but the efficiency of it. Nuclear is the hot-button hope, but it might be five, eight, or ten years out; nobody knows, which makes that bet a speculative asset.
So what is actually energy efficient right now? Noah argues for exposure in solar (Elon Musk has argued that if you build space data centers, solar panels plus batteries beaming data back down is the way to do it), hydro (the Hoover Dam already powers a city of millions), as well as domestic oil and gas.
Mom, HCA, and the First Trade
Noah’s mother has been in finance her whole career: Wall Street, then consulting, then CFO and C-suite roles across multiple health systems. The first trade is a story worth retelling. At the start of COVID, he was watching the market crash and asking his mom whether the family was going to be okay.
Her response was a framework, not a reassurance: what is the foundation of COVID? Healthcare. What is the largest health system in America? HCA. They bought it around $80 a share and rode it to the $400s over the next couple of years. The lesson that stuck: ask whether a company is a foundational business to society with a good leadership team. If the answer is yes, they will probably figure it out.
Boring Is the New Foundational
What still counts as foundational in an AI-driven economy? SaaS revenue was once treated as highly durable, with companies borrowing directly against ARR, but AI has made even long-term software contracts look less certain. Noah’s answer was that the next foundational businesses may be the ones that look the least exciting.
On the risk-on side, AI could be most transformative for old-line value companies that historically struggled to scale because of high operating costs, labor intensity, and thin margins. These businesses were considered “value” for a reason. Value itself is often a risk factor, reflecting structural inefficiency or limited growth.
But if AI can automate administrative work, customer service, scheduling, compliance, underwriting, and other labor-heavy functions, relatively small cost reductions can create enormous operating leverage. A boring business with stable demand and compressed margins can suddenly become a much better business when technology removes the cost structure that was holding it back.
On the defensive side, there are industries whose underlying demand is unlikely to disappear regardless of what happens in software. Compliance, healthcare, utilities, plumbing, housing, and physical infrastructure will continue to be needed. AI may improve these businesses, but it is less likely to eliminate them. Until robots can repair pipes, maintain buildings, install electrical systems, and care for patients at scale, the physical and unglamorous parts of the economy remain foundational.
The literal plumbing and electrical infrastructure supporting data centers represents a major investment theme for Noah. Eaton, or ETN, fits the electrical infrastructure side of the thesis. The same logic extends to real estate and private credit. Some of the strongest lenders, including groups such as Peachtree Group in Atlanta, have focused on lending against foundations rather than projections. Multifamily housing in the middle of a housing shortage fits that framework.
Rates, Oil, and the Macro Outlook
The macro backdrop was much less comfortable. Recorded the day before an FOMC meeting, the conversation came after a very hot CPI print, with oil emerging as the central inflation risk following the closure of the Strait of Hormuz.
At $120 a barrel, oil does not remain isolated to the energy sector. It flows through transportation, manufacturing, packaging, agriculture, construction, and nearly every physical supply chain. When a major input becomes dramatically more expensive, even temporarily, the cost does not simply disappear when prices normalize. It compounds through contracts, inventories, wages, and refinancing decisions.
That creates a serious problem for highly leveraged companies. Private credit lenders are beginning to recognize that borrowers cannot indefinitely absorb higher rates while lenders continue expecting the same returns and low default rates. Noah’s base case was that rates may need to remain flat. Cuts are difficult while conflict with Iran keeps energy prices elevated, and another hike cannot be ruled out.
The investment conclusion is not necessarily to abandon the market. It is to stay long the structural beneficiaries, particularly the boring businesses that either gain operating leverage from AI or provide services the economy cannot function without, while hedging the broader macro risk.
SpaceX, Anthropic, & OpenAI
Plenty of people argued that SpaceX was overvalued at $1.75 trillion, and it went up from there (at the time of recording it hit $3 trillion, before coming down). This naturally leads to dot-com comparisons, which Noah pushes back on. Part of why that bubble was such a disaster back in the 2000s was that those companies either had no revenue or were fraudulent in revenue (e.g. Enron).
Additionally, one may wonder if SpaceX at $3 trillion is a fair comp, what should Anthropic and OpenAI be worth? Both trade around $1 trillion in private markets, which arguably looks low: if SpaceX at 1.5 made Anthropic at 1 fair, then SpaceX at 3 implies something like 1.5 or higher.
Noah’s answer is about talent and transformation rather than multiples. These companies have changed the way we live, and they have allowed the average person to build a business worth millions. The market does not know how to price historic companies, the same way nobody knew how to price a train company when crossing the country suddenly stopped taking months. Because they are historic, they will probably be priced high, and they probably deserve to be.
AI Value Capture
Nonetheless, there is question regarding value capture. A company can create value X and capture only Y percent of it. A decentralized blockchain creates enormous value and captures almost none of it in fees; a bank pays pennies on your checking account and captures nearly all of it.
For the frontier labs and hyperscalers, the bear case runs through China: a distilled model that is 90% as good running the majority of inference tasks, backed by an energy infrastructure that is genuinely excellent and arguably over-invested, while the US is power constrained after years of underinvestment.
As an addendum, the thesis has begun to partially play out in the interval between recording and publication. U.S. semiconductors, neo-clouds, optics, photonics, and connectivity stocks have drawn down roughly 20% to 40% from highs as Moonshot AI, the Chinese frontier lab behind Kimi, released a model that reportedly outperformed Anthropic’s flagship model Fable across nearly all the benchmarks.
The market is reflecting growing concern that increasingly capable and efficient Chinese models could weaken the value-capture assumptions embedded in American AI valuations. Only time will tell whether Jevons paradox proves powerful enough, that falling inference costs cause global token consumption to expand so dramatically that today’s lofty valuations are ultimately justified, or whether AI becomes so strategically important that the required capital expenditure is partially underwritten, explicitly or implicitly, by the American taxpayer.
Either outcome could sustain enormous investment in AI infrastructure, but neither guarantees that today’s frontier labs and hyperscalers will capture returns proportionate to the value they create.
Professional Punters is presented by Freeport Markets: Freeport lets users trade 24/7, weekends included, with no KYC and up to 200x leverage across stocks, indices, commodities, crypto, rates, and more, with access to pre-IPO exposure in companies like OpenAI & Anthropic. For traders who already follow sharp sources across Twitter, Substack, hedge fund filings, corporate insider activity, and political trading disclosures, Freeport uses AI to read the sources they trust, connect the dots across markets, and surface trade ideas as narratives develop.
About the Host: Lihong Wang is the founder and CEO of Freeport Markets. Before starting Freeport, he traded discretionary semiconductor names at quantitative market-making firms including Jane Street and IMC Trading, building both systematic and discretionary strategies. Lihong graduated from Duke with a degree in mathematics and statistics, and has raised over $2.5M to build Freeport from investors including Y Combinator, Alliance DAO, and Informed Ventures. For more of his long-form research, visit freeportlogbook.substack.com.
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