Jim

Full time student of the stock market. Goal: To achieve Financial Freedom as early as possible and give back to community.

[Jim @madmedmd]

@ClonerToPower10 What is $UBER’s moat?

[Z @ZeeContrarian1]

𝗪𝗵𝗼 𝗠𝗼𝘃𝗲𝗱 𝗠𝘆 𝗘𝗱𝗴𝗲?

Most investors spend 95% of their time analyzing numbers.

Revenue growth.
Margins.
Guidance.
Valuation.

The problem is that everyone can see the same numbers.

Twenty years ago, investors like Warren Buffett could build an edge by reading financial statements better than almost everyone else.

Information moved slowly. Data was expensive. Analysis was limited.

Ten years ago, investors like Bill Ackman were still generating outsized returns through deep fundamental research, activism, and understanding businesses better than the market.

Today, every filing is instantly available to everyone.

Hedge funds, analysts, retail investors, and now AI systems can process the same information within minutes.

The market evolved.
Information evolved.
AI evolved.

Investors who didn’t evolve underperformed.

Even Bill Ackman’s most famous trade wasn’t finding a hidden line item in a balance sheet. It was recognizing a risk the market was largely ignoring and buying protection before COVID.

The edge moved.

I haven’t used Excel or a calculator in over 10 years.

Yet during that time I have outperformed the market by thousands of percentage points.

Not because numbers don’t matter.

But because numbers alone rarely provide an edge anymore.

If you’re only chasing valuations, you’re chasing information everybody already knows.

You can scroll through X all day and see endless posts saying:

“Bitcoin is so cheap.”

“PayPal is so cheap.”

And my personal favorite:

“This is the cheapest valuation in the company’s history.”

I probably see 10 posts like that every day.

So what?

Everybody can see the same valuation metrics.

Everybody knows the stock is trading at 8x earnings, 1x sales, or whatever ratio is being promoted that day.

If the opportunity is obvious to everyone, why would that be an edge?

In fact, some of the cheapest stocks get even cheaper.

The question isn’t whether something looks cheap.

The question is: what does the market believe that makes it cheap, and what is the market getting wrong?

One of my favorite edges is understanding the actions of the people who know a business best.

Not blindly following them.

Understanding why they’re doing what they’re doing.

In my latest three recommendations, $STAA, $WGS, and $IMDX, the numbers weren’t particularly attractive. Analysts were negative, and the last quarters weren’t great.

Yet within a few months, all three were up between 50% and 100%.

Almost nobody was talking about them.

You couldn’t scroll through X and find endless threads about how cheap they were.

At the same time, some of the people who knew these businesses best were buying aggressively.

That’s where I started paying attention.

Not because someone bought.

Because I wanted to understand why they bought.

There are entire funds and ETFs built around insider buying.

They scan thousands of companies, track insider transactions, apply statistical models, and buy based on those signals.

And they’re not wrong.

But that’s still only one piece of the puzzle.

Insider buying is not the thesis.

It’s one facet of the diamond.

It’s a clue.

The real work starts after you see the purchase, not before.

Who is buying?

How much are they buying?

What do they know?

What are their incentives?

Why are they acting now instead of six months ago?

Those are the questions that matter.

If you read the hedge fund letters on $STAA, you understood the thesis.

If you listened to the conference calls on $WGS and paid attention after insiders committed roughly $100 million of their own capital, you understood where the opportunity was.

Same thing with $IMDX.

The market is very good at pricing today’s numbers.

It’s much less efficient at pricing human behavior, incentives, and conviction.

[Jim @madmedmd]

@NIftek @Replit @HayaOdeh You should try Base44 instead!

[Jim @madmedmd]

@KrisPatel99 It’s for average person like yourself who doesn’t have the skill or talent to make even 0.1% wealth of what Buffet has made.

[Jim @madmedmd]

@ClonerToPower10 Me too. He is likely holding it

[Gavin Baker @GavinSBaker]

Much of Dwarkesh's argument hinges on this statment which *was* accurate but will be increasingly inaccurate on a go forward basis imo: 

“American labs port across accelerators constantly. Anthropic's models are run on GPUs, they're run on Trainium, they're run on TPUs. There are so many things you can do, from distilling to a model that's well fit for your chips.”

As system level architectures diverge (torus vs. switched scale-up topologies, memory hierarchies, networking primitives), true portability is eroding. The Mi300 and Mi325 had roughly the same scale-up domain size as Hopper while Blackwell’s scale-up domain is 9x larger than the Mi355 scale-up domain, etc.

Many frontier models are now being explicitly co-designed for inference on specific hardware like GB300 racks. Codex on Cerebras is another example. Those models run less efficiently on other systems and the performance differentials will only widen. A model that runs well on Google’s torus topology will run less efficiently on Nvidia’s switched scale-up topology and vice versa - the data traffic is fundamentally different as a byproduct of the models being parallelized across the different topologies.

Google’s internal teams - and increasingly the Anthropic teams as they become the most important customer of almost every cloud - have the luxury of operating across the stack (models, chips, networking) - but that is not the case for the rest of the market and other prospective users. Anthropic is the exception, not the rule. To wit, Anthropic and Google allegedly have a mutual understanding where Anthropic can hire the TPU engineers they need every year to ensure that they can continue to get the most out of the TPU.

Given the overwhelming importance of cost per token to the economics of the labs, models will be run where they run best. Most extremely large MoE models will run best on GB300s given the importance of having a switched scale-up network like NVLink for MoE inference. When training was the dominant cost for labs and power was broadly available, labs were optimizing to minimize capex dollars. Model portability was a way to create leverage over suppliers. I think that drove a lot of the focus on portability.

Today, inference costs as measured by tokens per watt per dollar are everything. Inference is way more important than training costs (inference is effectively now part of training via RL). Labs are therefore now optimizing for inference. This means increasing co-design and higher go-forward switching costs for individual models between systems. I do think this explains why Anthropic and Nvidia came together: Anthropic needed Blackwells and Rubins to inference at least *some* of their models economically. And Mythos might just end up being released coincident with the availability of Rubins for inference.

TLDR: as labs shift their focus from training to inference, the costs of portability and the upside of co-design to maximize tokens per watt per dollar both rise. Portability is likely to begin decreasing as a result.

I think what I might have respectfully added to Jensen’s answer is that systems evolve under local selective pressures.

The evolutionary pressure in America is a shortage of watts so it makes sense for Nvidia to optimize, as an American company, for power efficiency and tokens per watt and stay on copper as long as possible. China has a surfeit of watts. Chinese AI systems are already taking advantage of this with the Huawei Cloudmatrix 384 and Atlas SuperPoD having an optical scale-up domain that is much larger than anything offered by Nvidia today at the cost of *much* higher power consumption and much lower tokens per watt. The networking primitives for this Huawei system are very different than those for Nvidia’s systems and a model that runs well on Nvidia will not run well on that system and vice versa. This means that if a Chinese ecosystem gets momentum, Chinese models might stop running well on American hardware. And when Chinese models run best on American hardware, America is in a better position as this gives America a degree of leverage and control over Chinese AI that it risks losing to an all-Chinese alternative ecosystem.

This architectural fork makes porting and distillation less effective and strengthens the pro-American national security case for selling China deprecated GPUs imo.

Also I will attest that I did not wake up a loser this morning.

[Z @ZeeContrarian1]

𝗣𝗿𝗶𝗰𝗲𝗱 𝗜𝗻

Yesterday I wrote: markets at all-time highs, $VIX below 20, oil below 90. That tells you everything you need to know.

Despite what some doomsayers say, the rally itself is the message.

When I said “priced in,” a lot of people pushed back, pointing to the Global Financial Crisis.
They said: “That wasn’t priced in, so you’re obviously wrong.”

But that misses the point.

There’s a reason there’s a movie like The Big Short. It shows exactly what happened. Only a small group of people truly understood the risks in the mortgage market. The knowledge wasn’t widespread. Capital didn’t move until it did. And once understanding spread, everything repriced violently and fast.

That’s the key difference.

Back then, very few people knew. Today, everyone “knows.”

Scroll through X and you’ll see the same narratives repeated endlessly. Crude oil risk. Widening credit spreads. Software valuations. Private equity stress.. you name it….

If every average person is talking about it, it’s not hidden information.

It may not be 100% priced in, but it’s probably 90% there.

Markets don’t wait for certainty. They move on awareness.

And when something becomes common conversation, it’s no longer an edge.

That’s why there’s no real value in what you read or debate on X about these topics.

It’s already in the price.

[Brian Feroldi @BrianFeroldi]

Volatility transfers wealth from those who can't handle it to those who can

[Jim @madmedmd]

@TheShortBear What about $pdd? Got over it?

[THE SHORT BEAR @TheShortBear]

Would highly recommend to read this book.

Its a short read and you will be seeing through 90% of all headlines.

It is scary just how similar wars are in the end.

[Sahil Bloom @SahilBloom]

The biggest unlock of my 20s: Ask for things. The raise. The introduction. The favor. Most people never ask because they fear rejection. But your silence guarantees it. If you've done the work, ask for the crazy thing. The world rewards those who ask. Closed mouths don't get fed.

[Rose Celine Investments 🌹 @realroseceline]

Thoughts on $MELI

The past year proved that $MELI’s ecosystem is stronger than ever. Customer satisfaction hit record highs across Brazil, Mexico, and Argentina. Revenue grew 45% in Q4 and 39% for the full year. Operating income grew 22% even while they invested aggressively. Very impressive they can remain so profitable even while pedal to the metal investing in growth.

Ecommerce penetration in Latin America is roughly half of the US, UK, and China. They see no reason that gap cannot close significantly. With 121 million unique active buyers, the network effect keeps strengthening as more buyers attract more sellers.

Free shipping continues to drive massive growth. In Brazil, lowering the free shipping threshold to $19 accelerated buyer growth, frequency, and retention. GMV in Brazil grew 35% in Q4, sold items grew 45%, and unique buyers grew 26%. Even now, fewer than one third of Brazilians purchased from $MELI. Mexico and Argentina were also strong, both posting 35%+ GMV growth, with Chile, Colombia, and Peru growing even faster.

Logistics is a major competitive advantage. The network handled 41% more volume in 2025, nearly 500m additional shipments. Shipping costs fell 11% in Brazil in Q4, so scale is driving efficiency. This is somewhat hidden in their report and not emphasized “loudly”, but in my humble opinion speaks volumes to their execution. Cross border trade accelerated 74%, and they opened their first fulfillment center in China to support the so called “China LatAm corridor”.

Advertising is scaling quickly, up 67% in Q4 ($AMZN who?). AI is being embedded across search, personalization, seller tools, etc. Ads is going to be a monster business for $MELI due to its amazing economics. They are investing heavily across logistics, free shipping, cross border, loyalty, ads, and AI, while still growing revenue near 40%. Momentum is strong and the opportunity remains very large.

Fintech is becoming a core pillar. Pago is targeting millions who remain underbanked. Deposits scaled from $2b to nearly $19b in three years. Monthly active users more than doubled to 78m. Credit grew 90% in Q4 to $12.5b and is up more than 4x in 3 years. Spreads remain strong with NIMAL at 23.3%. The credit portfolio is their biggest risk, but also an enormous opportunity.

On consolidated financials, revenue grew 45% in Q4 to nearly $8.8b. Operating income was $889m including tax credits. Free cash flow was $763m in Q4. Full year investment was large, with $1.3b in capex and $6.5b deployed into credit. Management estimates a 5–6 point margin impact from strategic initiatives and views this as temporary pressure to expand the moat.

The message is clear. They are intentionally compressing margins in the short term to expand scale, engagement, and ecosystem dominance. The opportunity ahead remains enormous, and I personally couldn’t agree more.

🌹

[THE SHORT BEAR @TheShortBear]

There are 100x more people living in mediocrity because they were told they couldn’t than those who used that same doubt as fuel to rise.

“You can’t” isn’t guidance.
It’s usually projection, confession.

The spiral starts when people who never took the chance try to protect themselves from discomfort.

Watching you attempt, and possibly win, forces them to confront their own inaction. Your ambition can trigger their inferiority, so they try to shrink it.

But here’s the truth most overlook:

The biggest achievers in the world are often those who faced the highest levels of adversity, and turned it into work ethic, resilience, and relentless output.

Even envy, misunderstood and mislabeled as purely negative, can be one of the most powerful driving forces when redirected toward growth instead of the negative jealousy.

Most negativity pushed onto you isn’t about your limits.
It’s about someone else protecting theirs.

The strongest resistance often holds the greatest fuel potential.

Greatness doesn’t come from avoiding adversity.
It comes from pulling back the curtain, stepping through it, and using everything you find on the other side.

The world is a web of push and pull mechanism designed by our drive to conform to social norms.

[Grey @jgreyfriend]

In 2010, George Soros gave a 40-minute masterclass on why humans misjudge market reality.

He explained why:

- Markets distort reality
- Bubbles are logical, not irrational
- Regulators must fight markets

12 lessons from Soros that change how you see financial markets forever:

[Jim @madmedmd]

@realroseceline @jonnagap @grok which one is the best business as an investor? $msci $mco or $spgi?

[Finding Compounders @F_Compounders]

Li Lu’s recommend book list .

Seems like I’ve got a couple of books to add to my list.

[Steve Burns @SJosephBurns]

https://t.co/fMtEqY47d5

[Ray Dalio @RayDalio]

https://t.co/tjmbT5ytUN

[Steve Burns @SJosephBurns]

Warren Buffett reads Howard Marks writings, books, and market analysis.

Marks teaches about distressed assets and understanding market cycles.

Here is his investing wisdom condensed into a 65 minute video.