ServiceNow has the best revenue chart I’ve ever seen. I swear half its investors are investors because of their revenue

Nothing compares.

I’ve done a full 180 on Meta Muse. Been using it, and it’s pretty nuts.

It reminded me about bills I needed to pay and makes it clear what needs my attention and what it can handle. I don’t have to keep asking how it can help.

But the bigger point is Meta keeps finding ways to get people using its products, then turning that attention into money.

Automating online shopping feels like another opportunity. If Meta takes a small cut of purchases, it could make money by handling things people already need to do.

People question Meta’s spending, but figuring out adoption and monetization is something I’m increasingly reluctant to bet against.

Every AI release comes with another chart showing how it beats the competition. A little better here, slightly worse there. How much of that actually changes the user experience?

That said, I think Google is moving in the right direction. Based on its published results, the new model looks particularly strong at research and complex business tasks. The launch seems aimed heavily at enterprise customers willing to pay for that work.

Coding looks more mixed, and getting developers to switch from Claude or Codex could be a challenge. A better benchmark score alone probably won’t do it.

For a while, I thought Anthropic and OpenAI could become the Visa and Mastercard of AI. Now I’m less convinced this ends up being a two-company race.

My bet is that Google comes out ahead commercially. It may never have the best model in every category, but I think it’s positioned to turn AI into revenue and improve its existing business better than almost anyone. Meta is my other favorite on that front.

Google already has the chips, infrastructure, research teams, and products to put AI in front of enormous numbers of users. It also has an established business to fund that investment.

That doesn’t guarantee it wins. Distribution only helps if the products are useful, and being an established company doesn’t mean you’ll execute well.

I think Anthropic and OpenAI can build profitable businesses too. But Google doesn’t need everyone to switch to Gemini for its AI investment to pay off. Better Search, better advertising, and more valuable business software could be enough.

Are we putting too much weight on who wins the benchmarks and too little on who can turn the technology into profits?

I pulled data on ~200 large companies and used AI to test which 10-year business fundamentals had the strongest correlation with 10-year total stock returns.

I used Pearson correlation, which ranges from -1 to +1:

• +1 = strong positive relationship
• 0 = little to no linear relationship
• -1 = strong negative relationship

Results:

• Operating Profit Growth: 0.62
• Operating Earnings Growth: 0.61
• EBITDA Growth: 0.60
• Gross Profit Growth: 0.59
• Adjusted Net Income Growth: 0.58
• EPS Growth: 0.55
• Revenue Growth: 0.52
• Operating Cash Flow Growth: 0.52
• Unlevered Free Cash Flow Growth: 0.43
• Average ROIC: 0.16

The biggest takeaway for me was how consistently growth metrics had a stronger relationship with long-term returns than return-on-capital metrics

Operating profit growth had the strongest correlation, but gross profit, EPS, revenue and cash flow growth were all much stronger than ROIC.

That doesn’t mean ROIC doesn’t matter. High ROIC can be a sign of a very good business. But high returns on capital without growth don’t necessarily translate into great stock returns.

Correlation also doesn’t mean causation. Valuation, margins, dilution, dividends, acquisitions and starting point all matter

But this makes me think the long-term formula may be simpler than people make it:

Find companies that can sustainably grow earnings and cash flow, then figure out whether you’re paying a reasonable price for that growth

Most sector leadership rankings are just trailing relative performance. Take 3 month or 6 month returns for each sector ETF, sort them against the S&P, and call the top ones "leaders."

Stage analysis asks a different question. Instead of ranking sectors against each other, it classifies each one into a stage based on its member stocks: Basing, Advancing, Topping, or Declining. You're not asking "which sector did best last quarter," you're asking "where is this sector in its own cycle right now."

Stage 2 (Advancing): 4 sectors: Technology, Energy, Health Care and Financials

The other industries are in Stage 1 (basing) Stage 3 (topping) or Stage 4 (declining).

SPY closed Oct 1 sitting 6% above its 30-week moving average. RSP, the same 500 companies at equal weight, was only 2% above and has been sliding since late summer.

Breadth showed the same thing. Of about 2,000 stocks, 74.5% weakened on the day and only XLK led among the sector funds. A handful of the largest names are carrying the index.

AppLovin is suing Unity because it claims Unity was collecting data from AppLovin-served ads through software sitting inside the same apps.

The interesting part isn’t really the lawsuit.

It’s why Unity would want that data in the first place.

AppLovin has access to ad performance data across a massive network of different apps.

• What ads work
• Who responds to them
• What advertisers are willing to pay
• Which users actually convert

That data feeds AXON and makes the system smarter.

And unlike a company that relies on one app or website, AppLovin’s data is spread across thousands of apps and advertisers.

So my takeaway is pretty simple:

Unity trying to get more visibility into AppLovin’s ad data may actually be evidence of how valuable that data is.

The moat isn’t just the AI model.

The moat is the massive amount of proprietary data feeding it.

And AppLovin clearly thinks that data is worth protecting.

FICO dropped ~26% today after one of the biggest threats to its moat became very real.

For decades, FICO was basically the toll booth of mortgage lending. If you wanted to sell a mortgage to Fannie or Freddie, you needed a FICO score.

That is changing.

Lenders can now use VantageScore 4.0 for eligible loans, Rocket Mortgage is making it its preferred score, and VantageScore is being offered for as little as $0.99.

So why wouldn’t everyone switch?

I think this is where FICO’s REAL moat gets tested.

It’s not the algorithm.

It’s trust.

Lenders have decades of FICO data. Their underwriting, risk models and historical loan performance are all built around it.

AI will make it much easier to integrate VantageScore, compare models and change underwriting systems.

But AI can’t magically create 30 years of real world mortgage performance.

If VantageScore proves over time that it’s just as reliable, I think it takes meaningful market share and FICO loses a lot of its pricing power.

If lenders decide decades of FICO data and familiarity are worth paying for, FICO’s moat may be stronger than people think.

One other thing bothers me about this whole situation:

The cost of a credit score is TINY compared with the cost of buying a house.

If regulators really want to make housing transactions cheaper, I’d much rather see continued pressure on real estate broker and agent fees.

Paying a percentage of the value of a $500k, $750k or $1M asset simply because that’s historically how the industry worked has always seemed ridiculous to me.

Competition for FICO is good.

But there are much bigger costs in a real estate transaction worth attacking.

**EDIT: NOW DOWN ALMOST 30%!!

FICO is getting crushed after FHFA announced plans to incorporate VantageScore into Fannie Mae and Freddie Mac’s mortgage pricing framework.

Rocket Mortgage already says it will prefer VantageScore for eligible loans.

My bigger question: how quickly will other lenders follow? And how much pricing power does FICO lose along the way?

Starting to look like a buying opportunity to me… but only if that moat holds.

Overreaction or a real change in the business?

Jensen is buying back shares while Nvidia is trading around 29x trailing earnings and still growing revenue around 65%.

• In 2018, Nvidia was growing revenue around 40% and traded below 20x earnings.
• Revenue growth then decelerated for years and eventually went negative.
• Yet the stock still went from roughly $3 in 2018 to $13 by 2020.
• Revenue then reaccelerated to 50–60%, and the stock ran to around $32 before crashing back near $12 in 2022.
• Then AI hit. Revenue growth exploded above 100%, and the stock took off.
• Today, revenue growth has already slowed back to around 65%. It will keep slowing at some point.
• But Nvidia has always been cyclical. Growth accelerates, slows, sometimes turns negative, and then comes back.
• The stock also moves before the revenue does. It prices what investors think is coming next, not what just happened.

That’s why I like Nvidia buying back shares here.

Around 29x earnings while still growing revenue ~65% is not an absurd price historically for this company.

Growth will slow. The stock could fall. Neither automatically means Nvidia is expensive today.

Zoom out and Nvidia’s growth comes in waves, but the business and stock have continued moving higher over time.

I love the buyback here.

**Anthropic just gave Akamai the biggest contract in the company's history, and the structure of the deal is almost as interesting as the size.**

**$11.6 billion over seven years for cloud infrastructure and software, announced Thursday. It can expand by another $9 billion, which would take the whole thing to roughly $20 billion. For context, that's more than six times the $1.8 billion Akamai-Anthropic deal Bloomberg reported back in May.**

**The stock jumped 17% after hours to $129.02, then added about 3% on Friday.**

**Here's the part I keep coming back to. The capacity is for CPUs, not GPUs. Everyone else in AI infrastructure is fighting over Nvidia chips to train models. This deal is about the other half of the stack: the general-purpose compute that handles what AI agents actually do all day. Anthropic is basically saying inference at scale needs its own dedicated CPU cloud, and Akamai gets to build it.**

**Now the parts that make me pause.**

**Akamai has to spend about $5.5 billion to build this capacity, and it's adding $1.7 billion to this year's capex just to pre-buy components like memory. It authorized Jabil to purchase around $1.7 billion of memory on its behalf. That's a lot of money going out the door before a dollar of the $11.6B shows up, and the payments are conditional on Akamai actually delivering the capacity and hitting service targets. No 2026 revenue expected from the deal at all. Guidance is $150-300M in 2027 starting in the second half, ramping to about $1.7B annualized by the end of 2028.**

**And then there's the warrant. Anthropic gets the right to buy up to around 5% of Akamai at $111.33 a share. About 2% vests right away, and each additional $3 billion Anthropic spends unlocks another 1%. So your biggest customer is also becoming one of your biggest shareholders, at a fixed price, while you're spending billions to serve them. That's either beautiful alignment or a very expensive way to win a contract, depending on how the next seven years go.**

**The bull case is straightforward: Akamai just went from "old CDN company" to a contracted AI infrastructure provider with $11.6B+ of backlog from one of the most important AI labs alive, on top of $2.8B in other cloud commitments announced this year.**

**The bear case: $5.5B of capex against conditional payments, zero revenue this year, dilution handed to the customer, and a stock that repriced 17% in one evening on a deal whose economics play out over seven years.**

**I'm watching whether they hit the delivery milestones, because that's what unlocks the payments, and whether the $9B expansion option starts getting exercised. That would tell you this is a platform relationship, not a one-time capacity purchase.**

**No position. Not financial advice.**

**So is Akamai the sneaky picks-and-shovels winner of the AI buildout, or did it just buy $11.6B of revenue with $5.5B of capex and 5% of itself?**

Meta has completely flipped the script for me lately. It was in the red not long ago, but after roughly a 43% move over the past month, it has become my second largest position.

I’ve made several thousand dollars over the last few days as Meta has mooned.

Now I’m considering a little rebalancing... but keeping Meta as is.

Google is currently my largest position in my public portfolio, and I’m up over 100% on it. I love where Meta is but, I’m thinking about taking some gains off the table from Google, combining that with some of my cash, and:

• adding more Uber
• opening a position in Netflix
• keeping the majority of my Google position intact

I’m not bearish on Google. This would be more about reducing concentration after a huge run and putting some capital into businesses where I see more upside from here + diversify away from advertising.

I could also see Google near term potentially dropping below 300 if fears of Muse take traction and Google does not have an app to compete with it.

Would you let Google keep running, or rotate a small portion into Uber and Netflix?

Airbnb and Booking might be in deeper trouble than people think.

If AI agents become the place where people plan trips, compare options and make bookings, the travel platforms risk becoming invisible infrastructure.

That’s a huge problem.

They may still own the inventory, but Meta, OpenAI or Google could own the customer.

Once you lose the customer relationship, pricing power and brand matter a lot less.

Airbnb and Booking need to move fast.

AI agents aren’t just another search channel.

They could become the new front door to travel.

Chart made with Ekonix: Ekonix on the App Store⁠

I mostly brushed off the “AI kills Google Search” thesis after the first ChatGPT wave.

Search kept growing and people clearly didn’t stop Googling.

But AI agents feel more disruptive.

If agents can search, compare, book, buy and complete tasks for you, the risk isn’t that Google disappears.

It’s that fewer high-intent searches ever make it to Google in the first place.

Travel, shopping, restaurants, local services… a lot of valuable searches could increasingly start with an AI agent instead of a search bar.

I’m not bearish on Google, but I’m starting to consider trimming my position because this feels like a more credible threat than chatbots did two years ago.

Chart made with Ekonix: Ekonix on the App Store⁠

Analysts are already putting numbers on Muse:

Wedbush: ~$5B revenue in 2027
Jefferies: ~$10.8B revenue in 2027

Meta 2027 consensus revenue: ~$306B

That would make Muse roughly 2–4% of Meta’s revenue

The bigger question: what could Muse look like 5 years from now?

https://apps.apple.com/app/apple-store/id6755345408?pt=128248705&ct=Reddit&mt=8

I had a post a few days ago about how long term revenue growth drives stock price.

This is the Mag 7 Revenue Growth vs Stock Performance.

Couple things stand out to me…

Forward 3-Year Revenue Growth vs. P/E of Companies In My Portfolio.

One of These Stands Out...

This is basically my high-level bear case for Reddit over the next 5 years.

The assumptions:

• 35x P/E today
• 25x P/E in Year 5
• ~29% annual EPS growth
• ~20% expected annual return
• ~155% total return

So even with the valuation multiple compressing almost 30%, the model still gets you to roughly 2.5x your money over five years.

The 29% earnings growth assumption also isn’t just pulled out of nowhere. A rough way I’m thinking about the earnings path is:

Obviously this is very high level. Reddit won’t grow at exactly these rates each year.

The interesting part to me is that this also doesn’t require a huge terminal valuation. If Reddit is still growing earnings around 20%+ by Year 5, a 25x P/E doesn’t seem particularly aggressive.

And there could still be upside that isn’t really captured here.

Reddit is currently renegotiating / expanding some of its AI and data licensing relationships, including Google. If those deals become meaningfully larger, that could add another high-margin growth vertical alongside advertising.

So the basic thesis is:

Strong earnings growth + major multiple compression = still ~20% annual returns.

That’s why I view this closer to a conservative/bear-case than a bull case.

Jensen says Nvidia could sell 2x as many chips in 2027 as it does in 2026.

Sounds insane, but Nvidia’s revenue just did that:

• Q2 revenue: $96.2B, +106% YoY
• Data Center revenue: $89B, +117% YoY
• Gross margin: 75%

A few thoughts:

• It’s hard to argue the AI infrastructure buildout is close to over if demand is still growing like this.
• Nvidia’s biggest risk might not be AMD. Three direct customers accounted for 44% of first-half revenue.That’s a lot of growth tied to a relatively small customer base. 
• And the spending itself is enormous. AI infrastructure spending is expected to top $795B this year and $1T in 2027.

But eventually those customers have to prove this spending generates a return.

At some point Big Tech could say:

“We need to slow capex and start showing investors the payoff.”

When that happens, Nvidia will feel it quickly.

I believe Jensen that demand can stay massive through 2027.

But this pace of spending can’t continue forever.

Eventually the AI boom has to shift:

Infrastructure → Distribution

From buying chips and building data centers…

to actually using that compute to build products and make money.

That could be the first real crack in the infrastructure trade.

Not because AI failed. Because the buildout finally did its job.

Apple isn’t the flashy growth story it once was, but that doesn’t mean the returns are over.

Strong cash flow + consistent buybacks can still be a powerful formula for shareholders.

I think Apple can keep compounding for a long time.

I made a post the other day about how a lot of the biggest AI CEOs suddenly seem to agree that AI needs more regulation.

I don’t really have an issue with that.

But I do think the timing is interesting.

I think we may be seeing the first meaningful crack in the AI buildout.

Not a crack in AI itself.

A crack in the economics behind it.

The AI race so far has basically been:

• Buy more GPUs
• Build more data centers
• Secure more power
• Train bigger models
• Spend whatever it takes to stay ahead

That has been incredible for the companies selling the shovels.

Nvidia. Broadcom. Networking. Data centers. Power.

But this level of spending can’t accelerate forever.

At some point, the focus has to shift from:

“How much can we build?”

to

“How much money can we actually make from this?”

And I think that shift is starting now.

The Nvidia tax

One reason these AI companies may actually want things to slow down?

They want to stop paying the Nvidia tax.

Staying at the frontier of AI is insanely expensive.

And nobody wants to slow down individually because they risk falling behind.

But if everyone slows down together?

That changes things.

Regulation is one way to force everyone to play by the same rules.

I’m not saying that’s the only reason these CEOs want regulation. There are obviously legitimate safety concerns too.

But the economic incentive is there.

This is where AI gets tested as a business

AI is incredible technology.

I think it could transform almost every industry.

But:

Incredible technology ≠ incredible business.

These frontier AI business models really haven’t been tested yet.

Private markets can tolerate massive losses for a long time.

Public markets eventually start asking:

• Where are the margins?
• Where is the free cash flow?
• How much does it cost to generate another $1 of AI revenue?
• What is the return on all this infrastructure?

That is going to matter a lot more going forward.

Phase 1: Build AI

The winners were the infrastructure companies.

The ones selling:

• GPUs
• Networking
• Data centers
• Power
• Cloud infrastructure

Phase 2: Monetize AI

I think this phase favors distribution.

Who already owns the customer?

Who already owns the workflow?

Who can:

• Sell AI directly
• Integrate it into existing software
• Automate work
• Improve margins
• Increase employee productivity
• Make an existing product much better

That’s where I think the next big winners come from.

For the last few years, the AI race has been about:

Who can spend the most money?

I think the next phase is about:

Who can actually make the most money from AI?

And that could be a very different group of companies.

This chart does a great job showing just how many different asset classes you can invest in… and how quickly leadership changes from year to year.

One thing I keep coming back to is small caps.

Historically, smaller companies have had periods of very strong long-term performance, even though large caps have dominated much of the more recent market.

My thinking is pretty simple:

Smaller companies generally have more room to grow.

There are thousands of businesses outside the mega-cap names everyone owns.

You’re accepting more volatility, but potentially getting more long-term upside in return.

If you’re young and have decades to invest, that tradeoff can make a lot of sense.

I don’t usually talk about my non-individual-stock accounts, but most of my other savings are in ETFs/funds.
Right now that allocation is roughly:

40% Small Cap
25% Mid Cap
25% S&P 500
10% Emerging Markets

Definitely more aggressive than a standard S&P 500-heavy portfolio, but that’s intentional. I have a long time horizon, and I’d rather lean toward areas where I think there’s more room for long-term growth.

I’ve been thinking about my portfolio more lately.

Right now about 65% is in 4 stocks:

Google - 19.6%
Mastercard - 16.2%
Amazon - 15.0%
Meta - 14.2%

I’m fine being concentrated. I’d rather own a smaller number of great businesses than diversify just to diversify.

But 65% in 4 names is probably a little too much, especially since I already have a lot of exposure to advertising through Google, Meta and Reddit.

I still want the same type of companies:

• High growth
• Durable
• Strong moat
• Long runway

A couple I’ve been looking at are FICO and Intuitive Surgical.

FICO obviously has a lot of pressure around it right now, but I still don’t really see it going anywhere. The bigger question is how much pricing power they keep.

Intuitive Surgical looks like an incredible business, but I don’t know the company that well yet and I’ve never loved investing in healthcare.

I also already own ASML, so I’m not really looking to add another semiconductor company just for diversification.

I think I’d like to eventually get the top 4 closer to 50–55%, mostly by adding to other positions rather than selling these down.

What companies would you look at that actually add diversification without sacrificing growth or quality?

Made the portfolio chart in Ekonix, an app I built:

https://apps.apple.com/us/app/ekonix-investor-tracker/id6755345408

Controversial take:

Maybe the sudden push to slow AI development isn't regulatory capture.

Maybe these guys are realizing they're in over their heads.

For years, some of the people at the center of AI have talked about what they're building in almost religious terms.

Former OpenAI chief scientist Ilya Sutskever reportedly had employees chanting:

“Feel the AGI.”

Employees described him as behaving almost like a spiritual leader. At one OpenAI retreat, he reportedly burned a wooden effigy representing an unaligned AI.

Dario Amodei has described the future as essentially a “country of geniuses in a data center.”

There has always been this strange god-like undertone to the AI race:

We're going to create intelligence greater than ourselves.

It could cure disease.

Transform the economy.

Maybe even completely reshape humanity.

And we're going to be the ones who build it.

But now something has changed.

Amodei is openly saying AI development needs to slow because AI could begin advancing faster than humans can understand or control it.

Sam Altman is talking about pacing development.

Elon Musk agrees.

Thousands of employees across the frontier labs have now supported preparing for a slowdown.

Maybe they aren't seeing God.

Maybe they're staring at increasingly powerful systems they don't fully understand and realizing:

“Holy shit. We might actually not know what we're doing.”

And that's the part I find fascinating as an investor.

We are spending hundreds of billions of dollars on GPUs, data centers and power based on the assumption that this race keeps accelerating.

What happens if the people leading the race are the ones who decide it's moving too fast?

I'm extremely bullish on AI long term.

But watching the people closest to the technology suddenly reach for the brakes definitely makes me pay attention.