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Today at a glance:

1. 🕶️ Meta: AI Bet Takes Shape

2. 🚖 Tesla: $5 Billion CapEx Reversal

3. 🧴 P&G: Volume Returns

4. 🧠 Lam Research: AI's Storage Problem

5. ☁️ SAP: Backlog Redemption

6. ⚙️ Texas Instruments: Industrial Breaks Out

7. 🌐 IBM: Mainframe Comeback

8. 💳 Amex: Premium Trade-Off

9. 🧬 Abbott: A Leaner Future

10. 📞 AT&T: Cost of Convergence

11. 🦾 Intuitive Surgical: Cost of Expansion

12. 🛰️ Lockheed Martin: $292 Billion Backlog

13. 🎧 Spotify: Agentic Advantage

14. 💼 Moody’s: AI Financing Boom

15. 🚗 GM: Cash Over Growth

16. 🛩️ United: Pricing Power Meets Its Limits

17. 🛩️ Southwest: Fuel Squeeze

18. 🦅 American Airlines: Fuel Trap

19. 🏡 Appfolio: AI Goes to Work

1. 🕶️ Meta: AI Bet Takes Shape

Meta’s Q3 revenue rose 26% Y/Y to a record $64.6 billion ($1.2 billion beat), while adjusted EPS reached $7.40 ($0.60 beat). GAAP EPS of $3.83 included a $10 billion legal charge tied to its settlement of youth-safety lawsuits.

Shares fell 7% as investors looked past strong advertising growth and encouraging early traction for Muse, and focused instead on mounting AI spending and deteriorating free cash flow.

Ad revenue grew 26% Y/Y to $63.1 billion, with impressions up 10% and average price per ad rising 14%. That’s a shift from Q2, when impression growth (14%) outpaced pricing growth (12%). Meta is increasingly monetizing its existing audience more effectively rather than simply showing more ads. Family daily active people reached 3.64 billion (+3% Y/Y), while AI-powered recommendations and targeting continued to support engagement and conversion rates.

But the bigger story is Muse becoming a tangible business opportunity just six weeks after launch:

• More than 10 million users have tried Muse in the US and Canada, with approximately 25% returning on a given day. That’s promising early engagement, although retention and monetization remain unproven at scale.

• Shopping is the leading use case, with more than 25% of users engaging in commerce activities. Meta sees opportunities in subscriptions, transaction fees, and eventually advertising, extending its reach from influencing purchases to facilitating them.

• Muse Wonder, Meta’s first family of frontier models, is already powering new capabilities. Management expects to improve Muse’s compute efficiency by more than 10x over the next year, an important step toward making the agent economically viable.

Zuck believes Muse could eventually serve billions of people and generate more revenue per user than Meta’s existing social platforms. The ambition is enormous, but so are the obstacles. Amazon and other websites have blocked Muse from shopping on their platforms, illustrating why Meta is developing a Personal Agent Protocol to enable structured interactions between agents and businesses. Winning the interface doesn’t automatically guarantee access to the transaction.

Meanwhile, the AI bill keeps growing:

• Free cash flow turned negative for the first time, at -$2.1 billion, compared with $784 million in Q2 and $10.6 billion a year ago. Capital expenditures reached $34.9 billion, up from $31.1 billion in Q2.

• Meta raised the floor of FY26 CapEx guidance again, to $135–145 billion (from $130–145 billion). Even excluding the legal charge, operating margin declined to roughly 35% from 40% a year ago as AI infrastructure depreciation and R&D spending increased.

• Reality Labs lost another $4.1 billion. Management now expects annual losses of $18–19 billion for the segment in FY26 and approximately $15 billion in 2027 as resources shift toward AI.

Meta also announced a new class of non-voting shares, effectively creating a 2-for-1 stock split in January 2027. The Class C shares could fund acquisitions, employee compensation, or future capital raises without diluting Zuck’s voting control. It’s a notable move for a company that historically funded its ambitions through advertising cash flow.

More legal risks loom. Beyond the $10 billion Q3 charge, a New Mexico judge ordered Meta to pay $25 billion over alleged consumer deception related to privacy and content moderation. Meta plans to appeal, and the judgment is separate from the previously announced multistate settlement.

Meta guided Q4 revenue to $73–76 billion (midpoint above consensus) and raised FY26 expense guidance to $175–179 billion, incorporating the legal charge. Management still expects FY26 operating income, excluding that charge, to exceed last year’s level.

Takeaway: Meta’s ad business remains remarkably strong, and Muse offers a more tangible path toward a new revenue stream. But AI spending now exceeds operating cash flow. Can Muse become a meaningful business before infrastructure costs put further pressure on margins? Early adoption is encouraging, but monetization remains unproven.

2. 🚖 Tesla: $5 Billion CapEx Reversal

Tesla’s Q3 offered a mixed picture. Automotive margins recovered, and FSD adoption accelerated. But vehicle sales remain stagnant, while heavy investment in AI and robotics continues to weigh on profitability.

The biggest surprise? Tesla returned to positive free cash flow after burning cash last quarter, helped by a significant slowdown in capital expenditures.

• Revenue was flat Y/Y at $28.1 billion ($0.2 billion miss).

• Gross margin reached 20% (+2pp Y/Y and +3pp Q/Q).

• Operating margin fell to 3% (-3pp Y/Y).

• Non-GAAP EPS was $0.48 ($0.03 beat).

• Operating cash flow reached $6.3 billion, while free cash flow fell 62% Y/Y to $1.5 billion as CapEx declined Q/Q from $5.8 billion to $4.7 billion.

Tesla lowered its FY26 CapEx outlook from more than $25 billion to ~$20 billion. With $13 billion spent through September, that implies roughly $7 billion in Q4, still a meaningful acceleration from Q3.

Management expects spending to grow beyond 2026 as Tesla expands Robotaxi, Optimus, AI compute, solar, and semiconductor manufacturing. Tesla ended Q3 with $45.5 billion in cash and investments, plus about $30 billion in borrowing capacity it does not expect to draw on this year.

Now, here are the main insights from the call:

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Netflix has proved that streaming can generate enormous profits. But can it sustain growth when viewers have more entertainment choices than ever?

Management admits recent execution has been hit or miss, and the company is replacing longtime content chief Bela Bajaria with former Disney executive Peter Rice, signaling a renewed push for watercooler hits.

The stock price has dropped more than 45% over the past year, and the company recently announced its largest round of layoffs since 2022, eliminating roughly 1,000 positions.

Netflix is on track to surpass $50 billion in annual revenue, generates billions in free cash flow, and remains the world's largest paid streaming platform. Yet its share of US television viewing has plateaued, while YouTube and other competitors continue to gain share on TV.

Alongside its core programming, Netflix is experimenting with new ways to capture consumer attention. Live sports, video podcasts, creator content, and partnerships with traditional broadcasters offer new ways to attract and retain subscribers.

Today at a glance:

• 🍿 Netflix Q3 FY26

• 📈 YouTube pulls away

• 📺 Not all viewing hours are equal

• 💬 Salesforce buys Braze for $5.7B

🍿 Netflix Q3 FY26

Income statement:

• Revenue +12% Y/Y to $12.9 billion (in line).

• Operating margin 35% (+6pp Y/Y).

• EPS +49% Y/Y to $0.87 ($0.05 beat).

Balance sheet:

• Cash and short-term investments: $8.2 billion.

• Debt: $15.3 billion.

FY26 guidance:

• Revenue +13% to $51.1 billion (slightly below the prior midpoint).

• Operating margin 31.5% (+2pp Y/Y, unchanged).

• Free cash flow ~$12.0 billion (down from $12.5 billion).

So, what to make of all this?

• 📉 Growth keeps slowing: Revenue growth decelerated to 12%, its slowest pace in nearly three years, with Q4 guidance implying roughly the same rate. Operating margin surged 6 points to 35%, although the beat reflected favorable expense timing. Management reiterated its 31.5% full-year margin target despite restructuring costs in Q4. Subscription revenue is outperforming expectations, supported by price increases, membership growth, and improving churn. But margin expansion alone may no longer satisfy investors.

• 📢 Ads miss the mark: Netflix cut its 2026 advertising revenue forecast from ~$3 billion to ~$2.7 billion, blaming a slower-than-expected rollout of ad products. That represents a 10% cut, even though ad revenue is still expected to grow over 80% Y/Y. Netflix continues to expand programmatic access and measurement capabilities, with plans to introduce its ad tier in 15 additional countries in 2027. The opportunity remains substantial, but future growth will depend on technical execution and sustained viewer engagement.

• 🌍 Becoming an entertainment aggregator: Netflix is expanding beyond its own productions by integrating third-party programming directly into its service. Its partnership with French broadcaster TF1 is already generating incremental viewing, while a new Disney licensing agreement brings titles such as Ice Age to the platform. Management is open to additional integrations, potentially including subscriptions to other streaming services. The irony is that Disney is using Netflix to build awareness for its own upcoming theatrical releases. Even Hollywood's largest studios recognize Netflix's distribution value. Netflix may not always have the best original programming, but its distribution advantage is difficult to replicate. That could help the company expand its entertainment offering without continually increasing content spending.

• 💰 Record buybacks continue: Netflix repurchased another $4.8 billion of stock, its largest quarterly buyback ever, leaving $22.3 billion in remaining authorization. Free cash flow rose to $2.9 billion from $2.7 billion a year ago. Management reduced its full-year FCF forecast from $12.5 billion to about $12 billion, mainly because a real estate transaction is expected to close in Q4. Netflix continues to generate substantial cash and return it to shareholders, even as revenue growth moderates.

📈 YouTube pulls away

Streaming keeps taking share from traditional television, but Netflix isn’t keeping pace. According to Nielsen, streaming reached a record 49.0% of US TV viewing in July, up from 47.3% a year ago.

Netflix captured 7.8% of US TV time, down from 8.8% a year earlier. YouTube, meanwhile, reached a record 14.2%, widening its lead over Netflix from 4.6 to 6.4 percentage points in just 12 months. Americans now spend nearly twice as much time watching YouTube as Netflix on their TVs. And that doesn't even include YouTube TV.

Netflix had an unusually strong summer last year, when Squid Game and other hits dominated the charts. Monthly viewing fluctuates with release schedules, and Nielsen only measures US television screens (excluding mobile and laptop viewing). Still, the trend highlights how difficult it is to sustain engagement against free services and platforms built around seemingly endless creator content.

Netflix has acknowledged the challenge. Co-CEO Ted Sarandos said that engagement wasn’t growing as quickly as he wanted. New content chief Peter Rice previously oversaw Fox Searchlight, which delivered hits like Slumdog Millionaire, before taking senior leadership roles at Disney. His appointment suggests Netflix wants to improve its ability to produce breakout US hits that can travel internationally.

Yet Netflix’s own engagement data paints a more nuanced picture. Worldwide viewing hours rose 2% in H1 2026, up from 1.5% growth in 2025. Netflix hasn’t disclosed Q3 viewing hours, but management reported improving churn globally and in the US. Engagement continued to grow even as Netflix’s share of US television viewing declined. In short, subscriber retention remains healthy, but subscriber loyalty masks a gradual erosion of its share of screen time.

📺 Not all viewing hours are equal

Netflix argues that viewing hours alone don’t capture the value of its programming. Its live events illustrate why. Live programming represents just over 5% of Netflix’s content spending but only about 1% of viewing hours. Yet live events drove six of its 10 biggest sign-up days over the past five years.

That makes sports potentially more valuable than their modest engagement contribution suggests. A major NFL game or boxing match can attract new subscribers, create premium advertising inventory, and generate cultural relevance. The economics depend less on how long viewers watch than on whether they remain subscribers afterward.

Netflix is also exploring lower-cost ways to become a daily entertainment destination. Its partnership with French broadcaster TF1 achieved the broadcaster’s 18-month audience targets in less than three weeks. TF1+ daily audiences increased 16% during launch week, while Netflix reported growing viewing hours for TF1 programming. The early results are encouraging, although they don’t yet establish how much incremental value Netflix captures.

Video podcasts and creator-led programming offer another way to expand viewing occasions without the production costs of premium scripted entertainment. Netflix is even experimenting with theatrical releases. Greta Gerwig’s upcoming Narnia adaptation will receive a wide theatrical release before arriving on the service, testing whether the big screen can help build a streaming franchise.

Still, management isn’t abandoning its core business. Roughly 95% of spending remains focused on its core streaming offering, including series, films, and live events. These newer initiatives complement the core business rather than represent a fundamental shift in strategy.

Netflix’s challenge is to capture more valuable viewing occasions, not necessarily the most viewing hours. A blockbuster can drive sign-ups, a licensed show can improve retention, and live sports can attract advertisers. Each serves a different purpose.

Management points to improving churn and successful price increases as evidence of healthy engagement. But churn could be a lagging indicator. Subscribers could gradually spend less time watching Netflix long before they consider canceling.

Netflix has proven it can raise prices and expand margins. Now it needs to show that it can grow without relying primarily on charging existing subscribers more.

💬 Salesforce buys Braze for $5.7B

Salesforce agreed to acquire customer engagement platform Braze for $5.7 billion in enterprise value, offering $47.50 per share in cash, a 58% premium to its previous closing price. The acquisition raises an obvious question: Why does Salesforce need Braze when it already owns Marketing Cloud?

Braze specializes in real-time customer engagement. Rather than relying on scheduled marketing campaigns, its software reacts to customer behavior across mobile apps, websites, email, and other channels. Think of an abandoned shopping cart triggering a personalized offer, or a streaming service reaching out to a subscriber showing signs of leaving. Customers include Canva, Instacart, and Venmo.

Braze’s Q2 FY27 results illustrate both the appeal and the challenges of the business:

• Revenue grew 26% Y/Y to $227 million, with subscriptions accounting for 92%.

• Dollar-based net retention reached 110%, indicating continued expansion among existing customers.

• Sales and marketing consumed 41% of revenue, contributing to an $18 million operating loss despite a healthy 67% gross margin.

At roughly 6.2x annualized revenue, the acquisition isn’t outrageously expensive for a growing software business. Braze also generated $22 million in quarterly free cash flow, suggesting its underlying economics are improving despite its GAAP losses.

The strategic appeal is clear. Salesforce has customer data, enterprise relationships, and its Agentforce AI platform. Braze adds a specialized engagement engine that can translate those insights into personalized actions across consumer-facing channels. AI agents become more valuable when they can act on customer information. Salesforce could also accelerate Braze’s adoption through its massive enterprise distribution network.

But there are risks. Braze overlaps with Salesforce’s existing Marketing Cloud capabilities, creating potential integration challenges and product confusion. Its appeal as an independent platform could also suffer if customers fear being pushed into Salesforce’s broader ecosystem. And while cross-selling could improve margins, Salesforce must demonstrate that another acquisition actually accelerates organic growth.

The bigger picture is that Salesforce is trying to own more of the customer relationship, from data and AI-driven decisions to the interactions that follow. Braze could help close that loop. But can the integration deliver enough synergies and additional growth to justify the purchase price?

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Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.

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SpaceX just delivered its first profitable quarter as a public company.

Revenue surged 161% to $14.6 billion, and net income reached $4.4 billion. The rebranded Super Intelligence (SI) segment now generates more revenue than Starlink, transforming the rocket manufacturer into an AI powerhouse. But that growth came with nearly $20 billion in quarterly CapEx.

An even bigger move may be coming soon.

During the earnings call, Musk confirmed that SpaceX and Tesla have formed special committees to explore a merger. A combination could create a company worth more than $3 trillion, bringing Musk's AI, robotics, and space ambitions under one roof.

But would a merger unlock greater value for shareholders, or simply combine two enormous investment programs with very different economics?

Today at a glance:

• SpaceX Q3 FY26.

• Starlink opens new fronts.

• Key insights from the call.

• SpaceX + Tesla: One Musk empire?

1. SpaceX Q3 FY26

Income statement:

SpaceX beat Wall Street expectations in Q3, with revenue $1.6 billion ahead and GAAP EPS of $0.35 ($0.19 beat). Adjusted EBITDA quadrupled Y/Y to $8.8 billion, driven by growth across Connectivity and SI.

The three businesses are moving at very different speeds:

• 🚀 Space revenue grew 6% Y/Y to $1.8 billion (+84% Q/Q) as SpaceX completed 12 customer launches. The segment roughly broke even despite continued investment in Starship, which generated revenue for the first time. Its successful deployment of next-generation Starlink satellites marks an important step toward commercial operations.

• 🛰️ Connectivity revenue jumped 69% Y/Y to $5.2 billion (+22% Q/Q), supported by consumer growth (+57%) and enterprise and government demand (+91%). Starlink subscribers reached 13.6 million (+89% Y/Y), but average revenue per user fell 17% (+5% Q/Q) to $69 as the service expanded internationally. Operating margin improved 7 percentage points to 45%.

• 🤖 Super Intelligence revenue soared 830% Y/Y to $7.6 billion (+195% Q/Q), becoming SpaceX's largest segment with 52% of total revenue. Growth reflected new cloud computing agreements, increased Grok adoption, and the acquisition of AI coding platform Cursor, which contributed $608 million in revenue from its August 14 acquisition through September 30. The segment swung from a $1.7 billion operating loss a year ago to $431 million in operating profit, although its 6% margin remains far below Starlink's 45%.

Cash flow:

• Operating cash flow reached $9.6 billion in Q3 (+156% Y/Y).

• CapEx reached $19.6 billion, up from $18.4 billion in Q2. And management expects a much bigger jump in Q4.

• Free cash flow was negative ~$10.0 billion, compared with negative ~$16.0 billion in Q2.

Super Intelligence accounted for $16.4 billion, or 84% of total CapEx in Q3, compared with $1.6 billion for Space and $1.5 billion for Connectivity. AI compute capacity increased from 1.4 GW to 1.7 GW during the quarter.

The operating cash flow improvement is encouraging, but SpaceX still invests roughly twice as much as it generates from operations.

Balance sheet:

• Cash, cash equivalents, and marketable securities: $89.9 billion, down from approximately $100 billion at the end of Q2.

• Debt and finance leases: $38.8 billion, including $36.1 billion of long-term obligations.

• Net cash: approximately $51.0 billion, before other financing obligations or restricted-cash adjustments.

So, what to make of all this?

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Today at a glance:

1. 🔬 ASML: Demand Reaches 2030

2. 💊 J&J: MedTech Needs Work

3. 💼 UnitedHealth: Turnaround Broadens

4. 🇺🇸 Bank of America: Fine Masks NII Growth

5. 👔 Morgan Stanley: IPOs Feed Wealth

6. 📈 BlackRock: Fee Growth Hits 11%

7. 🏦 Schwab: Lending Boom

8. 🍕 Domino’s: Orders Up, Check Down

1. 🔬 ASML: Demand Reaches 2030

ASML’s Q3 revenue surged 55% Y/Y to a record €11.7 billion (€70 million miss), while GAAP EPS reached €12.93 (€2.14 beat). The EPS beat was partly flattered by a €0.5 billion valuation gain on ASML’s investment in Mistral AI, but the underlying quarter was still impressive. Gross margin reached a record 57%, well above guidance, as high-margin upgrades pushed Installed Base Management sales to €3.3 billion.

The biggest insight was how far out the AI capacity cycle has moved. Customers are increasingly placing orders for 2028 and asking ASML to accept orders for 2029 and even 2030. Management now says its currently planned supply will not fully meet demand in either 2027 or 2028. That is remarkable because ASML already planned to increase Low-NA EUV and immersion DUV capacity by roughly 30% in 2027, followed by another ~30% increase in 2028. It is now exploring an even larger ramp in 2028.

The demand is increasingly broad:

• Memory system sales are expected to grow ~75% in FY26, driven largely by DRAM capacity and greater EUV adoption.

• Advanced logic is growing more than 25%, even though total logic growth is closer to 10%.

• EUV sales should grow more than 45% this year, while Installed Base Management is now expected to grow roughly 50%.

• ASML shipped 113 new lithography systems in Q3, up from 86 in Q2.

ASML has now secured price increases on some of its most critical high-volume tools. Management expects the benefit to begin showing up in 2027 and become fully visible in 2028. Management stressed this isn’t opportunistic annual repricing, but it does confirm that ASML can capture more of the value its bottleneck technology creates.

High-NA is moving closer to actual production. Intel has now processed more than one million wafers using High-NA, Samsung plans high-volume adoption in 2028, and TSMC now targets 2030. ASML expects High-NA usage to expand node by node as customers validate the economics.

China remains the geopolitical wildcard. It represented 23% of Q3 system sales, while ASML still expects roughly 20% of full-year revenue from China. Management argues underlying Chinese demand remains strong, but further restrictions on immersion DUV could eventually accelerate development of domestic alternatives.

ASML raised FY26 revenue guidance again to €44–€45 billion (from €43–€45 billion) and now expects a gross margin of roughly 56%.

Takeaway: ASML has moved from ramping capacity to figuring out how much further it can stretch the supply chain. Customers are already signaling demand into 2030, while pricing increases and High-NA add further upside. Now it’s about ASML’s ability to build enough machines.

2. 💊 J&J: MedTech Needs Work

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💸 AI’s debt problem

Financing is the latest obstacle in the AI infrastructure boom.

CoreWeave shares plunged more than 15% on Wednesday, while yields on some of its bonds have climbed above 16%, a level associated with distressed debt. The NVIDIA-backed cloud provider has taken on tens of billions of dollars in debt to build the data centers powering the AI boom.

The pressure extends beyond CoreWeave. Oracle and other AI infrastructure companies have also faced selling pressure as investors reassess their borrowing costs and capital requirements. Higher interest rates make an already expensive buildout even more costly.

For highly leveraged AI infrastructure providers, the concern is shifting from demand for computing power to the cost of supplying it. Companies are committing enormous amounts of capital today against revenue they expect to collect over many years.

NVIDIA CEO Jensen Huang pushed back on Thursday, insisting that CoreWeave’s financial position is improving. But NVIDIA owns roughly 10% of CoreWeave and is also one of its biggest suppliers. Huang has every incentive to defend its expansion, even as bond investors demand higher returns to finance it.

Today at a glance:

• ⚡ TSMC is not slowing down

• 🤖 The model race heats up

• 🏠 Apple moves into the living room

💯 TSMC is already a 100-bagger

Its US-listed shares have returned more than 100x since 1997.

I put together a free 35-page report on what to look for in quality stocks before they become obvious.

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⚡ TSMC is not slowing down

Taiwan Semiconductor (TSMC) is investing at a scale few companies can match. Its CapEx budget has climbed from $41 billion in 2025 to $60–$64 billion in 2026, with cumulative spending over 2026–2028 expected to substantially exceed the previous three years.

CEO C.C. Wei has little interest in leaving opportunities to competitors:

“We have no intention to leave food on the table for others, and we want to capture every piece of business possible.”

Another record quarter backs his conviction. Q3 Revenue grew 41% Y/Y to $46.7 billion, and operating margin reached nearly 59%. TSMC is becoming more profitable even as it undertakes the largest manufacturing expansion in its history.

Q3 in numbers

Income statement:

• Revenue +41% Y/Y to $46.7 billion ($0.9 billion above the high end of guidance).

• Gross margin 66% (-1pp Q/Q).

• Operating margin 59% (+8pp Y/Y).

• EPS per ADR $4.60 (+69% Y/Y in local currency).

Cash flow:

• Free cash flow margin: 32% (+18pp Y/Y).

Balance sheet:

• Cash and marketable securities: ~$126 billion.

• Interest-bearing debt: ~$32 billion.

Guidance:

Management raised its FY26 revenue growth outlook to approximately 44% in USD, up from slightly above 40%. Q4 guidance of $52.3–$54.3 billion implies 58% Y/Y growth at the midpoint, a sharp acceleration.

So what to make of all this?

🤖 AI demand keeps surprising management. TSMC previously forecast mid-to-high-50% annual growth in AI accelerator revenue through 2029. C.C. Wei now suggests even that ambitious forecast could prove conservative:

“The demand is extremely robust. We did not see any sign of slowdown. Whether it is a high 50 CAGR, it does not matter. It probably will be higher.”

The forecast excludes conventional CPUs and networking chips, which are also benefiting from the rise of AI agents. TSMC’s opportunity extends well beyond GPUs, as its changing revenue mix shows. HPC (High Performance Computing) now accounts for 68% of revenue, up from 42% just three years ago.

🏗️ TSMC wants every available order. Yet the company isn’t simply taking its customers’ forecasts at face value. It checks their financial strength, visits data center construction sites, and verifies electricity availability before committing additional capacity. These checks help explain why management remains confident enough to invest tens of billions in factories that take years to build. They also connect directly to the financing concerns facing some AI infrastructure providers.

⚙️ Manufacturing leadership comes at a cost. The new 2nm process already accounts for 8% of wafer revenue, with capacity expected to more than double by 2027. Alongside advanced packaging and 3nm expansion, this reinforces TSMC’s position as the preferred manufacturer for the most demanding chips. However, the initial 2nm ramp contributed to the sequential decline in gross margin. Overseas expansion will add further pressure, with international factories expected to dilute consolidated gross margin by 3–4 percentage points over the next few years.

💰 How profitable can TSMC become? Despite rising investment and manufacturing costs, operating margin has climbed to nearly 59%. When asked about the longer-term potential, Wei replied:

“There is no ceiling for TSMC’s profitability. But a dramatic increase will not happen overnight either.”

TSMC’s technological lead and scarce capacity give it considerable pricing power. But management is also warning that rising component costs and macroeconomic uncertainty are hurting price-sensitive consumer markets. The company enters 2027 with exceptional AI demand, but not every part of the semiconductor industry is seeing the same conditions.

🤖 The model race heats up

Google and Meta made major moves in the AI model race this week, challenging OpenAI and Anthropic's perceived lead.

Google launched Gemini 4 Argon, its latest frontier model, claiming leadership across several benchmarks involving coding, finance, law, and cybersecurity. Its performance on professional tasks (Vals Index) is particularly noteworthy as AI agents move beyond answering questions to performing actual work.

Argon is available through Google’s API, Gemini app, Search, and coding tools. Google can distribute increasingly capable AI to billions of existing users, an advantage over independent AI labs that must build their own distribution.

Meanwhile, Meta introduced Muse Wonder and Muse Wonder Full, its most capable models to date. After struggling to keep pace at the frontier, Meta claims competitive performance with OpenAI and Anthropic. The models emphasize memory and the ability to carry out complex, multi-step tasks. Meta plans to bring these capabilities to its Muse personal AI agent.

More competition could pressure model pricing, while more capable agents could increase computing demand. Unlike chatbots that answer individual questions, agents can work through lengthy tasks involving dozens or hundreds of steps, consuming far more tokens along the way.

Better models and lower pricing don’t necessarily mean less infrastructure spending. They could make AI useful enough to justify even more investment.

🏠 Apple moves into the living room

Apple unveiled HomeView, a $299 smart-home display arriving November 13. It’s built around an upgraded Siri, alongside new HomePod and Apple TV devices. The seven-inch display recognizes household members and draws on personal information from their iPhones to answer questions and handle everyday tasks.

Apple also introduced cameras, thermostats, and smart locks developed with LG and Schneider Electric. These accessories work directly through Apple Home, without separate apps or accounts. The new Apple TV adds far-field microphones for hands-free Siri commands.

Apple is betting that its hardware ecosystem and access to personal data can make Siri the default interface for the connected home. But there’s a catch: HomeView still relies on a nearby, recently unlocked iPhone for personalized features, and many of its use cases already work on the phone itself.

The real test is whether Apple’s integration can compensate for Siri’s limitations. Amazon and Google already have established smart-home ecosystems, while AI agents are becoming increasingly capable of handling tasks without traditional apps.

That's it for today.

Happy investing!

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Disclosure: I own AAPL, AMZN, GOOG, META, and TSM in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members. 

Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.

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A new earnings season is here, with the big banks kicking us off.

Later this week, we’ll have a look at TSMC and ASML for our first signals from the AI infrastructure boom.

AI is already making waves in banking. JPMorgan CEO Jamie Dimon warned today about letting AI agents access customer accounts:

“If people are using them, they should understand the risk. […] We’re not going to allow just any outside agent to come in inside the company because that’s just too risky for clients and for JPMorgan.”

The concern goes beyond security. AI agents could eventually help consumers move their money between banks, threatening the cheap deposits that underpin their profitability. Wells Fargo CEO Charlie Scharf dismissed fears of an imminent disruption as “completely overdone.”

Four banking giants just reported $32 billion in combined quarterly profits this morning. Trading momentum is becoming uneven, but lending growth and resilient consumers continue to support earnings.

Let’s break down the results.

Today at a glance:

• JPMorganChase: Lending Takes the Lead

• Goldman Sachs: Equities Steal the Show

• Wells Fargo: Bigger Isn't Better Yet

• Citigroup: Turnaround Gains Ground

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The Big Picture

As a reminder, banks make money through two main revenue streams:

1. 💵 Net Interest Income (NII): The difference between interest earned on loans (like mortgages) and interest paid to depositors (like savings accounts). It’s the primary source of income for many banks and depends on interest rates.

2. 👔 Noninterest Income: The revenue from services unrelated to interest. It includes fees (like ATM charges), advisory services, and trading revenue. Banks relying more on noninterest income are less affected by interest rate changes.

Here are the significant developments shaping Q3 FY26:

• 📊 The trading boom splits: Equities remain on fire, led by Goldman Sachs (+72% Y/Y) and JPMorgan (+49%). But fixed-income trading declined at both banks as higher bond yields weighed on activity. Citi bucked the trend with 4% growth. Wall Street’s trading windfall is becoming increasingly uneven.

• 🏦 Higher rates cut both ways: JPMorgan raised its FY26 net interest income outlook to $106.2 billion, while Citi also lifted its guidance. Wells Fargo, however, held its outlook unchanged. Higher rates support lending income but also raise funding costs and create losses on existing bond holdings.

• 💳 Consumers keep spending: Card spending grew 10% at JPMorgan and 13% at Citi, while credit losses remained manageable across the group. Businesses are also holding up, supported by strong balance sheets and investment in AI infrastructure. Despite sticky inflation and higher borrowing costs, the anticipated credit deterioration has yet to materialize.

• 🎯 Profitability takes center stage: Citi raised its FY26 return target above 11% as its restructuring delivers results. Wells Fargo continues expanding its balance sheet after the removal of its asset cap, but revenue growth remains modest. The next challenge is converting growth into sustainable profits without letting expenses or funding costs erode the gains.

• 🔑 Takeaway: The banks are still growing, but the easy gains from the capital-markets boom are fading. Strong lending activity and healthy credit provide a solid foundation, while the divergence in trading and margins highlights how differently each franchise is positioned. Can earnings growth outlast the unusually favorable conditions of the past few quarters?

Let’s visualize them one by one and highlight the key points.

JPMorganChase: Lending Takes the Lead

• Net revenue grew 8% Y/Y to $50.2 billion ($0.3 billion miss):

  • Net interest income (NII): $27.0 billion (+13% Y/Y).

  • Noninterest income: $23.1 billion (+3% Y/Y).

• Net income: $15.3 billion (+6% Y/Y).

• Adjusted EPS: $5.75 ($0.19 miss), excluding $658 million in investment securities losses. Reported EPS was $5.57.

Key developments:

• 📉 Trading momentum fades: Trading revenue grew 15% Y/Y to $10.3 billion but missed expectations. Equities surged 49% to $5.0 billion, JPMorgan’s best third quarter ever, while fixed-income trading declined 4% to $5.4 billion. Investment banking fees rose 14% to $3.0 billion but fell sequentially as dealmaking slowed in September. Wall Street activity is cooling after an exceptional Q2.

• 🏦 Interest income surprises: NII rose 13% to $27.0 billion, prompting JPMorgan to raise its FY26 outlook to ~$106.2 billion (from ~$105.5 billion). Management also introduced a 2027 base case of ~$107 billion in NII excluding Markets, up ~10% from its FY26 estimate of $97.2 billion on the same basis. Loan growth and higher rates are strengthening the bank’s core earnings engine.

• 💳 Consumers remain resilient: Consumer banking revenue grew 9% to $21.2 billion, helped by 10% growth in card spending. JPMorgan added more than 650,000 net new checking accounts. The card net charge-off rate improved to 2.91% (from 3.34% in Q2), while management maintained its FY26 forecast of ~3.2%.

• ⚠️ Credit quality holds up: Credit-loss provisions fell 19% Y/Y to $2.7 billion, while net charge-offs declined to $2.2 billion. Consumer loan charge-offs improved to 1.27% (from 1.42% in Q2), while wholesale losses remained low at 0.11% (from 0.10%). However, JPMorgan added $452 million to wholesale reserves, reflecting loan growth and some changes in borrower credit quality.

• 💰 Buybacks slow sharply: JPMorgan repurchased just $1.1 billion of stock (from $6.7 billion in Q2), despite its new $50 billion authorization. Management favors investing in the franchise and supporting client lending over repurchases. With a 13.9% CET1 ratio, the slowdown appears to be a capital allocation choice rather than a constraint.

• 📈 Wealth management expands: Revenue rose 14% to $6.9 billion, supported by $263 billion in client asset net inflows. Assets under management reached $5.2 trillion (+13%), while total client assets climbed to $7.9 trillion (+15%). Growing fee-generating assets help diversify earnings beyond lending and trading.

• 💸 Expenses keep rising: Costs increased 12% to $27.3 billion, prompting JPMorgan to raise FY26 adjusted expense guidance to ~$108.6 billion (from ~$107.5 billion). Higher compensation, technology, and marketing spending drove the increase. Costs are growing faster than revenue, pressuring operating leverage despite healthy business activity.

• 🔑 Takeaways: JPMorgan’s earnings miss reflects weaker capital markets activity rather than broad deterioration. Rising NII, resilient consumers, and continued loan growth provide an offset. The decision to sharply reduce buybacks suggests management sees more attractive opportunities to deploy capital within the business.

Key quote:

CEO Jamie Dimon:

“Large fiscal deficits, sticky inflation, elevated asset prices, rising interest rates and geopolitical conditions continue to shift under the surface.”

Goldman Sachs: Equities Steal the Show

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Today at a glance:

1. 🥤 PepsiCo: International Takes Over

2. 🛩️ Delta: Fuel Eats the Upside

3. 🍺 Constellation: SpikedAde Joins the Mix

4. 🌿 Tilray: BrewDog Carries Growth

1. 🥤 PepsiCo: International Takes Over

PepsiCo’s Q3 revenue rose 6% Y/Y to $25.3 billion ($310 million beat), with core EPS of $2.34 ($0.04 beat). Organic revenue growth accelerated to 3.1%, its fastest pace since late 2023, helped by 3% beverage volume growth and 1% food volume growth.

The divergence between North America and the rest of the world keeps widening:

• International organic revenue grew ~8%, with EMEA up 9%. International now generates roughly 45% of PepsiCo’s profit year-to-date.

• North American Foods sales were flat, though volumes improved to low-single-digit growth after last year's declines.

• North American Beverages weakened, with CEO Ramon Laguarta admitting PepsiCo is “not competing well in soft drinks,” even as hydration and energy remain stronger.

GLP-1s and changing eating habits are pushing PepsiCo toward smaller portions and more protein- and fiber-focused products. Its “permissible” portfolio (better-for-you snacks) has already grown to roughly $3 billion, but it remains small relative to the traditional snack business under pressure.

PepsiCo also has an execution problem in its home market. Management plans to increase brand investment while cutting structural costs, and it is even open to selling more of its company-owned bottling operations to independent partners, making the business less capital-intensive.

Margins are getting harder to protect. Core operating profit grew 3%, but a $178 million tariff refund added roughly four percentage points to operating profit growth. Commodity costs are rising as hedges roll off, while PepsiCo is deliberately maintaining higher advertising spending to rebuild momentum.

PepsiCo now expects about 3% organic revenue growth in FY26, while cutting core constant-currency EPS growth to 2.5%–3.5% (from 4%–6%).

Takeaway: International is increasingly carrying PepsiCo while the North American turnaround takes longer than expected. With costs rising and US execution still uneven, management is moving beyond price tweaks toward a broader cost and operating-model reset.

2. 🛩️ Delta: Fuel Eats the Upside

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Great investing is 1% buying, 99% waiting

A 100-bagger is a stock that returns 100 times your initial investment.

Put in $10,000 and, eventually, you have $1 million.

Naturally, most investors focus on the first challenge: finding one.

But that is only half the battle.

In his book 100 to 1 in the Stock Market, Thomas Phelps showed how long it takes for compounding to turn an investment into a 100-bagger.

A stock compounding at 20% annually needs roughly 25 years to return 100 times your money. Even at an extraordinary 36% annual return, it still takes about 15 years.

And time is only part of the challenge.

Over those 15, 20, or 30 years, even the greatest winners can suffer brutal drawdowns. Apple endured repeated declines of more than 25% on its way to becoming a massive long-term winner, including two drops of roughly 80%.

Finding the right company is hard.

Holding it for 15, 20, or 25 years may be even harder.

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You will learn the following:

• 📊 What to look for

• 💯 A history of 100-baggers

• 🔎 Why they’re so hard to find

• 🧠 The mindset required to hold one

• ✅ A practical 100-bagger checklist

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📈 The power of compounding

A stock appreciates over time through two main forces:

• Earnings growth: The business makes more money.

• Multiple expansion: Investors pay more for each dollar of earnings.

Over long periods, earnings do most of the work. But exceptional companies can benefit from both. As profits compound and the market finally piles in, the valuation can get overextended.

That success creates the temptation to sell.

Once a stock has doubled or tripled, our natural instinct is to lock in the gains. If the valuation has expanded along the way, selling can feel even more prudent.

But extraordinary returns are heavily back-loaded. At a 20% annual return, an investment goes from roughly 38x after 20 years to nearly 100x after 25.

Those final five years create more wealth than the first 20 combined.

That is why selling a great company too early can be one of the most expensive mistakes an investor makes. Peter Lynch famously described this as “cutting the flowers and watering the weeds.”

The best businesses have a habit of repeatedly exceeding expectations. What looks expensive today can look much more reasonable several years later if earnings keep compounding.

Selling also creates another problem.

What do you buy instead? And when do you get back in?

None of this means you should never sell. If the competitive position deteriorates, earnings falter, or the original thesis breaks, the decision changes.

But I’ve adopted a simple rule:

A rising stock price is never a reason to sell on its own.

Compounding is beautiful. You have to resist the urge to interrupt it.

☕️ Pareto and the Coffee Can Portfolio

Stock returns are not evenly distributed.

In statistics, you’ll hear about two common patterns:

• Normal distribution: Most observations cluster around the average, while extreme outcomes are rare. Think human height or many natural measurements.

• Power law distribution: A small number of extreme outcomes account for a disproportionate share of the total. Think wealth, company size, or venture returns. Extreme outcomes occur far more often than under a normal distribution, creating a fat tail.

Investing follows the power law. More than a century of market history shows that a relatively small number of stocks have created a disproportionate share of long-term wealth.

That is why constantly rebalancing can work against you.

If one stock compounds much faster than the rest, it will naturally become a larger part of the portfolio. That concentration can feel uncomfortable and make the portfolio look weird.

But it may simply be the consequence of owning something exceptional.

If you repeatedly trim your best performers, you risk systematically cutting exposure to the very companies that can make the portfolio outperform.

Warren Buffett has famously allowed investments such as Coca-Cola and American Express to become very large positions rather than continually trimming them. Apple eventually grew to nearly half of Berkshire’s listed stock portfolio before Buffett began meaningfully reducing the position.

Robert Kirby captured the same idea with his Coffee Can Portfolio.

He once discovered that a client’s husband had quietly copied each of his stock recommendations, investing about $5,000 in every company. But with one crucial difference: he never sold.

Years later, the portfolio contained plenty of mediocre investments. But one position had grown so dramatically that it was worth more than the rest of the portfolio combined.

Kirby described the approach as being “passively active.”

• Active: Do the work upfront. Select the businesses carefully and decide what deserves your capital.

• Passive: Once you own them, give them time to compound.

Long-term investing demands active selection followed by patient ownership.

I can see that distribution clearly in App Economy Portfolio, the real-money portfolio I’ve shared publicly for the past decade.

A dozen holdings have compounded 5x, 10x, or 20x.

That creates a powerful asymmetry. Without leverage, a loser can cost you at most 100% of your investment. But a 10x or 20x winner can more than offset many losers.

You can see that in the visual below. Even holdings that were nearly wiped out look small next to the biggest winners.

AMD is an extreme example for me. I bought my first shares at $11, and they’ve appreciated roughly 60-fold since 2017. The return shown above is lower because I kept adding at higher prices rather than trimming as the stock rose.

Had I continually rebalanced AMD back toward its original weight, I would have systematically reduced my exposure to one of the best investments in the portfolio.

Your portfolio’s shape should reflect its performance over time.

Give your biggest winners room to run.

📊 The brutal reality of market returns

The Coffee Can approach makes even more sense when you look at how stock returns are actually distributed.

Blackstar Funds studied roughly 8,000 US stocks traded on the NYSE, AMEX, and Nasdaq from 1983 to 2006.

The most striking finding was that roughly 25% of stocks accounted for all of the market’s gains. The remaining 75%, collectively, contributed nothing.

The distribution underneath was brutal:

• Around 40% of stocks lost money over their lifetimes.

• Roughly one in five lost at least 75%.

And simply beating the market was rare:

• 64% underperformed the Russell 3000 over their lifetimes.

• Only about 6% beat the index by more than 500%.

The lesson is uncomfortable but important: most stocks are not great long-term investments.

In a 20-stock portfolio, some will disappoint, and many will be merely average. Fewer than a handful may ultimately drive most of the return.

The problem is that you rarely know which ones in advance.

Selling a winner can mean trimming the very stock you needed most.

You only need a few great investments. The hard part is not getting in their way.

Bottom Line

The secret to long-term investing lies in building a strategy you can stick with for decades, rather than guessing what the market will do next.

There will always be a reason to sell. A recession. A valuation scare. A new competitor. A market correction. But nobody knows what will happen next.

Long-term investing ultimately requires optimism that great businesses will keep adapting, growing, and creating value, despite the uncertainty.

Patience and persistence are among the most precious virtues in investing.

And you need both even if you are lucky enough to find the investment of a lifetime.

Sometimes the hardest thing is simply to leave a great investment alone.

Finding the winner is only the beginning. You have to let it get there.

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That's it for today.

Happy investing!

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Disclosure: I own AMD, ANET, AAPL, SHOP, and NVDA in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members. 

Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.

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Connecting the Dots

OpenAI used DevDay last week to unveil more than 20 products and features. The most important announcement was Dots.

Dots are always-on AI agents with their own cloud computer, access to more than 4,000 apps, and the ability to keep working toward your goals even when you are not actively prompting them. Sam Altman described the shift as a “whole new way to work with AI” and a new set of “superpowers.”

The contrast with Meta Connect was striking. Meta showed Muse booking trips, shopping, and handling everyday tasks. OpenAI showed Dots managing projects, coding, preparing documents, coordinating across Slack and Teams, and helping run a startup.

For now, the two companies are attacking the agent market from opposite ends.

• Meta is pushing toward AI for everyone.

• OpenAI is starting with AI for high-value work.

And that difference may explain where ChatGPT is heading next.

Today at a glance:

1. The pieces start to fit together

2. OpenAI wants to become the operating layer

3. The business model gets more interesting

4. The cost of autonomy

5. Who owns the workday?

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1. The pieces start to fit together

OpenAI has spent the past year turning ChatGPT into much more than a chatbot, but the pieces have not always fit together cleanly.

• ChatGPT started as the conversational interface.

• Codex became the place for agentic coding, organized around projects and files.

• ChatGPT Work extended that model to broader knowledge work, adding connections to SaaS apps and a persistent cloud environment.

More recently, OpenAI has started pulling those experiences into the same product.

The result is powerful, but increasingly complicated. You can have several projects, agents, threads, apps, and tasks running at once. At some point, the bottleneck becomes the person trying to keep track of it all.

That’s where Dots fit.

A Dot sits above your individual tools as a coordinating layer. It remembers what you are working on, follows projects over time, and can coordinate work across them. It acts like a control tower for your AI work.

The shift matters because the more work OpenAI hosts on its own cloud computers, the more persistent the relationship becomes. A chatbot conversation is relatively easy to move between providers. Recreating a collection of projects, files, permissions, preferences, and ongoing agents somewhere else is much harder.

In other words, Dots could make ChatGPT both easier to use and harder to leave.

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Today at a glance:

1. 🌐 Accenture: AI Creates More Work

2. 👟 Nike: Still Going Downhill

3. 🛳️ Carnival: Europe Comes Back

4. 🎿 Vail Resorts: Pass Sales Slide

1. 🌐 Accenture: AI Creates More Work

Accenture Q4 revenue rose 6% Y/Y to $18.7 billion ($660 million beat), with GAAP EPS of $3.29 ($0.11 beat).

Demand rebounded to a new high. New bookings grew 5% in local currency to $22.2 billion, reversing last quarter's decline. Managed services bookings hit a record $12.8 billion. Shares surged nearly 20% initially, their best post-earnings move on record.

So far, AI is creating more work for Accenture than it is destroying. Consulting revenue grew 6% (or 7% in local currency), and nearly 100 additional clients started their first advanced AI projects during Q4, bringing the FY26 total above 400. Management says much of the demand is coming from companies building the data foundations, digital core, and AI infrastructure needed before deploying agents at scale.

The economics are changing, though. Accenture acknowledged lower pricing in many parts of the business as AI improves productivity and competition intensifies. Hiring will also slow in FY27 as AI changes delivery. So the disruption thesis isn’t disappearing, but it’s showing up first in pricing and labor intensity rather than collapsing demand.

M&A remains another major growth lever. Accenture deployed $1.9 billion in Q4. The previously announced OT-security deals have now closed, and management expects roughly $5 billion of acquisitions in FY27. It also plans to return at least $9.5 billion to shareholders.

For FY27, Accenture guided to 3%–6% revenue growth in local currency. That said, acquisitions are expected to contribute 2%–2.5% to growth, so the organic outlook is considerably more subdued than the headline range.

Takeaway: The quarter doesn’t settle the AI disruption debate, but it brings some much-needed good news. Bookings rebounded, and more enterprises are hiring Accenture to build the infrastructure required for AI. The real pressure may increasingly show up in pricing and headcount rather than demand disappearing altogether.

2. 👟 Nike: Still Going Downhill

Nike Q1 revenue fell 4% Y/Y to $11.2 billion ($110 million miss), while EPS slipped 2% to $0.48 ($0.04 beat). Gross margin improved 60 bps to 42.8%, thanks to lower warehousing and logistics costs.

Nike’s performance portfolio remains encouraging. Running, football, training, and basketball collectively grew at a high-single-digit rate, suggesting Elliott Hill’s Sport Offense is gaining traction. But competitive pressure has intensified. Kylian Mbappé recently ended a two-decade relationship with Nike to join On (visualized here), which is now using him to launch its own football business in 2027.

North America was also the only major geography to grow. Elsewhere, the reset remains painful. Nike Sportswear fell by low double digits, while management acknowledged it had oversupplied Jordan retros and is now deliberately reducing launch volume to restore scarcity. NIKE Direct declined 8%, including a 13% drop in digital, while wholesale slipped 1%.

Greater China revenue fell 22% (or 26% in constant currency) to $1.18 billion, with wholesale down 28%. Nike is intentionally reducing promotions and narrowing its digital distribution there, which should improve brand health eventually. But it also means the reset will weigh on revenue for longer.

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🤖 Anthropic cracks open

Anthropic’s IPO prospectus hasn’t officially landed yet, but Reuters obtained a leaked draft this week, giving us the first real look inside one of AI's most extraordinary growth stories.

A few numbers already stand out:

• Revenue reportedly reached ~$4.6 billion in 2025, up 12x Y/Y.

• Compute and infrastructure spending topped $7 billion.

• Nearly half of revenue came through AWS and GCP, showing how Amazon and Google are simultaneously investors, infrastructure providers, distributors, and competitors.

Much more is hidden beneath those numbers, including customer concentration and cash burn. Anthropic has also agreed to spend at least $518 billion on future compute and infrastructure over the next decade, with roughly 80% of those commitments reportedly non-cancellable. I’m waiting for the public S-1 before doing the full teardown. Stay tuned for Anthropic visualized! 📊

Today at a glance:

• 🏢 Meta’s enterprise push

• ☁️ Micron raises the floor

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🏢 Meta’s enterprise push

Meta spent the past few weeks showing what AI could mean for consumers.

This week, it turned to businesses.

On Monday, Meta unveiled Meta Enterprise Platform, which Zuck called the company’s “next major pillar.” The new division will bring several pieces of Meta’s AI stack under one roof for businesses and developers, including:

• Muse, its personal AI agent

• Meta Business Agent

• Muse API

• Muse Code

• Meta’s underlying models and infrastructure

Meta also hired MongoDB CEO CJ Desai to lead the effort. Desai previously ran product and engineering at Cloudflare and spent nearly eight years at ServiceNow. He brings Meta deep enterprise software experience at the top, something it has historically lacked.

The announcement was ambitious enough to rattle the software sector. Salesforce, ServiceNow, Oracle, Adobe and other enterprise names sold off as investors contemplated another hyperscaler moving up the software stack.

That reaction may prove premature.

Building the technology is only part of enterprise software. Large customers also require long sales cycles, procurement, security reviews, integrations and implementation support. Meta has spent decades building consumer products and advertising tools, not selling mission-critical software to CIOs.

That’s why Tuesday’s announcement may be more interesting.

Small business first

Meta introduced Muse for Small Business, allowing companies to connect the agent to tools they already use, including Shopify, QuickBooks, Stripe, Canva, Slack, Notion, Figma, and Zoom, alongside their Facebook and Instagram business accounts.

The opportunity is not necessarily to replace those applications. It’s to sit above them.

A small business owner could ask Muse to understand why sales slowed, identify customers worth targeting, prepare new creative, and coordinate the work across several apps. Meta says actions involving publishing, sending, or spending still require approval.

That’s a much more natural fit for Meta. The company already works with more than 200 million businesses, primarily because they want access to customers on Facebook and Instagram. Many are small enough that the owner is simultaneously the marketer, operator, salesperson, and finance department (like yours truly).

An AI agent that can help across all four functions could be genuinely useful.

It also extends the same thesis we discussed last week without requiring Meta to win an entirely new market.

• For consumers, Muse wants to become the interface to the internet

• For businesses, it wants to become the interface to the software stack

The applications underneath may remain the same. What changes is who controls the layer where users express intent.

A new way to pay for AI

Meta is also pushing this aggressively for financial reasons.

The company is spending more than $100 billion on AI infrastructure this year. In previous earnings calls, almost every path to monetization eventually came back to advertising, leaving investors skeptical about how broadly Meta could monetize that spending. Enterprise software gives Meta another way to earn a return on the same models, agents, and compute.

Its AI monetization stack is quickly expanding:

• Advertising monetizes attention

• Subscriptions monetize heavy users

• Commerce could monetize transactions

• Business software could monetize the AI stack itself

💡 Takeaway: None of this means Meta suddenly becomes Salesforce or ServiceNow. But it is starting to turn its massive AI investment into businesses that extend beyond advertising. Enterprise is the boldest ambition, while small business looks like the lowest-hanging fruit given Meta’s existing customer relationships and distribution.

☁️ Micron raises the floor

Micron closed fiscal 2026 with another extraordinary quarter.

• Revenue surged 379% Y/Y and 31% Q/Q to $54.2 billion ($2.7 billion beat).

• Adjusted EPS: $33.42, up from $3.03 a year ago ($1.60 beat).

• Gross margin: 87%, up from 85% in Q3.

• Operating cash flow: $44.0 billion, while free cash flow reached $33.2 billion.

• Q1 FY27 guidance: Revenue of ~$61.5 billion (+351% Y/Y; ~$3.6 billion beat) and adjusted EPS of ~$38.15 (~$2.23 beat).

The scale is becoming difficult to process. Micron generated $133 billion of revenue in FY26, up from $37 billion a year earlier, while adjusted gross margin jumped to 81%. It ended the year with nearly $74 billion of cash and investments after producing more than $62 billion of adjusted free cash flow.

The growth is broadening beyond HBM. Core Data Center revenue reached $18.0 billion in Q4, up 56% sequentially, while Cloud Memory climbed to $16.3 billion. Even Mobile and Client posted a remarkable 90% gross margin, showing how far the memory shortage has spread beyond AI accelerators.

The shortage gets tighter

The key update was management’s view that the memory shortage is worsening.

Micron now expects supply-demand conditions in fiscal 2027 and 2028 to be tighter than they were in 2026, with no clear line of sight to when the market returns to balance. That matters because the biggest risk after this kind of earnings explosion is usually obvious: supply catches up, pricing rolls over, and the memory cycle turns.

Micron is also making more progress on the multi-year contracts we discussed last quarter. It now has 26 Strategic Customer Agreements, up from 16, with roughly $32 billion of customer commitments. These agreements cover more than 35% of revenue through 2030, and about three-quarters of that revenue includes defined pricing frameworks. The goal is to make the next downcycle less violent than the last one.

At the same time, Micron is spending aggressively to add capacity. CapEx reached $27 billion in FY26 and is expected to rise further, including roughly $25 billion in the first half of FY27 alone. But much of that new clean-room capacity will not arrive until 2028 and beyond, which helps explain why management still sees such a tight market.

The market is still assuming peak earnings

Despite the extraordinary numbers, Micron still trades at a single-digit forward earnings multiple. The market is effectively saying these margins and earnings cannot last.

That skepticism is understandable. Memory has always been cyclical, and an 87% gross margin looks like the kind of number investors normally associate with a peak.

But Micron is trying to change the evidence investors use to make that judgment:

• Long-term customer agreements are replacing annual handshakes.

• Customers are putting down billions in deposits and guarantees.

• Supply remains constrained well into 2027 and 2028.

• AI is increasing memory intensity across data centers, PCs, phones, and eventually physical AI.

The key question is no longer whether Micron is having an extraordinary cycle. It clearly is. The question is whether this cycle has become structurally different enough to deserve a different multiple.

💡 Takeaway: Micron just delivered the kind of quarter that would normally scream peak cycle. Yet supply is getting tighter, customer commitments are getting longer, and FY27 guidance is still moving higher. The market is still pricing Micron like memory eventually reverts to the old playbook. The question is whether AI has changed that playbook enough to justify a different multiple. Just remember the four most dangerous words in investing: “this time is different.”

That's it for today.

Happy investing!

P.S. I’m working on something new for investors looking beyond Big Tech.

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Disclosure: I own AMZN, GOOG, and META in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members. 

Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.

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🔥 The September report is here!

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• ✔️ Cut through the noise with clear, concise financial snapshots.

• ✔️ See revenue trends, profit margins, and key takeaways instantly.

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Inside the September report:

• ⚙️ Semis: Broadcom, Micron.

• 🤖 AI infra: Dell, Oracle, Nscale, SB Energy.

• 🛡️ Cybersecurity: Palo Alto Networks, Zscaler.

• 📊 Data: C3.ai, HPE, MongoDB, Rubrik, Samsara, Snowflake.

• 🖥️ Workflow: Adobe, DocuSign, GitLab, UiPath.

• 👟 Consumer: Costco, Lululemon, Oura.

• 😎 Travel & leisure: Carnival, Vail Resorts.

• And more, including Darden, Didi, Meituan, FedEx, GameStop, and Wealthfront.

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☁️ Nscale is going public

Nscale was spun out barely two and a half years ago.

It has already signed contracts worth up to $103 billion in future revenue.

Now it wants to build a full-stack AI hyperscaler from power to token.

The company secures power and land, builds data centers, buys the GPUs, sells the compute, and is now pushing further into AI software. And it’s scaling at an almost absurd pace.

Yet today, only 25,000 GPUs are active, compared with 461,000 active and contracted GPUs. Revenue reached just $141 million in the first half of 2026, even after growing 1,252% Y/Y.

The gap between what Nscale operates today and what it has already contracted is the main IPO story.

Can it turn extraordinary demand into a vertically integrated AI infrastructure platform before the capital requirements overwhelm the economics?

And then there is NVIDIA, which somehow shows up on almost every side of the transaction.

I condensed the S-1 into a clean breakdown, supported by our signature visuals. By the end, you’ll have a clear view of the Nscale investment case.

Today at a glance:

1. Overview

2. Business model

3. Financial highlights

4. Risks & challenges

5. Management

6. Use of proceeds

7. Future outlook

8. Personal take

1. Overview

Nscale was born out of a bitcoin miner.

Before the company was spun out in May 2024, its team was part of Arkon Energy, which had spent years assembling powered sites in markets where electricity was cheap and abundant.

The opportunity changed when AI demand exploded. The logic was simple. If models kept getting larger and more compute-intensive, power would become the bottleneck.

Power first

GPUs get most of the attention, but they are useless without enormous amounts of electricity behind them.

Nscale started by securing land and power in markets including Norway, Iceland, Portugal, and the US, then building compute capacity on top. Management now calls access to large blocks of reliable power the primary constraint on AI infrastructure.

As of August, Nscale had:

• 5 active and 12 contracted data-center sites

• 1.37 GW of active and contracted capacity

• Line of sight to roughly 10 GW of potential power capacity

The biggest example is Monarch, a 2,250-acre campus in West Virginia acquired in March. Nscale says the site could eventually support more than 8 GW of power, including over 6.5 GW of IT load. The first 1.37 GW is expected to be online in the first half of 2028.

Moving up the stack

Nscale does not want to stop at power and data centers.

It also owns and operates the GPUs inside them, then sells the compute through its cloud platform.

The planned acquisition of Anyscale, which builds software to run and orchestrate AI workloads, pushes Nscale another step higher into the software layer.

That is how Nscale gets to its pitch of becoming a full-stack AI hyperscaler.

But the current business still looks very different from that end state. Most of Nscale’s active capacity today sits in colocation or leased facilities, which lets the company get GPUs online while its own campuses are being built. By contrast, most of its contracted capacity is expected to sit in facilities Nscale owns.

Nscale is already selling the future version of the company while much of the physical infrastructure behind it still needs to be built.

Takeaway: Nscale’s original advantage was access to power. Now it’s trying to use that foothold to own more of the economics around AI compute, from the data center to the GPU to the software layer. The opportunity is enormous, but the vertically integrated company investors are being asked to value is still mostly under construction.

2. Business model

Nscale monetizes its infrastructure through two businesses:

• 🖥️ Nscale Infrastructure: Reserved AI compute sold under long-term contracts to hyperscalers, frontier labs, and large technology companies.

• ☁️ Nscale Cloud: On-demand compute and higher-value platform services aimed at AI-native companies, enterprises, and sovereign customers.

Today, the first business drives almost all of the economics.

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Today at a glance:

1. 🛒 Costco: Refunds Fund Price Cuts

2. 🍪 General Mills: Innovation Gains Traction

3. 🫒 Darden: Olive Garden Still Lags

1. 🛒 Costco: Refunds Fund Price Cuts

Costco Q4 revenue rose 11% Y/Y to $95.7 billion ($0.8 billion beat), while EPS increased 15% to $6.75. That included a $0.15 benefit from tariff refunds, so underlying EPS growth was closer to 12%. Comparable sales rose 9.4%, or 6.7% excluding gas and FX, essentially maintaining Q3’s 6.6% pace.

Fuel remained a tailwind, adding roughly 3 points to comps, but the core business was balanced. Adjusted traffic increased 3.3%, and average ticket rose 3.3%. Digitally enabled comps grew another 20%, with annual digitally enabled sales now exceeding $33 billion (~11% of Costco’s overall FY26 revenue).

Costco received $184 million in tariff refunds during Q4 and reinvested part of that into lower prices across produce, meat, beverages, furniture, and other categories. Management already received a similar amount in Q1 and plans to return most future refunds to members through better value.

Membership remains healthy, but growth continues to normalize. Paid members increased 4% to 84.1 million, while Executive memberships grew 9% to 42.3 million. US/Canada renewal improved to 92.3%. Costco also plans 33 warehouse openings in FY27, accelerating from 25 net additions in FY26.

Consumer insights from Numerator data show that Costco’s recent household growth is skewing toward Gen Z and lower-income shoppers, while lower-income customers are already making fewer trips and spending less per basket.

The stock is still expensive. Costco’s forward P/E has fallen from above 50x at its peak to roughly 41x, but that still leaves little room for error.

Bottom Line: The gasoline boost still hasn’t normalized, but Costco’s core comps are holding around 7% anyway. With membership growth returning to a more normal pace, the next leg of growth increasingly depends on getting more from each member through digital, pharmacy, Executive upgrades, and a broader services ecosystem. The market is certainly counting on it.

2. 🍪 General Mills: Innovation Gains Traction

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Muse breaks out

Muse launched on September 8 as Meta’s latest attempt to catch up in AI.

Despite being available only in the US and Canada, Meta’s new personal agent has reached more than 3.4 million downloads, according to Sensor Tower. Muse climbed to the top of the US app stores, and sent investors scrambling to understand what consumer agents could mean for everything from shopping and travel to banking and insurance.

At Meta Connect this week, the company made clear that Muse will not remain confined to an app, with plans to extend it across computers, communication channels, and smart devices.

Muse is Meta’s attempt to give consumers a digital Alfred. It’s “heavily inspired” by OpenClaw, according to product chief Nat Friedman. It can understand what you want, handle tasks, and increasingly act on your behalf. In doing so, Meta is making a play for the consumer AI interface that goes far beyond the chatbot.

If agents increasingly research, compare, communicate, and transact on our behalf, the implications stretch far beyond Meta. They could reshape who owns commercial intent, where advertising happens, which businesses get bypassed, and how much computing infrastructure it takes to run it all.

The internet was built around getting humans to click and scroll. The agentic internet may increasingly be built around AI choosing and acting for us.

Today at a glance:

1. 💬 Meta’s new aggregation play

2. 📦 Why Amazon is fighting back

3. 🛒 Who wins when the interface moves?

4. 🤖 Fewer clicks but more compute

1. 💬 Meta’s new aggregation play

What makes Muse different is that the agent can increasingly operate outside Meta’s own apps. Instead of keeping users inside Facebook, Instagram, or WhatsApp, Muse can sit between them and the rest of the internet, helping research a trip, compare products, book a restaurant, or buy something across multiple services.

These capabilities blur the boundaries between the internet’s traditional aggregators. Google aggregated information, Meta aggregated social attention, and Amazon aggregated shopping demand. Muse is trying to aggregate intent itself.

Meta wants to turn Muse into a persistent consumer service that knows your context and can keep acting on it. Meta gives the example of Muse turning a recipe saved on Instagram into a grocery list, remembering friends’ dietary restrictions, planning the dinner, and sending invitations. Those capabilities aren’t unique on their own. But combining memory, context, action, and distribution into one always-available consumer agent creates something much closer to a personal operating layer than a chatbot.

Meta is extending that idea into the physical world. Muse will come to its AI glasses, where it can act on what users see, including helping them shop for products in front of them. Meta is also dramatically expanding the hardware itself, with more than 100 glasses options expected across Ray-Ban, Oakley, and Meta Glasses by year-end.

Glasses can potentially capture the context in which intent emerges. See a product, restaurant, landmark, or problem in the physical world, and the agent increasingly has the context to understand what is happening and act from there.

Zuck’s “one more thing” made that ambition even more explicit. Muse Charm is a Tamagotchi-esque palm-sized device built specifically around Muse, giving users a way to talk to their agent without unlocking a phone or opening an app. He described it as the fastest way to access Muse when you are not wearing glasses, with Meta planning to ship it for the holidays.

The timing is notable because OpenAI is also preparing its first consumer device with former Apple design chief Jony Ive. The exact form factor remains uncertain, but Meta is already trying to establish Muse as an always-available interface before OpenAI enters the category.

Extending Muse into the physical world gives Meta access to high-intent activity close to the transaction. The company still says conversations and data inside Muse’s virtual machine are not shared with its advertising systems, reducing one obvious concern for users being asked to grant an agent access to more of their digital lives.

Meta also offered its clearest monetization roadmap yet at Connect. Zuck said Muse is free for most users, but Meta also offers $20 and $100 monthly subscription tiers for heavier usage. Meta is eventually expecting to make money by taking a small fee from transactions. That makes owning intent even more important: Meta does not necessarily need to show an ad or own the merchant if it can participate economically when Muse helps complete the purchase. Advertising could still find its way into Muse over time.

Takeaway: Meta spent two decades monetizing attention inside its own apps. Muse gives it a chance to own the layer where users express intent before money changes hands.

2. 📦 Why Amazon is fighting back

No company illustrates the stakes of this shift better than Amazon. It has spent decades becoming the default starting point for online shopping, owning both the final transaction and the discovery process leading up to it.

Consumers arrive knowing roughly what they want, search Amazon’s catalog, compare products, and buy. Amazon gets the data, the customer relationship, and the opportunity to monetize demand before checkout.

Muse challenges that sequence. If an agent can research products, compare options, and decide what to recommend before the user ever reaches Amazon, then Amazon may still fulfill the purchase while losing control of the journey that led there.

That helps explain why Amazon moved quickly to block Muse from shopping on Amazon.com.

The economics are substantial. Amazon generated $68.6 billion in advertising revenue last year, up 22% Y/Y. Advertising has more than doubled since 2021 (the fastest-growing segment), and now exceeds Amazon’s subscription revenue by nearly $19 billion.

For context, Amazon’s non-AWS segments generated $34.4 billion in operating income in 2025. Amazon does not disclose advertising margins, but ads alone generated twice that amount in revenue. It’s entirely plausible that Amazon’s underlying retail and logistics operations would be loss-making without the high-margin advertising layer sitting on top.

Instead of searching Amazon for headphones, imagine you tell the agent, “Find me the best noise-canceling headphones under $300 for long flights.” Muse can research the options, compare reviews and specifications, and make a recommendation before the user ever reaches Amazon.

The transaction might still happen there, but Amazon risks losing much of what happens beforehand, including search, sponsored-listing impressions, product discovery, and opportunities to increase the basket. Evidence already shows retailers give something up when an outside agent controls the experience. Walmart reportedly found that purchases through OpenAI’s Instant Checkout converted at roughly one-third the rate of customers shopping directly and produced smaller baskets.

That dynamic raises a new question about unit economics. If Muse takes a fee on purchases it completes, what does that look like? An affiliate-style take rate, a flat routing fee, or sponsored placement? Moving the interface forces a renegotiation of where the margin lives. If agents shrink baskets or reduce retailers’ ability to upsell, merchants will resist paying another toll on top.

Amazon has something Muse cannot easily replace. Over the past decade, it has built warehouses, transportation, inventory placement, and last-mile delivery infrastructure that lets it get products to customers incredibly quickly. AI can’t replace that physical network.

But that physical moat also depends on enormous volume. Amazon has spent heavily to build infrastructure with high fixed costs, which become more efficient as more purchases flow through the network. If agents gradually divert transactions toward Walmart or other retailers, every lost order spreads those costs across slightly fewer purchases.

Takeaway: Muse threatens Amazon’s control of discovery far more than its ability to fulfill the purchase. The battle is over who owns the customer before checkout.

3. 🛒 Who wins when the interface moves?

Amazon is only one side of the story. Companies will respond very differently to agents depending on where they sit in the value chain. Shopify and PayPal are leaning in because neither depends on owning a consumer search surface filled with ads. Shopify wants its merchants and Shop Pay accessible wherever consumers choose to shop, while PayPal is opening its merchant network to Muse. Both can benefit even if Meta owns the interface.

The incentives also look different for challengers. Walmart was quick to experiment with ChatGPT shopping because, as a distant number two in e-commerce, capturing demand that might otherwise go to Amazon can be worth giving up some control over the interface. Muse may be even more attractive because Meta is not trying to own the checkout itself.

That leaves us with a useful distinction. Companies that make money by owning discovery have more to protect, while companies that make money from the transaction or infrastructure underneath it can afford to be more open to agents.

Advertising itself probably does not disappear either. It may simply move closer to the agent. We are already seeing this with ChatGPT, where Amazon advertisers can reach users inside AI conversations. Amazon extends its ad business beyond Amazon.com, while OpenAI monetizes the intent generated inside ChatGPT.

Muse could eventually create similar arrangements. Meta has promised not to feed private Muse conversations into its own advertising systems, but that does not prevent commerce partners from paying to participate in the experience with user permission. One possibility is that Meta eventually monetizes some Muse activity through someone else’s advertising or transaction infrastructure rather than immediately building its own.

The consequences extend beyond advertising. Another class of business may be exposed for a different reason. Companies that benefit from consumer inertia. A chatbot can tell someone that their savings account pays too little. An agent can identify excess cash, compare yields, and move the money. The same logic can extend to insurance, brokerage, and other categories where incumbents benefit from customers not continuously shopping for a better deal.

Agents can shorten the distance between recognizing a better option and acting on it. For banks, the real proof would eventually show up in higher deposit costs and greater movement of customer balances.

The broader shift is more important than any one partnership. Today, advertisers largely pay to influence what humans click. In an agentic world, they may increasingly pay to influence what agents consider, making product data, availability, pricing, fulfillment, and trusted merchant relationships more valuable while reducing the importance of some of the webpages and ad placements that sit between intent and purchase.

Takeaway: Agents may not eliminate advertising. They could move the most valuable ad opportunity from winning the click to earning a place in the agent’s consideration set.

4. 🤖 Fewer clicks but more compute

There is one more economic shift. While agents simplify the internet for users, they make the underlying infrastructure much more demanding.

A traditional search might involve a few queries and clicks. An agentic task can involve repeated cycles of planning, searching, retrieving information, comparing options, calling tools, checking results, and eventually taking action. The user may see a much simpler experience, but the machine is doing considerably more work behind the scenes.

Muse’s early traction suggests consumer agents could dramatically expand the inference workload. If AI shifts from responding to discrete prompts toward agents continuously researching, communicating, and acting in the background, each user can generate considerably more computing activity.

Meta Connect offered a glimpse of what that requires. The new Muse Realtime Avatar required Meta to redesign its real-time inference stack to continuously generate synchronized voice and video. Meta says the resulting optimizations increased serving capacity 8x, allowing 12 concurrent video sessions on a single GB200 while keeping latency below one second.

The broader shift goes well beyond avatars. The first phase of the AI boom was dominated by training increasingly large models, concentrating attention on GPU accelerators. Agentic AI moves more activity toward continuous inference and orchestration, which can broaden demand into CPUs, memory, networking, and high-speed interconnects alongside GPUs.

AMD is a prime example of this architecture shift. Beyond competing in data center GPUs, its high-core-count EPYC server CPUs are increasingly important to agentic workloads, handling orchestration, data retrieval, tool calls, and the enterprise services that sit between prompts and accelerator inference. That’s one reason AMD has been a core pillar in App Economy Portfolio, where it recently became a 56-bagger and remains my largest holding.

Memory is another example. Agents continuously move between models, context, external data, and tool outputs, increasing the importance of larger pools of system memory rather than relying only on memory attached directly to accelerators.

The result is an interesting inversion:

• Above the model, AI could compress the internet by reducing searches, webpages, calls, and ad surfaces.

• Below the model, it could expand the infrastructure required to execute those same tasks, from compute and memory to communications, payments, and transaction software.

That's it for today.

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Disclosure: I own AMD, AMZN, ANET, GOOG, META, and NVDA in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members. 

Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.

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AI is changing business models in real time

OpenAI is building an advertising machine, Meta is pushing deeper into subscriptions, and Salesforce is rethinking how software gets monetized in an agentic world.

How do you make money when AI changes the interface?

AI is no longer just changing products. It’s reshaping the economics underneath them, determining who pays, what they pay for, and which parts of the stack capture the value.

This week, we look at what each company is building, why these shifts are happening now, and what they could mean for some of tech’s most durable business models.

Today at a glance:

• 🤖 OpenAI’s advertising machine

• ♾️ Meta’s second business model

• ☁️ Salesforce is going headless

🤖 OpenAI’s advertising machine

OpenAI spent years insisting advertising was not central to its business model. It took less than 200 days to build a $1 billion advertising business.

ChatGPT Ads reached that annualized run rate in August, with tens of thousands of advertisers already using the platform. Against more than 1 billion weekly ChatGPT users, that still works out to only about $1 per user.

For a rough sense of scale, Meta generated about $57 per daily active person across its Family of Apps last year. Of course, the metrics aren’t directly comparable. Meta uses a much more engaged daily-user denominator across multiple apps, while many ChatGPT users don’t see ads at all. But the gap shows how early OpenAI’s monetization still is.

The more interesting question is how much deeper into the purchase decision ChatGPT can move.

Google became an advertising giant because search captures intent. Someone searching for accounting software is already close to a purchase. ChatGPT can potentially see much more of the decision — the budget, existing tools, requirements, preferences, and tradeoffs discussed throughout a conversation. That could make the commercial intent unusually valuable.

Amazon is already validating that thesis. The company recently opened ChatGPT Ads to brands using Amazon Ads, despite historically restricting AI chatbots from accessing much of its storefront. Amazon manages the campaigns, while OpenAI determines where the ads appear based on the ChatGPT conversation.

The arrangement is clever for both sides. Amazon preserves its advertising economics and remains the destination for the purchase. OpenAI gets paid for the intent generated inside ChatGPT.

In effect, Amazon can sell advertisers the opportunity to become the Amazon link inside a ChatGPT recommendation.

The agent becomes the ad

OpenAI is now pushing further with Sponsored Agents.

Instead of clicking an ad and navigating an advertiser’s website, users can start a clearly labeled conversation with an AI representative of the brand. The agent can answer questions, explain differences, and help the user decide before eventually sending them to the advertiser when they are ready to act. Sponsored Agents are currently being tested with select US advertisers.

Conversational ads change what the ad itself can do. Instead of merely earning the click, it can help qualify the customer before the click happens.

Here’s one way to think about the evolution of digital ads. We went from monetizing the query (blue links) to attention (the endless scroll) to monetizing more of the decision itself (AI agents).

The decision matters most in categories where purchases require personalized research, such as software, travel, financial services, education, home improvement, or expensive consumer products. The conversation can absorb more of the work that currently happens across search results, comparison sites, product pages, and sales calls.

OpenAI is also making it easier for advertisers to plug into the system. New integrations with Shopify and HubSpot span everything from product catalogs and campaign creation to lead capture and measurement.

The tension is obvious. ChatGPT works because users trust that its recommendations are based on usefulness, not on who paid for placement. OpenAI therefore needs to monetize commercial intent without blurring that distinction.

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Jeff Bezos is buying into Liverpool FC.

Last month, the Amazon founder joined a consortium acquiring roughly a third of the English football club at a valuation north of $7 billion. Since then, record-setting sports deals have kept coming.

• ⚽ Liverpool FC: $7.0 billion — Bezos, Saverin, and Bhatia

• 🏈 Seattle Seahawks: $9.6 billion — Vinod Khosla and family

• 🏀 Los Angeles Lakers: $12.5 billion — Josh Kushner and Bob Iger

Just this week, Clearlake Capital also agreed to consolidate control of Chelsea FC in a deal valuing the club at roughly $6.8 billion.

These valuations look strange through a traditional investing lens. Liverpool generated £703 million in revenue last season and only £8 million in after-tax profit. European football clubs routinely reinvest most of what they earn into players, wages, and the pursuit of trophies.

Last summer, we explored those peculiar dynamics in Football Economics, including why even the world’s biggest clubs often struggle to turn enormous fan bases into consistent profits.

Yet while profits remain elusive, the assets themselves keep rising in value. FSG bought Liverpool for just £300 million in 2010. Sixteen years later, investors are looking at a ~15x return.

Today, we’ll explore why sports teams have become such coveted assets, and what Bezos may really be buying with Liverpool.

Today at a glance:

1. 📈 Teams keep getting more valuable

2. 🏟️ Why these assets are different

3. 🏈 Why the NFL and NBA are more profitable

1. 📈 Teams keep getting more valuable

Recent deals are extreme, but not isolated. Across major sports, teams have become some of the world’s most valuable private assets.

The NFL dominates the leaderboard. 15 of the world’s 20 most valuable sports teams are NFL franchises, with the Dallas Cowboys leading at $15.5 billion. The only outsiders are three NBA teams and two MLB teams.

European football is notably absent.

Even Real Madrid, the world’s most valuable football club, is worth an estimated $7.7 billion according to Sportico, below every team shown above. Forbes pegs the club valuation at $9.5 billion, but either way, the contrast is striking for the world’s most popular sport.

The average NFL franchise is now worth about $9.3 billion, nearly four times the $2.4 billion average Premier League club. NBA teams command more than twice the valuation, while even MLB sits comfortably ahead.

European football generated a record €40.2 billion in revenue last season, up 6% Y/Y, including nearly a quarter from the Premier League alone. The sport has moved well beyond its post-pandemic recovery as commercial revenue, expanded competitions, and global audiences continue to grow.

Yet team values are rising much faster than the businesses underneath them.

That leaves us with the more interesting question: why does an NFL or NBA franchise command such a large premium over a Premier League club?

2. 🏟️ Why these assets are different

Only a finite number of sports teams are truly compelling.

You can start another software company, restaurant chain, or even another football club. But you cannot recreate Liverpool FC. Its history, trophies, supporters, rivalries, and place in the culture have accumulated over more than a century.

That scarcity is becoming more valuable for three reasons:

• 🔒 Supply is fixed. Only so many teams have genuine global relevance, while the number of billionaires, private-equity firms, and sovereign investors who can buy them keeps growing.

• 📺 Live attention is scarce. Streaming and social media have fragmented entertainment, but sports still bring millions of people together at the same time. That makes elite games unusually valuable to broadcasters, streamers, advertisers, and sponsors.

• 🌍 Monetization is global. Anfield holds about 60,000 fans, but Liverpool can monetize supporters globally through sponsorships, merchandise, tours, licensing, and digital media. The stadium caps matchday revenue, but the brand reaches far beyond.

That last point is becoming increasingly important. Among the world’s 20 highest-revenue football clubs, commercial revenue has overtaken broadcasting as the largest income source, as clubs turn global followings into sponsorship, merchandise, and other brand revenue.

Liverpool itself offered a fresh example this month, signing Turkish Airlines as its next main shirt sponsor in a five-year deal reportedly worth more than £300 million.

Sports teams are also unusual because buyers don't necessarily maximize next year’s earnings. Returns can come from cash flow, long-term asset appreciation, and the non-financial value of ownership—prestige, access, influence, or simply owning a team capable of winning trophies.

That makes a club like Liverpool an unusual asset: scarce by definition, but increasingly monetizable.

Scarcity helps explain why sports teams keep appreciating. But it does not explain why the average NFL team is worth almost four times as much as the average Premier League club. For that, we need to look at the economics.

3. 🏈 Why the NFL and NBA are more profitable

At first, the valuation gap might seem easy to explain. You might assume American teams simply generate far more revenue. They don’t.

The average NFL franchise generates roughly $720 million a year in revenue, well ahead of the pack. But average revenue across the NBA, MLB, and Premier League is remarkably similar, at roughly $425–$450 million per team.

That makes the valuation discrepancy more surprising. The average Premier League club generates slightly more revenue than the average NBA team, yet clubs are worth less than half as much on average.

The real difference appears further down the income statement. The average NFL team generates around $127 million in operating profit, while the NBA averages roughly $113 million. Premier League clubs average only about $17 million.

Why are Premier League teams barely breaking even? The leagues create very different incentives to spend.

• 🪂 Failure has consequences. An NFL or NBA team can finish last and return the following season with its league membership and national revenue intact. Conversely, a Premier League club can be relegated. The all-important UEFA Champions League involves tens of millions in revenue at stake, but only for the teams finishing in the top four or five. Football teams therefore have to spend partly to protect the revenue they already have.

• 💰 Player spending is constrained. The NFL and NBA use salary caps and other spending rules that prevent owners from endlessly bidding against one another for talent. European football has introduced tighter financial rules, but clubs still have enormous incentives to funnel incremental revenue into wages and transfers.

• 📺 Revenue is shared. US leagues redistribute a large portion of national media revenue across teams, creating a much higher and more predictable floor. Every NFL team received about $453 million in shared league revenue last season, regardless of whether it won the Super Bowl or finished near the bottom.

The contrast can become almost comical. A Premier League club fighting relegation may spend heavily in January because losing could threaten the economics of the entire business. An NBA team whose season has slipped away can instead prioritize younger players and draft position, because losing games does not threaten its place in the league.

That difference in urgency matters. In European football, additional revenue often becomes additional player spending because standing still means falling behind.

Liverpool’s FY25 revenue flowed back into wages, player costs, and the infrastructure required to remain one of Europe’s elite clubs. Cutting those costs dramatically might improve margins today, but it could also reduce the odds of qualifying for the Champions League tomorrow.

The NFL and NBA have effectively designed that arms race out of their economics. Owners still compete fiercely to win, but the rules make it much harder to compete away all the profit. That helps explain why similar revenue levels can support dramatically different valuations.

💡 The scarcity trade

That brings us back to Bezos and Liverpool.

Liverpool combines something increasingly rare: a global audience, a century of brand equity, and a place in competitions that billions of people care about. Its economics may never resemble those of an NFL franchise, but the opportunity extends far beyond next year’s profit through higher media rights, commercial growth, and continued appreciation of the club itself.

That is the common thread across Liverpool, the Lakers, and the NFL’s most valuable franchises. Their economics differ dramatically, but truly iconic teams remain in fixed supply while the number of investors who can own them keeps growing.

So Bezos is not buying Liverpool for the earnings it produces today.

He is buying an asset the world cannot make more of.

That’s it for today.

Happy investing!

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Disclosure: I own AAPL, AMZN, GOOG, and META in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.

Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.

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⚡️ SB Energy is going public

If you have been watching the race to power the AI boom, this is one of the most aggressive bets on physical infrastructure yet.

The stakes just got higher. With AI leaders like Dario Amodei and Sam Altman openly discussing a more cautious pace at the frontier, investors face a fundamental question. How durable is the physical infrastructure buildout if frontier development slows?

SB Energy sits right at the center of that question.

The company generated just $139 million of revenue in the first half of 2026. It now reports roughly $439 billion of contracted backlog, almost entirely tied to data centers.

There’s one catch. None of those data centers are operating yet.

SB Energy started as a renewable-power developer. The AI boom has transformed it into something much bigger. It now develops gigawatt-scale data centers and the power infrastructure behind them, with OpenAI as its largest customer and NVIDIA and SoftBank helping fund the buildout.

In effect, SB Energy is becoming a physical landlord of the AI boom, owning the infrastructure underneath the compute.

The gap between SB Energy’s revenue today and the future revenue already under contract is the entire IPO story.

I condensed 300+ pages of the S-1 into a clean breakdown, supported by our signature visuals. By the end, you'll have a clear view of the SB Energy investment case.

Today at a glance:

1. Overview

2. Business model

3. Financial highlights

4. Risks & challenges

5. Management

6. Use of proceeds

7. Future outlook

8. Personal take

1. Overview

SB Energy’s transformation is easy to see in one place: Milam County, Texas.

In 2024, the company completed the Orion Solar Belt, a 900 MW solar complex with Google as its anchor customer. Two years later, SB Energy is building a 1.2 GW data center campus for OpenAI in the exact same county.

That captures the entire evolution of the company. SB Energy went from supplying clean power to third-party facilities to building and owning the physical data centers themselves. Milam County was only the beginning.

Mastering the grid (2019–2023)

SB Energy was founded in 2019 as a SoftBank Group company focused on large-scale renewable energy. It spent its early years mastering the hardest parts of infrastructure development: finding land, securing grid connections, contracting power, arranging project debt, and managing construction over multi-year cycles.

Those capabilities once looked like the plumbing of a slow-moving utility business. The AI boom changed that overnight.

Hyperscalers need massive compute, but chips are only part of the bottleneck. Gigawatt-scale data centers require land, high-voltage electricity, transmission access, environmental permits, and years of buildout. SB Energy already knew how to solve many of those problems.

Moving up the stack

Instead of remaining an upstream power supplier, the company moved up the stack. It now operates as an integrated data center and power platform, developing the physical infrastructure where AI hardware will live.

The scale changed rapidly. At its PORTS-Pike campus in Ohio, SB Energy has contracted 8 GW of capacity for OpenAI, with NVIDIA providing the compute infrastructure and initial phases coming online in 2028.

Across its portfolio, the company reports roughly 5 GW of power projects operating or under construction, along with three AI campuses in development.

SB Energy doesn't design chips, train AI models, or rent cloud instances. Its bet is on the land, power, and buildings that AI systems require.

Takeaway: SB Energy spent its first five years learning how to build power infrastructure at utility scale. The AI boom turned that expertise into physical infrastructure for the AI economy.

2. Business model

SB Energy monetizes that infrastructure through three segments:

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Today at a glance:

1. 🎨 Adobe: 100M Free Creators

2. 🐶 Chewy: Treats Get Cut

3. 📈 WealthFront: Assets Outrun Revenue

1. 🎨 Adobe: 100M Free Creators

Adobe Q3 revenue rose 13% Y/Y to $6.8 billion ($60 million beat), with non-GAAP EPS of $6.13 ($0.04 beat). Total ARR grew 11% Y/Y to $27.5 billion. Margins expanded, while operating cash flow hit a Q3 record of $2.5 billion.

Creative freemium MAU surpassed 100 million, up more than 70% Y/Y, while Adobe crossed 1 billion total monthly active users. AI-first ARR also climbed above $650 million, growing more than 150% Y/Y, but still only 2% of the total.

The audience growth comes with a near-term tradeoff. Management acknowledged that the shift toward freemium contributed to slower net new ARR and RPO growth, as Adobe prioritizes acquisition and engagement before monetization. Agentic products and credit-based AI usage remain the main path to converting that engagement into revenue.

Adobe also named Anil Chakravarthy as its next CEO, effective December 1, with Shantanu Narayen moving to Executive Chair. Chakravarthy currently leads Adobe’s Customer Experience business, signaling continuity around the push toward AI-powered workflows across creative and enterprise products.

Adobe raised FY26 revenue guidance slightly to $26.58–$26.63 billion (from $26.50–$26.60 billion). Q4 revenue guidance was slightly below expectations, sending shares modestly lower.

Bottom Line: Adobe is proving it can still attract users in the AI era. The harder part is converting them. Freemium creative users have now crossed 100 million and AI-first ARR is growing rapidly, but total ARR is still expanding only around 11%. The next phase of the story is monetization rather than adoption.

2. 🐶 Chewy: Treats Get Cut

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AI had a strange week

Meta launched Muse, a personal AI agent that can send emails, shop, book travel, and keep working after you close the app. Muse reached #3 in the US App Store within two days, and Meta says usage has already blown past expectations.

More importantly, it pushes Meta into the race to own the consumer agent, leveraging its massive distribution today and AI glasses tomorrow. New $20 and $100 monthly tiers also offer one of Meta’s clearest paths yet to monetizing AI beyond advertising.

Meanwhile, Anthropic researcher Jacob Coxon quit, warning that leading AI labs are "gambling with our lives." Anthropic alignment lead Evan Hubinger went further, putting the odds of AI killing all humans within the next decade above 10%. Not exactly ideal PR ahead of a potential IPO.

On that cheerful note, Apple just kicked off the Ternus era with its latest iPhone launch, while Oracle faced another test of the AI infrastructure boom.

Today at a glance:

1. 📱 Apple: The $2,000 iPhone

2. ☁️ Oracle: 850 Megawatts Later

1. 📱Apple: The $2,000 iPhone

New Apple CEO John Ternus just unveiled the iPhone Duo, the company’s first foldable phone, alongside the iPhone 18 Pro, new Watches, and AirPods.

The Duo starts at $1,999 and can reach an eye-watering $3,199 with 2TB of storage. It was actually less than some analysts expected given the current RAM-aggeddon. But beyond the sticker shock, the launch says a lot about where Apple sees its next growth opportunities.

Dominating a tiny market

Apple is seven years late to foldables. Samsung launched its first Galaxy Fold in 2019, yet the category still represents only about 2% of global smartphone shipments.

That could change quickly. Industry estimates suggest Apple could sell roughly 6 million Duos in 2026 despite launching in late October, potentially capturing around a quarter of the foldable market almost immediately. Some forecasts see first-year sales approaching 10 million units.

That would be an impressive entrance, but it also puts the opportunity in perspective. Apple sells well over 200 million iPhones each year. Even 10 million Duos would represent only a small fraction of its annual volume.

So Apple could become one of the largest foldable vendors almost overnight without foldables becoming a major growth driver.

Pushing iPhone prices higher

Apple generated roughly $210 billion of iPhone revenue last year, so even a successful Duo launch is unlikely to transform the company. If Apple sells 10 million at an average price around $2,000, that would represent roughly $20 billion of revenue. But most of those customers would have bought another iPhone anyway.

iPhone remains Apple’s core cash engine, but hardware growth now relies almost entirely on pricing power rather than unit volume.

What matters here are the incremental economics. Someone opting for a $1,999 Duo instead of a $1,299 Pro Max adds roughly $700 of revenue per customer. Across 10 million buyers, that’s ~$7 billion of incremental revenue before considering the much pricier storage configurations.

Apple is also raising prices elsewhere after the recent memory crunch. The new iPhone Pro and Pro Max both increased by $100, while several older models also became more expensive. There is no standard iPhone 18 this fall either, meaning anyone who wants the newest generation has to buy a Pro or Duo.

The Duo may remain niche, but it gives Apple room to push iPhone average selling prices materially higher.

Turning AI into an ecosystem feature

Apple also used the event to clarify where it fits in the AI race.

Ternus spent time describing an Intelligent Personal Hub, walking through the features the perfect AI device would need and making it sound like he was about to unveil something revolutionary. He concluded that this device already exists. It’s the iPhone. It sounded almost like an early rebuttal to OpenAI and Jony Ive’s work on a new AI device.

The new Siri can use personal context from messages, emails, photos and apps, understand what is on the screen, and take actions across the operating system. Apple is extending similar capabilities to the Watch, including AI-generated recaps and health insights.

That is a very different strategy from OpenAI or Anthropic. Apple does not need users spending hours inside an Apple chatbot. Its advantage is controlling the device, operating system, and personal context where AI can become useful.

Apple can also rely on outside models when needed while keeping control of the customer experience. In other words, Apple does not necessarily need to win the foundation-model race to benefit from AI.

The monetization is indirect too. Better AI can make the next iPhone more compelling, increase attachment to the Watch and AirPods, and make it harder to leave the ecosystem. Apple does not need a $20 monthly Siri subscription if AI helps sell more $1,200, $2,000, or even $3,000 devices.

Takeaway: The Duo is the flashy part of this week’s launch. But the broader strategy is familiar. New technology gives customers another reason to move up the hardware ladder and stay within the ecosystem.

2. ☁️ Oracle: 850 Megawatts Later

Oracle’s AI story has been easy to summarize over the past year. It signed enormous contracts first, spent enormous sums building the capacity to serve them, and promised the revenue would follow.

That promise finally started showing up in the August quarter.

Oracle delivered 850 megawatts of new data center capacity in Q1 FY27, almost triple what it delivered in Q4 FY26, while deploying more than 300,000 GPUs. OCI revenue more than doubled, total revenue accelerated to 30% growth, and the company still added another $26 billion to its enormous backlog.

The stock jumped after hours. After months of concern about Oracle’s debt, dilution, and massive AI spending, investors finally got more evidence of what all that money is buying. But even after the bounce, shares remain roughly 50% below their September 2025 peak.

Q1 FY27 in numbers:

• 🚀 Growth accelerated again: Revenue reached $19.3 billion, up 30% Y/Y and ahead of Oracle’s 27%–29% guidance. Adjusted EPS reached $1.92, comfortably above the $1.74 consensus.

• ☁️ OCI more than doubled: Cloud revenue grew 62% to $11.6 billion, led by OCI growth of 121% to $7.4 billion. Cloud applications grew a much slower 10% to $4.2 billion, while traditional software revenue fell 3% to $5.6 billion.

• 📦 The backlog is still growing: RPO reached $664 billion, up $209 billion Y/Y and $26 billion sequentially. Oracle booked more than $30 billion of additional AI cloud contracts during the quarter, even as it began converting more of the existing backlog into revenue.

• 💰 The CapEx number needs some context: Reported CapEx reached an extraordinary $28.5 billion, contributing to roughly $5 billion of negative free cash flow (and $29 billion in the past 12 months). But customer prepayments covered $11 billion of the infrastructure spending, bringing Oracle’s net cash outlay to roughly $18 billion. Oracle also completed its previously announced $20 billion equity offering. Oracle is burning cash to build infrastructure, but customer checks are softening the blow.

So what to make of all this?

• 📈 The backlog is becoming real revenue: OCI growth accelerated from 93% to 121% as Oracle nearly tripled the capacity it brought online. The bull case is no longer based only on a giant RPO number. Oracle is now showing it can turn that backlog into revenue, while demand for AI training and inference still exceeds available capacity.

• 💵 Customers are sharing the financing burden: Customer prepayments are becoming a meaningful part of Oracle’s AI funding model. The company still had to fund roughly $18 billion of net CapEx in Q1, but customers effectively financed more than $11 billion of the buildout. Oracle also said its latest $30+ billion of AI contracts require no incremental increase to its planned capital raise.

• ⚖️ Gross margins are under pressure: Oracle’s gross margin fell roughly 6 percentage points Y/Y as lower-margin OCI became a much larger part of the revenue mix. But that pressure has not flowed through to operating margins yet. GAAP operating margin expanded by nearly 6 points to 35%. So far, operating leverage elsewhere is absorbing the infrastructure mix shift.

What to watch

Oracle expects cloud revenue growth to accelerate again to 65%–71% in Q2 FY27. With demand already under contract, attention is increasingly shifting to execution and economics. Can Oracle bring capacity online fast enough, fund roughly $70 billion of net CapEx this year, and maintain attractive margins as infrastructure becomes a much larger part of the business?

Takeaway: Oracle no longer needs to prove that AI demand exists. Q1 showed that it can turn that demand into capacity and revenue at remarkable speed. The remaining question is whether the economics justify the enormous capital required to get there. This quarter was an important step toward proving they can.

That's it for today.

Happy investing!

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Thanks to Fiscal.ai for being our official data partner. Create your own charts and pull key metrics from 50,000+ companies directly on Fiscal.ai. Save 15% with this link.

Disclosure: I own AMD, AMZN, GOOG, META, and NVDA in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members. 

Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.