New year, same obsession: How market leaders actually make money.
While the internet is busy making bold predictions and hot takes, we’re starting the year the only way we know how: By following the money. 💰
Today at a glance:
📈 Sandisk Absolutely Crushed 2025
🧠 Meta Bets on AI Startup Manus
🤖 NVIDIA + Groq: The $20B Deal
🔎 Gemini’s Very Good Year
📈 Sandisk Absolutely Crushed 2025
If you spent 2025 watching only the Mag 7, you missed one of the big stories of the year. The best-performing stock in the S&P 500 wasn’t a hyper-scaler or an LLM designer. It was a company that didn’t even exist as an independent entity at the start of the year.
Sandisk (SNDK) delivered a staggering ~594% return since its spinoff from Western Digital (WDC) in February 2025.
For years, Western Digital’s stock was dragged down by a conglomerate discount, trying to run a legacy Hard Drive business and a high-growth Flash business under one roof. The spinoff finally freed Sandisk to be valued on its own merits. It became a pure-play on the next phase of the AI buildout.
But a nearly 7x surge in under a year requires more than just a corporate reshuffle. It requires a fundamental misunderstanding by the market of how a company makes money. That misunderstanding was amplified by years of NAND price cyclicality, which trained investors to treat every storage rally as temporary.
It’s not just USB drives
Many investors still associate Sandisk with the little SD card in their camera. While that Consumer segment still provides cash flow, it’s no longer the engine.
Sandisk makes NAND Flash memory, the essential technology for fast, non-volatile storage. Their revenue is now driven by three distinct pillars:
Datacenter: High-performance Enterprise SSDs for hyperscalers (like AWS and Google Cloud). These deployments carry longer contracts, higher switching costs, and disproportionate influence on valuation by turning volatile NAND pricing into infrastructure-like revenue streams critical to AI training workloads.
Edge: It covers the critical flash memory inside AI Smartphones, PCs, Internet of Things (IoT), and autonomous vehicles. As devices run more AI models locally (on-device AI), they need massive storage upgrades.
Consumer: The portable SSDs and memory cards we all know.
AI Multiple Valuation
Sandisk’s 2025 story was all about multiple expansion. The market stopped valuing Sandisk like a single-digit multiple NAND supplier and began pricing it closer to AI infrastructure peers with durable demand visibility.
In early 2025, the market valued Sandisk like a commodity hardware seller (a low P/E multiple). By mid-year, the narrative shifted. Investors realized that if AI is the new electricity, high-speed storage is the copper wire beneath it. Invisible, essential, and impossible to scale without.
Sandisk was “re-rated” from a boring hardware stock to critical AI infrastructure.
This re-rating was so powerful that the stock ripped higher even as gross margins compressed. Why? Investors recognized that Sandisk was executing a deliberate land grab, accepting near-term margin pressure as it ramps enterprise volumes and secures long-term hyperscaler relationships with data centers building out their AI architecture for the next decade.
What’s the moat? Sandisk’s advantage relies on the high switching costs of the "Qualification Cycle." In the AI era, hyperscalers cannot simply swap out storage components on a whim. Drives must undergo grueling 6-to-12-month testing regimens to ensure they don't cause latency spikes that idle expensive GPUs. Once Sandisk wins a socket and becomes the "Plan of Record," it locks in that revenue for the server's entire 3-to-5-year lifecycle. Furthermore, while competitors like Samsung are prioritizing HBM capacity for GPUs, Sandisk has capitalized on the less crowded lane of Enterprise SSDs, using specialized controllers and firmware to turn what used to be a commodity component into a sticky, essential layer of the AI infrastructure stack.
Takeaway: Spinoffs often unlock massive value by stripping away narrative noise and forcing markets to reprice the underlying economics. But more importantly, 2025 proved that the boring infrastructure layers of a tech boom can be far more lucrative than the flashy consumer apps built on top of them.
🧠 Meta Bets on AI Startup Manus
Meta Platforms just made another bold AI move, agreeing to acquire Manus for more than $2 billion, a deal reportedly agreed in just 10 days.
It might sound like a steep price, but Manus isn’t a typical chatbot. It’s a general-purpose AI agent designed to plan tasks, pull in tools, and deliver finished work. Think research reports, code, data analysis, or even websites, with minimal human input.
Manus was founded in China in 2022 and later moved to Singapore. It officially launched its agent in March 2025.
Key numbers
~100 Manus employees.
~$85 million raised so far.
~$125 million in ARR (crossed $100 million within 8 months of launch).
That puts Manus in rare territory for enterprise software adoption.
Meta plans to continue selling Manus as a standalone product while integrating its agent capabilities across Meta’s ecosystem: Facebook, Instagram, WhatsApp, ads, and AI hardware.
Why it matters
Meta has world-class models and massive distribution, but it has lagged peers like ChatGPT and Gemini in real, task-oriented AI applications. Manus fills that gap immediately.
Instead of waiting years to build an agent platform from scratch, Meta is buying:
A proven agent workflow.
Paying enterprise customers.
A subscription revenue stream.
A team already shipping in production.
It also gives Meta a path to meaningful AI revenue beyond ads.
Takeaway: This deal is all about monetization. Meta is using its balance sheet to shortcut the hardest part of AI: turning impressive technology into products businesses actually pay for. At $2 billion, Manus is expensive. But at $125 million in ARR in under a year, it gives Meta something rare in AI today: traction with economics attached.
🤖 NVIDIA + Groq: The $20B Deal
In a move that stunned Wall Street over the holiday break, NVIDIA announced a definitive agreement to pay $20 billion in cash to license the technology and hire the core engineering team of AI chip startup Groq.
This is not a traditional acquisition. Groq will continue to exist as an independent entity (renamed GroqCloud), led by former CFO Simon Edwards. However, NVIDIA is effectively absorbing Groq’s “soul”—its proprietary LPU (Language Processing Unit) patents and its visionary leadership, including founder Jonathan Ross (creator of the Google TPU).
Why now? To understand this deal, you have to look at the market shift that defined late 2025. We recently crossed a tipping point known as the Inference Flip. Global revenue from using AI models (inference) has officially surpassed the revenue from building them (training).
While NVIDIA’s GPUs are the undisputed kings of training (massive throughput), they have a vulnerability: Latency.
NVIDIA GPUs are like freight trains. Incredible capacity to haul massive data, but slow to get up to speed.
Groq LPUs are like Formula 1 cars. Lightweight, instant acceleration, and designed purely for real-time speed.
By securing Groq’s technology, NVIDIA solves its only real weakness: real-time, low-latency inference for agents and voice AI.
What it means:
Regulatory shenanigans: By structuring this as a “licensing and hiring” deal rather than a merger, NVIDIA sidesteps the antitrust gridlock that killed the ARM deal. It mirrors the strategy Microsoft used with Inflection AI earlier this year.
Capital efficiency: $20 billion sounds like a lot, but for NVIDIA, it represents just one quarter of Free Cash Flow. They essentially used three months of profit to neutralize a systemic threat and secure the next decade of inference dominance.
The moat widens: This deal deprives competitors (like Google and AMD) of some of the industry’s best inference talent. NVIDIA is no longer just selling shovels for the AI gold rush. It now owns the distribution network for the gold itself.
Takeaway: NVIDIA just signaled that it refuses to be disrupted from below. By integrating Groq’s speed into the upcoming Rubin architecture, NVIDIA ensures it remains the default operating system for the entire AI economy—from massive training runs to split-second inferencing.
🔎 Gemini’s Very Good Year
For most of 2024, the GenAI story looked simple: ChatGPT dominated, everyone else scrambled.
2025 quietly complicated that narrative.
Released on Christmas Day, new data from Similarweb tells one of the most important AI stories of the year:
Gemini is approaching a 20% shareof GenAI website traffic
ChatGPT has slipped from 87% a year ago to below 70% today.
The shift comes down to distribution.
Gemini now sits inside Google’s core products, including Search, Chrome, Android, and Workspace. It’s turning default placement into habitual usage. When AI lives where people already spend their time, adoption follows naturally.
Two signals stand out from SimilarWeb’s data:
Grok continues to gain traction, helped by tight integration with X and real-time content.
The market is consolidating into a ‘Big Two’ dynamic, with ChatGPT and Gemini pulling away from a long tail of task-specific tools.
Gemini is the only product meaningfully taking share, and it’s doing so via Google distribution and defaults. As model quality converges, power users might show little loyalty to a single interface. They switch to whatever is cheapest, fastest, or already embedded in their workflow.
That challenges the idea of a lasting moat at the AI interface layer.
OpenAI doesn’t expect to be profitable until 2030 and, according to Deutsche Bank, could burn more than $140 billion in cumulative negative free cash flow before getting there. Google’s parent Alphabet, by contrast, generated $74 billion in free cash flow over the past 12 months, even after heavy AI investment.
Takeaway: ChatGPT built the category and forced Google to respond. Now Google is using its balance sheet, distribution, and pricing power to compete aggressively, while OpenAI is still figuring out its unit economics. The clock is ticking on whether ChatGPT can meaningfully pressure Search before Gemini closes the gap.
That's it for today.
Happy investing!
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It’s the last post of 2025. So, it’s time to reflect!
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Most popular posts in 2025
Before we start 2026, here are 12 of the most popular posts of 2025.
Best and worst investments of 2025
2025 was another great year to be an investor.
Chances are, you are pretty happy with your portfolio today. After a catastrophic 2022, the market rose over 20% in 2023 and 2024, and now 18% in 2025.
S&P 500 annual returns (Macrotrends)
The S&P benchmark returned an average annual 10% from 1926 to 2024, and 74% of years were positive. The longer the time horizon, the higher the chance of positive returns. If you want to stack the deck in your favor, the easiest step by far is to trade less and ignore the doomsayers.
“Pessimists sound smart. Optimists make money.”
I share my stock portfolio with a community of long-term-focused investors. So I wanted to share the best and worst-performing investments this year:
📈 Best investment: AppLovin (APP): In March 2025, I wrote: “Several short reports have targeted the company, but they generally come from low-level analysts or content creators with no reputation or research teams. Conversely, hedge funds like Baillie Gifford, Coatue, and Sands Capital have invested in APP.” The market has been underestimating the leverage within AppLovin’s software platform. As the AI-driven AXON engine continues to optimize ad matching, we are seeing a decoupling of revenue growth from operational costs, leading to a massive repricing. The stock has nearly tripled since then, dramatically outperforming the market.
📉 Worst investment: The Trade Desk (TTD): I added to my existing position in February 2025, aiming to capitalize on what appeared to be a reasonable entry point. Since then, the stock has struggled with sentiment shifts in the ad-tech category and a slowdown in revenue growth, partially explained by tough comps from US political spending in 2024. The stock is down 50% from that specific purchase, making it our worst investment this year. TTD has also been the worst performer in the S&P 500. But consider this: TTD has been a holding since 2017, and despite the recent drawdown, the stock price remains over 10x our initial purchase price. It's another great reminder, if you needed one, that zooming out and keeping a long-term view is critical in investing.
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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.
Disclosure: I am long AAPL, AMZN, GOOG, META, PLTR, UBER, and TSLA in the App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members here.
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Today at a glance:
🌐 Accenture: AI Scaling, Outlook Stalling
👟 Nike: Middle Innings
🚚 FedEx: Increased US Volume
🛳️ Carnival: Dividend Returns
🍪 General Mills: Price Cuts Pay Off
🫒Darden: Traffic Returns
1. 🌐 Accenture: AI Scaling, Outlook Stalling
Accenture kicked off FY26 with Q1 revenue (November quarter) rising 6% Y/Y to $18.7 billion ($0.2 billion beat) and non-GAAP EPS of $3.94 ($0.22 beat).
New bookings rebounded12% Y/Y to $21 billion. They represent the value of new contracts signed during the quarter, which will fuel future revenue. A closer look at the past few quarters reveals a trend of stabilization rather than acceleration. New bookings have grown at a 10% CAGR in the past five years.
The drive behind the recent growth remains the same. Advanced AI bookings hit a record $2.2 billion for the quarter (up from $1.8 billion in Q4). This metric now includes not only GenAI, but also agentic and physical AI.
However, the stock dipped despite the beat, largely due to a tepid near-term outlook. Management guided Q2 revenue to ~$17.7 billion in the midpoint ($0.1 billion miss).
While they reaffirmed the full-year growth target of 2%–5%, CEO Julie Sweet noted that discretionary spending remains unchanged and there is no immediate catalyst for improvement. Management announced that Accenture won’t report Advanced AI metrics in future quarters, noting that AI is now embedded in every deal rather than as a standalone line item. This fits the narrative we covered yesterday about measuring AI revenue impact.
2. 👟 Nike: Middle Innings
Nike’s Q2 FY26 revenue managed to stay positive, growing 1% Y/Y to $12.4 billion ($0.2 billion beat), while EPS declined 32% Y/Y to $0.53 ($0.16 beat).
The Wholesale vs. Direct pivot remains the central story. Wholesale revenue climbed 8% to $7.5 billion, as the company re-engages with retail partners to drive volume. This strength helped offset a continued slump in Nike Direct, which fell 8% to $4.6 billion.
CEO Elliott Hill described the turnaround as being in the “middle innings.” The strategy is clearly working for NIKE Brand in North America, where sales jumped 9% driven by momentum in Running and new products.
However, this success was sharply contrasted by severe weakness elsewhere. Greater China revenue plunged 17% Y/Y to $1.4 billion, and Converse sales collapsed 30%. Profitability also took a significant hit. Gross margins fellto 40.6% (from 40.9% a year ago), pressured by the “Win Now” aggressive promotions and the looming impact of tariffs, which management confirmed is a ~$1.5 billion annualized headwind.
Reflecting the uneven recovery, Nike’s stock fell over 10% post-earnings. Management warned that the comeback “won’t be a straight line,” and revenue is expected to decline by low single digits in Q3. While the North American rebound is encouraging, the global turnaround is far from complete.
Amazon is reportedly negotiating a $10 billion investment in OpenAI, effectively mirroring the strategy Microsoft executed just last month.
Microsoft went all-in on OpenAI first ($13 billion), then hedged by backing Anthropic ($5 billion).
Amazon bet big on Anthropic first ($8 billion), and is now potentially hedging by backing OpenAI ($10 billion).
The era of exclusive AI marriages is over. Cloud giants have realized that picking a single winner is too risky. Instead, they are becoming arms dealers. Whoever prevails in the model war, the underlying cloud infrastructure wins. That’s particularly critical for Amazon’s custom Trainium chip, which OpenAI reportedly plans to use.
Meanwhile, OpenAI needs to raise hundreds of billions to fund its CapEx ramp. It also reinforces the growing trend of circular financing in AI. Just last month, OpenAI signed a $38 billion multi-year deal with AWS.
OpenAI once again demonstrates its ability to secure commitments. Tech giants are effectively tying part of their success to OpenAI’s ability to scale.
The Information also reports that OpenAI and Amazon are discussing commerce partnership opportunities. Plot twist?
Today at a glance:
ServiceNow On a Buying Spree
Enterprise Software “Show Me” Era
Micron’s Shocking Guidance
ServiceNow On a Buying Spree
ServiceNow (NOW) shares plunged nearly 12% on Monday, wiping out $21 billion in market cap for the software giant, and have not recovered since.
So what happened? The trigger may have been a report that the company is in advanced talks to acquire cybersecurity firm Armis for $7 billion. But it was likely less about the news itself, and more about a sum of developments of late:
💰 M&A overload: Armis would be the fourth sizable acquisition this year. Critics worry this aggressive spree signals that organic growth is harder to come by and that management is scrambling to make moves.
👔 KeyBanc downgrade: Analyst Jackson Ader raised concerns that AI could reduce IT back-office headcount, shrinking ServiceNow’s core total addressable market (TAM).
The reality check: Despite jittery sentiment, ServiceNow has been growing its top line by more than 20% (twice the pace of peers like Salesforce and Adobe), and cybersecurity remains a top priority for enterprise budgets. Management may simply be taking advantage of a more lenient regulatory environment for tech mergers.
Is the panic justified? Not if you look closely at the business momentum.
Here’s what we wrote for PRO members after the company’s earnings last month.
🧑💻 ServiceNow: AI Fuels Blowout
ServiceNow delivered another stellar quarter, with Q3 revenue rising 22% Y/Y to $3.4 billion ($50 million beat) and adjusted EPS soaring 30% Y/Y to $4.82 ($0.55 beat). The outperformance was driven by accelerating AI adoption and strong enterprise demand. Subscription revenue grew 22% to $3.3 billion, and cRPO climbed 21% to $11.3 billion. Large customer momentum continued, with customers spending over $5 million in ACV growing 18% Y/Y to 553.
Management highlighted exceptional AI traction, noting its AI products are on pace to exceed $0.5 billion in ACV this year, tracking ahead of plans. Reflecting this strength, ServiceNow raised its FY25 guidance significantly: Subscription revenue is now expected at ~$12.84 billion ($55 million midpoint raise), operating margin guidance was lifted 50bps to 31%, and free cash flow margin guidance jumped 200bps to 34%. Q4 guidance also points to continued strong growth (~19.5% subscription revenue).
ServiceNow’s board authorized a five-for-one stock split, pending shareholder approval. While management prudently factored potential government deal timing delays into Q4 guidance, the beats put the company on a strong trajectory.
Enterprise Software “Show Me” Era
The market has spent the last year punishing software stocks that promised an AI revolution but delivered only incremental features. Investors have largely dumped shares of any company that couldn’t draw a direct line between GPU spend and ARR growth, while OpenAI and Anthropic are sucking all the air in the room.
The billion-dollar club
While hyperscalers (Microsoft, AWS, Google, Meta) have captured most of the upside (as reflected in growth acceleration), some pure-play companies in the platform and apps categories have emerged. Notably, Palantir, Salesforce, and Databricks have all crossed the $1 billion AI revenue run rate milestone.
Palantir ($1.6 billion US Commercial run rate): The standout performer. Their AIP Bootcamps strategy has successfully converted pilot programs into committed revenue, driving US Commercial growth to 121%.
Salesforce ($1.4 billion Agentforce + Data): By bundling their new Agentforce with the less glamorous but essential Data Cloud, they generated $1.4 billion in annualized AI-related ARR, growing 114%. However, the market has focused on the overall organic growth (now at 9% excluding Informatica) and remains skeptical.
Databricks (>$.1.0 billion run rate from AI products): The Data and AI company just announced a $4 billion Series L investment, valuing the company at $134 billion. They crossed $4.8 billion in overall revenue run-rate in Q3, growing over 55% Y/Y.
Here’s a look at the other large movers.
As you can see, the aforementioned ServiceNow follows, alongside Adobe. The latter has been deemed the ultimate AI victim, though the jury is still out.
Can incumbents also win after all?
The data highlights a critical divergence: software incumbents are not building flashy models. Instead, they are capturing the “boring” enterprise context that makes those models useful.
Companies like ServiceNow and Salesforce are executing a small-ball M&A strategy by acquiring niche tools to feed their agents.
Massive acquisitions like Warner Bros. are essentially focused on the old economy.
Smaller AI acquisitions are happening weekly. They may not capture headlines, but they focus on what comes next, from data management to cybersecurity. Those are the acquisitions that can be overlooked and still deliver massive returns if integrated properly.
This unsexy integration is fueling the trifecta of software economics:
Acquisition: AI as the hook to get in the door (see Palantir Bootcamps).
Retention: Embedded agents that learn enterprise context, making them painful to rip out.
Monetization: Usage-based pricing (Databricks/Snowflake) or premium agent consumption (Salesforce) that scales with value.
If we judge the AI revolution solely by the few billions here and there directly attributed to AI products, the math looks broken. But this view misses the massive, invisible uplift in core businesses. For giants like Google and Meta, AI is the algorithm making their existing ad machines smarter, boosting revenue lines that are part of the core business. In fact, having an existing business that can be significantly enhanced by AI may be the fundamental aspect of the AI boom that favors incumbents.
Micron’s Shocking Guidance
Last quarter, we discussed how Micron’s reorganization revealed the HBM-powered recovery (High-Bandwidth Memory).
The company just released its Q1 FY26 earnings (ending November 2025), and the numbers shattered estimates. The story has moved from recovery to what management calls the “most significant disconnect between supply and demand in 25 years.” And while Q1 results were strong, the market was stunned by the Q2 forecast.
Wall Street expected a steady climb, but Micron delivered an explosion. The Cloud Memory unit generated $5.3 billion, a 2x year-over-year increase.
Revenue surged 57% Y/Y to $13.6 billion ($0.8 billion beat), and EPS was $4.78 ($0.82 beat). Margins continued to expand to their highest level since 2018, marking a historic expansion in profitability driven by pricing power.
Micron’s revenue guidance was 31% higher than expectations. The forecast implies a staggerring 38% sequential revenue growth and nearly 59% operating margin. This P&L is starting to look like NVIDIA’s!
Why is guidance so high? We are officially out of chips. Executive VP Manish Bhatia described the current environment as the most severe supply/demand imbalance he has seen in his career.
HBM is sold out: Micron is sold out of HBM chips for 2026.
Rationing: CEO Sanjay Mehrotra admitted they can currently meet only 50% to 66% of demand for key customers.
What it means for Micron
As Micron shifts production lines to build complex HBM chips for AI, there is now a shortage of standard memory for PCs and smartphones. This allows Micron to raise prices across the board, not just in AI.
Despite 25 upward revisions heading into the print (usually a setup for disappointment), Wall Street still dramatically underestimated the reality.
What to watch next
CapEx: Micron raised its fiscal 2026 CapEx forecast to $20 billion (up from $18 billion). They are spending aggressively to build capacity in Idaho and New York.
Consumer Impact: Watch for price hikes in consumer electronics (PCs, phones) as commodity memory becomes scarce.
HBM4: Production is slated for early 2026. With current generation chips sold out, the race for next-gen allocation has already begun.
The shortage is real, the margins are historic, and the cycle is accelerating. We’ll find out next quarter if Micron was still conservative with its $18 billion promise.
Just don’t forget: memory remains the quintessential cyclical business.
That's it for today.
Happy investing!
How They Make Money Premium members unlock hundreds of visuals every quarter
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 ADBE, AMD, AMZN, CRM, GOOG, META, NOW, PLTR, 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.
