Broadcom just dropped a bombshell on their latest earnings call — and Wall Street is still trying to figure out what hit them.The company quietly revealed they're tracking toward $100 billion in AI revenue. Not someday. Not in a decade. Within the next three years. And behind that number? Six customers. Just six. The most elite AI buyers on the planet, each building million-chip clusters that will reshape how we think about computing infrastructure.In this episode, we break down everything Hock Tan said on the call — and more importantly, what he didn't say. We go customer by customer through Broadcom's six hyperscaler relationships: Google, Anthropic, Meta, OpenAI, and the two mystery buyers Wall Street can't stop speculating about (ByteDance and Apple are the leading candidates). Each one tells a different story about where AI infrastructure spending is headed.We dig into why AI spending is irreversible once you've made the switch, why training isn't dead despite what the DeepSeek crowd keeps saying, and how the demand picture has actually gotten stronger — not weaker — since the last earnings cycle. Meta alone is scaling from hundreds of thousands of accelerators to millions. Anthropic is talking about 3 gigawatts of power demand. These aren't science projects. These are the biggest infrastructure buildouts since the internet itself.Then we do the math on Broadcom's path to a $3 trillion market cap, why Wall Street's current models are still underestimating the opportunity, and what this all means for investors watching the AI infrastructure trade play out in real time.Whether you're long Broadcom or just trying to understand the AI capex cycle, this episode connects the dots between earnings calls, datacenter buildouts, and where the smart money is actually flowing.0:00 Intro0:20 Broadcom's $100B Bombshell1:22 Why AI Spending Is Irreversible1:53 Hock Tan's Earnings Call4:18 Not a Zero-Sum Game5:57 Why Training Still Matters7:48 Training vs Inference: Two Tracks9:48 Demand Durability11:14 The 6 Elite Customers12:53 Customer #1: Google17:12 Customer #2: Anthropic20:31 Anthropic's 3GW Scaling21:31 Customer #3: Meta25:09 Meta's Two-Track Strategy26:47 Customer #4: OpenAI28:29 Customers #5 & #628:59 The Math Behind $100B31:56 Wall Street's Disconnect33:53 Key Investor Insights36:10 The Road to $3 Trillion38:00 Outro

Meta, Microsoft, Amazon, and Google are pouring over $1 trillion into AI infrastructure. I break down each company's strategy, rank them 1–4, and explain why one of them might never earn it back.0:00 Intro — The $1 Trillion AI Question0:29 Subscriber Jamie's Question2:22 GPU Real Estate Model — How Cloud CapEx Works4:26 META — All-In on AI (Not Renting GPUs)7:30 Meta's Hidden Opportunity Wall Street Is Missing8:25 Zuckerberg's Vision: Social Media Becomes a Life Tool13:34 Meta's Competitive Moat vs TikTok & Snapchat17:21 Meta's Power Move — Building the Next Era19:35 Why This Isn't the Metaverse Again23:11 MICROSOFT — The Azure & Copilot Play27:46 Microsoft's Familiar Playbook (Teams 2.0?)31:15 Can Copilot Become the AI Orchestration Layer?35:50 Enterprise Lock-In: Microsoft's Safety Net37:50 The Copilot Question — Will They Pull It Off?39:28 AMAZON — Instant Demand for Every GPU43:27 Amazon's Secret Weapon: They Don't Need Frontier AI45:13 The Golden Goose — $500B E-Commerce + AI48:39 GOOGLE — The Only Fully Vertically Integrated Stack52:07 Google's AI Specialization: Video, Images & DeepMind55:38 The Future of Ads — Why Clicks Get More Valuable58:25 RANKINGS — #1 Amazon, #2 Meta1:00:33 RANKINGS — #3 Google, #4 Microsoft1:03:47 Outro & How to Submit Questions

Wall Street is lumping all software stocks together and selling them off. That's a mistake. I walk through the 4-box checklist I use to find the software stocks that will actually recover — and which ones to avoid.

0:00 — Snowflake replaces Salesforce CRM, saves $5M/year

01:22 — Wall Street lumping all software into one basket

02:17 — Software stock charts breaking down (Salesforce, ServiceNow, Adobe, Trade Desk)

04:08 — The vibe coding bear argument

06:22 — Why Wall Street is making a big mistake

08:02 — Legacy software vs. the new infrastructure layer

10:16 — Software that actually does the work vs. UI software

15:08 — Snowflake proves growth without headcount (30% revenue growth, +37 employees)

17:39 — "Growth is no longer tied to headcount"

23:02 — The 4-Box Checklist begins

24:18 — Box 1: Where does the software live in the stack?

28:01 — Box 2: Is AI adoption accelerating?

31:21 — Box 3: Is revenue decoupling from headcount?

34:03 — Box 4: Does the chart confirm it?

40:54 — Recap and key takeaways

Wall Street is obsessed with legacy software and the cloud data center. But the agentic layer is guaranteed to replace humans inside organizations — and that work isn't going to the cloud. I break down why on-prem hardware is about to have its biggest moment in 15 years, which companies benefit (Apple, Nvidia, Dell, Intel, AMD, IBM, Oracle), and why the choice between renting the cloud and owning your own AI will be obvious.

SaaS stocks are selling off and Wall Street is debating whether Salesforce and Adobe can pick themselves up off the mat. But the bigger opportunity isn't in old software — it's in a brand new layer of the economy. AI agents aren't tools you use. They're workers you deploy. And that changes everything — payments, security, identity, hardware, and operating systems all need to be rebuilt for this new layer. I walk through the three phases of how this rolls out, who wins at each phase, and why smart investors are getting positioned now while Wall Street is still a year behind.

Nvidia can only sell what TSMC can build. OpenAI can only ship what compute allows. Even the power grid is a constraint. I walk through why every AI winner so far has been supply-limited, why Jevons Paradox means removing those limits won't slow spending, and where the uncapped Phase Two opportunity emerges. Plus the inference flip data from this week's article.

A subscriber asked what drives asset manager stocks. Here's a deal BlackRock and KKR aren't the same business. I break down the two camps what makes each one move, and why the coming wave of IPOs could be a catalyst for both. Plus the AI infrastructure connection nobody's talking about yet.

For two years, AI capex was funded by excess — buybacks stayed huge, free cash flow stayed solid. In 2026, that changes. Meta, Google, Microsoft are now spending beyond their free cash flow. The demand is real, but certain investors can't hold these names anymore. I explain the shift from "funded by excess" to "funded by commitment" — and why patient investors have the edge.

The SaaS sector just had its worst day since Covid. Wall Street is selling everything — but they're making the same mistake they always make: lumping all software together. AI isn't killing software, it's replacing salaries. That's great for some companies and devastating for others. I walk through the three camps of SaaS and why the same trend destroying one group is fueling another.

Google and Meta are building the full AI stack. Apple, Amazon, and Microsoft are partnering. Both can't be right. If LLMs commoditize, the partners win. If LLMs stay differentiated, the builders win. I break down the divergence, the stakes, and what it means for each stock heading into 2026.