Dylan Patel - The Infinite Demand for Tokens, Claude Mythos, and Supply Constraints - [Invest Like the Best, EP.468]
Dylan Patel - The Infinite Demand for Tokens, Claude Mythos, and Supply Constraints - [Invest Like the Best, EP.468]
Podcast45 min 19 sec
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Note: AI-generated summary based on third-party content. Not financial advice. Read more.
Quick Insights

Investors should prioritize Micron (MU) and other memory leaders like SK Hynix, as a structural supply shortage is expected to double or triple DRAM prices through 2027. NVIDIA (NVDA) remains a high-conviction hold as older GPU models maintain high rental value and the upcoming Blackwell launch scales compute power to unprecedented levels. To capitalize on TSMC’s (TSM) projected $100 billion capital expenditure cycle, investors should look toward equipment suppliers ASML, Applied Materials (AMAT), and Lam Research (LRCX). Beyond chips, the skyrocketing demand for AI-driven reinforcement learning makes CPUs a critical, "sold-out" bottleneck worth monitoring in cloud infrastructure. Finally, watch for a major breakthrough in Robotics over the next 6 to 18 months, which will shift market focus toward physical hardware and actuators.

Detailed Analysis

Anthropic (Private)

• Anthropic is experiencing explosive revenue growth, moving from an estimated $9 billion to a projected $40-$45 billion ARR (Annual Recurring Revenue) in a very short window. • The company recently released Opus 4.7 and has an internal model called Mythos that is rumored to be a massive leap in capability (moving from "L4" to "L6" engineer level). • Demand for their tokens is so high that they are operating with high gross margins (estimated floor of 72%), despite the massive compute costs. • They are currently "sold out" of capacity, leading to strict rate limits for non-enterprise users.

Takeaways

Enterprise Priority: For businesses, securing an Anthropic Enterprise contract is becoming a competitive necessity to bypass rate limits and gain early access to frontier models. • Margin Expansion: The shift from 30% to 70%+ gross margins suggests that frontier AI labs have immense pricing power as long as they maintain a lead in intelligence. • Access Arbitrage: Investment firms (like Citadel) may seek exclusive "first-look" access to tokens, creating a new form of market advantage where the wealthiest firms get the smartest models first.


NVIDIA (NVDA)

• NVIDIA continues to dominate the hardware layer with 75% gross margins. • The "useful life" of GPUs is proving to be much longer than the 5-year estimate many analysts predicted; older A100 and H100 clusters are being re-signed for additional years at higher prices. • The upcoming Blackwell chips represent a massive scale-up in compute power (100,000 Blackwells being equivalent to hundreds of thousands of previous-gen chips).

Takeaways

Extended Asset Value: The longer-than-expected lifespan of GPUs suggests that the depreciation hit to companies buying them might be lower than feared, while the rental value remains high. • Supply Chain Tightness: Bottlenecks remain in specialized components like copper foil, glass fibers for PCBs, and lasers, which are essential for NVIDIA’s hardware.


Taiwan Semiconductor Manufacturing Co. (TSM)

• TSMC is aggressively increasing capital expenditure (CapEx). Dylan Patel predicts they could spend $100 billion on CapEx by 2028. • They are currently squeezing every possible bit of output from existing fabs to meet demand. • Unlike memory providers, TSMC has been slower to aggressively hike prices, potentially leaving some "value" on the table for their customers (like NVIDIA and Apple).

Takeaways

Long-term Infrastructure Play: The massive projected CapEx suggests a multi-year "super-cycle" in semiconductor manufacturing equipment. • Downstream Beneficiaries: Companies that supply TSMC, such as ASML, Applied Materials (AMAT), and Lam Research (LRCX), are positioned to benefit from this massive spending trajectory.


Memory & DRAM Sector (MU, SK Hynix, Samsung)

• Memory is identified as a critical bottleneck. Capacity can only grow at low double-digit percentages annually. • DRAM prices are expected to double or triple from current levels because capacity cannot be added fast enough to meet AI demand. • New capacity decided upon today likely won't hit the market until 2027 or 2028.

Takeaways

Bullish Sentiment: The "memory story" is not overplayed; the supply-demand imbalance is structural and will likely lead to sustained high pricing and record margins for memory makers. • Demand Destruction: High prices will be the only way to "ration" supply, meaning only high-value AI applications will be able to afford the latest memory-intensive hardware.


Investment Themes & Sectors

The "Software-Only Singularity" and Robotics

• We are entering a phase where software implementation is becoming "cheap and easy" due to AI. • The next frontier is Robotics. Patel expects a breakthrough in the next 6 to 18 months in "few-shot learning" for robots (showing a robot a task once or twice for it to learn). • This will shift the focus from software back to physical hardware and actuators.

CPU Demand in AI

• While GPUs get the headlines, CPUs are currently "sold out" and seeing skyrocketing demand. • Reason 1: Reinforcement learning environments (where AI "practices" tasks) run on CPUs, not GPUs. • Reason 2: The "slop code" and apps generated by AI must ultimately be hosted and run on traditional CPU-based cloud servers.

"Phantom GDP" and Deflation

• AI is creating massive economic value that isn't showing up in traditional GDP stats. • Example: One person using AI can do the work of 200 economists or a full engineering team. This is highly deflationary for services but creates "Phantom GDP" where output rises while costs (and thus measured GDP) might actually shrink.

Risk Factors: Social Backlash

• Patel predicts large-scale protests against AI companies within the next three months. • Public perception is turning negative due to fears of job displacement and the "uncharismatic" communication from AI lab CEOs. • Investment Risk: Regulatory crackdowns or social unrest could impact the speed of data center build-outs and power consumption approvals.


Mentioned Private Companies & Tools

Ramp: AI-automated expense management (Sponsor). • WorkOS: Enterprise-ready APIs for AI startups (Sponsor). • Rogo (Felix): Personal finance AI agent for investment firms (Sponsor). • Vanta: Automated compliance and security (Sponsor). • Ridgeline: Unified platform for asset management (Sponsor). • CoreWeave: Specialized GPU cloud provider.

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Episode Description
This is my second conversation with Dylan Patel. Dylan is the founder and CEO of SemiAnalysis, where he tracks the semiconductor supply chain and AI infrastructure buildout. This conversation is about the supply and demand of tokens. On demand, Dylan describes something completely explosive. He explains why the frontier model is the only model anyone wants, and willingness to pay for it is nearly unbounded. His own firm has gone from tens of thousands of dollars in AI spend last year to seven million this year. On supply, we walk through the bottlenecks across memory, logic, and fab equipment that will determine how fast any of this can scale. We also cover Claude Mythos and what the leading labs need to do to fix their growing public perception problem. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp’s⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Visit ⁠vanta.com/invest⁠.  ----- ⁠WorkOS⁠ is a developer platform that enables SaaS companies to quickly add enterprise features to their applications. Visit⁠⁠ ⁠WorkOS.com⁠⁠⁠ to transform your application into an enterprise-ready solution in minutes, not months. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Intro: Dylan Patel (00:03:09) Semi Analysis AI Spend: Zero to $7M (00:05:16) Real-World Examples of Claude Code (00:11:41) Token Demand: “Completely Explosive” (00:14:48) Why Everyone Wants the Frontier Model (00:15:36) Mythos: Biggest Model Capability Jump in Two Years (00:20:54) Fear of Rapid Model Progress (00:23:45) Robotics as the Next Demand Wave (00:26:03) Scaling Laws & Compute Efficiency (00:27:24) OpenAI vs. Anthropic (00:31:33) Supply Side: Bottlenecks Across the Stack (00:33:26) TSMC CapEx Could Cause a Shortage (00:36:45) CPUs, ASICs, and FPGAs (00:40:12) Tokenomics (00:42:20) Protests & AI Backlash
About Invest Like the Best with Patrick O'Shaughnessy
Invest Like the Best with Patrick O'Shaughnessy

Invest Like the Best with Patrick O'Shaughnessy

By Colossus | Investing & Business Podcasts

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