"what used to matter a lot was execution was very very fucking difficult and ideas were cheap now ideas are cheap and plentiful but execution is very easy so really only the good ideas are the ones that can justify the spend on super cheap implementation" - Dylan Patel [00:18:57]
"if you don't use more tokens you'll never escape the permanent underclass" - Dylan Patel [00:29:31]
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"if this person can do the work of five to 10 to 15 people using cloud code then all of a sudden I should probably cut people" - Dylan Patel [00:02:20]
"ai is less popular than politicians... people hate AI" - Dylan Patel [00:42:11]
"if I sell you information for a dollar you're only buying it for a dollar because you know that information helps you make a decision that lets you make more than $1" - Dylan Patel [00:09:54]
"TSMC is going to spend hundred billion on capex... maybe two years from now... in 2028" - Dylan Patel [00:36:29]
Speakers & Credentials
Host: Patrick O'Shaughnessy (Invest Like The Best)
Guest: Dylan Patel (Founder & Chief Analyst at SemiAnalysis, leading expert on the semiconductor supply chain, AI infrastructure, and the economics of computing tokens).
1. Executive Summary
Enterprise demand for frontier AI tokens is compounding at an explosive rate, moving beyond engineering teams and fundamentally restructuring operational spend, as seen with SemiAnalysis scaling to a $7 million token spend against a $25 million payroll.
The bottleneck of the global economy has officially shifted from the cost of execution to the quality of ideas; AI has collapsed implementation costs, allowing non-technical operators to build massive data models and functional applications in weeks rather than decades.
Anthropic has secured a dominant lead in capability with internal models (Mythos) reaching "L6 Engineer" proficiency, forcing an intense battle for token allocation and compute resources among major enterprises.
The physical infrastructure layer is facing critical, unavoidable bottlenecks: hardware life cycles are extending to 7-8 years out of necessity, memory prices are surging due to fixed capacity expansion rates, and physical components from copper foil to FPGAs are completely sold out.
The next frontier of capability scaling will bridge the "software-only singularity" into the physical world, utilizing data-efficient "few-shot" models to rapidly advance robotics, acting as a massive deflationary force while sustaining insatiable token demand.
2. Chronological Table of Contents
00:00:30 - The Explosion of Enterprise Token Spend
00:11:18 - Anthropic's Lead & The Economics of Capability
00:22:10 - Overcoming the "Software-Only Singularity" in Robotics
00:30:33 - Supply Side Bottlenecks & Expanding Margins
00:41:56 - Societal Backlash & The PR Failures of AI Labs
3. Detailed Thematic Summary
The Explosion of Enterprise Token Spend & "Claude Psychosis" [00:00:30]
The internal usage of AI at SemiAnalysis shifted from a marginal tens-of-thousands-of-dollars expense in 2023 to a massive core operational cost, entirely driven by non-technical staff adopting Claude for coding and data manipulation [00:00:54].
By April 2026, the firm reached a $7 million annualized spend rate on Claude Code [00:01:30], accounting for over 25% of their total $25 million salary expense [00:01:47]. Patel noted that this trajectory could eclipse 100% of their salary expense by the end of the year [00:02:01].
The leverage gained is existential for knowledge businesses: an individual utilizing cloud code can achieve the output of 5 to 15 traditional employees [00:02:20], fundamentally threatening the survival of incumbent data services that do not adapt rapidly to increased token integration [00:06:08].
Capability Jumps, Anthropic's Dominance, and The Frontier Squeeze [00:11:18]
Anthropic has achieved staggering revenue growth, scaling from an initial $9 billion benchmark up to a targeted $35-$45 billion ARR, reportedly adding $10 billion a month in revenue velocity [00:11:18].
Early in the year, leaked funding documents showed Anthropic operating at ~30% gross margins [00:12:02]; however, hyper-demand has allowed them to restrict rate limits, pushing gross margins to a floor of 72% [00:11:47].
The progression of Anthropic's internal models has outpaced their own goals: they targeted achieving an L4 software engineer capability by the end of 2025 [00:16:43], but their unreleased "Mythos" model—internally available in February—demonstrated L6 (Senior) engineering capabilities [00:17:00].
The raw cost of intelligence is collapsing, with DeepSeek matching GPT-4 class capabilities at 1/600th the cost [00:15:34]. Yet, demand continues to skyrocket because end-users constantly push for the bleeding-edge frontier models (like Claude Opus 4.7) to generate maximum economic value [00:15:41].
The Hardware & Infrastructure Supply Crunch [00:30:33]
The supply chain is structurally incapable of matching the exponential demand curve. To compensate, operators are extending the useful life of previous-generation clusters (A100 and older Hopper units) from an assumed 5 years to 7 or 8 years, resigning leases at higher rates [00:31:10].
Fundamental components are experiencing severe bottlenecks. Memory (DRAM and NAND) manufacturers can only grow capacity at 20-30% annually, meaning true incremental supply won't come online until 2028, ensuring memory prices will double or triple from current levels [00:34:39].
Massive capital expenditures are rippling downstream. TSMC has upped its 2024 capex to $56-$57.4 billion [00:36:20], and Patel projects they will hit an unprecedented $100 billion in annual capex by 2028, violently whipping downstream suppliers like ASML, Lam Research, and Applied Materials [00:36:29].
CPUs are experiencing a hidden demand crisis. While training occurs on ASICs/GPUs, the complex simulation environments required for Reinforcement Learning (RL)—grading trajectories and testing model outputs on CAD or file systems—run exclusively on CPUs, causing them to completely sell out [00:37:54].
Auxiliary components like FPGAs (requiring 120 units per next-gen AI rack), copper foil, and glass fibers for PCBs are entirely allocated, with hardware manufacturers demanding massive prepayments, fundamentally altering the return on invested capital across the tech sector [00:36:01].
The Transition to Robotics & Physical AI [00:22:10]
We are currently experiencing a "software-only singularity," limited because programming actuators and microcontrollers for physical space remains notoriously difficult and data-inefficient [00:22:28].
Current Vision-Language-Action (VLA) models scale poorly due to physical data bottlenecks [00:23:10]. However, the collapse of software implementation costs will soon yield large-scale pre-trained robotic models capable of "few-shot learning" (mimicking human sample efficiency), projected to cause massive breakthroughs in the next 6 to 18 months [00:23:45].
Societal Backlash & The Measurement of Phantom GDP [00:39:39]
AI has become a deeply unpopular concept with the general public, polling worse than politicians and ICE according to Pew surveys [00:42:11]. Patel predicts large-scale physical protests against entities like Anthropic and OpenAI within 3 months as economic disruptions become visible [00:42:06].
The leaders of frontier labs (Sam Altman, Dario Amodei) have failed at public relations, lacking charisma and unnecessarily stoking public fear by constantly predicting apocalyptic, world-changing shifts rather than focusing on present-day utility [00:43:11].
Traditional macroeconomics fails to capture the true value generation of tokens, resulting in "Phantom GDP." High-volume users arbitrage AI intelligence to produce massively superior informational outputs, causing output to rise while nominal costs plummet, effectively masking the true technological acceleration from standard GDP metrics [00:41:17].
The Reference Vault
4. Data & Figures
Data Point
Value
Context
Timestamp
SemiAnalysis Total Salary Expense
$25 Million
The firm's annual baseline payroll for context against their AI budget.
The Implementation Cost Collapse: The paradigm shift where the historical bottleneck of business (executing a good idea) has fallen to zero cost. The new constraint is the ability to generate hyper-valuable ideas to deploy the AI compute against. If implementation is essentially free, the treadmill of deploying ideas accelerates exponentially [00:18:57].
Phantom GDP: An economic framework crafted by Malcolm (SemiAnalysis Economist), describing how output and productivity drastically increase via AI, but because the cost of that output plummets, it nominally shrinks the GDP statistics, creating a massive delta between measured economic growth and actual societal utility [00:41:17].
The Permanent Underclass (Token Arbitrage): The thesis that the future economy relies entirely on three pillars: using maximum tokens, generating value from them, and capturing that value. Failure to participate in this heavy-token leverage immediately consigns a worker or business to an unbridgeable economic deficit [00:29:31].
Demand Destruction via Pricing: A supply-chain reality where physical constraints (e.g., Memory/DRAM production caps) cannot be fixed by immediate capital injection due to multi-year lead times. Therefore, the only way markets balance the lack of supply is to raise prices until the poorest buyers are priced out [00:35:17].
The Software-Only Singularity: The recognition that AI capabilities are temporarily trapped within digital domains. The gap is bridged by moving away from inefficient Vision-Language-Action models toward Few-Shot Learning models for robotics, which mimics human sample efficiency to overcome physical actuator programming limits [00:22:28].
6. Anecdotes
The Oregon Reverse Engineering Lab: SemiAnalysis spent 1.5 years building a hardware lab with electron microscopes. A single ex-Intel employee used a few thousand dollars of Claude code on a CoreWeave server to build a fully GPU-accelerated application. Now, when fed an image of a microchip, the app instantly performs finite element analysis, mapping copper, tantalum, germanium, and cobalt. This entirely replaced what used to be a full, highly-paid engineering team's job [00:02:37].
Malcolm's 2,000 Task Phantom GDP Map: Malcolm, a former bank economist, utilized massive API pulls of FRED and BLS data. Operating completely solo with an AI, he analyzed over 2,000 discrete BLS job tasks against an AI capability rubric, doing a year's worth of work for a 200-person economics department in just a few weeks [00:04:11].
Jeremy vs. The 100-Person Incumbents: In three weeks, a SemiAnalysis employee named Jeremy spent $6,000 a day on tokens to scrape the geographic and functional data of every power plant and major transmission line in the US. He built an energy supply/demand dashboard that shocked hedge-fund clients, proving superior to a specialized competitor company that had 100 employees working on the same problem for a decade [00:06:39].
Begging for Mythos on Bended Knee: To illustrate the desperation for frontier models, Patel told a story about himself and Leopold Aschenbrenner literally getting on their knees in front of an Anthropic co-founder, begging for access to the unreleased "Mythos" model while pretending it didn't exist [00:13:44].
Sam Altman's Molotov Cocktails: Highlighting the deep-seated public hatred for AI leaders, Patel referenced news articles covering incidents where Molotov cocktails were thrown at OpenAI CEO Sam Altman's house twice in two weeks, noting that the general public in the comment sections were actively cheering for the attacks [00:42:49].
7. References & Recommendations
AI Labs, Software & Models
Anthropic / Claude Opus 4.6 & 4.7: The dominant frontier model provider, heavily utilized by SemiAnalysis, resulting in L4/L6 level coding capabilities and sparking "Claude Psychosis" inside the firm [00:01:26].
Mythos (Project Earwig/Glasswig): Anthropic's heavily restricted, unreleased frontier model deployed selectively for cyber-security firms, representing a massive jump in capability and cost [00:13:59].
OpenAI & GPT-4: The former default frontier leader, which Dylan suggests is losing the enterprise adoption battle to Anthropic due to compute restrictions, though they possess a superior hardware funding pool [00:26:18].
Spud: An alleged upcoming AI model release from OpenAI that was discussed in the tech press [00:26:27].
DeepSeek: Mentioned as the prime example of capability cost-collapse, achieving legacy GPT-4 performance for fractions of a penny [00:15:34].
FRED & BLS: Economic databases scraped by SemiAnalysis via API to map the deflationary impacts of AI task replacement [00:03:52].
Hardware, Semiconductors & Materials
TSMC: The central foundry of the AI revolution, projected to massively expand capex to $100B, creating massive downstream strain [00:35:34].
ASML, Lam Research, Applied Materials, MKS Instruments (MKSI): The wafer fabrication and equipment supply chain that will benefit directly from TSMC's cascading capex expansion [00:36:46].
Carl Zeiss: The German optical systems manufacturer cited as a primary bottleneck for ASML's expansion capabilities [00:32:05].
H100 / A100 / Hopper: Nvidia hardware architectures that are seeing their useful enterprise lifespans artificially extended due to supply scarcity and cost constraints [00:30:53].
TPUs, Trainium, FPGAs: Alternative compute architectures; FPGAs specifically noted as being required at high density (120 per rack) for modern AI setups [00:37:26].
Siemens: Mentioned as the provider of complex physics simulations and CAD systems used to generate reinforcement learning environments for models [00:38:40].
CoreWeave, Oracle, SoftBank, Amazon, Microsoft: Hyperscalers and capital providers mentioned as heavily supplying OpenAI with compute scale and hardware like Trainium to offset their compute constraints [00:27:11].
Copper Foil, Tantalum, Germanium, Cobalt: Raw materials identified either in the tight hardware supply chain or dynamically mapped by SemiAnalysis' AI reverse-engineering applications [00:03:12].
People, Media & Institutions
Dario Amodei & Sam Altman: Lab CEOs criticized by Patel for lacking public relations charisma and driving negative consumer sentiment by focusing on apocalyptic scale rather than localized utility [00:43:11].
Doug O'Laughlin: President of SemiAnalysis, credited with aggressively pushing non-technical adoption of Claude Code throughout the firm [00:01:01].
Leopold Aschenbrenner: Colleague of Patel who joined him in pleading with Anthropic executives for access to restricted intelligence models [00:13:44].
Ken Griffin (Citadel) & Jane Street: Used as archetypes for massive, hyper-capitalized legacy firms that may attempt to corner token access through sheer financial brute force, starving smaller competitors of intelligence [00:21:25].
Tucker Carlson: Mentioned by Patel regarding an interview with Sam Altman, which Patel claims negatively impacted OpenAI's public perception among conservatives [00:43:18].
The Information: The media publication explicitly mentioned as the source of rumors regarding OpenAI's "Spud" release [00:26:35].
Pew Research & ICE (Immigration and Customs Enforcement): Mentioned together to highlight polling data that shows AI is less popular with the public than both ICE and politicians [00:42:11].
8. The Bottomline (by AI)
The structural bottleneck of the global economy has definitively rotated from human execution to raw hardware supply, as frontier models have entirely collapsed the cost of implementing complex business logic. Enterprises must aggressively abandon legacy pacing and immediately convert capital into maximum token consumption, or risk irrelevance as capabilities compound. Watch for an impending socio-political crisis within the next quarter, as the deflationary "Phantom GDP" created by these non-technical operator efficiencies begins triggering widespread public backlash against the AI labs.
Jul 16, 2026
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Peak Individual Token Spend
$6,000 / Day
The amount SemiAnalysis analyst Jeremy spent daily to scrape and map the US power grid.