Gavin Baker - Nvidia v. Google, Scaling Laws, and the Economics of AI - [Invest Like the Best, EP.451]
Episode
84 min
Read time
2 min
Topics
Startups, Sales & Revenue, Artificial Intelligence
AI-Generated Summary
Key Takeaways
- ✓Blackwell Product Transition: NVIDIA's Blackwell chips require liquid cooling, 130 kilowatts per rack versus 30 for Hopper, and weigh 3,000 pounds versus 1,000 pounds. This eighteen-month delay would have stalled AI progress without reasoning models bridging the gap until deployment scaled in late 2024.
- ✓Google's Cost Advantage: Google operates as the lowest cost token producer using TPU v6 and v7 chips, running AI at negative 30% margins to extract economic oxygen from competitors. This advantage disappears when Blackwell-trained models deploy, fundamentally changing strategic calculations across the industry.
- ✓Reasoning Model Economics: Reinforcement learning with verified rewards and test time compute create multiplicative scaling laws beyond pretraining. These enable the flywheel effect where user interactions generate data to improve models, similar to how Netflix and Amazon achieved increasing returns to scale over decades.
- ✓SaaS Margin Trap: Application software companies preserve 80% gross margins while AI natives run agents at 40% margins, repeating brick-and-mortar retailers' ecommerce mistake. Companies like Salesforce and ServiceNow must accept lower margins or face displacement by venture-funded competitors accessing their data through APIs.
- ✓Data Centers in Space: Solar energy in space provides six times more irradiance than Earth with no battery costs, free cooling via radiators, and faster laser communication through vacuum than fiber optics. Starship launch economics make this viable for inference workloads within three to four years.
What It Covers
Gavin Baker analyzes the AI infrastructure race between NVIDIA and Google, explaining how Blackwell chip delays, TPU advantages, scaling laws for pretraining, and the economics of token production shape competitive dynamics among frontier AI labs.
Key Questions Answered
- •Blackwell Product Transition: NVIDIA's Blackwell chips require liquid cooling, 130 kilowatts per rack versus 30 for Hopper, and weigh 3,000 pounds versus 1,000 pounds. This eighteen-month delay would have stalled AI progress without reasoning models bridging the gap until deployment scaled in late 2024.
- •Google's Cost Advantage: Google operates as the lowest cost token producer using TPU v6 and v7 chips, running AI at negative 30% margins to extract economic oxygen from competitors. This advantage disappears when Blackwell-trained models deploy, fundamentally changing strategic calculations across the industry.
- •Reasoning Model Economics: Reinforcement learning with verified rewards and test time compute create multiplicative scaling laws beyond pretraining. These enable the flywheel effect where user interactions generate data to improve models, similar to how Netflix and Amazon achieved increasing returns to scale over decades.
- •SaaS Margin Trap: Application software companies preserve 80% gross margins while AI natives run agents at 40% margins, repeating brick-and-mortar retailers' ecommerce mistake. Companies like Salesforce and ServiceNow must accept lower margins or face displacement by venture-funded competitors accessing their data through APIs.
- •Data Centers in Space: Solar energy in space provides six times more irradiance than Earth with no battery costs, free cooling via radiators, and faster laser communication through vacuum than fiber optics. Starship launch economics make this viable for inference workloads within three to four years.
Notable Moment
Baker reveals his career origin: planning to be a ski bum and wildlife photographer, he took one finance internship at his parents' request. Reading research reports while mailing them to clients, he realized investing combined history, current events, and competitive skill in ways nothing else offered.
Episode Transcript
Here's an interesting question to think about. If your finance team suddenly had an extra week every month, what would you have them work on? Most CFOs don't know because their finance teams are grinding it out on lost expense reports, invoice coding, and tracking down receipts until the last possible minute. That's exactly the problem that Ramp set out to solve. Looking at the parts of finance everyone quietly hates and asking why are humans doing any of this? Turns out they don't need to. Ramp's AI handles 85% of expense reviews automatically with 99% accuracy, which means your finance team stops being the department that processes stuff and starts being the team that thinks about stuff. Here's the real shift. Companies using Ramp aren't just saving time, they're reallocating it. While competitors spend two weeks closing their books, you're already planning next quarter. While they're cleaning up spreadsheets, you're thinking about new pricing strategy, new markets, and where the next dollar of ROI comes from. That difference compounds. Go to ramp.com/invest to try Ramp and see how much leverage your team gains when the work you have to do stops getting in the way of the work that you want to do. To me, Ridgeline isn't just a software provider. It's a true partner in innovation. They're redefining what's possible in asset management technology, helping firms scale faster, operate smarter, and stay ahead of the curve. I wanna share a real world example of how they're making a difference. Let me introduce you to Brian. Brian, please introduce yourself and tell us a bit about your role. My name is Brian Strang. I'm the technical operations lead, and I work at Congress Asset Management. How would you describe your experience working with Ridgeline? Ridgeline is a technology partner, not a software vendor, and the people really care. I get sales calls all the time, and I ignore them. Ridgeline sold me very quickly. We went from 7,000,000,000 to 23,000,000,000, and the goal is 50,000,000,000. Ridgeline was the clear front runner to help us scale. In your view, what most distinguishes Ridgeline? They reimagined how this industry should work because obviously they were operating on another level. It's worth reaching out to Ridgeline to see what the unlock can be for your firm. Visit ridgelineapps.com to schedule a demo. One of the hardest parts of investing is seeing what's shifting before everyone else does. AlphaSense is helping investors do exactly that. You may already know AlphaSense as the market intelligence platform trusted by 75% of the world's top hedge funds, providing access to over 500,000,000 premium sources from company filings and broker research to news, trade journals, and over 200,000 expert transcript calls. What you might not know is that they've recently launched something game changing, AI powered channel checks. Channel checks give you a real time expert driven perspective on public companies, weeks before they show in earnings or consensus revisions. AlphaSense uses an AI interviewer to run …
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“These enable the flywheel effect where user interactions generate data to improve models, similar to how Netflix and Amazon achieved increasing returns to scale over decades.”
“Companies like Salesforce and ServiceNow must accept lower margins or face displacement by venture-funded competitors accessing their data through APIs.”
“Companies like Salesforce and ServiceNow must accept lower margins or face displacement by venture-funded competitors accessing their data through APIs.”
“These enable the flywheel effect where user interactions generate data to improve models, similar to how Netflix and Amazon achieved increasing returns to scale over decades.”
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