Eric Vishria

Mentioned in 1 episode

"At that time TensorFlow was dominant, so the first flow was dominant, ResNet was still a thing. The actual ML, the AI looked nothing like it does today."

Mentioned in 1 episode

"What Michael and team at Cursor did so well from the very beginning is they really understood the jagged capability and built a product that allowed that translation from developer to that jagged capability and kept on iterating on that as the edge changed. You saw the evolution of Cursor from like IDE to tab autocomplete to agentic work, over and over again — they were obsoleting their work from six months ago."

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"It's like 'Anthropic is going to do everything' — right, is that really right? To me, that view that it's just like one company's going to eat it all doesn't hold."

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"Everybody from AWS to Azure to GCP to the neoclouds to the Fireworks-based Together AIs of the world — they all run these stock open-source models that are available in their developer pools."

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"It's not just Snowflake. You had Confluent and Elastic and Databricks — all of these companies."

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"Sierra is feeling its way through what are really cool things that we can do for end consumers — enable companies to do for end consumers, starting with customer service. They're doing real AI work, experimenting with the models, building agents, and they're very close to the metal of the models and the capability."

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"Of course you got Oracle and SQL Server and a whole slew of smaller players that did really really well in databases."

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"Nvidia was worth like 40 billion, not 4 trillion. The TPU hadn't been announced. None of that — so this is like early early. But the whole idea, as soon as he said it, I was like, 'Oh, of course.'"

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"Of course you have Snowflake, direct competitor to Amazon Redshift, ran on Amazon. You're out-Amazoning Amazon on Amazon."

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"The thesis that AWS was going to eat everything was massively wrong — not because of all the examples I just mentioned, but because Azure and GCP were irrelevant then. Fast forward to 2026 and they're unbelievable businesses."

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"There's of course TSMC, which everyone knows, but it's not just TSMC — there's 30 other vendors that like matter and putting all this stuff together."

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"Even outside of those big three, you have Cloudflare, which is a cloud provider in a different sort, which is another hundred billion dollar company. So you have these smaller players that emerge as hundred billion dollar companies outside of it."

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"If I take Saji and Benchling — Benchling is a life sciences SaaS company that's absolutely crushed it and done really really well. And then of course you had this biotech crash and the company became hard, became grindy."

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"We're investors in Sunday Robotics, which is going after this exact pipeline company. They're doing household robots, but the key part before you get to household and all of that stuff matters much less actually than: can you get this training pipeline to work? Sunday uses gloves that are designed just with the robot hands, so they're perfect — you get very good data transferability from one to another."

Mentioned in 1 episode

"When I was 20 years old, I was working at an investment bank. Horowitz, Marc, Ben had started LoudCloud — it was still in stealth — and Ben gave me an offer to be his assistant."

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"The first use cases were very much language-oriented. They were very much like essay writing and editing and marketing. I think the first application that really took off was Jasper, which was just like writing marketing copy."

Mentioned in 1 episode

"The first slide was just like 'GPUs actually suck for deep learning — they just happen to be a hundred times better than CPUs.' And as soon as he said it, I was like, 'Oh, of course.' You would have 450,000 cores on it. You'd have something like 20 gig of SRAM on the chip, so you never have to go off chip to get to memory. And because they were all in the same wafer, the communication between cores is maximized."

Mentioned in 1 episode

"We have an investment in a company called New Lantern which is approaching this — but the big hurdle was wait a minute, all of the aggregated training data set doesn't exist anywhere."