20VC: Inside Coatue's $70BN Machine: Why Price Matters Least | Why Mega Markets are the Most Important | How to Assess Durability of Revenue and Margins in AI with Lucas Swisher
episode
Previously titled “20VC: Inside Coatue's $7BN Growth Fund: Why Price Matters Least | Why Mega Markets are the Most Important | How Mega Funds Can Still Do 5x Returns | How to Assess Durability of Revenue and Margins in AI with Lucas Swisher” — renamed by the publisher on Aug 2, 2026
I think price does matter, but I think it matters least.Margin matters, but early, it can be a misleading indicator.Data is a prerequisite.It is not the answer.One of the places where we don't spend time, these pre-revenue companies are really high valuations.
How can investors find value in public SaaS during the AI wave?
Today's guest is rarely ever on a podcast, Lucas Swisher.He co-leads the growth fund at Cotu, and they've backed some of the best companies of the last few years, like OpenAI, Harvey, Deal, Canva, Anthropic, and many more.I'm He also previously worked at Klein & Perkins with the one and only Mamoun Hamid.And this is one of his few appearances where we really delve deep into the investment process at CO2 and what they look for in great, great companies and founders.But before we dive into the show today, over 80% of Fortune 100 companies are running their businesses with Airtable. Airtable combines AI with the scale of an award-winning, infinitely flexible no-code system, a platform where you can see all of your data in one place and use it to make really big picture decisions.
How do market size and founder quality influence investment success?
It lets you use your data to inform strategy, monitor progress, and take action.Every cell is capable of performing hundreds of AI-powered tasks like web research or localization. and using those results to inform and update hundreds or thousands of other sales and workflows in real time.Unlock the true scale of your workflows at www.airtable.com forward slash 20 VC, Airtable, the infrastructure of innovation.And just like Airtable organizes your workflow data, MetaView organizes your conversation insights.
Can mega funds still achieve venture-like returns?
This episode is brought to you by MetaView.Who says hiring has to be fair?Every founder, VC, and exec I speak with knows this.Your ability to hire is the biggest constraint on your company's growth.
What defines an exciting return for growth stage investments?
But recruiting is slow, it's subjective, and only getting more competitive. And that's why teams like Eleven Labs, Brex, Replit, Deal, and 5,000 other organizations use MetaView, the AI company giving high-performance teams a real unfair advantage in hiring.
How do investors assess the risks of overestimating market potential?
MetaView's built a suite of AI agents that behave like recruiting co-workers.They proactively find candidates, they take interview notes automatically, and they help you surface the best candidates in process. For the first time, AI handles the recruiting toil and gives you a single source of truth.That means hours saved per hire and a team focused on what matters most, winning the right candidates as fast as possible.Don't let your competitors out hire you.MetaView customers close roles 30% faster.Try MetaView today and get a free month of sourcing at metaview.ai forward slash 20VC. After MetaView captures what was said, Turing helps you build with the people who can deliver after it.Frontier labs keep facing the same limitation.
Models perform well on benchmarks, but they fall short once they enter real coding tasks, real tools, and real workflows.That disconnect between synthetic evaluation and actual system behavior is now a core blocker for agentic models.That's why NVIDIA, Anthropic, Salesforce, Gemini, and other leading lab partners partner with Turing.Turing is the research accelerator focused on post-training reliability.They build realistic RL environments, next generation data quality systems built from real world operational traces and coding data sets that stress models under conditions where failures matter. state changes, workflow branching, brittle tool calls, and the coding errors that break RL agents but never appear in benchmark reports.
In reality, a model may demonstrate correct reasoning in your evaluation setup, yet still select the wrong parameter or mishandle a code update in a realistic interface.
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