20VC: Why OpenAI and Anthropic Won't Win the App Layer | Why Teams Will Get Bigger Not Smaller in a World of AI | Why AI Removes Incumbents Advantage of Bundling | China vs America: Who Wins the AI War with Arvind Jain, Co-Founder @ Glean
Episode
54 min
Read time
2 min
Topics
Productivity, Investing, Startups
AI-Generated Summary
Key Takeaways
- ✓Model Commoditization: Over 90% of enterprise use cases can already be handled by open source models, making frontier model pricing increasingly difficult to justify. Glean actively routes workloads to cheaper open source alternatives, including models like GLM 5.2, projecting that the majority of enterprise AI workloads will run on open source within three years.
- ✓Consumption Pricing Breaks Bundling: Microsoft's Copilot bundling strategy loses structural advantage as AI shifts toward consumption-based billing. When enterprises pay per unit of work rather than per seat, they can deploy multiple best-of-breed tools simultaneously without vendor consolidation pressure, allowing specialized platforms to compete directly against bundled Microsoft offerings.
- ✓AI ROI Requires Context Investment: Enterprises burning tokens on brute-force MCP server connections see slow, expensive results because models waste compute assembling raw context. The fix is pre-investing in structured, curated enterprise context layers before deploying agents, which reduces token costs and dramatically improves task completion speed and accuracy across workflows.
- ✓Team Size Will Expand, Not Contract: Jain's counterargument to headcount reduction: companies that shrink while competitors maintain larger teams using identical AI tools simply produce less output. Glean plans to grow from 1,000 to 5,000 employees, betting that AI amplifies productivity per person but market demands scale proportionally, requiring more people to capture available opportunity.
- ✓Chinese Open Source as Enterprise Threat: The primary barrier to enterprise adoption of Chinese open source models like GLM is not technical capability or data sovereignty when run on-premise, but reputational and political risk. Early enterprise adopters willing to absorb that perception risk gain significant cost advantages, with Glean internally validating GLM 5.2 for majority workload deployment.
What It Covers
Arvind Jain, co-founder of Glean, argues that frontier model providers like OpenAI and Anthropic will not dominate the enterprise app layer, that AI teams will grow rather than shrink, and that consumption-based pricing dismantles Microsoft's bundling advantage across large enterprises.
Key Questions Answered
- •Model Commoditization: Over 90% of enterprise use cases can already be handled by open source models, making frontier model pricing increasingly difficult to justify. Glean actively routes workloads to cheaper open source alternatives, including models like GLM 5.2, projecting that the majority of enterprise AI workloads will run on open source within three years.
- •Consumption Pricing Breaks Bundling: Microsoft's Copilot bundling strategy loses structural advantage as AI shifts toward consumption-based billing. When enterprises pay per unit of work rather than per seat, they can deploy multiple best-of-breed tools simultaneously without vendor consolidation pressure, allowing specialized platforms to compete directly against bundled Microsoft offerings.
- •AI ROI Requires Context Investment: Enterprises burning tokens on brute-force MCP server connections see slow, expensive results because models waste compute assembling raw context. The fix is pre-investing in structured, curated enterprise context layers before deploying agents, which reduces token costs and dramatically improves task completion speed and accuracy across workflows.
- •Team Size Will Expand, Not Contract: Jain's counterargument to headcount reduction: companies that shrink while competitors maintain larger teams using identical AI tools simply produce less output. Glean plans to grow from 1,000 to 5,000 employees, betting that AI amplifies productivity per person but market demands scale proportionally, requiring more people to capture available opportunity.
- •Chinese Open Source as Enterprise Threat: The primary barrier to enterprise adoption of Chinese open source models like GLM is not technical capability or data sovereignty when run on-premise, but reputational and political risk. Early enterprise adopters willing to absorb that perception risk gain significant cost advantages, with Glean internally validating GLM 5.2 for majority workload deployment.
Notable Moment
Jain revealed Glean built an engineering triage agent handling 95% of production alerts automatically, replacing work previously done by a 15-person on-call team. The agent cost one million dollars per month to run, raising genuine internal debate about whether it was actually cheaper than the human team it displaced.
Episode Transcript
90% or greater of use cases cannot be fully handled by many, many different models, including open source models. I think, like, for almost all other AI companies that are not doing frontier model training, they should see the model companies as a as a huge asset. So once you move towards consumption, there's no inherent bundling advantage. You have to do 10 times the work to get the same model revenue from your customers. This is 20 VC with me, Harry Stebbings. Now, I have to admit, I started fasting and the trouble with fasting is you can get a little bit hangry. Now, I did this show late in the afternoon and Arvind Jain, the incredible founder of Glean, is one of the technology luminaries of the last decade. He founded Rubrik before which obviously IPO'd very successfully and is a brilliant public company now. He's gone on to found Glean, an incredible business today that's raised money from Kleiner Perkins and many other great investors. And I was, I would say, divisive in this show. I'm almost slightly nervous to listen back because I really pushed him in a way that I probably don't push other guests. But it actually led to one of the most phenomenal discussions that we've had in recent times on the show, which makes me think I should probably be hangry a little bit more. But it was a great show. I'd love to hear your thoughts. Do Do you like Happy Harry or Hangry Harry more? But before we dive into the show today, most companies have tried AI. Most aren't seeing results. Not because AI doesn't work, it's because AI hasn't reached the workflows yet. That's the gap Asana is built to close. Asana is the operating system for human agent teams. Your easy button for AI productivity across every team. Ready to go AI teammates, prebuilt for marketing, ops, and IT. No prompt engineering, no setup. They show up where the work is happening, already onboarded in your workflows, ready to deliver. With Asana, your whole company can work on the same plan towards the same goal, whether you're a team of 10 or a team of 10,000. Asana, where humans and agents workflow together. Try it at asana.com. That's asana.com. While Asana aligns the roadmap, MongoDB powers the build. MongoDB has always been the database developers love. Well, now it's the data platform AI agents need. Agents need accurate context fast. MongoDB stores, searches, and reasons over your data in real time. JSON native database, vector search, and Voyage AI embeddings all in one place. One system instead of 10. No data pipelines to maintain. Ugh. This sounds too good to be true. Build and scale from your first user to billions of vectors. Run on any cloud on prem or your laptop even. That's why 75% of the Fortune a 100 run their most critical apps on MongoDB, moving trillions of dollars every single day. And it's …
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Books, tools, and gear mentioned in this episode
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Tools
“Glean actively routes workloads to cheaper open source alternatives, including models like GLM 5.2, projecting that the majority of enterprise AI workloads will run on open source within three years”
by Microsoft
“Microsoft's Copilot bundling strategy loses structural advantage as AI shifts toward consumption-based billing”
company
“frontier model providers like OpenAI and Anthropic will not dominate the enterprise app layer”
- GleanBy guest
“Arvind Jain, co-founder of Glean, argues that frontier model providers like OpenAI and Anthropic will not dominate the enterprise app layer”
“frontier model providers like OpenAI and Anthropic will not dominate the enterprise app layer”
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