Glean CEO Says Own Learning, Rent Models As Open Source Commoditizes AI
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OWN THE LEARNING, RENT THE MODEL @jainarvind, Founder & CEO, @glean, interviewed by @HarryStebbings Stebbings (@20VC) Summary: The model layer is commoditizing fast. Arvind Jain says more than 90% of enterprise use cases can now run on many different models, including open source, and he expects the majority of enterprise workloads to run on open models within three years. The move that matters is owning your context and the institutional learning that accumulates inside your agents, because whoever owns that learning owns the compounding advantage. Jain also makes a contrarian bet: AI grows companies. He plans to take Glean from 1,000 to 5,000 people because per-person productivity climbs, but you have to do ten times the work to earn the same revenue. 1. Model-Layer Commoditization. More than 90% of enterprise use cases can now be handled by many different models, including open source. Glean turns that into a product: it picks the right model for each task and reaches for open source whenever the answer quality holds, which it sells to customers as cost control. The value has moved off the model and onto the orchestration around it. When any competent model can do the job, the layer that chooses and routes captures the margin. 2. The Open-Source Inflection. Open source has just reached within three months of frontier capability, and Jain expects most enterprise workloads to run on open models within three years. The trigger was concrete: a Chinese model, GLM, is the first his own team trusts to carry the majority of their workloads. The driver is cost, now that AI budgets blow past their annual number in a month or two. Once quality is close enough, an order-of-magnitude price gap decides where the work runs. 3. Own The Learning. The real danger with frontier providers is operational dependence. As an agent does a job over and over, the undocumented institutional learning accumulates inside that agent. If you do not run the agent or own what it learns, you have handed your operations to the AI company. Jain's rule for enterprises: use these models, but keep control of the compounding learning, because it belongs to you. 4. China Or Not. The open-source decision comes down to one question: are you comfortable running a Chinese model? Everyone will be fine with open source itself; the hesitation is whether a Chinese model hides a back door or could later be used against them. The models run inside your own contained environment with nothing sent back, so what holds people back is comfort. The early movers make the leap first, and then it becomes normal. 5. Labs As Asset. For any company not training frontier models, the labs are a huge asset. The vertical products Anthropic and others launch into design, legal, and finance are shallow, and Jain sees AI expanding the market: non-designers now use Claude for design while designers keep using Figma. Founders losing sleep over the labs should stop worrying and go solve problems. The frontier labs let Glean ship a product it could never have built alone. 6. Bundling Breaks. Microsoft is Glean's most significant competitor, and its play of shipping a product 70% as good but bundled into the enterprise suite genuinely works. What weakens it is consumption pricing: when you pay per unit of work, the bundle loses its built-in advantage, because users can pick the best tool for each job and you pay only where the work happens. Stebbings pushes back that enterprises still prefer one approved vendor over fifteen. Jain concedes the real killer is price, because it is hard to compete with free. 7. Investing Around The Model. Getting ROI from AI is mostly a throughput problem. Most enterprises bolt AI onto their systems through MCP and let the model brute-force its way to the context it needs, which is slow and expensive because most tokens get burned just assembling raw materials. The fix is to invest around the model and feed it the right context so it works faster and cheaper. Companies chasing "replace the worker" are aiming at the wrong target. 8. The Headcount Bet. While most CEOs shrink teams, Jain plans to grow Glean from 1,000 to 5,000 people. His logic: if you and your competitor both have the same AI and you shrink while they keep their people to build a 10x better product, they win. Per-person productivity will climb, but the bar climbs with it, since you now have to do ten times the work to earn the same revenue. The bigger company aimed at more ambitious output takes the market. 9. Don't Replace Yourself. Telling employees to replace themselves with AI is the wrong goal, because it gives the technology too much credit. AI can handle 90% of most roles but not the last intangible, the way an assistant cannot quite pick your spouse's birthday present. In competitive work a 90% solution loses to a rival who runs the same AI and keeps a sharp human on top. Aim to make each person the best at what they do. 10. The Million-Dollar Agent. Glean built a triage agent that resolves 95% of production issues for a 15-person on-call team, and it costs a million dollars a month. Even Jain calls it questionable whether that beats the humans it replaced, a reminder that today's AI can be absurdly expensive for what it delivers. His bet is that inferencing costs fall by orders of magnitude, and that the recent per-token price increases were labs proving they are good businesses before going public. If he is right, the economics of every AI agent look very different in two years. 11. The Token Power Law. Glean's approach to token budgeting was to do nothing and let people figure out what the technology could do. The result is a power law: some employees spend $10,000 to $15,000 a month in tokens while others spend $20. Everyone adopted the basic use case, question answering, which is the single largest application of AI in the world, but the advanced use cases sit with about 5% of the workforce. Adoption is universal but shallow, and the deep use stays rare. 12. Capital As A Failure Path. Too much available capital is quietly creating failure paths for startups. A seed-stage company will pay half a million dollars for an engineer while Google, which knows it does not need to buy talent that way, will not, and that structure does not last. At the same time this is a genuine land grab: every company wants a product like Glean's, and getting in today is ten times easier than getting in later. Jain even admits his own discipline may now be too conservative for the moment