Majority of enterprise code written by AI now
At Glean, essentially 100% of initial code is now written by AI, though the company enforces mandatory human code reviews before any code is merged.
Snapshot · The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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
Explore episode
Jul 11, 2026
Where this was said
"Where's the ROI?" Why Enterprises Are Starting to Question the AI Hype
At 23:02 · chapter starts 20:53
Harry declares that 2026 H2 and 2027 will be the years when enterprises stop writing AI checks on faith and demand ROI accountability. Arvind doesn't disagree but provides texture: customer support is the clearest win because productivity is directly measurable (cases resolved per day per agent). Coding is where the biggest AI spend has gone, but the paradox is real — coding speed increased dramatically, but product shipping speed hasn't followed, because coding is only one component of shipping. At Glean itself, Arvind admits it's hard to isolate AI's contribution from team growth and tenured experience. He then delivers the core insight: AI ROI is a throughput problem. [1] — Arvind Jain "Most enterprise AI deployments fail not because models are bad but because agents spend most of their time and tokens searching for the rig…" 24:50 Enterprises deploy AI in a brute-force way, letting agents assemble context from scratch for every task, burning tokens and time on setup rather than actual work. The fix is investing in context infrastructure upfront.
AI Coding: 100% Written by AI, 0% Shipped Without a Human
Glean now has essentially 100% of its initial code written by AI — but enforces mandatory human code review before any commit. The paradox: review is now the bottleneck, not writing. Some companies eliminate reviews entirely, but Glean believes maintaining oversight is worth the cost while the industry is still in a learning phase.
Why Glean Thinks AI ROI Is a Throughput Problem
Most enterprise AI deployments fail not because models are bad but because agents spend most of their time and tokens searching for the right context. Treating AI like a brute-force tool burns money and delivers slow results. The solution is investing in context infrastructure before deploying agents.
Context Is the Bottleneck, Not Models
Most enterprises connect AI to their systems and let it brute-force its way through context assembly — burning tokens, slowing down, and delivering poor results. The real ROI unlock is investing in context infrastructure so agents start with the right information rather than searching for it.
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