The Capability Gap Crisis

Sarah Friar (OpenAI's CFO) and Vinod Khosla recently discussed the state of AI adoption. They talked about multi-agent systems, the 10-year adoption curve, and the future of work. But when asked for examples of AI actually delivering value? Contract review. Accounting automation. SDR workflows. Sarah described her finance team's workflow: contracts pulled overnight, AI flags non-standard terms, suggests rev-rec treatment. Khosla shared a company doing $150M ARR with ONE accountant. Another replaced 10 SDRs with 1 SDR supervising AI. These aren't moonshots. They're systematic, boring wins. Meanwhile, Khosla dropped this stat: single-digit percentage of users leverage even 30% of AI's capabilities. Sarah's framing: "We've handed them the keys to a Ferrari. They're still learning to back out of the driveway." This isn't a technology gap. It's a capability gap. And Khosla estimates a 10-year journey to close it. That's paradoxical given how fast technology is changing. They say it's a decade-long journey. They also say 2026 is when multi-agent systems mature. I'm not sure both can be true, especially when their best examples are simple automation. But what I do know: change is hard. Getting people to learn new technology while doing their normal work is hard. That's why simple automation is the entry point, to get people hooked, to "refind their jobs" as Sarah puts it. It's where you build the muscle. Getting to agents requires something most organizations haven't developed: the sophistication to handle decisions, context management, and governance. Only 14% of enterprises are using agentic features today. Not because the technology isn't ready. Because they haven't earned the right to use it. They skipped the fundamentals, bought the platform, and now wonder why their agents hallucinate or make decisions no one authorized. Earn your complexity before you buy it. The organizations closing the capability gap aren't the ones with the most sophisticated tools. They're the ones building capability systematically, proving they can govern simple before attempting complex.