This week, AI Insider hosted a dinner, sponsored by Dash0, in San Francisco for tech executives. I started with a simple question.
“Who had an AI agent help them at work today?”
Every hand went up.
Then I asked a follow-up.
“Who had more than five AI agents helping them at work today?”
Again, every hand stayed in the air.
Around the table were Zoom CTO XD Huang; Figma VP of Engineering Josh Clemm; Coursera CTO Mustafa Furniturewala; ServiceNow executive Ashraf Karim; Glean CTO T.R. “Vish” Vishwanath; Netlify CEO Matt Biilmann; PagerDuty engineering leader Manu Gurudatha; Yahoo Mail GM Kyle Miller; Lime CTO Jon Jenkins; Proofpoint AI security chief Ravi Ithal; Temporal cofounder and CTO Maxim Fateev; Inworld CEO Kylan Gibbs; Xero engineering leader Diya Jolly; MinIO CTO Ugur Tigli; Tribe AI partner Pooja Brown; Basis cofounder Creston Brooks; and Empirik CEO Kartik Chandrayana.
No one debated whether AI will kill us. These are experienced tech leaders from the trenches wrestling with real questions, such as: How do you make AI agents actually useful inside a company?
Context
One phrase kept coming up all evening: context.
Zoom’s Huang said AI agents won’t become genuinely useful until they understand far more about your company, your colleagues, your meetings, and your work.
“Context is all you need,” XD quipped, riffing on the famous AI research paper “Attention is All You Need.”
Glean’s Vishwanath agreed. He said AI agents often fail because they’re connected to too little information — or worse, the wrong information. They confidently produce plausible answers that aren’t actually correct. Giving the right context to AI systems at work could now be more important to building smarter models.
That theme cropped up repeatedly.
Ashraf Karim, who leads ServiceNow’s internal rollout of its own AI products before customers get them, said the company first used AI to help human support staff before gradually allowing AI agents to handle simpler customer requests themselves. Humans using AI are now delivering even higher customer satisfaction scores than humans working alone because they resolve problems faster and generate more personalized answers.
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Biilmann, the CEO of Netlify, argued that writing code is rapidly becoming a commodity. The scarce skills are increasingly domain expertise and software judgment. Companies, he said, may eventually reorganize engineering teams around those strengths rather than around people simply writing code.
Hiring
Hiring sparked one of the liveliest debates of the evening.
Several executives described what one attendee called a “barbell” strategy: hiring very experienced engineers who can question AI-generated work alongside younger graduates who are naturally fluent with AI tools.
Clemm, Figma’s engineering VP, warned that engineers in the middle of their careers could face the most pressure as organizations flatten and managers increasingly favor senior hires.
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Inworld’s Gibbs said startups don’t have that luxury. His company is leaning even harder into experienced engineers who can solve the difficult technical problems AI still can’t. If AI can already do something in a day, he argued, it probably isn’t where your competitive advantage lies.
Optional software
At one point, Tigli, the CTO of MinIO, showed me something that captured another theme of the evening: How AI is changing the software business and making some software unnecessary.
Earlier that day, he’d asked Claude Code to create a presentation. Not in PowerPoint. Not in Google Slides. Claude generated a single HTML file instead. He opened it on his laptop, then AirDropped the same file to his iPhone. It worked perfectly on both devices. No presentation software. No cloud service. Just a file.
This is often how software gets disrupted. Not with a dramatic product launch, but with people quietly discovering a simpler way to get the same job done. If AI can build a presentation from a prompt and package it into a portable HTML file, how much do you really need Microsoft PowerPoint or Google Slides?
Predictions
I ended the dinner with one final question: “What will we laugh about doing manually two years from now?”
Several people predicted status meetings would largely disappear because AI agents would already know what everyone had accomplished. That echoes what OpenAI is already seeing internally: some research teams have stopped holding troubleshooting office hours because researchers increasingly rely on AI instead.
Then Lime CTO Jon Jenkins produced one of the night’s biggest eyebrow-raisers.
He suggested software engineers won’t be the only people wondering whether AI can do their jobs.
Within two years, perhaps C-suite executives (like those around the table) will be asking themselves the same uncomfortable question.
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