A week in AI is like a year in other industries. I hope these issues become your weekly source of AI information, inspiration, and ideas. If we haven’t met before, I’m Amanda Smith. I write about AI and the fascinating folks who are building in this brave new world.
Good morning. October flew. Five stories went out this month, and if any of them got buried in a busy week, this is a good moment to catch up.
A video company that did things the hard way for a decade, a man who spent years chasing dental records by fax, a Coinbase veteran building identity for AI agents, a VC who took DigitalOcean to IPO and thinks most of what’s being funded right now is wrong, and a former teacher using AI to help 80 million Americans find the money the government already set aside for them.
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🤝 This edition is kindly brought to you by Datadog
Teams are shipping models faster than they retire them.
Datadog’s State of AI Engineering report analyzed production LLM telemetry from 1,000+ organizations: most are now multi-model by default, agent framework adoption has doubled, and hidden token costs keep piling up while prompt caching goes unused. It’s a benchmark for what AI actually costs and breaks at scale.
Hope Horner didn’t start in AI. She started in sales, launched an on-demand fitness company before Uber made “on demand” a word people used, then co-founded a video production company at a time when a single commercial cost $300,000. Lemonlight spent a decade doing things the hard way before AI arrived. Now they’re doing $25-30M ARR, just signed deals with Comcast and Amazon, and made the Inc. 5000 eight times. There’s a lot of go-to-market detail in this one worth reading.
Shaju Puthussery spent decades in enterprise software before he turned his attention to dental insurance. The back office of this industry still runs on faxes, manual eligibility checks, and billing processes that eat hours and kill margins. LightSpun is building AI infrastructure to fix it. Not glamorous. Exactly the kind of unsexy, essential problem that tends to become a very large business.
Everybody is building AI agents. Fewer people are asking how you know whether to trust one. Isha Bhatnagar is building that. Beltic is verification infrastructure for AI agents — a way for businesses to audit what an agent did, whether it did it correctly, and whether it stayed in bounds. She started with a simple question: if an AI agent makes a financial decision on your behalf, how do you check it? The answer didn’t exist. So she built it.
Karl Alomar spent years as an operator. He knows what it looks like when founders get swept up in something. And he thinks a lot of what’s being sold as an AI revolution right now is noise. That’s not the interesting part. The interesting part is that he still believes in the underlying technology enough to bet his career on it as a VC at M13. He’s not anti-AI. He’s anti-hype. There’s a difference, and in this conversation he draws it clearly.
Laura Glaab started as a teacher, became a principal, then scaled an AI reading tutor to statewide partnerships. Her next move was MyFriendBen, a screener that tells families which of 40+ government programs they likely qualify for in about six minutes. 175,000 households across nine states have used it. Then she built Benji, an AI assistant available at 2am when human navigators aren’t. She raised $4M to scale it. As Glaab puts it: a wrong answer about food assistance isn’t a bug to patch. It’s a missed meal.
How's the depth of today's edition?
If one of these stories stuck with you, I’d love to hear which one.
Speak soon,
Amanda
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