Remotely.Local

The scariest scam calls don’t sound like strangers anymore. They sound like family.

Ryo Koyama and Mike Johnson talk to Patrick Coughlin, CEO of Savi Security and author of Dark Side of the Boom, about how AI has turned scamming into the fastest growing crime in the country, from an AI-cloned kidnapping call aimed at his own mother to a voice-cloned scam that fooled one of Remote.it’s own employees.

Patrick talks about why scams go underreported, why every family should have a safe word, and why he thinks the real danger isn’t a rogue AI model, but the quiet flood of AI-powered scams already hitting everyday people’s phones.

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Some of the most striking art on the playa is wired as much as it’s welded. On this episode of Remotely Local, Ryo Koyama and Mike Johnson sit down with Kevin Clark, founder of Reared In Steel, whose fire sculptures have been lighting up Burning Man for more than a decade. His pieces range from the 26-snake Medusa to the 80-foot Flower Tower and the 110-effect, MIDI-controlled Fire Cathedral.

Kevin tells how he got hooked on Burning Man in the early days of the temple movement, helping Jack Haye and David Best. He explains why scale matters when you’re building in the middle of the desert. Mike, who built the control systems behind the pieces, walks through how the tech grew from push buttons to Raspberry Pis, relay banks, MIDI, and remote access from the playa. That includes the night a stranger plugged in a homemade keytar and played the Fire Cathedral.

They also get into Guma, the climbable Burning Man 2025 piece Kevin built with his daughter, including the story behind the viral bike crash. Kevin talks about teaching teenagers to weld, why he sees AI as a tool for getting ideas out faster rather than as the finished art, and why he’d happily put a few robot welders on the crew.

Chapters

Links

Watch the video version of this podcast (includes the crash clip): https://www.youtube.com/watch?v=QsIO-rHsD8o

Reared In Steel on Facebook: https://www.facebook.com/rearedinsteel/

Guma dino crash video: https://www.youtube.com/watch?v=5HHFIPKMISc

Remotely Local: https://remotelylocal.com

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Somebody has to build the road before you can drive on it.

Sam Cox, CTO of Mountain Vector, joins Ryo Koyama to talk energy data, legacy utilities, and the unglamorous infrastructure behind Physical AI — from dot-matrix-era utility bills to running hundreds of remote devices with a 12-person team.

Mountain Vector does energy management: utility bill analysis, solar and battery evaluations, and analytics that show customers where their consumption and spend actually go.

The hard part isn’t the AI. It’s the data. Utilities are among the most conservative industries in existence, and many municipalities still issue bills in formats that look like dot matrix printouts — poor-quality account numbers, inconsistent layouts, barely legible to a human. Before any insight is possible, someone has to make that readable to a machine.

Mountain Vector serves customers coast to coast:

One industrial customer tracks locational marginal pricing on 15-minute and five-minute intervals, deciding in near-real-time whether to curtail operations when costs spike. Mountain Vector is building predictive capability on top of that, so customers can plan ahead instead of reacting.

The models get the attention. The roads don’t. Factories, sensors, meters, and edge devices have to be connected and reliably reporting before AI can do anything useful with them.

With hundreds of devices deployed nationwide and a small team in New Mexico, Mountain Vector can’t send a technician every time something breaks. Remote access is what makes the model work:

Sam had scoped building this himself — reverse SSH tunnels, custom infrastructure — before finding Remote.It and concluding it was cheaper and faster not to. He also recounts a support case where the root cause turned out to be an MTU issue on his own end, and Remote.It tracked it down anyway.

Sam’s sharpest observation: software has always required you to specify how. AI shifts that to what. Describe the outcome — pull these fields from ten thousand inconsistent utility bills — and let the system work out the method.

Sam’s experience runs against the prevailing anxiety. AI hasn’t reduced his workload; it’s multiplied his throughput. Where he shipped one feature a week, he now ships six. The queue empties faster and refills faster.

His analogy: Excel didn’t eliminate accountants. It let them serve far more clients and do far more sophisticated work.

Language models are very good at producing exactly what you asked for — which is not always what you needed. In production energy systems, that gap matters. Human expertise becomes more valuable as the tools improve, because someone has to judge whether the output actually survives contact with reality.

Video version: https://www.youtube.com/watch?v=1zMs9TFK5v4

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