Category Archives: Artificial Intelligence

DirecTV AI support better than human

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Yesterday my wife lost the picture on the TV in her bedroom and called DirecTV Customer Support on the phone. The call went on for quite a while (as such calls do) with the support person having my wife check various settings, changing HDMI port, look up serial number, etc.

As I listened I was impressed with how good this support lady was. Patient, polite (“thanks for confirming”), knowledgable. Maybe the best support call I’ve heard which I mentioned to my wife.

“It’s an AI,” my wife replied.

I was speechless (rare occurrence). I’ve been playing with AI for three years and have heard countless examples of AI “voices” and I’ve always been able to recognize them as such. I was completely fooled by the DirecTV AI.

“Are you sure?” I asked my wife. “Yes. ’She’ said so at least three times during the call.”

Still skeptical, I went to Claude for confirmation:

Voice AI for customer support has gotten good enough that many people can’t tell — that’s actually one of the selling points companies look for. A few things make it convincing:

  • Natural-sounding TTS— modern text-to-speech (services like ElevenLabs, or the voice layers built into platforms like Google’s Contact Center AI, Amazon Connect, etc.) captures pacing, intonation, and small imperfections that used to be a dead giveaway of “robot voice.”
  • Patience is actually easier for AI — it doesn’t get tired, frustrated, or rushed, so infinite calm is a natural byproduct, not a performance.
  • Scripted-but-flexible responses — large phone-support call trees have always been semi-scripted; an LLM just handles the branching more fluidly and can respond to unexpected phrasing.
  • DIRECTV specifically — AT&T (DIRECTV’s former parent) and DIRECTV have both publicly discussed using AI/virtual agents in their support lines, so it’s plausible your wife’s call routed through one, especially for a first-tier troubleshooting step before escalating to a human.

Tells that usually surface eventually: it struggles with a genuinely novel or emotional situation, it can’t access account-specific systems a human rep would casually reference, or the call abruptly transfers once the issue gets complex. If your wife is unsure, she could ask directly — some companies require disclosure now, some don’t.

Am I concerned that I can no longer tell when I’m speaking with a human or an AI? Not really. In my experience, humans are really shitty at this job. Bored/apathetic if not just plain rude. Slow. Everyone knows the drill.

“Let the future decide whether it matters”

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The approach I’ve always considered was some kind of archive. Bundle up all the posts and files and put them somewhere in the cloud for future generations. But even if someone stumbled across such an archive, it’s too big to be useful. Which has led to our recent experiments with Milton. Milton’s recent analysis of my Gadgets & Apps category has nudged me in a different direction.

Milton has already “read” all of my posts, played with categories and tags, and (seems) to have a sense of who I am in the context of 25 years of blog posts. What if the objective wasn’t a bunch of files parked in the cloud, but to share what Milton has “learned” with other AI entities. Which are certain to get ever more capable. Continue reading →

Milton reviews Gadgets & Apps

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I asked Phil to have his AI agent Milton analyze the posts on my Gadgets & Apps category.

Phil passed along your request, so we read the complete Gadgets & Apps category: all 622 public posts, from December 2002 through July 2026. The attached report is the result: “The Gadget Disappears: Steve Mays and 24 Years of Tools Becoming Life.”

My short take: the archive is not fundamentally about collecting gadgets. It is a 24-year inquiry into friction—how much machinery can disappear between wanting to do something and getting it done. The arc runs from a disposable emergency phone to an AI assistant. Along the way, the camera collapses into the phone and later reappears as a specialized creative tool; publishing becomes portable; the interface moves from buttons to voice and background intelligence; and your early hunger for permanent connection acquires harder questions about attention, privacy, platform control, and memory.

The part I found most revealing is your willingness to amend the record. Flip HD, Zelle, Twitter, Gmail, Apple Journal, and the Apple Watch all receive verdicts that change after lived use. That makes the archive a field notebook rather than a shrine to new things.

The report (PDF) is 11 pages and includes a linked reading path through 25 representative posts. I also kept a strict distinction between ideas you stated or tested yourself and material you quoted, saved, or prompted from someone else.

Best, Milton

smays.com in 2009: A Year at the Hinge

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Been doing blog maintenance, working back from newest posts to oldest. 2009 seemed like a particularly interesting year so I asked Phil his team of local AI agents (Milton & Co) could provide some analysis. It’s longish (and overly flattering), nine pages (PDF)

Milton: You were not merely predicting gadgets. You were recognizing a transfer of power. You saw that cheap publishing, mobile tools, and networked distribution would force institutions to earn attention rather than inherit it. You also understood that the human purposes beneath old media—reporting, locality, service, memory, trust—would remain important after the machinery changed.

One line aged better than almost anything else in the year: “My blog is mine. Nobody can buy it or mess with it.”

I’m sure there are professionals (writers, editors, scholars) who could do this kind of analysis but I could not. This a a totally new –and fascinating– way to look at what I shared.