I Drove 1,800 Miles to Pick Up My Daughter from College. I Brought AI Along for the Ride.

I am a serial entrepreneur and the CEO of OpsCanvas, an AI-native platform that helps companies manage their cloud infrastructure using AI. I spend most of my working life thinking about what AI can and cannot do. So when I found myself with sixteen or seventeen hours of solo driving ahead of me -- Alexandria, Virginia to New Orleans and back -- to pick up my daughter from her graduation at Tulane, I thought: why not turn this into an experiment?

What would it actually feel like to use AI as a road trip copilot? Not for coding or analyzing spreadsheets, but as a genuine productivity partner for a founder trying to get real work done from behind the wheel?

Over five days and two full legs of driving, I ran a loose, ongoing experiment. I used ChatGPT in live voice mode, Claude for document work, and tested Siri, Gemini, and a handful of other tools. Some of what I found surprised me. A lot of it confirmed suspicions I already had.

The Dream, and the First Reality Check

The pitch for AI in the car sounds perfect. You are alone, hands on the wheel, with nothing but windshield and time. Talking to an AI feels like the obvious solution.

The first thing I discovered is that "live voice AI" and "voice dictation" are not the same thing. Not even close.

When you use Siri to send a text or Google to navigate, you are using dictation: you speak, the system transcribes or executes a command, and the loop ends. What I was doing with ChatGPT's voice mode is fundamentally different. The AI is listening, reasoning, remembering context within the conversation, and responding in real time. It feels like calling a smart colleague.

The limitation, at first, was basic: connectivity. Driving through rural Virginia and Tennessee, I kept going in and out of coverage. Every time I dropped signal, the conversation just stopped. ChatGPT had no idea it had lost me. There was no graceful recovery, no "I noticed we lost a few minutes, let me catch up." It was like the other end of the phone going dead with no indication anyone noticed. This is a fixable problem, but no one has fixed it yet.

The Siri Problem, the Gemini Irony, and the Context Wall

At some point during day one, I tried Siri instead. That did not go well.

Here is the analogy that kept coming to me: using a modern large language model feels like the moment smartphones went from single-touch to multi-touch. Suddenly everything was possible. Pinch, zoom, rotate, gesture. The whole paradigm shifted. Siri still feels like single-touch. You phrase your request in exactly the right way, it executes. You do not, it fails. I tried to manage my calendar through CarPlay, and had to go event by event, instruction by instruction, with no ability to say "move everything after 3pm" and have it just happen.

Gemini was a different kind of frustrating. The irony is hard to overstate: Google has your email, your calendar, your documents, your entire digital life, plus one of the best AI models in the world. And yet Gemini in Google Workspace often refuses to access your own information, citing privacy and security policies. Part of that is genuine. But part of it feels like an economics problem dressed up as a privacy policy. Reading email in real time, summarizing meetings, querying calendars continuously costs compute, and at Google's scale, it adds up fast.

Whatever the reason, the result is the same: everything is in one ecosystem, and the AI still cannot connect the dots.

At OpsCanvas, it is exactly the problem we think about every day for cloud operations: data is everywhere, and nobody has a unified picture.

The Return Trip: AI as Co-Creator

The most productive AI work I did happened not on the drive down, but on the way home. And I was not even driving.

My daughter took the wheel for the first morning of the return trip, up through Birmingham and into Chattanooga, and I sat in the passenger seat with my phone. What followed was one of the most productive half-days I have had in months.

I had been meaning to work on a three-year product adoption playbook for OpsCanvas. I opened Claude and started talking it through. Back and forth, building sections, refining arguments, pushing back when something did not feel right. Within an hour, I had a strong, complete document.

Then I did something interesting. I took that playbook and brought it into a separate Claude project I had been building around a homepage redesign. When I tried to make specific edits, though, the model kept changing things I had not asked it to touch. So I invented a workaround on the fly: I went back to the playbook thread and said, write me the exact prompt I should feed into the website thread so that only these five changes are made and nothing else moves. Claude wrote the prompts. I ran them. I did this three or four times. The final product was exactly what I wanted.

This is a workflow I think is genuinely new: using AI not just to create, but to orchestrate its own instructions across parallel workstreams. I was directing a process, from my phone, in the passenger seat, somewhere on I-81 in Tennessee.

Work Snacking vs. Getting a Real Meal Done

Somewhere between Knoxville and Bristol, I found the analogy that tied the whole experiment together.

Most of what we call "working on the go" is snacking. At the doctor's office, in transit, on a road trip, without a desk: you reply to an email, fire off a Slack message, maybe hop on a quick call. These are small, reactive actions. They feel productive, but they rarely move anything important forward.

What I did in the passenger seat felt different. I finished a strategic document. I redesigned our homepage. I wrote a briefing document to explain a contested strategic decision to my team -- all from a phone, without a desk.

That felt like a meal.

We have not fully internalized what that means yet.

What I Learned

The most important insight: the tools that will win are not the ones with the most capable model in isolation. They are the ones that solve context. The AI that knows what you are working on, what you decided last week, what your calendar looks like, and what your customers are asking for becomes indispensable. The AI that only knows what you told it three minutes ago is useful, but ultimately limited.

---

My daughter graduated. We drove home through Birmingham, up through Chattanooga and Knoxville, and spent the night in Bristol, Virginia, right on the Tennessee border. We had some genuinely great BBQ. And for most of the drive, she controlled the music -- a playlist of her favorites that turned out to include a surprising number of songs I used to love too, back in a different era of road trips.

I thought about what the next version of all this might look like: an AI that notices when you have been driving for three hours, suggests you take a break, reads you a summary of your afternoon emails, and asks if you want it to reschedule your 4pm. Not because you asked, but because it knows you well enough to know you would want it to.

We are not there yet. But after 1,800 miles, I can tell you: we are closer than most people think.