Just got back home after a conference and wanted to jot down a few thoughts to mark the moment in time. I wanna be able to look back at this in a couple years and revisit where we were then, kinda like how I made notes about what life was like during COVID.
William Gibson supposedly said, “The future is already here – it’s just not very evenly distributed.”
Self-driving cars are like that. A close friend of mine has a brand new Tesla Model X, one of the last ones, equipped with the Hardware 4 gear and the Full-Self-Driving 14 software. It opens his garage door for him, meets him in the driveway, opens the car door as he approaches, closes the car door after he’s inside, closes the garage door as it drives him out of the subdivision, navigates through the suburbs and onto the highway, and takes him right to his destination. Granted, we live in Vegas, a nearly-always-sunny location with clearly marked roads, but for all practical purposes, it feels like the future we were promised.
I have other friends with Teslas that have earlier versions of the hardware, not capable of getting FSD 14, and they have very different levels of experiences. And then of course there’s me, and none of my cars even stay in their own lanes, let alone do any kind of self-driving. It’s not like I’m poor, either: it’s just that the Venn diagram of cars I wanna own, and cars that have good self-driving, have nearly no overlap.
So if you ask six people to describe the abilities of self-driving cars today, you can get six different answers ranging from “it’s amazing” to “it will kill you”, much like the parable of the blind men describing an elephant. All of those answers are simultaneously correct, and yet also incorrect.
AI is like that today, too.
The exact conference, presentation, and presenter doesn’t really matter here, so I’m not going to name them. The point is that at this recent conference, I sat through sessions where I heard a few presenters say things from the podium like:
- “AI doesn’t have runtime access to your database, so it can’t see how its recommended query changes will perform”
- “AI’s knowledge is limited to only what it was trained on, and it can’t infer new things about new systems”
- “AI can’t evaluate query optimizer cost calculations because it doesn’t understand your data distribution”
- “AI can’t identify the root cause of performance issues”
- “AI can’t reason about CPU, memory, and I/O tradeoffs while tuning a query”
Uh… maybe your AI can’t.
But there are most definitely AIs out there today that do all of that stuff, and I should know, because I use them on a daily basis.
I’m a Microsoft SQL Server consultant and trainer, and I run Claude Code Desktop on my MacBook Pro. I use it for writing demo queries, First Responder Kit changes, and client work. Claude Code can still comprehend exactly what’s going on, despite being on a Mac, and it can test its work against Azure SQL DB, Amazon RDS SQL Server, local SQL Server VMs, and some in Amazon EC2. It’s fast, works well with Github, and accomplishes things in minutes while I’m off doing other stuff.
When something in my environment goes wrong, I can ask Claude Code to troubleshoot it. I don’t maintain production Availability Groups or failover clusters, but if I did, I’d be quite comfortable using Claude Code to troubleshoot those as well, plus make plans for upcoming deployments or environmental changes.
Claude Code can connect to your database, read your statistics, try different versions of a query, measure its overhead in terms of CPU/memory/IO, help you understand the tradeoffs, and much more. Just yesterday, while sitting in an airport, it was helping me use undocumented tricks against undocumented DMV columns – stuff that most definitely wasn’t a part of its training data, but it was experimenting and trying new stuff so we could both learn together.
The only thing that would have been cooler is if all this was happening in a self-driving car.
Careful when you listen to AI sessions at conferences.
The state of the art is advancing really quickly, and the people standing up on stages talking about the state of the art are like six blind folks describing an elephant.
- Some vendor staff will over-sell what their own products can do
- Some vendor staff will be honest about what their own products can’t do
- Many vendor staff will just be plain ignorant about what their competitors can do
- End users in the field will have the most relevant experience to share, but they need to be clear about which AI platforms they’re discussing, and make it clear that they’re only describing the part of the elephant they’ve been able to put their hands on
- Some (most?) of this stuff will be outdated within 2-4 months of the conference talk
- Some of it was already outdated when the slides were due, but the busy practitioner didn’t have time to update their slides, so listen really carefully to what they say aloud, and ask plenty of questions
I don’t think any of these people are being purposely misleading or malicious. They’re just blind folks describing an elephant. We’ll get a better picture of the elephant over time.