BlazeSQL

AI model improvements don't matter much at this point. OpenAI, Anthropic etc. want you to believe otherwise, but that's not where 90% of value will come from... Most knowledge work can already be automated with the current AI models. The bottleneck isn't intelligence, it's context and workflows. When AI output doesn't match human quality, it's probably due to: - Missing context. You know more about your business than the AI does. - Not enough task-specific guidance. If the AI output for a particular task isn't great, you can put your feedback in the prompt you use for that task. If you do this right, you'll eventually have no more feedback and the output will always be great. -Trying to do everything in one step. The output will be WAY better if you break it down into steps (ex. planning, reviewing, and iterating) If you think about it, you'll realize the above applies to humans too. Doing all of the above for any given task is hard, but specialized AI tools (ex. for marketing, analytics, etc.) do most of it for you. That's why: 1. Specialized AI tools for analytics, content creation, customer support etc. often work WAY BETTER than generalist chatbots like ChatGPT or Claude. 2. Incumbents (ex. Salesforce, Power BI) that slap on AI features aren't as good as solutions from AI-Native companies because it's easy to slap AI onto your product, but it's hard to make it work well. This difficulty isn't obvious, because you don't see the complexity under the hood when AI chatbots or agents work well. If it's not the company's specialty, their AI capabilities usually aren't great. Most people just use ChatGPT, Claude, and whatever other software they were already using (which now has AI bolted-on), and blame AI models when something doesn't work. 90% of the value that AI delivers in the coming years won't come from more intelligent models, it will come from more companies realizing that specialized tools built by AI-native experts often work far better. If you want to keep it simple, you can always check the ChatGPT app store or Claude connectors to let your generalist chatbot access those specialized abilities.

The founder of Twitter and Square fired nearly half his company (4000 people) because of AI... Future companies will keep teams smaller in the first place.

People blame "AI" for mistakes, as if it's all the same. Then they avoid it, and that's an expensive mistake. There are thousands of specialized AI tools and models. Lumping them together is like saying "software doesn't work" because one app crashed. This Reddit post is rage bait (it's fake, and was removed by moderators). But this kind of story goes viral every time because people think it confirms that "AI" can't be trusted. People don't understand that it's incredibly easy to build AI that looks impressive right now, and that making it reliable is a completely different skill set. That's true everywhere. Incumbents and startups ship beautiful demos built by software engineers who've never solved AI reliability problems. Internal teams build shiny prototypes and say "Look, we can build this ourselves!" Most people can't tell the difference between any of this and AI that actually works. So they use a tool that claims to do everything instead of a specialized one, don't test it thoroughly enough, and don't look for something better when it doesn't deliver. We built testing into BlazeSQL as a standard part of onboarding. That was standard practice in data science & AI long before ChatGPT existed. Every time a story like this goes viral, a bunch of people decide AI "doesn't work" and go back to doing things manually. Meanwhile others find a specialized, reliable tool for their use case and win the massive benefits that everyone else thinks are a myth.

"We barely talk about dashboards anymore"

A user executed an agreement on Agree.com at 11:59:10 on New Year's Eve — this was a fun query to pull!