Hunting for a Flywheel | Refactoring’s Luca Rossi

**Ben Lloyd Pearson** (0:00)
Hey, Andrew, I heard you have been doing some really cool experiments with generative AI. Why don't you tell our audience about what you're up to?

**Andrew Zigler** (0:08)
I have been in the lab this week, full confession, working on the developer experience for Linear B's GitStream, where we're using the power of context with generative AI in the code review process. I imagined the process a bit like a restaurant. Bear with me. The model is the chef, and it's skilled and trained and capable of preparing an incredible variety of things depending on what you give it. And the data, that's like the ingredients. Without good ingredients, even the best chef will struggle. And when the ingredients are local as opposed to store-bought, they're often better. But the context is the order from the customer. And the customer is everything. They tell the chef what to prepare, how to season it, if they have any allergies, whether to mix it in or put it on the side. And no matter how great the chef is, or the ingredients are, if the order is unclear or missing, the dish doesn't meet your expectations, and you're not going to be happy.

**Ben Lloyd Pearson** (1:07)
I think I'm starting to see where you're going with this.

**Andrew Zigler** (1:10)
So at Linear B, we're focusing on that power of the context, to give the customer exactly what they ordered. Because no one cares if you're chefs from an elite academy, or if your ingredients are fully organic, if you order a steak and you get a pizza. To that end, Linear B released the AI Starter Kit for PR reviews. It's a collection of workflow automations that make it easy for engineering teams to start experimenting now with the power of context in their pull reviews. It brings the power of context to your AI models and services, along with native GitStream capabilities.

**Ben Lloyd Pearson** (1:45)
Yeah, so this is really cool because we're seeing so many products out there that are emerging to tackle this space. There's definitely a lot of confusion about what works versus what doesn't.
And I really like the focus we've been taking with this so far with this experiment is on responsible adoption. So if you're just getting started with AI, like you've maybe just bought one or two tools and you have an API that your developers can query. Or if you're somebody who's actually already purchased a bunch of AI tools and is trying to roll them out responsibly and securely and effectively, we're building these tools that are here to help you mature that process. So where can our audience get their hands on this?

**Andrew Zigler** (2:23)
Well, anywhere that Google searches or search engine results are served is probably going to point you in the right way. The GitStream documentation is going to have the best starting places for getting started with the AI starter kit, as well as the other features available on GitStream. But if you want someone to walk you through a more in-depth guide or walkthrough based upon your own needs, definitely reach out on the Linear B website. It's really easy to book a demo with an expert and get your hands on some great ideas for how to take this back to your team. And we'll put the links for this in the show notes as well.

**Ben Lloyd Pearson** (2:54)
Yeah, awesome.

**Andrew Zigler** (2:55)
So Ben, what have you been reading this week?

**Ben Lloyd Pearson** (2:58)
Last week we were talking about all these investments that are going into AI. Of course, this was before DeepSeek completely upended the market. But I am proud to say that many of our predictions from then were relatively accurate. Some might say we have some foresight here. Another story that has caught my attention for reasons that we'll get into in a moment is IBM. A company that has been around forever at this point, very early to the AI space with Watson. But a company that I think we've all kind of forgotten about for a while because there just hasn't been a lot of groundbreaking news other than maybe them buying Red Hat and HashiCorp. Their Q4 earnings report just came out. Usually these are not interesting at all. But they showed some extremely bullish growth in one area in particular, and that was their AI services. So they announced $5 billion in total sales, and $2 billion of that was in Q4 alone. So a massive, massive quarter over quarter growth. Naturally, there's a huge spike in their stock price, sort of bucking the trends actually this week of many of the companies in AI. But actually, there's one finer detail that was hidden under all of this, and that 80% of this is actually consulting.