I think we need to continue to be mature and responsible about how we do it. I think that there should be more openness in terms of how these large models are being trained, what kinds of rule sets are being applied to them, what kinds of data sets are going into them. And I think that without additional transparency, it’s going to be hard to trust continued development of it. And so I definitely support continuing to put pressure on this eco stem. It’s not something that we should allow to just like run unchecked.
Operator: And our next question comes from Pinjalim Bora with JPMorgan.
Pinjalim Bora: One — just one question for me. Isabelle, you were assuming it seems like a degrading degradation in the macro environment in the guidance. I want to ask you if you saw the macro headwinds intensify through the quarter or towards the end of the quarter or through Q1 so far?
Isabelle Winkles: So we haven’t necessarily seen things getting worse in Q1 other than some of the disruption that we’re seeing or that one could expect as a result of the most recent sort of banking uncertainty that obviously will have impacts on potential buyer behavior. But we’re not necessarily seeing anything more negative in Q1 specifically.
Operator: And our next question comes from Hannah with Piper Sandler.
Unidentified Analyst: First one for you, Bill. You touched on this earlier but can you just talk about what you’re seeing in terms of adoption from outside of marketing such as data engineers and product teams and what you’re doing to kind of encourage adoption from those other users?
Bill Magnuson: Yes, absolutely. We’ve been really encouraged to see just how the interdisciplinary nature of the teams that use Braze has been — year-over-year has continued to increase. We’ve been excited to see more users coming to the base dashboard, accomplishing more use cases. You heard us announce feature flags as well back at Forge. And so that’s been bringing more product engineering teams into the fold. So when we look at things like cloud data injection, a lot of the work that’s being done around data transformation, bringing currents into the fold, the expansions to the product catalog capabilities as well as I referenced earlier that there are subsets of the artificial intelligence feature set that we’re building that do require being some sophistication in terms of how you train it, how it learns and how you nurture it over time which does end up bringing more data scientists into the fold.
And so we’re definitely we’re understanding of our position right now and we’re not like rushing to go and try to sell directly to the out we think there’s a tremendous amount of value to be had around bringing those persona into as fold, having them collaborating with the marketer and the marketing budget that we are selling into today. That increases our awareness and our reputation in those groups and we think it creates a lot of great optionality for us in the future.
Unidentified Analyst: Super helpful. And then can you just talk about the feedback and adoption for your cloud data ingestion product and how that’s trended? And remind us when that goes GA?
Bill Magnuson: Yes, absolutely. So actually, to answer your last question first, we actually had Snowflake go into GA on February 7. And excitingly, Redshift went into early access on the same day. And so actually, the product momentum there has been quite rapid. We also are off to a strong start in terms of customer adoption. We’ve got an early wave customers actively sinking data from their warehouse or Braze. We’re already counting the number of data points with units of $1 billion which is exciting to see, especially in the early days. We’ve also been really happy with how quickly people have been able to get those connectors up and running and integrated and configured. And you should also expect much like with our launch of currents that we’ll plan to roll out a few more connectors to cover the most popular data warehousing solutions in our customer base.
