# New products need startup-like autonomy inside a large company

Markdown copy of https://realalpha.app/s/new-products-need-startup-like-autonomy-inside-a-large/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Ali Ghodsi](https://realalpha.app/builders/ali-ghodsi/) · Ali Ghodsi is cofounder and CEO of Databricks. · Co-founded Databricks, which he has led as CEO since 2016, and helped create Apache Spark.
- Host: [Logan Bartlett](https://realalpha.app/people/logan-bartlett/) · Software Investor, Redpoint Ventures · Backs enterprise software companies like Ramp, Cribl, Cyera and Legora.
- Show: The Logan Bartlett Show
- Episode: [Databricks CEO on Super AGI and Unlocking AI’s Untapped Potential](https://realalpha.app/v/pAcF3GV4ygM/), Nov 22, 2024
- Moment: 44:16 to 47:46 of the episode (4 min), playing on https://realalpha.app/s/new-products-need-startup-like-autonomy-inside-a-large/; the episode from there: https://realalpha.app/v/pAcF3GV4ygM/?t=2656

## Quote

> “the startups move **three to four times faster** than a company like Databricks.”
> — [Ali Ghodsi](https://realalpha.app/builders/ali-ghodsi/) at [46:56](https://realalpha.app/v/pAcF3GV4ygM/?t=2816) · The Logan Bartlett Show

## Takeaways

1. Keep early product teams outside the large company's processes.
2. Use a separate codebase to let teams iterate quickly.
3. Integrate the product after it shows product-market fit.

## About this segment

Ali Ghodsi gives new products a small team, fewer processes and a separate codebase until product-market fit. He says startups move three to four times faster than Databricks.

From “Databricks CEO on Super AGI and Unlocking AI’s Untapped Potential”, Nov 22, 2024. [Watch the full episode](https://realalpha.app/v/pAcF3GV4ygM/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[44:21](https://realalpha.app/v/pAcF3GV4ygM/?t=2661) Now, as you think about expanding into new products, I've heard you actually will set up different teams for each product as it gets going. What have you learned about product management and organization as you can need to scale and iterate on these different product areas? Yeah, I think that when you build a product zero to one, you kind of have to do the same thing that you did in the original kind of first two, three years of the company.

[44:43](https://realalpha.app/v/pAcF3GV4ygM/?t=2683) So there's a lot of processes in any company once you're over 150 employees, let alone thousands. So you got to cut that and say, look, for new products, you don't need to satisfy all the security stuff. You don't need to have all the processes, Have a small core team that iterates really, really quickly. Figure out, first of all, who's the sort of innovator type that you have in those early startups.

[45:05](https://realalpha.app/v/pAcF3GV4ygM/?t=2705) Put them on the project. These are people that are not necessarily good at when the company scales and they're leading mature products. You have to have one such anchor person that you put everything around and you put them in charge. They start building that product. And then you let them iterate really quickly. So get them off the iteration cycle that the big company goes on, but get them on this much quicker, smaller one.

[45:26](https://realalpha.app/v/pAcF3GV4ygM/?t=2726) and encourage them to just iterate inner dev loop, outer dev loop, that's super, super fast and just focus on getting users and getting product market fit. Once that happens, you can kind of move it back into the mothership. So ideally even do it outside of the whole product if you can. So we have a product right now called Genie, which lets you just, you ask in English questions and it can answer immediately.

[45:47](https://realalpha.app/v/pAcF3GV4ygM/?t=2747) It produces, it understands all the semantics of your data. We call it data intelligence. So it understands your data and it then programs and asks the questions and give you just straight up the answer. But to know that it's not hallucinating, you can actually see how it coded it up and behind the scenes and so on. That product, we actually just built it completely outside of Databricks code base.

[46:06](https://realalpha.app/v/pAcF3GV4ygM/?t=2766) And it didn't satisfy any of this stuff. We didn't build on anything. We just iterated quickly, just do that outside of the norm, like a startup almost. And then only when we saw that the results are really good and people like it, we went through the kind of painful process of, okay, let's rewrite it, let's move it into the mothership so it's almost like an acquisition at that point happens and you have to integrate it into the whole system and make it secure and so on. So you'll give people autonomy to make the decisions on not the shared primitives necessarily or if you guys are using some type of I don't know single sign on or whatever cloud structure underneath you'll get people full autonomy to do it and then you'll come back and reconcile it once it actually finds product market fit.

[46:45](https://realalpha.app/v/pAcF3GV4ygM/?t=2805) Literally do it outside in a separate repository do it in a separate GitHub repository outside of the big system and do it iterate very quickly is the most important thing because that's what you're doing as a startup. As a startup, you operate three to four times. I've seen it, we've done acquisitions. And if you measure it, the startups move three to four times faster than a company like Databricks.

[47:04](https://realalpha.app/v/pAcF3GV4ygM/?t=2824) And I think we move very fast and we're very innovative, but still it takes us three, four times longer. Why is it? It's the security processes. It's the legal sign off. It's making sure that it works on all the clouds, in all of the environments. And the biggest thing that slows you down is that you have thousands of thousands of customers that you can't regress. So you've got to unshackle the new products.

[47:25](https://realalpha.app/v/pAcF3GV4ygM/?t=2845) Otherwise, you're just going to move too slowly. And that's what we're seeing, honestly. They're really, really big companies. They operate that way. It takes them too long. That's why you have startups in the ecosystem. So you have to kind of go back and reproduce the kind of environment that you had when you had a small startup. I'm curious. So now you're coming up on, I guess,