The hidden costs of pre-computing data | Chalk's Elliot Marx

**Andrew Zigler** (0:06)
Welcome to Dev Interrupted. I'm your host, Andrew Zigler.

**Ben Lloyd Pearson** (0:10)
And I'm your host, Ben Lloyd Pearson.

**Andrew Zigler** (0:12)
This week, I'm sitting down with Elliot Marx of Chalk, the data platform for AI and machine learning. We invited Elliot on-site to ELC Annual this year to discuss how AI relies on real-time pipelines rather than traditional storage, and the costly dangers of pre-compute in a world that's increasingly consumption-driven. So if you're transforming data into business results, and let's face it, that's what we're all doing, then don't miss this one. But first, let's discuss today's new stories. OpenAI's Code Red, Google quietly launching its Workplace Studio, a pragmatic guide for LLMEvals for devs, and ignoring the spotlight as a staff engineer. Ben, where do you want to start?

**Ben Lloyd Pearson** (0:54)
Yeah, let's just start right at the top, because I've been involved in this conversation on LinkedIn, and it's a pretty fascinating story. But Microsoft's advantage in artificial intelligence is evaporating as Google Gemini surges ahead. So what's going on here, Andrew?

**Andrew Zigler** (1:08)
Yes. So it's no news to anybody that Google Gemini 3, it's surpassed OpenAI's Chat GPT-5 and multiple performance tests and benchmarks. And the Nano Banana Pro image generation tools are also outperforming competitors like Dolly. You know, OpenAI CEO Sam Altman, he declared a code red in the last week. He canceled planned marketing and monetization timelines and warned staff that Google Gemini poses a serious existential threat with expected growth potentially slowing to single digits for OpenAI through 2026 And you know it's bad when the company cancels the ad product. So we've covered this recently, but even just last week, we talked about how OpenAI faces financial strain. We looked at a number of graphs showing their capital expenditures, requiring hundreds of billions to fulfill a 1.4 trillion in compute commitments over the next decade. And meanwhile, their primary partner, Microsoft and their integration of the AI features, you know, they've received mixed results and it trails Google's seamless ecosystem advantage. This is a sea change event in the foundation model race. Ben, what do you think is going on here?

**Ben Lloyd Pearson** (2:19)
Yeah, well, first of all, Andrew, you and I both know that Sam Altman is a regular listener of this podcast. So we know he's been hearing my advice on all these frontier AI model companies.

**Andrew Zigler** (2:32)
Yeah, so Sam, listen up for this week as well.

**Ben Lloyd Pearson** (2:34)
Yeah, yeah. So I'm going to start sounding like a broken record on this, but there really are just no motes within the frontier model space yet. But this story is really significant, I think, because it may actually be the first example of a company actually starting to establish a moat within AI model space, and that's Google, because they have a lot of incumbency and they have a lot of data. So yeah, I think that's very significant in this situation.

**Andrew Zigler** (3:00)
And it's interesting too to note that Google's moat in this world, it doesn't come from the frontier model alone, but from its full ownership of the stack that makes that model possible.

**Ben Lloyd Pearson** (3:09)
Exactly.

**Andrew Zigler** (3:10)
They created the chips, the tensor processor units, the TPUs that trained the model. They aren't beholden to NVIDIA or CUDA. They serve their own inference on their distributed cloud platform, where they serve all the rest of their services as well. And this creates a nearly untouchable position that simply other players in the market can't emulate.

**Ben Lloyd Pearson** (3:30)
Yeah, I mean, it's really easy to forget about Google in this race because I think a lot of us remember the extraordinary flop that they had when they launched BARD a few years back. But I started using Gemini this week for the first time really, and I think it still has a lot of room to improve, particularly around user experience. But when you think about it, they've got just a treasure trove of images and videos that they can use for training and the hardware to scale it. So that's why I think NanoBanana Pro in particular is receiving a lot of attention because it's just an extraordinary image generator that is, in my opinion, miles ahead of everyone else. But I think there's two big things our audience should take away from this. The first is data quality. So Google is really proving the importance of super high quality data sets. If you have really great data hygiene, really great accessible data within your organization, you can give AI the context it needs to do really awesome stuff. So because Google has all these images and videos, it's probably going to be very hard for other companies to sort of replicate their success on that.