WEBVTT 00:00:00.480 --> 00:00:03.018 Thank you all for joining the webinar today. 00:00:03.101 --> 00:00:05.997 Uh we're very excited to have with us a discussion 00:00:06.080 --> 00:00:11.193 uh between our CEO Ali Gatsi and uh via Sam Alman uh CEO of OpenAI. 00:00:11.276 --> 00:00:13.597 So thank you very much both for 00:00:13.680 --> 00:00:16.364 for being here. Thank you. Yeah, excited. Yeah. Well, 00:00:16.447 --> 00:00:18.557 let's get started and I hope we can have a 00:00:18.640 --> 00:00:23.031 fun kind of thoughtful discussion. um you know data bricks and open AAI we recently 00:00:23.115 --> 00:00:23.517 announced 00:00:23.600 --> 00:00:27.642 kind of first-of-a-kind partnership uh to make the open AAI models natively 00:00:27.725 --> 00:00:28.797 available within data 00:00:28.880 --> 00:00:32.566 bricks and agent bricks so maybe actually starting with Ali first you know what's 00:00:32.649 --> 00:00:33.357 the importance of 00:00:33.440 --> 00:00:36.849 partnering with open AI on this on this endeavor yeah I mean every one of 00:00:36.932 --> 00:00:37.997 our enterprise customers 00:00:38.080 --> 00:00:41.283 want to use openAI like you know uh they all want to have the models available they 00:00:41.366 --> 00:00:41.917 want to use them 00:00:42.000 --> 00:00:46.478 on their enterprise data uh and you know they're you know getting just those two 00:00:46.562 --> 00:00:47.277 things working 00:00:47.360 --> 00:00:51.574 together is non-trivial because it's like the data is sensitive They want to have 00:00:51.657 --> 00:00:54.928 their privacy, you know, they want to have auditing, they want to have GDPR rights, 00:00:55.011 --> 00:00:56.157 but they want to like, you know, 00:00:56.240 --> 00:00:59.907 they want to use, you know, the models to build agents to get insights. So, 00:00:59.990 --> 00:01:04.298 it's like it's really customer demand that uh, you know, has been overwhelming. Yeah, 00:01:04.381 --> 00:01:05.677 we're we're thrilled to do 00:01:05.760 --> 00:01:08.347 this. Enterprise is becoming uh one of our biggest focuses. 00:01:08.430 --> 00:01:10.157 We've had like 7x enterprise growth this 00:01:10.240 --> 00:01:13.795 year and uh you know we we really think that we're heading into the phase of AI 00:01:13.879 --> 00:01:14.717 where the models are 00:01:14.800 --> 00:01:18.567 getting so good that enterprises will need to use them want to use them bring them 00:01:18.650 --> 00:01:19.037 into their 00:01:19.120 --> 00:01:22.910 uh into their whole ecosystem and we cannot imagine a better partner than data 00:01:22.993 --> 00:01:23.357 bricks to 00:01:23.440 --> 00:01:26.395 to make that happen. So we're thrilled. We're also very happy to be a data bicks 00:01:26.478 --> 00:01:27.117 customer. Yeah know 00:01:27.200 --> 00:01:30.112 that's amazing. you know, as as you said, you know, the partnership, 00:01:30.195 --> 00:01:33.861 not only is of course data bricks bringing open AAI models to the M, you know, 00:01:33.944 --> 00:01:35.677 all of our enterprise customers, um, 00:01:35.760 --> 00:01:40.371 but also for some time now, OpenAI has been using data bricks for data analytics. 00:01:40.455 --> 00:01:41.357 So maybe starting 00:01:41.440 --> 00:01:44.627 with maybe Ali, like how has that partnership and that work been going? 00:01:44.710 --> 00:01:45.917 It's awesome working. I mean 00:01:46.000 --> 00:01:50.777 like extremely technical teams that push us to you know our boundaries and uh you 00:01:50.860 --> 00:01:51.437 know demand 00:01:51.520 --> 00:01:54.756 more and one more of us and uh you know it's nice that the two companies are so 00:01:54.840 --> 00:01:59.154 close geographically so it's just easy to walk over and iterate on the product. 00:01:59.237 --> 00:02:01.117 Uh but uh yeah it's a one of our 00:02:01.200 --> 00:02:05.583 most demanding customers in a very good way like made us just way way way better 00:02:05.667 --> 00:02:06.477 as a company. Um 00:02:06.560 --> 00:02:10.782 maybe moving separately a little bit you know Sam you and open have really 00:02:10.865 --> 00:02:11.597 revolutionized 00:02:11.680 --> 00:02:16.057 AI for consumers. Um how do you think on this new chapter as you mentioned about 00:02:16.141 --> 00:02:17.117 going to enterprise 00:02:17.200 --> 00:02:21.631 how data bricks and open AI together you know can revolutionize AI for enterprise 00:02:21.714 --> 00:02:22.077 as well? 00:02:22.160 --> 00:02:26.562 Yeah we always plan to do enterprise also but the models started with a lot 00:02:26.645 --> 00:02:30.761 of problems they started fairly weak. It was easier to get consumers to adopt them. 00:02:30.844 --> 00:02:32.477 They're coming to the point in their 00:02:32.560 --> 00:02:38.081 maturity where we are clearly seeing together huge enterprise demand. Um, 00:02:38.164 --> 00:02:40.077 and the need to figure out 00:02:40.160 --> 00:02:44.410 how to bring this technology to enterprises in a way that they can use it 00:02:44.493 --> 00:02:45.597 with all their data, 00:02:45.680 --> 00:02:48.887 with all their constraints, with all of the concerns they have about security 00:02:48.970 --> 00:02:49.357 and safety, 00:02:49.440 --> 00:02:56.411 but but also uh to sort of quickly jump into this new world where you'll have AI 00:02:56.495 --> 00:03:01.383 doing an increasing amount of the intellectual work in an enterprise. 00:03:01.467 --> 00:03:03.517 um this feels like it's really 00:03:03.600 --> 00:03:08.598 the time for I think in 2026 2027 we'll see a huge transformation um of how 00:03:08.682 --> 00:03:10.157 enterprises think about 00:03:10.240 --> 00:03:14.147 this maybe the the example we can look at from 2025 is what's happened with how code 00:03:14.230 --> 00:03:14.717 gets written 00:03:14.800 --> 00:03:19.420 right yes and now if you imagine that for every other function of the enterprise uh 00:03:19.503 --> 00:03:20.157 that would be 00:03:20.240 --> 00:03:25.085 quite a big deal and thrilled to get to go do that together yeah I'm super excited I 00:03:25.168 --> 00:03:25.437 mean I 00:03:25.520 --> 00:03:29.692 think there's a lot of uh context in the data that enterprises have it's very 00:03:29.775 --> 00:03:30.797 proprietary right So 00:03:30.880 --> 00:03:34.261 uh the consumer side initially I presume started with like okay we have all 00:03:34.344 --> 00:03:35.277 this data in the world 00:03:35.360 --> 00:03:39.590 that's you know public mankind is created over thousands of years what if the AI 00:03:39.673 --> 00:03:40.237 could really 00:03:40.320 --> 00:03:43.913 sort of compress that and understand that and reason on that but this then there's 00:03:43.996 --> 00:03:44.317 this like 00:03:44.400 --> 00:03:49.460 data that's like not available to the LMS which we now can bring together so now you 00:03:49.543 --> 00:03:50.317 know providing 00:03:50.400 --> 00:03:55.129 the data that they have that's proprietary uh in the enterprise as context to 00:03:55.212 --> 00:03:56.317 the agents which is 00:03:56.400 --> 00:04:00.327 really what they need that I think is going to be like the big unlock just 00:04:00.410 --> 00:04:01.357 excited to uh build 00:04:01.440 --> 00:04:06.220 that together. Yeah. And you know on that point both Sam and Ali on sort of 00:04:06.303 --> 00:04:07.517 the model capability 00:04:07.600 --> 00:04:10.791 reaching a point where you know enterprises make a lot of sense. 00:04:10.874 --> 00:04:11.917 The other piece on top 00:04:12.000 --> 00:04:17.117 of that has been this sort of agents kind of age of agents sort of arising and I 00:04:17.200 --> 00:04:18.157 want to ask both 00:04:18.240 --> 00:04:22.957 of you a little bit more on the research side. you know now with AI research moving 00:04:23.040 --> 00:04:27.358 um from you know building great great frontier models to now including hey you 00:04:27.441 --> 00:04:28.317 mentioned context 00:04:28.400 --> 00:04:33.186 management right you mentioned agents and and multi-agentic patterns and systems 00:04:33.270 --> 00:04:33.917 where do you 00:04:34.000 --> 00:04:37.633 think the future innovations like across these research buckets will really kind 00:04:37.716 --> 00:04:38.237 of drive this 00:04:38.320 --> 00:04:42.807 move into into enterprise I mean I think there will be all of these different 00:04:42.890 --> 00:04:43.757 research buckets 00:04:43.840 --> 00:04:47.207 like we'll keep pushing on pre-training we will you know get these multi- aent 00:04:47.290 --> 00:04:47.517 systems 00:04:47.600 --> 00:04:51.395 that are doing complicated things the models will get smarter across the board. 00:04:51.478 --> 00:04:55.703 But there are maybe kind of two axes that I would think about here. 00:04:55.786 --> 00:04:58.077 Uh one is nothing at all to do with 00:04:58.160 --> 00:05:01.679 model intelligence or very little. It's about how tightly can you integrate it 00:05:01.762 --> 00:05:02.557 into an enterprise, 00:05:02.640 --> 00:05:05.400 all of the knowledge they have, all of the different data sources, 00:05:05.483 --> 00:05:06.477 their business processes, 00:05:06.560 --> 00:05:10.928 the the way that they want to work. Like you could bring a very brilliant physicist 00:05:11.011 --> 00:05:11.357 and drop 00:05:11.440 --> 00:05:14.625 them into data bricks and maybe they wouldn't get that much done on the first 00:05:14.709 --> 00:05:15.517 day because they'd be 00:05:15.600 --> 00:05:19.323 missing all of that understanding. they would know how to even though they had 00:05:19.406 --> 00:05:20.397 crazy intelligence. So 00:05:20.480 --> 00:05:26.186 can be a problem. Yeah, I'm sure. Uh so so I think there's this one layer that 00:05:26.270 --> 00:05:27.597 the whole ecosystem 00:05:27.680 --> 00:05:30.797 together this will not just be either our companies but the whole world is going 00:05:30.880 --> 00:05:31.197 to have to 00:05:31.280 --> 00:05:38.280 build kind of an integration uh into these models and sort of with these models 00:05:38.632 --> 00:05:40.317 and that is that is 00:05:40.400 --> 00:05:44.066 a lot of work to really figure out how you're going to you know teach a model 00:05:44.150 --> 00:05:48.491 to be a sort of productive employee in a particular enterprise or whatever. 00:05:48.574 --> 00:05:50.557 Um and then the other category we 00:05:50.640 --> 00:05:57.640 uh we have seen a dramatic increase in how long of a horizon a model can work on 00:05:57.814 --> 00:05:58.717 a task for. 00:05:58.800 --> 00:06:02.321 Yeah. Yeah. So the way the way that I think about this is for a particular class 00:06:02.405 --> 00:06:06.646 of task once you've given it that sort of connection to an enterprise that kind 00:06:06.729 --> 00:06:07.437 of integration 00:06:07.520 --> 00:06:14.520 um how long of a task does the model have a 50% chance of success at gone from if we 00:06:15.867 --> 00:06:16.877 think about 00:06:16.960 --> 00:06:23.960 say coding we've gone from 5-second tasks at the launch of GPT3.5 to 5 minute tasks 00:06:24.440 --> 00:06:24.717 with 00:06:24.800 --> 00:06:31.800 various GPT4 iterations to 5-hour tasks with um GP5 and that's remarkable. 00:06:34.310 --> 00:06:36.797 Uh but a lot of what 00:06:36.880 --> 00:06:42.468 an enterprise does is tasks that require months or years sometimes and also we can 00:06:42.552 --> 00:06:43.437 only currently 00:06:43.520 --> 00:06:48.606 do this in this one vertical. So lengthening the the horizon that these 00:06:48.689 --> 00:06:49.917 models can work on 00:06:50.000 --> 00:06:53.912 um giving them all of the context that exists inside an enterprise or the world 00:06:53.995 --> 00:06:54.317 and then 00:06:54.400 --> 00:06:58.317 adding that to like many more verticals I think will be the important thrust. Yeah, 00:06:58.400 --> 00:07:02.968 I mean I I think that one is the 50th percentile task completion time horizon 00:07:03.051 --> 00:07:04.237 is super interesting. 00:07:04.320 --> 00:07:08.112 uh and I think if you bring more context so the first thing you mentioned also 00:07:08.195 --> 00:07:08.957 with more context 00:07:09.040 --> 00:07:13.049 uh you know you can increase that horizon like you know lengthen that so the task 00:07:13.133 --> 00:07:13.757 something that 00:07:13.840 --> 00:07:18.598 would take a human a day to do the the the agents can now do um uh not that we have 00:07:18.682 --> 00:07:19.357 a day yet but 00:07:19.440 --> 00:07:22.677 I'm saying if you add that context you can get that longer and longer and one thing 00:07:22.760 --> 00:07:23.117 that we saw 00:07:23.200 --> 00:07:27.708 that's really cool is just optimizing the context automatically like you know we we 00:07:27.791 --> 00:07:28.317 developed a 00:07:28.400 --> 00:07:33.262 technique called uh ja uh which like basically um uh inspired by genetic 00:07:33.345 --> 00:07:35.117 algorithms But uh basically 00:07:35.200 --> 00:07:38.170 gets that enterprise context that you have and gets it into the model 00:07:38.254 --> 00:07:39.277 but automatically so that 00:07:39.360 --> 00:07:42.437 you don't have to have humans that are sitting there and optimizing the context 00:07:42.520 --> 00:07:43.037 so that you can 00:07:43.120 --> 00:07:46.677 live feed the relevant context that's sitting in the different docs and inside 00:07:46.760 --> 00:07:47.517 of the enterprise. 00:07:47.600 --> 00:07:50.651 Uh so I think that's that's a really good metric to look at and see you know and 00:07:50.735 --> 00:07:51.357 the progression of 00:07:51.440 --> 00:07:56.750 it has been like insane as Sam said like from like seconds now to like many many 00:07:56.833 --> 00:07:57.357 hours. So 00:07:57.440 --> 00:08:01.420 very excited about that. It always yeah it always strikes me that the the model 00:08:01.503 --> 00:08:02.397 capability today is 00:08:02.480 --> 00:08:06.431 the dumbest it will ever be for the rest of my lifetime you know and that's like 00:08:06.515 --> 00:08:07.037 a incredible 00:08:07.120 --> 00:08:11.137 thought to have whenever you think about the potential applications of this thing. 00:08:11.220 --> 00:08:15.680 Um and you know as as we mentioned sort of agents getting deeper enterprise operating 00:08:15.763 --> 00:08:16.877 on hours and hours and 00:08:16.960 --> 00:08:20.111 potentially even taking actions in the enterprise what are the main things 00:08:20.195 --> 00:08:21.117 to think about in terms 00:08:21.200 --> 00:08:26.324 of ensuring kind of responsible AI and governance over the tools and the data 00:08:26.407 --> 00:08:27.677 and and the actions? 00:08:27.760 --> 00:08:32.356 Yeah, I mean we've worked very closely on that and we've made sure that uh we we 00:08:32.440 --> 00:08:35.466 sort of build with privacy and security from the ground up. 00:08:35.549 --> 00:08:38.077 So the whole uh partnership and integration builds 00:08:38.160 --> 00:08:41.101 on having the guardrails in place, making sure that you have audit logging 00:08:41.184 --> 00:08:41.837 on everything that 00:08:41.920 --> 00:08:45.081 happens in the enterprise so that you can track like what did it exactly do so you 00:08:45.165 --> 00:08:45.517 can go back 00:08:45.600 --> 00:08:49.648 and track those things. Having access control uh making sure I mean simple 00:08:49.732 --> 00:08:50.877 things for enterprises 00:08:50.960 --> 00:08:55.315 that maybe you know sometimes takes for granted but uh you know is it on brand 00:08:55.399 --> 00:08:56.397 yeah what the model 00:08:56.480 --> 00:08:59.298 is saying is it recommending a competitor things like that. 00:08:59.381 --> 00:09:01.117 So you can build those guard rails in 00:09:01.200 --> 00:09:05.529 from the beginning into the uh into the models and that's so if you use 35 now 00:09:05.612 --> 00:09:06.717 inside data books you 00:09:06.800 --> 00:09:10.306 get all of that out of the box. Uh so we're very excited about that. 00:09:10.389 --> 00:09:11.837 This is I think this is going 00:09:11.920 --> 00:09:15.424 to become the fundamental limiter for adoption of AI in the enterprise. 00:09:15.507 --> 00:09:18.317 Uh it won't be about the intelligence. It won't be about the price. 00:09:18.400 --> 00:09:20.557 We're going to I believe the research teams and the sort 00:09:20.640 --> 00:09:22.957 of infrastructure teams will figure that out. 00:09:23.040 --> 00:09:25.677 But I think enterprises are are starting to realize 00:09:25.760 --> 00:09:30.881 very quickly just how critical this is and it will be the limiting reagent. 00:09:30.964 --> 00:09:32.477 Yeah 100%. I agree. You 00:09:32.560 --> 00:09:37.160 know maybe this is a question for Sam but as we and data bricks can attest you know 00:09:37.243 --> 00:09:37.837 the open way 00:09:37.920 --> 00:09:42.587 model GPOSS have been a gamecher for a lot of our companies both in terms of 00:09:42.670 --> 00:09:43.837 the capabilities and 00:09:43.920 --> 00:09:47.742 the use cases can tackle the customizability that an open way model you 00:09:47.825 --> 00:09:49.117 know involved and I think 00:09:49.200 --> 00:09:54.760 openai just recently released uh the GBD OSS uh safeguards version as well curious 00:09:54.843 --> 00:09:55.517 yeah what's 00:09:55.600 --> 00:10:00.782 the future for open source and open weights from from open AAI there's clearly 00:10:00.865 --> 00:10:01.997 demand for for it. 00:10:02.080 --> 00:10:06.695 There's honestly much less demand than there is for the most capable uh you know 00:10:06.778 --> 00:10:07.517 models that we 00:10:07.600 --> 00:10:10.983 can run in a cloud. But I think we the answer is we're you know a bigish company 00:10:11.067 --> 00:10:11.677 and we should do 00:10:11.760 --> 00:10:16.040 both. People people want a model that they control and that they can run on 00:10:16.124 --> 00:10:17.437 their device or in their 00:10:17.520 --> 00:10:21.975 own system and there needs to be some version of some way we support them. 00:10:22.059 --> 00:10:22.957 Yeah. Um so I am 00:10:23.040 --> 00:10:27.268 curious like we're seeing huge demand on GP2oss like people really want you know 00:10:27.352 --> 00:10:28.077 especially like 00:10:28.160 --> 00:10:32.713 you know an American open source model that like kind of uh uh is at the very 00:10:32.796 --> 00:10:33.917 frontier. Uh what is 00:10:34.000 --> 00:10:41.000 open opens plans going forward? Like is is G2S the last? No no I I I hope not. 00:10:41.248 --> 00:10:43.117 Um I we are trying to 00:10:43.200 --> 00:10:49.062 figure out how we can do a someday do a model that is like the quality of GPT5 uh 00:10:49.146 --> 00:10:50.237 running on a you 00:10:50.320 --> 00:10:54.450 know one device open source W model. We don't know how to do that yet but uh we 00:10:54.533 --> 00:10:55.517 didn't think we were 00:10:55.600 --> 00:11:00.454 going to get a model as good as GPT OSS running you know in 120 billion parameters 00:11:00.538 --> 00:11:05.527 either. Um, so again, I don't think this will be the what most people want, 00:11:05.610 --> 00:11:07.837 but the people who want it really 00:11:07.920 --> 00:11:11.513 want it. And I think we'll find we'll try to find ways to offer incredible open 00:11:11.596 --> 00:11:14.378 source models. Wow, that's pretty amazing. So you could run on your, you know, 00:11:14.462 --> 00:11:15.677 if you could get that form factor down 00:11:15.760 --> 00:11:19.364 running on your laptop. We we think about what it's going to look like to make 00:11:19.447 --> 00:11:21.917 a sort of, you know, a new computer for like the AI era. 00:11:22.000 --> 00:11:23.997 I think that there's a whole bunch of reasons 00:11:24.080 --> 00:11:26.338 why that form factor is not quite right. Yeah. 00:11:26.422 --> 00:11:29.037 But one part of this is it should run locally a great 00:11:29.120 --> 00:11:33.090 model if you wanted to. Yeah. Yeah. So if Wi-Fi is down, you know, 00:11:33.173 --> 00:11:35.117 you should AI if Wi-Fi is down or 00:11:35.200 --> 00:11:38.953 you're just like, hey, from a privacy perspective, 00:11:39.037 --> 00:11:42.637 I I I I believe that privacy and freedom will be 00:11:42.720 --> 00:11:46.734 two extremely important principles for how people use AI if it becomes as important 00:11:46.817 --> 00:11:52.633 in people's lives as we expect. And the current tech industry, public policy, 00:11:52.716 --> 00:11:55.517 cluster, whatever, leaves some area 00:11:55.600 --> 00:11:58.997 for concern. Yeah. And and so I think people will want good local models. 00:11:59.080 --> 00:12:03.275 Oh, that's super exciting. Yeah, looking forward to that future. Um, you know, 00:12:03.358 --> 00:12:05.837 maybe this is a question for Ali, but uh we 00:12:05.920 --> 00:12:09.113 as a community today are so focused on vibe coding and, you know, 00:12:09.196 --> 00:12:10.877 these small kind of agentic apps in 00:12:10.960 --> 00:12:13.932 the short term. What are we underestimating about this technology 00:12:14.016 --> 00:12:15.437 in the long term, especially for 00:12:15.520 --> 00:12:19.028 for the enterprise? Yeah, I mean, I think Sam touched on this. I mean, 00:12:19.111 --> 00:12:20.157 we're going to see it. 00:12:20.240 --> 00:12:23.463 We're going to have co-workers that are agents and they're going to there there's 00:12:23.546 --> 00:12:26.662 so many different aspects of the work that they're going to be involved in. 00:12:26.745 --> 00:12:28.157 Like even on coding now we're like 00:12:28.240 --> 00:12:32.127 just thinking about okay can you produce the code but you know coding at databbooks 00:12:32.210 --> 00:12:32.797 is only 20% of 00:12:32.880 --> 00:12:36.133 what R&D or an engineer does. We measure it it's only 20% of their time what are 00:12:36.216 --> 00:12:36.717 they doing the 00:12:36.800 --> 00:12:41.592 rest of the time but it's like design docs it's PRDS it's you know discussions 00:12:41.675 --> 00:12:42.717 meetings but every 00:12:42.800 --> 00:12:45.295 one of them when you start looking at it it's like there's a place for agents too 00:12:45.378 --> 00:12:45.677 and actually 00:12:45.760 --> 00:12:48.524 if agents were involved in the whole process it would probably also make 00:12:48.607 --> 00:12:49.117 the coding much 00:12:49.200 --> 00:12:52.372 much much better. It's context that's missing when they're writing the code. 00:12:52.455 --> 00:12:55.322 So, we're going to see, you know, I think end to end for, you know, 00:12:55.406 --> 00:12:57.837 all of this kind of productive work. We're going to see 00:12:57.920 --> 00:13:01.138 them uh show up and help accelerate everything that's happening inside of 00:13:01.221 --> 00:13:04.488 the enterprise. And it's going to happen, you know, slowly. It will take time, 00:13:04.571 --> 00:13:06.077 but that task horizon is just getting 00:13:06.160 --> 00:13:08.877 longer and longer and longer, which means they can take on more and more complicated 00:13:08.960 --> 00:13:14.312 tasks over time. So, it's exciting times. Yeah. Yeah. Um, and then maybe Sam, 00:13:14.395 --> 00:13:18.906 for you, I think you've said before that we're in this like once in a history kind 00:13:18.990 --> 00:13:20.397 of transition, right? Okay, 00:13:20.480 --> 00:13:23.633 now the world is changing much faster than than we think. 00:13:23.716 --> 00:13:25.677 Um maybe if you zoom out 5 10 years, 00:13:25.760 --> 00:13:28.996 what's like the most I don't know surprising institution, right? 00:13:29.080 --> 00:13:33.627 Schools, cities, religions, hospitals that you think will look unrecognizable, right, 00:13:33.710 --> 00:13:35.837 with those generations. I think once in 00:13:35.920 --> 00:13:39.095 history is too big of a statement. Okay, I can say once in a generation. 00:13:39.178 --> 00:13:42.821 Once in a generation. Yeah. Um the econ I think the economy changes a lot. 00:13:42.904 --> 00:13:45.597 I I I don't think it's I don't think we're going to 00:13:45.680 --> 00:13:49.245 like run out of meaning or purpose or fulfillment or anything like that. Um, 00:13:49.328 --> 00:13:50.157 I think humans have 00:13:50.240 --> 00:13:55.458 such main character energy. We're so able to like not care about what the machines 00:13:55.541 --> 00:13:56.557 do. Like already, 00:13:56.640 --> 00:14:00.231 you know, GPT5 is probably smarter than most people and they seem entirely 00:14:00.314 --> 00:14:03.602 unbothered. I feel pretty unbothered even though I'm like, "Oh, you know, 00:14:03.685 --> 00:14:05.357 I just had said host or whatever." Um, 00:14:05.440 --> 00:14:10.915 I so I don't think we have any of those crisis of meanings in quite the way 00:14:10.998 --> 00:14:14.477 that or I'm I'm less convinced. Maybe we do. Future's hard to predict. 00:14:14.560 --> 00:14:16.557 I'm less convinced than other people are, but the 00:14:16.640 --> 00:14:19.988 economy feels like it should just absolutely radically transform and I you 00:14:20.071 --> 00:14:20.637 know maybe the 00:14:20.720 --> 00:14:24.742 whole like maybe the structure of it changes in a pretty big way. 00:14:24.825 --> 00:14:26.637 Any uh predictions or thoughts 00:14:26.720 --> 00:14:30.850 on your end on this Ali? I think you know Sam has you know he he's the ones who 00:14:30.933 --> 00:14:31.917 touched on are those 00:14:32.000 --> 00:14:36.011 are excellent. I'll give boring enterprise you know what I see in my kind of vicinity 00:14:36.095 --> 00:14:36.397 which is 00:14:36.480 --> 00:14:40.365 uh you know I think uh you know there's like this whole um you know in sales 00:14:40.448 --> 00:14:41.357 for instance in B2B 00:14:41.440 --> 00:14:46.968 organizations there's sales engineering you know but I think that's actually even 00:14:47.052 --> 00:14:48.077 more than R&D uh 00:14:48.160 --> 00:14:52.211 this kind of technology agents is applicable there uh the marketing function 00:14:52.294 --> 00:14:53.517 inside of a B2B function 00:14:53.600 --> 00:14:57.043 like there's so much that the agents already doing at data bricks like you know 00:14:57.126 --> 00:14:57.757 in the marketing 00:14:57.840 --> 00:15:03.813 funnel like preparing u campaigns and you know preparing the material that's for 00:15:03.896 --> 00:15:07.523 the different audiences all that that's already being done by agents. 00:15:07.606 --> 00:15:09.357 Uh so I think we're going to see that 00:15:09.440 --> 00:15:12.935 uh change a lot of the inside of an enterprise like data bricks there's so 00:15:13.018 --> 00:15:13.757 many different op 00:15:13.840 --> 00:15:17.507 ops functions and the people that basically are analysts they're very smart 00:15:17.590 --> 00:15:18.557 people with financial 00:15:18.640 --> 00:15:22.483 backgrounds you know they're typically sitting in excel every day um a lot 00:15:22.567 --> 00:15:23.757 of that is already being 00:15:23.840 --> 00:15:26.950 completely transformed and we're going to see that actually be even more 00:15:27.033 --> 00:15:27.837 revolutionized so at 00:15:27.920 --> 00:15:31.805 least when I look at inside of a company like data bricks it's pretty much every 00:15:31.888 --> 00:15:32.797 function is going to 00:15:32.880 --> 00:15:36.372 be completely transformed that doesn't answer the society question but but you 00:15:36.455 --> 00:15:37.197 know there's a lot 00:15:37.280 --> 00:15:40.411 of enterprises out there that kind of a little bit look like data bricks and I 00:15:40.495 --> 00:15:41.277 think already they're 00:15:41.360 --> 00:15:45.224 they're going to be vastly different in five years or maybe three. Yeah. Yeah. 00:15:45.308 --> 00:15:46.237 And maybe you know I 00:15:46.320 --> 00:15:50.345 know we talked about 5 10 years from now but what are the most exciting enterprise 00:15:50.428 --> 00:15:51.197 AI use cases that 00:15:51.280 --> 00:15:56.367 that you both see today. I mean there's there's so many uh and you know it starts 00:15:56.450 --> 00:15:57.197 like it we're 00:15:57.280 --> 00:16:00.609 taking on the smaller tasks. So like when you zoom in it might not look like 00:16:00.692 --> 00:16:04.040 that exciting but it's like what Sam said with the task horizons. 00:16:04.124 --> 00:16:07.357 Uh but you know like one one one thing that I thought was 00:16:07.440 --> 00:16:11.174 interesting. Astroenica had a use case with agents that basically sift through 00:16:11.257 --> 00:16:14.923 400,000 documents which is something no human could have done. Yeah. 00:16:15.006 --> 00:16:16.797 Uh and it's uh you know our in in 00:16:16.880 --> 00:16:19.889 finance we have a lot of financial organizations that are now going through 00:16:19.973 --> 00:16:20.797 all of the SEC filings 00:16:20.880 --> 00:16:25.133 all the related documents and they're gleaning out alpha for investments 00:16:25.216 --> 00:16:28.253 that then they're giving to analysts that are then using them. 00:16:28.336 --> 00:16:30.397 Humans that are using them but it's just this 00:16:30.480 --> 00:16:34.330 would be extremely tedious. human would have to do that before and now it's just 00:16:34.413 --> 00:16:35.117 you know we take 00:16:35.200 --> 00:16:37.652 for granted it's like yeah of course sift through all those thousands of pages 00:16:37.735 --> 00:16:38.237 and give give back 00:16:38.320 --> 00:16:41.994 the response quickly to me. uh in insurance we're seeing it you know 00:16:42.078 --> 00:16:43.597 underwriting again like in in 00:16:43.680 --> 00:16:46.886 in the healthcare space there's if you think about if you go to a hospital 00:16:46.969 --> 00:16:47.997 there's like thousands of 00:16:48.080 --> 00:16:52.008 thousands of pages of papers that you have to sign that are produced for every 00:16:52.091 --> 00:16:53.037 hospital visit and I 00:16:53.120 --> 00:16:57.675 don't know if anyone ever even looks up those uh but now in the healthcare sector 00:16:57.758 --> 00:16:58.477 they're having 00:16:58.560 --> 00:17:01.520 agents more and more companies and startups are having actually all of 00:17:01.603 --> 00:17:02.477 that being fed to LLMs 00:17:02.560 --> 00:17:07.909 that are going through it and you know extracting tidbits figuring out risk uh so 00:17:07.993 --> 00:17:09.117 so much of society 00:17:09.200 --> 00:17:12.281 is going to be changed uh but it's going to happen you know as you said with those 00:17:12.364 --> 00:17:12.404 you 00:17:12.480 --> 00:17:16.460 know simpler and simpler tasks that gets more and more complicated and you know 00:17:16.543 --> 00:17:17.437 over time increases 00:17:17.520 --> 00:17:23.030 I I super agree with that answer but just to add two other directions I find very 00:17:23.113 --> 00:17:23.997 interesting um 00:17:24.080 --> 00:17:27.453 obviously it's great that people are doing things you know faster and better 00:17:27.536 --> 00:17:28.317 and cheaper but the 00:17:28.400 --> 00:17:32.101 categories of things that you just wouldn't do at all before AI or and you 00:17:32.184 --> 00:17:33.277 already touched on this 00:17:33.360 --> 00:17:37.362 but things that you just simply couldn't do are are both interesting to me so 00:17:37.445 --> 00:17:41.165 there's more focus on stuff you couldn't do like read 4,000 pages of documents. 00:17:41.248 --> 00:17:42.637 And I think that's amazing that 00:17:42.720 --> 00:17:47.417 AI is just now churning through this. And um it's so superhuman at literature search 00:17:47.500 --> 00:17:48.157 as a specific 00:17:48.240 --> 00:17:53.086 example of what that's meant. Um but this wouldn't do category just doesn't get 00:17:53.170 --> 00:17:54.397 as much attention. So 00:17:54.480 --> 00:17:58.631 if you I use coding again because it's the kind of thing I have the best mental 00:17:58.714 --> 00:17:59.757 personal mental model 00:17:59.840 --> 00:18:05.090 for. Um coders they can do their work faster but they also there's just features 00:18:05.173 --> 00:18:06.157 they would never 00:18:06.240 --> 00:18:08.806 think they would never try. that that would be at the bottom of someone's 00:18:08.889 --> 00:18:11.855 forever. I will never do do that. I'm just not ever going to get there. 00:18:11.938 --> 00:18:14.317 And now if I can just like kick off one extra agent, 00:18:14.400 --> 00:18:18.652 I am willing to do that. So seeing there's just like there were these good ideas 00:18:18.735 --> 00:18:22.612 that you just wouldn't do. And if you make the activation energy low enough, 00:18:22.695 --> 00:18:24.397 you make it cheap enough, you just 00:18:24.480 --> 00:18:27.769 you say, "Okay, fine. I'll try it." That's been like an interesting thing for me 00:18:27.852 --> 00:18:28.317 to see. Yeah, 00:18:28.400 --> 00:18:33.919 that's awesome. Yeah. Yeah. So yeah, the ability to launch async parallel basically 00:18:34.002 --> 00:18:34.797 agentic jobs, 00:18:34.880 --> 00:18:38.747 right? uh really changes how you think about the process of actually doing work 00:18:38.830 --> 00:18:39.597 which is which is 00:18:39.680 --> 00:18:43.301 very exciting. Yes. I mean it's uh it's so cool because it's so fast and you can do 00:18:43.384 --> 00:18:43.837 so much with 00:18:43.920 --> 00:18:47.527 it but now I feel like I'm so frustrated like why is it not why is it not like why 00:18:47.610 --> 00:18:50.117 it's taking so long like a minute this to write 20,000 lines. 00:18:50.200 --> 00:18:52.077 You know this is an area that I think we as an 00:18:52.160 --> 00:18:57.619 industry are underinvested in is how how we make this stuff dramatically faster. 00:18:57.702 --> 00:19:00.872 Yeah. 100%. We're going to push on that. Yeah. Exciting. Yeah. 00:19:00.955 --> 00:19:03.277 I mean we've done this in our database why Lindy is 00:19:03.360 --> 00:19:07.169 king right for any interactive experience whether it be a dashboard and the same 00:19:07.253 --> 00:19:07.997 principle applies 00:19:08.080 --> 00:19:11.988 to agents as well industry took a bet that that uh which I think was the right bet if 00:19:12.071 --> 00:19:12.317 you had 00:19:12.400 --> 00:19:15.674 to do one or the other which is we were going to prioritize cost per token over 00:19:15.758 --> 00:19:16.397 you know token to 00:19:16.480 --> 00:19:20.764 token latency and design most hardware that way there's clearly demand for a much 00:19:20.847 --> 00:19:21.357 higher cost 00:19:21.440 --> 00:19:24.922 per token at a much lower latency interesting is that now we're getting 00:19:25.005 --> 00:19:26.077 peaks into the hardware 00:19:26.160 --> 00:19:30.119 you're building the hardware that we'll eventually build not the first thing we'll 00:19:30.202 --> 00:19:33.752 do but We want to we want to do that at some Yeah. Yeah. Great. 00:19:33.836 --> 00:19:36.797 Uh we have a lot of uh folks dialing in here from 00:19:36.880 --> 00:19:41.677 uh you know where data scientists or data engineers or some even cos and leaders at 00:19:41.760 --> 00:19:46.816 at these enterprise companies. Um with this sort of generative AI revolution 00:19:46.900 --> 00:19:48.237 maybe what's like one 00:19:48.320 --> 00:19:52.730 thing that you know CEO or leader should do like immediately now to like prepare 00:19:52.814 --> 00:19:53.517 yourself right 00:19:53.600 --> 00:19:58.010 for this wave that's already here and it's like continuing to to roll to roll 00:19:58.093 --> 00:19:58.477 forward. 00:19:58.560 --> 00:20:03.569 I think uh you know I'll start with the boring stuff because it's uh it's people 00:20:03.652 --> 00:20:04.397 people um you 00:20:04.480 --> 00:20:08.127 know they're excited about you know what they can accomplish with AI which is great 00:20:08.210 --> 00:20:08.397 uh but 00:20:08.480 --> 00:20:11.108 before you get there you have to have a good data foundation and you have to make 00:20:11.191 --> 00:20:11.677 sure that you can 00:20:11.760 --> 00:20:15.390 secure it and you can make sure that you have the right guard guardrails in place 00:20:15.473 --> 00:20:18.518 and then also getting access to as much of your data in the enterprise. 00:20:18.601 --> 00:20:20.077 there's a lot of data that's siloed 00:20:20.160 --> 00:20:24.514 and tucked away like that's sitting in a database that's not accessible or that's 00:20:24.597 --> 00:20:25.117 you know um 00:20:25.200 --> 00:20:28.782 actually not even this is on premise it's not in the cloud and so on so making sure 00:20:28.866 --> 00:20:29.357 that you just 00:20:29.440 --> 00:20:34.409 can get all of that in one place uh so that it's accessible as context to the AI 00:20:34.493 --> 00:20:35.357 will be kind of 00:20:35.440 --> 00:20:38.489 like that differential whether the AI immediately gets it right whether it's 00:20:38.572 --> 00:20:39.437 within the task horizon 00:20:39.520 --> 00:20:43.296 or whether it's uh you know it's just trying many many things because you just 00:20:43.380 --> 00:20:44.237 didn't give it that 00:20:44.320 --> 00:20:47.659 little context that's sitting tucked away in that silo data set that's exactly what 00:20:47.743 --> 00:20:48.576 I was going to 00:20:49.200 --> 00:20:52.205 I think that's I think that's it sounds so boring but it's so important to get 00:20:52.288 --> 00:20:52.957 in this work. Yeah. 00:20:53.040 --> 00:20:57.321 Yeah. And and you know we've seen sort of natural language is great but it's also 00:20:57.404 --> 00:20:58.237 ambiguous. So you 00:20:58.320 --> 00:21:03.597 know when when our Ali knows this or when our customers ask hey please compute the 00:21:03.680 --> 00:21:05.716 uh churn for me. Well what is what is churn? 00:21:05.800 --> 00:21:08.077 Like it's it's really dependent on the enterprise 00:21:08.160 --> 00:21:11.566 context and and the enterprise use. Yeah it is I mean it is that definition exists 00:21:11.649 --> 00:21:12.077 somewhere in 00:21:12.160 --> 00:21:16.299 the organization in a company. how you define revenue or how you define churn 00:21:16.382 --> 00:21:16.957 or what when 00:21:17.040 --> 00:21:21.714 a fiscal year starts or what the quarters are um and you know so it's just can you 00:21:21.798 --> 00:21:22.237 find that 00:21:22.320 --> 00:21:26.400 as a context is is that is the AI connected to that if it is it will be able 00:21:26.483 --> 00:21:27.277 to figure it out 00:21:27.360 --> 00:21:30.917 because the models are so freaking smart now and they're getting smarter every 00:21:31.000 --> 00:21:31.757 release that comes 00:21:31.840 --> 00:21:34.973 u they're getting smarter and smarter so we just have to make sure we can feed 00:21:35.056 --> 00:21:35.757 that context though 00:21:35.840 --> 00:21:39.689 there's a little bit debate raging now on like whether these agent patterns are just 00:21:39.772 --> 00:21:40.157 agents and 00:21:40.240 --> 00:21:44.809 tools in a loop or whether you got to handgineer all the individual uh pieces 00:21:44.893 --> 00:21:49.328 of the agents and wire them into some deterministic kind of kind of workflow. 00:21:49.411 --> 00:21:51.517 the models what you see if models can 00:21:51.600 --> 00:21:54.448 like do novel math they're certainly smart enough to figure out how to do this out 00:21:54.531 --> 00:21:54.877 without hand 00:21:54.960 --> 00:21:58.491 engineering may take us a little bit of time and again you do need in the same way 00:21:58.575 --> 00:21:59.197 that an employee 00:21:59.280 --> 00:22:01.997 needs some training needs to know where to look where the enterprise stuff is you'll 00:22:02.080 --> 00:22:02.397 have to like 00:22:02.480 --> 00:22:06.044 make that knowledge available but they will not need custom training for it yeah 00:22:06.127 --> 00:22:06.637 totally agree 00:22:06.720 --> 00:22:10.546 isn't a bitter lesson uh it is it is a bit lesson bitter lesson you know it's like 00:22:10.629 --> 00:22:11.357 and we've seen it 00:22:11.440 --> 00:22:14.866 with most of the agents that people are building the successful agents when when 00:22:14.950 --> 00:22:15.437 you when when 00:22:15.520 --> 00:22:18.583 the people built them talk about it's like hey I actually did the simplest thing I 00:22:18.666 --> 00:22:19.197 didn't have lots 00:22:19.280 --> 00:22:23.149 of lots of lots of agents that call each other and lots of hand tune things. 00:22:23.232 --> 00:22:24.397 I just let the AI figure 00:22:24.480 --> 00:22:30.054 it out. Yeah. Yeah. Great. Um, thank you both for uh joining us today. 00:22:30.138 --> 00:22:35.018 We really really appreciate it. Uh, for the folks who are dial in here, you know, 00:22:35.101 --> 00:22:37.117 if you want to get ready and get started 00:22:37.200 --> 00:22:41.721 uh using uh open a on data bricks, uh please check out uh AI uh playground 00:22:41.804 --> 00:22:43.277 and also our agent bricks 00:22:43.360 --> 00:22:47.714 product uh to get starting building agents uh with open AAI and the GBT5 models 00:22:47.797 --> 00:22:48.640 on data bricks. 00:22:49.200 --> 00:22:52.950 Thank you both for being here. Thanks so much. Great discussion. Thank you.