WEBVTT NOTE This file was generated by Descript 00:00:00.000 --> 00:00:02.129 Victoria: What are the challenges that you see and, and that 00:00:02.129 --> 00:00:03.224 you guys are trying to tackle? 00:00:03.694 --> 00:00:04.444 Ned: Oh man. 00:00:04.493 --> 00:00:05.153 Let's see. 00:00:05.393 --> 00:00:07.068 The market's insatiable. 00:00:07.258 --> 00:00:13.208 The customer base wants more, wants it faster, wants it in different flavors, 00:00:13.208 --> 00:00:17.568 wants it in different ways packaged differently sustainably, of course. 00:00:17.968 --> 00:00:26.318 So that's really hard as well to try to get more from the existing 00:00:26.318 --> 00:00:30.068 team, sometimes even a smaller team because of economic issues. 00:00:30.518 --> 00:00:36.668 So the old adage of how do we do more with less is, is very, very present right now. 00:00:36.698 --> 00:00:38.948 So that's, that's a big challenge. 00:00:39.738 --> 00:00:43.248 The, the other challenges are that this stuff is really hard. 00:00:43.658 --> 00:00:47.828 You know, chemical development, material science development is hard. 00:00:47.828 --> 00:00:49.868 It's, it's multidimensional. 00:00:49.868 --> 00:00:52.508 It has an unbelievable amount of variables. 00:01:02.623 --> 00:01:05.423 voiceover: A key component of the modern world economy, the chemical 00:01:05.423 --> 00:01:08.963 industry delivers products and innovations to enhance everyday life. 00:01:09.483 --> 00:01:13.923 It is also an industry in transformation where chemical executives and workers 00:01:13.943 --> 00:01:17.563 are delivering growth and industry changing advancements while responding 00:01:17.563 --> 00:01:22.093 to pressures from investors, regulators, and public opinion, discover how 00:01:22.093 --> 00:01:25.943 leading companies are approaching these challenges here on the chemical show. 00:01:26.413 --> 00:01:29.753 Join Victoria Meyer, president of Progressio Global and 00:01:29.763 --> 00:01:31.183 host of the chemical show. 00:01:31.458 --> 00:01:35.658 As she speaks with executives across the industry and learns how they are leading 00:01:35.658 --> 00:01:41.238 their companies to grow, transform, and push industry boundaries on all frontiers. 00:01:41.578 --> 00:01:43.828 Here's your host, Victoria Meyer. 00:01:46.019 --> 00:01:47.519 Victoria: Hi, this is Victoria Meyer. 00:01:47.759 --> 00:01:49.439 Welcome back to The Chemical Show. 00:01:49.889 --> 00:01:53.789 Today I am speaking with Ned Weintraub, who is the Chief 00:01:53.819 --> 00:01:56.229 Revenue Officer at NobleAI. 00:01:56.429 --> 00:02:00.899 Noble's a pioneer in science-based AI solutions for chemicals 00:02:00.929 --> 00:02:02.279 and material companies. 00:02:02.559 --> 00:02:07.119 And Ned has been involved in digital innovation for industry throughout 00:02:07.119 --> 00:02:11.939 his career at companies including Seven Signal Verana and HP Cloud. 00:02:12.539 --> 00:02:16.584 We're gonna be talking about business challenges with accelerating chemical 00:02:16.584 --> 00:02:20.424 development, innovation, how AI fits into that and a whole lot more. 00:02:21.024 --> 00:02:22.614 Ned, welcome to The Chemical Show. 00:02:23.122 --> 00:02:24.502 Ned: Thank you for having me, Victoria. 00:02:25.254 --> 00:02:29.334 Victoria: So you've spent your career in technology and growth oriented firms. 00:02:29.754 --> 00:02:34.244 What got you started in this space and what ultimately brought you to NobleAI? 00:02:34.642 --> 00:02:36.172 Ned: Yeah, it's a good question. 00:02:36.222 --> 00:02:42.182 For me, I've always tried to work for Mission-driven companies, and 00:02:42.182 --> 00:02:44.012 Noble is a mission-driven company. 00:02:44.012 --> 00:02:49.532 We believe that AI can solve the world's problems better 00:02:49.537 --> 00:02:51.932 than it can destroy the world. 00:02:52.032 --> 00:02:53.832 And so we believe that that. 00:02:54.767 --> 00:02:59.967 This is, this is a fantastic way of, of really trying to solve health 00:02:59.972 --> 00:03:03.817 issues, environmental issues all sorts of different ways that we 00:03:03.817 --> 00:03:08.377 can, you know, get over what we're trying to, to fight every day. 00:03:09.829 --> 00:03:10.069 Victoria: Yeah. 00:03:10.099 --> 00:03:10.579 Makes sense. 00:03:10.579 --> 00:03:13.819 And I, and I like that mission driven space because I think we sometimes 00:03:14.059 --> 00:03:15.679 lose track of that in business. 00:03:16.079 --> 00:03:16.229 And 00:03:16.298 --> 00:03:17.081 yet it all starts 00:03:17.254 --> 00:03:19.079 out with a, a bigger purpose. 00:03:19.872 --> 00:03:20.472 Ned: You got it. 00:03:20.472 --> 00:03:21.342 That's exactly right. 00:03:21.347 --> 00:03:22.902 It helps us get up in the morning. 00:03:23.414 --> 00:03:24.074 Victoria: Absolutely. 00:03:24.344 --> 00:03:27.794 So the chemical industry it is definitely in a period of 00:03:27.794 --> 00:03:29.744 accelerated innovation, right? 00:03:29.844 --> 00:03:33.714 We're seeing this, in fact, you and I recently met up at ACI, 00:03:33.736 --> 00:03:35.444 One of their themes was innovation. 00:03:35.664 --> 00:03:42.169 So innovation is everywhere, and yet we're still challenged in commercializing these 00:03:42.169 --> 00:03:44.629 new ideas, new products, new innovations. 00:03:45.689 --> 00:03:49.589 So first of all, what trends do you really see driving innovation 00:03:49.619 --> 00:03:52.359 in the chemical space when you go out and talk with customers? 00:03:52.586 --> 00:03:56.726 . Ned: I would say one of the biggest ones, was sustainability. 00:03:57.226 --> 00:04:02.966 Sustainability has finally arrived and moved from sustainability washing 00:04:03.116 --> 00:04:06.416 to really putting budgets behind it. 00:04:06.846 --> 00:04:08.346 Again, a mission. 00:04:08.606 --> 00:04:14.056 Where companies can feel good about what they're doing and really driving 00:04:14.056 --> 00:04:16.306 sustainability through everything. 00:04:16.311 --> 00:04:20.326 And frankly, product development and, and optimization is a big one. 00:04:21.016 --> 00:04:22.696 That's the biggest one. 00:04:22.701 --> 00:04:27.466 But I would also say adapting and agility to regulatory issues. 00:04:27.516 --> 00:04:31.486 That was the other big focus and we hear this really from every 00:04:31.486 --> 00:04:35.386 single one of our customers, which is the world is changing fast. 00:04:36.056 --> 00:04:37.136 How do we adapt? 00:04:37.226 --> 00:04:38.786 How do we become more agile? 00:04:38.786 --> 00:04:43.226 How do we become proactive instead of reactive to a lot of these things? 00:04:43.466 --> 00:04:49.396 Those are the two biggest ones that we see kinda driving a lot of the the innovation. 00:04:49.728 --> 00:04:53.098 Victoria: Yeah, so I think the drivers are strong and as you say, they are 00:04:53.098 --> 00:04:57.298 mission-based in many ways, and yet there are still some challenges. 00:04:57.298 --> 00:05:00.523 What are the challenges that you see and, and that you guys are trying to tackle? 00:05:00.992 --> 00:05:01.742 Ned: Oh man. 00:05:01.792 --> 00:05:02.452 Let's see. 00:05:02.692 --> 00:05:04.367 The market's insatiable. 00:05:04.557 --> 00:05:10.507 The customer base wants more, wants it faster, wants it in different flavors, 00:05:10.507 --> 00:05:14.867 wants it in different ways packaged differently sustainably, of course. 00:05:15.267 --> 00:05:23.617 So that's really hard as well to try to get more from the existing 00:05:23.617 --> 00:05:27.367 team, sometimes even a smaller team because of economic issues. 00:05:27.817 --> 00:05:33.967 So the old adage of how do we do more with less is, is very, very present right now. 00:05:33.997 --> 00:05:36.247 So that's, that's a big challenge. 00:05:37.037 --> 00:05:40.547 The, the other challenges are that this stuff is really hard. 00:05:40.957 --> 00:05:45.127 You know, chemical development, material science development is hard. 00:05:45.127 --> 00:05:47.167 It's, it's multidimensional. 00:05:47.167 --> 00:05:49.807 It has an unbelievable amount of variables. 00:05:49.842 --> 00:05:56.212 Everything from price to market pressures to supply chain risk, personnel risk. 00:05:56.212 --> 00:05:57.532 This stuff is really hard. 00:05:57.532 --> 00:06:04.792 So how can technology like science-based AI help in these situations, and 00:06:04.792 --> 00:06:06.357 that's really why we're here. 00:06:06.683 --> 00:06:09.293 Victoria: And it seems to me, I mean, I think part of it, you talk 00:06:09.293 --> 00:06:12.473 about the people challenges and just the fact that it's really hard. 00:06:12.803 --> 00:06:17.753 I know what we're seeing is the continual graying of the chemical industry. 00:06:18.033 --> 00:06:20.343 Although some of us like better living through chemistry, so 00:06:20.343 --> 00:06:22.203 we wash that gray away and then 00:06:22.237 --> 00:06:22.812 what have you, 00:06:22.863 --> 00:06:23.133 right. 00:06:24.561 --> 00:06:24.841 Ned: Absolutely. 00:06:25.403 --> 00:06:28.883 Victoria: But I think what I'm hearing from people across the industry 00:06:28.883 --> 00:06:33.833 is a real concern that they're losing really experienced staff. 00:06:34.473 --> 00:06:34.703 Yeah, 00:06:34.953 --> 00:06:39.813 across the board, and it seems particularly in the product development 00:06:39.813 --> 00:06:43.983 and formulation development space because they're retiring, right? 00:06:43.983 --> 00:06:44.103 So 00:06:44.117 --> 00:06:44.553 they're just, 00:06:44.733 --> 00:06:49.163 they're, they're moving on to greener pastures or however we wanna say it. 00:06:49.463 --> 00:06:49.987 Um, and, 00:06:50.063 --> 00:06:53.983 you know, we, we just don't have the same knowledge base in the industry. 00:06:53.983 --> 00:06:58.963 And it seems like, AI is one of the ways that we can really harness 00:06:58.963 --> 00:07:03.883 and leverage some of that existing knowledge, even perhaps when the 00:07:03.883 --> 00:07:06.938 people that developed it have moved on. 00:07:07.491 --> 00:07:07.851 Ned: Yeah. 00:07:08.601 --> 00:07:09.321 Unbelievable. 00:07:09.321 --> 00:07:11.581 I thought there's two issues. 00:07:11.631 --> 00:07:13.431 Number one, you're absolutely right. 00:07:13.481 --> 00:07:16.851 Senior leaders are retiring, right? 00:07:16.851 --> 00:07:18.682 They've been in this industry for a very long time. 00:07:19.161 --> 00:07:24.441 But the other one, which is really interesting is that there's a talent war. 00:07:24.861 --> 00:07:27.141 And people are leaving. 00:07:27.141 --> 00:07:30.711 It's not the old days of sticking with your company and getting a 00:07:30.711 --> 00:07:32.751 gold watch at the end of 30 years. 00:07:33.281 --> 00:07:34.661 The world has changed. 00:07:34.661 --> 00:07:37.776 So how can companies de-risk? 00:07:38.541 --> 00:07:41.961 You know, the personnel flight, the brain drain as they call it. 00:07:42.011 --> 00:07:46.896 So is there a way to really mimic or really institutionalize or 00:07:46.896 --> 00:07:49.686 codify that institutional knowledge 00:07:49.882 --> 00:07:52.701 and not be at risk when that person walks out the door. 00:07:52.771 --> 00:07:55.511 That's also a really interesting conversation that 00:07:55.511 --> 00:07:56.561 we're having with customers. 00:07:57.116 --> 00:07:58.296 So, you're a hundred percent right there. 00:07:58.823 --> 00:07:59.183 Victoria: Right. 00:07:59.483 --> 00:08:03.943 So I mean, you guys are at the, the front end of moving AI into 00:08:03.948 --> 00:08:07.183 product development and developing solutions to support the industry. 00:08:07.453 --> 00:08:10.238 What do you see as the role and what are the conversations that 00:08:10.238 --> 00:08:14.798 you're having about how people want to integrate AI in this space? 00:08:16.116 --> 00:08:16.656 Ned: Yeah. 00:08:16.706 --> 00:08:20.576 The best thing that's happened to us is generative AI, right? 00:08:20.606 --> 00:08:25.046 You know, the chat GPTs and the Geminis and all of these others have 00:08:25.046 --> 00:08:27.056 really brought it to the forefront. 00:08:27.368 --> 00:08:31.418 Now remember, artificial intelligence has been around 30 years longer 00:08:31.418 --> 00:08:33.008 than the internet has been around. 00:08:33.008 --> 00:08:36.938 So just just to give people an understanding, this is 00:08:36.938 --> 00:08:39.188 not, it's 15 minutes of fame. 00:08:39.518 --> 00:08:41.378 It's been around for a long time. 00:08:41.378 --> 00:08:46.958 It just now has, because of the internet, has been able to take all of this data, 00:08:47.288 --> 00:08:49.598 the oceans of data that they can mine. 00:08:50.693 --> 00:08:55.683 What's great for creating speeches and my kids' homework doesn't 00:08:55.683 --> 00:09:00.423 work for science necessarily because there's not a lot of data. 00:09:00.423 --> 00:09:04.983 There's not an ocean's amount of data because we're trying to create things. 00:09:05.163 --> 00:09:08.614 And so, you know, if it were that easy, everybody would do it. 00:09:08.853 --> 00:09:15.893 We have to be able to help our customers drive new innovation optimize 00:09:15.923 --> 00:09:21.143 current products with not a lot of data or completely spread out data. 00:09:21.223 --> 00:09:27.673 For us, this notion of specialized AI, or science-based AI, especially 00:09:27.673 --> 00:09:32.503 in chemical and material science is really critical because we can 00:09:32.503 --> 00:09:34.843 do a lot with very little data. 00:09:35.083 --> 00:09:38.773 We can be very prescriptive in what problems we we're being 00:09:38.773 --> 00:09:43.443 asked to solve and really be able to accelerate based on that. 00:09:43.713 --> 00:09:47.743 That's a big piece of, , it's great that we have generative AI 00:09:47.773 --> 00:09:52.003 but you know, specifically focused in this world that you and I live 00:09:52.008 --> 00:09:54.313 in, it has to be specialized. 00:09:54.555 --> 00:09:54.945 Victoria: Right. 00:09:55.065 --> 00:09:55.245 Yeah. 00:09:55.245 --> 00:09:55.875 It's interesting. 00:09:55.875 --> 00:09:57.705 So you say that there's not a lot of data. 00:09:57.705 --> 00:10:00.465 I think people would assume we are awash with data. 00:10:00.685 --> 00:10:06.145 And certainly it's true in, when I think about chemical companies and just 00:10:06.145 --> 00:10:09.925 their overall business and business operations, we have a ton of data 00:10:09.925 --> 00:10:11.477 about customers, we have a ton of data 00:10:11.477 --> 00:10:15.775 ton of data about manufacturing we probably have a lot of it about 00:10:15.775 --> 00:10:17.095 product and product development. 00:10:17.245 --> 00:10:21.460 Although I will say, I reflect back and think about my time in industry, 00:10:21.460 --> 00:10:25.450 when I worked really closely with our formulation guys to help get new 00:10:25.455 --> 00:10:29.590 products out into market and stuff, I was sometimes shocked by like how many 00:10:29.590 --> 00:10:31.450 data points, how few data points were 00:10:31.494 --> 00:10:32.034 actually on 00:10:32.140 --> 00:10:33.520 a curve or on a graph or what 00:10:33.594 --> 00:10:33.850 have you. 00:10:33.850 --> 00:10:34.180 So 00:10:34.510 --> 00:10:37.770 it's it's interesting twist on this 'cause I sometimes think. 00:10:38.790 --> 00:10:43.030 We feel like there's just all this data and yet maybe it's not always 00:10:43.030 --> 00:10:45.160 the right data or in the right place 00:10:45.850 --> 00:10:46.660 at the right time. 00:10:47.033 --> 00:10:47.573 Ned: All of it. 00:10:47.753 --> 00:10:48.353 Absolutely. 00:10:48.353 --> 00:10:49.733 All, all of the above. 00:10:49.793 --> 00:10:51.913 You're right, these companies have been around for. 00:10:52.513 --> 00:10:55.753 You know, some a hundred years and you would think that the data 00:10:55.763 --> 00:10:58.853 is A, there and B accessible. 00:10:59.243 --> 00:11:01.733 And very often it's not. 00:11:01.793 --> 00:11:03.683 And neither one or one of them. 00:11:03.683 --> 00:11:07.973 And it's, it's spread all over the world in different lab notebooks, physical 00:11:07.973 --> 00:11:09.683 and electronic, maybe electronic. 00:11:11.123 --> 00:11:12.713 It's very, very difficult. 00:11:12.853 --> 00:11:18.858 And so, how do we help customers get started without having to rake the ocean 00:11:18.948 --> 00:11:21.258 full of ones and zeros to get started? 00:11:21.758 --> 00:11:27.548 That's really where building that science into the AI right from 00:11:27.548 --> 00:11:32.228 the get go alleviates a lot of that need for a lot of that data. 00:11:32.228 --> 00:11:34.748 Because we already know a lot of that information. 00:11:34.748 --> 00:11:39.968 We can build it in institutionally and really accelerate that development. 00:11:40.900 --> 00:11:41.890 Victoria: So what does that look like? 00:11:41.890 --> 00:11:43.330 So say more a little bit about that. 00:11:43.330 --> 00:11:44.170 'cause I think it 00:11:44.192 --> 00:11:44.650 stills 00:11:44.770 --> 00:11:47.020 kinda, you know, 20,000 miles 00:11:47.064 --> 00:11:47.569 up in the, 00:11:47.740 --> 00:11:48.670 the atmosphere. 00:11:48.990 --> 00:11:51.470 You know, what does it mean to be science-based? 00:11:51.470 --> 00:11:52.350 Is it generative? 00:11:52.402 --> 00:11:53.347 this generative? 00:11:53.557 --> 00:11:55.507 And how does it work? 00:11:56.135 --> 00:11:56.645 Ned: Yeah. 00:11:56.745 --> 00:12:04.245 So it's, it is generative in a way because we can generate new insights, 00:12:04.605 --> 00:12:09.915 but fundamentally, when we talk about specialized AI or science-based AI, we're 00:12:09.915 --> 00:12:16.770 building the fundamentals of physics and chemistry into the models that we 00:12:16.770 --> 00:12:19.140 build with our customers in partnership. 00:12:20.130 --> 00:12:23.790 I always like to say for the senior executives who aren't necessarily 00:12:23.790 --> 00:12:28.440 scientists, and I don't have a scientific background, you know, elephants don't fly. 00:12:28.440 --> 00:12:30.450 We all know elephants don't fly. 00:12:30.510 --> 00:12:35.070 But with, with commercial AI, something you get off the shelf, 00:12:35.640 --> 00:12:39.630 you have to train it, that elephants don't fly and therefore. 00:12:40.105 --> 00:12:41.035 It takes time. 00:12:41.035 --> 00:12:44.305 And then a lot of the answers early on you get, well wait a second, 00:12:44.305 --> 00:12:45.535 this doesn't make any sense. 00:12:45.535 --> 00:12:47.185 Why are we even going down this path? 00:12:47.555 --> 00:12:48.305 So they give up. 00:12:48.755 --> 00:12:54.155 Where by building all of that knowledge upfront into the models and then using 00:12:54.160 --> 00:13:00.625 different models, solving different problems that really accelerates 00:13:00.625 --> 00:13:05.095 the insights and that gets us to, even in the first rudimentary models 00:13:05.095 --> 00:13:06.595 that we build with our partners. 00:13:08.070 --> 00:13:09.510 There are aha moments. 00:13:09.540 --> 00:13:14.250 We've, we've solved some very fundamental problems for customers that have been 00:13:14.250 --> 00:13:18.870 struggling with, you know, maybe it's a PFAS chemical that they're trying to 00:13:18.930 --> 00:13:20.850 get out of one of their formulations. 00:13:21.180 --> 00:13:25.440 We were able to, you know, give them insights within 30 days, something 00:13:25.445 --> 00:13:29.520 that they've been trying for years to solve, or at least, you know, the last 00:13:29.520 --> 00:13:34.200 two years, within 30 days, we gave them directionally approaches to head to. 00:13:34.795 --> 00:13:38.875 So by building that institutional and, and that scientific knowledge 00:13:38.935 --> 00:13:43.855 into those models early on, we really accelerate that. 00:13:43.860 --> 00:13:46.555 And then we can train those models as we continue. 00:13:46.885 --> 00:13:52.255 And then customers use them once they're mature to drive insights to 00:13:52.285 --> 00:13:56.285 really do a lot of testing that they wouldn't otherwise be able 00:13:56.285 --> 00:13:58.725 to do on a bench, if you will. 00:13:59.302 --> 00:13:59.542 Victoria: yeah. 00:13:59.962 --> 00:14:00.412 Makes sense. 00:14:00.632 --> 00:14:00.992 Yeah. 00:14:01.142 --> 00:14:01.382 Yeah. 00:14:01.382 --> 00:14:02.552 30 days seems fast. 00:14:02.552 --> 00:14:02.792 I know. 00:14:02.792 --> 00:14:06.122 In the world of chat, GPT, if I have to wait longer than 30 seconds 00:14:06.173 --> 00:14:06.848 for my answer, 00:14:06.962 --> 00:14:08.762 it seems like it's taking a long time. 00:14:08.762 --> 00:14:12.192 But, but to your point, this is I guess a much more rigorous 00:14:12.822 --> 00:14:14.202 approach as needed, right? 00:14:14.202 --> 00:14:17.412 I mean, it has to be rooted in the scientific principles, whether 00:14:17.412 --> 00:14:18.792 it be chemistry or physics or 00:14:18.873 --> 00:14:19.653 material science 00:14:19.782 --> 00:14:20.952 to, to make that happen. 00:14:21.295 --> 00:14:21.535 Ned: right. 00:14:21.535 --> 00:14:24.295 We're, we're dealing with scientists, by the way, so, you 00:14:24.295 --> 00:14:25.735 know, they, they want facts. 00:14:25.735 --> 00:14:25.915 They 00:14:26.112 --> 00:14:27.093 Victoria: They want facts and, 00:14:27.192 --> 00:14:29.292 and they're probably a little bit risk averse. 00:14:29.862 --> 00:14:31.143 So let's, let's talk 00:14:31.152 --> 00:14:32.142 about those risks. 00:14:32.142 --> 00:14:32.322 So 00:14:32.358 --> 00:14:32.868 what, you know, I 00:14:32.922 --> 00:14:37.092 think what are the risks that you guys see when, or that you talk 00:14:37.097 --> 00:14:40.302 about and you work to alleviate with your clients when you think about 00:14:40.307 --> 00:14:42.102 using AI and product development? 00:14:42.505 --> 00:14:42.925 Ned: Yeah. 00:14:43.915 --> 00:14:49.915 So honestly, the risk that we see our people are gripped by this notion of 00:14:49.915 --> 00:14:52.225 not having all the data in one place. 00:14:52.405 --> 00:14:57.930 And honestly it's a bit of the boogeyman that, you know, part of the industry who 00:14:57.930 --> 00:15:02.680 is about trying to collect all of your data in one place before you get started. 00:15:02.780 --> 00:15:04.040 That's their message. 00:15:04.040 --> 00:15:09.770 And the reality of it is, is that the risk of not getting started now. 00:15:10.295 --> 00:15:14.345 You're allowing your competitors to, to distance themselves from you, right? 00:15:14.345 --> 00:15:18.665 To either gain the edge or to expand if, if you can't do that, you know, 00:15:18.670 --> 00:15:22.715 part of the reason why we do a lot of work with mid-size companies is because 00:15:22.715 --> 00:15:29.285 they can't throw money and bodies at this internally, and they have to do 00:15:30.395 --> 00:15:34.805 what they have to do in terms of closing the gap with those big companies. 00:15:34.895 --> 00:15:39.545 And we see that every day and they genuinely see AI as. 00:15:40.640 --> 00:15:45.440 Both a panacea, so we have to kind of temper their enthusiasm, but also give 00:15:45.440 --> 00:15:50.570 them the true value, you know, vision of what it can really do for them. 00:15:50.570 --> 00:15:54.470 So the risks, getting back to your question, are just getting started. 00:15:54.620 --> 00:15:55.370 That's one. 00:15:55.955 --> 00:16:01.085 The other risk is the industry has spent a hundred years hugging 00:16:01.085 --> 00:16:06.215 their IP and not allowing it out into the world because genuinely 00:16:06.215 --> 00:16:07.835 that is the keys to their kingdom. 00:16:07.835 --> 00:16:12.785 How do they work with partners, feel comfortable about working 00:16:12.785 --> 00:16:16.025 with partners, but still have the. 00:16:16.330 --> 00:16:21.810 Security literal and figurative, figuratively, to be able 00:16:21.810 --> 00:16:25.260 to really collaborate with people outside their four walls. 00:16:25.260 --> 00:16:28.800 And that's a, that's a perceived risk as well, right? 00:16:28.980 --> 00:16:31.680 The cloud industry had to go through this, right? 00:16:31.685 --> 00:16:33.770 It's all of our data needs to be here. 00:16:33.775 --> 00:16:38.060 And then people realize that AWS and Azure are probably even more 00:16:38.060 --> 00:16:40.220 secure than your own network itself. 00:16:40.220 --> 00:16:42.590 So, these are early days. 00:16:42.680 --> 00:16:46.395 Those are the, those are the challenges that we work through with our customers. 00:16:47.512 --> 00:16:48.142 Victoria: I can see that. 00:16:48.142 --> 00:16:52.942 And certainly the ip, the intellectual property and data privacy is, is 00:16:52.942 --> 00:16:54.712 probably the thing I hear the most. 00:16:54.952 --> 00:16:55.218 Um, in 00:16:55.222 --> 00:16:59.672 many ways it's maybe the most misunderstood in my opinion. 00:16:59.677 --> 00:17:02.572 And, and I've done some work around this some folks that. 00:17:03.172 --> 00:17:06.712 You know, there's this perception of, oh, if I, if I put it out there, 00:17:06.712 --> 00:17:08.122 it's there for the public domain, 00:17:08.291 --> 00:17:08.482 Right, 00:17:08.482 --> 00:17:08.826 It's like, well, 00:17:08.842 --> 00:17:10.642 no, no, no, there are firewalls. 00:17:10.642 --> 00:17:15.042 And by the way, don't put your don't put your test data into chat GPT 00:17:15.047 --> 00:17:18.852 because chat GPT is open domain, right? 00:17:18.857 --> 00:17:20.382 So buyer beware. 00:17:20.432 --> 00:17:22.742 But there are other, I mean, heck, there's a version of chat that 00:17:22.747 --> 00:17:23.792 you can buy that's private and 00:17:23.956 --> 00:17:24.511 obviously 00:17:24.512 --> 00:17:28.022 when, if you are working with a company like Noble, there's firewalls 00:17:28.022 --> 00:17:30.122 and privacy protections to protect. 00:17:30.307 --> 00:17:31.027 All that data. 00:17:31.440 --> 00:17:31.770 Ned: Yeah. 00:17:31.770 --> 00:17:33.330 I'll, I'll even go one further. 00:17:33.330 --> 00:17:34.740 So you're a hundred percent right. 00:17:35.670 --> 00:17:39.541 There's the risk on the generative AI side that you are putting all of your. 00:17:40.335 --> 00:17:42.105 Your information out on the internet. 00:17:43.125 --> 00:17:47.115 So our customers do have and are working through these 00:17:47.115 --> 00:17:48.915 policies for their employees. 00:17:48.915 --> 00:17:54.655 So that should be, that should be looked at where science-based 00:17:54.660 --> 00:17:58.225 AI folks like NobleAI work. 00:17:58.705 --> 00:18:02.035 Is within their customers domain. 00:18:02.035 --> 00:18:07.895 So we have the ability to build these models and serve them to our 00:18:07.900 --> 00:18:10.985 customers within their private cloud. 00:18:11.495 --> 00:18:17.435 So that's a big differentiator for us because we feel like, yes, we're 00:18:17.435 --> 00:18:21.815 not gonna try to change the hearts and minds about people's ip, right? 00:18:21.815 --> 00:18:23.375 It's, we don't have enough time. 00:18:23.640 --> 00:18:26.250 To, to try to, to create a sea change there. 00:18:26.250 --> 00:18:31.740 So for us, we feel like we, we have the ability to work within that. 00:18:32.145 --> 00:18:34.905 You know, that framework, and that's been very successful. 00:18:34.905 --> 00:18:41.325 The second thing is, the other challenge is in the AI world is this notion 00:18:41.330 --> 00:18:44.455 of, of who owns the models, right? 00:18:44.455 --> 00:18:46.795 Who owns this ip, right? 00:18:46.795 --> 00:18:51.655 Is it the, is it the AI company or is it the chemical company 00:18:51.655 --> 00:18:53.155 that's bringing that data? 00:18:53.605 --> 00:18:57.235 And historically, all of the last five to 10 years. 00:18:58.075 --> 00:19:00.385 The AI companies have said, oh, no, no. 00:19:00.385 --> 00:19:01.615 Those are our models. 00:19:01.675 --> 00:19:02.875 Those are our models. 00:19:02.880 --> 00:19:08.755 And so it's really set up to be this very confrontational you know, is it ours? 00:19:08.755 --> 00:19:10.585 Is it theirs, is it co-owned? 00:19:10.585 --> 00:19:11.635 How do we do that? 00:19:11.640 --> 00:19:14.695 If we wanna publish it, I mean, it, it can become a nightmare. 00:19:15.385 --> 00:19:17.245 We've taken a different approach. 00:19:17.245 --> 00:19:24.245 We, we build customized models specific to our customers, and they own those 00:19:24.245 --> 00:19:30.995 models because for us, it's, it's important that they can build from there. 00:19:30.995 --> 00:19:31.835 It's theirs. 00:19:32.170 --> 00:19:35.320 I always use the, the analogy of. 00:19:36.315 --> 00:19:39.405 Steven Spielberg writes a screenplay. 00:19:39.775 --> 00:19:41.095 He wants a movie made. 00:19:41.095 --> 00:19:43.405 He goes and raises money and go, gets it made. 00:19:43.405 --> 00:19:45.145 He takes it to a filmmaker. 00:19:45.145 --> 00:19:46.225 He takes it to, 00:19:46.615 --> 00:19:46.686 you 00:19:46.735 --> 00:19:53.340 know Skywalker Rancher and they own the way the movie gets made, 00:19:53.430 --> 00:19:57.630 the special effects and all of the different ways that it becomes a 00:19:57.990 --> 00:19:59.880 fabulous Steven Spielberg movie. 00:20:00.300 --> 00:20:02.040 Steven Spielberg owns that movie. 00:20:02.250 --> 00:20:04.890 The movie company doesn't own that movie. 00:20:04.890 --> 00:20:07.590 And so we, that's our approach. 00:20:07.590 --> 00:20:10.920 We feel as though, and our customers appreciate that because 00:20:11.280 --> 00:20:12.690 they don't have to focus on. 00:20:13.285 --> 00:20:16.705 Oh my God, are they gonna turn around and sell this to a competitor? 00:20:16.705 --> 00:20:20.255 Which is obviously in business a very real situation. 00:20:20.255 --> 00:20:24.045 So that's, that's how this industry is starting to evolve. 00:20:24.045 --> 00:20:25.905 We feel like we're on the forefront of it. 00:20:26.242 --> 00:20:28.342 Victoria: Yeah, that ownership risk, that's great. 00:20:28.492 --> 00:20:29.392 The other thing. 00:20:29.902 --> 00:20:35.512 That I hear, and this is a widespread concern regarding all AI and all 00:20:35.512 --> 00:20:40.132 generative AI, is that we're using a limited data set and that we're 00:20:40.132 --> 00:20:46.912 just kind of creating this very narrow spiral based on limited data. 00:20:46.917 --> 00:20:50.092 And once it gets skewed, the truth gets skewed. 00:20:51.125 --> 00:20:51.275 Ned: Yeah. 00:20:51.275 --> 00:20:53.195 The bias, the bias comes in. 00:20:53.705 --> 00:20:54.095 Yeah. 00:20:54.095 --> 00:21:01.815 That bias is always a, an omnipresent thought around building these models in 00:21:01.815 --> 00:21:07.665 AI, it's the, it is one of the biggest reasons why companies should be partnering 00:21:07.670 --> 00:21:10.995 with companies like NobleAI because. 00:21:12.495 --> 00:21:15.405 Institutional bias happens within the same four walls. 00:21:15.405 --> 00:21:21.075 It's the same people building these models, and they, and they own that box. 00:21:21.225 --> 00:21:26.845 Now, AI does a great job of, especially science-based AI and specialized 00:21:26.845 --> 00:21:32.700 AI to broaden your horizons, right, your design space, but. 00:21:34.015 --> 00:21:39.545 Bringing in people with external experiences, a from either people 00:21:39.545 --> 00:21:42.665 within the industry or even better yet, people outside the industry. 00:21:42.915 --> 00:21:45.075 What are the people in oil and gas doing? 00:21:45.075 --> 00:21:48.115 Although it's related, upstream is very different. 00:21:48.175 --> 00:21:51.145 What, how, what are they, what are they doing in exploration? 00:21:51.245 --> 00:21:53.165 What are people doing for alternative energy? 00:21:53.165 --> 00:21:56.255 Is there something there that we can bring to the packaging industry? 00:21:56.525 --> 00:22:00.635 What are people doing in, in Biosynthetics and, and what can we bring to that? 00:22:01.145 --> 00:22:06.905 So, you're right, that's if you are trying to do it internally and 00:22:06.905 --> 00:22:12.105 without multiple different ways of building these models with NobleAI. 00:22:12.515 --> 00:22:16.930 I think for our, our domain of different models, we've got over 00:22:16.930 --> 00:22:23.760 45 different ways of building models that could all be combined. 00:22:23.790 --> 00:22:27.510 It's not just kind of repurposing the same model over and over. 00:22:27.780 --> 00:22:29.280 'cause that generates bias. 00:22:29.777 --> 00:22:30.137 Victoria: Yeah. 00:22:30.377 --> 00:22:30.677 Yeah. 00:22:30.737 --> 00:22:34.817 And I suppose once a solution is identified, let's just say you, 00:22:34.817 --> 00:22:36.377 you brought up the PFAS example. 00:22:36.377 --> 00:22:40.517 Once the new alternative formulation that replaces PFAS is identified, 00:22:40.877 --> 00:22:42.497 there's still lab work that gets 00:22:42.578 --> 00:22:42.707 done. 00:22:42.707 --> 00:22:43.163 There's all kinds 00:22:43.307 --> 00:22:43.937 of testing. 00:22:43.937 --> 00:22:46.547 And so new data is created 00:22:47.370 --> 00:22:47.970 Ned: absolutely. 00:22:48.042 --> 00:22:48.917 Victoria: into the model. 00:22:48.917 --> 00:22:51.527 As long as I guess it, you know, you understand where it goes in 00:22:51.927 --> 00:22:55.317 and to your point, there's, there's always biases always existed in, 00:22:55.570 --> 00:22:57.970 Ned: Always existed even more so without 00:22:58.122 --> 00:22:59.232 Victoria: let's just say it's in 00:22:59.500 --> 00:23:00.130 Ned: well, yeah. 00:23:00.190 --> 00:23:01.540 I mean, even before, right? 00:23:01.545 --> 00:23:05.290 I mean, you have your scientists who are brilliant, but they know what they know. 00:23:05.395 --> 00:23:08.205 That automatically instills bias. 00:23:08.205 --> 00:23:13.225 But you brought up a very good point which is this notion that 00:23:13.225 --> 00:23:16.895 AI is going to wash away jobs. 00:23:17.645 --> 00:23:22.925 That may be the case in other industries, but absolutely not in our space. 00:23:23.185 --> 00:23:26.035 The need for, first of all, these are scientists. 00:23:26.035 --> 00:23:26.755 They don't trust. 00:23:27.215 --> 00:23:29.975 Anything they've gotta verify, they've gotta double verify, 00:23:29.975 --> 00:23:30.995 they gotta triple verify. 00:23:30.995 --> 00:23:37.175 So whatever we do in silico on the computer is going to be wet verified. 00:23:37.175 --> 00:23:41.615 It's gotta be verified in a lab that, yes, this makes sense, I'm 00:23:41.620 --> 00:23:47.525 replicating this and therefore this gets me to my ultimate goal faster. 00:23:47.935 --> 00:23:49.645 So we are not seeing that at all. 00:23:49.645 --> 00:23:54.455 What we see is the advent of doing. 00:23:55.130 --> 00:23:58.280 A lot more, a lot faster. 00:23:58.340 --> 00:24:01.640 They have moonshot projects that have been on our whiteboard for 00:24:01.640 --> 00:24:03.260 two years and haven't moved. 00:24:03.260 --> 00:24:06.110 And you know, there's a sign that says Do not erase. 00:24:06.115 --> 00:24:08.810 And you know, people have left that, but it's always stayed 00:24:08.810 --> 00:24:10.340 in the upper left hand corner. 00:24:10.700 --> 00:24:13.190 Now these things are starting to get pulled into view. 00:24:13.460 --> 00:24:16.910 These same folks, they're not losing their jobs by any stretch. 00:24:16.910 --> 00:24:18.650 They now get to work on. 00:24:19.700 --> 00:24:22.400 Four times the amount of projects than they ever had, 00:24:22.400 --> 00:24:24.380 so that's been very exciting. 00:24:24.712 --> 00:24:25.192 Victoria: That's cool. 00:24:25.462 --> 00:24:26.152 That's very cool. 00:24:26.332 --> 00:24:30.232 So, so this is maybe a good segue to our next topic, which is really around 00:24:30.232 --> 00:24:34.402 customers and customer acceptance, maybe even the customer experience. 00:24:34.407 --> 00:24:34.702 And I know 00:24:34.776 --> 00:24:35.362 that Ned, you're 00:24:35.722 --> 00:24:38.512 an expert in sales and business development and that's the 00:24:38.512 --> 00:24:39.532 role that you've played, 00:24:39.802 --> 00:24:42.892 um, with a number of companies really helping drive that 00:24:42.892 --> 00:24:44.302 customer and that value. 00:24:44.992 --> 00:24:49.012 Um, and I know that you're out talking to chemical companies and people 00:24:49.012 --> 00:24:50.662 across the value chain every day. 00:24:51.362 --> 00:24:56.612 What are your customers excited and and or concerned about when 00:24:56.612 --> 00:25:01.142 they think about bringing in an AI based solution to their company? 00:25:01.591 --> 00:25:06.796 Ned: I would say the, the folks who are looking inside the operational folks 00:25:06.796 --> 00:25:09.226 are worried about disruption, right? 00:25:09.226 --> 00:25:10.846 Transformation is scary. 00:25:11.186 --> 00:25:15.026 And so that, that's number one. 00:25:16.076 --> 00:25:23.786 Number two is do we really have the people in house that can leverage it? 00:25:24.266 --> 00:25:28.291 It's not just enough for a partner to hand this to us and run with it. 00:25:28.321 --> 00:25:30.331 We have to have the people that can run with it. 00:25:30.451 --> 00:25:34.931 And very often there is some change over there. 00:25:35.031 --> 00:25:43.031 But I would say for the most part ultimately because it is new the finance 00:25:43.031 --> 00:25:45.731 folks can't really qualify it right? 00:25:45.731 --> 00:25:47.321 Or quantify it actually. 00:25:47.451 --> 00:25:51.111 Therefore, it becomes this, are we risk averse 00:25:51.761 --> 00:25:53.321 are we really ready for this? 00:25:53.771 --> 00:25:59.476 So my job as a business development person and my team is really 00:25:59.476 --> 00:26:04.036 there to help them understand what the business value is, right? 00:26:04.036 --> 00:26:07.676 The scientific value, I think is pretty demonstrable. 00:26:07.986 --> 00:26:14.736 The economic value is really where the senior executives want to be able 00:26:14.736 --> 00:26:19.056 to sign off on it, but they're not necessarily willing to jump into the 00:26:19.061 --> 00:26:21.186 deep end of the pool without some, 00:26:21.542 --> 00:26:24.542 Either a reference or, you know, some business case built. 00:26:24.542 --> 00:26:27.957 So we spent a lot of time, you know, what, what would an 00:26:27.957 --> 00:26:29.967 acceleration of this project or. 00:26:31.242 --> 00:26:33.992 Financially for you what are the risks that you're seeing 00:26:33.992 --> 00:26:35.282 now from a supply chain? 00:26:35.282 --> 00:26:38.312 We have one customer who had to take a product off the market 00:26:38.312 --> 00:26:41.102 for eight weeks because one of their small little chemicals. 00:26:41.102 --> 00:26:41.252 Yeah. 00:26:41.252 --> 00:26:42.422 Eight weeks is a major 00:26:42.799 --> 00:26:43.429 Victoria: That's a lot. 00:26:43.609 --> 00:26:43.909 Yeah. 00:26:43.987 --> 00:26:44.432 Ned: a lot. 00:26:44.432 --> 00:26:46.142 I mean, so it's millions of dollars. 00:26:46.142 --> 00:26:50.702 And so when you have that and it's visceral like that is. 00:26:51.712 --> 00:26:53.707 You, you get to figure that out pretty quickly. 00:26:53.807 --> 00:26:57.817 But there are others that are just trying to figure this stuff out. 00:26:58.583 --> 00:27:02.033 business wise, it has to move a needle, right? 00:27:02.033 --> 00:27:05.483 We always talk about it's gotta save money, it's gotta make money, it's 00:27:05.483 --> 00:27:07.493 gotta de-risk, or it's gotta transform. 00:27:07.883 --> 00:27:13.163 If you can't do two of the four, then, you know, probably shouldn't do it. 00:27:13.355 --> 00:27:14.133 Victoria: Yeah, action then. 00:27:14.421 --> 00:27:15.741 Ned: that exactly right. 00:27:16.233 --> 00:27:17.643 Victoria: Who usually brings you in? 00:27:17.883 --> 00:27:18.693 Where does that happen? 00:27:18.693 --> 00:27:19.443 Does that happen at. 00:27:20.823 --> 00:27:24.183 The, you know, at the the lab level, let's just say, or the product 00:27:24.183 --> 00:27:27.633 development guys, is it, is it the executive team that says, oh yeah, we 00:27:27.633 --> 00:27:28.983 know we need to do something different. 00:27:28.983 --> 00:27:33.333 Where do you see, how do you guys normally enter an organization? 00:27:33.723 --> 00:27:37.243 And then, we kind of touched on this, there's obviously different 00:27:37.303 --> 00:27:40.273 organizational priorities depending on where you sit and what you're 00:27:40.279 --> 00:27:40.963 looking at. 00:27:41.413 --> 00:27:42.943 How do you bridge those gaps? 00:27:44.531 --> 00:27:50.021 Ned: Yeah, we just met with the CEO of a very, very large Fortune 1000, maybe even 00:27:50.021 --> 00:27:55.871 500 CEO and his entire executive team, and they broke it out into four stages and 00:27:55.971 --> 00:28:00.441 research and development for a chemical company has a very tall pole in that 00:28:00.441 --> 00:28:03.601 tent, so is manufacturing and engineering. 00:28:03.601 --> 00:28:08.791 So they bucket their priorities based on. 00:28:10.361 --> 00:28:11.171 Revenue. 00:28:11.231 --> 00:28:11.651 Right? 00:28:11.681 --> 00:28:14.981 I mean, that's ultimately, especially if you're a publicly traded company, 00:28:15.186 --> 00:28:17.681 it's, it's, it's what moves the needle. 00:28:18.161 --> 00:28:20.231 So who brings us in? 00:28:20.231 --> 00:28:24.431 To get back to your question, number one is very often it 00:28:24.436 --> 00:28:27.041 will be a product development. 00:28:27.041 --> 00:28:27.101 I. 00:28:27.581 --> 00:28:32.531 Manager, someone who is either behind the eight ball on their 00:28:32.531 --> 00:28:34.991 product development goals, right? 00:28:34.991 --> 00:28:37.391 Their product is delayed, it's over budget. 00:28:37.751 --> 00:28:39.521 Those are the folks who have the budget. 00:28:40.151 --> 00:28:43.511 But they're not the ones who can go and run these experiments. 00:28:43.511 --> 00:28:48.321 They're the ones who then have to bring us into the data science teams 00:28:48.321 --> 00:28:50.211 or the r and d teams very often. 00:28:50.211 --> 00:28:52.881 We need all three of those to get consensus. 00:28:53.526 --> 00:28:57.066 It's a challenge in my world of, of selling and, and business 00:28:57.066 --> 00:29:00.426 development because you do cross all of these domains, right? 00:29:00.426 --> 00:29:04.056 If you're selling IT security, you get to sell to the security team and 00:29:04.366 --> 00:29:07.216 it's a pretty linear path for us. 00:29:07.276 --> 00:29:09.706 It cuts across all business lines. 00:29:09.711 --> 00:29:14.096 It cuts across manufacturing, it cuts across engineering, from how do we 00:29:14.096 --> 00:29:15.926 get from the bench to the market? 00:29:16.196 --> 00:29:18.926 either the r and d team brings us to product development. 00:29:18.986 --> 00:29:22.106 Product development brings us to the r and d teams, the two. 00:29:22.526 --> 00:29:26.036 But nothing happens until we're speaking with senior executives 00:29:26.036 --> 00:29:29.276 because they're the folks who are looking outside the boat, as we say. 00:29:29.556 --> 00:29:31.626 And they're looking for icebergs. 00:29:31.926 --> 00:29:36.816 They're the ones who are saying, how can we get more gas into this engine? 00:29:36.816 --> 00:29:41.096 How can we do it without, without hiring a boatload of people without 00:29:41.096 --> 00:29:43.136 spending unbelievable amounts. 00:29:43.376 --> 00:29:45.806 Spending millions to make millions doesn't make sense. 00:29:45.866 --> 00:29:50.951 And so they're the folks who ultimately we need to get to, to really drive this. 00:29:51.898 --> 00:29:53.068 Victoria: Yeah, makes sense. 00:29:53.068 --> 00:29:56.458 And I mean, ultimately they hold the purse strings and make the big decisions. 00:29:56.758 --> 00:29:59.608 It also strikes me, Ned, that there is there's a real need for 00:29:59.613 --> 00:30:05.833 change management because this is a change in business processes, 00:30:06.103 --> 00:30:06.694 um, 00:30:06.823 --> 00:30:10.603 that have probably been in place, as you say, you know, maybe for 00:30:10.603 --> 00:30:12.013 a hundred years in some cases. 00:30:12.343 --> 00:30:16.513 And it's a change that hits, interestingly, not just r and d 00:30:16.513 --> 00:30:20.113 and product development, but it's also a marketing effort and it's 00:30:20.383 --> 00:30:20.914 potentially 00:30:21.103 --> 00:30:23.328 a manufacturing and engineering effort. 00:30:23.748 --> 00:30:25.848 How do you see this playing out? 00:30:25.908 --> 00:30:32.388 The, the change management of introducing really a significant new tool and, and 00:30:32.388 --> 00:30:35.608 new approach to chemical innovation 00:30:35.682 --> 00:30:36.203 in a company. 00:30:36.986 --> 00:30:39.116 Ned: Yeah, it's really interesting. 00:30:39.146 --> 00:30:42.656 Some of this is just absolutely institutional. 00:30:42.996 --> 00:30:46.986 So part of what we work through is are they culturally 00:30:46.986 --> 00:30:49.936 ready to really adopt a change. 00:30:49.966 --> 00:30:50.866 Now we do it. 00:30:51.391 --> 00:30:54.211 In a crawl, walk, run methodology. 00:30:54.211 --> 00:30:56.971 So we're not asking people to burn the boats and the bridges 00:30:56.971 --> 00:30:58.501 and adopt this new path. 00:30:58.921 --> 00:31:03.301 So we, we, we bring them along on this journey, but you're right, 00:31:03.301 --> 00:31:06.136 it's everything from administrative. 00:31:06.166 --> 00:31:10.036 How do we deal with people getting access to our systems? 00:31:10.366 --> 00:31:13.036 How do we give them access to our data? 00:31:13.591 --> 00:31:17.251 Do we want to, who, how do we minimize that scope? 00:31:17.551 --> 00:31:19.681 All sorts of different processes. 00:31:20.241 --> 00:31:24.651 That's why this crawl, walk, run definitely works because they get 00:31:24.651 --> 00:31:29.121 the taste of it, they see the value of doing one or two projects, and 00:31:29.121 --> 00:31:33.561 then our customers almost across the board, add multiple use cases 00:31:33.561 --> 00:31:35.541 and models onto the platform. 00:31:35.541 --> 00:31:39.441 So you're a hundred percent right, especially in new technology. 00:31:40.416 --> 00:31:44.536 Those who are looking out of the boat tend to adopt earlier 00:31:44.536 --> 00:31:45.706 and make it happen, right? 00:31:45.706 --> 00:31:50.956 Transformation is never, is never easy, and so it's hard. 00:31:51.076 --> 00:31:51.796 Exactly. 00:31:52.488 --> 00:31:53.808 Victoria: hard stuff, so that's great. 00:31:53.813 --> 00:31:55.698 What's, so, what's next for you? 00:31:55.788 --> 00:31:58.968 What should we be looking at for NobleAI in 24? 00:31:58.968 --> 00:32:02.298 What should we be looking at for AI and chemical innovation, 00:32:02.538 --> 00:32:02.889 uh, as 00:32:02.958 --> 00:32:04.398 we look ahead into 2024? 00:32:04.861 --> 00:32:08.831 Ned: I think you're gonna see the rise of more and more partners 00:32:09.131 --> 00:32:11.471 adopting the specialized AI. 00:32:11.901 --> 00:32:16.561 For us it's just, going deeper and wider with our customers 00:32:16.566 --> 00:32:18.601 finding more opportunity to show 00:32:19.063 --> 00:32:19.353 Victoria: Yeah. 00:32:20.191 --> 00:32:23.656 Ned: really, Victoria, there's no, there, there's nothing that we 00:32:23.666 --> 00:32:27.241 can't work on and move a needle on. 00:32:27.241 --> 00:32:28.921 If it has anything to do with science, 00:32:29.791 --> 00:32:32.701 we wanna, we want to at least take a shot at it. 00:32:33.721 --> 00:32:37.021 The second thing that we're really working on is expanding 00:32:37.021 --> 00:32:38.841 our presence through Microsoft. 00:32:38.841 --> 00:32:41.211 Microsoft is one of our lead investors. 00:32:41.241 --> 00:32:43.431 They say tremendous opportunity. 00:32:43.436 --> 00:32:46.371 Not only are they an investor, but they're also a customer. 00:32:46.711 --> 00:32:51.111 They wanna bring this science-based AI or, you know, science AI 00:32:51.111 --> 00:32:53.451 for science to their customers. 00:32:53.451 --> 00:32:57.531 And so that's really been a, a starting to really take off. 00:32:57.901 --> 00:33:02.821 We're gonna be down at CERA week with them and very excited for that as well. 00:33:02.821 --> 00:33:03.241 So 00:33:07.172 --> 00:33:08.952 voiceover: We've come to the end of today's podcast. 00:33:09.362 --> 00:33:11.912 We hope you enjoyed your time with us and want to learn more. 00:33:12.321 --> 00:33:14.581 Simply visit TheChemicalShow. 00:33:14.591 --> 00:33:17.341 com for additional information and helpful resources. 00:33:18.031 --> 00:33:22.081 Join us again next time here on The Chemical Show with Victoria Meyer. 00:33:27.881 --> 00:33:31.961 Ned: yeah, there's just a ton for us to do, but boy, there's, you 00:33:31.961 --> 00:33:33.641 know, right in our sweet spot. 00:33:33.911 --> 00:33:38.281 There's just a ton of customers to work with and ton of problems to solve. 00:33:38.683 --> 00:33:38.973 Victoria: Yeah. 00:33:39.323 --> 00:33:39.613 Cool. 00:33:39.828 --> 00:33:40.248 Awesome. 00:33:40.518 --> 00:33:41.808 Well, Ned, this has been great. 00:33:41.838 --> 00:33:44.058 Thank you for joining us today on The Chemical Show. 00:33:44.611 --> 00:33:45.271 Ned: Absolutely. 00:33:45.271 --> 00:33:47.281 I really appreciate you inviting me, Victoria. 00:33:47.748 --> 00:33:48.048 Victoria: Yeah. 00:33:48.048 --> 00:33:50.583 I'm so glad to have you here and thank you everyone for listening. 00:33:50.583 --> 00:33:54.393 Keep listening, keep following, keep sharing, and we will talk again soon.