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The Fragility of the Traditional Supply Chain
The world's supply chains, set around just-in-time outsourcing, are delicate structures that need a better understanding for successful operations. These chains significantly affect aspects like carbon footprints and national security. With certain nations having overpowering control over crucial minerals, it becomes imperative to evaluate these networks thoroughly.
The Digitization Shift in Supply Chain
History indicates a transformation from paper-based operations to digital supply chains. The advent of artificial intelligence (AI) and large language models has catalyzed this shift, making it possible to construct comprehensive views of international supply network systems.
Impact of AI Models on Industry Transparency
Artificial intelligence has substantially alleviated traditional opaqueness in industries. Tracing the origin and path of products, tracking carbon footprints, recognizing forced labor, and monitoring potential business interruptions have become feasible due to AI technologies.
AI’s Take on Job Transformation and Automation
Artificial Intelligence plays an instrumental role in automating and expediting work processes in areas such as customs clearance and commercial compliance. This cognitive technology vastly improves the cross-border movement of goods, thereby altering the nature of labor and tasks within the global supply chain.
The Need for an Actively Managed Networked Business Structure
AI can also assist in implementing supplier diversity strategies - from granting redundancy within the supplier base to evaluating employee rights practices. However, introducing AI into supply chains without due consideration can lead to unanticipated consequences and vulnerabilities. Thus, businesses should transform into actively managed and networked value webs to ensure prosperity in the contemporary global business panorama.
AI and Larger Language Models: A Value Chain Analysis
AI, in collaboration with larger language models, enables an in-depth analysis of the value chains – starting from raw materials and intermediate goods to finished products and customers. This tech propulsion aids in better managing global supply chains.
AI Enhancing the Nature of Work
AI is set to redefine the nature of work by automating mundane and repetitive tasks while functioning in tandem with human creativity and judgment. This allows for more focus on value generation, creative collaboration, and the construction of more robust and sustainable supply chains.
AI and the Understanding of Carbon Footprints
Artificial intelligence aids in comprehending the carbon footprints of logistics companies. This understanding helps deal with forced labor issues and inspires the creation of new forms of insurance, like business interruption risk exposure.
The Impact of AI in a Post-Pandemic Era
The COVID-19 pandemic has had a colossal impact on global supply chains. However, the integration of generative AI can lead to significant transformation and progress in the industry norms.
New AI Models Outpacing Competition
Recent advancements have seen the development of new AI models exceeding competitors in benchmark tests. These models, fine-tuned to corporate needs, are employed by premium content providers to generate top-notch AI products.
Supply Chains: The Fabric of World Economy
The supply chain footprint is scattered all over our daily lives, with AI having a huge role to play in global operations. In essence, AI is an indispensable tool for any business eager to create a more sustainable, efficient, and reliable supply chain in today's ever-evolving global landscape. Its role is only set to increase in the coming years.
Topics Covered in This Episode
1. Importance of Supply Chain
2. Implication of AI in Supply Chain
3. Value Chain and AI Integration
4. AI Addressing Industry Challenges
5. Impacts of COVID-19 on Supply Chains and Role of AI
Podcast Transcript
Jordan Wilson [00:00:16]:
Whether you know it or not, your life is very much impacted by the supply chain. Yes. Something that you probably don't pay much attention to at all. It's impacting our daily lives in good ways and maybe bad ways, but something impacting the, global supply chain and and industries, obviously, bringing in goods from from all over the country and internationally, AI AI is impacting their day to day operations. And, I think today's conversation, whether you work, around in in supply chains and logistics or not, I think today's conversation is one that you're definitely going to want to pay attention to. Alright. I'm excited to get into that. But before we do, let me welcome you to the show.
Jordan Wilson [00:01:03]:
This is everyday AI. My name is Jordan Wilson, and I'm the host. And everyday AI, it is for you. This is your daily livestream podcast and free daily newsletter, helping everyday people like you and me understand what the heck is going on in the world of generative AI and how we can learn what's going on and learn from experts like our guests today and apply it to grow our companies and to grow our careers. So that sounds like something you wanna be a part of. Yeah. Learning from experts, growing your career. Sounds good.
Jordan Wilson [00:01:29]:
Make sure you go to your everydayai.com. Sign up for the free daily newsletter where we recap our interview every single day as well as give you all the AI news, tips, and tools that you need. So if you're listening on the podcast, thank you. Make sure to check out your show notes for that link to go sign up for the free daily newsletter. Alright. Before we get into today's conversation, well, also a reminder, go check out our thanks a million, giveaway on our website and newsletter. But let's go over the AI news for today. So Microsoft has unveiled its PHY 3.5 AI model.
Jordan Wilson [00:02:02]:
So Microsoft has launched actually 3 new AI models in its PHY series, marking a significant advancement in AI technology and providing developers with powerful tools for innovation. So the new models include PHY 3.5 Mini Instruqt, PHY 3.5 mixture of experts or MOE, and PHY 3.5 vision instruct designed for various tasks such as cogeneration reasoning and multimedia or multimodal processing. So each model shows some impressive performance in benchmark tests outpacing competitors like, Google's Gemini 1.5 Flash and even OpenAI's, GPT 4 o Mini in certain areas. So this new model from, Microsoft is a smaller language model and is meant to be, used locally on devices. So it's available under a Microsoft branded MIT license. These models allow developers to use and modify them freely. That's a big part there. Right? The difference between proprietary closed source and open source, promoting collaboration.
Jordan Wilson [00:03:06]:
Alright. 2 pieces of OpenAI news. So OpenAI has introduced fine tuning for its largest and most powerful model, GPT 4 o, targeting corporate clients. So OpenAI is making headlines with the launch of its fine tuning feature that allows businesses to tailor its advanced AI model, GPT 4 o, using their own proprietary data. So this development comes at a time when competition among AI startups is intensifying as companies seek to leverage AI for improved business outcomes. So OpenAI's new, customization option in fine tuning enables companies to enhance the performance of GPT 4 o by integrating their unique data sets by training the model. So the move is obviously strategically timed as businesses are under increasing pressure to demonstrate the effectiveness, of generative AI and show a return on investment. Also worth noting, OpenAI did announce this earlier, but developers do have up to 1,000,000 free tokens every single day.
Jordan Wilson [00:04:11]:
That came after Google just announced a 1,000,000,000 free tokens a day. Yes. Billion with a b. Alright. Last but not least, OpenAI has partnered with Conde Nast to utilize premium media content. So OpenAI has announced a multiyear agreement with Conde Nast, the media company allowing OpenAI to use content from renowned brands such as the New Yorker, Vogue, GQ, Vanity Fair, and Bon Appetit in its AI products. So the partnership highlights the growing trend of collaboration between AI companies and traditional media outlets aiming to enhance news discovery and delivery while maintaining quality and accuracy and probably fewer lawsuits as well. Alright.
Jordan Wilson [00:04:55]:
So there's a lot more AI news and more in today's newsletter. Make sure you go sign up. But if you're listening, you probably want to hear about how AI is transforming the global supply chain. And that's something I'm not an expert in. Don't worry. But we do have an expert, to help guide us through this because I think it's something that impacts our daily lives. So, I'm excited. So please help me.
Jordan Wilson [00:05:18]:
Welcome to the show. There we go. So we have Evan Smith, the CEO and cofounder of Altana. Evan, thank you so much for joining the Everyday AI Show.
Evan Smith [00:05:26]:
Yeah. I'm looking forward to it.
Jordan Wilson [00:05:27]:
Alright. Cool. So, Evan, just tell us a little bit, about Altana and what it is that you all do.
Evan Smith [00:05:33]:
Altana's built the world's largest data network on the supply chain, chain, probably that's ever been created. And, we we render a bottoms up living, breathing view of the global supply chain using artificial intelligence. So think about it like Google Maps for the world supply chain, connecting governments, businesses, insurance providers, global logistics companies, all into one common operating picture to do a range of jobs, move goods across the border, target fentanyl precursors that are moving around the world or going to the cartels to, you know, just planning supply chains and making them more resilient and shock resistant. So, wide range of use cases all on that one data and AI platform.
Jordan Wilson [00:06:18]:
Yeah. And and give us just just, to set the kind of tone here, Evan. Give us your who's your average, you know, client or customer and, you know, really what are they using the platform for ultimately?
Evan Smith [00:06:30]:
Well, it's a lot. So we work with governments. We work with, customs and border protection, with the Department of Commerce, with Department of Defense, some civil agencies and national agency national security agencies, in the US and abroad. We work with global businesses with physical supply chains. So everybody from, like, a brand or a retailer to a manufacturer in aerospace or auto or apparel, so really sweeping coverage of, of global business. And then we work with financial services companies like insurance providers, and we work with global logistics providers that are moving goods around. So we're connecting all of those different stakeholders who, you know, before us really had no way of, seeing the same set of facts. You know, who am I doing business with? Where are these goods going? What's the nature of these goods? We connect them all into one common operating picture where they can have that, shared source of truth.
Jordan Wilson [00:07:25]:
So let's do this because I'm sure there's, some people, Evan, tuning in who work, you know, in logistics and, on the global supply chain, and then there's people that maybe don't. For everyone else, right, that maybe doesn't work, in or around the industry, how does the supply chain affect us every day? I assume it affects probably just about me every time that I I I leave my house or and, obviously, when I'm in here as well. But, you know, how does the supply chain impact the average American on a day to day basis?
Evan Smith [00:07:56]:
Well, like, in literally every moment of the day. Right? So, like, the the supply chain is the fabric of the world's physical economy. Right? That's the way to think about it. It's it's how all of the goods and even most of the services that we depend on, get to us. Right? And it's abstracted away. It's invisible to a lot of us. We're not paying attention to it unless something goes wrong. And, you know, a big thing did go wrong in COVID.
Evan Smith [00:08:23]:
That was kind of the big wake up call where it was like, this outsourced just in time supply chain is actually pretty fragile, and we need to start to know what that whole network looks like. But supply chain, you know, is is like it's everything. It's your carbon footprint. It's your national security exposure to China who controls 60 to 98% of the world's critical minerals that all of our semiconductors and weapons and automobiles and everything else depends on. So it's it's embedded in really every part of our lives. And, you know, for for a long time, it's been sort of a paper based, back office function, you know, just just, like, make sure that things get there on time, right, and at a low cost. And now it's really rising to the fore as a strategic, on in the national security context, like a theater of geopolitical competition. And as a business context, the the the supply chain is actually becoming a strategic driver.
Jordan Wilson [00:09:23]:
So, I mean, let's just kind of start by answering some of those, hypothetical questions that I started out with. You you know, what are some of the biggest ways that generative, AI because, yes, you know, in most industries as big as the, you know, global supply chain, we've had traditional AI machine learning, deep learning in play for many decades. But how it has generative AI in large language models, most impacted, the supply chain and, you know, how is it already transforming it?
Evan Smith [00:09:54]:
So, a little bit of just kind of arc of history on this. So for a long time, the supply chain broadly, the global supply chain, was paper based. So all of the transacting parties that are, you know, going from raw materials to a intermediate component to a finished product are transacting and then, you know, moving goods with their logistics providers or declaring some to the government. It was all these paper documents that humans were filling out. Right? And that, over the last 10 or 20 years, has started to migrate to a digitized format. So it's like PDFs. It's, you know, rows and columns, but it's still really messy stuff. You know, it's in Chinese and Spanish and English.
Evan Smith [00:10:38]:
And if you've ever seen supply chain data, the listeners have, you kinda know what I'm talking about. So, so really, why does that matter? Historically, it's been impossible to really know what that whole supply chain network looks like. Right? So you've gotta get the data somehow, and you've gotta process all that, you know, text information, billions and billions and billions of records, and and, like, no human can no group of humans can do that. It would take 1,000,000 of years of of people's time. So, enter AI. So we can take this unstructured data, documents, you know, semi structured, rows and columns, and we can now apply large language models and deep learning. And we can start to construct a bottoms up view of that global supply chain network for the first time. So, you know, this company is making the cotton at this location, which moves to this mill, which turns into a rolled textile, which moves into this other garment manufacturing facility, which turns into your t shirt, which goes over the ocean to this retailer, and this is the store you buy from.
Evan Smith [00:11:45]:
So that whole network of production, you can now actually map out continuously and build this, like, bottoms up view of the physical economy. So that's, that's kind of thing 1 is, like, it's now possible for us to actually know and model the whole global supply chain network.
Jordan Wilson [00:12:02]:
And and, you you know, hey. As a reminder to our livestream audience here, thanks for tuning in. Michael, Woosie, Fred, Monica, Colby, everyone else. If you have any questions on, how AI is impacting the global supply chain, please drop them in now. So one thing that, you know, I'm curious, about, Evan, is, you know, even what you just said right there that, you know, the industry even in the last, you know, decade or 2 is just paper based. Right? Which which to me, it's like I assume every single, you know, global industry has been digitized, you know, since the nineties, but I know that's not always the case. You know, can you talk a little bit specifically even, you know, large language models? And you you talked a little bit about how one of their greatest strengths is being able to take unstructured data, and to bring some structure to it and to be able to tell the story. Yep.
Jordan Wilson [00:12:54]:
How have you even seen those impacts firsthand maybe with either customers or clients? And what does that also mean? Right? Something that is seemingly as simple as, you know, helping turn a a paper industry with, you know, on, you know, unlabeled data, different languages, not not, you know, formatted properly and organized, how can generative AI on a simple scale even just help the industry communicate better? And then what does that mean?
Evan Smith [00:13:20]:
Well, it it has a lot of implications, but I'll just pick a few. So if you're, if you're a global business, let's say you're a name brand retailer and you're buying, you know, consumer products and apparel and you're selling those through through your store. Because of all that opacity and the data problem that I was speaking to, it's really it's historically been impossible to know how the goods were made. Right? So where did that to my example, where did that cotton come from? And, therefore, you can't answer questions like, what's the carbon footprint of that t shirt? Or was there forced labor involved in making that t shirt? Or am I gonna have a business interruption 6 months from now because, you know, 5 or 6 hops away in the network that made that that T shirt, it's called the value chain, we're seeing a massive disruption. You know, there's a flood. There's a factory outage, something like that. So everything until now has been obscured in a fog. You couldn't answer those questions.
Evan Smith [00:14:22]:
So, so that's one. I mean, I I think another one, we, we work a lot on at Altana is, the movement of goods across the border. So, for non trade nerds listening, you you have to declare your your product to a government. This is the oldest form of taxation, actually. It's called a customs duty. So these tariffs and duties when you move goods across the border, are how governments manage trade compliance and collect revenue. So the the reading of documents and the judgment about what's the nature of the goods and I have to declare this this code and therefore I'm paying a 5% duty to the government. That's that's happening on 1,000,000,000 and 1,000,000,000,000 of dollars across border movements of goods.
Evan Smith [00:15:10]:
And that industry of of licensed humans that's doing that work is about a $150,000,000,000 per year business. So these are, you know, credentialed licensed customs brokers who are who are just reading documents, making a judgment about the nature of the goods, and helping file a a regulatory attestation. So we can actually look to accelerate and even automate a lot of that work, and that's, profoundly value creating in the movement of goods.
Jordan Wilson [00:15:37]:
And I guess where at least my mind goes when when you say that, Evan, and, I'm sure other people's does as well, is it sounds like there's just been these, just almost wild inefficiencies, right, right, in your industry, which makes sense, right, when it is literally connecting the world and everyone has different, you know, access to different technology, everyone has different resources. Right? But how does, you know, AI and and generative AI and large language models, how does it impact the work that humans will be doing, you know, in the future? Because it sounds like so many of these jobs maybe aren't needed in their current state. Right? So I'm sure it's gonna change. But how do you even see the jobs and the roles in the long term changing, across the global supply chain?
Evan Smith [00:16:26]:
Well, I'm certainly not the first guest to tell you that, it's gonna change the nature of work. Right? So some of these some of these manual tasks, certainly, the the repetitive wrote tasks are gonna be automated. A lot of the, more sort of judgment oriented and creativity oriented jobs will, I think, be copiloted with AI. I think humans will stay in the loop for a very long time, if not forever. And so the the nature of of work in the supply chain is gonna be around value added, creative, you know, judgment oriented, and even, I I would say, collaborative workflows. Right? So so it's not just me and my silo typing re retyping data into a new form for the, you know, trucking carrier who needs to pick up the load or their customs broker who needs to declare the goods to the government. All that stuff, all that data processing is gonna happen in the background, and then the humans are gonna be involved in, okay. So how do I actually make a more resilient supply chain? How do I make a more sustainable supply chain? How do I better connect not just my direct buyer and supplier, but I'm I'm gonna go to the tier 2, the tier 3, the tier 4, and we're gonna work together as a network.
Evan Smith [00:17:48]:
So those are the things that are gonna be possible here with with AI doing the heavy lifting on what has historically just been these, you know, document oriented processes.
Jordan Wilson [00:17:58]:
And, you know, I think I think that was a really good viewpoint on, you know, the near future and and how industry is working on the supply chain might be impacted. Evan, I'm I'm wondering, you know, because, there are probably a lot of people that that work in this industry tuning into this episode. You know, aside from, you know, oh, go check out Altana and, you know, things that you offer. How can, you know, people who, are in these positions aside from, you you know, a complete overhaul of of their data and their operations. Right? Because I understand sometimes that's easier said than done. So for everyone else that's maybe, you you know, struggling to kind of come into this generative AI area who works in these industries, what can they be doing or what should they be looking at? What are some of the biggest, you know, kind of, low hanging fruit, so to speak, around the, you know, logistics and, supply chain industries?
Evan Smith [00:20:04]:
Well, I don't think anything is is that easy, where you can just where where anybody without organizational buy in can can just get to work. I I shouldn't say that. There there will be there will be parts of supply chain logistics that, you know, you can feed a here's here's a good one. So if you're processing PDFs, stop. You know, feed it to one of the large language models, have that accelerate your workflow. Right? Like, return structured data. So that's that's something anybody can just go do now, from their own desk. I think, you know, bigger projects do require more organizational buy in.
Evan Smith [00:20:46]:
So you've gotta make your company's data available to some AI system. You've gotta organize that data and get it in one place. You've gotta, you know, either yourself or through a vendor, you've gotta begin to structure and apply that data into training and inference workflows. So the, the the the sort of business transformation through AI does require organizational buy in.
Jordan Wilson [00:21:14]:
So, you know, one thing that you mentioned a little bit earlier, Evan, is, you know, how AI right now can help, you know, companies or people in logistics, really better understand, you know, things like, oh, their their carbon footprints or, you know, was there forced labor involved somewhere in this process. Right? And I'm sure that those things were all available, that information was available, but was maybe harder to track down or, you know, just took more people. Right? What are some of the some of those biggest, advantages that the rest of us may not necessarily see in the in the products and services? Right? But, what are maybe some of the biggest advantages or new capabilities, that the industry is now seeing, with generative AI and large language models?
Evan Smith [00:22:02]:
Well, so the the one of the ones I'm really excited about is being able to pioneer new kinds of insurance. So, if you think about global property and casualty insurance for businesses, right, it's it's everything from, like, cargos to factories. You have you're insuring against the loss of the goods or of the capital equipment. The factory burns down, I get paid. But these policies also have what's called a business interruption limit. So you so I I'm gonna transfer some of that business interruption risk through insurance. And so, the global insurance industry all has this, this business interruption risk exposure through the supply chain. So if if my factory burns down or there's some interruption there and then I can't, I can't operate, well, you know, now I now I have, like, a $1,000,000,000 business interruption loss.
Evan Smith [00:23:02]:
If my supplier's supplier's factory burns down, I have a contingent business interruption loss. So you're looking at, you know, 1,000,000,000,000 of dollars of, of loss or or rather of insured value and, 100 of 1,000,000,000 of dollars of insurance premium all with this hidden risk exposure through the supply chain that nobody's been able to model or underwrite until now. So as we, as you think about, like, where does global business go and how you apply AI in the supply chain, and I'm a little bit talking about work we're doing, but, I think it's more general. Because we can now process these billions of data points and and model these kinds of impacts that cascade through a network, we can ensure against supply chain losses. We can model those supply chain losses. So businesses can get better coverage. Insurance providers can do better underwriting and risk selection. The whole ecosystem is gonna benefit.
Jordan Wilson [00:23:57]:
You know, one thing you said there is just talking about business interruption. Right? And one thing that I think men many of us outside of the industry, you know, I think COVID was maybe the first time at a large scale that many of us, you know, felt, kind of what it means when there's large scale disruption or, you know, business interruption in the global supply chain. Right? And the timing of it. Right? So, it was about a year or so later, many businesses, started to gain access to to generative AI, right, via whether it was via ChatGPT or, you know, other companies starting to use the early tech, GPT technology. How do you think, kind of this this combination of, you know, so many suppliers were going through a lot during that period with generative AI and and kind of these things happening almost back to back. How do you think that that has changed the global supply chain in in the long run or maybe did it not at all?
Evan Smith [00:24:59]:
Well, I think it's changing it. So, the technology now exists to, get ahead of these problems and not just, you know, see a problem earlier as it's happening through a network and then act on it, but to actually design and model a more resilient supply chain or value chain network. And this is a corporate board level initiative for just about every global business right now. They have, you know, a supply chain resilience or business resilience initiative. So, you know, global businesses are trying to solve this problem. Now that requires a lot of business transformation and new ways of doing things, and you've gotta, you know, not just model it in your software, but you've gotta then, you know, do the much slower work of, okay. Now I've gotta go work with new factories in new geographies, build compliant and, you know, on spec products and bring those online. So that business transformation, is gonna take years to build these more shockproof resilient supply chains.
Evan Smith [00:26:02]:
But, every c suite and board, that I talk to has some version of a, you know, top 3 priority focused on this. Because if you think that, you know, the world's gonna get less predictable and more volatile in the 21st century, which I certainly do, then, you can expect that supply chain dislocation, climate dislocation, geopolitical dislocation is going to increase and not decrease. This is something you have to get ahead of.
Jordan Wilson [00:26:29]:
A great great question here, from Cecilia. So thanks for this. So she's asking, Evan, have you seen the use of AI for a supplier diversity strategy effectively?
Evan Smith [00:26:42]:
Yeah. So I, supplier diversity to just for the audience, can can mean 2 different things. 1 is, what it like like, basically, having redundancy in your supplier base, so you're not being sole sourced to a single supplier. And another way that that term is used in the industry is, with respect to the workforce and the, kind of human rights, profile of the suppliers themselves. So is it, you know, is it is there child laborers and all male workforce? Is it are you sourcing from, veterans or people of color? So those those are used in in, 2 different ways in the industry. So, the answer, to both is yes. And, I think it's easier said than it's it's it's easier to accomplish on the first category where you can look across a whole network and, you know, tier 2, tier 3, tier 4, and then you can start to model those single points of failures and those dependencies. In the case of, you know, the the sort of firmographic profile of the companies you're doing business with, that really requires, a lot of underlying attestations and data points to come together to build that profile.
Evan Smith [00:27:56]:
Right? So is are they telling me the truth about the nature of the workforce? So that's where AI can play a role. You know, you can scrape LinkedIn. You can look at the, you know, the the people who work for the company. You can look at different regulatory filings. You can look at different, public declarations that are made about that company or, you know, chat board. So so there are AI companies and strategies and products built around profiling the the workforce and the the, DEI sort of posture of these companies and then framing that in terms of supplier diversity.
Jordan Wilson [00:28:31]:
Evan, so one thing I think that we can all see, you know, the huge possibility and and the tremendous upside, right, of of generative AI, you know, when it comes to the global supply chain. But maybe are there unexpected ways, that AI might disrupt, you know, traditional supply chain dynamics maybe that we're not thinking of? Maybe companies are a little, you know, too quick to try to sprinkle AI on everything without even having a strategy. So maybe is is is there a rush, to this that companies maybe are are missing that are maybe creating some new vulnerabilities that we might not have been able to predict?
Evan Smith [00:29:09]:
I I I would say not yet. Just my my firsthand experience with, these organizations, you know, in supply chain logistics, sustainability compliance, they tend to not be very, fast moving.
Jordan Wilson [00:29:24]:
So,
Evan Smith [00:29:24]:
I I don't think we're in danger of some runaway unintended consequence anytime soon. What I think, you know, if you fast forward into the future, will be interesting is a lot of the routing and what's called planning. You know, it's coordinating supply and demand across buyers and suppliers and doing that across the network. You could imagine a future where that that's pretty lights out. Like, the the analogy would be, Google Maps and Waze, right, where it just starts to automatically route you. So that's a future that, will probably exist. And in that future where there's, you know, an overlay of of big disruptions, dislocations, I can imagine some some unintended consequences and some fragilities that, you know, the thing we we built the the automation we built to adapt to the thing is actually gonna, create some some of those same unintended consequences.
Jordan Wilson [00:30:24]:
Evan, maybe what's a a a viewpoint that you have on, you know, AI's impact on the supply chain that maybe some of your peers might look at you and be like, oh, that's interesting. I wouldn't have expected that out of you.
Evan Smith [00:30:37]:
So I have 2. So, if you're running a global business, if you're doing business across borders, then, if you're not reconceiving of your business as a value chain network so in other words, it's not just who I sell to and who I buy from directly, but it's my supplier supplier supplier back to the soil. It's my customers customers customers all the way to the end use. Then you're not gonna succeed in the 21st century. I I really believe that. So the the world is is changing in the these ways that, you know, we're going from a outsourcing paradigm and globalization to this new thing that people are talking about in terms of deglobalization, near shoring, resilience. You have great power competition that's really refactoring the whole supply chain network because of China and US competition. So if you're not organizing your business as a active managed value chain network and you're just, you know, in the traditional mode of I have spires and I have customers, and that's my worldview.
Evan Smith [00:31:39]:
Good luck. So that's that's one take. And I think the other one, you you you told me to give a a GenAI hot take. So the so I was invited to, to the, US Senate to give testimony on AI safety. I was part of that that group. And my my contribution to the discussion, I think, surprised a few people in the government. Bipartisan panel, and it was very well intentioned. It was it was actually a great conversation.
Evan Smith [00:32:10]:
But, but one of my contributions was I actually think that the the way we're having this conversation, where it's all about defending the public and the government and government data and public data from abuses of AI and from bias and other, you know, issues that come up, is actually the wrong conversation to be having. And the conversation we need to have as the United States is how do we win the AI race in the 21st century? Because these are these are gonna be the commanding heights of, of geopolitics. And the government is one of the holders largest holders of data. And if you think about the positive applications of AI in education, in health care, in medical research, oncology, right, in being able to manage global supply chain. So the, you know, Department of Homeland Security is one of the biggest data holders in in supply chain in the world. So I would I would, like us to reframe this and say, how can government proactively cooperate in partnership with industry to leverage data in order to achieve these beneficial, safe, positive, like, net net positive outcomes of artificial intelligence and ensure that we win the economic and geopolitical competition of this century. Mhmm.
Jordan Wilson [00:33:34]:
Such such a great call out there. Yeah. We're looking at you government. Work work with private industry a little bit more. Make the industry better for everyone. Alright. I don't know about everyone else. I am feeling, much, more informed now.
Jordan Wilson [00:33:47]:
But, Evan, maybe what is your one biggest takeaway? You know, because you probably peaked a lot of people's interest. So maybe for those who work in the industry, what's your one biggest takeaway on how people can start using generative AI in large language models to really move their piece or their part of the, global supply chain forward?
Evan Smith [00:34:07]:
So I would say you can now know and manage your value chains. Raw materials, intermediate goods, finished products, customers at whole chain, it's possible to know it, and it's not just something you inherit blindly.
Jordan Wilson [00:34:20]:
Love it. Love to see it. Alright. This was a great one. I I think, Evan, that you really just at at least for me even. Right? I didn't know a ton. I didn't know a ton about, you know, how AI is impacting the global supply chain, but I think you just helped walk me and the rest of our audience through it. And we thank you so much.
Jordan Wilson [00:34:38]:
So thank you, Evan, so, for joining the Everyday AI Show. We really appreciate your time.
Evan Smith [00:34:43]:
Thanks, Jordan. This was fun.
Jordan Wilson [00:34:44]:
And, hey, as a reminder, everyone, there was a lot there, a lot of value. So if you haven't already, make sure you go to our website, your everydayai.com. We're gonna be recapping, today's conversation with Evan putting in a lot of additional resources and some takeaways for you because, yeah, we covered a lot there. So, make sure you do that, and then make sure you join us tomorrow and every day for more everyday AI. Thanks y'all.