How to Get Started with AI in Your Business (Practical Tips & Real Technologies) - Part 2

Watch this episode on YouTube! https://youtu.be/UbNr7vF4CC8?si=VqX2owW86GNq7Ods

What does it really take to get started with artificial intelligence in a small or mid-sized company right here in the U.S.?

In Part 2 of this two-part series, Matt Kirchner continues breaking down A Manufacturer’s Guide to AI Tech — exploring the final 7 technologies reshaping how organizations operate, automate, and make decisions.

From autonomous mobile robots and smart drones to AI-powered industrial robots, next-gen metrology, and smart materials, Matt explains how these tools are already being used across industries. He also connects these innovations to larger questions about the workforce, education, and the future of human capability in an AI-driven economy.

Listen to learn:

• How autonomous mobile robots and drones are transforming logistics and manufacturing
• What next-gen metrology and 3D scanning mean for quality, speed, and precision
• Why AI-powered robotics is redefining human-robot collaboration
• How AI is accelerating material discovery and sustainability
• What these technologies reveal about the future of the workforce and human ingenuity

Including…the final 7 technologies from A Manufacturer’s Guide to AI Tech.

FULL SHOW NOTES (plus links & resources): https://techedpodcast.com/appliedai

Want to see all the videos and data? Watch the episode on YouTube: https://youtu.be/UbNr7vF4CC8?si=VqX2owW86GNq7Ods

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Transcript

TechEd Podcast Introduction: 0:09

Announcer, this is the TechEd podcast, where we feature leaders who are shaping, innovating and disrupting technical education and the workforce. These are the stories of organizations leading the charge to change education, to rethink the workforce and to embrace emerging technology. You'll find us here every Tuesday on our mission to secure the American Dream for the next generation of STEM and workforce talent.

Melissa Martin: 0:35

Welcome back to the TechEd Podcast. I'm Melissa Martin, your producer, and this week, we're wrapping up our two part series on how to get started with AI in your business. Last week, in part one, Matt shared his insights from his recent trip to China. He talked about why small and mid sized businesses can't afford to wait on AI adoption, and he introduced the first five technologies every business leader should know from his manufacturer's guide to AI tech. These include AI agents and MCP servers, digital twins, vector databases, gpts and embedded smart technologies. If you missed part one, be sure to go back and listen first. That sets the stage for everything that you're going to hear in today's episode. You can go back and catch part one at TechEd podcast.com/applied AI. All right. In part two, Matt will pick up where we left off, diving into the final seven technologies that are transforming how businesses operate, automate and make decisions, and most importantly, he'll talk about what the future of work looks like in this age of AI, and what that means for your job. Now, here's Matt.

Matt Kirchner: 1:47

All right, let's talk about intelligent data prediction. You know, in my early days of manufacturing, we spent a lot of time looking over our shoulder, right? We would get to the middle of get to the middle of the month of November, and we were looking back in October, and maybe we said, Wow, that was a great month. I wonder what went well, or, man, we really underperformed in October. What a disaster. And so you're sitting there 15 minutes after the end of the month, wishing that you had it to do over again, right? Because something went wrong, and you should have probably played your cards a little bit different when it comes to financial performance, is what I'm referring to, by the way. So we're looking at our financial statements, you know, then we got to a point where we were starting to measure data in real time. So in the world of manufacturing, you know, you're pulling metrics in real time. We, you know, we were doing Income Statement projections every single week. So we get to the end of the second week of the month, and we have a good idea of what we think revenue is. We know what our what our spend is, and where we're you know what our labor cost is going to be, materials cost, maybe some of our energy costs, and so on. And so you could start to predict out where your business is going to end up. And if you didn't like what you were seeing, you had time to fix it. You had time to redirect that was a really good time in business. And those of us that figured that out, and I was, I was an early adopter, proud to say, of that kind of a model where we were, there were no surprises in the manufacturing businesses. I ran from about 2005 until, you know, until I jumped out in 20, 2015, we knew what was happening as it was happening, and that was great. Well, now we're at a point in time where that's not good enough anymore, and we need to start looking out into the future. And if we don't like what we're seeing in the future, making the change now so that future never happens. Changing in real time is no longer good enough. As John Maxwell said earlier in this podcast, or as I referenced earlier in this podcast, quoting him, you know, it used to be you could just if you're a leader, you were a leader because you could see more, not anymore. Now you're a leader because you can see before. We need to see ahead. Let's talk about that. In one of our businesses, we use a CRM, a customer relationship management software called Microsoft Dynamics, 365 we pull all kinds of data into that, into that CRM, into that platform. And so one of the things that we look at is every single order that, according to our business development or sales teams is going to close in the next 90 days, and we have another list of every order that is going to close in the next 180 days. And if we know that what's going to close in the next 90, what's going to close in the next 180 have a pretty good idea of what. You know how my business is going to perform, except that we all know that biz dev. People aren't infallible, and their projections are not always super precise. And we may have one person who is a constant, you know, kind of Pollyanna person, who glasses not just full, but like, triple full, and everything's going to close. Then we have somebody else that's like a total sandbagger, and nothing is going to close. And so we just take we just accept the fact that people are going to project their territories differently, but we track it over time. And so we call it our sandbagger index. And we know if you constantly over project or under Project, your expected performance. We know that, and so we just adjust your projection. If you're an over projector, we'll back it off based upon your previous performance. And if you're a. On our projector, we might juice it up a little bit to get a really good idea of what we think in terms of orders, what's going to close. We also know what products those customers are ordering. We know the lead time, so we know when they're going to ship. And then we also know those customers payment history, so we know when they're going to pay, so we know when we're going to get our cash. And we really we put all that data into and some other data sources as well, into Azure Data Factory formatted the data for us use it. Used Microsoft. Azure basically created an, what they call an inference pipeline for what that's worth, imputed new data, output new predictions, and here are the results, right? So this is just pause on this for a minute and think back to we used to look over our shoulder on November 15 and say, how we how did we perform? And then correct for the rest of November if we didn't like what we saw for our October performance. So we got to the point where we would correct in real time, and that was great. These are the results of what we did through that project by early 2024 so by the beginning of last year, that model was predicting EBITDA, if you're not familiar with that, that term earnings before interest, taxes, depreciation and amortization, basically a financial measure of the cash flow of a business, not quite the same as net income, but if you're if you're less familiar with financial models, that's a good enough analogy. So how much is the company going to make in a specific period of time? How much cash is it going to produce? We're predicting EBITDA 14 months in advance, plus or minus 4% by early 2024 and then at the end of 2023 that same model made a prediction for 2024 calendar year EBITDA, and that prediction for total cash flow for that period of time, calendar year 2024 was with when within 1.5% so we literally know in our businesses now, not just how they're performing last month, not not just how they're performing this month, but how they're going to perform for the next 14 months. How much more risk can you take in a business, if you know between now and all the way through 2026 how your business is going to perform, and how can you take calculated risks in that business, knowing that you're going to have the cash flow to be able to support those risks that you're taking, and what you're able to do in terms of innovation and reinvest in reinvestment in your organization is light years ahead of where we used to be looking over our shoulder at the prior month. So really, really cool stuff happening using predictive analytics and AI, you know, we also use artificial intelligence to do all the show notes for the podcast, at least the first draft of those, the episode titles, chapter markers, social posts, a lot of those are done transcripts and all the recordings. So so much of what you're hearing today is done using artificial intelligence. And so we've got phenomenal marketing people in our in our company, the TechEd media group that owns the podcast. We have an amazing producer that I love talking about, Melissa Martin, who really is the heart of the podcast. Say that all the time, and I mean it, but just a great team of people, but those people are doing things that are value added, building relationships, finding new guests, looking at trends and understanding our markets, and a lot of the blocking and tackling that happens in a podcast, including, like I said, all the production, the social media, first pass of the show, notes and so on. All that is done using AI back platforms. We use them in our in our marketing pieces as well. I mean, you get a picture sometimes that is a beautiful picture you want to use in a marketing piece, but it's like, really busy or has a bunch of stuff in the background. We can use a platform like Canva is one of them that we use. Just take the background out of the picture. Another great example, we'll get pictures sometimes that aren't perfect, that we want to use in a marketing marketing materials, maybe a picture of a guest that is, you know, a beautiful picture, a nice, bright smile, but maybe the shoulder is cut off in the picture. We can use AI to give that person a shoulder so it looks normal when we put that out in social media. One of my favorite examples is, you know, we get, we get photos every once in a while from folks where they're not quite high res enough. We can use AI just to turn them into an into a high res image. It's not, not hard to do. And then the other one is, I might love this example too. Now Secretary Doug Burgum, Secretary of the Interior United States of America. Is a friend of the podcast, friend of Matt kirkners. We were doing a marketing piece not too long ago, and we had a great picture of the two of us together, big smiles, happy day. But I had a red tie on, and it was clashing with the concept for the podcast or for the marketing materials. I should say that we were putting out. Hey, just used AI to give me a blue tie. It's just that easy. It's not, not difficult at all. So really, really cool applications for artificial intelligence, for gpts, practical applications in manufacturing. And if you're looking for an easy place to start, some of those, some of those turnkey software platforms, really, really great places to start. I've got, we use probably 40 of them. By the way, in our businesses, altogether, the list may be even longer than that. I've got at least 10 of them that I use to do slides, to do photos, to do, you know, to generate imagery, all that kind of stuff when I speak, just tapping into really cool AI technology. All right, staying on our manufacturing topic, let's talk now about what we call autonomous mobile robots. So. So I spent some time in the Nike of China, right? So you think about Nike and how prolific that shoe brand is here in the United States, when I was traveling to China earlier in 2025 I spent some time in their distribution facilities in the Nike of China. And what I saw absolutely, absolutely fascinated me. So we saw all kinds of shoe box after shoe box, package after package, and they were all being moved autonomously. They had people at the front end of the process at receiving where product was coming in, especially if it was coming in in a non standardized nature. In other words, you might get a truck full of boxes that weren't standard or so on, and it was a little hard to automate that. We saw people working in that part of the business. We saw people working on the back end of the business, sending product out to individual retail stores. Everything in the middle, everything in the middle was automated, wall climbing, autonomous mobile robots. So they would pull up to a huge rack. Some of these racks that we saw could literally be 1015, stories tall. Think about a rack that tall in a distribution facility, but these things would drive up and down aisles, climb up and down racks, pick up inventory, move it to where it needed to be. We had, we saw whole autonomous mobile robots and AGVs, automated guided vehicles. Difference being the amount of intelligence embedded in the in the unit itself. AMRs have way more intelligence. But we saw both and and these were things that are moving inventory all over a facility. No people driverless fork trucks just moving tons and tons of inventory all over that facility. And as I suggest, the only place that we really saw people working after they unloaded the truck at receiving was at the final shipping facility, where they were loading up product at final inventory. But we didn't see any people other than those. So really cool applications for autonomous mobile robots in all kinds of facilities, by the way, when we asked about people about deploying this technology in the United States, so we said, Hey, this looks really cool. We'd like to do it in the US. This autonomous. They call it ASRS, so automated storage and retrieval systems. The people in China literally laughed at us. They said it would be four times as expensive to do it here in the United States as it was in China. And they said it's, you know, it's a combination of safety regulations. And look, you're not going to get any argument from me that we're going to comply with safety regulations here in the US, the most important thing in a business is to send our people home safe and sound healthy to their families at the end of the day. I'm all good with the safety side of things. But they said, look, the cost of of exporting automation, whether it's robot or robots or other automation, into the United States super, super expensive, in part because of the the implications of tariffs, and then the other part of it is you're building a big inventory storage system. They said the cost of steel in the US because of of tariffs. Huge implications there too. They said it'd be four times more expensive to deploy that technology here in the US. So, you know, just a little bit of a thought. It's not a political thought, by the way. I could, I could sit here for 10 minutes and make a really strong case for for tariff policy here in the United States, and I could sit here and and make a really strong case for not having tariffs here in the United States for products coming out of outside of the country. I'm generally a free trader. I understand the administration's position on tariffs, and in many circumstances, I accept the logic and I don't argue with it. The one where I think we have to keep a really close eye on what we're doing is with automation. You know, we're not going to stand up automation companies here in the United States fast enough to service the needs of us, manufacturers, and if we are making it prohibitively expensive for them to automate by placing tariffs on automation equipment coming into our country, we're putting putting our manufacturers here in the US at a competitive disadvantage. So that's not a political statement I have, I have had this conversation with people extremely high up about as high as you can go in the US Congress, conversations with people in the administration, and then they know how we feel here at the TechEd podcast, about about making it prohibitively expensive for us manufacturers to automate. So keep your eye on that. If you're a manufacturer, we've got to make sure that our manufacturers can efficiently and cost consciously, implement manufacturing and automation technology here in the United States of America. But let's talk now a little bit more about autonomous mobile robots. I want to mention a great company that I've had the opportunity to get to know well. Is auto This is deployed artificial intelligence, right? So what you're thinking about is a driverless fork truck driving around a manufacturing facility, driving around a distribution facility, in many cases, engaging with other people, who are individuals who are driving material handling equipment, but they can take packages and pallets and boxes and containers and move them wherever you want them to go autonomously. There's a ton of AI content on these autonomous mobile robots. So smart sensors at the edge gathering data in real time. We've got LiDAR, we've got 2d cameras. We have a microprocessor sending information to a computer network, sending that information to the cloud. This is the edge to cloud continuum at its most. Prolific here in the United States, these things will literally they're driverless fork trucks without optimized material flow through a manufacturing environment, through a distribution facility, without a person doing anything. So keep your eyes on AMRs. One of the people that I know that had his eyes on AMRs is the CEO and founder of ClearPath robotics company that owns auto motors, the company that we were just talking about autonomous mobile robots. His name is Matt Randel. He, too has been a guest on the TechEd podcast. We'll link that one up for you. He, by the way, a couple years ago, a company that he had founded with, I think it was three of his college roommates. You can you can validate that, confirm that on the on the podcast, sold that company two years ago to another friend of mine by the name of Blake Moret, and Blake is the CEO of Rockwell Automation, just an iconic automation company, kind of the godfathers of the programmable logic controllers. Great US company and their CEO, Blake. Blake Moret was on the podcast. Point being, Matt Rendell sold his company to Blake, to Blake's company, Rockwell Automation, for $600 million so don't don't tell me there aren't opportunities for young people to innovate. Don't tell me there aren't opportunities in advanced manufacturing, because Matt Rendell would probably have something to say about that. And I will also, while we're talking about Blake Moret will make sure that his episode is also in what may be perhaps the the episode of The TechEd podcast with the most links we've ever done in the show notes. We'll make sure Blake's episode is in there as well. His company Rockwell Automation will give them a little bit of credit. Every year they produce the annual State of smart manufacturing report. This is the 10th one that they've done here in 2025 really, really good content. And just a pull quote from that particular report that was published here earlier in 2025 95% of the respondents to their survey, and they had over 15,000 respondents. 95% of them have either invested in or plan to invest in artificial intelligence, machine learning and generative AI, or what we call causal AI, little bit outside the scope but another, another form of artificial intelligence in the next five years, AI adoption in the manufacturing sector is outpacing other industries, and they point to large companies, especially among those companies that are more than a billion dollars in revenue. But that is coming to small to mid sized companies, and it is coming fast. So you can check that report out again. It's called the 10th annual State of smart manufacturing, and there's some great data in there. Okay, number eight, smart drones in manufacturing. Yet another episode that, yes, we will link up on the show notes, was our episode with Shawn Mitchell of a company called gather AI. Now, if you've never worked in manufacturing, if you've never worked in accounting, if you've never worked in a distribution facility and had to do year end inventory, or what we call cycle counts. So doing inventory from time to time to make sure that what your books, what your financial statements, what your inventory list says you have, is what you actually have in inventory, Count yourself lucky if you've never done that, because it is a hassle and and I've been through tons and tons of physical inventories, and they always end up with a surprise or two. You hope the surprise is a small one and not a big one. But believe me, I've had both. Sean Mitchell's company is solving for this challenge, and they're basically deploying drones. So they have drones flying in a manufacturing facility, flying in a distribution facility, and they're gathering all kinds of data, reading RFID, reading barcodes, looking using what we call classification machine learning, which is a category of AI, category of machine learning, which is a category of AI that's called classification machine learning, basically looking at something and figuring out what it is, using a vision system. They're using that technology to do inventory, to confirm what's in the manufacturing plant, to do piece counts, to do cycle counts, to figure out what pallets are there, what boxes are there, in real time. And boy, that just solves all kinds of challenges for a manufacturing environment. But think about being able to deploy a drone in your plant and have it count your inventory in real time and feed all that data to your ERP or MRP system on an ongoing basis. Really cool technology. So you're going to see more and more applications for both ground drones and and aerial drones in manufacturing, because all the things that we can do autonomously using computer vision technology, which is a really, really key along with LIDAR technology that we're seeing in terms of allowing our machines to see in manufacturing and then creating inventory intelligence, understanding what's there, where you may have an issue, where you may have a bottleneck, where you may have less inventory than you thought you did, or, for that matter, maybe you've got overstock or something as well, having all that in real time, super, super valuable in manufacturing. Okay, now let's talk about what we call AI powered industrial robots. We will link Ariane Kabir s episode up as well, gray matter robotics company out of San Diego, California. I met Ariane at a Automation Conference in 2023 we became fast friends. Fascinating dude. He's raised tons of money in his in his company, gray matter robotics, from from investors that just want to get a piece of the great things he's doing. Let's talk about what Aryan is doing. And we'll start by talking about a problem that I faced all the time when trying to automate Manufacturing Company. 90s. It's just one example. For 10 years, I ran one of the largest, probably the largest, contract metal finishing companies in the United States. So when we think about metal finishing, if you think about anything that's plated using zinc plating or black oxide or tin plating or silver plating, like the contacts in your phone, so many things in automotive, for example, and your vehicle are plated in zinc to protect the substrate, to protect the steel from rusting. You know, think about paint or powder coat. Anything that you have that's painted or powder coated your your patio furniture would be an example. So we, we ran a company though, a huge company, 16 automated production lines that we're doing more metal finishing and plating than almost anyone else in the world, and we had so many different parts that came through that plant, right? So 16,000 different what we call skews. So that's not 16,000 units of inventory. That is 16,000 different distinct types or designs of parts, right? So millions upon millions of parts running through that plant, but 16,000 different versions of them, so no two days were ever alike. And so we were in what we call a high mix, low volume environment. So we've got every order is a smaller order, huge, huge mix in terms of the different products that we're running in that manufacturing plant. So automating that process is hard. It's one thing. I was in a plant in China where they make 8 million of the same thing every day. That's really easy to not easy. It's relatively easy to automate, compared to automating something where you have 16,000 different things you make every year and every single day is different. So it's really hard in the past to automate processes with lot sizes that are small. Well now, with lot sizes as small as one, let me talk to you about what Arians product is doing. You know, we used to have, for instance, people using grinders, grinding parts in a metal finishing facility. So we've got parts that come in, and we need to grind those. We need to surface finish those that somebody with a hand grinder. You could picture something you might buy at Home Depot or Ace Hardware in your grinding parts with, with a with a rotary grinder and just just grinding the surface of a part. Well, what our hands company did, and I think this is really, really fascinating, is they basically took a 3d scanner and put it at the end of a robot, and then presented that scanner to a production part in a lot size, as small as one, and they scan that part using 3d scanning, and then they use that 3d scan, and they create what we call an STL file, or a CAD file, basically a digital file of the design of that part. So they create the digital file of the design of the part using 3d scanning, using CAD software, and then they program the robot using AI to finish the part. So now, rather than having human beings holding a sand, a sander or a grinder, not a glamorous job, not a not a particularly fascinating job, and not always a safe job, because there's a lot of potential for soft tissue injuries and repetitive motion injuries, and now we've automated that and made that job safer, taking the technology closer to the robot, the people that are now programming and operating those robots making more money than they would if they were just sanding those parts. So just an example of the advancing technology, but an example of how we can use these AI enabled robots to improve performance. The same thing is happening in the space of measurement engaging. So if you think about how we used to measure parts, and in many cases, still do in a manufacturing environment, a lot of folks are still using what we call micrometers, or dial dial calipers, where you're basically dialing a measurement system and then measuring parts based upon, you know, based upon their physical measurement. So they'll be like using a tape measure, something a little bit more, quite a bit more advanced and more precise than that, but measuring parts in a in a manufacturing environment to make sure, you know, form, fit and function, make sure that we're within, within our tolerances for how big that part can be. Well, that's manual, and it's open to error, and it's it's not particularly efficient. Well now with next gen AI metrology. So Metrology is kind of a fancy word for how do we measure stuff in manufacturing or in other in other disciplines. So using next gen artificial intelligence metrology, what I learned from my friend Fannie trishaun, who is also a guest here on the podcast, her she's the president of a company called Creative form, linking that one up too. So when Fanny joined me on the podcast, we talked about her technology and her company's technology, what she has done is taken a 3d scanner, similar somewhat to what gray matter robotics did, and put it at the end of a robot. We put it at the end of a collaborative robot. We can put it at the end of a a six axis traditional industrial robot. We can also use a handheld scanner as well, but either either putting it at the end of a robot and automating it, or scanning a part by hand using 3d scanning technology. Now we can scan that part. So think about running, you know, a unit about the size of the, you know, maybe three or four times the size of your of your phone. Or, you know, in the case of a, what they call their Metro scan, it's about the size of a basketball. But run that across a part, over the top of a part. Right, and you get all the dimensions of the part. So you can, if there's key dimensions that you're measuring, you can measure all of that. You can get all of your dimensions. You can test you can test all your tolerances. You can make sure the part is fitting the specification using technology, as opposed to using some of those hand driven systems that we used in the past. Also a great way, by the way, to create a CAD file of a part. So if you want to reverse engineer something, yeah, an example would be somebody out on a on a Navy ship in the middle of the ocean, and they have a failure on a part they use to to run that naval vessel. They can't just order that on Amazon, right, have it delivered out in the middle of the ocean. You can build it on the ship. So scan, scan apart, create a CAD file, and then create the physical manifestation of that part using next gen AI metrology and 3d scanning. So another example where we can use artificial intelligence in a manufacturing opportunity or environment without building our own ACP server, MCP server, I should say, without building our own agent, without building our own knowledge graph. It's somebody else that has engineered this for us and and we bring it into our facility. A lot of these technologies, by the way, when we think about capex or capital expenses, spending money and investment in manufacturing, Aryan Kabir is product or gray matter. Robotics is a is basically a paper pay for service. You're basically paying a monthly fee to use the technology. So it allows it to become an operating cost in many cases, and and also helps you avoid that huge upfront investment, in some cases, in technology, because you're paying it over the course of time. Not to say that his, his his product, is prohibitively expensive, and that wasn't the implication. But rather than, if a company, for whatever reason, wants to make investments in other parts of his business with its cash, they can turn this into an ongoing expense, as opposed to having to pay for it up front. All right. Keeping going here, having some great conversations about advanced manufacturing technology. Now we are on a topic. Back to my friend Leo Reddy, my 92 year old friend, who is learning all about artificial intelligence. He did work for the State Department. He worked there from for a number of years in the 70s and 80s. During part of that time, he reported to the person who was secretary of state from, I believe, 1974 to 77 give or take a year, I might have it perfect if you want to think about who was the Secretary of State of the United States of America from 1974 to 1977 and if you guessed Henry Kissinger, you would be right. Kissinger, as many know, passed away toward the end of 2024 he was close to 100 might have been 100 but Kissinger wrote a great book, and it's one of the it's, I would just say, if you're starting your AI journey, or if you haven't read the book yet, it's a must read. And it's a book called Genesis. He wrote it with two other people, Craig Monday, who is the Senior Vice President of Strategy for Microsoft. And Eric Schmidt, who is the CEO of Google, and the three of them, wrote this book called Genesis. It's called artificial intelligence, hope and the human spirit. How do you not get excited about that particular subtitle? So check the book out. But here's one of the things they talk about, all kinds of things in AI. One of the things they go deep in is this whole idea of AI generated smart materials. I'm going to quote from the book. It says, quote, AI will be put to use researching and developing increasingly cheap and abundant sources of raw materials for its own inputs as AI is simultaneously deployed in manufacturing, that's our topic. It could reduce the capital needed for any given good. So think about that. AI is going to be developing its own cheap and abundant sources of raw materials for its own inputs. In other words, as AI tries to expand itself, it's going to be developing really, really innovative materials. And so to quote from another part of the book, quote using new, sustainable synthetic materials, AI could AIS could build cities around the world to provide shelter, regulate temperature, ensure access to power and digital connections and provide clean water, food, medicine and sanitation. So those are the kind of things that we are going to be using AI for in the future. And again, that is a quote from the book Genesis, written by Henry Kissinger and two of his co writers. So we start thinking about what we call AI, accelerated discovery and design. That's where all this is going, that the materials we use in manufacturing are going to look different, and we're going to have all kinds of different materials. We're not just going to be talking about, is it mild steel? Is it stainless steel? You know, in all the other variables that we're tracking, we're going to have all of these other variables in manufacturing. We're going to be able to create materials and machine materials and form materials and in mold materials that we have never thought of in the past. They're going to have all kinds of really, really cool properties that we've also never thought of. Some of the great examples that I love. They're talking about having, literally programmable matter, shape shifting materials with embedded intelligence that can transform as needed. It can transform on demand for adaptive applications. Another example would be self healing composites. So we saw this in the metal finishing space for a long time, where if we had a part that got damaged, we could self heal it an example. Would be a chromate converted part, where we put zinc plating on a part, and then we put a Promate chromate conversion over the top of it. And if that chromate got nicked, it would actually grow back over the Nick. Over time, we're going to see all kinds of applications there materials that can detect their own damage, repair their own damage automatically, and that, of course, will expand the and extend the lifespan of a product, in many cases, dramatically. But other, you know, other examples are quantum engineered materials, materials that are AI designed to stand up to extreme environments, biometric nano structures. We talked about carbon negative materials that could be could be accretive to sustainability. So all kinds of great things happening in the world of materials here in the age of AI. All right, so I saw this quote not too long ago. It's from a guy named Jensen Wong. If you don't know Jensen Wong's name, he is known to many, known to many of us, and is the CEO of Nvidia. And here's what he said, I'm gonna, I'm gonna put a blank in here and let you answer the question in your head. He said, cars, drones and blank are the only three types of robots that can scale to extremely high volumes because they can be deployed to the world as it is. I'm going to repeat that quote again. It's one from Jensen Wong of Nvidia. Quote, cars, drones and blank are the only three types of robots that can scale to extremely high volumes because they can be deployed to the world as it is. What's the answer? If you guessed humanoid robots, cars, drones and humanoids are the only three types of robots that can scale to extremely high volumes, because they can be deployed to the world as it is. If that was your guess, you are absolutely correct. Let's talk about number 12 humanoid robots. When I was in China, I saw no less than, I bet it was eight, at least six humanoid robot companies. So you think about a humanoid robot, I think most of our audience probably knows what that is, but, man, they look just like people, or at least like robotic people. They've got arms, they've got legs, they can march, they can walk, they can balance. They can see. They can stand up. They can sit down. You can knock them down. They can stand back up. They can sit in a chair. They can, you know, load inventory. They can rack and unrack parts, not perfectly yet, so there's still some innovation that has to happen before we see these widely deployed in in the world of manufacturing. But it's coming. Elon Musk is one example. Predicts a day where we will have 10 billion humanoid robots on the planet, more so than the than the number of humans doing all kinds of things, doing our laundry, making our dinner, washing our cars, driving our cars. If we even need a humanoid, obviously those will be self driving, but all kinds of tasks, and then, certainly in manufacturing, think about any application where we could benefit from humanoids, racking unwrapping, performing dangerous or monotonous jobs as the cost of labor goes probably to about $1 an hour over time, through through humanoid robots and manufacturing, but lots of really crazy stuff happening. What I love about humanoid robots, when we think about I do a whole separate discussion. Maybe we'll do this as a separate podcast sometime this, this year here, before the end of 2025 on the different materials and the different technologies that are advancing advanced manufacturing, many of which we've talked about today, advanced materials, autonomous systems, battery technology, biomimicry, so mimicking what we see in nature as we do our innovation, the cost and the power of compute, the global positioning systems, electrification, vision systems like Lidar and standard vision, smart sensors, telemetry. All these technologies are innovating different aspects of manufacturing. They are all innovating humanoid robots. So we have humanoid robots because of all that, that entire list, which I won't go through again, you can, you can hit your 15 second rewind if you want to hear it again. But those technologies that are totally transforming manufacturing, we see every single one of them on humanoid robots. And so as we talked about a moment ago, we are seeing huge improvements in efficiency relative to the number of people we need in manufacturing. If you look back in the 1980s in order to produce a million dollars of revenue in real dollars in the 1980s so in real dollars, what we mean by that, of course, is inflation adjusted. So taking the effect of inflation out of the numbers we're sharing with you, seven and a half people per million dollars of revenue in 19 in the early 1980s that's how many people it took to produce a million dollars of revenue in manufacturing. And the s, p5, 100, by the way, is the is the sample source, or the source of data for that number, seven and a half people down to in the year 2022 the last number last year. We have the data for this, by the way, according to both Bank of America and a study that they did, down to two people. So we went from seven and a half people to produce a million dollars of revenue down to two people to produce a million dollars of revenue over the course of that period of time, 1980s to 2022 and if you think about that path that we're on, that trajectory, the downward project trajectory, it's going to continue to shrink. We're going to produce more and more more and more revenue with fewer and fewer people. One of my favorite quotes from Sam Altman open AI, he expects that that we're going to see. One person, billion dollar company. He said that at TechCrunch, actually. And so it's super, super fascinating that if you think about we're going to get to a point where we will see one individual, one person, leveraging AI, leveraging technology, creating a billion with a B dollars of revenue. So the amount of labor that we're going to need in manufacturing relative to the the amount of revenue that we can produce is going to continue to shrink in with the advent of humanoid robots, that's going to become even more acute, which leaves us with all kinds of questions, as we kind of get near the end of this episode of the podcast, all kinds of questions about the future of work. So let's talk about the breakthroughs of and the impact of AI in this age and talk about the future of work. Another great study, this one was done by three economists, Edward Felton from Princeton, Manav Raj from the University of Pennsylvania, and then Robert Siemens from New York University. Those three individuals did a fascinating did some fascinating research, and they looked at how language modelers like chatgpt and other generative pre trained transformers are going to affect both occupations and industries. They looked at 1016 different job categories. And those job categories the study, by the way, I read about in the book co intelligence, by Malek, which is another great book from 20, 24,016 job categories. And they looked at the degree to which those job categories were going to be disrupted in the age of artificial intelligence, what they found was 36 of them. A full 36 of those 1016 jobs were unlikely to overlap with AI. And look, I know some of us are afraid of artificial intelligence. Some of us don't like change. Some of us are worried about the world of work changing like crazy. Well, if you're one of those people, and you are a pile driver operator, or you are a roofer, or you are a professional dancer, the news is actually really good for you, because your world is unlikely to change significantly. For the rest of us that aren't in those 36 jobs, those jobs, or the other 33 jobs like them, the world is going to change like crazy in the age of artificial intelligence. It really is, and we need to be ready. We have a lot of educators that listen to this podcast, I can tell you that same study said that of those of the top 20, of the top 20 jobs that were most likely to be disrupted by artificial intelligence, of the top 20 jobs most likely to be disrupted by AI, 10 of those are in the education space. Now, most of those are university professors. So depending upon what level of education you're working in, maybe a little bit less innovation and disruption in the short term in spaces like K 12 or Technical and Community Colleges, although, trust me, it's coming in the space of higher education at the university level, I think we're in for some really, really significant disruption in the next three to five years. Anyway, a lot of us are worried about losing parts of our job to artificial intelligence. I know a little bit about what that is like. We talked earlier about how manufacturing people love spreadsheets, right? There are weeks where, if I could live my whole life in a spreadsheet, I would just kind of grew up with, you know, first it was first, it was lotus, 123, and there were a couple other Apple based platforms. And then, of course, Microsoft Excel for the last 30 years. And love Excel, love data. I was always the guy in the office they would come to with questions, right? If somebody wanted to know about a macro, if they wanted to know about a formula, if they wanted to format a spreadsheet, who would they come to? They come into my office and they say, I'm just, can you just tell me to show me how to do this? Struggling with this, I was always happy to do it. And then I noticed about a year and a half ago that it had been a while before or since anybody had walked in and asked an Excel question. And I actually praised myself a little bit, right? All these years of trying to train my teammates on how to use Excel, we finally reached the end of my knowledge. There wasn't anything I could help them with. They were off on their own. I was so proud. And then I thought for another moment and realized that that had nothing to do with it. That really the reason that these folks were no longer coming to me with questions. And by now, I've dwelled enough on the subject that you've all figured it out they weren't coming to me because they were going to chatgpt, right? They were going to perplexity, they were going to Claude asking a question, asking for a formula, asking for a macro, asking how to do something, and it was giving them the answer at the snap of their fingers. So I know a little bit about what it's like to lose part of your job to AI, because I did now some 18 months ago. It's okay, though, it happened with my son too. I have this son that lives in Washington, DC. He works in consulting, and he TechEd me not too long ago, and he said, Have you ever used chatgpt to write an Excel macro? He said, I did it yesterday, and it was life changing. So, so in that case, generative AI certainly took part of my job away. What was life changing for my son? Is okay, though, by the way, that is the same son who told me quote I always say, Please and thank you to chat GPT, because when the robots take over, I want them to like me. All right. Well, I'm not worried about the robots taking over, but I will tell you that our world is going to change here in manufacturing. I'm going to close this extended episode of the podcast with a little bit of a story. It's an oldie, but a goodie. If you heard it before. Bear with me, but I love this one. It's about a woman who lives in a home, and in her house she has. A floor that creaks. And every time somebody walks across the floor, it makes this creaking noise. It's in the kitchen, if somebody is like relaxing or even sleeping in the next room, and somebody walks through the floor, the creak is so loud that it can wake them up. And she gets sick of hearing this creak day after day, week after week, month after month, year after year, and so she finally breaks down and says, I'm going to call the carpenter and have him come and fix this. And so she she calls him, and the carpenter comes, and he stands in her kitchen, and she shows him exactly where the squeak is. And he he patiently walks one way across the floor, and he listens to the squeak. Then he turns around, and he patiently walks the other way across the floor, and he listens to the squeak. And then he looks at the floor for a bit, and he kneels down on the floor, and he and he pulls one nail, a single nail, out of his out of his tool belt. Then he in with his other hand, he pulls a hammer out of his tool belt, and he places the nail carefully in one specific spot on the floor, and he drives the nail into the floor using the hammer, and then he stands up and he walks across the floor, and there's absolutely no squeak. And he looks at the woman and invites her to do the same thing. And she does, and she walks across the floor, and she's in and there's absolutely no squeak. And she said, Oh, thank you. She said, I can't tell you that was just driving me crazy. It was so loud, you know, sometimes people literally couldn't sleep in the next room. I am so, grateful to you for what you did here. And he pauses for a moment, and then he takes a piece of paper out of his tool belt. Then he writes on the piece of paper. He writes invoice across the top of the piece of paper, and then he writes on the invoice, fix squeaky floor, $75 and he hands it to the woman, and the woman looks at it, and she says, Well,$75 she's like, thank you so much. I mean, I really appreciate the work that you did, but $75 that seems like a lot of money. All you did was pound one nail. And he takes the invoice back from the woman, and he crumples it up, and he puts it in his pocket, and he takes out another piece of paper, and across the top, he writes invoice, and he writes pound one nail. And next to that, he writes $1 and then below that, he writes, knowing where to pound the nail,$74 total, $75 and he hands it back to the woman. You see, the truth of the matter is, my friends, that many of us, and especially our parents, grew up in an era of physical labor, right? They grew up in an era where you could get paid for what you did, right? If you worked on a production line, if you were a carpenter and you and you built houses, I mean, what you did with your physical body, what you did with your time, that was a great way, in many cases, to earn a living. And I have people in my family who I'm super, super close to that earned great living. Earn a great living working in manufacturing, in, you know, out on production lines, in manufacturing environments, and supported their families and created great futures for themselves and so on. So the truth is that individuals in those spaces were living in a physical labor era. They were living in a physical economy, and they got paid for what they do and what they did. Well, then we evolved into a knowledge economy where it was no longer an economy where we can get paid for what we do. The world moved on. We automated a lot of processes, and it became all about what we know. And so no longer were we paid for pounding the nail. We were paid and we are paid for knowing where to pound the nail, and we've been in a knowledge economy for quite some time, some might argue, 50 or 60 years, where knowledge is power, and the more we know, the more earning power we have. Well, we are quickly evolving into a time where knowledge isn't going to be good enough. We are moving into what we call the intelligence economy, and that is going to be an economy in which we are paid for how we use artificial intelligence, and the better more adept you are at implementing turnkey AI systems, the more adept you are at understanding artificial intelligence, whether that's generative, pre trained transformers, whether it's embedded technology like that we talked about, whether it's predictive analytics, the Folks that understand how to deploy artificial intelligence and how to leverage it in the work world own the day in the intelligence economy. So as we move toward that period of time, it is really, really important that we stay on top of this stuff. The reason I wanted to do this podcast, and the reason that we dedicated such a long episode to advancing technologies in the world of manufacturing, and particularly artificial intelligence in the world of manufacturing, is that this is coming quick. I started in my trip to China. They're using AI in all kinds of ways. We're starting to do it in many ways here in the United States, in some ways, we're leading here in the US. The race is still in process, and who ends up winning the day is yet to be determined. I'm betting on the United States of America. I never will bet against our country, but we've got to get this right, and especially for our small to mid size companies, we've got to be investing and understanding how to deploy artificial intelligence to improve our processes, because other companies in your space are doing it. The ones that do are going to be the ones. End up way ahead and five years from now, the ones that don't, I worry, and I'm quite certain, are the ones that are going to fall behind, many of them, to the point where they won't even be in existence five years from now. So as we close our time together, I will just reiterate the fact that back in early in the knowledge economy, and even partly in the physical labor economy. I worked in manufacturing 30 years, running, leading, owning, investing in industrial companies, advanced manufacturing companies, and I lived and benefited from that knowledge economy. And while we were still in that knowledge economy, and after selling our manufacturing company, my partners and I did at the end of 2014 I dedicated the rest of my career to securing the American Dream for the next generation of STEM and workforce talent, and just as important, literally, securing it for the next generation and manufacturing here in the United States, such a huge part of our economy is going to play a huge part in securing that American dream. Adopt artificial intelligence. Understand how it's going to change your market space. How it's going to disrupt your market space? Upskill yourself, equip your team with the skills they'll need to be successful in the age of AI. Let's get going on our AI journey. And when we input and when we implement this technology in advanced manufacturing, together, we will secure the American Dream for the next generation. Thanks so much for tuning in to this episode of The TechEd podcast. We are going to put this episode at TechEd podcast.com/applied AI, that is TechEd podcast.com/a P, P, L, I, E, D, A, I, so check it out there. And when you're done, come see us on social media. We've got all kinds of great stuff that we're doing on social whether it's Facebook or Tiktok or LinkedIn or YouTube, I follow our podcast on every one of my feeds. It's amazing. The kind of cool stuff that our team puts up all the time. I learn from the things that end up on social media from the TechEd podcast. So be sure to tune in and follow us. And the other thing we should make sure you do is tune in next week to the TechEd podcast, where this week and every week, we impart all kinds of great knowledge to the incredible people doing incredible work in education and manufacturing and across the entire US economy. My name is Matt Kirkner. I'm your host for the TechEd podcast, and thanks so much for joining us.