Episode Transcript
[00:00:00] Speaker A: Welcome to the Tech Advantage. I'm Roland Parker and today we show how practical technology and AI can strengthen operations and create measurable business results. You're now watching Now Media.
Today I'm diving into one of the most important business conversations of our time. How emerging technology moves from idea to impact.
My guest today is Todd Thomas, a recognized voice in AI, energy, innovation and entrepreneurship. He's the best selling author of Hyperscale and Unleashing Abundant Energy Trilogy and the founder and CEO of Woodchuck AI.
What I love about the conversation is that Todd Thomas is not just just talking about the future. He is actively building solutions that turn efficiency into opportunity and help reimagine how we create value in a changing world.
I'm going to open by exploring how Todd Thomas built a career around spotting the business potential inside emerging technologies.
I want to establish his leans on innovation, what he looks for in a market shift, and why he believes technology should create measurable business advantage, not just headlines.
So, Todd, I want to hear you start with your bigger lens. What drew you to that intersection of AI, energy, innovation and entrepreneurship in the first place?
[00:01:43] Speaker B: Well, it's a pretty exciting place right now. We're seeing obviously this massive surge in demand for AI which is driving a huge need for more AI data centers.
As we're all well aware by reading the headlines, AI data centers consume a massive amount of energy.
So we're seeing this interesting convergence of big tech and kind of that sleepy old industry which is utilities, and they're really converging together.
And then we have interesting innovators and entrepreneurs and startups trying to solve the problem, which is how do we generate enough power to support this ever growing need for more AI and more AI. It's a pretty exciting, fast moving field, fun place to be.
[00:02:35] Speaker A: It's quite interesting that you say that because being in the technology side and we're supporting businesses, it's interesting to see how that the prices are being pushed up because of this huge demand for memory and storage and these huge data centers that are consuming an AI. So it's definitely having a ripple effect.
So when I look back at your career, I see a pattern of identifying what is next before it becomes obvious. So what do you think you've learned about recognizing real opportunity early?
[00:03:18] Speaker B: I think if you look at particular market, it's really easy to know what the right decision was after the fact.
Everybody has 20, 20 after the fact. The trick is trying to figure out where do you think this market or this industry is going in the next three, four or Five years. And how can you get ahead of that?
I think what we're seeing in AI data centers right now is really going to change the model of how energy is interacting with data centers. What I mean by that is historically, data centers have been passive energy consumers. The local utility produces power, and the AI data center sits at the end of the line, consuming the energy that they need.
The challenge right now is the energy grid in the US is fairly well maxed out.
We see rolling blackouts in Texas and California and New York.
There isn't really a lot of excess capacity that's just readily available.
So then the question becomes, if we have to have more data centers, where do we put them and how do we power them? The search has been where is there excess capacity where we can put these data centers?
But more and more the answer is that there isn't excess capacity anywhere.
What's the next solution? I think the next solution is we're going to see a change in how data centers interact with the grid. Instead of being simply passive energy consumers, they're going to need to start to be active participants in that grid.
And that can be things like flexing their capacity and flexing their consumption based on availability of power on the grid. I think that'll be. We're beginning to see that already.
But I think the next thing that we're starting to see is AI data centers being their own power producers. So they're kind of distributed power.
So an AI data center won't simply connect to the grid. They will build a solar field or a wind field, or they'll add liquid natural gas, or they'll add biomass power, and they will produce their own power. And then we'll see some type of hybrid relationship where when the AI data center is producing more power than they're consuming, they can feed power back to the grid. And when there's a surge in power capacity, they can pull energy off the grid when. So they really become an active participant within the grid.
[00:05:54] Speaker A: So I guess in some ways it's kind of like getting solar panels for your house, you selling the power back to your grid, and then you're using it when you. When you use excess power.
So how do you come into that equation? Are you acting as an advisor to the companies?
What are your thoughts on how do you get involved in making this into. Into a solution that's going to benefit everybody?
[00:06:21] Speaker B: So we kind of interact in two ways.
Woodchuck was originally created to essentially provide bioenergy producers with a reliable, renewable source of feedstock. So Woodchuck works with construction companies and manufacturing companies and we divert wood out of their waste streams. So instead of sending all of their wood waste to a landfill, where it simply fills up the landfill and eventually breaks down, releases gases, instead we divert all of that wood, we process it into biomass, and we provide it to biomass energy producers who produce renewable energy.
So our first connection is through the current utilities producing power, giving them another source of renewable energy. United States has massive woody biomass reserves and so being able to tap into that can be a really reliable long term energy source for really all of North America.
At the same time, we provide a really nice benefit on the construction side. So as big tech companies are building gigawatt data centers, these big data centers actually produce more waste than your typical commercial construction. And that's because all of the servers are delivered on site in heavy packaging, big six sided hardwood pallets filled with cardboard and plastic. And right now most of that material goes to the landfill or it fills up the landfill because it's very voluminous and ultimately breaks down, releases gases. We can deliver all of those materials, we can turn the wood into, into biomass for energy production. We can recycle the cardboard and the plastic. And by partnering during the construction of an AI data center, we can have a meaningful impact on the upfront carbon footprint of actually building that facility by diverting considerable amount of waste. I'd say for an average gigawatt data center, there's over 10,000 tons of wood waste. We can divert those 10,000 tons of wood waste, which will avoid about 16,500 tons of carbon from being released. And we can use that to actually generate more power, to generate renewable energy. So we're reducing waste and providing more power and really interacting both with the construction of the data center and ultimately with the utility company that's turning that woody biomass that used to be wood waste into renewable energy.
[00:08:56] Speaker A: So really you're kind of taking the theory out of AI and applying practical knowledge to go, how can we operationally and strategically benefit both the company environmentally?
It's a much bigger picture than just going, well. We're talking about AI and it's this nebulous thing that sits somewhere in the cloud and something's happening somewhere. You're actually bringing this down into real terms and how people can save money and the environment at the same time.
[00:09:29] Speaker B: That's exactly right. And you know, when people talk about AI, AI is not one thing. There are so many different versions of AI and different models and different things that it can be used for. We are specifically talking about image recognition or some people call it computer vision.
It's not really that new. AI image recognition has been around for quite a while and it's been fairly well matured in other industries. It's fantastic in healthcare because AI image recognition can be trained to see a level of detail that the human eye can't see. So it's really revolutionizing healthcare. We also see it in manufacturing. It is fantastic at identifying errors, again, smaller, detailed errors that the human eye can't see. So we're simply taking that existing AI technology and applying it to a vertical that has been historically void of technology, really ripe for disruption.
Waste hauling and material recovery is the state of it today is very low tech, very manual, very inefficient. By leveraging this fantastic AI image technology and, and using that to help us sort and segregate these different materials, it really brings a level of efficiency that not only results in the environmental benefits of reducing waste, but also there's great economic efficiencies there which allows us to actually reduce cost to our partners.
We firmly believe that in order for a solution to be truly sustainable, it can't just be environmentally friendly. It has to be financially sustainable. It's not financially sustainable. People aren't going to keep doing it. It will die. So I would say in order for it to be truly sustainable, it has to be financially sustainable.
And I think waste diversion done correctly, particularly with the use of AI image technology, we can drive real efficiencies and real cost reductions.
So it's a really.
You can do the right thing and save money at the same time.
[00:11:42] Speaker A: Great. That's good news. I'm going to close out this first part by framing the rest of our conversation around one key idea.
Technology becomes powerful when leaders know how to align it with real operational and strategic needs.
We'll be right back with more practical strategies and real world technology solutions for growing businesses. Stay with us. And we're back. I'm Roland Parker and this is the Tech Advantage on NOW Media Television. Let's continue.
Now I'll move into the company story.
I really want to unpack the origins of Woodchuck AI, the operational problem it addresses, and how AI can actually help organizations both reduce landfill usage, increase efficiency and unlock hidden value.
This is where the episode becomes very tangible for business leaders.
So, Todd, what was the moment you realized that waste itself could be reimagined as a value stream instead of just being a cost center?
[00:12:54] Speaker B: So the origin of Woodchuck really comes back to Northstar Clean Energy.
Northstar Clean Energy is the renewable energy arm of the public electric utility for the state of Michigan, Consumers Energy.
So Northstar Clean Energy has three bioenergy facilities in the state of Michigan that can burn biomass to produce electricity.
Their challenge was they didn't have enough biomass, so they were forced to augment their fuel supply with fossil fuels.
They wanted to get completely off fossil fuels, go 100% biomass.
Their additional challenge was not only do they want to catch up on biomass immediately, but they think they need to double their energy production over the next 10 years. So they were looking for a solution that could provide immediate help, but also grow with them over the next 10 years. So they reached out to me. I have a kind of an eclectic background across construction and manufacturing and technology and sustainability.
And so we started to look at wood waste, particularly construction and manufacturing wood waste. There's a massive supply of wood waste from construction manufacturing in the United States alone. Last year we sent over 41 million tons of construction wood waste to landfill. So our landfills are literally filling up with wood waste. That wood has great energy content in it. It's very valuable.
So we thought if we could build a technology platform to help us identify that material and segregate it and divert it away from landfills, it could solve two really big problems. First of all, it could dramatically reduce the waste going to landfills and secondly becomes a really nice feedstock for energy production.
So we're really solving two major problems there.
So we brought this idea, we launched this and we reached out to several manufacturing companies and several construction companies and the reception we got was huge. There are people looking for solutions. There are people that don't want their waste going to landfills.
Everybody's interested in efficiency, everybody's interested in cost reductions. Those are non political.
And that's exactly what our solution does.
Now previous companies have tried to look at recovery materials, but typically they are trying to do that after the fact. So they've got a big dumpster and they throw all their trash in there. And different companies have tried to sort that material after the fact, after it's already all co mingled and smashed together. No one's really figured out a cost effective way of doing that. It's just, it's highly labor intensive and it's very difficult.
And the quality of the materials get lessened because they're mixed and commingled and contaminated. So we took a different strategy. We use AI image recognition to help us identify materials at the front end and we pre sort them. So when we go to a manufacturing company or to a construction company. We don't simply put one dumpster for trash, we put multiple dumpsters. We have a container for wood, we have a container for plastic, we have a container for cardboard and a container for metal. And we use the AI to help us make sure that we are only getting the appropriate materials in each one of those containers. Then at the end of the day or the end of the week, we have a container full of pure wood. And we typically get greater than 95% compliance. So our wood is really clean wood coming off of the site.
We can then process that wood. So what was wood waste? We process into biomass and deliver that to Northstar Clean Energy that produces clean, renewable energy.
We take the cardboard, the plastic, the metal, we bail that up and essentially turn it into a commodity. So if you have a bunch of loose mixed plastic, that's trash. If you separate the different types of plastic and you bail them into thousand pound bales, well, that's a commodity with value on a market. So now all of a sudden you're taking what used to be waste, you're separating it, you're really recovering the materials and turning it into monetizable commodities. So for our clients, construction companies and manufacturing companies, instead of paying someone to haul their waste away, they now partner with Woodchuck. We turn that same material into essentially soft revenue back to the company. So we're able to dramatically reduce their waste cost. So we're reducing waste, we're providing savings to the construction manufacturing companies, and we're providing a renewable, reliable source of energy for the state of Michigan. So it works really well.
[00:17:42] Speaker A: So that's interesting because generally we tend to think of waste as just an environmental issue.
But really what you're saying, it's. It's now a data issue, a logistics issue, and a leadership of that company issue.
So when you're discussing with the business leaders, where do you think the biggest gains normally show up? Is it about the cost reduction, Is it about the efficiency, Is it about sustainability? Or is this kind of like a new revenue opportunity for that company?
[00:18:16] Speaker B: It's really all of the above.
There are certainly efficiencies that create more efficient job sites, whether that's construction or manufacturing, more efficient job site. It creates soft revenue opportunities back to the company. So it's a cost reduction, which everybody loves. It certainly has environmentally friendly impacts. We're literally reducing waste and producing more renewable energy. So those are all good things.
And I would add there that, you know, we feel very strongly that sustainable solutions have to be financially sustainable. I Think when we, when we talk with companies, lots of companies want to do the environmentally responsible thing and they always assume it's going to cost them more money. And in the past it often has. But the Woodchuck solution not only allows them to do the environmentally friendly thing, but it also is a waste reducer and a cost reducer. So they're doing the environmentally responsible thing and they're saving money. So it's fantastic. But honestly, I think the real power here, when we circle back to AI data centers, AI data centers are facing a lot of headwinds from local communities, local communities that are concerned about energy costs and water availability and the impacts that these data centers are having in their local community.
So if we can go in there and partner with the data center and reduce their carbon footprint up front as they're building the facility and help to increase the renewable energy available in that community, it really can be a positive for the big tech companies as they are working to work with the communities and build partnerships with the communities. So we've had some of our clients tell us they really view us as a strategic advantage in advancing their AI data center projects. Of course, we're thrilled to help play that role.
There's multiple levels of benefits for the end owners of the data centers, for the contractors that are building those data centers, for the local community.
Lots of layers of benefit.
[00:20:24] Speaker A: It's quite interesting though that you're actually using AI to actually help the AI data centers.
And it seems to me like it really is a win win situation.
So are there any downsides to it? What are the implications with the company going well, we don't know where to start.
How do we get this ball rolling when we're facing headwinds from a manufacturing or construction or AI data center point of view?
We've got all this waste.
How do we make the most out of it?
At what point can. Are there other companies like yours that are out there that are doing similar things or are you kind of like spearheading this?
[00:21:11] Speaker B: So we're pretty unique.
I'm not aware of any other companies doing what we are doing. Hopefully there will be more.
We're scaling rapidly, but we're a third year startup and we're now expanding outside of Michigan across the Great Lakes area. We'll be doing projects in Indiana and Ohio and Kentucky and looks like we might be doing one in Texas. So we are having some, some great growth and some great scale.
But the solution is wonderful. It really reduces waste, provides more energy, provides benefits to everyone throughout the community.
So yeah, I'd love to see other companies kind of embrace this technology and bring it. I mean, I guess the beautiful world would be if we never sent any wood, cardboard or plastic to landfills anymore. If we could divert all of those materials for a better second use, that would be a spectacular outcome.
[00:22:02] Speaker A: And at what point, from a company size point of view, does it make sense to start doing things like this? We deal with a lot of manufacturing and construction companies, but they may be on the smaller size where they building schools and that type of thing, but not huge construction companies.
So at what point does it really make sense for somebody of that size that's got a couple of hundred employees to actually connect with you
[00:22:32] Speaker B: immediately? The bulk of our clients are major national companies.
So Walbridge is a fantastic partner. Barton Malo is a fantastic partner. Both are two of the biggest national contractors that are building a lot of data centers.
Their end clients, Google, Amazon, Ford.
So these are all really big companies.
The bigger the company, the bigger the project, the more waste we're talking about. If you're building a gigawatt data center, you're probably going to produce 10,000 tons of wood waste. It would be a real shame to send 10,000 tons of wood waste to the landfill where it provides no value.
If we are able to divert 10,000 tons of wood waste, that will divert 16,500 tons of carbon. So there is immediate value to any of these large companies and we'd love to talk to any of them that are interested.
Go to Woodchuck AI and check us out. We'd love to look at your projects and see if we can support you.
[00:23:34] Speaker A: Thanks, Todd.
We'll be right back with more practical strategies and real world technology solutions for growing businesses. Stay with us. And we're back. I'm Roland Parker and this is the Tech Advantage on NOW Media Television. Let's continue.
I'm back with Todd Thomas. And now I want to go bigger into the future of abundant energy, the role of AI and how leaders can prepare for what is coming next.
In this segment, I'm going to elevate the conversation to strategy and future readiness. I want Todd Thomas to connect the dots between energy innovation and the business transformation while keeping the discussion grounded enough that executives can see why it matters right now.
So, Todd, you talk about an abundant energy future.
What does that vision mean in business, business terms and not just in theory.
[00:24:39] Speaker B: So when you look at the energy landscape right now, obviously we have an exploding demand for energy.
So the immediate question is how do we meet this demand? What Are the resources that allow us to do that.
People are often looking for a single silver bullet that's going to solve that problem.
Is it renewables? Is it solar?
What's going to solve this energy problem? And the reality is the problem is really big and that the real answer right now, today is all of the above. We need renewables, we need fossil fuels, we need nuclear, we need all of it in order to meet this surging demand in energy.
Energy to some extent is localized. Right? It's expensive to transport energy and there's a loss of power when you transmit energy.
So to the extent you can take advantage of the natural resources as close to you as possible, the better solution you're going to have. So if you're in Wyoming, you should be using thermal.
If you're in Arizona, you should be using solar.
Texas is a great place. Texas has so much oil and natural gas, but they're also a leader in renewable energy. They have more solar farms and wind farms than anybody.
So as you, if you can build an energy strategy for your local community where first and foremost you take advantage of whatever energy resources are in your immediate vicinity so that you are importing as little energy as possible, that's going to provide you the most cost effective energy production.
Now the one caveat to that is nuclear fusion. Nuclear fusion is really promising.
It's just not available right now. Right. There are companies that are developing it quickly.
Helion is building a nuclear fusion facility in Washington and the intent of that facility is to power a Microsoft data center right there. And they believe they'll be able to do that by 2028. So if that's the case, that would be spectacular. Nuclear fusion is very clean, kind of infinite in quantity. So if we can in fact solve for nuclear fusion, of course it'll still take decades to build out the infrastructure, but that could literally unlock an abundant energy future. But in advance of that, for the next several decades at least, we're going to need all the different energy resources that are available to us. And every community needs to build their own energy solution based on what's most readily available to their geography.
[00:27:17] Speaker A: So I guess in some ways when people hear the word nuclear, they get a little bit of fear in their stomachs and they kind of go, you know, it's a bit of the unknown.
There's all this, I guess, bad theory around nuclear, but you're saying it's actually a good thing.
So what I want to do is kind of look at how do we look at nuclear? That's a good Thing and then AI and how do you.
We've spoken about a lot of energy things, but how do you really take AI and energy and make them work together in a, in a way that's understandable for the average business person or the average end user?
[00:28:01] Speaker B: So that the funny dichotomy about AI, as you mentioned earlier in the show, AI is partially responsible for this surge in demand. People want more and more AI. AI consumes a ton of energy. So it's the use of AI that's demanding this huge growth in energy capacity. At the same time, AI is a very powerful tool and can be part of building the solutions and finding newer efficiencies that can provide reduction in energy, more efficient energy use of the energy. We have better management and distribution of the energy that's available.
So AI can absolutely be part of the solution.
Now, when we talk about nuclear, nuclear, similar to AI, is a big thing that isn't one thing. It has lots of different components. And when you talk about nuclear, right now, most people think about nuclear fission, which is the nuclear that we're, you know, we have some experience with. We built some really big plants, we built weapons.
Nuclear fission is when you split an app and atom to release the energy.
And that's what we have to date. And yeah, it's, it creates a lot of waste that lasts forever. It has some risk to it.
Nuclear fusion is when you bring two atoms together and that also releases energy. Nuclear fusion is the type of nuclear energy that powers the stars, powers our suns. It is a, it is a cleaner reaction with less waste and less risk because the process can be stopped more quickly. Now, right now, today, there are safer nuclear fission options.
There are available micro reactors or mini reactors. So instead of, you know, these gigantic reactors that we've seen that have melted down and put black spots on the earth, these micro reactors or mini reactors, you can put a small unit at an AI data center and that single micro unit micro reactor can produce enough energy to provide all the energy needed for that data center and ideally a little bit extra that can be fed back to the grid to help with local community utility costs.
But these microraftors are smaller. So just in the fact of the scale, it reduces the risk. But they are more efficient, they have better safety built into them. So these microreactors are a, an actual solution that are available now that does have lower risk and less kind of permanent toxic waste that comes from them.
Nuclear fusion, of course, is a different thing. It's a different type of reaction. The challenge with nuclear fusion is it takes a ton of energy to ignite a nuclear fusion reaction. And the challenge scientifically or physics is how do you put enough energy into to ignite a nuclear fusion reaction and get a net energy positive result so that you're actually providing energy back as a solution instead of simply consuming it. So I mentioned Helion a little earlier. They're really interesting company. They've come up with a unique, a unique approach to that in that they're not trying to reach nuclear ignition. They are pulsing material with power and then capturing the resulting agitated power with magnets and sucking it off directly without ever actually reaching ignition. And they are able to generate power positive reactions.
And that's basically the approach that they're using for their energy facility in Washington. So they believe they will be able to produce net positive power cost effectively without ever actually reaching nuclear ignition. Now some, you know, physics physicists and PhDs like, hey, that's cheating, that's not advancing science.
And helium saying, we're not interested necessarily in advancing science, we're interested in providing abundant energy and so ignition isn't necessary. So really interesting discussion and exciting to see if they are able to succeed and produce net positive power in 2028. That would be really exciting.
[00:32:11] Speaker A: And 2028 is really just around the corner. And it's exciting to see these new technologies coming through where people are just taking ideas and actually turning them into reality.
So how does that compare to say for example, the wind farms and the solar farms that we've seen going up? Do you think that this could ultimately be a cleaner and more efficient way of actually producing power power, or is it really going to end up being a combination of all of those industries actually merging together to provide the technology and the electricity and energy that the US Is going to be consuming?
[00:32:54] Speaker B: Well, right now, today, it's definitely an all of the above solution. We need all of these different sources of energy.
Wind and solar have become much more efficient.
Thermal has become much more efficient.
Hydro is limited by the geography. You have to have the geographic conditions that allow you to generate hydro. But thermal has kind of borrowed some technologies from the oil and gas industry and they've learned how to do horizontal drilling.
And historically, thermal has always looked for high hot water deep underground that can be pulled out for energy. And what they've realized now is really all they need is hot rocks.
They can drill down to where the hot rocks are and inject fluid, inject water or other fluids, more efficient fluids, and you can extract the energy out of those hot rocks. So thermal is becoming more efficient. So there are lots of different energy sources out there. And I think AI is, is a fantastic tool in helping to identify those efficiencies and help build the best solution.
I think we're also seeing a real rise in hybrid solutions where you use a combination particularly of renewable energy and more legacy fossil energy.
One of the challenges or limitations and efficiency of solar and wind has always been that they're intermittent, right? You only get solar when it's sunny, you only get wind when the wind is blowing. And so there always has to be battery and energy storage infrastructure that is built along with those energy producers and that dramatically increased the cost and reduce the efficiency.
But now we're starting to see some hybrid solutions where if you combine say a solar farm with liquid natural gas, you can remove the necessity of, of a huge storage infrastructure. When the sun is shining, use the solar when the sun is not shining, crank up the liquid natural gas. So you always have the energy you need to power your facility, but you don't have the need for the big power infrastructure, storage infrastructure because you can use one or the other all of the time. I think we're going to see more kind of creative hybrid solutions.
And again, AI can help us balance that not just across a single facility, but also across an entire grid with multiple different distributed power sources. AI can really help us manage that and maximize the efficiency of that.
[00:35:30] Speaker A: And I guess using AI to do the simulations that before would be that much more complicated, you can use AI to simulate those things, manage those things and balance your load a lot better. So I'm going to end the segment by bringing the conversation right back to leadership.
And I think the companies that win the future are often the ones that learn to read, change early and act with intention.
We'll be right back with more practical strategies and real world technology solutions for growing businesses. Stay with us. And we're back. I'm Roland Parker and this is the Tech Advantage on NOW Media Television. Let's continue.
So I'll close by focusing on founder leadership, commercialization and practical lessons for decision makers. This is where Todd Thomas can speak directly to entrepreneurs, CEOs and innovators who want to move from big thinking to actual, disciplined action.
So Todd, you've built around bold ideas, but commercialization requires discipline.
What have you learned about turning that vision into a market ready business?
[00:36:51] Speaker B: I think the key is to start with the problem. What is the problem you're trying to solve for? I think too often people fall in love with a particular technology or a particular solution.
And they run around looking for a way to monetize it. And I think we're seeing that certainly in AI projects today. Everybody wants to do an AI project and companies are launching tons and tons of AI projects just so they can say that they're doing AI. And I think that's backwards and really succeeds.
In order for a project to work, you need to start with the problem and let the problem define the solution. And so for your particular problem, maybe AI is a fit. And again, there's lots of different flavors of AI, right? There's tons of different models out there and different types of AI technology.
So the problem should define whether you need AI or not, what the correct flavor of AI is that's going to be most efficient for your problem.
And let that problem then define the solution. And if you have a problem that is significant and clients are willing to pay to solve that problem, well, now you've got a real solution that you can monetize.
[00:38:07] Speaker A: So where do you think founders often judge the difference between that invention and the execution?
[00:38:18] Speaker B: Well, again, I think lots of founders are working on a particular solution, a particular technology that they're really interested in. And so they're building something and they're looking for where does this fit, who might be interested in this? How do we monetize that? And again, I think that's backwards. You don't start with a solution and go look for a problem that's very difficult.
The best way is really to start with an industry you're familiar with that has a problem that you are well aware of, that you know, the players in that industry struggle with and would be willing to pay to solve and then build a solution. Or in the ideal world, you actually have a client that will pay you to help you come up with a solution and solve that solution. So you have a built in client or built in market fit, if you will, and you can work the closer, you can work with that client to, to get feedback throughout the process and really build a solution that solves their problem and meets their needs, that they're willing to pay to solve.
That's the way to create a monetizable solution.
[00:39:32] Speaker A: So as a visionary, you really have to spend that time with your clients, getting to know them and finding out what the problems are.
Because at the end of the day, there's no point in actually coming up with something that not only doesn't have it, you need to find that solution to the problem and as you were saying, find out what the root cause of the problem is and how you can find a solution and monetize it, how can you save them money and possibly make money out of the situation?
So if I'm a business leader watching this and trying to build a smarter business in uncertain times, what the you think is the one mindset shift that you would want me to leave with?
[00:40:21] Speaker B: What are the challenges that your business is facing right now and what are the potential solutions to solve that problem? Maybe it's a technology solve, maybe it isn't.
But you need to understand that first. Now, the great thing about AI is, as we've talked about several times, there are so many different AI technologies out there and it is so powerful that it really can drive huge efficiencies. Or I like to think of that as a force multiplier, where if you select the right AI tool for your particular problem, you can really force multiply the work that you're getting out of your team or out of your company.
So it really drives such great efficiencies that if your team becomes efficient and well versed in using the correct AI tool, you can get double, triple, 4 times 5x the amount of productivity out of your existing team. So more work, higher quality work. And the reality is, if you're not exploring these AI tools and you're not picking the right ones and leveraging the right AI tools, your competitors are. If you are relying on legacy manual processes that can be replaced by AI and provide more efficiencies and higher quality production with AI, and you're not embracing that, you're literally giving an advantage to your, to your competitors. And in fact, I was reading an article this morning saying the US Economy is growing, but wages and employment are not.
People aren't necessarily getting laid off, they're staying in the jobs they have, but companies aren't hiring more people. But the economy is still growing, which suggests increased productivity from our existing labor force. And that's exactly what AI does. It's a force multiplier, allowing greater productivity out of your existing workforce.
And if you're not embracing that, you're giving it away to your competitors.
[00:42:28] Speaker A: Yeah, I think one of the fears that people have is AI is going to replace people. And I think the misconception is AI is not really going to replace people, but businesses that don't adopt AI are going to be replaced by those businesses that do adopt it.
Because I think businesses are not looking necessarily at all the time, how can I use AI to replace people, but how can I use AI to boost the productivity of the people? And that's the interesting thing that you were talking about was we're not necessarily seeing a decrease in the number of workers, but an increase in that output. And I think every business, whether you small or large, needs to look at how can I adopt AI into my business to make my team more efficient so I can expand my business and be more productive with that same number of people that I currently have?
Do you think that's an accurate assumption?
[00:43:31] Speaker B: But we do need to recognize that some jobs will go away.
People that perform redundant, repetitive tasks.
Those jobs are at risk. Doesn't mean they lose, they become unemployed, but they do need to change their job. I think a great example of that is developers. Right? Developers has been one of the fastest growing jobs for decades and now all of a sudden it isn't.
Now AI prompting is one of the fastest growing jobs. So once upon a time you might have a team of five developers with one leading a team of four developers.
Well now instead of actually writing that code by hand, the AI can do the coding for you. You can get agentic agents to do that for you. Now instead of having a team of five developers working on one project with a lead, each one of those five developers can have 5, 10, 15 agentic AI clusters working under them. So they can produce a lot more work, a lot faster and better code, frankly.
So they're not doing the same job they used to do. They're not developers anymore. They're running teams of AI that are doing the coding. So it is a job shift. There is retraining required. But we always see that with every new technology that comes out, certain jobs become obsolete, but are often or usually replaced by better high paying jobs.
The NASA story about the human computers, people that used to compute, all of the math and then all of a sudden they got the big IBM machine and they didn't need human computers anymore, but they did need people to program those machines. That gave birth to the biggest growth in jobs in decades. Just as we said a minute ago, now you had the growth in developer jobs that was the fastest growing job for decades. That came by replacing people computing with paper and pencil. So yeah, that job became obsolete, but was replaced by, I don't even know the total numbers, but massively more higher tech jobs that paid more. And that has always been the case with every technology development.
And that certainly will be the case with AI.
[00:46:00] Speaker A: Well, I guess you look at it, 60% of the jobs that are in place today didn't even exist 60 years ago. So that's the kind of rapid change that we've had. And we've just got to accept that as a business owner, we've got to adopt AI, but as an employee, those workers need to start looking at.
Every employee has got to start learning about AI and how they can use AI to benefit corporations. Because at the end of the day, it's the employees that adopt AI and the businesses that adopt AI are going to be the ones that succeed.
So today I got to speak with Todd Thomas about what it really looks like to turn technology into practical advantage, and not just through AI, but actually through better thinking, smarter systems, and the courage to create value where others see limitations.
This conversation reminded me that the innovation is not about chasing noise. It's about building what matters, executing with clarity, and staying ready for the future before it arrives.
Yeah, you can ask him, like, how people can reach him and he. They give you the opportunity to. He talks about his book.
So, Todd, if business owners want to get hold of you, how are they going to do that?
How would they be able to reach out for you? And, and you can let them know how they can benefit from putting AI into their business.
[00:47:44] Speaker B: So please reach out to Woodchuck AI. That's our website, not.comaiwoodchuckai. and we'd love to connect with you and see how we can support your businesses.
You can reach me on LinkedIn. I'm just Todd Thomas on LinkedIn. I'm readily, readily available.
And if you found this discussion interesting and you're interested in learning more about data centers and AI and what I would say is the next great expansion of global energy, then I have a new book out called Hyperscale. You can find it on Amazon. Just go to Amazon.com and search for Hyperscale and you can find this book. And it'll give you a lot of the information we talked about today, but a lot more detail around data centers and how we're going to power them and what our realistic options are, that's me. Happy to connect.
[00:48:35] Speaker A: Thanks, Todd. It's been a pleasure speaking to you today.
