# When the Smartest in the Room is No Longer Human — Transcript

Who's excited for a great day? Woohoo. Let's get some energy going. It's an early morning.

There you go. I love it. So special call out to all of those that are putting on this fabulous summit. My name is Casey Webster, and I'm co-founder of the PrecisionX system.

I also am CEO and founder of Profit Quiver, which is an AI business transformation company. And I'm joined by Dave Mahofsky, who is my co-founder again for PrecisionX, and he is the CEO and founder of Mindset Innovations Consulting. So many of you have stopped by our booth. If you have not done that, please do put that on your list and in your app.

Many people want to know what is the Precision X system. You're going to kind of notice any archery lovers in the room. Come on, where are my archery people? So I have this fascination for archery.

And so you're going to notice that my business is named around archery, but the Precision X system metaphor that we often use is that no matter who is using AI, if it's done appropriately and correctly, and that we have prepared organizations from a human-centered perspective, that everyone can hit the bullseye and that we can create velocity and results within our organizations. So our PrecisionX system is focused just on that. Not only do we go in and evaluate tech stacks and look at what are the pain points and problems that we need to solve for businesses, but we start with the 12 domains of human-centered adoption. And that means psychological safety.

It means leadership evolution, infinite mindset, and using diagnostics and assessments to ensure that we are being intentional. and that we are going to create human AI human experiences, and that we're going to amplify those human skills that are so critically important. So we have some activities. Stop by and see us. Excited to be here.

Special thanks to everyone for letting us be your premier sponsor. We're honored to do that. And I'll turn it over to Dave for the keynote introduction. Thank you, Casey.

I guess I don't have to add anything else to that. She did pretty good. So, okay. So next up, we have JC Hoyer from Pella Corporation.

And so JC is really the leader of AI, data science, and advanced analytics at Pella Corporation. It's one of the North America's largest manufacturers of premium windows and doors. With experience spanning manufacturing, e-commerce, energy, financial services, and real estate, JC brings a cross-industry perspective on how organizations can move from experimentation to real-world AI impact. He holds master's degrees from Iowa State University.

Round of applause, maybe. There we go. There we go. Go State.

Okay. Both in business and data science. He is known for translating complex capabilities into practical, actionable strategies. In his keynote, JC will challenge a core assumption many organizations still hold.

As AI capabilities accelerate and costs decline, intelligence itself is no longer scarce. The question is no longer how to access powerful tools, but how to build organizations that can effectively work alongside of them. Drawing on production grade examples from Pella and beyond, he will explore what it means to operate in a world where AI is not just a tool, but a teammate. From computer vision at scale to agentic systems that can plan and act, this session will examine how leadership, governance, and organizational design must evolve to keep pace.

So on behalf of PrecisionX System, Please join me in welcoming JC Hoyer. Excuse me. Excuse me. Excuse me.

That is a maze to get up here. So if I bumped anybody, forgive me. Good morning, everyone. Thank you for the introduction, Paul and team.

Excited to be here. It's a great day. Fantastic day. We're going to learn about, talk about AI.

The first place I want to start is with a question for you all. Now, I'm going to preface this question, though, emphasizing that you don't have to answer this out loud. Some of you may be more confident, and some may be more humble than others, but somewhere in between, and that's OK. But the question I have for you is I ask you to look around the room and consider for yourself whether or not you are the smartest thing in this room.

So glance around, don't answer out loud, but just think on that for a moment. Now, to frame that a little bit more, it's probably fair that for any one of us, we are the smartest in a certain domain or certain expertise, right? But it's unlikely that across every single domain and area of expertise, we are the smartest thing in this room. and beyond, right? We go further on that.

We know that intelligence, smartness, comes in different forms, from the carbon-based biological form that we are, to the silicon-based form, that is the machines that we're building, to what I believe is the likelihood. There's probably many other forms of intelligence across the vastness of the cosmos as you go out into the universe, right? Maybe we discover, maybe we don't. We know that intelligence also comes in different capabilities, from intelligence and science and mathematics, governance and politics, religion and art and music.

And it's in music that I really want to start this morning. Music fascinates me as a domain of intelligence, because it combines both science and mathematics as well as creativity. And creativity from an intelligence standpoint is hard to validate. It's hard to verify, right?

In music, if I put something out into the world, I don't know beforehand if it's going to be accepted as good. There's no clean answer to that. It may be a small group. It may go viral.

It may be the next Taylor Swift or whatever it is, but it's hard to judge that. I think it's a fascinating space for intelligence because of that. And so we're going to explore this space of music for just a couple moments here to start off with. So I'm going to play three different songs, just short bits of three different songs, by three different artists.

The challenge for you all is to decide whether or not that artist is human or AI. Sound fair? All right, let's try this. So when I tested it this morning, my caveat is that the sound system works.

I think it's going to be good. So we'll see how this goes. So the first one, We're going to try. Ready?

When I look at my life And all that I've been Every scar, every fall Every place I've been in I'm still here breathing Still finding my way It was a good song, right? Setting aside It may not be your genre of preference, but I think it was a good quality song for a few seconds there. You could go look it up on Spotify, I'm sure, and find it out. So the answer to this, was this human or AI?

This song was written by an artist and performed by an artist named Inga Rose. And Inga is an AI. So this song, as we celebrate, is the name of this song. It made it to the top of the iTunes charts recently, and it's had some good popularity over the last couple of months as well.

But it's entirely an AI song, right? The next one we've got for you. Ready for it? Oh, it's hearts on fire and crazy dreams Oh, the nights ignite like gasoline And light up those streets and never sleep when the sky goes dark.

Now we're the wild things on. Good song, right? Maybe a little more popular in the song, but different beat, more country in its vibe. The song is from Luke Combs.

So Luke Combs is a Grammy award-winning country artist. He actually had a concert just down the street here a few weeks ago, which was a great concert if you're able to go to it. Crazy amount of people were there, but it was a good concert, right? One more for us.

Last one. Every scar's a story that I survived. I've been through hell, but I'm still alive. They said, slow down, boy.

Don't go too fast. But I ain't never been one to live in the past. I keep A lot of emotion in that one, different style of song, a little rattle in it as well. So this song was by an artist named Breaking Rust, which the name might give this away, because I think it's the most country-sounding name I've ever come across, right?

Breaking Rust is an AI. So this is a song of Breaking Rust that went viral last fall and made it to the top of a few different charts as well, but entirely AI. So that's three different artists, three different songs, two of them AI, one of them human. I think across all three of them, what's interesting is that you can start to make the claim that AI is at least, in a sample set, equivalent to human.

I wouldn't say between the two AI and the one human that there was a significant difference. So in this domain of intelligence, there's at least an equivalence. I think you start to see as well that you could argue that AI is beginning to exceed human capability here too, right? There's A proliferation of more AI songs being put out on the Spotify and different streaming services as well, and they're gaining in popularity over time as well.

Now across the human and the AI in this, in the space of intelligence, there's one common tie in and a nuance of this I want to step into a little bit. And that's whether it's human, whether it's non-human, the intelligence is born out of what is the most abundant resource in existence. And this is not, this is going to be a science joke, stay with me. It's not hydrogen.

Anybody gets that? Sun, stars are made out of hydrogen, helium, they fuse together, it's a giant nuclear reactor, right? But the most abundant resource in existence From the smallest of the small, the Planck length, the largest of the large, super clusters of galaxies, and the universe, the multiverse, and beyond, the most abundant resource is information. Intelligence is emergent out of information.

There's many, I think it's a great framing of this, that humans, we're actually nothing more, this is very reductionist to me, but nothing more than very capable information processing systems, right? Our senses take in any form of information and we put out what usually is intelligence out in the world, right? There's a great quote from Demis Hassabis on this as well that I think is just beautiful and elegant and captures this well. Demis is one of the co-founders of DeepMind.

It's now Google DeepMind, Google bought him a few years ago. This is the group that's building Gemini. They built AlphaFold. AlphaFold won a, helped Demis and some others win a Nobel Prize for unlocking A decades-old challenge in biology related to protein structures.

But this statement, a machine that can navigate an infinity of data will be infinite in its reach. I think it's just beautiful, telegant, captures it so well. the notion of how important information is to intelligence emerging. And so it's this concept of information, it's going to be the through line through the rest of the time this morning. Information, it's availability, our capability to process it, it's what drives the exponential growth of AI that we're witnessing and it's going to continue on its exponential curve.

So we'll start there and we'll take the through line of that to frameworks for embracing intelligence that we have now, knowing that intelligence is only going to improve over time as more information, more computing capacity continues to become available to process that information. We'll wrap up here this morning with some of my thoughts on probabilities of the future. We know the probability that the future is nothing more than just some probability density function with any number of paths behind it. This is maybe too statistical for anybody here, but there's any number of things that can unfold in the future is the concept.

I'm going to share a couple of thoughts on what I believe two of those might be meaningful for you all to take away for the rest of the day. So the capability of AI and its exponential is where we start. Now, there's one core takeaway that I want you all to grasp from this. I'll go into more detail over this in the next few minutes.

The takeaway is that the capability of AI is doubling every four months. And that four-month window is continuing to compress shorter and shorter. And this is what I believe is the root of why there's such a whirlwind sensation of every day there's some new advancement or new feature or new breakthrough out in the world. If you're staying up to date with it, you have the sensation that just yesterday I learned how to do something.

Today I have to learn something different, and tomorrow it's going to be something brand new again. A new model emerges or whatever that might be. From an enterprise standpoint, this drives a lot of challenge in trying to plan, to strategize, to allocate resources, and I start here because this is truly one of the areas, one of the topics I think I have conversations around the most often, at least weekly at Pella, discussing between the groups that believe the capability is real and the groups that are still doubters, right? And then the vast space of in-between of what's out there.

All trying to work out how fast and far we actually need to integrate, should we integrate these capabilities of intelligence into the organization. The tool I use to help frame this and understand this is a forecast. Forecast is nothing more than three parts. You have some expectation of the future based upon some understanding of today, informed by the past.

Nothing novel in that. It's A forecast. The forecast that I found to be most useful for this comes from an AI nonprofit research group called the AI Futures Project. This group, what I think is great about them, is Their forecast inputs and assumptions are very robust and it's very transparent.

So some point today or when you leave here, using your AI tool of choice, go out and research this, look into it. The transparency of what they put in the forecast is great, from considerations for allocation to efforts to continue growing the grid capacity to advancements in compute, to advancements in foundational models and talent coming in the space and considerations for national security concerns and geopolitical concerns and so on. It's very broad, very deep in its inputs. Specifically what this group seeks to forecast is their call on when artificial general intelligence and super intelligence will be achieved.

Now we'll give more definition to that in a moment. General intelligence, superintelligence, they put out a forecast on this last year. The first forecast came out in April 2025. The title of it was AI 2027.

So the title gives us the way. The initial call was we'd achieve general intelligence in 2027. They made an update earlier this year. They bumped it out to 2028 as factors in their forecast have evolved. 2028 is now their mean of that call.

The midpoint is early 2030s, and there's a long tail in this distribution that goes out in the 30s and 40s, all focused on making that call of when AGI and superintelligence will be achieved. There's more voices out that they've come across. They're beginning to converge on 2030 as being that kind of point in time when general intelligence is achieved. And again, I'll give more definition to that in a moment.

But there's two parts then of this forecast that I think are meaningful and illustrate this quite well. And the first part we start on is this plot on your right, which is sourced from a AI benchmarking platform called Meter. It's M-E-T-R. Meter seeks to evaluate the task horizon capability of a given AI model.

In other words, what they're looking for is that for any given task, can that AI do in a moment's time what it would take a human To do in some equivalent amount of time, right, or framing differently again, if you had a task that takes a human one week to accomplish at some level of proficiency, say 80% accuracy. Can an AI do that same task at the same level of proficiency? That's what they're looking to evaluate. Further, what they evaluate is the autonomous capability of any given AI.

This begins to reveal agentic AI, the concept of agentic AI. Now, hopefully some of you, many of you have at least started to read about, use agents. But agents are the vehicle that are going to drive this transformation, deliver this transformation to an organization, right? The agentic enterprise is a term that's out there.

We'll use agents more and more throughout our daily lives and everywhere in between. But an agent by itself is just, it's an entity that is given a task, a target, it's given tools to use, some outcome, and it's set for you to go execute. It goes and does it, comes back, might be a day, might be a week, might be months eventually. no different than a human. A human you give some target, some task, some tools, goes and executes, it comes back, and you've got some outcome, right?

That's what the agent is doing. So a plot on the right is evaluating that exponential curve and these models that continue to be on that curve. There's one point of significance that the authors call out then. It's the one-year time horizon.

Now, one-year time horizon is in their forecast, where they make the call that this is when we will achieve a true automated coder capability. It takes us to the left plot, right, the plot on the left-hand side. Automated coder, I wrestle with the right framing or term for this. The other term I'd give to it, it's a true agentic software developer.

The definition they give on this left-hand plot is we'll achieve that by June of 2028. This is the idea that you'll have an agentic AI that is able to do the entirety of a software development function, an organization. Everything from planning to designing to building to deploying to debugging to supporting to maintaining, everything in that space will be able to be done by an agentic AI at that point in time. That's the call being made.

Now, if you're using agentic tools right now, agentic software tools, programming tools, Maybe we're ahead of pace on that. There's some good evidence out there. You can make arguments kind of on either side, but it's seeming like it's probably going to be at least close to being that June of 2028 time frame. So once we hit that agentic coder, then you move all the way up to the right-hand side of this curve.

By May of 2029, the call is that is when we achieve artificial superintelligence. Superintelligence broadly is the idea that it's AI, whatever form it is, that exceeds human capability across all domains. The definition they use in this forecast specifically is that it's AI that is two times better than the world's best human relative to the average human in that domain. I'm going to use my finger puppets to reframe that a little bit.

So if you can see this, if you have the world's best human is this one here, you've got my thumb is the average human. There's a gap between those two. then you've got AI that's way up here. There's an exponential difference is what they're illustrating in this forecast, right? There's one other milestone of significance I want to call out on this.

You step back one level from superintelligence, it's the superintelligent AI researcher that they call out on here. This is significant because the idea is this milestone is when we achieve what's termed the takeoff. The takeoff is the the idea that AI is now able to self-improve. So it's AI that's researching new methods, new algorithms, training on those new methods and algorithms, deploying the improved version of itself, and then this massive flywheel begins to take off.

And once you achieve that, then it's thought to be a quick jump to superintelligence, because you've got AI that can now improve itself for whatever challenge that it has and get to superintelligence, right? So 2 plots illustrating that capability. I've got one more plot that I want to touch on to build this sentiment of the realness of the capability that is at least my perspective on it, right? This plot, I think, brings it to more of an immediate kind of perspective because it's focused on economics, on dollars.

This is depicting the annualized revenue run rate of a number of leading frontier model providers. The 2 at the top are the specific ones to call out. You've got OpenAI at the very top, GPT models, and then Anthropic is the one right below it, which are the Claude models. And for the narrative here, focusing on Anthropic, you can see that at the top right of their line, the curve, that curve begins to steepen.

The slope goes up more. And this illustrates well what has been the rapid pace of growth in terms of people, enterprises, voluntarily saying, I'm going to give you my dollars to get more of your capability. I think it's as telling as anything of how real that is. It's that true desire emerging, I need more, I need more of this, right?

Specifically for Anthropic, so they're founded 2020 or 2021, so they're almost five years old. From 2023, the annualized revenue is $100 million. 2024, it was $1 billion. 2025, it was $10 billion. As of April of this year, it's $30 billion. And they're on pace that by the end of this year, it'll hit $100 billion in annualized revenue.

That's 10xing each of those years, which is just unfathomable growth, but reflects significantly how real the capability is and how much demand there is for this capability. So I land on this because I think it's as telling as anything of it's real, and sorting out how to begin to embrace this technology, and the term is diffuse, diffuse it in the organization becomes the real challenge for all of us. The framework I then have for this, we've had our capability, now how do we walk through embracing this? The framework I use for this, and candidly, I'm going to admit, I don't have all the answers for it.

My team at Pella doesn't have all the answers for it. I don't think I've chatted with anybody yet that truly has an answer for how to actually embrace and embed this technology through the organization. But the framework I use is one of the factory. So you've got in the physical world, Pella manufactures windows and doors.

And then that factory, we've got saws and nailers and glass cutters and computer vision and robotics. And you've got people that are operating that equipment, ultimately trying to build quality, good windows and doors products for our customers, right? So the agentic factory is the same concept. So in the core of it, you've got the factory that's trying to provide safe, secure, trusted agents and capability to the organization.

We've got agentic operators. So The novel ideas, you've got the technology itself, the AI itself running that factory, and you've got those that are consuming it, right? We're going to give more detail to this in a moment, but there's one other aspect of this I want to illustrate. I think it's foundationally important to how we bring this capability to life in the organization.

It's one of culture, specifically the culture of experimentation, right? So not every organization, not every person natively has this culture of experimentation, but it's critical in the space of AI. My argument for this is because AI fundamentally is a probabilistic technology. It can use deterministic tools, but at its core, it's probability.

And with probability, you don't really know how it's going to act until you deploy it, see what happens, and then refine around that, right? So having in place the right structure to be able to do that rapidly becomes critical. SpaceX, It's a fantastic example of this culture. This culture of rapid experimentation is at the core of what SpaceX is.

And there's probably, I'm sure, some other SpaceX nerds out here in some form or some space, right? So forgive me if I don't mean to be offensive calling anybody a nerd, but I'll admit to be one of them, right? And so what you see on the screen are three Raptor engines from SpaceX, Raptor 1, 2, and 3. I think what's most immediately evident is how beautiful simple and elegant Raptor 3 is.

Raptor 3 is the most powerful rocket engine in the world. Of the three on the screen, it's the most performant. Of the three on the screen, it costs the least to produce and costs the least to operate. A little side tangent within this, if you may have seen this, last week, SpaceX launched the 10th experimental flight of their Starship rocket, which is their super large rocket.

They strap 33 of those rocket engines together, and that's what powers Starship, right? It shakes the earth when it launches. If you can go watch it, it's super incredible to see. The Raptor 3, SpaceX got to this not through careful planning and trying to design out all the risk, but they got there through rapid iteration, right?

We're going to build it, we're going to launch it, and we're going to blow it up over and over and over and over again, right? We're going to feel the ephoria of it, We're going to fail, we're going to get up again. They buy in wholly to push the boundaries of engineering and physics to fail to learn as much as we can as quickly as we can. I think this concept becomes vital to how...

I think I need to watch that again because it's just entertaining, right? That's great. That concept of rapid failure in a safe, secure kind of structure. I don't think it's anything new, but it's critical to AI more so than ever, because there's just an unknown nature of it all, and you don't know until you actually try and actually deploy it, right?

And it is not always easy to illustrate that story to different leaders and different groups that shareholders and so on, but it's critical. Okay, so let's step into each of these. The first is the factory. So this is a quick snapshot, quick illustration of this concept.

At the core of the factory, the Agenta factory, are all these components that allow for observing what an agent is doing, observing how and why an agent is making a decision. which in itself is still an unsolved space of research. It gets into what's called mechanistic interpretability, which is opening up the neuron kind of pathway of how a decision is being made. This is components for an agent registry, so you can see which agents are available in an organization. You've got management of safe, secure access to systems and data and tools. protocols built into this around MCP and A2A.

These are all things that you're probably going to get some flavor of throughout the rest of the day and through the different talks and topics that you sit on as well. But it all gets to, in this framing, creating a governance and guardrail system that allows for safe, secure, trusted output of your intelligence. There's 2 components of this that I'm going to touch on with a little more in depth. The first is context.

So context is another framing on information. Context specifically here, though, is within your organization, the enterprise, it's giving whatever AI you bring in the ability to gain expertise of that domain. Generally, I believe that as we go forward, models will continue to improve broadly, but there's always going to be some nuance of your organization that probably makes your organization special. that you need to be able to give context to that. Example I have of this, maybe somewhat crude, is surgery.

I'm not a trained surgeon by any means. But if you give me a knife, a scalpel, needle, and thread, I can do surgery on you, move some stuff around, cut you open, sew you back up again. You probably won't be happy with the outcome. The quality won't be great, but I can do it.

Versus if I spent the years learning the human body, learning how healing happens, learning about you, And then did the surgery, will I be a trained surgeon with context and I could do it with quality, right? The other example here I think drives us home well is NFL Thursday Night Football. So this is hosted by Amazon. Some of you may have seen this.

They have a version of their telecast that is the Next Gen. Stats telecast. They do this for a few different sports now. But in real time, what they're showing is they'll predict what the next play is likely to be, everything from the kind of simple, benign, percentage of time they go left, right, or down the middle, to the more challenging of predicting a blitz.

That's what they're doing on the screen here. And blitz in football is relatively a surprise. It is a surprise, right? It's an unexpected action by the defense.

Catch the offense off guard to try to make a big play. And you see this on the screen. Right before the ball is snapped, a little red circle pops up on that Denver Broncos player on the top of the screen, the player in the white uniform. They're predicting that player is going to blitz.

Circle pops up. He rushes across the line, makes a tackle for a loss. It's a good thing in football. And they're doing this by providing the AI with vast amounts of context.

So the players all have IOT sensors, so devices on their bodies. They're tracking location on the field, acceleration, orientation, all this kind of information coming out of it. They've got the field itself. It's constantly gathering information on the environment, the current situation.

The color commentators are commenting on the video, the game that's annotating video for them. And what might be the most useful of all of this is they've got a roster of Hall of Fame coaches and players that they have watched game film and then just stream their consciousness. Just talk about In this situation, what's this player doing? What's the coach thinking about?

And it's that knowledge, that tribal knowledge, that tribal expertise, that fills in all these cracks at the hard data, the sensors, the video, doesn't capture. But it's probably the most important knowledge to give context to your AI. So Pella, we're trying to do this on a shop floor. We've got decades of knowledge in different areas of the shop floor, as an example.

Framing I have for it is There's someone that might be working on a machine for 20 years. The machine itself is 50 years old. They just know how to smack the machine on the side of it to make it work. Well, that context becomes so important to an AI, being able to understand, to be able to be effective in that environment.

It's more likely that 50-year-old machine is going to have another 50 years of life. It probably is that we're going to invest in a new one as well. So figuring out how to extract that context and give it to the AI becomes a critical component of enterprise AI, agentic AI capabilities emerging in the organization context, right? The other core element in this factor I'm going to touch on, and this kind of like spiritual visual that's out there, is what's called constitutional AI, or the soul of your AI.

And so in this factory, working to create a core soul in which all other agents emerge from and have a link into becomes important. This is where ethical alignment, cultural alignment, practices of how you want the organization to function and act all tie back to what is the soul. Anthropic goes as far as to have they have an individual on their team whose sole responsibility is to craft the soul of Claude. They actually go further and they train that soul intrinsically into the core of Claude itself.

And it's their attempt to try to solve what's called the alignment problem. So there's an alignment between where AI is emerging into and whether or not it aligns to human values. It's a real concern that's out there and they're trying to solve it with that. But from an enterprise standpoint, we can attach our own soul to the agents that are merging and again, drive that consistency or that alignment to our business as well out of that.

So that's a factory. Now we need something to run this factory. This is the agentic operators. And so the idea behind this is that you've got a core group of agents that are tasked with running this factory.

These are agents that are orchestrating interaction with the factory, that are helping to navigate intent, helping to understand tools and how they can be used, helping to manage security. This visual, I think, or animation helps illustrate this further. It comes from A developer built this. It's called Pixel Agent.

You can go out and use this yourself. But I think it captures the sentiment well. Each of these little pixel characters is an agent in itself that you can see working with other agents, interacting with other agents. So this is exactly what this concept, this idea is, and that we're trying to build at Palo is this set of agentic operators that are in the midst of all this.

And there's one specific part of this what these operators are tasked with doing that I want to step into for a moment. And it's the operator that manages compute, memory, and cost. And this is significant right now because there is, and some of you may, I'm sure, have seen some of this, an imbalance in the supply and demand of available compute resources and demand on top of trying to access all of that. And supply and demand mechanics would say if there's an imbalance, demand is above supply.

What's going to change is cost. And so cost is beginning to be experimented with in this space. The framing I've got on this is, so AI usage is measured in energy. There's globally right now 20 gigawatts of AI compute available.

Anthropic has claimed the 2 1/2 gigawatts. OpenAI has claimed the roughly 2. They're working to try to secure deals to gain more access to this. I just read about a company last night that is They are working to put, they're working with Pulte Group, which is a home builder, for homeowners to have many data centers on the side of their house.

And then as you build a network of all these many data centers, they can link together and help try to resolve some of this capacity constraint that's out there as an example. Well, there's 20 gigawatts available now. Given the rate of manufacturing of new chips and capability to manufacture new chips by 2030, We likely get to 200 gigawatts, which will vastly still, vastly will be much smaller than what the demand still is in 2030. I also struggle to get that one out.

And so what begins to change is cost. Anthropic is beginning to experiment with moving from their subscription-based buffet-style pricing to true usage-based pricing. So you can no longer consume all you want at a flat rate. And they're even beginning to experiment with outcome-based pricing.

So pricing that varies based upon how the token, the output token, so AI is based upon tokens input and output, how that token drives value for you. So token that might be used for customer service is probably going to be a relatively low value token because customer service is broadly available, broadly similar, versus the token that's used for inventing the new molecule or the new drug that's going to be a billion dollar thing by itself. That new invention is going to be priced significantly different than the token that's helping with customer service. So I share this, I touch on this because it's significant from a planning standpoint.

In an organization, it influences how fast and far do you start to ingrain these capabilities into business-critical functions and workflows is the key concept. So we've got our factory, we've got our operators, and we need the consumption of it. And so there's two parts to this I'm going to touch on. The first is now you've got safe, trusted, quality output.

And so at this point, as a consumer, I can start to order or use these agents. And this is where the importance of what's called an agent registry comes to life. I frame it as a draft board, so you can go out and see for any given agent, what's that agent capable to do, what task can it accomplish, what tools and services can it connect to, how does the community feel about that agent? All this becomes available.

As a project manager or someone in the business, I can decide upon, hey, I've got this project I need to go execute on. I can go grab these three agents, bring them into the business, and... give them a target, some reward that they're going to get, and keep them accountable, right? Not any different than how you would treat a human. You have a human on the team, I'm going to give them a target, I'm going to give them tools, give them a reward, and they're going to go to work, right?

So the concept here is really pushing into these agents becoming more and more embedded into project teams and into different functions and becoming more managed like a human would be managed. And that takes me to the role of humans. Now, candidly, I don't know the answer for this. And I've touched on this because it's probably the question I get the most often is what does this all mean for my team, for my individuals, for humans, right?

No one knows the answer to this is my position on this. Specifically, no one knows. You think about, you know, I started this off by illustrating artificial superintelligence being here in 2 1/2 years, whatever it is. What does that actually mean for humans at that point?

No one's going to know, right? But at least Right now, I think it's still certainly the case that the role of human is AI and humans building together. And the human is more and more focused on three things. It's judgment, accountability, and decision making, judgment of what's right and wrong, accountability to the outcomes, decision making on we're going to go this way, we're going to go that way.

That's my frame for it. I wish I could be more specific or more forthcoming and what the future's going to hold, but it'd be a guess, right? Okay, so we've got our capability. We've got our factory framework.

You're probably going to get components of this in different forms throughout the rest of the day. The last bit here I'm going to touch on is let's fly into the future. So I've got two thoughts on this. One part of this is near term.

And the other thought in this is a little bit more further out, but keep in mind this is an exponential, so further out is maybe 5 to 10 years or so is the framing for this. First one is the convergence of digital AI and physical AI becoming the same, right? Everything up to now that I've been thinking of or sharing with you all is really on the digital AI side. Most of how we consume AI right now is very much in the digital space.

It's AI that has an indirect impact on the physical world. An example of this would be algorithmic high-frequency trading. So AIs that buy and sell shares, and something like 80 or 90% of all share transactions are AI-driven right now. Well, that buying and selling of shares influences capital allocation, which influences investment, influences building of buildings, manufacturing of goods, providing of services comes out of that as well.

It's an indirect impact. The physical AI side is its direct impact on the physical world. This is the humanoid robots. This is the autonomous vehicle.

So last year, Tesla had their first car roll off the end of the assembly line and autonomously deliver itself to the customer. This is digital and physical AI working together. The digital AI is evaluating the environment. Physical AI is in taking that evaluation decision from the digital and acting on it, steering the wheel, accelerating, braking, all that kind of stuff.

The humanoid side of this, last year there was the first humanoid Olympics. There'll be another one this year. Most recently there was a humanoid first human marathon in China where the humanoids blew away the human records. That's another example.

And there's many in this space that believe there's really two last sort of big milestones or discoveries to be made. One is solving for the information scarcity. So context, again, giving enough context to whatever form of physical AI there is to understand how to operate in the world. Getting that data is a challenge, but it's being resolved.

An engineering challenge, specifically around the human hand. The human hand is a marvel of engineering, so figuring out how to create the humanoid version of that is another big challenge. I'd argue as well that Physical AI is going to take the form of whatever the application desires or needs, right? So a roofing robot or a robot that's installing Windows may not be a humanoid form, but it's going to be here soon, right?

Pella, as an example, is starting to experiment with some of our own versions of multi-form physical AI in our factories. The last thought I have for you all, touches on, at least my perspective, of how we start to solve this capacity constraint back to cost. And frankly, it's just a fun kind of thought exercise to walk through. It has to do with us going to the moon and harvesting the moon to build chips, right?

So stay with me on this. I'm going to go fast through this. So focus is on SpaceX. SpaceX is merging with xAI to go public later this year.

It'll give them greater access to public markets. Public markets are much larger than private markets. In doing so, they're going to build a TeraFab. So TeraFab will be producing 1 terawatt of AI energy and one structure.

They're already starting to build this thing. It's outside Austin. That's where they're at. So you go from 20 gigawatts of AI energy available globally to one vertically integrated manufacturing structure that has a terawatt available.

They're going to 50 times that terrestrially, and they're going to go to space. So SpaceX is right now launching one rocket every two days. I saw one this spring. I took my family on a spring break trip down to Cape Canaveral and forced us to go watch a SpaceX for rocket launch, right?

But they're pacing from one rocket every two days to one every 5 minutes. The target is that they'll start to build what's called the Dyson swarm. Dyson swarm is basically this giant array that is trying to consume as much of the sun's energy as possible. And that sun's energy is going to power AI data centers in space.

And as we make our way to the moon, whether it's NASA and or SpaceX or others, and set up the infrastructure to start manufacturing and mining on the moon, we'll mine the moon and then using mass drivers, which are depicted here, start launching chips out into space to join that Dyson swarm, eventually get to harvesting one one-millionth of the sun's energy, which is, another context on that would be, that's roughly 2,000 Earth's worth of energy consumption. All of that to go to power our growing need for AI. over and over again. And I'll end on what I think is probably the most fascinating fact of all this, not even tied to AI. But once we're able to manufacture on the moon, it will be cheaper to build something on the moon and launch it to the earth and land it somewhere on the earth than it is to manufacture on the earth and ship it around the earth.

I think it's just such a fascinating thought to me to think about that. We're shooting things back to the earth. So that's what I have for you all. Thank you all.

Go and have a great day. Sure. Let's see if we can get time. All right.

So does anyone have any questions for JC or are you afraid? I have one. Okay, we have one. Excellent.

And let's get the microphone. Let's be patient with the microphone. Here we go. So I was wondering if you, what are your thoughts on China's court decision that AI cannot be used to replace human employees strictly for monetary purposes?

Yeah, that is a very good question. Candidly, I don't know if I have a good thought on that yet, because I quickly get into ethical considerations, and it's a dicey space, right? I think economics, right or wrong, are going to win out. And so the pressure's always going to be on how do you produce goods and services at either a lower cost, greater profit margin, I think is going to hold, at least in the near term.

And so whether or not that decision in China sets off a trend globally, we'll have to wait and see. I wish I had a better answer for that, but it's a dicey sort of area with no clean answer behind it. What happens to economics, though, when unemployment rates are crazy because of AI. So you're not producing something that nobody can buy.

Yeah, So the promise or the, my framing for this, I go back to why is AI even a thing from its start. What's target is to at some point push the cost of goods and services to 0, as close to 0 as we can get. And you quickly get into this notion of some future where there's infinite abundance. So the scarcity that drives all of our economics goes away and I can go and build something, consume something, get it, whatever I need in any form.

And there's going to be some interim period between now and getting to that. It's going to be very interesting. And I don't have the answer for what it's going to look like by any means. So it's...

We've got another question here. I'm just going to suggest that we could make a conference for AI in society. That's not what this particular conference is, but I totally understand your issue. Mark Adressing had an interview three months ago, and he talked about that AI with the boom just started, right?

We haven't even started. And he focuses on productivity, and he mentions something that We don't think productivity is the way it was like in the 1800s and the possibilities of growth. The way society was in the late 1800s to what it is in the early 2000s, it's really different, right? Everybody was expecting growth.

And what he mentions is there's the way we're building things is actually, since everybody can do it, might reduce the cost of getting things like you mentioned. But one of the things, there's a discussion between him and Peter Thiel about there's not going to be new stuff, new technology being built. But what's your take on that? Because I find it kind of weird those two guys are fighting over.

Like we've done everything under the sun and the other guys. No, there's like, there's more stuff coming down the road. And what's your take on that? Great question.

I land on the side there's more, there's more stuff coming, and the simple framing I have on. This is where I ended with space, right? Space is truly the next frontier that we're starting to step into and embrace, and I was chatting with a group last night as an example. There's a whole other mirror economy to emerge in space, so you've got the notion of... take how we produce goods right now.

How do you get a spatula into your kitchen? Well, there's, it's produced in China, gets on a barge, goes across the ocean, gets on a truck, gets to the store, and so on. That same supply chain has a place in space as well. SpaceX will be that barge that gets stuff in the low orbit.

There'll be another economy, companies that emerge that then take that off the barge and they'll orbit and move it to the moon, to Mars, and so on, all that's going to require new invention, new technology to continue to emerge, right? So I'm very much in the camp of there's more technology, more invention, more creation to emerge, and AI is going to enable that, is my perspective. Productivity, right? The change we see that, some of us have seen in productivity, that is opening. other doors that some people even imagine that will be doing that stuff.

Correct. Productivity is, I think, the easy place to start, right? Because it reflects on there's a fundamental inefficiency to how we run our economies or organizations, whatever it is. And so there's a gap there that can be closed with AI, which comes to life in productivity, right?

And so it's a great starting point. Good question. My question comes from your initial bit about your music, your examples. And I'm thinking about the inspiration, right?

The idea to take something old and make it new in a new way, like that new idea that didn't exist, like that inspiration part. I suspect the AI generated songs, a human still did that. Like it would still generate human inspiration, like combine these different things in this way with this outcome. How does the inspiration or that part become AI?

That's what I'm trying to understand and think through. That's a good question. I mean, so Where I go with that is I get to what is the root of intelligence? And so is there something unique about humans that is beyond just finding patterns and information?

I would argue that humans maybe aren't really that unique in that sense, right? It's all just we're consuming information and building patterns out of it. So from an AI standpoint, Our form of AI right now is really based upon the transformer architecture, which may not be the form that gets us to super intelligence it may change, it may come in a different architecture, right? But it's still baked into patterns, and so...

There's no reason that an AI can't form that inspiration and create some new pattern that might seem like a net new-- it's never existed before, but everything is born out of something from the past, right? So that's my framing on it. So my probability is somewhere in there, maybe, right? So you mentioned the importance of context.

How do you envision and the tribal knowledge, how do you envision the extraction of that from a long-tenured employee without disrupting that work? Yeah, it's a good question. Culture drives this, so being diligent and trying to historically in the manufacturing environment, there's a concept of lean manufacturing, which In simple terms, it's basically like any extra action was waste. So it was all focused on how do you take out unneeded actions.

Well, a shift that has to be made is that action to get tribal knowledge out of the individuals is now of value to the organization, to the AI, and so on. And so that's the first step in this, is now building that back in to the process and trying to build it in as passive as you can. And so in our state, it's either allocating some amount of time, or it's just in their day to go and have an interview, an interaction with an AI that can help them work through some question and answer to extract that, to putting a device on them where they're just wearing this and speaking into a recorder throughout the day or for some period of time. There's no easy answer to that.

It's going to vary as you go across enterprises and industries and so on to try to get that knowledge out of the individuals. Some areas it might be easier if The typical, the amount of work you do is focused on a computer. It's easier to track that. Meta's doing this as an example.

It came out last week that they're forcing all their employees to have tracking software on their computers. So all their mouse clicks, everything is now going to be tracked and they don't have an option out of it. It's easier to enforce it there than it is on the factory floor. And it's going to be a challenge for any organization to have to wrestle with is what's the cultural impact of me trying to get information out of my workforce because it's going to benefit my AI to now be performant.

All right. And