Dabnis AI, The Green Solution Transcript

AI Generated

Speaker 1 00:00:00
So the next time you're sitting at your desk, you know, typing a prompt into an AI, I want you to imagine taking a standard plastic bottle of fresh drinking water, just unscrewing the cap, and pouring it directly onto the floor.

Speaker 2 00:00:14
Just dumping it right out.

Speaker 1 00:00:15
Yeah, exactly. Just dumping it. Because according to the research we are diving into today, a typical session with a large language model, which is, like, what, maybe 20 to 50 relatively short queries that literally evaporates roughly 500 milliliters of fresh water.

Speaker 2 00:00:32
Wow.

Speaker 1 00:00:33
Yeah. It just vanishes into the atmosphere.

Speaker 2 00:00:35
It kind of, it completely shatters that really comforting illusion we have about the digital world, right?

Speaker 1 00:00:41
Yes.

Speaker 2 00:00:41
Because you use the term the cloud, mainly because we want our technology to feel weightless.

Speaker 1 00:00:45
Right. Like, it's just magic.

Speaker 2 00:00:46
Exactly. We want to believe our emails and our, you know, our AI assistants just exist in some infinite ethereal vapor up in the sky, completely detached from the actual messy physical reality of the earth.

Speaker 1 00:00:59
But when you actually read through the stack of sources we've gathered for you today about this current AI boom, you quickly realize the cloud is, well, it's definitely not in the sky.

Speaker 2 00:01:08
No, not at all.

Speaker 1 00:01:09
The cloud is actually made of millions of tons of concrete, reinforced steel, high voltage power lines, and, like we said, actual physical rivers of water. I mean, it is rapidly eating the physical world.

Speaker 2 00:01:21
It is. And the defining bottleneck for the whole future of AI has shifted entirely. Like, it is no longer about who can write the smartest software or, or even who can manufacture fastest silicon chips.

Speaker 1 00:01:33
Right.

Speaker 2 00:01:33
The hard limit on commercial AI right now is the absolute physical boundary of our local environments. I mean, we are just hitting a massive wall of thermodynamics.

Speaker 1 00:01:43
And that is exactly what we're unpacking for you today on this deep dive. We are going to explore the staggering physical toll of commercial AI, the massive data centers, the power grids that are just totally buckling under the pressure, and, of course, the local water supplies being drained to keep these systems from, you know, literally melting down.

Speaker 2 00:02:02
Yeah, huge issue.

Speaker 1 00:02:03
But we aren't just looking at the crisis here. The sources actually point to a radical paradigm shift happening right now in response to all this. We're going to look at what happens when businesses decide to just abandon this massive cloud infrastructure entirely.

Speaker 2 00:02:18
Which is a huge deal.

Speaker 1 00:02:19
It is. They're looking at moving in favor of local autonomous solutions. Specifically, we're focusing on a system called Dabness AI today. We'll explore how this local approach is being positioned as a massive green alternative and the economic shockwave it could cause.

Speaker 2 00:02:37
Right, because if this shift really takes hold, we could see billions or, you know, potentially trillions of dollars of current AI infrastructure investments rendered completely redundant.

Speaker 1 00:02:46
Just stranded. It's wild.

Speaker 2 00:02:47
That really is. But, to appreciate why a local solution like DabnessAI represents such a massive disruption, we really have to start by looking under the hood of the current cloud model.

Speaker 1 00:02:57
Right.

Speaker 2 00:02:58
We have to look at what's being called the 1,000 x power problem.

Speaker 1 00:03:01
So let's break that down for everyone because I think a lot of people just completely misunderstand what's actually happening when they use an AI. Because, you know, when you type a prompt into a generative AI model, the interface looks exactly like a traditional Google search.

Speaker 2 00:03:14
Yes, just a little white text box.

Speaker 1 00:03:16
Exactly. You hit enter and text pops out. But, mechanically, under the surface, the compute power required is just fundamentally different.

Speaker 2 00:03:25
Oh, completely. Because a traditional web search is, well, it's essentially a retrieval task. It's like asking a librarian to go into the stacks, find a specific book that has already been written, and just hand it to you.

Speaker 1 00:03:38
Which doesn't take much effort.

Speaker 2 00:03:39
Right. It takes a very small amount of energy to locate and retrieve existing data. But generative AI is completely different. It is probabilistic synthesis.

Speaker 1 00:03:48
So it's more like, like hiring an author to sit down and write a brand new, totally original book for you, word by word, every single time you ask a question.

Speaker 2 00:03:57
That analogy actually gets right to the core of the compute mechanism because the AI is literally calculating the mathematical probability of every single word or, you know, every pixel being generated in real time.

Speaker 1 00:04:09
And that takes a ton of juice.

Speaker 2 00:04:11
Exactly. The sources indicate that because of this intense computational load, a single AI related task can consume up to 1,000 times more electricity than a traditional web search.

Speaker 1 00:04:22
1,000 times. And when you multiply that by, I don't know, millions of enterprise users integrating AI into their daily workflows, the power demands become astronomical.

Speaker 2 00:04:32
They do.

Speaker 1 00:04:32
I was looking at a projection in the notes, from Goldman Sachs Research, and they are forecasting that data center power demand will surge by 165% by the year 2030 just compared to 2023 levels.

Speaker 2 00:04:44
A 165% increase on a baseline that was frankly already straining the grid. I mean, McKinsey is estimating that meeting this new demand will require a $5,200,000,000,000 investment in AI-ready infrastructure.

Speaker 1 00:04:56
Trillion? With a T?

Speaker 2 00:04:57
With a T, yeah. And a key reason for this continuous power draw is a fundamental shift in how AI operates out in the wild. Basically, we are moving from the training phase to the inference phase.

Speaker 1 00:05:08
Okay, let's clarify those terms for anyone who might not be deep in the developer weeds. So, training is the initial phase, right?

Speaker 2 00:05:15
Yeah, exactly.

Speaker 1 00:05:15
It ires when a company feeds just billions of documents into a massive supercomputer to teach the AI how to understand language. It errors this massive energy spike, but it eventually finishes. Inference is what comes after, right?

Speaker 2 00:05:29
Right. Inference is the deployment. It errors the actual everyday usage of the model by you and me.

Speaker 1 00:05:34
The everyday stuff.

Speaker 2 00:05:35
Yeah. The AI running in the background, analyzing a spreadsheet, drafting an email, or, you know, generating code. And sources note that inference now accounts for roughly 80% to 90% of all AI computing.

Speaker 1 00:05:47
So instead of a one time massive energy spike, inference represents this permanent twenty four seven baseline draw on the power grid. Like, it just never stops.

Speaker 2 00:05:56
Never. Which completely forces the hyperscalers, you know, the massive tech companies operating these millions of square feet of server farms, to totally rethink their infrastructure.

Speaker 1 00:06:06
How so?

Speaker 2 00:06:07
Well, historically, you wanted to build a data center close to major population hubs

Speaker 1 00:06:13
Right.

Speaker 2 00:06:13
Or financial centers.

Speaker 1 00:06:14
Right. For speed.

Speaker 2 00:06:15
Exactly. You wanted the physical distance between the server and the user to be as short as possible to reduce latency so the data traveled instantly.

Speaker 1 00:06:23
But the sources show that geography is, it's basically no longer dictating where these facilities are built. It is purely thermodynamics now.

Speaker 2 00:06:31
Quanta percent.

Speaker 1 00:06:31
They are looking at places like Alberta, Canada, or specific pockets of The US Midwest or even The UAE. I mean, they are moving away from traditional tech hubs simply because they are hunting for regions that have gigawatts of surplus electricity just sitting on the grid.

Speaker 2 00:06:47
They are essentially defecting from the public utility model altogether. Yeah, because the power requirements are so vast that hyperscalers are realizing standard municipal grids simply cannot support them. The research actually highlights companies signing a 150 megawatt wind power purchase agreements or actively looking into funding nuclear small modular reactors - SMRs - just to guarantee an uninterrupted power supply for a single facility.

Speaker 1 00:07:13
That's insane. But, I actually want to challenge this narrative for a second, though. Sure. Because one of the defining characteristics of the tech industry over the last, like, fifth years has been this incredible leap in hardware efficiency. You know, Moore's Law, better architecture, smarter software. Right. Companies like NVIDIA and AMD - they are constantly releasing new chips that can perform way more calculations per watt of electricity. So shouldn't this hardware efficiency eventually just solve the power crisis naturally?

Speaker 2 00:07:42
I mean, it seems intuitive, right, that more efficient chips would lead to lower overall power consumption. But in economics, there is this phenomenon known as the Jevons paradox. It's named after a nineteenth century economist, William Stanley Jevons. And he observed that when technological improvements increase the efficiency of coal usage, the overall consumption of coal actually skyrocketed rather than dropping.

Speaker 1 00:08:06
Oh, because the efficiency made coal cheaper and more useful, so industries just found entirely new ways to burn it.

Speaker 2 00:08:13
Exactly. And when you apply the Jevons paradox to AI hardware, the mechanics are identical. Right. As GPUs become more efficient at running AI models, the cost of generating a response drops significantly.

Speaker 1 00:08:25
So it becomes faster and cheaper to deploy.

Speaker 2 00:08:27
Which means businesses don't just use it for a few queries a day. If it's cheap and fast, they integrate it into absolutely everything.

Speaker 1 00:08:34
Right. They have AI reading every single incoming customer service email, summarizing every meeting, monitoring every line of code.

Speaker 2 00:08:42
Exactly. The scale of deployment expands so aggressively that it completely overwhelms the efficiency gains of the individual hardware components. The demand just wildly outpaces the optimization, driving aggregate power consumption right through the roof.

Speaker 1 00:08:56
Okay, that definitely explains the electrical grid strain. But electricity is only half of the thermodynamic equation here, right?

Speaker 2 00:09:02
That's right.

Speaker 1 00:09:03
Because there is a fundamental law of physics at play. Every single watt of electricity pumped into a computer chip eventually turns into heat.

Speaker 2 00:09:12
Yes, 100%.

Speaker 1 00:09:14
And when you pack tens of thousands of these high performance GPUs into a single building, you are generating just an incomprehensible amount of thermal energy. Which brings us back to that plastic water bottle we dumped on the floor at the beginning.

Speaker 2 00:09:29
Hidden thirst.

Speaker 1 00:09:31
We have to talk about it.

Speaker 2 00:09:32
We do. Because to prevent these densely packed servers from literally melting down under their own thermal load, they require massive cooling systems. Now for decades, data centers just used industrial air conditioning.

Speaker 1 00:09:44
Which makes sense.

Speaker 2 00:09:45
But air is a relatively poor conductor of heat, and the new generation of AI chips runs so hot that air cooling is physically incapable of removing the heat fast enough.

Speaker 1 00:09:54
So they have to move to liquid cooling, right? Because liquids can absorb and transfer heat much more efficiently than air.

Speaker 2 00:10:00
Up to 3,000 times more efficiently, actually. Wow. Yeah. And the most common method involves chilled water loops. Basically, cold water is pumped into the data center, absorbing the heat from the servers. But that water, which is now incredibly hot, has to be cooled back down before it could be circulated again.

Speaker 1 00:10:20
And this is where the physical water loss actually happens. So, walk us through the mechanism of an evaporative cooling tower.

Speaker 2 00:10:26
Right. So, the hot water from the data center is piped up to these massive cooling towers, usually up on the roof of the facility. Outside air is blown across this hot water, and through the process of evaporation, the heat is released into the atmosphere.

Speaker 1 00:10:39
But in doing so, a significant volume of the water itself is evaporated as steam, right? It physically leaves the system.

Speaker 2 00:10:45
Yes. That is the 500 milliliters for every 20 to 50 prompts we talked about.

Speaker 1 00:10:50
So the heat generated by your local computer query literally boils a bottle's worth of water into the sky at some remote data center.

Speaker 2 00:10:58
Exactly. And when you scale that up to the enterprise level, the numbers detailed in these sources are just staggering.

Speaker 1 00:11:04
They really are.

Speaker 2 00:11:05
Take a look at the training phase for GPT-four. A massive portion of that compute took place in a cluster of data centers in West Des Moines, Iowa.

Speaker 1 00:11:15
Oh, I read about this.

Speaker 2 00:11:16
Yeah. According to the research, in a single month, those facilities consumed 11,500,000 gallons of freshwater.

Speaker 1 00:11:23
And just to put that in perspective, 11,500,000 gallons accounted for approximately 6% of that entire district's water usage for the month.

Speaker 2 00:11:31
Just for one facility?

Speaker 1 00:11:33
Yeah. And that was just during the training phase. Hyperscale facilities running continuous, 247 inference workloads, the daily consumption can range anywhere from 1,000,000 to 5,000,000 gallons of water every single day.

Speaker 2 00:11:44
It's unbelievable.

Speaker 1 00:11:45
It really is. But I do want to bring up a counterargument I saw while reading through the broader context and the sources. Sources.

Speaker 2 00:11:51
Okay, let's hear

Speaker 1 00:11:51
it. Because nationally, like across the entire United States, data centers account for less than 1% of the total water supply. And when you compare that to something like agriculture, which uses roughly 70% - I mean, less than 1% - sounds like a rounding error. So why is this being categorized in the research as a crisis?

Speaker 2 00:12:09
That's a common pushback. But looking at the national aggregate fundamentally misunderstands the physical mechanics of hydrology.

Speaker 1 00:12:17
How so?

Speaker 2 00:12:18
Well, water is incredibly heavy and extremely expensive to transport over long distances. There is no national water grid where you can seamlessly route surplus water from a flooded region to a drought stricken one.

Speaker 1 00:12:30
Ah, I see.

Speaker 2 00:12:31
Water is hyper local. It is drawn from specific, localized watersheds, aquifers, and municipal reservoirs.

Speaker 1 00:12:39
Right. So, you can't just take water from a rainy week in Seattle and use it to cool a data center down in Arizona.

Speaker 2 00:12:44
Exactly. Which means that while the national percentage is tiny, dropping a hyperscale facility that drinks 5,000,000 gallons a day into a single municipality creates an immediate severe shock to that specific local environment.

Speaker 1 00:12:58
It just drains the area.

Speaker 2 00:12:59
Yeah. It puts the tech facility in direct competition with local drinking water supplies and local farming.

Speaker 1 00:13:04
And the sources actually cite a very clear example of this friction in Santiago, Chile.

Speaker 2 00:13:10
Yes, that was a huge case.

Speaker 1 00:13:12
A major tech giant proposed a data center project there that faced massive public and legal backlash. The facility was projected to evaporate around 7.6 million liters of potable water daily, but the problem was it was slated to be built in a region suffering from a historic mega drought.

Speaker 2 00:13:32
People were furious.

Speaker 1 00:13:33
Yeah, the local population was literally protesting under the slogan Your cloud is drying my river. I mean, it turns a global technological achievement into a highly concentrated local resource stream.

Speaker 2 00:13:46
It really does. And the physical constraints of the cloud model, you know, the buckling regional power grids and the hypolocal water conflicts

Speaker 1 00:13:53
-

Speaker 2 00:13:53
they are forcing the industry to look for alternatives.

Speaker 1 00:13:56
Because the current trajectory relies on this massive centralization which just isn't sustainable.

Speaker 2 00:14:00
Right. But the sources highlight a growing movement toward extreme decentralization. What if a business didn't need the massive water guzzling hyperscale facility at all?

Speaker 1 00:14:10
And this is exactly where we get into the paradigm shift the sources are pointing to. They introduce a completely different architectural approach to commercial AI, and it focuses heavily on a solution called Dabness AI. And the defining characteristic of a Dabness AI solution is that it operates entirely locally and autonomously.

Speaker 2 00:14:29
And we really need to define exactly what local means in this context because it's not just some marketing buzzword.

Speaker 1 00:14:35
Definitely not.

Speaker 2 00:14:36
It means the AI software is deployed directly onto the physical hardware sitting inside a company's own office or, you know, the private server

Speaker 1 00:14:44
room. So no remote connection?

Speaker 2 00:14:46
None. It requires zero internet connection to function. It does not ping a remote data center. It does not need a dedicated power station. It is completely air gapped.

Speaker 1 00:14:55
It is essentially taking that sprawling, multimillion square foot factory and just shrinking the intelligence down so it operates independently, completely off the grid.

Speaker 2 00:15:05
Exactly.

Speaker 1 00:15:05
And the sources explicitly promote this dabness AI model as the ultimate green solution to commercial AI. Because if a business runs their AI locally like that, they're completely removing themselves from the hyperscale ecosystem.

Speaker 2 00:15:18
The environmental math there is just incredibly straightforward.

Speaker 1 00:15:22
Yeah.

Speaker 2 00:15:22
A local deployment requires absolutely no massive cooling towers Yeah. Evaporating millions of gallons of water. It requires no dedicated nuclear reactors or wind farms just to keep the lights on. It just uses the existing standard power draw of the business's own local servers.

Speaker 1 00:15:39
It's amazing. But beyond just the green initiatives, the sources dig heavily into why a business would actually make this pivot from an operational standpoint. Like, why leave the convenience of the cloud at

Speaker 2 00:15:51
all? Right.

Speaker 1 00:15:52
It really comes down to absolute data privacy and predictable costs. Let's talk about API tokens for a moment, because I like to think of the cloud AI model as basically renting a supercomputer, by the word.

Speaker 2 00:16:04
That is mechanically exactly how an API or application programming interface functions in this space.

Speaker 1 00:16:10
Okay, bring that down for us.

Speaker 2 00:16:11
So when a company uses a cloud based AI, they have to send their proprietary data over the Internet to the Hyperscaler's remote server. The server processes it and sends the answer back, and the company is billed based on tokens which are roughly equivalent to pieces of words.

Speaker 1 00:16:25
Right. So every single time your employees use the tool to, say, summarize a document or analyze a private financial spreadsheet, you are paying a meter that is just constantly spinning.

Speaker 2 00:16:35
The costs add up incredibly fast.

Speaker 1 00:16:37
Oh, I bet. The more you integrate AI into your business, the higher your monthly operating expense just climbs. Plus, you are constantly piping your most sensitive corporate data out of your secure building and into a third party server.

Speaker 2 00:16:49
Which is a huge security risk. But a local, autonomous solution like Dabness AI fundamentally flips that entire economic and security model.

Speaker 1 00:16:58
Because it's local.

Speaker 2 00:16:59
Because the system is completely air gapped and runs on the company's own hardware, the data literally never leaves the building. Absolute data privacy is essentially guaranteed by physics.

Speaker 1 00:17:10
Right. There is no physical connection to the outside Internet to be breached.

Speaker 2 00:17:14
Exactly. And furthermore, the business is completely immune to API token costs. They own the local processing capability outright.

Speaker 1 00:17:22
You buy the asset instead of renting it by the minute? Yeah. And you have zero connectivity dependence. Like, if a cuts the fiber optic internet cable outside your office, your local AI just continues to function flawlessly because it doesn't need to reach the cloud.

Speaker 2 00:17:37
When you combine that absolute privacy, the immunity to unpredictable token costs, and obviously the elimination of the massive environmental footprint, The sources argue that local autonomous AI is poised to become the new standard for enterprise deployment.

Speaker 1 00:17:54
Which brings us to the final and honestly perhaps the most disruptive point in today's deep dive.

Speaker 2 00:17:58
The financial side.

Speaker 1 00:17:59
Yeah. If local solutions like Dabness AI do become the norm for businesses because, you know, it solves the privacy, the costs, and the sustainability crises all at once, we have to look at the financial shockwave this will cause to the current AI establishment.

Speaker 2 00:18:13
Oh, it would be massive. We discussed earlier that the hyperscalers, the major tech giants, are pouring just unprecedented amounts of capital into building centralized cloud infrastructure.

Speaker 1 00:18:22
Unprecedented is putting it lightly?

Speaker 2 00:18:24
Very lightly. The sources show they're projected to invest over $350,000,000,000 in data centers in the year 2025 alone.

Speaker 1 00:18:32
$350,000,000,000 in a single calendar year.

Speaker 2 00:18:35
It's hard to even fathom.

Speaker 1 00:18:36
And the construction costs for these specific AI-ready facilities are compounding by 7% annually. A single building can now cost anywhere between $500,000,000 and $2,000,000,000 to complete.

Speaker 2 00:18:48
And because of the massive power constraints we explored earlier, these companies aren't just pouring concrete.

Speaker 1 00:18:54
What else are it's doing?

Speaker 2 00:18:54
They are locking themselves into decades long energy contracts, underwriting nuclear facilities, and purchasing vast amounts of carbon credits to try and offset their massive footprint. Yeah. They are essentially building an architecture designed for a world where every single enterprise inference task must travel to their centralized servers.

Speaker 1 00:19:14
So here is the multi trillion dollar question for you. What happens to that projected $5,200,000,000,000 infrastructure build out if the business world collectively decides they just don't want to pipe their private data to the cloud?

Speaker 2 00:19:27
Right.

Speaker 1 00:19:28
What happens if they look at the environmental destruction, look at the unpredictable API costs, and decide to adopt a local autonomous solution like DabnessAI instead.

Speaker 2 00:19:37
Well, the sources suggest we could be witnessing the inflation of a historic infrastructure bubble. A bubble? Yeah. If enterprise demand shifts from the centralized cloud to local edge computing, those massive AI factories risk becoming stranded assets.

Speaker 1 00:19:51
Okay, stranded assets. Explain the mechanics of that term for our listeners who might not be familiar.

Speaker 2 00:19:56
So, a stranded asset in economics is an investment that has suffered an unanticipated devaluation, or conversion into a liability long before the end of its useful life.

Speaker 1 00:20:07
Give me an example.

Speaker 2 00:20:08
Imagine a massive $2,000,000,000 facility sitting on a multi acre footprint. It requires a dedicated power plant just to keep its servers idling and a steady stream of water to keep the ambient temperature stable.

Speaker 1 00:20:20
So the overhead costs to simply keep the building turned on are astronomical.

Speaker 2 00:20:24
Exactly, and that facility only generates a return on investment if millions of corporate users are constantly pinging it, paying those API token fees for inference tasks.

Speaker 1 00:20:34
But if they stop pinging it?

Speaker 2 00:20:35
Right. If a massive swath of those corporate users pull their workflows entirely in house onto local machines running Dabness AI, the revenue stream for that specific cloud server just evaporates.

Speaker 1 00:20:46
But the massive facility is still sitting there. The decades long energy contract still has to be paid. The servers are still drawing power, but no one is renting them by the word anymore.

Speaker 2 00:20:55
Which means millions, if not billions, of dollars of present AI infrastructure investment could be rendered completely redundant almost overnight.

Speaker 1 00:21:04
Just wiped out.

Speaker 2 00:21:05
Pretty much. We're talking about hyperscale real estate, dedicated high voltage power pipelines, massive evaporative cooling systems, and long-term utility commitments all becoming financially unviable.

Speaker 1 00:21:17
Simply because the demand vanished from the cloud and moved directly into the local offices of businesses themselves.

Speaker 2 00:21:23
Yes. It is a fascinating look at the cycle of technological disruption.

Speaker 1 00:21:27
It really is.

Speaker 2 00:21:27
I mean, we are watching the giants build an unimaginably massive, centralized, resource intensive monopoly. And the research suggests they could be disrupted by a decentralized autonomous solution that requires a fraction of the hardware.

Speaker 1 00:21:43
A solution that operates entirely off the grid and is fundamentally more secure for the end user.

Speaker 2 00:21:47
The real vulnerability of the hyperscalers here is their lack of agility. You just cannot easily pivot a $2,000,000,000 concrete monolith that is physically hardwired to a local power grid and watershed.

Speaker 1 00:22:01
It's stuck there.

Speaker 2 00:22:02
It is. If local AI becomes the preferred enterprise model, the financial write downs across the centralized cloud sector would be unprecedented in the history of modern technology.

Speaker 1 00:22:11
It really forces you to reevaluate absolutely every assumption about where this technology is heading. I mean, we started this deep dive looking at the physical reality of the cloud - the 165% surge in power demand, the billions of gallons of hyperlocal water being evaporated just to cool the servers, and the staggering financial cost of sustaining that model.

Speaker 2 00:22:32
Which is a heavy toll.

Speaker 1 00:22:33
And the trajectory is colliding directly with the physical limits of our environment. But the sources make a really compelling case that the solution isn't to stop using AI but to fundamentally change where and how the compute happens. By severing the connection to the cloud entirely and running autonomous systems like Dabness AI locally, businesses might just sidestep the resource crisis entirely while simultaneously protecting their most sensitive data.

Speaker 2 00:22:59
It definitely leaves you with a very different perspective on the physical landscape of the future.

Speaker 1 00:23:03
For

Speaker 2 00:23:03
sure. I mean, the next time you deploy an AI tool or, you know, approve an enterprise software budget, consider that the future of computing might not be about building bigger power plants or draining larger rivers to support massive corporations.

Speaker 1 00:23:17
Right.

Speaker 2 00:23:17
True power and efficiency might actually lie in total independence from the grid, Because you really have to wonder, how long will it be until the sprawling, multibillion dollar data centers being built today become the abandoned industrial ruins of tomorrow.

Speaker 1 00:23:33
It's a massive shift in perspective. Leaving the cloud behind might just be the most grounded decision a business can make. Thanks for joining us on this deep dive, and we'll catch you next time!