CES 2026: The Year Humanoid Robots Got Shipping Dates and Price Tags | AI workforce transformation

CES 2025 showed us the promise. CES 2026 delivered production timelines, price tags, and deployment dates. The question has shifted from "if" to "when" — and increasingly, the answer is "soon."

In this special AI Dispatch, we unpack the seismic shift that just happened in Las Vegas, from Boston Dynamics announcing factory deployment by 2026 to the $25,000 Engineered TRIO humanoid robot shipping this summer.

In this episode, you'll learn:

• The 9 humanoid robots that dominated CES 2026 — with real prices and ship dates
• Boston Dynamics' factory deployment timeline and what it means for manufacturing jobs
• The $25,000 TRIO humanoid robot: why consumer-grade robots change everything
• LO3's autonomous home robot that actually thinks for itself with no hidden human operator
• Why every major chipmaker is now positioning itself as a robotics company
• What humanoid robots at scale mean for your career in the next 3 to 5 years

Subscribe to Surviving AI and leave a review — it helps other workers find this show.

Surviving AI podcast, CES 2026 robots, humanoid robots 2026, Boston Dynamics factory, TRIO robot $25K, Carlo Thompson, robotics job impact, AI robots replacing workers, CES AI announcements, robot deployment timeline, manufacturing automation robots, consumer robotics, physical labor automation, robotics career impact

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Transcript

SPEAKER_00 0:00

Welcome back to the deep dive. This is the place where we take a mountain of critical sources, confidential reports, academic research, and uh industry forecasts, and we really just distill the actionable truth.

SPEAKER_01 0:11

Also that you, the learner, can cut through the noise, understand the complexity, and get straight to those strategic insights.

SPEAKER_00 0:17

And today, the signal could not be more urgent. We are doing a really crucial, time-sensitive deep dive that directly supplements the surviving AI with Carlo Thompson curriculum.

SPEAKER_01 0:30

This is absolutely essential listening, especially for anyone who's tracking the AI employment revolution right now.

SPEAKER_00 0:35

That's right. And if you are part of the curriculum, you already know our core premise. It's uncompromising. The AI revolution isn't some future worry.

SPEAKER_01 0:43

It's here. It's right now.

SPEAKER_00 0:45

Exactly. Our mission isn't to, you know, indulge in theoretical sci-fi discussions, it's to provide a practical survival guide and one that is focused purely on the immediate present.

SPEAKER_01 0:54

We're talking actionable frameworks, real case studies, and most importantly, those critical high certainty timelines.

SPEAKER_00 1:00

Aaron Powell The ones that fall between 2027 and 2030.

SPEAKER_01 1:04

Right. And to get that level of precision, we've spent a lot of time dissecting the data coming out of the technology world's biggest proving grounds over the last two years.

SPEAKER_00 1:13

Aaron Powell You're talking about CES 2025 and CES 2026.

SPEAKER_01 1:17

Exactly. And this side-by-side comparison, it's not just interesting history. It gives us a crystal clear metric to track the transition from, well, from pure theoretical AI hype.

SPEAKER_00 1:28

The kind that gives you a marginally clever chatbot.

SPEAKER_01 1:31

Yes, that stuff. And it lets us track the transition all the way to the emergence of real, deployable physical infrastructure, the kind that is already dramatically reshaping the employment landscape.

SPEAKER_00 1:41

We're talking logistics, manufacturing.

SPEAKER_01 1:44

Even domestic services. It's happening across the board.

SPEAKER_00 1:46

So if you, the learner, walk away with only one thing today, what is the core mission statement we're trying to fulfill in this deep dive?

SPEAKER_01 1:54

The goal is actionable, granular intelligence. We are moving past the general sort of existential worries and getting directly into hard dates, specific product specs, and you know, the economic realities.

SPEAKER_00 2:06

So you can use this data.

SPEAKER_01 2:08

You need to use this data to accurately assess your specific job automation risk, to build or uh refine your concrete 24 to 36 month action plan.

SPEAKER_00 2:19

And that plan has to be based on confirmed corporate roadmaps, not speculation.

SPEAKER_01 2:24

Precisely. And most importantly, you have to internalize the reality that AI as a tool has now functionally left the screen. It is entering the physical world.

SPEAKER_00 2:33

So we're going to look closely at the machines.

SPEAKER_01 2:35

The machines capable of performing complex real-world work in factories, in homes, and on the road. This gives you the necessary external data to make immediate strategic choices.

SPEAKER_00 2:44

Okay, let's unpack this. It sounds like a monumental shift. And we're using the recent CES events as our chronological markers to define what we're calling the AI revolution's inflection point.

SPEAKER_01 2:55

That's the perfect framing for it.

SPEAKER_00 2:56

So let's start with a quick rewind. Let's go back to CES 2025. That was the year that AI's transformative potential was really cemented in the public eye. I mean, the numbers were huge, over 141,000 attendees.

SPEAKER_01 3:08

And 4,500 exhibitors. It was massive.

SPEAKER_00 3:10

But if you look back at the sources from then, the focus was still very much on what we might categorize as incremental innovation.

SPEAKER_01 3:19

Aaron Powell That categorization is spot on. 2025 was all about making existing devices smarter, faster, and more personal. The AI applications were largely contained within the digital sphere. Trevor Burrus, Jr.

SPEAKER_00 3:31

Where they were just enhancing existing hardware functions. Right. I mean, we saw the debut of NVIDIA's Cosmos platform, which looking back, it certainly signaled their long-term intentions for physical systems.

SPEAKER_01 3:42

Oh, for sure. The writing was on the wall. Trevor Burrus, Jr.

SPEAKER_00 3:44

But for the average consumer, for the professional on the ground, the immediate buzz was about AI-powered gadgets.

SPEAKER_01 3:50

Trevor Burrus, Jr.: Smart health tracking mirrors.

SPEAKER_00 3:51

Trevor Burrus, Jr.: Enabled home appliances, some initial breakthroughs in really specialized robotics. And generative AI was just starting to redefine mantend.

SPEAKER_01 4:00

Trevor Burrus, Right. Think hyper-personalized advertising, some breakthrough tools for creators. But the core theme was, as you said, incremental.

SPEAKER_00 4:07

It was about optimizing what we already do.

SPEAKER_01 4:10

Aaron Powell Yes. But within that optimization, we saw this unexpected, almost psychological trend emerge in 2025, which the sources started calling affectionate AI.

SPEAKER_00 4:21

Aaron Powell Affectionate AI. That term sounds less like technology and more like a relationship status. What did the sources actually mean by that?

SPEAKER_01 4:29

Aaron Powell Well, it was a serious trend, even if it sounds a little unsettling. Corporations like LG, for instance, specifically debuted appliances with affectionate intelligence.

SPEAKER_00 4:38

Okay, what is that?

SPEAKER_01 4:40

These were things like conversational refrigerators and TVs. They weren't just designed to be tools, but to actively build a sense of, well, companionship and trust with the user.

SPEAKER_00 4:50

I have enough trouble getting my human family members to listen to me. A conversational fridge sounds like an emotional burden I do not need before my morning coffee.

SPEAKER_01 4:58

I get that.

SPEAKER_00 4:59

What's the psychological angle here? Are they trying to manufacture dependency on a refrigerator?

SPEAKER_01 5:03

The intention, at least according to the marketing materials, was to blur the line between a utility and a companion, to reduce the perceived friction of interaction.

SPEAKER_00 5:13

Just make the tool feel more personal.

SPEAKER_01 5:15

Exactly. And we also saw specialized emotional robotics emerge, like the robotic pet Mirumi. Its whole purpose was companionship and trust building through this refined, almost human-like interactive design.

SPEAKER_00 5:28

So it's still AI enhancing an existing product, but with this clear emotional overlay.

SPEAKER_01 5:34

A very deliberate one, an attempt to make our tools personable.

SPEAKER_00 5:37

Now hold that thought about emotional AI because when we fast forward just one year to CES 2026, the entire discussion just completely changed. It is a night and day difference. The sources all agree that AI stopped being a simple feature or, you know, acute addition. It became pure, unavoidable infrastructure. What exactly marked this critical shift in 2026?

SPEAKER_01 5:58

It was a fundamental change in architectural design. And it's really summarized by two key transformations that happened right in that 12-month span between the two shows. These are critical for anyone tracking that 2027-2030 employment impact.

SPEAKER_00 6:11

Okay, so give us the big two.

SPEAKER_01 6:12

Number one, on device processing, AI moves significantly, almost decisively, from relying on the cloud and server farms to running directly on the device itself. And this shift, which we'll get into the weeds on in the silicon section, it fundamentally liberates physical AI. It cuts it loose from bandwidth constraints, server latency, and that persistent dependence on external compute resources.

SPEAKER_00 6:36

There was a rule of thumb in one of the sources, right?

SPEAKER_01 6:38

Aaron Powell A very stark one. It basically said if your gadget still relies entirely on a distant server farm to perform its primary function, it is, in a professional sense, already obsolete.

SPEAKER_00 6:50

So local intelligence means speed, immediate responsiveness, better user privacy, and most crucially for industrial and logistical use, reliability.

SPEAKER_01 6:59

Aaron Powell You said it earlier. You can't have a robot pause assembly because the Wi-Fi dropped.

SPEAKER_00 7:03

No way.

SPEAKER_01 7:04

Precisely. And the second transformation, which is really the operational consequence of local processing, is agentic productivity.

SPEAKER_00 7:10

The agentic productivity.

SPEAKER_01 7:12

This is the philosophical leap. It's the jump from AI as a passive assistant, the traditional model that just answers your questions, to systems that function as true butlers. But they take a high-level complex goal and they autonomously execute all the multi-step workflows, coordinating multiple devices to achieve it.

SPEAKER_00 7:32

Let's break that down for the learner. Because the difference between asking, what's the weather and plan and book my entire trip to Bali is Well, it's the difference between optimizing one single task and automating an entire job function. It fundamentally changes the relationship between the human and the machine. In the old paradigm, the human user was a micromanaging supervisor, right? You were guiding the AI step by step.

SPEAKER_01 7:54

Right. Okay, now search for flights. Okay, now look for hotels.

SPEAKER_00 7:57

Exactly. In the new agentic paradigm, the user stops micromanaging and starts delegating a complex outcome. The system reasons through constraints, schedules, contacts, and it coordinates all the necessary digital and physical assets across an entire ecosystem to fulfill that delegated goal.

SPEAKER_01 8:14

And that orchestration capacity.

SPEAKER_00 8:15

That's the aha moment. That defines what AI is capable of doing to a whole suite of job functions. That said, while the corporations were busy rolling out these ambitious agentic butlers, the sources suggest a pretty serious point of friction emerged on the consumer side.

SPEAKER_01 8:31

Oh, absolutely. Widespread fatigue and skepticism toward the AI everywhere marketing push.

SPEAKER_00 8:37

This is that essential consumer reality check. It's vital for a balanced view.

SPEAKER_01 8:41

Well, that corporate push led directly to a widespread backlash, which we are now calling AI washing.

SPEAKER_00 8:47

AI washing. We've seen this play out before with smart tech or blockchain, right? Where a company just slaps a buzzword on a product to inflate its perceived value.

SPEAKER_01 8:57

Aaron Powell It was egregious in 2026. One industry analyst noted it. We saw examples cited of simple hard-coded algorithms like a washing machine calculating load weight variables or a cleaning vacuum detecting a large object.

SPEAKER_00 9:10

Things I've done for years.

SPEAKER_01 9:11

For years. Yeah. And they were getting immediately rebranded as AI just to capitalize on the trend. They're changing the wording by releasing functionally identical incremental products.

SPEAKER_00 9:21

The fact that a computer uses a basic formula to calculate variables doesn't make it a revolutionary AI system.

SPEAKER_01 9:27

But the marketing said otherwise. And it created enormous confusion. It reached a point where Dell's head of product had to comment publicly.

SPEAKER_00 9:36

Right. I remember that. He said consumers were definitely not buying based on the AI label because, and this is a quote, the term probably confuses them more than it helps them understand a specific outcome.

SPEAKER_01 9:48

It's true. Consumers are still fundamentally buying based on core performance metrics, processing power, display quality, storage, memory, the basics.

SPEAKER_00 9:58

So the consensus from industry watchers on the ground in 2026.

SPEAKER_01 10:01

Was that the whole event felt less like a consumer electronics show and much more like a corporate electronics show. It was devoted to showcasing things that were primarily interesting to shareholders and executives looking for that next big growth driver.

SPEAKER_00 10:15

Which brings us precisely to the source of that shareholder excitement, the engine behind the agentic and physical transition, the hardware that makes it all possible. This is where the infrastructure wars truly ramp up, shifting from digital supremacy to controlling the physical world. When we talk about infrastructure for the physical AI world, one company has so clearly cemented its status as the foundational backbone, the one selling all the picks and shovels.

SPEAKER_01 10:42

NVIDIA.

SPEAKER_00 10:42

NVIDIA.

SPEAKER_01 10:43

Their strategy is a masterclass in platform development. They realized early on that the computational demands for machines to physically interact with the real-world calculating physics, real-time sensor fusion, generating synthetic training data are just far, far greater than generating a pretty image on a screen.

SPEAKER_00 11:02

Their focus has shifted from digital visualization to real-world modeling. And their Cosmos platform perfectly illustrates this. Our sources highlight that Cosmos isn't just a powerful chip, it's an entire ecosystem specifically designed to accelerate physical AI systems like autonomous vehicles and robots.

SPEAKER_01 11:19

Right. Cosmos provides the critical tool set, generative world models, and accelerated data pipelines. Think of it as the ultimate digital playground or simulator.

SPEAKER_00 11:28

A place where these complex physical machines can learn billions of scenarios and behaviors in a synthetic environment.

SPEAKER_01 11:34

Before they ever step foot on a factory floor, this is what makes scalable, safe robot deployment possible.

SPEAKER_00 11:41

And their reach is massive. It extends far beyond the lab.

SPEAKER_01 11:44

Oh, absolutely. Their D drive platform is the established engine for these huge partnerships across the automotive and logistics sectors. They're collaborating with giants like Toyota, Aurora, Uber, Continental.

SPEAKER_00 11:57

This isn't RD. This is foundational infrastructure for the global supply chain for transportation systems.

SPEAKER_01 12:02

And for the impending era of driverless trekking.

SPEAKER_00 12:04

And then on the sheer hardware side, the scaling is just relentless. Nvidia announced that its next generation AI superchip platform, dubbed VeraRubin, is already in full production.

SPEAKER_01 12:15

Yes.

SPEAKER_00 12:16

What does that move to a new superchip architecture signal about the demands of physical AI?

SPEAKER_01 12:22

Aaron Powell The Vera Rubin announcement confirms that the current generation of silicon simply isn't fast enough. It's not fast enough for the agentic systems that corporations are designing for that 2027-2030 window. The demands are just increasing exponentially.

SPEAKER_00 12:35

It's the physics, right?

SPEAKER_01 12:36

It's the physics. Physical AI requires constant real-time recalculation of physics, motor control, visual perception. It needs massive parallel processing power. Their Rubin is all about consolidating immense AI compute capabilities into a single power-efficient platform.

SPEAKER_00 12:53

Aaron Powell And one that's designed not just for data centers.

SPEAKER_01 12:56

Right. Potentially for deployment into high-end physical machines like the next generation of humanoid robots.

SPEAKER_00 13:02

Aaron Powell Okay, so here's where the infrastructure wars get tactical. If NVIDIA controls the large industrial backbone, the competition is fierce in that other key 2026 theme: local on-device intelligence.

SPEAKER_01 13:15

Aaron Powell As we established, agentic AI cannot rely entirely on a distant cloud server.

SPEAKER_00 13:20

The chip race is now a race for embedded intelligence.

SPEAKER_01 13:23

It has to be. If a device has to ask permission from the cloud every time it needs to make a decision, it's just too slow and too fragile for the physical world.

SPEAKER_00 13:30

So if NVIDIA is supplying the supercomputers, who is winning the battles in the desktop and laptop realm?

SPEAKER_01 13:36

Well, we saw commitments from both Intel and AMD at CES 2026, and they were clearly defining their strategies for local compute. Intel focused on the massive mobile market. They committed to unveiling their Panther League CPUs.

SPEAKER_00 13:50

Also marketed as the Intel Core Ultra Series 3.

SPEAKER_01 13:53

Right. And those include dedicated neural processing units, or NPUs, built directly into the silicon for laptops.

SPEAKER_00 13:59

Let's provide that deeper technical explanation our audience expects. For the learner, what exactly is an NPU and how does it fundamentally change what your laptop can do locally compared to relying on the main CPU or the GPU?

SPEAKER_01 14:12

That's a great question because NPUs are the true enabler of local agentic AI. Think of the traditional CPU, the central processing unit, as a brilliant generalist. It handles a huge variety of tasks, but mostly sequentially.

SPEAKER_00 14:25

Okay.

SPEAKER_01 14:25

The GPU, the graphics processing unit, is a massive parallel processor. It's optimized for math that can be broken into thousands of tiny identical chunks like rendering graphics.

SPEAKER_00 14:35

And the NPU.

SPEAKER_01 14:36

NPU is a specialist. It's designed for the specific math of AI matrix multiplications and convolutions.

SPEAKER_00 14:42

And why is that specialization so necessary?

SPEAKER_01 14:45

Because AI tasks like real-time background blurring during a video call, local real-time translation, or running a large language model on your device, they require these low precision, high-volume parallel calculations. The NPU handles these operations with significantly lower power consumption and higher efficiency than the CPU or even the main GPU.

SPEAKER_00 15:07

So the AI workloads get handled right on the machine itself.

SPEAKER_01 15:10

Which frees up the CPU, it conserves battery life in a laptop, and crucially, it preserves user privacy.

SPEAKER_00 15:16

Because the data never has to leave the device and travel to a server farm.

SPEAKER_01 15:20

That's the architectural foundation for agentic productivity on personal devices.

SPEAKER_00 15:24

Right. And meanwhile, what was AMD's play?

SPEAKER_01 15:26

Well, AMD, led by CEO Lisa Sue, they showcased a different segment, the high performance enthusiast market. They featured the Ryzen 7 9850 X3D.

SPEAKER_00 15:35

A chip aimed squarely at creators and high-end gamers.

SPEAKER_01 15:38

People who want massive local AI performance without sacrificing the traditional performance metrics demanded by high frame rate gaming or complex rendering jobs.

SPEAKER_00 15:47

And speaking of gaming, NVIDIA's DLSS 4 is a perfect example of how this AI enhancement translates into real-time performance. And the underlying tech has massive implications for future robot vision.

SPEAKER_01 15:59

Oh, absolutely. DLSS4 Deep Learning Super Sampling is a truly pivotal piece of generative technology. It utilizes AI models, specifically its new multi-frame generation architecture, to solve a fundamental physics problem in real-time computer graphics.

SPEAKER_00 16:15

So instead of the GPU having to calculate every single pixel of every frame.

SPEAKER_01 16:20

Right, which is the traditional way. DLSS4 doesn't do that. It renders the image at a lower resolution, and then, using deep learning models trained on vast amounts of data, it intelligently reconstructs and upscales the image to a much higher resolution.

SPEAKER_00 16:32

That's the upscaling part. But what about the multi-frame generation? That seems like the real leap.

SPEAKER_01 16:37

That is the revolutionary part. It creates entirely new frames between the frames that were actually rendered by the GPU. Instead of just interpolating existing pixels, the AI predicts what the next frame should look like based on motion vectors and the surrounding context.

SPEAKER_00 16:50

So the GPU might render, say, 60 frames P second, but the user experience is 120.

SPEAKER_01 16:57

Exactly. You get ultra-sharp visuals and dramatically smoother frame rates without maxing out the hardware.

SPEAKER_00 17:03

Okay, so if you, the learner, aren't a gamer, why should you care about this sophisticated graphics tech?

SPEAKER_01 17:09

Because the underlying generative world models and the predictive AI are directly applicable to robot vision.

SPEAKER_00 17:15

How so?

SPEAKER_01 17:16

The same technology that allows a gaming GPU to predict the movement of a shadow in the next frame is the core capability that allows an autonomous vehicle or a humanoid robot to predict the trajectory of a pedestrian.

SPEAKER_00 17:28

Or the best way to grip an oddly shaped object in an unstructured environment.

SPEAKER_01 17:32

Precisely. It's the engine of real-time perception for the physical world.

SPEAKER_00 17:36

This chip race isn't about bragging rights anymore. It's the engine room for the next section, which is the most disruptive for the traditional employment market. Physical labor done by physical machines.

SPEAKER_01 17:46

This is where it gets real.

SPEAKER_00 17:48

When we compare the humanoid robotics on display at CES 2025 to those in 2026, the shift is just staggering. In 2025, we saw robots like Aptronix Apollo showing off basic pick and place tasks with a friendly show.

SPEAKER_01 18:02

Right.

SPEAKER_00 18:04

But by 2026, our sources confirmed we had passed the theoretical tipping point. We've entered the era of confirmed deployable systems.

SPEAKER_01 18:12

It was a transition from demonstrating potential in a controlled demo environment to announcing commercial contracts and factory deployment. The key difference was the clear intent. They had defined jobs, signed commercial customers, and most importantly, they presented concrete production timelines that fall directly into that critical 2027-2030 forecast window.

SPEAKER_00 18:32

Okay, let's start with the undisputed heavyweight, the most concrete deployment plan and the sources. Boston Dynamics Atlas and its deep integration into the Hyundai ecosystem.

SPEAKER_01 18:41

The public debut of the production ready electric atlas at CES 2026 was it was monumental. This wasn't the hydraulic atlas we were used to seeing dance on YouTube.

SPEAKER_00 18:51

No, this was a machine built for the industrial floor.

SPEAKER_01 18:53

It demonstrated not only full rotational freedom of its joints, but also autonomously rising from a prone position using this complex non-human maneuver. It showed incredible control over its body mass.

SPEAKER_00 19:07

And the specifications alone read like something out of a military contract.

SPEAKER_01 19:10

They're designed for heavy, continuous industrial deployment. Atlas boasts an incredible 56 degrees of freedom.

SPEAKER_00 19:18

56? What does that actually mean?

SPEAKER_01 19:20

It means it has dozens of joints and actuators that allow for highly complex, flexible movements, far beyond what a traditional industrial arm can achieve.

SPEAKER_00 19:29

And its reach and lift capacity.

SPEAKER_01 19:31

A substantial 7.5 foot reach and a lifting capacity of 110 pounds. And for reliability, it runs on a four-hour battery designed for hot swappable autonomy.

SPEAKER_00 19:40

To minimize downtime on the assembly line.

SPEAKER_01 19:43

Zero downtime is the goal.

SPEAKER_00 19:44

So why is 56 degrees of freedom so important? Is that just a high number, or does it relate directly to job function?

SPEAKER_01 19:50

It relates directly to operating in unstructured human environments. Industrial robots typically have maybe six or seven axes. 56 degrees of freedom is what you need for true, bimanual, dexterous manipulation.

SPEAKER_00 20:01

The kind of complex task a human does instinctively.

SPEAKER_01 20:04

Like threading a cable or tightening a bolt in an awkward tight space while bracing their body. That level of agility signals a machine ready for complex, dynamic assembly, not just stacking boxes.

SPEAKER_00 20:17

And the brain behind that brawn is the key to its agentic capability.

SPEAKER_01 20:21

Absolutely. The machine is integrating with Google DeepMind's Gemini Robotics AI.

SPEAKER_00 20:26

Yeah.

SPEAKER_01 20:26

This is the strategic partnership that matters. It enables Atlas to move past just pre-programmed movements.

SPEAKER_00 20:33

And instead.

SPEAKER_01 20:34

It can reason through high-level, complex instructions and operate effectively in the unstructured, unpredictable reality of a busy factory floor.

SPEAKER_00 20:43

Okay, let's focus on the hard data for the learner, the deployment timeline. This is the brass tax for that 24 to 36 month action plan.

SPEAKER_01 20:50

Hyundai's timeline is precise and it's highly aggressive. Initial units of the electric atlas are scheduled for deployment in 2026.

SPEAKER_00 20:57

This year.

SPEAKER_01 20:58

This year, at Hyundai's MetaPlant in Georgia. They will start with basic material handling and sequencing tasks by 2028. And critically, the leap to more complex assembly operations where humanoids begin taking over intricate manufacturing steps is scheduled to begin by 2030.

SPEAKER_00 21:14

So that provides human labor in those specific roles with a visible hard deadline.

SPEAKER_01 21:19

A deadline for when augmentation or replacement becomes a corporate reality.

SPEAKER_00 21:24

And Hyundai isn't just acquiring the robot, they are leveraging their massive corporate structure to industrialize and scale this technology rapidly.

SPEAKER_01 21:33

That's the key to the speed of this deployment. The entire Hyundai Motor Group is creating this powerful internal value chain for AI robotics by leveraging its automotive mass production capabilities.

SPEAKER_00 21:44

So other affiliates are getting involved?

SPEAKER_01 21:46

Absolutely.

SPEAKER_00 22:00

And they're also dedicating physical space to train this new robotic workforce.

SPEAKER_01 22:04

Correct. The sources detailed the establishment of the Robot MetaPlant Application Center, or RMAC, in 2026. This is a dedicated facility. Its sole purpose is to continuously train and validate their AI robotics solutions using authentic, unpredictable factory conditions.

SPEAKER_00 22:22

So data is constantly flowing between the factory floor and the RMAC.

SPEAKER_01 22:27

It creates a continuous loop of improvement. This infrastructure ensures that Atlas is tested and proven in the most demanding environments manufacturing before it ever expands to logistics or energy or construction sites.

SPEAKER_00 22:39

So Atlas represents the heavy-duty, high-performance industrial worker, but CES 2026 also showed us the path toward mass market deployment and affordability.

SPEAKER_01 22:48

This is the democratization of the robotic workforce. It signals that this isn't just a domain for billion-dollar corporations.

SPEAKER_00 22:54

And Unitree Robotics was a big part of that story.

SPEAKER_01 22:57

A huge part. They showed both high affordability and exceptional agility. Their live demonstrations of the smaller G1 robot involved high-speed martial arts and boxing movements. It demonstrated motor control that could rival human athleticism.

SPEAKER_00 23:10

And they're positioning themselves for a robot as a service model.

SPEAKER_01 23:13

Right, where businesses can lease or rent these systems rather than buying them outright. A huge change in accessibility.

SPEAKER_00 23:20

And Unitree also brought industrial muscle with the H2 and specialized tools like the JU2 robot dog.

SPEAKER_01 23:26

Yes. The Ju2 robot dog is designed for high precision, dangerous tasks like facility inspections and fire rescue operations. Its utility rivals the popular Boston Dynamics spot.

SPEAKER_00 23:37

But the Unitree brand is aggressively pursuing a more accessible price point.

SPEAKER_01 23:42

Which means wider, quicker adoption across various sectors that currently rely on human inspection teams.

SPEAKER_00 23:47

Let's talk about that crucial price point, the one that determines the economic viability of replacement. Engine AI put a hard number on deployment costs that seems designed to shock the market.

SPEAKER_01 23:58

The Engine AI T800 confirmed production with an astonishing starting price of just$25,000.

SPEAKER_00 24:03

$25,000.

SPEAKER_01 24:05

With shipments scheduled for mid-2026. This machine is a full-scale humanoid, 1.73 meters tall, built with a strong magnesium aluminum alloy frame, and it's capable of generating up to 450 nanometers of peak torque.

SPEAKER_00 24:18

And it's powered by the Nvidia Jetson Thor platform.

SPEAKER_01 24:22

So it has massive onboard compute.

SPEAKER_00 24:24

Wait, hold on a second.$25,000. That's the price of a mid-level sedan. It's just a little over half the average annual wage for a factory worker in many regions. That number sounds revolutionary, but what about the hidden costs?

SPEAKER_01 24:38

That's the necessary critical probing.

SPEAKER_00 24:40

A business owner needs to factor in certification, ongoing maintenance, the integration costs to connect this robot to legacy machinery. Is$25,000 truly disruptive, or is that just a good marketing number that hides the real expenditure?

SPEAKER_01 24:55

So you're right. The sources agree that the total cost of ownership, the TCO, is the real metric. And that includes software licenses, specialized maintenance, training, liability insurance.

SPEAKER_00 25:04

Aaron Powell So it's more than$25,000.

SPEAKER_01 25:06

Definitely more. However, the psychological effect of that$25,000 number is what matters to the learner. It dramatically lowers the perception of the economic barrier for small and medium-sized enterprises.

SPEAKER_00 25:18

So even if the TCO is, say,$45,000 per year.

SPEAKER_01 25:22

It makes the economic calculus for replacing human labor much clearer and quicker for a company that's struggling with wage inflation or labor shortages.

SPEAKER_00 25:30

And the NENURA Robotics 4NE1, Gen 3, seems to be designed specifically to break down the walls between industrial and home use.

SPEAKER_01 25:38

That's the German company's core strategy. Build a highly capable machine for both. The 4NE1 features a unique combination of high industrial capability, a lifting capacity up to 100 kilograms, paired with safety features like patented artificial skin for immediate collision detection.

SPEAKER_00 25:55

And it runs on the Nerverse OS.

SPEAKER_01 25:57

Which is an operating system designed to enable skill sharing. If one robot learns a complex task in one facility, that skill is instantly shared across the entire fleet in real time.

SPEAKER_00 26:06

That shared learning model is terrifyingly efficient.

SPEAKER_01 26:09

It creates an immediate exponential ROI. You don't train one worker, you train the entire networked workforce instantly.

SPEAKER_00 26:16

Beyond these general-purpose humanoids, 2026 also saw the rise of specialized robotic roles that are already commercially deployed in high precision fields.

SPEAKER_01 26:24

Two sectors stand out advanced surgery and personal care. The LEM Surgical Dynamis is already a commercially deployed surgical humanoid.

SPEAKER_00 26:34

For spinal and orthopedic procedures.

SPEAKER_01 26:36

Right. It utilizes a multi-arm architecture to mimic complex human bimanual workflows, and it operates with sub-millimeter accuracy. This is high-skill specialized labor being automated right now in hospitals.

SPEAKER_00 26:49

And on the softer, more care-focused side, designed for direct human interaction.

SPEAKER_01 26:53

Fourier Robotics debuted the GR3, which is designed specifically for healthcare and public service roles. It features a soft shell exterior, which is critical for approachability in non-industrial settings like nursing homes or hospitals.

SPEAKER_00 27:06

So what did they have it doing?

SPEAKER_01 27:07

During demonstrations, it engaged visitors in live chess matches and performed synchronized dance routines, just showcasing its real-time perception capabilities and its mission to assist humans in delicate high contact roles.

SPEAKER_00 27:20

The lesson here seems clear.

SPEAKER_01 27:25

No, it's a variety of specialized and general humanoids. From the heavy lifting atlas to the gentle GR3, all entering the workforce with confirmed operational timelines between now and 2030.

SPEAKER_00 27:37

So if the factory floor is getting its synchronized workforce of humanoids, the domestic sphere is getting its sophisticated agentic assistant. And this is driven by LG's highly ambitious vision for a zero labor home.

SPEAKER_01 27:51

LG's debut of the Slealoid Home Robot at CES 2026 was the definitive physical centerpiece of this domestic vision. Now the term zero labor home is certainly optimistic.

SPEAKER_00 28:02

A bit of marketing flair there.

SPEAKER_01 28:04

Just a bit. But the demonstrations were strikingly tangible. They showed Cleoid performing real, complex, time-consuming household chores that humans typically dread.

SPEAKER_00 28:13

What were the specific tasks Sleoid was demonstrated doing? I'm curious.

SPEAKER_01 28:17

Well, Cleoid was demonstrated successfully folding laundry, loading a dishwasher, and even performing basic food preparation. Like what? Like retrieving a specific container of milk from a refrigerator and accurately placing a croissant into an oven. It functions as both a dedicated physical assistant and a mobile smart home hub, deeply integrated with the LG Think ecosystem to orchestrate other connected appliances.

SPEAKER_00 28:40

And the technology that allows it to execute these tasks, to see, to understand the object's context, and then act upon it, is the perfect complex illustration of agentic AI leaving the screen. Which converts images and video input into structured language-based understanding.

SPEAKER_01 29:05

It doesn't just see pixels. It understands that the pile of fabric is a dirty shirt. Second, the vision language action model, or VLA, which translates those visual and verbal inputs into complex physical motor actions. And this entire model was meticulously trained on thousands of hours of annotated household task data.

SPEAKER_00 29:22

So the robot doesn't just see a shirt, it understands the intent that the shirt needs to be folded or placed in the laundry hamper based on the high-level goal it was delegated.

SPEAKER_01 29:30

Exactly. And its ability to orchestrate tasks across disparate devices is magnified by its integration with LG's ThinkQ platform.

SPEAKER_00 29:41

Coordinating the washing machine, the oven, the vacuum.

SPEAKER_01 29:44

All based on the high-level goals delegated by the human.

SPEAKER_00 29:48

But LG isn't alone in pushing this agentic capability directly into appliances. Samsung's AI refrigerator is another prime example of delegation taking over tasks once performed by humans.

SPEAKER_01 29:59

Right. And Samsung is proving that you don't require a fully bipedal robot to achieve agentic productivity. Their AI refrigerator is designed to manage the entire grocery lifecycle.

SPEAKER_00 30:11

It monitors food freshness.

SPEAKER_01 30:13

Using internal sensors, yeah. It suggests recipes that utilize near-expiry ingredients to minimize waste, and it autonomously builds and manages grocery lists.

SPEAKER_00 30:21

And the automated purchasing feature is truly agentic.

SPEAKER_01 30:24

It is a significant leap. It features AI vision for effortless grocery replenishment. When you remove the laugh carton of milk, the fridge senses the removal, it identifies the item, and automatically adds it to an Instacart order.

SPEAKER_00 30:35

Which you can then review and complete directly from the fidge screen.

SPEAKER_01 30:39

You move from the cognitive labor of managing your inventory to simply delegating the entire shopping list to the fridge itself.

SPEAKER_00 30:46

Beyond the major appliances, the CES floors were packed with practical AI gadgets and wearables focused on personalization and convenience.

SPEAKER_01 30:54

Right, moving AI to the point of sale and communication. We saw devices focused on solving real-world friction points. For instance, Roborox Soros Vacuums debuted back in 2025 with an AI-enabled robotic arm.

SPEAKER_00 31:08

This device could identify and a clear fallen objects like socks, charging cables, and slippers from its path.

SPEAKER_01 31:14

Which eliminated the need for human pre-cleaning intervention. That set a new standard for AI smart devices, handling real-world chaos.

SPEAKER_00 31:21

And other gadgets focused on hyper-personalization in health and beauty.

SPEAKER_01 31:25

Yes. The Samsung Micro LED Beauty Mirror from 2025 used AI to analyze skin conditions with remarkable precision, providing tailored skincare recommendations and product suggestions based on real-time data. It's moving beyond simple diagnostics to prescriptive personalized routines.

SPEAKER_00 31:41

What about wearables? Things focusing on communication and assistance for professionals on the go?

SPEAKER_01 31:47

The focus there was on discrete, real-time assistance and memory augmentation. Rocket AR glasses from 2025 provided discrete displays for real-time translation and teleprompting for presentations.

SPEAKER_00 31:59

And then in 2026?

SPEAKER_01 32:01

In 2026, we saw the debut of the Vochi ring. It's a small, stylish wearable that offers personal note-taking and voice recording capabilities with a range of up to five meters, ensuring that spontaneous ideas or meeting notes are captured passively.

SPEAKER_00 32:15

And we also saw that early concept that raised some significant eyebrows regarding the necessary privacy trade-off for agentic assistance.

SPEAKER_01 32:22

You're talking about Motorole's experimental Project Maxwell concept.

SPEAKER_00 32:26

That's the one.

SPEAKER_01 32:27

It was pitched as a continuous capturing device, constantly listening to and visually capturing your surroundings, all intended to provide recommendations, insights, and memory retrieval.

SPEAKER_00 32:37

And the sources noted that its concept status was likely emphasized for a reason.

SPEAKER_01 32:41

Absolutely, because a continuous, ever-capturing device immediately triggers massive scrutiny around privacy, data storage, and the inevitable security implications of having your life constantly streamed.

SPEAKER_00 32:54

Now let's pivot back to a sector that has the most immediate, quantifiable economic implications for the learner, specifically those in logistics. Automotive innovation and autonomous transport. This is a field where our sources provide hard, specific, and frankly alarming timelines.

SPEAKER_01 33:12

AI in the automotive sector, specifically in freight, is arguably the leading indicator for the employment revolution impacting blue-collar labor. These dates are crucial for anyone in the trucking or transportation industry. The transition is not theoretical, it's contractually binding.

SPEAKER_00 33:27

So what are the immediate near-term deadlines we need to put into that 24-36 month plan?

SPEAKER_01 33:33

Companies like Continental and Aurora, leveraging the foundational compute power of NVIDIA's D drive platform, are targeting mass commercial production and scale deployment of driverless trucks by 2027.

SPEAKER_00 33:43

That's the immediate horizon. We're talking the next year to 18 months.

SPEAKER_01 33:47

Before autonomous freight transport becomes common on major highway routes.

SPEAKER_00 33:52

What makes driverless trucks such an immediate threat compared to, say, level 5 passenger autonomy?

SPEAKER_01 33:58

Freight logistics is highly structured and predictable. Highway driving is a constrained environment compared to navigating a complex city street. Trucks operate hub-to-hub.

SPEAKER_00 34:08

Meaning human drivers can still handle the chaotic last mile delivery?

SPEAKER_01 34:13

While the AI manages the long, high-cost, high-risk highway miles, the economic savings in fuel, labor costs, and operational hours make the ROI on autonomous trunking immediate and massive.

SPEAKER_00 34:24

And for passenger vehicles, Ford's roadmap provides a useful tiered timeline.

SPEAKER_01 34:29

It shows the stage deployment model. Ford's AI assistant, focused on personalization and vehicle diagnostics, will debut in its smartphone app first before expanding to vehicles in 2027. And that roadmap culminates in eyes off driving capability in its sophisticated Blue Cruise ADS system by 2028. This means the technological and legal framework for the human driver to completely disengage under certain highway conditions is expected to be in place within three years.

SPEAKER_00 34:54

And across the broader OEM landscape, we're seeing a similar tiered approach to autonomy.

SPEAKER_01 35:00

We are. Toyota, while adopting NVIDIA drive Orin, is explicitly prioritizing these platforms for advanced driver assistance systems, or ADS. They're focusing on safety and collision avoidance rather than immediate, full level 5 autonomy.

SPEAKER_00 35:13

In Honda.

SPEAKER_01 35:14

Honda showcased level three autonomous EVs with AI-driven personalization features. The transition is happening, but it's structured, with clear milestones for when humans cede control to the machine. And it starts with the highest ROI application, freight transport.

SPEAKER_00 35:29

These developments, robots and factories, butlers in homes, and driverless trucks on the road, they all intersect with the larger, often paradoxical economic picture. Let's dedicate some serious time to the economic and strategic implications that tie all these physical AI innovations together.

SPEAKER_01 35:44

It's a critical piece of the puzzle.

SPEAKER_00 35:46

The economic backdrop of CES 2026, according to the CTA's January forecast, was defined by these persistent economic headwinds, despite the massive AI investment boom. That creates a critical paradox we need to unpack.

SPEAKER_01 35:59

That paradox is essential for the learner to understand the operating environment. In January 2026, the CTA forecast showed a very mixed picture for the U.S. economy. On one hand, we saw unemployment at 4.6%.

SPEAKER_00 36:13

The highest since September 2021.

SPEAKER_01 36:15

Right. And that was coupled with a significant weakening in consumer confidence, which hit its lowest level since mid-2022.

SPEAKER_00 36:22

That consumer fatigue and lower confidence aligns perfectly with the AI washing backlash we discussed earlier.

SPEAKER_01 36:28

Exactly. And then there's the persistent threat of sticky inflation. Core inflation was expected to hold stubbornly above the Fed's 2% target all through 2026.

SPEAKER_00 36:37

Contributing to ongoing financial pressures for middle class consumers.

SPEAKER_01 36:41

Right. And despite modest positive GDP growth expectations, major layoffs across tech, finance, and manufacturing were announced, showing real cracks in the labor market.

SPEAKER_00 36:51

Okay, so despite those strong headwinds, higher unemployment, low confidence, sticky inflation, the tech industry, fueled entirely by AI, showed incredible resilience. It was acting as a massive tailwind for the broader economy. How does one sector's investment stabilize the whole economy against such strong negative forces?

SPEAKER_01 37:08

This is the core of the AI paradox. Forecasts from leading banks suggested that the sheer scale of AI investment would significantly drive up economic growth, primarily through two channels.

SPEAKER_00 37:21

Okay, what are they?

SPEAKER_01 37:22

First, major capital investments in AI infrastructure. And you have to think about where that money is going. It's not just software. It's funding new fabrication plants or fabs for those Vera Rubin chips. It's building specialized data centers for training massive LLMs.

SPEAKER_00 37:37

And constructing the massive robot metaplant application centers, the RMACs.

SPEAKER_01 37:42

That's a large industrial construction boom, creating high-paying jobs in the interim. And the second channel is productivity boosting. By automating complex tasks, AI enables existing workers to achieve significantly higher output, which economists translate into national productivity games.

SPEAKER_00 37:58

And this focus on AI-linked stocks powered the market to record levels.

SPEAKER_01 38:01

Creating a substantial wealth effect. It enabled wealthier consumers to continue spending on high-end goods and services, which offset the spending pullback from struggling middle-class consumers.

SPEAKER_00 38:12

And the trade policy environment complicates all of this. The specter of tariffs hangs heavily over the consumer tech sector.

SPEAKER_01 38:19

Right. The rising inventory costs due to tariffs imposed by the previous administration remain a serious market pressure point. Sources indicated that President Trump imposed the highest U.S. import tariffs in nearly a century back in 2025. And while companies initially cushioned the blow by pulling forward shipments, meaning they shipped vast quantities of product earlier than necessary to beat an anticipated deadline, those stockpile goods eventually depleted.

SPEAKER_00 38:46

And now.

SPEAKER_01 38:46

Now those tariffs and the ongoing supply chain uncertainty are expected to fully translate into higher consumer tech retail prices in 2026, putting pressure on consumer spending and corporate margins.

SPEAKER_00 38:57

And there is significant regulatory uncertainty specifically around the legality of these trade actions.

SPEAKER_01 39:03

That's right. The report noted that the U.S. Supreme Court is expected to rule on the legality of tariffs imposed under the International Emergency Economic Powers Act, or IEEE PA, in early 2026.

SPEAKER_00 39:14

And the IEPA, for the learner who might not know, is the act that grants the president broad authority to regulate commerce and property during a declared national emergency.

SPEAKER_01 39:23

Exactly. And even with a court ruling pending, the former administration was reportedly preparing alternative tariff measures that could be imposed immediately after the decision, keeping the tech industry on high alert regarding supply chain diversification and cost management.

SPEAKER_00 39:38

Moving from macroeconomics to strategy, the conversation among business leaders at CES 2026 shifted decisively. It moved from simply adopting AI to the much harder work of operationalizing it with execution and control.

SPEAKER_01 39:52

The C-suite consensus was clear. The immediate competitive advantage is gone. Leaders are no longer asking if they should use AI. They're asking how do we deploy this without creating massive operational or brand risk.

SPEAKER_00 40:04

So the immediate value is clear. Throughput.

SPEAKER_01 40:07

Throughput. Faster iterations, more versions, quicker approvals, less friction between strategy and market execution. Efficiency gains are now table stakes for survival.

SPEAKER_00 40:16

They are non-negotiable.

SPEAKER_01 40:18

But the most successful entities, as the sources highlight, must simultaneously pursue those essential efficiency gains and the path of true innovation, the AI unlocking the previously impossible.

SPEAKER_00 40:31

The strategic challenge is walking both paths.

SPEAKER_01 40:33

Maximizing short-term productivity while driving long-term genuine breakthrough. And that leads directly to the core problem of brand safety and governance in the age of generative AI.

SPEAKER_00 40:44

The competitive advantage in 2026 is using AI with control.

SPEAKER_01 40:48

Right. Rushed implementation creates massive brand risk because AI makes it ridiculously easy to scale errors instantly. Think about a deep fake scandal, widespread misinformation generated by a bot, or simply deploying inaccurate or offensive marketing copy across a billion impressions.

SPEAKER_00 41:04

So what are business leaders doing to mitigate this? Can you give us a concrete example of a rule or an approval path that was discussed?

SPEAKER_01 41:11

One clear takeaway was the institutionalization of the human-in-the-loop check for all high-risk generative output. For example, one major tech company reportedly instituted a rule that any generative marketing copy destined for public consumption must pass through a human editor.

SPEAKER_00 41:24

One who's trained in brand voice and ethical guidelines.

SPEAKER_01 41:28

And with a turnaround time of under two hours, the goal isn't to stop the AI, but to embed governance directly into the workflow, ensuring high speed doesn't compromise brand trust. This is the ultimate evolution of consumer marketing. It's similar to how SEO changed everything two decades ago. As agentic AI systems like the ones in Samsung's Smart Refrigerator or LG's Sea Aloid Home Hub Peg, over-evaluation and decision making about product purchases, agencies must prepare for a future where algorithms are the primary target audience.

SPEAKER_00 42:09

How does an agency even begin to understand algorithm evaluation? What metrics matter to an AI agent?

SPEAKER_01 42:15

The rules of evaluation shift completely. An AI agent doesn't care about emotional appeal or celebrity endorsements. It cares about quantifiable metrics that optimize the delegated goal.

SPEAKER_00 42:24

So agencies need to start focusing on different data points.

SPEAKER_01 42:27

Exactly. Things like product reliability scores, verified sustainability credentials, energy consumption ratings, hyper-precise nutritional data. If you tell your fridge agent, order the lowest carbon footprint milk, the AI will filter based on metrics that most brands don't currently optimize for in their marketing copy. Understanding the agent's utility function will become critical.

SPEAKER_00 42:49

Finally, we have to discuss the ethical cost. The debate around content provenance and compensation was clearly a major tension point at CES 2026.

SPEAKER_01 42:59

The sources highlighted the passionate argument put forth by Actor Turn founder Joseph Gordon Levitt. He stressed that the current AI business model, driven by pure business incentives and unchecked data scraping, is leading us down a dark path.

SPEAKER_00 43:12

What was the essence of his argument regarding training data and human labor?

SPEAKER_01 43:16

He argued forcefully that AI systems, especially large language models, are built on the foundational knowledge and creative output of humanity. Everything humans have put their time and energy and labor into over decades.

SPEAKER_00 43:28

So he contended that AI companies should be legally required to get consent.

SPEAKER_01 43:32

And provide fair compensation for the data and content used to train their models. His point was that content theft should not be silently forgiven simply because the technology utilized is revolutionary.

SPEAKER_00 43:43

And that is the central ethical tension of this revolution the ambition for unparalleled efficiency, clashing directly with the rights and compensation of the human labor that created the foundational data sets.

SPEAKER_01 43:54

And that tension will only intensify as physical AI starts automating more tasks once performed. Human labor. It's a core policy and legal debate that will shape the profitability and deployment speed of these systems over the next five years.

SPEAKER_00 44:07

Okay, so let's synthesize all of this.

SPEAKER_01 44:08

To synthesize everything we've covered, the primary essential takeaway for you, the learner, is that the era of theoretical AI is definitively over. The necessary infrastructure, the hardware backbone from Nvidia, the local intelligence chips from Intel and AMD is now fully enabling physical agentic systems.

SPEAKER_00 44:28

Systems like the Atlas Humanoid and the Celoid Home Robot. And the most important metric is the timeline. We are talking about confirmed corporate roadmaps, not guesses.

SPEAKER_01 44:36

Driverless trucks and Ford's AI assistance starting in 2027. Atlas handling sequencing tasks by 2028, and moving to complex assembly operations by 2030. These are visible, hard deadlines.

SPEAKER_00 44:50

And this specific external data provides the necessary clarity to refine your surviving AI 24 to 36 month action plan.

SPEAKER_01 44:58

Your professional value and your job security will increasingly depend not on your capacity to use a sophisticated chatbot, but on your ability to work alongside, manage, and strategically interact with these agentic systems in the physical world.

SPEAKER_00 45:11

We've thoroughly analyzed the impressive roadmap laid out by corporations at CES 2026. But here is the final provocative thought for you to chew on as you implement your strategy.

SPEAKER_01 45:20

The real test for the next year isn't what these systems can do in a controlled environment or what a$25,000 price tag looks like on a slide deck. The test is what happens when they encounter the unpredictable chaos of reality.

SPEAKER_00 45:31

What happens when that highly complex, agentic, sealoid home robot has to coordinate devices from half a dozen different brands that are not properly communicating? What happens when a sophisticated, expensive robot like Atlas encounters a spill, a fire, or a tool that was misplaced in the messy, unpredictable environment of a factory floor?

SPEAKER_01 45:54

Something it was never trained for.

SPEAKER_00 45:55

Exactly.

SPEAKER_01 45:56

The long-term winners in this revolution won't just be the companies who built the fastest AI. It will be the companies and the individuals who design systems and processes that are robust enough to handle failure, ambiguity, and the beautiful, complex, unpredictable nature of human environments.

SPEAKER_00 46:12

That capacity for resilience.

SPEAKER_01 46:14

Both in the machine and in the human workforce, that is where true enduring value will be found.

SPEAKER_00 46:19

Thank you for joining us for this special AI News segment for Surviving AI with Carlo Thompson. If you want to continue following the curriculum and receive future actionable insights as this landscape continues to shift at breakneck speed, make sure you sign up and enable notifications. We'll see you on the next deep dive.