-SOURCE-Tonal-(cropped).jpg&w=3840&q=100)
All Home Gyms Should Look as Good as a Tonal 2
Tonal is a high-tech slab of ambition—a gym in a mirror for those wanting to trade dumbbells for algorithm-powered gains. Tonal 2 is sleeker, sharper, and somehow even more expensive than before. At $4,295 (plus several add-on costs), it's both a statement piece and a serious financial commitment.
I wanted to know if Tonal could change the way I train. After weeks of testing, I analyzed everything from installation to daily performance to see if Tonal 2 could keep me motivated—no small feat given my squirrel-like attention span—or if it would just turn my living room into the set of a Black Mirror episode. New and Improved
Tonal 2 sticks with the same wall-mounted design as its predecessor (7/10, WIRED Recommends) but refines the aesthetic with an all-black finish and chrome accents. It looks cool in any home gym—as long as you wipe off the inevitable smudges after every workout. The most noticeable upgrade is the built-in camera, which enables real-time form feedback on-screen. Previously, Smart View required a separate mobile device to analyze movements, but now corrections are immediate. You can still integrate your phone for a side-angle, which improves the coaching cues.
Photograph: Boutayna Chokrane
In my experience, Smart View caught subtle but critical posture mistakes—like hunching my shoulders or letting my chest drop. But it wasn't flawless. For example, it didn't flag when I performed an entirely different exercise, like a narrow hamstring curl instead of a wide bicep curl. Certain exercises also make it hard to glance at the screen mid-set, so I relied on the post-workout videos and feedback for review more than in real time.
Connectivity has also improved, with stronger Wi-Fi and Bluetooth stability. Your internet still matters—if your connection is spotty, expect occasional lag—but I didn't experience any streaming disruptions during testing.
Perhaps the most meaningful upgrade is its durability. Tonal 2's new cables are 75 percent stronger, pushing the digital resistance cap from 200 to 250 pounds—a huge plus for advanced lifters. Tonal also introduced a new workout modality, Aero HIIT, a cardio and strength training hybrid, that adds five new moves to the list: Aero Pull, Aero Mini Pull, Aero Twist, Aero Chop, and Aero Lunge. Drop sets are also now built-in, making pushing through fatigue possible without manual weight adjustments. Not Just Weights
Tonal 2 requires professional installation, adding another $295 (or more, depending on your location). It mounts flush to the wall, keeping your space clutter-free, but it demands at least 7'x7' of floor space and a 7'10' ceiling height for a full range of motion. If you live in a smaller space, be prepared to shuffle furniture around before workouts. The actual installation took about 45 minutes and went smoothly. Though fair warning, the drilling is loud. If you have neighbors, maybe give them a heads-up.
Lifting with digital resistance feels different from free weights. According to Tonal, the pin-loaded machine can feel 23 percent heavier than barbell dumbbells. There's no momentum, so your muscles stay engaged throughout the motion. It's a more controlled (and limiting) way to train. The touchscreen is responsive, making it easy to adjust weights, follow programs, and track progress.
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WIRED Editor At Large Steven Levy sits down with Google DeepMind CEO Demis Hassabis for a deep dive discussion on the emergence of AI, the path to Artificial General Intelligence (AGI), and how Google is positioning itself to compete in the future of the workplace. Director: Justin Wolfson Director of Photography: Christopher Eusteche Editor: Cory Stevens Host: Steven Levy Guest: Demis Hassabis Line Producer: Jamie Rasmussen Associate Producer: Brandon White Production Manager: Peter Brunette Production Coordinator: Rhyan Lark Camera Operator: Lauren Pruitt Gaffer: Vincent Cota Sound Mixer: Lily van Leeuwen Production Assistant: Ryan Coppola Post Production Supervisor: Christian Olguin Post Production Coordinator: Stella Shortino Supervising Editor: Erica DeLeo Assistant Editor: Justin Symonds - It's a very intense time in the field. We obviously want all of the brilliant things these AI systems can do, come up with new cures for diseases, new energy sources, incredible things for humanity. That's the promise of AI. But also, there are worries if the first AI systems are built with the wrong value systems or they're built unsafely, that could be also very bad. - Wired sat down with Demis Hassabis, who's the CEO of Google DeepMind, which is the engine of the company's artificial intelligence. He's a Nobel Prize winner and also a knight. We discussed AGI, the future of work, and how Google plans to compete in the age of AI. This is "The Big Interview." [upbeat music] Well, welcome to "The Big Interview," Demis. - Thank you, thanks for having me. - So let's start talking about AGI a little here. Now, you founded DeepMind with the idea that you would solve intelligence and then use intelligence to solve everything else. And I think it was like a 20-year mission. We're like 15 years into it, and you're on track? - I feel like, yeah, we're pretty much dead on track, actually, is what would be our estimate. - That means five years away from what I guess people will call AGI. - Yeah, I think in the next five to 10 years, that would be maybe 50% chance that we'll have what we are defined as AGI, yes. - Well, some of your peers are saying, "Two years, three years," and others say a little more, but that's really close, that's really soon. How do we know that we're that close? - There's a bit of a debate going on in the moment in the field about definitions of AGI, and then obviously, of course, dependent on that. There's different predictions for when it will happen. We've been pretty consistent from the very beginning. And actually, Shane Legg, one of my co-founders and our chief scientist, you know, he helped define the term AGI back in, I think, early 2001 type of timeframe. And we've always thought about it as system that has the ability to exhibit, sort of all the cognitive capabilities we have as humans. And the reason that's important, the reference to the human mind, is the human mind is the only existence proof we have. Maybe in the universe, the general intelligence is possible. So if you want to claim sort of general intelligence, AGI, then you need to show that it generalizes to all these domains. - Is when everything's filled in, all the check marks are filled in, then we have it- - Yes, so I think there are missing capabilities right now. You know, that all of us who have used the latest sort of LLMs and chatbots, will know very well, like on reasoning, on planning, on memory. I don't think today's systems can invent, you know, do true invention, you know, true creativity, hypothesize new scientific theories. They're extremely useful, they're impressive, but they have holes. And actually, one of the main reasons I don't think we are at AGI yet is because of the consistency of responses. You know, in some domains, we have systems that can do International Math Olympiad, math problems to gold medal standard- - Sure. - With our AlphaFold system. But on the other hand, these systems sometimes still trip up on high school maths or even counting the number of letters in a word. - Yeah. - So that to me is not what you would expect. That level of sort of difference in performance across the board is not consistent enough, and therefore shows that these systems are not fully generalizing yet. - But when we get it, is it then like a phase shift that, you know, then all of a sudden things are different, all the check marks are checked? - Yeah. - You know, and we have a thing that can do everything. - Mm-hmm. - Are we then power in a new world? - I think, you know, that again, that is debated, and it's not clear to me whether it's gonna be more of a kind of incremental transition versus a step function. My guess is, it looks like it's gonna be more of an incremental shift. Even if you had a system like that, the physical world, still operates with the physical laws, you know, factories, robots, these other things. So it'll take a while for the effects of that, you know, this sort of digital intelligence, if you like, to really impact, I think, a lot of the real world things. Maybe another decade plus, but there's other theories on that too, where it could come faster. - Yeah, Eric Schmidt, who I think used to work at Google, has said that, "It's almost like a binary thing." He says, "If China, for instance, gets AGI, then we're cooked." Because if someone gets it like 10 minutes, before the next guy, then you can never catch up. You know, because then it'll maintain bigger, bigger leads there. You don't buy that, I guess. - I think it's an unknown. It's one of the many unknowns, which is that, you know, that's sometimes called the hard takeoff scenario, where the idea there is that these AGI systems, they're able to self-improve, maybe code themselves future versus themselves, that maybe they're extremely fast at doing that. So what would be a slight lead, let's say, you know, a few days, could suddenly become a chasm if that was true. But there are many other ways it could go too, where it's more incremental. Some of these self-improvement things are not able to kind of accelerate in that way, then being around the same time, would not make much difference. But it's important, I mean, these issues are the geopolitical issues. I think the systems that are being built, they'll have some imprint of the values and the kind of norms of the designers and the culture that they were embedded in. - [Steven] Mm-hmm. - So, you know, I think it is important, these kinds of international questions. - So when you build AI at Google, you know, you have that in mind. Do you feel competitive imperative to, in case that's true, "Oh my God, we better be first?" - It's a very intense time at the moment in the field as everyone knows. There's so many resources going into it, lots of pressures, lots of things that need to be researched. And there's sort of lots of different types of pressures going on. We obviously want all of the brilliant things that these AI systems can do. You know, I think eventually, we'll be able to advance medicine and science with it, like we've done with AlphaFold, come up with new cures for diseases, new energy sources, incredible things for humanity, that's the promise of AI. But also there are worries both in terms of, you know, if the first AI systems are built with the wrong value systems or they're built unsafely, that could be also very bad. And, you know, there are at least two risks that I worry a lot about. One is, bad actors in whether it's individuals or rogue nations repurposing general purpose AI technology for harmful lens. And then the second one is, obviously, the technical risk of AI itself. As it gets more and more powerful, more and more agentic, can we make sure the guardrails are safe around it? They can't be circumvented. And that interacts with this idea of, you know, what are the first systems that are built by humanity gonna be like? There's commercial imperative- - [Steven] Right. - There's national imperative, and there's a safety aspect to worry about who's in the lead and where those projects are. - A few years ago, the companies were saying, "Please, regulate us. We need regulation." - Mm-hmm, mm-hmm. - And now, in the US at least, the current administration seems less interested in putting regulations on AI than accelerating it so we can beat the Chinese. Are you still asking for regulation? Do you think that that's a miss on our part? - I think, you know, and I've been consistent in this, I think there are these other geopolitical sort of overlays that have to be taken into account, and the world's a very different place to how it was five years ago in many dimensions. But there's also, you know, I think the idea of smart regulation that makes sense around these increasingly powerful systems, I think is gonna be important. I continue to believe that. I think though, and I've been certain on this as well, it sort of needs to be international, which looks hard at the moment in the way the world is working, because these systems, you know, they're gonna affect everyone, and they're digital systems. - Yeah. - So, you know, if you sort of restrict it in one area, that doesn't really help in terms of the overall safety of these systems getting built for the world and as a society. - [Steven] Yeah. - So that's the bigger problem, I think, is some kind of international cooperation or collaboration, I think, is what's required. And then smart regulation, nimble regulation that moves as the knowledge about the research becomes better and better. - Would it ever reach a point for you where you would feel, "Man, we're not putting the guardrails in. You know, we're competing, that we really have to stop, or you can't get involved in that?" - I think a lot of the leaders of the main labs, at least the western labs, you know, there's a small number of them and we do all know each other and talk to each other regularly. And a lot of the lead researchers do. The problem is, is that it's not clear we have the right definitions to agree when that point is. Like, today's systems, although they're impressive as we discussed earlier, they're also very flawed. And I don't think today's systems, are posing any sort of existential risk. - Mm-hmm. - So it's still theoretical, but the problem is that a lot of unknowns, we don't know how fast those will come, and we don't know how risky they will be. But in my view, when there are so many unknowns, then I'm optimistic we'll overcome them. At least technically, I think the geopolitical questions could be actually, end up being trickier, given enough time and enough care and thoughtfulness, you know, sort of using the scientific method as we approach this AGI point. - That makes perfect sense. But on the other hand, if that timeframe is there, we just don't have much time, you know? - No, we don't. We don't have much time. I mean, we're increasingly putting resources into security and things like cyber, and also research into controllability and understanding of these systems, sometimes called mechanistic interpretability. You know, there's a lot of different sub-branches of AI. - Yeah, that's right. I wanna get to interpretability. - Yeah, that are being invested in, and I think even more needs to happen. And then at the same time, we need to also have societal debates more about institutional building. How do we want governance to work? How are we gonna get international agreement, at least on some basic principles, around how these systems are used and deployed and also built? - What about the effect on work on the marketplace? - Yeah. - You know, how much do you feel that AI is going to change people's jobs, you know, the way jobs are distributed in the workforce? - I don't think we've seen, my view is if you talk to economists, they feel like there's not much has changed yet. You know, people are finding these tools useful, certainly in certain domains- - [Steven] Yeah. - Like, things like AlphaFold, many, many scientists are using it to accelerate their work. So it seems to be additive at the moment. We'll see what happens over the next five, 10 years. I think there's gonna be a lot of change with the jobs world, but I think as in the past, what generally tends to happen is new jobs are created that are actually better, that utilize these tools or new technologies, what happened with the internet, what happened with mobile? We'll see if it's different this time. - Yeah. - Obviously everyone always thinks this new one, will be different. And it may be, it will be, but I think for the next few years, it's most likely to be, you know, we'll have these incredible tools that supercharge our productivity, make us really useful for creative tools, and actually almost make us a little bit superhuman in some ways in what we're able to produce individually. So I think there's gonna be a kind of golden era, over the next period of what we're able to do. - Well, if AGI can do everything humans can do, then it would seem that they could do the new jobs too. - That's the next question about like, what AGI brings. But, you know, even if you have those capabilities, there's a lot of things I think we won't want to do with a machine. You know, I sometimes give this example of doctors and nurses. You know, maybe a doctor and what the doctor does and the diagnosis, you know, one could imagine that being helped by AI tool or even having an AI kind of doctor. On the other hand, like nursing, you know, I don't think you'd want a robot to do that. I think there's something about the human empathy aspect of that and the care, and so on, that's particularly humanistic. I think there's lots of examples like that but it's gonna be a different world for sure. - If you would talk to a graduate now, what advice would you give to keep working- - Yeah. - Through the course of a lifetime- - Yeah. - You know, in the age of AGI? - My view is, currently, and of course, this is changing all the time with the technology developing. But right now, you know, if you think of the next five, 10 years as being, the most productive people might be 10X more productive if they are native with these tools. So I think kids today, students today, my encouragement would be immerse yourself in these new systems, understand them. So I think it's still important to study STEM and programming and other things, so that you understand how they're built, maybe you can modify them yourself on top of the models that are available. There's lots of great open source models and so on. And then become, you know, incredible at things like fine-tuning, system prompting, you know, system instructions, all of these additional things that anyone can do. And really know how to get the most out of those tools, and do it for your research work, programming, and things that you are doing on your course. And then come out of that being incredible at utilizing those new tools for whatever it is you're going to do. - Let's look a little beyond the five and 10-year range. Tell me what you envision when you look at our future in 20 years, in 30 years, if this comes about, what's the world like when AGI is everywhere? - Well, if everything goes well, then we should be in an era of what I like to call sort of radical abundance. So, you know, AGI solves some of these key, what I sometimes call root node problems in the world facing society. So a good one, examples would be curing diseases, much healthier, longer lifespans, finding new energy sources, you know, whether that's optimal batteries and better room temperature, superconductors, fusion. And then if that all happens, then we know it should be a kind of era of maximum human flourishing where we travel to the stars and colonize the galaxy. You know, I think the beginning of that will happen in the next 20, 30 years if the next period goes well. - I'm a little skeptical of that. I think we have an unbelievable abundance now, but we don't distribute it, you know, fairly. - Yeah. - I think that we kind of know how to fix climate change, right? We don't need a AGI to tell us how to do it, yet we're not doing it. - I agree with that. I think we being as a species, a society not good at collaborating, and I think climate is a good example. But I think we are still operating, humans are still operating in a zero-sum game mentality. Because actually, the earth is quite finite, relative to the amount of people there are now in our cities. And I mean, this is why our natural habitats, are being destroyed, and it's affecting wildlife and the climate and everything. - [Steven] Yeah. - And it's also partly 'cause people are not willing to accept, we do now to figure out climate. But it would require people to make sacrifices. - Yeah. - And people don't want to. But this radical abundance would be different. We would be in a finally, like, it would feel like a non-zero-sum game. - How will we get [indistinct] to that? Like, you talk about diseases- - Well, I gave you an example. - We have vaccines, and now some people think we shouldn't use it. - Let me give you a very simple example. - Sure. - Water access. This is gonna be a huge issue in the next 10, 20 years. It's already an issue. Countries in different, you know, poorer parts of the world, dryer parts of the world, also obviously compounded by climate change. - [Steven] Yeah. - We have a solution to water access. It's desalination, it's easy. There's plenty of sea water. - Yeah. - Almost all countries have a coastline. But the problem is, it's salty water, but desalination only very rich countries. Some countries do do that, use desalination as a solution to their fresh water problem, but it costs a lot of energy. - Mm-hmm. - But if energy was essentially zero, there was renewable free clean energy, right? Like fusion, suddenly, you solve the water access problem. Water is, who controls a river or what you do with that does not, it becomes much less important than it is today. I think things like water access, you know, if you run forward 20 years, and there isn't a solution like that, could lead to all sorts of conflicts, probably that's the way it's trending- - Mm-hmm, right. - Especially if you include further climate change. - So- - And there's many, many examples like that. You could create rocket fuel easily- - Mm-hmm. - Because you just separate that from seawater, hydrogen and oxygen. It's just energy again. - So you feel that these problems get solved by AGI, by AI, then we're going to, our outlook will change, and we will be- - That's what I hope. Yes, that's what I hope. But that's still a secondary part. So the AGI will give us the radical abundance capability, technically, like the water access. - Yeah. - I then hope, and this is where I think we need some great philosophers or social scientists to be involved. That should hopefully shift our mindset as a society to non-zero-sum. You know, there's still the issue of do you divide even the radical abundance fairly, right? Of course, that's what should happen. But I think there's much more likely, once people start feeling and understanding that there is this almost limitless supply of raw materials and energy and things like that. - Do you think that driving this innovation by profit-making companies is the right way to go? We're most likely to reach that optimistic high point through that? - I think it's the current capitalism or, you know, is the current or the western sort of democratic kind of systems, have so far been proven to be sort of the best drivers of progress. - Mm-hmm. - So I think that's true. My view is that once you get to that sort of stage of radical abundance and post-AGI, I think economics starts changing, even the notion of value and money. And so again, I think we need, I'm not sure why economists are not working harder on this if maybe they don't believe it's that close, right? But if they really did that, like the AGI scientists do, then I think there's a lot of economic new economic theory that's required. - You know, one final thing, I actually agree with you that this is so significant and is gonna have a huge impact. But when I write about it, I always get a lot of response from people who are really angry already about artificial intelligence and what's happening. Have you tasted that? Have you gotten that pushback and anger by a lot of people? It's almost like the industrial revolution people- - Yeah. - Fighting back. - I mean, I think that anytime there's, I haven't personally seen a lot of that, but obviously, I've read and heard a lot about, and it's very understandable. That's all that's happened many times. As you say, industrial revolution, when there's big change, a big revolution. - [Steven] Yeah. - And I think this will be at least as big as the industrial revolution, probably a lot bigger. That's surprising, there's unknowns, it's scary, things will change. But on the other hand, when I talk to people about the passion, the why I'm building AI- - Mm-hmm. - Which is to advance science and medicine- - Right. - And understanding of the world around us. And then I explain to people, you know, and I've demonstrated, it's not just talk. Here's AlphaFold, you know, Nobel Prize winning breakthrough, can help with medicine and drug discovery. Obviously, we're doing this with isomorphic now to extend it into drug discovery, and we can cure terrible diseases that might be afflicting your family. Suddenly, people are like, "Well, of course, we need that." - Right. - It'll be immoral not to have that if that's within our grasp. And the same with climate and energy. - Yeah. - You know, many of the big societal problems, it's not like you know, we know, we've talked about, there's many big challenges facing society today. And I often say I would be very worried about our future if I didn't know something as revolutionary as AI was coming down the line to help with those other challenges. Of course, it's also a challenge itself, right? But at least, it's one of these challenges that can actually help with the others if we get it right. - Well, I hope your optimism holds out and is justified. Thank you so much. - And I'll do my best. Thank you. [upbeat music]
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