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Why fears of a trillion-dollar AI bubble are growing

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Why fears of a trillion-dollar AI bubble are growing


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For almost as long as the artificial intelligence boom has been in full swing, there have been warnings of a speculative bubble that could rival the dot-com craze of the late 1990s that ended in a spectacular crash and a wave of bankruptcies.

Tech firms are spending hundreds of billions of dollars on advanced chips and data centers, not just to keep pace with a surge in the use of chatbots such as ChatGPT, Gemini and Claude, but to make sure they’re ready to handle a more fundamental and disruptive shift of economic activity from humans to machines.

The final bill may run into the trillions. The financing is coming from , debt and, lately, some more unconventional arrangements that have raised eyebrows on Wall Street.

Even some of AI’s biggest cheerleaders acknowledge the market is frothy, while still professing their belief in the technology’s long-term potential. AI, they say, is poised to reshape multiple industries, cure diseases and generally accelerate human progress.

Yet never before has so much money been spent so rapidly on a technology that remains somewhat unproven as a profit-making business model. Tech industry executives who privately doubt the most effusive assessments of AI’s revolutionary potential—or at least struggle to see how to monetize it—may feel they have little choice but to keep pace with their rivals’ investments or risk being out-scaled and sidelined in the future AI marketplace.

Sharp falls in global technology stocks in early November underscored investors’ growing unease over the sector’s sky-high valuations, with Wall Street chief executives warning of an overdue market correction.

What are the warning signs for AI?

When Sam Altman, the chief executive of ChatGPT maker OpenAI, announced a $500 billion AI infrastructure plan known as Stargate alongside other executives at the White House in January, the price tag triggered some disbelief. Since then, other tech rivals have ramped up spending, including Meta’s Mark Zuckerberg, who has pledged to invest hundreds of billions in . Not to be outdone, Altman has since said he expects OpenAI to spend “trillions” on AI infrastructure.

To finance those projects, OpenAI is entering into new territory. In September, chipmaker Nvidia Corp. announced an agreement to invest up to $100 billion in OpenAI’s data center buildout, a deal that some analysts say raises questions about whether the chipmaker is trying to prop up its customers so that they keep spending on its own products.

The concerns have followed Nvidia, to varying degrees, for much of the boom. The dominant maker of AI accelerator chips has backed dozens of companies in recent years, including AI model makers and cloud computing providers. Some of them then use that capital to buy Nvidia’s expensive semiconductors. The OpenAI deal was far larger in scale.

OpenAI has also indicated it could pursue debt financing, rather than leaning on partners such as Microsoft Corp. and Oracle Corp. The difference is that those companies have rock-solid, established businesses that have been profitable for many years. OpenAI expects to burn through $115 billion of cash through 2029, The Information has reported.

Other large tech companies are also relying increasingly on debt to support their unprecedented spending. Meta, for example, turned to lenders to secure $26 billion in financing for a planned data center complex in Louisiana that it says will eventually approach the size of Manhattan. JPMorgan Chase & Co. and Mitsubishi UFJ Financial Group are also leading a loan of more than $22 billion to support Vantage Data Centers’ plan to build a massive data-center campus, Bloomberg News has reported.

So how about the payback?

By 2030, AI companies will need $2 trillion in combined annual revenue to fund the computing power needed to meet projected demand, Bain & Co. said in a report released in September. Yet their revenue is likely to fall $800 billion short of that mark, Bain predicted.

“The numbers that are being thrown around are so extreme that it’s really, really hard to understand them,” said David Einhorn, a prominent hedge fund manager and founder of Greenlight Capital. “I’m sure it’s not zero, but there’s a reasonable chance that a tremendous amount of capital destruction is going to come through this cycle.”

In a sign of the times, there’s also a growing number of less proven firms trying to capitalize on the data center goldrush. Nebius, an Amsterdam-based cloud provider that split off from Russian internet giant Yandex in 2024, recently inked an infrastructure deal with Microsoft worth up to $19.4 billion. And Nscale, a little-known British data center company, is working with Nvidia, OpenAI and Microsoft on build-outs in Europe. Like some other AI infrastructure providers, Nscale previously focused on another frothy sector: cryptocurrency mining.

Are there concerns about the technology itself?

The data center spending spree is overshadowed by persistent skepticism about the payoff from AI technology. In August, investors were rattled after researchers at the Massachusetts Institute of Technology found that 95% of organizations saw zero return on their investment in AI initiatives.

More recently, researchers at Harvard and Stanford offered a possible explanation for why. Employees are using AI to create “workslop,” which the researchers define as “AI-generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.”

The promise of AI has long been that it would help streamline tasks and boost productivity, making it an invaluable asset for workers and one that corporations would pay top dollar for. Instead, the Harvard and Stanford researchers found the prevalence of workslop could cost larger organizations millions of dollars a year in lost productivity.

AI developers have also been confronting a different challenge. OpenAI, Claude chatbot developer Anthropic and others have for years bet on the so-called scaling laws—the idea that more computing power, data and larger models will inevitably pave the way for greater leaps in the power of AI.

Eventually, they say, these advances will lead to artificial general intelligence, a hypothetical form of the technology so sophisticated that it matches or exceeds humans in most tasks.

Over the past year, however, these developers have experienced diminishing returns from their costly efforts to build more advanced AI. Some have also struggled to match their own hype.

After months of touting GPT-5 as a significant leap, OpenAI’s release of its latest AI model in August was met with mixed reviews. In remarks around the launch, Altman conceded that “we’re still missing something quite important” to reach AGI.

Those concerns are compounded by growing competition from China, where companies are flooding the market with competitive, low-cost AI models. While U.S. firms are generally still viewed as ahead in the race, the Chinese alternatives risk undercutting Silicon Valley on price in certain markets, making it harder to recoup the significant investment in AI infrastructure.

There’s also the risk that the AI industry’s vast data center buildout, entailing a huge increase in electricity consumption, will be held back by the limitations of national power networks.

What does the AI industry say in response?

Sam Altman, the face of the current AI boom, has repeatedly acknowledged the risk of a bubble in recent months while maintaining his optimism for the technology. “Are we in a phase where investors as a whole are overexcited about AI? In my opinion, yes,” he said in August. “Is AI the most important thing to happen in a very long time? My opinion is also yes.”

Altman and other tech leaders continue to express confidence in the roadmap toward AGI, with some suggesting it could be closer than skeptics think.

“Developing superintelligence is now in sight,” Zuckerberg wrote in July, referencing an even more powerful form of AI that his company is aiming for. In the near term, some AI developers also say they need to drastically ramp up computing capacity to support the rapid adoption of their services.

Altman, in particular, has stressed repeatedly that OpenAI remains constrained in computing resources as hundreds of millions of people around the world use its services to converse with ChatGPT, write code and generate images and videos.

OpenAI and Anthropic have also released their own research and evaluations that indicate AI systems are having a meaningful impact on work tasks, in contrast to the more damning reports from outside academic institutions. An Anthropic report released in September found that roughly three quarters of companies are using Claude to automate work.

The same month, OpenAI released a new evaluation system called GDPval that measures the performance of AI models across dozens of occupations.

“We found that today’s best frontier models are already approaching the quality of work produced by industry experts,” OpenAI said in a blog post. “Especially on the subset of tasks where models are particularly strong, we expect that giving a task to a model before trying it with a human would save time and money.”

So how much will customers eventually be willing to pay for these services? The hope among developers is that, as AI models improve and field more complex tasks on users’ behalf, they will be able to convince businesses and individuals to spend far more to access the technology.

“I want the door open to everything,” OpenAI Chief Financial Officer Sarah Friar said in late 2024, when asked about a report that the company has discussed a $2,000 monthly subscription for its AI products. “If it’s helping me move about the world with literally a Ph.D.-level assistant for anything that I’m doing, there are certainly cases where that would make all the sense in the world.”

In September, Zuckerberg said an AI bubble is “quite possible,” but stressed that his bigger concern is not spending enough to meet the opportunity. “If we end up misspending a couple of hundred billion dollars, I think that that is going to be very unfortunate, obviously,” he said in a podcast interview. “But what I’d say is I actually think the risk is higher on the other side.”

What makes a market bubble?

Bubbles are economic cycles defined by a swift increase in market values to levels that aren’t supported by the underlying fundamentals. They’re usually followed by a sharp selloff—the so-called pop.

A bubble often begins when investors get swept up in a speculative frenzy—over a new technology or other market opportunity—and pile in for fear of missing out on further gains. American economist Hyman Minsky identified five stages of a market bubble: displacement, boom, euphoria, profit-taking and panic.

Bubbles are sometimes difficult to spot because market prices can become dislocated from real-world values for many reasons, and a sharp price drop isn’t always inevitable. And, because a crash is part of a bubble cycle, they can be hard to pinpoint until after the fact.

Generally, bubbles pop when investors realize that the lofty expectations they had were too high. This usually follows a period of over-exuberance that tips into mania, when everyone is buying into the trend at the very top.

What comes next is usually a slow, prolonged selloff where company earnings start to suffer, or a singular event that changes the long-term view, sending investors dashing for the exits.

There was some fear that an AI bubble had already popped in late January, when China’s DeepSeek upended the market with the release of a competitive AI model purportedly built at a fraction of the amount that top U.S. developers spend. DeepSeek’s viral success triggered a trillion-dollar selloff of technology shares. Nvidia, a bellwether AI stock, slumped 17% in one day.

The DeepSeek episode underscored the risks of investing heavily in AI. But Silicon Valley remained largely undeterred. In the months that followed, tech companies redoubled their costly AI spending plans, and investors resumed cheering on these bets. Nvidia shares charged back from an April low to fresh records. It was worth more than $4 trillion by the end of September, making it the most valuable company in the world.

So is this 1999 all over again?

As with today’s AI boom, the companies at the center of the dot-com frenzy drew in vast amounts of investor capital, often using questionable metrics such as website traffic rather than their actual ability to turn a profit. There were many flawed business models and exaggerated revenue projections.

Telecommunication companies raced to build fiber-optic networks only to find the demand wasn’t there to pay for them. When it all crashed in 2001, many companies were liquidated, others absorbed by healthier rivals at knocked-down prices.

Echoes of the dot-com era can be found in AI’s massive infrastructure build-out, sky-high valuations and showy displays of wealth. Venture capital investors have been courting AI startups with private jets, box seats and big checks.

Many AI startups tout their recurring revenue as a key metric for growth, but there are doubts as to how sustainable or predictable those projections are, particularly for younger businesses. Some AI firms are completing multiple mammoth fundraisings in a single year. Not all will necessarily flourish.

“I think there’s a lot of parallels to the internet bubble,” said Bret Taylor, OpenAI’s chairman and the CEO of Sierra, an AI startup valued at $10 billion. Like the dot-com era, a number of high-flying companies will almost certainly go bust. But in Taylor’s telling, there will also be large businesses that emerge and thrive over the long term, just as happened with Amazon.com Inc. and Alphabet Inc.’s Google in the late 90s.

“It is both true that AI will transform the economy, and I think it will, like the internet, create huge amounts of economic value in the future,” Taylor said. “I think we’re also in a bubble, and a lot of people will lose a lot of money.”

Amazon Chairman Jeff Bezos said the spending on AI resembles an “industrial bubble” akin to the biotech bubble of the 1990s, but he still expects it to improve the productivity of “every company in the world.”

There are also some key differences to the dot-com boom that market watchers point out, the first being the broad health and stability of the biggest businesses that are at the forefront of the trend. Most of the “Magnificent Seven” group of U.S. tech companies are long-established giants that make up much of the earnings growth in the S&P 500 Index. These firms have huge revenue streams and are sitting on large stockpiles of cash.

Despite the skepticism, AI adoption has also proceeded at a rapid clip. OpenAI’s ChatGPT has about 700 million weekly users, making it one of the fastest growing consumer products in history. Top AI developers, including OpenAI and Anthropic, have also seen remarkably strong sales growth. OpenAI previously forecast revenue would more than triple in 2025 to $12.7 billion.

While the company does not expect to be cash-flow positive until near the end of this decade, a recent deal to help employees sell shares gave it an implied valuation of $500 billion—making it the world’s most valuable company never to have turned a profit.

2025 Bloomberg L.P. Distributed by Tribune Content Agency, LLC.

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Managing traffic in space

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Managing traffic in space



Chances are, you’ve already used a satellite today. Satellites make it possible for us to stream our favorite shows, call and text a friend, check weather and navigation apps, and make an online purchase. Satellites also monitor the Earth’s climate, the extent of agricultural crops, wildlife habitats, and impacts from natural disasters.

As we’ve found more uses for them, satellites have exploded in number. Today, there are more than 10,000 satellites operating in low-Earth orbit. Another 5,000 decommissioned satellites drift through this region, along with over 100 million pieces of debris comprising everything from spent rocket stages to flecks of spacecraft paint.

For MIT’s Richard Linares, the rapid ballooning of satellites raises pressing questions: How can we safely manage traffic and growing congestion in space? And at what point will we reach orbital capacity, where adding more satellites is not sustainable, and may in fact compromise spacecraft and the services that we rely on?

“It is a judgement that society has to make, of what value do we derive from launching more satellites,” says Linares, who recently received tenure as an associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “One of the things we try to do is approach these questions of traffic management and orbital capacity as engineering problems.”

Linares leads the MIT Astrodynamics, Space Robotics, and Controls Lab (ARCLab), a research group that applies astrodynamics (the motion and trajectory of orbiting objects) to help track and manage the millions of objects in orbit around the Earth. The group also develops tools to predict how space traffic and debris will change as operators launch large satellite “mega-constellations” into space.

He is also exploring the effects of space weather on satellites, as well as how climate change on Earth may limit the number of satellites that can safely orbit in space. And, anticipating that satellites will have to be smarter and faster to navigate a more cluttered environment, Linares is looking into artificial intelligence to help satellites autonomously learn and reason to adapt to changing conditions and fix issues onboard.

“Our research is pretty diverse,” Linares says. “But overall, we want to enable all these economic opportunities that satellites give us. And we are figuring out engineering solutions to make that possible.”

Grounding practical problems

Linares was born and raised in Yonkers, New York. His parents both worked as school bus drivers to support their children, Linares being the youngest of six. He was an active kid and loved sports, playing football throughout high school.

“Sports was a way to stay focused and organized, and to develop a work ethic,” Linares says. “It taught me to work hard.”

When applying for colleges, rather than aim for Division I schools like some of his teammates, Linares looked for programs that were strong in science, specifically in aerospace. Growing up, he was fascinated with Carl Sagan’s “Cosmos” docuseries. And being close to Manhattan, he took regular trips to the Hayden Planetarium to take in the center’s immersive projections of space and the technologies used to explore it.

“My interest in science came from the universe and trying to understand our place within it,” Linares recalls.

Choosing to stay close to home, he applied to in-state schools with strong aeronautical engineering departments, and happily landed at the State University of New York at Buffalo (SUNY Buffalo), where he would ultimately earn his bachelor’s, master’s, and doctoral degrees, all in aerospace engineering.

As an undergraduate, Linares took on a research project in astrodynamics, looking to solve the problem of how to determine the relative orientation of satellites flying in formation.

“Formation flying was a big topic in the early 2000s,” Linares says. “I liked the flavor of the math involved, which allowed me to go a layer deeper toward a solution.”

He worked out the math to show that when three satellites fly together, they essentially form a triangle, the angles of which can be calculated to determine where each satellite is in relation to the other two at any moment in time. His work introduced a new controls approach to enable satellites to fly safely together. The research had direct applications for the U.S. Air Force, which helped to sponsor the work.

As he expanded the research into a master’s thesis, Linares also took opportunities to work directly with the Air Force on issues of satellite tracking and orientation. He served two internships with the U.S. Air Force Research Lab, one at Kirtland Air Force Base in Albuquerque, New Mexico, and the other in Maui, Hawaii.

“Being able to collaborate with the Air Force back then kind of grounded the research in practical problems,” Linares says.

For his PhD, he turned to another practical problem of “uncorrelated tracks.” At the time, the Air Force operated a network of telescopes to observe more than 20,000 objects in space, which they were working to label and record in a catalog to help them track the objects over time. But while detecting objects was relatively straightforward, the challenge came in correlating a detected object with what was already in the catalog. In other words, is what they were seeing something they had already seen?

Linares developed image analysis techniques to identify key characteristics of objects such as their shape and orientation, which helped the Air Force “fingerprint” satellites and pieces of space debris, and track their activity — and potential for collisions — over time.

After completing his PhD, Linares worked as a postdoc at Los Alamos National Laboratory and the U.S. Naval Observatory. During that time he expanded his aerospace work to other areas including space weather, using satellite measurements to model how Earth’s ionosphere — the upper layer of the atmosphere that is ionized by the sun’s radiation — affects satellite drag.

He then accepted a position as assistant professor of aerospace engineering at the University of Minnesota at Minneapolis. For the next three years, he continued his research in modeling space weather, tracking space objects and coordinating satellites to fly in swarms.

Making space

In 2018, Linares made the move to MIT.

“I had a lot of respect for the people and for the history of the work that was done here,” says Linares, who was especially inspired by the legendary Charles Stark “Doc” Draper, who developed the first inertial guidance systems in the 1940s that would enable the self-navigation of airplanes, submarines, satellites, and spacecraft for decades to come. “This was essentially my field, and I knew MIT was the best place to continue my career.”

As a junior faculty member in AeroAstro, Linares spent his first years focused on an emerging challenge: space sustainability. Around that time, the first satellite constellations were launching into low-Earth orbit with SpaceX’s Starlink, which aimed to provide global internet coverage via a huge network of several thousand coordinating satellites. The launching of so many satellites, into orbits that already held other active and nonactive satellites, along with millions of pieces of space debris, raised questions about how to safely manage the satellite traffic and how much traffic an orbit can sustain.

“At what level do we reach a tipping point, where we have too many satellites in certain orbital regimes?” Linares says. “It was kind of a known problem at the time, but there weren’t many solutions.”

Linares’ group applied an understanding of astrodynamics, and the physics of how objects move in space, to figure out the best way to pack satellites in orbital “shells,” or lanes that would most likely prevent collisions. They also developed a state-of-the-art model of orbital traffic, that was able to simulate the trajectories of more than 10 million individual objects in space. Previous models were much more limited in the number of objects they could accurately simulate. Linares’ open-source model, called the MIT Orbital Capacity Assessment Tool, or MoCAT, could account for the millions of pieces of space debris, in addition to the many intact satellites in orbit.

The tools that his group has developed are used today by satellite operators to plan and predict safe spacecraft trajectories. His team is continuing to work on problems of space traffic management and orbital capacity. They are also branching out into space robotics. The team is testing ways to teleoperate a humanoid robot, which could potentially help to build future infrastructure and carry out long-duration tasks in space.

Linares is also exploring artificial intelligence, including ways that a satellite can autonomously “learn” from its experience and safely adapt to uncertain environments.

“Imagine if each satellite had a virtual Doc Draper onboard that could do the de-bugging that we did from the ground during the Apollo missions,” Linares says. “That way, satellites would become instantaneously more robust. And it’s not taking the human out of the equation. It’s allowing the human to be amplified. I think that’s within reach.”



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Meta Glasses Are Comfortable, Functional, and Make My Spouse Recoil in Fear

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Meta Glasses Are Comfortable, Functional, and Make My Spouse Recoil in Fear


Every time I’ve written about Meta’s AI-enabled glasses, I invariably get asked these questions: Why do you even want these? Why do you want smart glasses that can play music or misidentify native flora in a weirdly cheery voice? I am a lifelong Ray-Ban Wayfarer wearer, and I’m also WIRED’s resident Meta wearer. I grab a pair of Meta glasses whenever I leave the house because I like being able to use one device instead of two or three on a walk. With Meta glasses, I can wear sunglasses and workout headphones in one!

Meta sold more than 7 million pairs in 2025. Take a look at any major outdoor or sporting event, and you’ll see more than a few people wearing these to record snippets for Instagram or TikTok. Meta’s partnership with EssilorLuxottica has made smart glasses accessible, stylish, and useful and is undoubtedly the reason why Google, and now Apple, are trying to horn in on the market. After the notable flop that is the Apple Vision Pro, Apple is recalibrating its face-wearable strategy, moving away from augmented reality (AR) toward simpler, display-less, and hopefully good-looking glasses.

That’s not to say that you shouldn’t be careful how you use these glasses. Meta doesn’t have the greatest track record on privacy, and the company has continued to push forward with policies that are questionable at best. Even if you’re not concerned that face recognition will allow Meta to target immigrants or enable stalkers to find their victims, at the very least, people really do not like the idea that you could start recording them at any moment.

Probably the biggest hurdle to wearing Meta glasses is that even doing so seems like a gross violation of the social contract. After all, these are Mark Zuckerberg’s “pervert glasses.” When I pop these on my head, I’ve had friends (and my spouse) recoil and say, “I have apps to warn me away from people like you.” The best part, though, is that Oakley and Ray-Ban already make really great sunglasses. Even if the battery runs out or you don’t use Meta AI at all, these are stellar at shading your eyes from the sun.

Anyway, if you decide to try them, here’s what you should get. If you do chicken out, check out our buying guides to the Best Smart Glasses or the Best Workout Headphones for more.

Table of Contents

Best Overall

  • Photograph: Boone Ashworth

Ray-Ban

Meta Glasses (Gen 2)

Last year, Meta upgraded the original Meta Ray-Ban Wayfarers that became a smash hit. These are Meta’s entry-level glasses, and they come in a variety of lens styles. You can order them with clear lenses, prescription lenses, transition lenses, or the OG sunglass lenses, as well as in a variety of fits, including standard, large, or high-bridge frames. Improvements to this generation include an upgrade to a 12-MP camera and up to eight hours of battery life; writer Boone Ashworth’s testing clocked in at five to six hours.



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The Smart Home Gadgets to Amp Up Your Curb Appeal

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The Smart Home Gadgets to Amp Up Your Curb Appeal


I tried the battery version, which does require you recharge it every couple of weeks, but the wired-in version is the top recommendation on our guide to the Best Video Doorbells.

A Better Birdhouse

I had a new-to-me problem this spring: bird invasion. A little bird made a nest in my front-door wreath without us noticing. One evening, my sister opened the door, and the bird flew out of the nest and straight into our house. After a 30-minute battle to get it outside again (and keep my cat from eating it), it wasn’t until we saw the bird fly off the door again the next day that we realized it was calling our home its home, too.

If this is a common problem at your house, our resident bird-gear tester Kat Merck has a solution: a smart nesting box. Birdfy makes a few different smart bird feeders we like for bird-watching, and the Nest Duo is a birdhouse that lets you watch the birds while they nest inside of it. It’s a slim, attractive box that will add to your front yard’s style while also packing two solar-powered cameras (one facing the entrance, one focused inside) so you can bird-watch from multiple angles. It comes with different hole sizes to appeal to different species, metal predator guards to prevent chewing around the hole, and a remote control to reset or recharge the camera without disturbing your feathered neighbors.

Stylish Smart Lights

Image may contain: Electronics, Mobile Phone, Phone, Light, Computer Hardware, Hardware, Mouse, Appliance, and Blow Dryer

Govee

Outdoor Clear Bulb String Lights

I’ve liked Govee’s smart outdoor string lights before, usually for my holiday decor, and have previously recommended something similar with a bistro-light-like look that happened to be smart. These clear bulb string lights are part of Govee’s current lineup and have a contemporary twist with a triangle in the center instead of the wire filament. These are a fun option for outdoor lights you can enjoy on warm nights, and they can do every color and shade of white without looking as bulky as permanent outdoor lights. (Added bonus, these lights are also Matter compatible!)

Fresh Bulbs

Image may contain: Lighting, Electronics, LED, Light, Appliance, Blow Dryer, Device, and Electrical Device

Cync

Smart LED Light Bulb, PAR38

If you have light fixtures you want to remote-control, add an outdoor smart bulb. There are tons to choose from, and you can usually find one from any brand you already have at home. The only downside is that outdoor-rated smart bulbs are usually 4.75-inch-diameter PAR38-style bulbs, so they’re best for downward-facing floodlights on your porch or balcony. They’ll likely be too big to fit in a wall fixture as a replacement for a normal-sized bulb. Don’t just grab any smart bulb—not all are outdoor-rated. Check for mentions of outdoor use and waterproof ratings to make sure they’re safe to use. I’m a big fan of Cync bulbs, and the brand has an outdoor version of the Cync Full Color bulbs I like to use indoors. You’ll be able to add fun colors as well as shades of white, so you can turn the porch a spooky orange or red for Halloween, pink for Valentine’s Day, or the colors of your favorite sports team on game day.

Remote-Controlled Garage

Chamberlain

MyQ Smart Garage Controller

Chamberlain

MyQ Smart Garage Door Opener with Integrated Camera

If your garage is the centerpiece of your home’s curb appeal, you can control it as easily as a smart door by adding a smart controller. You can do two different styles: I have the Chamberlain MyQ professionally installed smart garage opener, which means the device that controls my garage has these smarts built into it (plus a camera, but I find it doesn’t work great with how far the device is from my Wi-Fi router), or you can get a smart garage controller that can add smart features onto an existing garage door. Both let you check whether the garage is open or closed and operate it remotely, and you can add a video keypad that doubles as a video doorbell and can let you open or close the garage without your phone.

Smart Shades

SmartWings

Motorized Roller Shades

Lutron

Caseta Smart Shades

The front of my home faces west, so it’s absolutely baking at the end of the day. What I need to add are some of our favorite smart shades to automate closing the shades on that side of the house at the right time of day. These also give your home a nice, cohesive look and immediate, controllable privacy from the outside world. WIRED reviewer Simon Hill recommends the SmartWings shades as his top picks, and Lutron’s Caseta shades if you’re looking for a more upgraded look.

Invisible Swaps

Looking to add some smarts without touching your existing setup? These switch-ups can make your front door and yard smart without being visible.

Yale

Approach Lock

This smart lock just swaps out the inner half of your front-door lock to make it smart without requiring a new key or changing your exterior hardware. You can also add on a keypad—or not, if you’d rather keep the smarts a complete secret.

Cync

Outdoor Smart Plug

This outdoor plug is visible at the outlet itself, but if the outlet is covered by something or is around the corner from your front door, no one will know that your lights or other electrical devices are connected to this smart plug.


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