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NBA announces it’s using smart basketballs and wearable tech in preseason games

1 hour 45 minutes ago

The NBA is experimenting with two pieces of technology in the leadup to the season, the league announced Monday.

Select exhibition games will include a smart basketball that has a Bluetooth sensor in it and can collect data for future use in real-time officiating decisions. The league said the information from the Wilson-developed connected balls will be for research only and not affect calls made during those games.

Officials in select games will also use wrist wearables to communicate with the replay center in designated situations, including replay reviews, scoring changes and clock malfunctions.

The wearables went through what the NBA called a successful pilot program during the summer league. The technology will not extend into the regular season.

—Associated Press

Associated Press

Why America can’t build the iPhone

4 hours 33 minutes ago

For six decades, I’ve worked on product design and manufacturing for companies such as Polaroid, Seiko, and Apple, as well as startups. When I worked at Polaroid and Apple, they still had their own factories and chose to go to Asia to build products. As noted in Patrick McGee’s bestseller Apple in China, I was responsible for bringing Apple’s first product to Taiwan in 1994. As a product design engineer and program manager, I was always involved in where my products were manufactured, because of the impact manufacturing has on a product’s success.

In my jobs at Polaroid and Apple, I turned to Asia for different reasons. I brought manufacturing to Asia for Polaroid in the 1970s, when it was rarely done. By the time I did it at Apple in the 1990s, Apple was late to the game.

Today, as pressure mounts for Apple to build iPhones in the U.S., I know from all my experiences that it is an impossible demand. We don’t have the attitude, the infrastructure, or the resources. We lost that battle decades ago, and cannot will it back.

The U.S. never invested in building consumer tech manufacturing at the scale of China or Taiwan. We prioritized R&D, design, and innovation, and didn’t value low-margin manufacturing work. During the Cold War, U.S. government investment and incentives were directed toward aerospace and defense, not consumer electronics. Our manufacturing base became dominated by contractors focused on military specifications, not consumer mass production.

Manufacturing consumer electronic products, in contrast, requires an infrastructure of suppliers, competitive labor costs, large workforces, and flexibility. Resources in China and Taiwan have allowed many U.S. companies to dominate the world in technology. While China became the manufacturer to the world, it helped us become the innovators to the world.

Going offshore: the early days

When I joined Polaroid in 1966, I designed consumer cameras that sold millions per year. Once the design was done, we’d hand it off to our internal manufacturing group, only to have them come back to us months later with requests for small design tweaks to save a few cents here and there.

It was a slow, iterative process with little concern for time to market. I often wondered why I was working so hard to meet an aggressive design schedule and then had to wait around for the product to finally ship. Analysts and industry leaders emphasized the importance of time to market as a critical factor for a product’s success, especially in competitive and fast-evolving markets.

I wondered why we couldn’t be as creative in the rest of the process as we were in the design stages. So, I began to look for ways to get products to market more quickly.

Outsourcing manufacturing to companies building similar products seemed like a good approach. They already had the experience and knowledge, so they could move quickly. That allowed the manufacturing and design teams to collaborate earlier in the process.

It wasn’t that I didn’t try to do manufacturing in the United States. I reached out to companies with manufacturing capabilities that looked to be synergistic, but it often took weeks to hear back. Many of the companies were focused on defense-related products and showed little interest in building consumer tech products with tighter schedules and lower margins.

I wondered why this was the case. Could it be that these U.S. resources were focused on the defense industry during the Cold War and never invested in consumer manufacturing? Or perhaps the defense business was just more lucrative. It was disappointing, but, fortunately, I found another solution.

At Polaroid, I turned to a few companies in Japan that had been supplying some of our components and wanted a chance to build the entire product. They offered to take our design early in the process, improve it for manufacturing, and deliver the product in high volume much more quickly than we could on our own. We were being offered the opportunity to tap into their product expertise and quality-obsessed manufacturing that Japanese companies were known for, and to learn from them in the process.

It turned out that these companies were so good that we could almost drop off a drawing package and pick up the product months later. They brought speed, experience, and predictability, and enabled us to be much more effective designers with smaller teams. Every time I’d return from a visit to Japan, I’d feel a sense of accomplishment and excitement. It was like night and day compared with using our internal manufacturing organization.

This pattern repeated itself over the next few decades as I worked at other companies. When Japan became too costly and less efficient, I went to Taiwan and later China, as each of these countries developed their manufacturing capabilities.

The U.S. could have created similar capacity over the years, especially after the Cold War, but never did. Manufacturing was just never considered an important need. It was not glamorous and never considered to be strategic.

In the 1990s, when a company I cofounded, ThinkOutside, invented the Stowaway folding keyboard, we found a Taiwan keyboard company that was experienced with manufacturing tiny key mechanisms, and partnered with them. Like many other companies working to get products to market quickly, we were doing what we did best and letting others do what they did best.

At this time, there were a few contract manufacturers in the U.S., including Solectron and Flextronics, that could manufacture others’ products in their plants in the U.S. and elsewhere in the world. They started with printed circuit boards and had expanded into complete product manufacturing; they also had some design capabilities.

This evolution mirrored the tech industry’s growing reliance on outsourced manufacturing, driven by cost, scale, and time-to-market pressures. But I found that the U.S.-based options were bureaucratic lumbering giants, reminding me of my Polaroid manufacturing experiences. They wanted only to take on large-volume projects from well-established companies, and rarely took the kind of risks that Asian companies did.

Asia becomes the world’s manufacturer

Meanwhile, Taiwan and China were becoming destinations for companies to build technically complex products because they were so good at it. They were aided by huge government investments in office parks, infrastructure, and free-trade zones.

Taiwan quickly moved from building calculators in the 1970s to notebook computers in the 1980s. James Fallows chronicled the rise of China in his famous three-part 2007 article in The Atlantic, “China Makes, The World Takes,” as China became the manufacturer for the world.

Scores of Chinese companies became available to manufacture a customer’s original design. They were known as original equipment manufacturers, or OEMs. If they offered their own designs that a customer could sell under its name, they were called original design manufacturers, or ODMs.

It was a win-win partnership. We used China to build our products, and they relied on us keep their factories running by marketing and selling products they designed.

Companies often specialized in specific product categories. If you wanted to build a particular type of product, you’d go to a company that was already building something similar. You tapped into their expertise, supplier relationships, and buying power, and everyone would benefit from the economies of scale and relevant experience.

While some first-time customers worried about two competing products being made by the same OEM, each company’s areas were usually secured from the other. When I worked on the Barnes & Noble Nook e-reader, we used Foxconn, the same manufacturer that built the Amazon Kindle. That allowed us to go from an initial concept to market in less than a year.

Product designers large and small now had a destination where they could bring their product designs and get them manufactured quickly and efficiently. You no longer needed to be a Sony to have access to manufacturers. Chinese factories enabled thousands of companies across the United States and the world to turn their ideas into viable products.

As these OEMs and ODMs grew in number, component manufacturers and other suppliers opened their own factories nearby, close to the facilities using their parts, providing even greater efficiency and faster time to market.

As an example of how effective this was, I was about to ship the initial lot of a new consumer electronic product to the U.S. from Shenzhen, the Chinese technology hub adjacent to Hong Kong, and it was to arrive just before going on sale. At the last moment, I found an error in the instruction manual that needed to be fixed before the product could ship. We called the printer, which was a few blocks away. It made the correction, and delivered correctly printed manuals two hours later.

During this time, we never considered that we were taking jobs from Americans. Nothing like this ever existed in the U.S.—not the industry, the factories, nor the workers. What China offered was unique.

Apple was late to the game

When I joined Apple in 1994 to work on the Newton MessagePad personal digital assistant, the company was manufacturing most of its computers in its own factories in California, Colorado, Ireland, and Singapore, even though other computer companies had been outsourcing most of their notebooks to Taiwan for years.

The first version of Newton, the MessagePad 100, was developed with Sharp Electronics in Japan because of that company’s expertise in building handheld products such as its Wizard organizer. But its cost came in way above plan, and I was asked to quickly develop a new model at a more affordable cost.

Apple’s internal manufacturing organization had neither the interest nor experience to master products like the Newton. Its leaders considered our product to be a nuisance and encouraged us to find our own manufacturer.

Taiwan was the obvious answer. After all, that’s where Sharp was now building its second-generation Wizards, and where most of the world’s calculators and notebook computers were being built, other than Apple’s. The new Newton MessagePad 110 was the first time Apple developed a product to be manufactured in Taiwan.

I flew to Taipei and spent two weeks visiting potential manufacturers, mostly notebook and calculator manufacturers. We selected Inventec, a $320 million manufacturer at the time. It was busy producing calculators for Texas Instruments, phones and fax machines for telecom companies, and notebook computers for Compaq and Zenith.

But we ran into an immediate challenge. Inventec would need an $800,000 machine to assemble the processor chip powering the Newton, machinery not yet used in Taiwan. The company’s CEO, Richard Lee, insisted on buying it at Inventec’s expense. He never considered it a burden, explaining it would give them new capabilities.

Inventec assigned a group that began working with our Apple design team. We began with a handshake deal while Lee suggested we negotiate a contract in parallel. We all knew drawing up contracts could take many months, particularly at Apple, and he didn’t want to lose that time.

Our joint team of about a half-dozen engineers met monthly in Taipei. The industrial designer on the team, Jony Ive, had recently joined Apple. This would be one of his first projects, and his first time he’d been to Taiwan. (I later learned that he complained to his boss about how hard I made him work.)

The product was completed in eight months and went on to fail almost as badly as the first model. Sales volumes were about 10% of plan. A few months later when I apologized to Lee, he told me not be concerned. He was happy to be working with Apple and considered this to be a win. He was thinking of the long term, and was proud of the Apple relationship. I was embarrassed.

Inventec went on to develop and build notebook computers, servers, and a range of other products for most of the world’s technology companies, including Apple. Its revenues for 2025 were $22 billion.

How design impacts manufacturing

The Newton was constructed like most other consumer tech products at the time. It consisted of a molded plastic clamshell enclosure, a large printed circuit board populated with electrical components, a pressure sensitive touch screen, a stylus, and a battery compartment that held four AA cells. Ive’s design contribution was creating a beautiful fluid-looking package that made the product look smaller than it was. It was nicely done, but its fit and finish were not dissimilar from other products being produced at the time, such as Nokia phones. 

Years later, when Ive became Apple’s senior VP of industrial design, he elevated its design to a level never seen in consumer products. I recall seeing the first iPhone and marveling at how precisely the parts fit together, the flawless finishes of the parts, and the use of new materials never employed at mass scale.  His designs set a new standard for all consumer tech products. Other products suddenly seemed crude by comparison.

Apple’s design and manufacturing engineers were responsible for putting these new designs into mass production. It certainly could not be done in the U.S., which didn’t even have the ability to build much simpler products. The new design standards with the ultraprecise fit and finish meant that the products needed to be built China.

When I hear politicians demanding that iPhones be built in the U.S., it’s clear they have no understanding of how products are created and built. You can’t just wish it to happen. You would need to create much of what China and Taiwan have built over the past 30 years.

It’s not just setting up factory buildings filled with assembly machinery and workers. You would need the entire infrastructure of suppliers and manufacturers of the parts that go into the products: the factories to build displays, batteries, and electronic components. You’d require the experience, and the right attitude.

Shipping the components from China and doing final assembly in the U.S. would also cause huge issues. Let’s suppose you bring displays in from China, and they have a defect. If you were building the product in Shenzhen, you’d call the display company that’s an hour away and they would have engineers in your factory in a few hours to address the problem or provide new parts. Try that from the United States, and you would likely shut down your assembly line for several weeks.

Now consider what would happen once you multiply this by the thousands of parts and processes that go into a product. Not only is it inefficient, it’s also just not practical to build these products here. It makes as much sense as building an automobile in the Arctic from parts made in Detroit.

The Mac Mini isn’t an iPhone

With all this said, Apple, under pressure to bring products back to the U.S., has agreed to build Mac minis in Houston. They have asked Foxconn, the same company that has built, staffed, and managed much of Apple’s China manufacturing, to create and operate the assembly operation.

The Mac mini is a much simpler and lower volume product than the iPhone. Essentially, it’s a printed circuit board assembly housed in an aluminum box. But it will still require many of its components to be shipped from China. Politically, it may be a good move, but practically, it means very little.

It will be interesting to see if Apple’s new CEO, John Ternus, makes any fundamental changes to Tim Cook’s manufacturing strategy. As a product designer, I’m guessing that Ternus—who is also a product designer—wants to get his new products to market in the fastest and most efficient way possible and will continue to rely on Asia. That approach has helped make so many of his past products huge successes.

Phil Baker

Why America can’t build the iPhone

4 hours 33 minutes ago

For six decades, I’ve worked on product design and manufacturing for companies such as Polaroid, Seiko, and Apple, as well as startups. When I worked at Polaroid and Apple, they still had their own factories and chose to go to Asia to build products. As noted in Patrick McGee’s bestseller Apple in China, I was responsible for bringing Apple’s first product to Taiwan in 1994. As a product design engineer and program manager, I was always involved in where my products were manufactured, because of the impact manufacturing has on a product’s success.

In my jobs at Polaroid and Apple, I turned to Asia for different reasons. I brought manufacturing to Asia for Polaroid in the 1970s, when it was rarely done. By the time I did it at Apple in the 1990s, Apple was late to the game.

Today, as pressure mounts for Apple to build iPhones in the U.S., I know from all my experiences that it is an impossible demand. We don’t have the attitude, the infrastructure, or the resources. We lost that battle decades ago, and cannot will it back.

The U.S. never invested in building consumer tech manufacturing at the scale of China or Taiwan. We prioritized R&D, design, and innovation, and didn’t value low-margin manufacturing work. During the Cold War, U.S. government investment and incentives were directed toward aerospace and defense, not consumer electronics. Our manufacturing base became dominated by contractors focused on military specifications, not consumer mass production.

Manufacturing consumer electronic products, in contrast, requires an infrastructure of suppliers, competitive labor costs, large workforces, and flexibility. Resources in China and Taiwan have allowed many U.S. companies to dominate the world in technology. While China became the manufacturer to the world, it helped us become the innovators to the world.

Going offshore: the early days

When I joined Polaroid in 1966, I designed consumer cameras that sold millions per year. Once the design was done, we’d hand it off to our internal manufacturing group, only to have them come back to us months later with requests for small design tweaks to save a few cents here and there.

It was a slow, iterative process with little concern for time to market. I often wondered why I was working so hard to meet an aggressive design schedule and then had to wait around for the product to finally ship. Analysts and industry leaders emphasized the importance of time to market as a critical factor for a product’s success, especially in competitive and fast-evolving markets.

I wondered why we couldn’t be as creative in the rest of the process as we were in the design stages. So, I began to look for ways to get products to market more quickly.

Outsourcing manufacturing to companies building similar products seemed like a good approach. They already had the experience and knowledge, so they could move quickly. That allowed the manufacturing and design teams to collaborate earlier in the process.

It wasn’t that I didn’t try to do manufacturing in the United States. I reached out to companies with manufacturing capabilities that looked to be synergistic, but it often took weeks to hear back. Many of the companies were focused on defense-related products and showed little interest in building consumer tech products with tighter schedules and lower margins.

I wondered why this was the case. Could it be that these U.S. resources were focused on the defense industry during the Cold War and never invested in consumer manufacturing? Or perhaps the defense business was just more lucrative. It was disappointing, but, fortunately, I found another solution.

At Polaroid, I turned to a few companies in Japan that had been supplying some of our components and wanted a chance to build the entire product. They offered to take our design early in the process, improve it for manufacturing, and deliver the product in high volume much more quickly than we could on our own. We were being offered the opportunity to tap into their product expertise and quality-obsessed manufacturing that Japanese companies were known for, and to learn from them in the process.

It turned out that these companies were so good that we could almost drop off a drawing package and pick up the product months later. They brought speed, experience, and predictability, and enabled us to be much more effective designers with smaller teams. Every time I’d return from a visit to Japan, I’d feel a sense of accomplishment and excitement. It was like night and day compared with using our internal manufacturing organization.

This pattern repeated itself over the next few decades as I worked at other companies. When Japan became too costly and less efficient, I went to Taiwan and later China, as each of these countries developed their manufacturing capabilities.

The U.S. could have created similar capacity over the years, especially after the Cold War, but never did. Manufacturing was just never considered an important need. It was not glamorous and never considered to be strategic.

In the 1990s, when a company I cofounded, ThinkOutside, invented the Stowaway folding keyboard, we found a Taiwan keyboard company that was experienced with manufacturing tiny key mechanisms, and partnered with them. Like many other companies working to get products to market quickly, we were doing what we did best and letting others do what they did best.

At this time, there were a few contract manufacturers in the U.S., including Solectron and Flextronics, that could manufacture others’ products in their plants in the U.S. and elsewhere in the world. They started with printed circuit boards and had expanded into complete product manufacturing; they also had some design capabilities.

This evolution mirrored the tech industry’s growing reliance on outsourced manufacturing, driven by cost, scale, and time-to-market pressures. But I found that the U.S.-based options were bureaucratic lumbering giants, reminding me of my Polaroid manufacturing experiences. They wanted only to take on large-volume projects from well-established companies, and rarely took the kind of risks that Asian companies did.

Asia becomes the world’s manufacturer

Meanwhile, Taiwan and China were becoming destinations for companies to build technically complex products because they were so good at it. They were aided by huge government investments in office parks, infrastructure, and free-trade zones.

Taiwan quickly moved from building calculators in the 1970s to notebook computers in the 1980s. James Fallows chronicled the rise of China in his famous three-part 2007 article in The Atlantic, “China Makes, The World Takes,” as China became the manufacturer for the world.

Scores of Chinese companies became available to manufacture a customer’s original design. They were known as original equipment manufacturers, or OEMs. If they offered their own designs that a customer could sell under its name, they were called original design manufacturers, or ODMs.

It was a win-win partnership. We used China to build our products, and they relied on us keep their factories running by marketing and selling products they designed.

Companies often specialized in specific product categories. If you wanted to build a particular type of product, you’d go to a company that was already building something similar. You tapped into their expertise, supplier relationships, and buying power, and everyone would benefit from the economies of scale and relevant experience.

While some first-time customers worried about two competing products being made by the same OEM, each company’s areas were usually secured from the other. When I worked on the Barnes & Noble Nook e-reader, we used Foxconn, the same manufacturer that built the Amazon Kindle. That allowed us to go from an initial concept to market in less than a year.

Product designers large and small now had a destination where they could bring their product designs and get them manufactured quickly and efficiently. You no longer needed to be a Sony to have access to manufacturers. Chinese factories enabled thousands of companies across the United States and the world to turn their ideas into viable products.

As these OEMs and ODMs grew in number, component manufacturers and other suppliers opened their own factories nearby, close to the facilities using their parts, providing even greater efficiency and faster time to market.

As an example of how effective this was, I was about to ship the initial lot of a new consumer electronic product to the U.S. from Shenzhen, the Chinese technology hub adjacent to Hong Kong, and it was to arrive just before going on sale. At the last moment, I found an error in the instruction manual that needed to be fixed before the product could ship. We called the printer, which was a few blocks away. It made the correction, and delivered correctly printed manuals two hours later.

During this time, we never considered that we were taking jobs from Americans. Nothing like this ever existed in the U.S.—not the industry, the factories, nor the workers. What China offered was unique.

Apple was late to the game

When I joined Apple in 1994 to work on the Newton MessagePad personal digital assistant, the company was manufacturing most of its computers in its own factories in California, Colorado, Ireland, and Singapore, even though other computer companies had been outsourcing most of their notebooks to Taiwan for years.

The first version of Newton, the MessagePad 100, was developed with Sharp Electronics in Japan because of that company’s expertise in building handheld products such as its Wizard organizer. But its cost came in way above plan, and I was asked to quickly develop a new model at a more affordable cost.

Apple’s internal manufacturing organization had neither the interest nor experience to master products like the Newton. Its leaders considered our product to be a nuisance and encouraged us to find our own manufacturer.

Taiwan was the obvious answer. After all, that’s where Sharp was now building its second-generation Wizards, and where most of the world’s calculators and notebook computers were being built, other than Apple’s. The new Newton MessagePad 110 was the first time Apple developed a product to be manufactured in Taiwan.

I flew to Taipei and spent two weeks visiting potential manufacturers, mostly notebook and calculator manufacturers. We selected Inventec, a $320 million manufacturer at the time. It was busy producing calculators for Texas Instruments, phones and fax machines for telecom companies, and notebook computers for Compaq and Zenith.

But we ran into an immediate challenge. Inventec would need an $800,000 machine to assemble the processor chip powering the Newton, machinery not yet used in Taiwan. The company’s CEO, Richard Lee, insisted on buying it at Inventec’s expense. He never considered it a burden, explaining it would give them new capabilities.

Inventec assigned a group that began working with our Apple design team. We began with a handshake deal while Lee suggested we negotiate a contract in parallel. We all knew drawing up contracts could take many months, particularly at Apple, and he didn’t want to lose that time.

Our joint team of about a half-dozen engineers met monthly in Taipei. The industrial designer on the team, Jony Ive, had recently joined Apple. This would be one of his first projects, and his first time he’d been to Taiwan. (I later learned that he complained to his boss about how hard I made him work.)

The product was completed in eight months and went on to fail almost as badly as the first model. Sales volumes were about 10% of plan. A few months later when I apologized to Lee, he told me not be concerned. He was happy to be working with Apple and considered this to be a win. He was thinking of the long term, and was proud of the Apple relationship. I was embarrassed.

Inventec went on to develop and build notebook computers, servers, and a range of other products for most of the world’s technology companies, including Apple. Its revenues for 2025 were $22 billion.

How design impacts manufacturing

The Newton was constructed like most other consumer tech products at the time. It consisted of a molded plastic clamshell enclosure, a large printed circuit board populated with electrical components, a pressure sensitive touch screen, a stylus, and a battery compartment that held four AA cells. Ive’s design contribution was creating a beautiful fluid-looking package that made the product look smaller than it was. It was nicely done, but its fit and finish were not dissimilar from other products being produced at the time, such as Nokia phones. 

Years later, when Ive became Apple’s senior VP of industrial design, he elevated its design to a level never seen in consumer products. I recall seeing the first iPhone and marveling at how precisely the parts fit together, the flawless finishes of the parts, and the use of new materials never employed at mass scale.  His designs set a new standard for all consumer tech products. Other products suddenly seemed crude by comparison.

Apple’s design and manufacturing engineers were responsible for putting these new designs into mass production. It certainly could not be done in the U.S., which didn’t even have the ability to build much simpler products. The new design standards with the ultraprecise fit and finish meant that the products needed to be built China.

When I hear politicians demanding that iPhones be built in the U.S., it’s clear they have no understanding of how products are created and built. You can’t just wish it to happen. You would need to create much of what China and Taiwan have built over the past 30 years.

It’s not just setting up factory buildings filled with assembly machinery and workers. You would need the entire infrastructure of suppliers and manufacturers of the parts that go into the products: the factories to build displays, batteries, and electronic components. You’d require the experience, and the right attitude.

Shipping the components from China and doing final assembly in the U.S. would also cause huge issues. Let’s suppose you bring displays in from China, and they have a defect. If you were building the product in Shenzhen, you’d call the display company that’s an hour away and they would have engineers in your factory in a few hours to address the problem or provide new parts. Try that from the United States, and you would likely shut down your assembly line for several weeks.

Now consider what would happen once you multiply this by the thousands of parts and processes that go into a product. Not only is it inefficient, it’s also just not practical to build these products here. It makes as much sense as building an automobile in the Arctic from parts made in Detroit.

The Mac Mini isn’t an iPhone

With all this said, Apple, under pressure to bring products back to the U.S., has agreed to build Mac minis in Houston. They have asked Foxconn, the same company that has built, staffed, and managed much of Apple’s China manufacturing, to create and operate the assembly operation.

The Mac mini is a much simpler and lower volume product than the iPhone. Essentially, it’s a printed circuit board assembly housed in an aluminum box. But it will still require many of its components to be shipped from China. Politically, it may be a good move, but practically, it means very little.

It will be interesting to see if Apple’s new CEO, John Ternus, makes any fundamental changes to Tim Cook’s manufacturing strategy. As a product designer, I’m guessing that Ternus—who is also a product designer—wants to get his new products to market in the fastest and most efficient way possible and will continue to rely on Asia. That approach has helped make so many of his past products huge successes.

Phil Baker

The 3 next big things from midsize companies for 2026

5 hours 33 minutes ago

The continued growth of AI is streamlining tasks, such as event planning, that once required tabbing between spreadsheets, emails, and vendor websites. It’s also unleashing new possibilities for innovations that can tackle its need for reliable and affordable energy. These companies, employing between 100 and 499 people, are taking advantage of both types of AI opportunities.

BoomPop
For making corporate event planning less of a headache
Corporate events are a $1.5 trillion market, but actually planning them has historically left companies downing in a sea of disparate apps. BoomPop has created an AI-native platform that can find locations for events from corporate retreats to customer dinners, gather rates and quotes, and enable booking with just a few clicks. The platform, which relies on a proprietary database of roughly 1.5 million vendors, can also craft invitations, track RSVPs, and integrate with calendar software. The company has booked more than $60 million in business for venues in the past two years, assisting clients such as Google, Netflix, Amazon, and Dick’s Sporting Goods.

Lightmatter
For using light to connect the chips that power AI
Modern AI models rely on small neural networks called “experts” that are focused on different types of problems and are typically spread across hundreds of GPU chips in a data center. These chips need to share data, but electrical connections, and even traditional optical networks, aren’t fast enough to shuffle information at the speed required. Lightmatter’s Passage networking system, powered by microscopic lasers, delivers speedy connections that let models train up to three times faster and deliver results up to 11 times faster while using a fraction of previous power requirements. In March 2026, Lightmatter and Qualcomm announced they had achieved a record-breaking 1.6 Tbps bandwidth per fiber. The company has launched an open standards initiative within the Open Compute Project aimed at setting standards for integrating fiber connections with computing chips.

Torus
For helping the electrical system run smoothly even as loads rapidly shift
Torus builds electrical storage systems that can help boost grid capacity and more smoothly address sudden shifts in demand. Using a mix of the company’s Torus Spin fast-responding mechanical flywheel systems and battery storage, its Torus Station technology can respond in milliseconds to the needs of the grid without degrading over time like pure battery systems. An AI orchestration platform called Torus Overwatch anticipates peaks and handles preventive maintenance, while built-in cybersecurity technology keeps the system shielded from hackers. In 2025, Torus helped utilities respond to more than 360 real-world demand response events, opened a 545,000-square-foot manufacturing facility in Salt Lake City, and announced $200 million funding from asset manager Magnetar.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Steven Melendez

The 5 next big things in media and entertainment for 2026

5 hours 33 minutes ago

The next big things in media and entertainment remove friction for both creators and consumers. From deploying AI to handle the tedious aspects of animation to leveraging new technology to make the experience of viewing sports more dynamic, these innovations handle the busywork so humans can focus on the good stuff.

Autodesk
For empowering animators to spend more time on creative work
MotionMaker, a new feature in Autodesk’s Maya animation software, uses an autoregressive motion generation model to predict character movement. As a result, animators can spend less time on repetitive production tasks and more time on iteration and refinement. The technology makes animation more like directing a digital actor than manually piecing together movement frame by frame.

L-Acoustics
For making massive venues sound small
L1, the latest sound system from French professional audio company L-Acoustics, debuted at the Hollywood Bowl in May 2026. It does more with less when powering concerts in large venues, using software-steered sound to deliver consistent, studio-quality audio. A compact L1 column delivers the power of a traditional speaker stack up to three times its size, meaning fewer blocked sight lines and thus fewer “bad seats.”

Lemonlight
For streamlining video production
Lemonlight’s Hero is AI that’s clearly built by video production insiders. The technology enables production teams to storyboard, generate shot lists and call sheets directly from scripts, and apply varying levels of creative control. Directors can fine-tune every detail, or move from concept to production with a single click. The result is not just faster video, it’s better video. 

Linguana
For giving global scale to creator partnerships
For brands, the fundamental issue with influencer partnerships is that they don’t scale. Linguana changes that equation. The AI-powered platform lets brands run a single campaign that can be seamlessly integrated into creator content across languages and markets. The tech also works for creators, allowing them to expand their reach without being limited by their native language or geography. 

Peripheral
For bringing spatial intelligence to all levels of sport
Peripheral’s neural rendering systems deliver broadcast-quality 3D reconstructions of sporting events at one-tenth the cost of previous products. The technology gives broadcasters access to storytelling formats that traditional camera tech cannot produce, offering Gen Z and Alpha fans the any-angle replays they’re familiar with from gaming. It became part of the NBA’s Launchpad program in January 2026, quickly leading to pilot deployments with NBA-affiliated teams.

Utopai Studios
For solving the problem of narrative continuity in AI
Because Utopai is, at its core, a film studio, it has firsthand knowledge of the needs directors are looking to address with AI. The company’s PAI video generator supports up to three continuous minutes of 4K video generation with a single narrative flow, and it can maintain character consistency across different shots and environments. Directors and creatives can use PAI on its own or as part of broader creative workflows. The product hit $11 million in annual recurring revenue within six weeks of its release.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Maia McCann

The 3 next big things in global innovation for 2026

5 hours 33 minutes ago

While U.S. companies continue to dominate global news, these companies are solving problems abroad. The three honorees, from diverse industries, are addressing specific challenges in the markets they serve: reducing drag on boats, helping ranchers manage livestock, and improving customer interactions. Each company is applying new technology to a practical problem in the market where it operates.

Halter
For giving ranchers a high-tech solution to manage their livestock
Livestock ranching has traditionally relied on labor-intensive work. Halter is making it easier for ranchers to manage livestock, helping them track elements like grazing systems and herd movement while relying less on physical infrastructure. This is especially important as the effects of climate change increase and ranchers need to develop solutions for more sustainable food production.

Synthesia
For using AI to improve customer interactions
Synthesia makes AI avatars that help employees practice interactions, including role-play scenarios like handling issues during sales calls. Employees then receive feedback, while the software tracks their skills progression. A USC study found AI avatars statistically equivalent to human presenters in terms of message impact. The company raised a $200 million Series E at a $4 billion valuation.

Vessev
For creating technology that reduces drag on boats
Vessev makes electric boats that move people across the water more efficiently by adding carbon-fiber underwater wings called hydrofoils, which lift the hull above the water as the boat moves, cutting down on drag. The company’s technology is used by ferries and in the tourism industry to give riders a more comfortable alternative to typical passenger boats.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Yasmin Gagne

The 7 next big things in health and medicine for 2026

5 hours 33 minutes ago

As these honorees show, the future of medicine is looking more and more . . . futuristic. They include AI that diagnoses more than a dozen serious conditions from a single abdominal scan, a sci-fi-worthy clinic that reimagines the preventive care model, and a psychedelic that tackles treatment-resistant depression with a synthetic version of a Sonoran Desert toad secretion. Need help with paying rising out-of-pocket medical costs? There’s even a solution for that.

Aidoc
For massively streamlining the interpretation of medical imaging 
Roughly one in four adults will need medical imaging this year, creating a daunting caseload for radiologists who interpret the scans. AI can help, but most tools assess single conditions, resulting in diagnostic blind spots and critical care delays. Aidoc’s operating system for clinical radiology, called aiOS, consolidates these separate workflows. In January 2026, the FDA cleared Aidoc’s comprehensive abdomen CT triage tool, which can diagnose 15 acute conditions—including appendicitis, aortic dissection, and bowel obstruction—from a single scan. With a total of 30-plus FDA-cleared algorithms to date—spanning cardiology, oncology, and vascular conditions—Aidoc has analyzed more than 120 million patient cases at over 1,600 hospitals and raised more than $500 million to date. In August, the company announced the creation of the Diagnostic AI Consortium, in which a dozen of the largest hospitals and health systems in the U.S. will develop standards for AI usage in clinical decision-making. 

Akido
For expanding healthcare capacity through improved teamwork 
Akido‘s ScopeAI clinical automation platform expands the effectiveness of  multidisciplinary care teams by enabling nonmedically trained outreach workers to begin patient intake and diagnosis before a doctor is available. Trained on more than 10 million real-world clinical encounters, it listens to patient interviews on a tablet or laptop and suggests follow-up questions. An initial diagnosis and treatment plan is then sent to a physician to sign off on or modify. In Akido’s street medicine program in Los Angeles, ScopeAI enabled provider teams to double the number of patients they could serve, driving a 55% reduction in emergency department visits and achieving 82% retention at three months in a population with traditionally high dropout rates. The company has expanded into clinics in Rhode Island and New York, including a chronic-disease management program for New York City rideshare drivers. Akido now serves 500,000 patients annually across nearly 100 clinics and 26 specialties. 

AtaiBeckley
For bringing next-generation psychedelics into the mainstream
In July, Eli Lilly announced it would acquire AtaiBeckley in a deal worth up to $3.8 billion—a major validation of a pioneer in regulated psychedelic therapies. Its lead asset, BPL-003 (mebufotenin benzoate), is a nasal spray based on a compound found in the defensive secretions of the Sonoran Desert toad. After early studies showed substantial improvement over existing approaches to treatment-resistant depression, the FDA granted BPL-003 Breakthrough Therapy Designation in October 2025. The spray could shrink clinical visits to about two hours, versus six to eight hours for first-generation therapies based on psilocybin. With phase 3 trials initiated in June 2026, the drug could become the first next-generation psychedelic medicine to obtain FDA approval.

Axoft
For developing a more brain-friendly brain-computer interface
Made from a bio-inspired material called Fleuron that mimics the soft tissues of the brain, Axoft‘s minimally invasive brain-computer interface (BCI) can be surgically implanted through two tiny holes in the skull. With 32 times more sensors per thread than current flexible probes and 60% less signal loss than BCIs made from conventional polyimide, it gives high-resolution access to neural activity not available through traditional exams or external imaging. Because it can be safely implanted in any brain region, including previously unreachable deep areas, Axoft’s device is uniquely suited to evaluating patients in a coma or vegetative state. The device has been implanted in 11 patients so far across three clinical sites, including Mass General Brigham.

Neko Health
For making the annual physical fast—and kind of fun 
Cofounded by Spotify founder Daniel Ek and Hjalmar Nilsonne, Neko Health is reinventing preventive healthcare through a growing network of spa-like clinics offering a comprehensive health assessment in one hour. Combining data from its proprietary Dermascan imaging device, biometric measurements, blood testing, advanced sensor readings, and a consultation with a human physician, the Neko Health exam identifies early indicators of cardiovascular disease, metabolic conditions, circulation issues, and skin cancers. The company has opened two clinics in Sweden, six in London, and one in New York City. Neko Health has raised over $1 billion to fund growth, including a $700 million funding round this July.

Paytient
For making out-of-pocket health costs more manageable
Americans spent $98 billion out of pocket on medications in 2024, a cumulative 25% increase over five years, according to analytics firm IQVIA. Paytient’s healthcare affordability platform, which is typically underwritten by employers, acts like an interest-free line of credit to help people pay for things that health insurance doesn’t, including copays, coinsurance, and deductibles. Members get a Paytient card that pays providers in full up front, then repay over time via payroll deduction, bank account, or HSA/FSA. The company now serves more than 26 million members and partners with nearly 7,000 employers, insurers, and providers. In 2026, Paytient expanded into pharmacy benefits, partnering with GoodRx and Cost Plus Drugs and adding transparent drug pricing to its app.

SetPoint Medical
For easing rheumatoid arthritis pain without pills 
SetPoint Medical‘s is the first FDA-approved neuroimmune modulation device for rheumatoid arthritis. The multivitamin-size wireless neurostimulator is implanted on the vagus nerve and delivers a daily one-minute electrical signal to regulate inflammation without drugs. In a trial of 242 patients with RA, 75% of participants managed their symptoms with SetPoint alone at 12 months. Since the FDA nod in July 2025, adoption has accelerated—the therapy is now available in six markets, with expansion planned in up to 30 new cities by the end of 2026. In April 2026, the company began enrollment for a pilot study evaluating the device as a potential therapy for relapsing multiple sclerosis.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Adam Bluestein

The 7 next big things in foundational AI for 2026

5 hours 33 minutes ago

AI innovation may feel like it’s utterly dominated by a few giants, but the reality couldn’t be more different. These honorees have thrived by zeroing in on specific challenges, including teaching the technology to interpret sensor data, speeding up inference, and giving researchers better insight into how models actually work.

Archetype AI
For turning sensor data into intelligence
Large language models may know a lot, but because they have been trained on text and images, that limits their understanding of our world. By contrast, Archetype AI’s Newton foundation model can interpret data from cameras, lidar, radar, inertial measurement units, and other sensors used in enterprise domains such as construction, logistics, manufacturing, and urban planning. Customers ranging from the city of Bellevue, Washington, to data services firm NTT Data to construction giant Kajima Corporation have adopted the model, which outperforms the major general-purpose LLMs on sensor-related tasks.

Cerebras Systems
For putting inference’s pedal to the metal
If you’ve ever asked AI to do something and then drummed your fingers waiting for it to respond, you’ve run up against slow inference. To pick up the pace, Cerebras developed its own inference platform from the semiconductor wafer up. In January 2026, the company unveiled a partnership with OpenAI to launch a 750-megawatt deployment of its technology. Two months later, it announced that its systems had been deployed in Amazon’s AWS data centers. It’s also working with AWS on an AI architecture that pairs the companies’ technologies to quintuple a hardware footprint’s token capacity.

d-Matrix
For powering real-time AI
In June 2026, d-Matrix’s Corsair AI platform entered full production. It reduces typical latency inherent to AI by placing compute and memory alongside each other, an approach that an independent benchmark showed slashing AI response time to 2 seconds from 24 seconds. The company’s investors including Microsoft, SK Hynix, and Samsung, along with the Qatar Investment Authority and Singapore’s EDBI.

Goodfire
For taking the mystery out of models
Silico, Goodfire’s AI development and interpretability platform, is designed to overcome AI models’ notoriously opaque nature. It allows researchers to examine the innards of a model, make precision adjustments, and steer its behavior in ways that might otherwise be impossible. Mayo Clinic adopted Silico to catalog 4.2 million genetic variants and predict their likelihood of causing disease. Rakuten used it to excise sensitive data from its agent platform, resulting in 58% fewer hallucinations at approximately 90 times less cost than typical approaches.

Liquid AI
For helping AI think small
AI running at massive scale in data centers gets most of the industry’s attention. Liquid AI’s LFM2.5 is a set of AI models optimized to run on processors in laptops, smartphones, vehicles, and IoT devices—no internet connection necessary. Instead of being based on transformers like a conventional model, each one is customized by Liquid AI’s own meta-AI system to operate within specific processor and memory constraints. Chipmakers AMD, Intel, and Qualcomm are shipping LFM2.5 versions for their respective platforms. Last April, Liquid AI also signed a multiyear deal with Mercedes-Benz to embed the technology directly into vehicles.

Merge
For plugging enterprise AI into the real world
Released in October 2025, Merge’s Agent Handler lets organizations use AI to unlock the value of data that lives in their essential applications. Instead of engineering teams having to wire up Model Context Protocol (MCP) connections themselves, they can use Merge’s library, which covers of hundreds of enterprise tools, such as Salesforce, Workday, Gmail, and GitHub. These connectors are prebuilt and designed with security and observability in mind. Some 1,700 paying customers call on the platform for more than 1.1 billion daily API requests; it also powers Perplexity’s Enterprise Pro service.

Runpod
For delivering AI infrastructure as a service
Runpod is a cloud provider focused entirely on helping organizations train and deploy fast, reliable AI without having to invest in GPU hardware or manage it on their own. Its platform provides access to more than 30 Nvidia and AMD GPU models in 31 global regions and offers proprietary technologies such as FlashBoot, which speeds inference by cutting cold-start times to under 200 milliseconds. The company says it’s passed $120 million in annual recurring revenue from more than 1 million customers.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Harry McCracken

The 4 next big things in food and agriculture for 2026

5 hours 33 minutes ago

Climate change is making food harder to produce, even as agriculture remains a major source of emissions. At the same time, farmers are contending with labor shortages, rising costs, and pressure to get more out of every acre. New technologies are helping them do that, from robotic beekeeping and AI systems that reduce crop loss and pesticide use to tools that identify the genes behind more resilient crops.

Beewise
For automating beekeeping at scale
For commercial beekeepers, keeping tabs on thousands of hives across miles of farmland is a challenge. A hive can develop a health problem without anyone noticing, simply because it’s not physically possible to visit every hive often enough. ROBI, Beewise‘s solar-powered robotic beekeeper, is designed to help by autonomously moving down flexible rails that sit between rows of beehives. It captures images of each frame in a hive so software can analyze colony health. It can also feed bees and treat disease as needed. Some of the largest pollination providers in the U.S. are now beginning to roll it out.

Farmwave
For using AI to reduce crop loss
Farmers harvesting crops such as corn and soybeans can lose tens of thousands of dollars in produce because tractors struggle to adapt to varying field conditions, leaving portions of the crop unharvested. Farmwave uses cameras and AI to track grain loss in real time so farmers can adjust tractor settings, reducing loss by three to eight bushels per acre. The tech runs on Dell’s Pro Rugged tablets, designed to work in dusty, demanding field conditions. Thousands of images captured by the AI system each day are processed directly on the device, allowing the technology to work in areas with limited or no Wi-Fi connectivity.

Heritable Agriculture
For finding the genes behind better crops
Conventional plant breeding is slow—scientists can spend as long as ten years just figuring out which gene matters for a particular trait. Heritable Agriculture uses AI to identify key genes in 14 to 18 months. The startup spun out of X, Alphabet’s “Moonshot Factory,” in 2024 after five years of research. It uses deep learning and large language models to analyze plant data, including DNA and proteins, alongside environmental data on soil and water to help develop crops optimized for a particular location and adapted for climate change. In one project backed by the Gates Foundation, Heritable is developing drought- and heat-tolerant crops for sub-Saharan Africa.

Verdant Robotics
For slashing pesticide use with AI
Verdant Robotics’s SharpShooter technology, mounted on tractors, uses AI to aim herbicide directly at weeds and not at crops, reducing herbicide use by as much as 99%. The system builds a live digital twin of the field, including every crop plant and weed, and it targets unwanted plants with millimeter-level accuracy. Farms can save hundreds of thousands of dollars on labor and roll out treatment at the moments it’s most useful, something that’s challenging with current labor shortages. The payback period is between six and 18 months. In one study, yield at early harvest more than doubled.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Adele Peters

The 3 next big things in fintech, crypto, and blockchain for 2026

5 hours 33 minutes ago

Fintech and blockchain technology has seen its fair share of boom and bust cycles in the past decade, from the cryptocurrency explosion during the COVID pandemic to the AI era launched by ChatGPT. But the tech has continued to evolve in step with it all, and the work being done in this space is exciting—from companies that are challenging social norms to those that are betting on futuristic visions.

Mesh
For connecting crypto’s fragmented payment ecosystem
Even as cryptocurrency matures, a pain point for consumers remains: The system is fragmented by design. Hundreds of different blockchains, digital wallets, exchanges, and payment platforms exist as islands on their own. That may be frustrating for the more than 900 million global crypto users—who made over $27 trillion in transactions with stablecoins alone this year—as moving money around often requires custom engineering. Mesh bridges the islands. It built a layer of infrastructure that links wallets, exchanges, and platforms at a single point, turning a cacophony into a harmonious orchestration—a shopper in Lagos could pay in Bitcoin, Solana, or any other token owned, and a merchant in Manila could instantly settle in USDC, dollars, euros, yen, or any currency preferred. With Mesh, those conversions are automatic rather than manual. The company stands as a neutral party—it does not mint tokens, run a trading desk, or hold custody itself—and bills itself as future-proof for the age of AI agents, even giving a live demo of an AI-driven e-commerce transaction at Singapore’s TOKEN2049. In January, Mesh raised $75 million in funding at a $1 billion valuation. It powers crypto payments for PayPal and Kalshi, and partnered with Google for the emerging autonomous payments economy.

Seon
For making fraud intelligence portable across AI models
AI might enable more fraud, but Seon is enabling more fraud protection across AI bots. In June, the company released a fraud detection and anti-money-laundering tool that can be freely layered onto any existing AI model—from Anthropic’s Claude to OpenAI’s ChatGPT to Google’s Gemini. That’s in stark contrast to other fraud tools on the market, which lock customers into proprietary experiences that don’t talk to each other. According to Seon, companies often stack those disconnected tools—one for identity checks, one for activity monitoring, et cetera. But Seon’s “portable intelligence layer,” or “Model Context Protocol,” sends its 900 real-time signals to any AI, allowing it to seamlessly reference them across the full user journey. And that makes fraud detection more effective—for example, it could catch criminal rings by flagging clusters of activity that only seem suspicious when put together versus being viewed in isolation. Seon processes 15 million transactions daily and is used by more than 5,000 businesses globally across fintech, retail, and gaming, including Afterpay, Bally’s, and Tecovas. It helped a top fast-casual restaurant chain across North America, Europe, and the Middle East cut fraud by 40%. In 2025, it grew its annual recurring revenue by 80%, from new customers and deeper adoption.

Wealth.com
For bringing estate planning into the digital age
Nearly 3 in 4 Americans regard estate planning as important—yet simultaneously, nearly 3 in 4 Americans lack even a basic estate plan, according to Caring.com. That’s begotten unfortunate consequences: More than half (58%) of families have squabbled over inheritances after a death in the absence of a proper estate plan—which has, counterproductively, locked up that wealth for months if not years, InvestmentNews reports. Why don’t Americans plan for post-mortem? Studies suggest it’s a combination of confusion about the process, concerns that it will be too costly, and the belief that they don’t have enough net worth to justify an estate plan. But with new AI technology that cuts expensive attorneys and lengthy paperwork out of the equation, Wealth.com is hoping to de-frictionize the process. Its engineering teams leveraged artificial intelligence to translate thousands of pages of state-specific legislation and factor in decades of estate-planning logic, creating a platform that can walk clients in all 50 states (plus Washington, D.C.) through the whole journey, end to end. It claims that what once took months can now be done in 30 minutes. Since its founding in 2021, Wealth.com has delivered 100,000-plus estate plans and is used by 5 out of 7 of the nation’s largest private banks, representing more than $15 trillion in client assets. Following four consecutive years of over 300% revenue growth, in 2026, the company raised $65 million from Google Ventures, Charles Schwab, Citi Ventures, and other investors.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Connie Lin

These innovations of 2026 could have a particularly broad impact

5 hours 33 minutes ago

Some innovations have potential beyond the bounds of a single industry. These honorees include a company that is combining AI and advanced genetic life science to better track disease pathology, and another developing models that could help avoid animal testing by simulating human organ functions. 

Algen Biotechnologies
For blending genetics and AI to advance pathology
One of the most promising health applications for AI is earlier identification of genes that can be targeted by therapies to reverse disease processes. Spun out of Nobel Laureate Jennifer Doudna’s lab, Algen is the creator of AlgenBrain. It uses CRISPR and AI to better understand disease biology and accelerate next-generation approaches. The system maps causal links between gene regulation and disease progression, going beyond data analysis to generate solutions. The company recently announced a $555 million partnership with AstraZeneca to develop AI-powered drug discovery, giving Algen access to the pharmaceutical giant’s immunology pipeline.

Emulate
For developing human organ simulators that can supplant animal testing
Organ-on-a-Chip technology from Emulate uses microfluidic devices containing human cells to replicate key aspects of human organ functions. That offers more relevant data for modeling—for example, drug reactions—than traditional 2D cell culture models. Emulate’s next-generation platform focuses on scalability, attacking the high-cost and labor-intensive workflows that its first systems required. The company’s Human Liver Chip is in the final stages of the FDA’s ISTAND qualification program, which could enable it to partially replace animal testing in regulatory submissions for pharmaceuticals.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Ross Rubin

These established companies are still innovating like startups

5 hours 33 minutes ago

As companies emerge from early startup stage, pressure mounts to focus on execution. But they must also have an eye toward opportunities to improve, expand, and serve evolving marketplace needs. This year’s honorees have used AI to streamline and automate common back-office tasks, help companies meet mission-critical requirements, and serve doctors and mothers by more precisely determining pregnancy due dates.

Rippling
For teaching AI “that’s how we do things here“
The more AI understands about our work, the better it can help us navigate and perform tasks in that world. Rippling is a platform focused on streamlining and automating many of the administrative tasks in areas such as HR, IT, and finance. Its agentic prowess stems from having access to the native data structures, context, and controls defined within a company. That creates outputs that reflect the actual state of a business, helping humans in these roles focus on more strategic tasks. The company, which largely serves mid-market customers, recently crossed $1 billion in annual recurring revenue and raised $450 million at a $16.8 billion valuation. 

Seekr
For bringing AI to mission-critical tasks
While many organizations are piloting AI across a range of jobs, the vagaries around its operation have kept it out of many mission-critical applications. Seekr’s SeekrFlow aims to change that with an end-to-end AI platform for creating, deploying, and managing LLMs using an organization’s own data. Designed for enterprises, government agencies, defense organizations, and regulated industries, it addresses challenges around such issues as data quality and oversight via high-quality AI datasets and domain-specific models and agents. Earlier this year, the company partnered with General Dynamics Information Technology to build agentic AI solutions for federal agencies. It’s available now on AWS GovCloud.

Ultrasound AI
For delivering more accurate pregnancy due dates
For hospital staff, an expectant mother’s due date is key to a range of obstetric care decisions, such as how to time an induction or cesarean or when to prepare for a potential high-risk birth. Predictions made via traditional means can be off by more than two weeks. Ultrasound AI‘s Delivery Date AI (DDAI) replaces the manual formula measurements with one derived from analysis of a standard ultrasound image in less than a minute, creating a more accurate delivery date prediction. In February, Ultrasound AI received FDA De Novo clearance for DDAI tech, paving the way for broader adoption.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Ross Rubin

These big companies showed their innovative side in 2026

5 hours 33 minutes ago

Large companies have scale, but scale can also make change difficult. This year’s enterprise-size honorees show what happens when established organizations use new technology to rethink core operations, from hiring tens of thousands of workers to managing sprawling retail networks. Their innovations are notable not just for what they do but for how widely they can be deployed.

Albertsons Companies
For putting AI to work across the grocery business
Rather than deploy AI through isolated pilots, grocery giant Albertsons Companies has built a unified data and AI platform that connects operations across its stores, supply chain, corporate functions, and digital channels. The system is being used to improve demand forecasting and inventory management, optimize promotions and product assortments, and power more personalized digital shopping tools. Albertsons has also introduced AI tools for employees to reduce repetitive work and to help associates make faster decisions. The company says these efforts are contributing to a multibillion-dollar productivity target.

Chipotle Mexican Grill
For using AI to speed up restaurant hiring
Chipotle partnered with Paradox to build Ava Cado, an AI-powered hiring assistant that guides restaurant applicants from initial application through interview scheduling and offer. The mobile-first system answers questions, collects candidate information, coordinates interviews, and sends offers around the clock, including during the roughly 30% of interactions that occur outside normal business hours. Since its launch, Chipotle says the average time from application to start date has fallen from 12 days to four, while application completion rates have climbed from about 50% to nearly 88%. Applicant flow has nearly doubled, and 96% of offers are accepted.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Max Ufberg

The 5 next big things in enterprise technology for 2026

5 hours 33 minutes ago

As enterprises scale their use of LLMs and autonomous agents, the big questions are shifting from adoption to control and cost. Instead of providing organizations with generic AI tools, this year’s honorees bring domain expertise and niche solutions to solve complex problems. They’re helping legacy organizations such as government agencies and hospitals modernize processes to improve compliance and quality while reducing cost.  

Integrate
For modernizing government project management
Integrate is helping to modernize government project management, where complex programs and secure data need to work across fragmented systems, government agencies, and contractors. Its platform can run over classified networks, with permissions that can limit access down to individual data elements while keeping project timelines continuously updated instead of relying on periodically refreshed reports. These upgraded systems could be even more important as government agencies work with more AI model providers, software platforms, and outside partners. In 2025, the company signed a $25 million contract with the U.S. Space Force to help improve management and coordination of space programs over the next five years.

Knox Systems
For taking the headache out of government sales
Cloud companies selling to federal agencies often need to prove they can securely handle government data, which can involve a lot of red tape and technical hurdles. Instead of making companies start from scratch, Knox Systems created a way to fast-track the process by connecting its own prehardened infrastructure and security controls within existing environments. It’s also adapting for FedRAMP’s focus on continuous vulnerability monitoring, with KnoxAI already in use by more than 100 government agencies, including the U.S. Navy and U.S. Treasury. Knox, which says it helps cut FedRAMP approval time to less than 90 days and slash first-year costs by 90%, has customers such as Adobe and Celonis. It recently announced an infrastructure partnership with Microsoft Azure Government Cloud. 

Midstream Health
For helping hospitals find hidden revenue 
It’s no secret that hospital finance is notoriously slow and fragmented. Hospitals such as Houston Methodist and Mount Sinai are turning to Midstream Health for help. Instead of optimizing coding or billing, the company uses AI to find money hospitals are already owed but haven’t collected yet from missed rebates, pricing errors, and underpayments. One of the largest not-for-profit health systems used Midstream’s AI to analyze data from 20 disconnected systems of record, uncovering “tens of millions” in uncollected rebates. Over the next five years, Midstream plans to expand its portfolio of agents across healthcare supply chains, managed care, and pharmacies to tackle other operational and economic issues.

Sonar
For applying quality control to coding agents
As AI makes code generation cheap and abundant, verification and trust become even more valuable. Sonar has built a new independent verification layer around that shift by evolving its SonarQube platform for the agent-centric development cycle to improve accuracy, security, and reliability. New tools include ways to guide coding agents with repository-specific context, hunt for hard-to-spot security flaws, automatically fix or flag existing issues within agentic workflows, and verify outputs. Rather than trusting one LLM to grade another, it verifies outputs by analyzing code deterministically as an independent analysis. Sonar works with coding platforms such as Claude Code and Cursor, and it recently acquired the AI code-review startup Gitar. Its customer base covers over 75% of the Fortune 100, including Nvidia, ServiceNow, AstraZeneca, and Ford.

Tailscale
For bringing order to API key chaos
Expanded use of AI agents and LLMs has made usage harder to track. To give security and finance teams more granular visibility into usage and spending, Tailscale’s Aperture platform centralizes API credentials and better connects agent and LLM traffic to users, devices, and workloads. Earlier this year, it made its first acquisition when it bought the Vancouver-based firm Border0, which helps companies manage and monitor how autonomous AI tools access servers and databases. Tailscale also recently added browser-based AI access, universal data connectors, and sandbox support to help tackle “shadow AI” by giving employees easier ways to use approved models. 

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Marty Swant

The 2 next big things from long-established companies for 2026

5 hours 33 minutes ago

Innovation can be challenging, even for companies with a clean slate. Those who have an established track record of 15 or more years of business must navigate history, culture, product lines, and other entrenched forces that can be both assets and inhibitors. Our honorees have shown that past success doesn’t preclude tomorrow’s progress by leveraging robot fleets for construction and extending real-time intelligence to billions of devices.

DeWalt and August Robotics
For getting fleets of robots to drill down into construction efficiency
Gargantuan data centers do not spring up overnight. Their many construction complexities include holes for structural support as well as mechanical, electrical, and plumbing systems. To streamline what can be a particularly labor-intensive process, DeWalt and August Robotics developed robots that can be operated together as a fleet to handle physically demanding tasks such as concrete drilling while supporting a collaborative human-machine model. In July, August Robotics reported that its drilling robot delivers up to 10x faster drilling speeds versus traditional methods, reducing construction times by 190 weeks across 26 major projects.

Synaptics
For developing edge intelligence in an open-source model
With AI token expenditures soaring in many businesses and security, privacy, and latency concerns preventing many applications from using cloud-based AI, many companies are eager to explore edge-based options. Best known for its pioneering work in developing the touchpad 30 years ago, Synaptics has developed Astra, an open platform to bring real-time intelligence into devices such as factory equipment, medical devices, home appliances, and retail systems. At Google’s developer conference, Synaptics showed off how an Astra-powered development board equipped with Google’s Coral NPU could generate real-time music based on the tracked movement of jellyfish.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Ross Rubin

The 3 next big things in education tech for 2026

5 hours 33 minutes ago

Much of the discussion around AI and young people has focused on what career opportunities may exist in the future. But the technology is already changing education. These honorees have leveraged it to personalize preparation for standardized tests as well as help teachers generate compliant curricula and supporting educational materials.

Acely
For personalizing college entrance exam preparation
Many families pay about $1,000 for college entrance exam prep programs that offer one-size-fits-all instruction, with private tutoring stretching well beyond that. Acely seeks to flip that investment on its head with a personalized program starting at $49 per month that requires only about 20 minutes a day. While that still adds up, the service provides personalized, responsive instruction, not just a barrage of drills. In August, the company formed a partnership with the organization behind the ACT to co-market its prep platform nationwide.

Chalkie
For making AI a helpful teacher’s pet
The exceptional emotional intelligence needed to effectively connect with young minds likely leaves teaching relatively low on the list of jobs humans will soon cede to AI. Chalkie keeps the technology squarely in the toolset of human teachers by creating educational resources grounded in sound pedagogy, learning objectives, activities, assessment, and recap tasks, with a special emphasis on making lessons academically and structurally effective. It can also tailor curricula to national and state standards such as Common Core and even adapt lessons to meet IEP-mandated requirements. In March, the edtech startup raised a $4 million seed round. It also recently crossed the million-teacher mark in its user base.

ClassDojo
For helping to develop early reading skills
Education platform ClassDojo’s Dojo Sparks coaches young readers using specially tuned speech recognition, an adaptive science-driven curriculum, and a child-friendly design that integrates motivation and repetition. The company says that using it for 15 minutes a day in classrooms can take some children from pre-literacy to second-grade reading in six months or less. Beyond the classroom, ClassDojo has been expanding its online tutoring and school communications offerings. It recently shared that it is used by 45 million families and teachers worldwide.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Ross Rubin

The 6 next big things in data for 2026

5 hours 33 minutes ago

Training data begat large language models—and ever since, they’ve been intimately bound. This year’s honorees include a database tuned for AI agents, a dataset for AI-aided diagnosis of breast tumors, and a platform that gives companies more control over how their data is acquired and used.

Databricks
For bringing databases up to AI speed
According to Databricks, AI agent creation of test and development environments on its platform has grown from 0.1% two years ago to 97% today—a dynamic that traditional database architectures have been ill-equipped to accommodate. In response, the company developed Lakebase, a ground-up rearchitecture of databases that can spin them up and shut them down at AI speeds, facilitating the multitude of small, fast workloads of AI-driven development. In August, Databricks raised $5 billion in a strategic funding round, valuing it at $190 billion. The company’s revenue run rate has grown 80% year over year.

DataPelago
For transparently routing code to the optimal processor
While GPUs and NPUs are the processor architectures most closely aligned with AI, high-performance computing tasks also take advantage of two other kinds of processors, CPUs and FPGAs. Effectively routing the right computations to the right processors can reduce significant overhead. DataPelago’s Nucleus modernizes CPU-centric designs of legacy systems and acts as a bridge between data lakes and query engines. It routes computing requests to the most efficient processor type available without requiring any code changes. The company says that its Accelerator for Apache Spark delivers up to 10x faster performance at 80% lower compute costs. In August, DetaPelago was acquired by NetApp, which put Nucleus at the core of its full-stack AI offering.

DDN
For scaling storage to supercomputer workloads
Training ever larger and more capable data models has been hampered by slow data path throughput, which can result in GPU underutilization. DDN’s EXAScaler is a parallel file system technology. One of its highest-profile clients is Google Cloud’s Managed Lustre, a file service used for AI and high-performance computing models. Salesforce and Nvidia are also customers. The managed service can scale up to 10 terabytes per second by combining parallel I/O and high-throughput shared storage. DDN is on track to double revenue to $1 billion in 2026 on surging AI storage demand.

Hydrolix
For improving site traffic insights by watching the bots
AI agent and bot traffic now accounts for more than half of all web traffic, according to Cloudflare. Hydrolix’s Bot Insights makes this behavior visible, providing personalized dashboards for different job roles and allowing customers to decide which ones to block. They can also query the data to answer questions about latency during checkouts or rising search costs. Hydrolix reports that Bot Insights reduces incident attribution time by 95%. The company recently raised an $80 million Series C venture round and was featured as the engine powering AWS’s Agentic Intelligent Operations platform for streaming media

iMerit
For improving breast cancer diagnosis through a breakthrough dataset
When detected early, the five-year survival rate for breast cancer exceeds 90%. The clinical standard for screening—Digital Breast Tomosynthesis, also known as 3D mammography—outperforms traditional 2D mammography for detecting lesions that could be cancerous, particularly in women with dense breast tissue. Unfortunately, this method is held back by a shortage of biopsy-confirmed datasets. iMerit has worked with Segmed and Advocate Health to release an open-source, freely available dataset of more than 500 biopsy-confirmed exam results. The dataset was released in accordance with HIPAA regulations and split roughly between benign and malignant cases. The dataset should help researchers hone AI’s ability to more accurately and quickly assist diagnosis. In August, data and AI company EXL completed its $310 million acquisition of iMerit.

Mozilla Data Collective
For giving usage control to dataset owners
One of the most prominent controversies around AI concerns compensation of the parties behind the data sources used in its training. While content owners are striking deals with AI giants or blocking bots to pave the way for negotiations, the Mozilla Data Collective takes a more proactive approach. Entities that own datasets can share them under their own terms while retaining ownership and control over how that data is used, including compensation. The collective includes more than 190 organizations sharing over 1,700 curated datasets. This year, Mozilla spun out the Data Collective as an independent entity.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Ross Rubin

The 6 next big things in consumer tech for 2026

5 hours 33 minutes ago

Consumer technology is at its best when it’s solving problems that exist outside of the tech world. This year’s honorees include better ways to clean your floors, put on your shoes, hear the conversation around you, and practice music effectively. There’s also a less-intrusive smart lock, a refreshingly simple microwave oven, and—if you’ll allow one solution to a problem caused by technology itself—a way to spare your smartphone screen from prying eyes.

JBL/Harman
For giving guitar practice an AI upgrade
While the modern musician will find no shortage of backing tracks on YouTube for practice purposes, they’re seldom as fun to jam with as the original material. JBL’s BandBox Solo and Trio guitar amplifiers can play music from streaming services over Bluetooth but with AI audio separation that controls the volume of individual instruments. Users can mute a song’s guitar track while playing along themselves, or turn down the vocals and drums to better hear the notes in a tricky solo. Software tools such as LALAL.AI and Moises have long allowed aspiring musicians to create backing tracks from recorded music, but by building this capability directly into the hardware, JBL is adding a new layer of convenience.

Kizik
For making footwear hands-free
For nearly a decade, Kizik has been making shoes you can squeeze your heels into without having to use your hands. But while the company has expanded into new categories such as snow boots, running shoes have always been a major challenge because of the need for a stable fit with no performance compromises. The company finally cracked the problem this year with its Freedom Run line, with a heel design that compresses when you step into it but then snaps into a rigid position that prevents slippage. Tests at Brigham Young University confirmed that the shoe’s heel stability while running is comparable or better than that of leading brands. The company is also working with Nike and New Balance and plans to expand into other areas of active footwear, such as hiking and winter sports, with the eventual goal of making every shoe hands-free.

Legato
For using smart eyewear to enhance what you hear
While Big Tech companies like Meta and Google hoover attention with their smart glasses platforms, Legato is building frames for a more practical purpose. Its Legato Frames use on-device AI audio processing to enhance human speech while quieting background noise, serving as a stylish and smarter alternative to traditional hearing aids. The company has a big rival in EssilorLuxottica, whose Nuance Audio glasses serve the same purpose. Still, its team of former Bose engineers figured out how to solve some key problems, such as amplifying the voices of others without unnaturally boosting the wearer’s own voice. Frames remains on a waitlist, though Legato has raised $12 million to help bring the product to market by year-end.

Level Home
For making smart locks look less dumb
Smart locks tend to be eyesores, with bulky enclosures to house all the necessary motors, sensors, and connectivity. With the Level Lock Pro, Level managed to fit all of those components into a smart lock no larger than a standard deadbolt. The company says it’s 62% smaller than traditional smart locks. Corporate parent Assa Abloy laid off most of Level’s staff in June, according to The Verge, but the Swedish lockmaker intends to maintain Level as a separate brand. Even if the Lock Pro ends up being Level’s swan song, here’s hoping it inspires the next generation of rival smart locks to shrink down.

Roborock
For literally bringing robot vacuums to the next level
Climbing stairs has always been a holy grail for robot vacuums. So Roborock naturally made a stir at CES in 2026 with its Saros Rover, a prototype robot vacuum with a pair of retractable wheels on legs. They allow the device to make its way up a staircase, cleaning each step on the way. While Roborock has not shared concrete launch plans, the company says it’s a “real product in development.” And with rival Dreame developing its own stair-climbing concept (albeit one that can’t clean the stairs itself), the race to solve robot vacuums’ multistory problem is well underway.

Samsung
For offering a solution to shoulder surfing
Samsung’s Privacy Display is the first new smartphone feature in years that feels like a truly practical breakthrough. Debuting on the company’s flagship Galaxy S26 Ultra, the feature applies a matrix to the display that narrows the beams of light emitting from each pixel, making the screen harder to read from off-angles. This can prevent strangers from peeking at your phone’s contents in public places such as trains, planes, and elevators. Unlike aftermarket privacy-protecting screen protectors, the Galaxy S26 Ultra lets users toggle the feature on and off or limit it to specific apps. This allows the screen to remain bright and shareable when you’re not worried about who might be looking.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Jared Newman

The 6 next big things in computing, chips, and foundational technology for 2026

5 hours 33 minutes ago

Advances in computing aren’t just about faster processors. As these innovations show, progress is driven by everything from the technologies used to transmit data to the systems that ensure data centers get reliable power without overwhelming the grid. If many of them are largely invisible to most of us—well, that’s kind of the point.

Amazon
For skating where the AI puck is going
Announced at AWS re:Invent in December 2025, Trainium3 is Amazon’s newest AI chip. It’s designed to take on not just today’s dominant AI workflows but also to anticipate the needs of emerging ones such as world models. Equipped with up to 144 chips, Amazon’s Trainium3 UltraServer quadruples the compute performance and energy efficiency of its predecessor. Uber is adopting Trainium3 for tasks such as calculating arrival times, and video-generation platform Decart has used it to achieve 4x higher frame throughput and reduce latency by 75%.

Cadence
For teaching AI agents to design processors
Historically, advances in processor design have been propelled by human engineering talent, a resource that’s difficult to scale. Introduced in early 2026, Chip design giant Cadence’s Super Agents take on essential elements of the process such as creating test plans, design node migration, and digital implementation. Already adopted by big names like Nvidia, Qualcomm, and Altera, they offer up to 10x productivity increases across design and verification, according to Cadence.

HP
For building a workstation today for tomorrow’s GPUs
Out of the box, HP’s Z8 Fury G6i desktop workstation can support for up to four powerful Nvidia RTX Pro 6000 GPUs. But the company also looked ahead to future GPUs. The workstation’s optional Max Side Panel expands its case by 1.4 inches and adds cooling fans, allowing tool-free expansion to accommodate larger, hotter GPUs that require more power and would otherwise require the purchase of a new workstation. HP uses recycled plastic, steel, and rare earth materials to build the Z8 Fury G6i and put it through 360,000 hours of reliability testing.

Marvell
For harnessing light to move AI data
No matter how fast an AI system’s GPUs are, it may be held back by the electrical interconnects and copper pathways used to shuttle data in and out. Marvell’s Phototonic Fabric platform replaces them with light-based transport, allowing the same energy footprint to deliver up to three times the performance. The company gained the technology as part of its $3.25 billion acquisition of Celestial AI, announced in December 2025, and has since been engineering products that will support it both as an upgrade to existing AI systems and as a core element of future ones.

ON.energy
For giving AI the uninterruptible power it needs
AI data centers don’t just draw vast amounts of power—they do it in oscillating bursts that stress the electrical grid and cause problems for everyone else on it. After developing an uninterruptible power supply for a Mexico City bakery prone to blackouts, ON.energy realized that its design was also applicable to AI. That led to the AI UPS. In tests at the U.S. Department of Energy’s National Laboratory of the Rockies, it prevented load swings from 30% to 100%, in intervals as fleeting as 10 milliseconds, from impacting the grid.

Silicon Quantum Computing
For pioneering quantum-enhanced AI
In October 2025, Australia’s Silicon Quantum Computing launched a quantum-enhanced machine learning system called Watermelon. The first commercially available quantum reservoir computer, it uses quantum technology to spot patterns in datasets that can’t be identified by classical computers. The technology requires the placement of phosphorous atoms in silicon with 0.13 nanometer accuracy and has been validated by partnerships with Telstra, Nvidia, and the Australian Department of Defense.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Harry McCracken

The 3 next big things in commerce tech for 2026

5 hours 33 minutes ago

AI is poised to transform virtually everything about online and physical retail, from product exposure and recommendations to consumers even having to complete the transactions for themselves. These honorees are setting the stage for agentic commerce, allowing critical medical supplies to take flight and providing physical retailers with comprehensive intelligence about shopping variables within and beyond the store.

American Express
For putting agents on the checkout line
During the early days of e-commerce, retailers strived to simplify online purchasing for consumers. In the AI era, a new wave of accommodations must be forged to allow frictionless transactions by agents. American Express’s Agentic Commerce Experiences (ACE) toolkit leverages the company’s experience across a range of financial roles and includes a new trust model focused on identity, intent, accountability, and visibility. But Amex is still keeping humans in mind through a new agent purchase-protection program that protects eligible card members from charges resulting from certain AI agent errors. Early-access developer partners have recently signed on to ACE, which could further drive the record card-member purchases the company reported in its most recent quarter.

CVS Health
For making delivery of critical products airborne
While several companies have deployed drones during emergencies and others have piloted broad drone delivery programs, CVS’s Air Response program targets a scenario that falls in the middle: serving patients who need critical supplies such as naloxone, epinephrine, and automated external defibrillators (AEDs). As a retailer serving mass markets, CVS has designed its drone network to operate safely in urban environments. The company is now exploring integration with 911 systems and has announced partnerships with Thales and SkyfireAI as it frees up investment dollars through its exit of the Omnicare long-term-care pharmacy segment.

WVN
For developing holistic retail site intelligence
Physical-store owners have long envied e-retailers that have nearly perfect accounts of the digital pathways purchasers travel through their storefronts. WVN seeks to create a comparable level of market intelligence for brick-and-mortar players. StoreBrain is an AI agent that acts as a persistent AI intelligence layer for physical storefronts, capturing and analyzing not only onsite indicators like transactions, foot traffic, and shelf input but also inputs beyond the store, such as online reviews. The company’s training data also covers demographics, psychographics, economics, and even weather data. The company reports that 93% of stores using its platform report improved conversion. It recently inked a partnership with menswear brand Mugsy, which operates eight physical stores.

The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.

Ross Rubin
Checked
1 minute 28 seconds ago
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