Tech Brew keeps business leaders up-to-date on the latest innovations, automation advances, policy shifts, and more, so they can make informed decisions about tech.

Tech Brew

Quarter Century Project

Patrick Kulp

emergingtechbrew.com

From a small research lab “free from financial obligations” to one of the most valuable private businesses in the world, the company that ushered in the generative AI era has evolved a lot over its decade of existence.

Now, OpenAI is on the verge of more change. Its longtime strategic partnership with Microsoft 

. The company’s long drift into for-profit-dom is 

. And as the company still at the center of the generative AI boom, OpenAI’s structural pivots and alliance shifts have implications for the rest of the industry.

“This is going to be a very, very interesting next 12 to 18 months because of all these dynamics,” Ritu Jyoti, group VP at market intelligence firm IDC, told Tech Brew.

 Having gathered top research minds in the AI space from day one, OpenAI has always encompassed a series of competing and evolving visions for what the next generation of AI should look like.

That was one of the takeaways Tyler Johnston, executive director of the nonprofit Midas Project, arrived at as he spent the past several months sifting through more than 200 news articles, legal filings, employee testimonials, and other information about OpenAI’s history. The group recently published the result of this work as a research report called the 

 in partnership with advocacy group Tech Oversight Project.

“OpenAI is a company that has existed on this insanely accelerated lifespan,” Johnston told us. “It’s just astonishing how much has happened in the 10 years that it’s existed, and how many different visions of the organization have, at various times, flourished or been squashed based on how convenient they were to the leadership at the time.”

Backed by big-name investors and co-founded by Sam Altman and Elon Musk, among others, OpenAI immediately made a splash upon its launch in 2015 as a nonprofit. The lab had already assembled a laundry list of top researchers, with the not-for-profit purpose as part of the recruiting pitch, according to a 

“We just talked about every specific structure and thought this one had the most advantages,” Altman told 

 of the nonprofit mission. “I think that the misaligned incentives [of profit-seeking] would be suboptimal to the world as a whole.”

 OpenAI once again grabbed headlines in 2019 when it declared it would not release the full second iteration of its generative pre-trained transformer model (GPT-2) due to “concerns about malicious applications of the technology.” The model was capable of producing “synthetic text samples of unprecedented quality.”

A month later, OpenAI converted to a capped profit structure, where returns beyond 100 times an investment would be passed to the nonprofit board. A few months after that, Microsoft invested $1 billion to kick off a “multiyear partnership.”

“This was brilliant, by Microsoft, by [CEO] Satya [Nadella],” Vasant Dhar, a data science professor at NYU’s Stern School of Business and author of a 

 on the history of AI, told Tech Brew. “He just saw this thing coming, you know, from much further away than most people did, and he clinched a really solid deal.”

 But as OpenAI perhaps continues down its path toward full for-profit status—despite some pushback and potential setbacks—experts we talked with say Microsoft and OpenAI were destined to drift apart eventually. The companies have reportedly clashed over their revenue-sharing agreement and OpenAI’s proposed acquisition of coding platform Windsurf. (The deal ultimately fell through when 

 with a shock acquihire of top Windsurf employees.) OpenAI has signed competing cloud agreements, and Microsoft has rolled out competing models.

Microsoft and OpenAI said in a joint statement emailed to Tech Brew that they “have a long-term, productive partnership” and are “optimistic we will continue to build together for years to come,” a statement also provided to 

, Microsoft reiterated that its partnership would continue with OpenAI through 2030, while also softening its cloud exclusivity terms.

Part of the tension, too, hinges on language around the original agreement, which ends when OpenAI achieves what its board determines to be 

, which it defines as “a highly autonomous system that outperforms humans at most economically valuable work.” But AGI does not have a single agreed-upon definition, and that’s 

“That is fundamentally a very, very slippery thing to base your benchmark on, especially as over the past 50 years, we’ve kind of continuously redefined what AGI looks like in practice,” Rowan Curran, principal analyst with Forrester Research, told us.

While the pressure campaign to keep OpenAI a nonprofit appeared to 

 earlier this year, Johnston argued that the framing of the story was misleading.

“In my mind, OpenAI is very conveniently describing control in such a way that they retain a lot of the freedom that they wanted…while still acting as if they had made a concession,” Johnston said.

But an eventual Microsoft-OpenAI split could also be a boon for the overall ecosystem and the companies that are using this technology as it spurs new competition, Jyoti said.

“It would diversify the AI providers—it will not be just centered around Microsoft and OpenAI. It will create some new alliances,” Jyoti said. “It will give developers and enterprises more choice, but it can also [add] more complexity.”

OpenAI Co-Founder & CEO Sam Altman speaks onstage during TechCrunch Disrupt at Moscone Convention Center in San Francisco, 2019. 

Nearing its 10th anniversary, OpenAI is at a crossroads

The company is drifting from its longtime backer and toward for-profit status—but not without resistance.

Tricia Crimmins

Jordyn Grzelewski

A decade ago, before all-electric vehicles were routinely spotted on roads, drivers of Volkswagen’s diesel offerings thought they were making an environmentally conscious choice. The company had touted a “best of both worlds” approach: It said its diesel vehicles produced lower emissions than competitors, but still gave customers more energy per gallon compared to gasoline.

“It was this amazing promise in vehicle technology,” Christopher Hennigan, a University of Maryland professor of environmental engineering, told Tech Brew. “But it turned out to be a lie.”

That lie involved Volkswagen putting what the Environmental Protection Agency called “defeat devices” in model year 2009–2016 cars to deceive federal emissions tests: When the cars were being tested, emissions were low. But on the road, they emitted a “major excess” of NOx, or gas particles that can lead to illness and premature death

 when then-International Council on Clean Transportation Senior Fellow John German and his team tested Volkswagen diesel vehicles on the road—not in a test facility. Using a machine in the car’s trunk and a probe down the length of the exhaust pipe, they uncovered extremely high emissions.

As a result, the EPA and Department of Justice found that Volkswagen, which included the Audi and Porsche brands, violated the Clean Air Act, a federal law passed in 1970 to regulate emissions from stationary sources, like factories, and mobile sources, like cars. Under the law, the company agreed to pay multiple settlements and pleaded guilty to criminal charges.

“The Clean Air Act worked as it was intended to—in the sense that we had a company that was violating it and when that was discovered, [there were] massive penalties in response,” Amanda Halter, a partner in Pillsbury Winthrop Shaw Pittman’s Environmental & Natural Resources practice, told us. “It’s the kind of law, frankly, that you just couldn’t get passed by today’s Congress.”

As part of the settlement, Volkswagen spent $2 billion to establish a public fast-charging network, Electrify America, which today is the second largest in the US. It now has more than 4,800 charging ports across the country, second only to 

“The contribution that Electrify America has made to the public charging infrastructure is really substantial,” Brent Gruber, executive director of JD Power’s EV practice, told Tech Brew.

However, the network has been criticized by EV drivers and electrification advocates over reliability issues, as detailed in a 

 by The Washington Post. An executive told the outlet that the network was “working hard to improve reliability.”

 on consumer satisfaction with public fast chargers in a 2024 JD Power study. It scored 559 on a 1,000-point scale, while Tesla ranked highest, with 731.

In general, there are two major problems with US public charging, Gruber said: lack of availability and 

The EV “adoption rate is outstripping the pace of charger installation by about two to one,” he said. And about one in five public charging visits fail, per JD Power and 

, because of issues like faulty chargers and long wait times.

Solving these issues is important because public-charging concerns remain the No. 1 barrier to EV adoption, Gruber said. But now, that effort must proceed without federal help, as the Trump administration 

Gruber said he expects to see a near-term slowdown in charger installation, but that states and private-sector players will push ahead, because strong public charging is key to an electric future in which manufacturers are heavily invested.

. It reported that its customers’ 16 million charging sessions represented 50% growth in 2024, and it plans a 30% expansion this year. Electrify America declined to comment on Tech Brew’s questions.

The network is working to increase the number of “Hyper-Fast” 350 kilowatt-hour chargers, per a company report, as well as opening larger stations. It says it’s implemented policies, like idle fees, aimed at reducing wait times.

“Increasing customer satisfaction and station performance is a top priority for Electrify America,” the network said in its Q1 2025 report to the California Air Resources Board. The network will report to the board annually on charger station uptime and “strive to achieve target uptime thresholds on its California network of chargers,” under the terms of its latest maintenance plan.

Meanwhile, Gruber said he was heartened to see significant progress in Q1 on customer satisfaction and charger reliability.

The public charging failure rate dropped to 16% in Q1, the largest improvement since JD Power launched the survey in 2021, according to Gruber. And the satisfaction level with public fast chargers went up by six points from Q4 2024, to 656 out of 1,000.

The results, Gruber said, “gave me a lot of hope that we’re starting to see the tides turn with public charging.”


Volkswagen Passats are offered for sale at a dealership on September 18, 2015, in Chicago, Illinois. The Environmental Protection Agency (EPA) has accused Volkswagen of installing software on nearly 500,000 diesel cars in the U.S. to evade federal emission regulations. 

Dieselgate spawned a large US EV charging network. Why is public charging still so vexing?

Volkswagen agreed to spend $2 billion to build out a network of EV chargers as part of a settlement in the wake of an emissions scandal. A decade later, drivers frequently cite charger unavailability as a roadblock to EV adoption. 

Edwin Olson can still hear the faint sound of the autonomous vehicle he helped build as part of MIT’s team for the DARPA Urban Challenge in 2007 as it returned from the hourslong competition.

“It was the most amazing feeling,” he told Tech Brew.

Nearly 20 years later, Olson is the CEO of an AV company of his own, 

, and the industry is in a much different position than it was in the early days of self-driving vehicle technology.

Plenty of would-be contenders have come and gone. Leaders have emerged. Lots of modern vehicles now come equipped with some automated features, and fully self-driving vehicles are operating on a limited basis—progress is being made. But hype around people being ferried to and fro in AVs on a mass scale has collided with technical, regulatory, and scalability challenges. Yet industry stakeholders are still working toward a future where many more of our transportation needs are met by driverless vehicles—and someday by steering wheel- and pedal-free ones.

“The public sentiment was significantly ahead of the technical reality…And then what basically happened is, the technical reality got ahead of public sentiment. And I think that’s where we are right now,” Olson said. “It can’t do everything. But it works pretty damn well, and it can solve real problems.”

Research into self-driving vehicles dates back decades, but many point to the DARPA challenges as seminal events that helped kickstart the modern AV sector.

In the early 2000s, the US Defense Advanced Research Projects Agency (DARPA) started testing AVs on a 150-mile off-road course, according to the University of Michigan. The DARPA Grand Challenge series pitted major universities like MIT, Stanford, and Carnegie Mellon against each other, starting with a competition in the desert in 2004 in which none of the AVs completed the course.

Olson was part of MIT’s team for the DARPA Urban Challenge in 2007, and recalled it as a “humbling” experience that involved creating what was essentially a rudimentary joystick to control the vehicle.

“The hardware was terrible…The vehicle control was very imprecise,” Olson said. “Everything was dodgy. The ride quality was bad. The autonomy was really bad. But we got there.”

The challenges are widely credited with establishing the foundation of the AV sector as we know it today, including contributing to the start in 2009 of Google’s self-driving vehicle project—what is today Waymo, the robotaxi sector’s leader.

“Since then, we’ve gone up and down in hype and disappointment and adjusting expectations and whatnot,” Jeremy Carlson, associate director for autonomy at S&P Global Mobility, told Tech Brew, “because it turns out that it’s a pretty challenging task, what we’re trying to do in building autonomous vehicles to replicate and improve upon human driving.”

While the DARPA challenges generated lots of momentum around AV tech, it became clear soon enough that there were massive technical hurdles to overcome, according to experts and stakeholders. One of them, Carlson noted, is that it’s nearly impossible to teach AV systems the infinite number of complex driving scenarios they could encounter in the real world.

“We don’t have a system today that’s going to address every single possible corner case in the world,” Carlson said. “Whether or not we need to build that in the first place is an argument that most developers will say, ‘Hey, it’s impossible to actually cover every corner case.’ To do that kind of rules-based approach, which is where a lot of the software and programming side of this started, it’s just kind of unrealistic.”

Then there are the realities of the automotive business, which is capital-intensive and slower-moving than the tech industry’s hype cycles, Carlson said. And AV tech in and of itself is incredibly costly—not just in terms of hardware like cameras, sensors, and radar, but in terms of establishing operating bases and maintaining large vehicle fleets.

These challenges are one reason why we’ve seen much of the traditional auto industry put its resources into 

, or ADAS, which partially automate the driving experience (for example, allowing drivers to take their hands off the wheel on the highway or automatically change lanes) but still require human involvement.

In one of the starkest examples of this shift, GM last year 

 it was pulling the plug on its robotaxi business, Cruise, and focusing, at least for now, on ADAS. The company attributed the decision to “the considerable time and resources that would be needed to scale the business, along with an increasingly competitive robotaxi market.”

 that rules-based, software approach to what we might describe as an end-to-end architecture, something that’s a little bit more flexible in identifying the situation, understanding what it’s supposed to do…that will cover a lot more of those corner cases,” Carlson added. “It will make those systems a lot more adaptable, very much like a human brain would be.”

Though the highest levels of vehicle automation operating on a wide scale may still be many years away, in the next several years, experts expect to see expansions in ADAS features and the more sophisticated automation we’re already seeing today for use cases like ride-hailing and trucking. Think of packages being delivered via autonomous vans, 

 no longer featuring human drivers behind the wheel.

“You’ve got Waymo out showing everybody this can happen,” Olson said. “And I think this is really important, too, because the way that we build our cities today…this personal car ownership thing, it’s a disaster.”

He and other industry stakeholders describe a future in which multi-car households go down to one car, and eventually, just maybe, to zero—perhaps eventually spurring complementary zoning and development changes to meet the needs of a society less dependent on personal vehicles.

Johann Jungwirth, EVP of autonomous vehicles for 

, touted potential benefits like restoring lots of free time to people no longer beholden to their daily commutes, increased productivity at work, more choices around where people live if transportation is less of a concern, better transit accessibility for people with disabilities, and huge safety improvements. These boons, he believes, will be delivered both by autonomous personal vehicles and fleets.

“I think we are just at the beginning of this,” Jungwirth said. “There is so much more to come, once vehicles can be designed without steering wheels or pedals.”

In the next five years, Carlson expects to see AVs become more integrated into many people’s everyday lives—largely in the areas where the tech is available today, like 

“Those vehicles are very much a part of people’s lives today. It’s obviously in limited areas,” Carlson said. “But that is beginning to expand quite quickly.”

He also expects to see gradual advancement into small- and medium-sized cities in the coming years, and that technology advancements will “also make it possible to put it into locations where we might have inclement weather or other obstacles that we don’t see them being deployed in today.”

May Mobility, which has partnerships with 

, is working toward a future of transportation in which autonomy, paired with public transit, outcompetes the experience of owning a car.

“I think we’re very, very close to that,” Olson said. “When people ride in an autonomous vehicle…it’s like getting somewhere in a way that doesn’t suck.”

A member of the SciAutononics II team gives a thumbs-up gesture from the chase vehicle as they follow their autonomous vehicle from the starting line in a quest for a million-dollar prize at the DARPA (Defense Advanced Research Projects Agency) Challenge on March 13, 2004 near Barstow, California. 

Our self-driving future has yet to materialize. What’s next for AV tech?

Autonomous vehicles have yet to live up to the hype around them, but industry stakeholders still see them as a way to transform transportation as we know it.

Slack, Gmail, Monday.com, Google Meet, Zoom, Microsoft Teams, Google Docs—this is a (surely incomplete) list of apps and platforms used over the course of reporting and writing this story.

It may ring familiar to many digital workers who have grown accustomed to constantly toggling among the seemingly infinite number of workplace productivity tools that have come to populate our professional lives.

One of the most influential of these tools, Gmail, 

 on April 1, 2004, and is credited with forever changing the ways we use email—and influencing the trajectory of the internet and workplace tech.

At the time, the service’s offering of 1,000 megabytes of storage seemed so improbable that many assumed it was an April Fool’s joke.

“Google’s rivals have copied Gmail so thoroughly that it’s hard to remember just how terrible webmail was before Gmail came along,” Slate recounted in a 

 marking Gmail’s 10th birthday. “Pages were clunky and slow to load, search functions were terrible, and spam was rampant. You couldn’t organize messages by conversation. Storage capacity was anemic, and if you ran out of space, you had to spend hours deleting old emails or buy more storage from your provider. Gmail…taught us that Web apps could run as smoothly as desktop applications. And it taught us the power of cloud storage.”

Aamer Baig, a senior partner at McKinsey, recalled how in the early days of email, communications were limited within a company. Now, the entire world is theoretically connected.

“Now it is literally a countless number of channels,” Baig told Tech Brew. “You have Slack, you have text, you have email, then you have collaboration spaces in documents and workflow, in applications. On top of that you’re also getting AI that is getting embedded into all these tools.”

All of this innovation has made us more connected than ever—and yet!—somehow still exhausted and struggling to optimize our work lives. So how can we work smarter and not harder?

 is the phenomenon of becoming overwhelmed and exhausted from toggling between various apps and platforms. Research has demonstrated that switching between different contexts or tasks doesn’t just make workers feel burned out—it actually reduces productivity.

In response to a 2023 survey by project management software provider Wrike, 58% of knowledge workers said they’d like to use fewer apps at work: “They are crying out for clear, streamlined, and consolidated processes.”

The report also pointed out that the proliferation of apps and platforms makes it more difficult for business leaders to have visibility into what employees are doing, which “leads to wasted efforts, delayed or canceled projects, and employee burnout.”

 found that each employee working on “a single supply-chain transaction” at a Fortune 500 consumer goods company toggled 350 times between 22 apps and websites. In the course of one day, every person toggled more than 3,600 times.

The study found that this act of “context switching” causes workers to lose up to five working weeks per year, or nearly a tenth of their time on the clock––what the authors dubbed the “toggling tax.”

And it appears that workers are struggling to effectively use all of these tools. Nearly half of digital workers reported “that at least half the time they look for information they couldn’t find it,” in 2022, according to Gartner. In 2024, only 31% reported having that issue, but those who used AI were more likely to struggle. And solutions like intranets and new employee communications platforms haven’t solved the problem.

Tori Paulman, a VP analyst at Gartner, told Tech Brew that in the last two years, digital workers’ reported satisfaction with the apps they use for work has plummeted.

Amid all this exhaustion, frustration, and dissatisfaction, workers are devising their own solutions and turning to their personal favorite apps to meet their professional needs.

“This causes a huge problem,” Paulman said. “This is one of the things that’s fueling the ROI crisis with AI, is that information is fragmented across hundreds of apps.”

To that end, Paulman pointed to personalization as the solution that employees are clamoring for.

Gartner’s 2024 Digital Worker Survey, for example, found that 51% of digital workers “customize or build tools leveraging the digital technology their organization provides,” or get their own.

“Getting people to use the thing you give them, rather than the thing they like, it’s the perennial issue,” Paulman said.

Instead of trying to get employees to buy into a consolidated platform or yet another new tool, Paulman said it’s best to first determine how employees are using their existing tools. It may be that rather than a new tech solution, what’s needed is clearer communication at the front end of a new project about which tools will be used and how.

“The leaders need to bring some sanity to how and when we use these technologies, in order to calm that cognitive fatigue,” Paulman said.

McKinsey’s Baig, too, offered a low-tech solution: creating individual and company-level rubrics to guide how employees use workplace productivity tools.

Time sensitivity, for example, is a rubric he uses in his own work. Employers might encourage workers to use Slack and text for urgent matters and emails for non-time-sensitive ones. And workers might block off chunks of time to work without constant interruptions from notifications, with incoming communications limited to higher-ups who need a quick response.

“I’m exceptionally excited about the tools we have at our disposal,” Baig said. “But I don’t think all of them should be used all the time.”

He believes we’ll continue to find ways to integrate technology into our lives and careers in beneficial ways, but to make the most out of what technology has to offer, he suggests less multi-tasking.

“Sometimes the most productive and the most effective we are is when we are mono-tasking,” he said.

Of course, one of the much-touted solutions to the problems surrounding workplace productivity is AI.

Google, for one, is integrating AI into Gmail and its Google Workspace suite of apps.

“Our goal is to provide powerful, genuinely useful technology that clears people’s path to productivity instead of complicating it,” Gmail said in a statement. The email service now has AI-enabled capabilities like summaries of email threads, a Smart Reply feature, and a Q&A tool that enables users to ask questions about their inbox.

“With Gmail connected to the broader Google Workspace suite of apps, and with AI assistance woven in seamlessly,” according to Gmail, “all of our tools work together so people can stay in their flow of work without app or context switching.”

Both Paulman and Baig said they’re hearing more and more about workers using AI to manage emails, generate content, and summarize information—raising the possibility that we might someday be much less attached to our inboxes.

“It might be, actually, that AI attached to email might get us out of our inboxes,” Paulman said. “But only if we can get out of our inboxes.”

Laptop screen displaying the original Gmail logo.

How can we work smarter in an era of endless productivity tools?

It’s been more than 20 years since the advent of Gmail. We’re now awash in workplace apps and platforms.

There once was a time when a piece of software would be loaded from a disk onto your computer. And that was that; the software lived on that computer in perpetuity.

Over the last 25 years, that relationship between software provider and user has evolved drastically, transforming how the entire tech industry operates in the process. The growth of cloud computing and widespread internet connections gave rise to a new model for how businesses and people alike access digital technology: software as a service, or SaaS.

That model boosted a generation of bootstrapping startups that might not otherwise have been able to afford top-shelf enterprise software. Now, a new revolution in giant generative AI models could be poised to further shift tech industry business models.

 But let’s back up. In September 2003, Adobe, already one of the top software makers in the world for graphic design, bundled its various offerings—Photoshop, Illustrator, InDesign, and others—into a Creative Suite for the first time, setting the stage for the monumental shift to the web-only Creative Cloud nearly a decade later.

Also in 2003, a burgeoning Salesforce held its first Dreamforce conference, when the company’s tagline was “the end of software,” an early encapsulation of the SaaS model. And the same year, Amazon executives huddled for a retreat at Jeff Bezos’s house, where they began to conceive how the infrastructure they’d developed for online shopping could serve as the “operating system for the internet,” according to 

. The debut of Amazon Web Services (AWS) three years later would become a pivotal event in the evolution of SaaS.

“Adobe was very much a license model when they created Creative Cloud. Without an Amazon Cloud underneath, it would have been really hard to do,” Scott Snyder, chief digital officer at life sciences services company Eversana and senior fellow at the Wharton School, told Tech Brew.

“AWS was kind of the only game in town. Eventually, then, Microsoft and Azure and Google got in, but AWS made it easy for these SaaS companies, from Salesforce to Zoom to Slack to Adobe, to stand up their SaaS platforms,” he continued.

 The surge in cloud computing that followed the AWS rollout in 2006 changed the math of what it took to start a business. Companies no longer needed to make big up-front investments in data centers, software installations, and IT teams to maintain it all, according to Tim DeStefano, a research associate professor at Georgetown’s McDonough School of Business who specializes in digital technology and AI.

“Cloud computing represents a fundamental shift in the way in which firms access digital technology, and this fundamental shift has basically meant that it’s shifted the adoption of technology away from an up-front sunk investment to being able to access this technology as a service,” DeStefano said. “Small and young firms that don’t have a lot of human capital and are financially constrained can more easily access these tools.”

On the software provider side, SaaS has allowed companies to bring in a steady revenue stream and save costs on customer acquisition, according to Icheng Robert Chiang, a consultant and professor at Fordham University’s Gabelli School of Business. That, in turn, has also led to a host of new entrants into the market.

“What SaaS has brought is the barrier to entrance has decreased dramatically,” Chiang us. “So today, we are still driven by ideas rather than the up-front capital needed to realize that idea.

Vasant Dhar, a professor at NYU Stern School of Business specializing in data science, said the SaaS arrangement has ultimately been much more of a boon to software vendors than users of the products.

“You could track how your product is being used, you got rid of a lot of piracy issues that had plagued the previous paradigm of software, and [you’re no longer] relying on those slow product release cycles—one every few years. You could do it kind of arbitrarily,” Dhar said.

Adobe officially made the switch from a traditional software provider to a full subscription model in 2012, leading to a 35% drop in its profit a year later, according to a Harvard Business Review 

. But by 2016, Adobe’s stock price had tripled as the company retooled for a new era.

 the latest generative AI advances into their platforms. But Snyder said this technology could also ultimately pose a threat to the SaaS model if user companies decide it simply makes more financial sense to build their own AI tools.

“People could say, ‘Well, why am I using a service—[like] a Salesforce customer service bot—when I can just build my own at a very low cost because I’ve already got the AI infrastructure set up?’” Snyder said. “Either the SaaS companies will move fast enough and keep the cost reasonable enough that the economics and the switching costs still make sense for big companies to stay on the SaaS platforms. But if the switching costs aren’t that high, these larger companies may roll out their own, and I think that’s going to be the interesting tension going forward.”

Adobe Systems corporate headquarters, computer software manufacturer in Silicon Valley, Mountain View, California in 2004.

How the rise of SaaS transformed the tech industry

Software makers like Adobe have transformed over the past 25 years. AI could bring even more disruption.

The inconveniences caused by scooters controlled via smartphone contributed to something of a micromobility meltdown a few years ago.

But in 2025, the micromobility sector aims to learn from the mistakes of its past and ride a fresh wave of interest in solutions that can help people get from Point A to Point B without a car. Last-mile delivery of both goods and people, return-to-office mandates, and the soaring popularity of e-bikes are among the factors fueling micromobility’s next chapter.

“The big story is that, in my view, the next big winner in micromobility does not exist today. That company has yet to be built,” Jiten Behl, partner at venture-capital firm Eclipse Ventures, told Tech Brew. “It’s a wide-open field for an actual Tesla of micromobility.”

The history of micromobility dates back decades, but in December 2001, the US saw the introduction of a product that would in many ways represent the sector’s promises and failures: the Segway, an electric scooter that never quite delivered on its mission to transform transportation (unless your name is Paul Blart).

Washington, DC, was home to the first bikeshare pilot in the US in 2008, according to a National League of Cities report. Then, in 2013, New York City launched Citi Bike, followed soon after by bikeshare programs in San Francisco and Chicago. Chinese bikeshare company Ofo in 2014 introduced a dockless bike with GPS tech. Electric scooter startups Bird and Lime came onto the scene in 2017 and quickly drew massive investments.

The sudden surge in micromobility deployments drove interest in the concept, Chinmay Malaviya, CEO and co-founder of micromobility startup Ridepanda and a Lime alum, told us––but the underlying business models didn’t make sense. It turned out to be extremely costly to scale and manage large fleets of bikes and scooters, especially when the vehicles were strewn across cities all over the country.

“While the demand was there, and we saw it literally in front of our eyes, it was not a great business model, because of poor quality vehicles to start with and just a heavy operational overload, added on top of the vandalism, theft and all that,” Malaviya said. “A mix of these reasons led to what’s seen as a bubble in 2020, 2021.”

Ridepanda launched in 2020 as a shared micromobility platform, but pivoted to offering micromobility solutions to enterprise customers like Amazon that wanted to offer transit options to employees. Now, Ridepanda’s platform offers a curated selection of bikes and scooters on a subscription basis. The idea is that employers benefit by giving workers the means to get to the office, while saving on parking costs and promoting sustainability and wellness.

“With our model, people actually take care of their vehicles, because they’re the ones that are going to be using them the next day,” Charlie Depman, Ridepanda’s CTO and co-founder, said.

Behl, too, pointed the finger at the sector’s unwieldy cost structure.

“There’s all this margin stacking going on in the supply base, with little or no innovation, resulting in an inflated cost base, but not an exciting performance,” he said. “That has to switch.”

Following some of the micromobility startup failures of the early 2020s, the sector is seeing some positive signs. Smart Cities Dive 

 that the number of shared bike and scooter rides in the US grew 16% in 2023. Bird recently 

And in September, Lyft affirmed its commitment to micromobility following a restructuring into what’s now called Lyft Urban Solutions, a division that encompasses eight Lyft-operated micromobility programs in the US and supplier partnerships in dozens of global markets. In some cities where Lyft doesn’t have its own micromobility solutions, it’s partnered with Bird and Spin to give users access via the Lyft app.

“We are more confident than ever about the promise that bikes and scooters represent,” CEO David Risher said.

As part of the restructuring, Lyft opted to do away with dockless solutions and go all-in on docked bikes and scooters.

“We’ve really made the decision to no longer be distracted by that, and really lean into what we do best, which is providing station-based programs in a long-term context,” Caroline Samponaro, VP and head of external affairs at Lyft Urban Solutions, told us.

Lyft looked at its own data and decided to double down on micromobility: The company saw “major ridership records” across its bikeshare systems for the third consecutive year, including 50% growth in e-bike rides on Citi Bike in New York, per a company report.

Another trend that shows up in Lyft’s data: the rise of e-bikes. Lyft reported a 169% surge in e-bike ridership in Washington, DC, and a 96% increase in Toronto, for example, and in 2023 some 800,000 people took their first e-bike ride on the company’s platform.

“E-bikes have made biking for transportation something that a much wider range of people can now do,” Samponaro said. “And that’s obviously great for the environment, great for congestion, etc.”

To support this trend, Lyft is in the process of installing a charging network for its e-bikes, and recently introduced a new modular charging station called Pillar. It also launched a next-gen e-bike that it designed and built from the ground up, featuring new safety and software features.

Pairing e-bikes with innovative software solutions is key to capitalizing on their popularity, Behl said.

“When you insert an amazing software experience along with an e-bike architecture, it’s basically Peloton on wings,” he said. “Software-defined e-bike is a category that, in my view, does not exist in its purest and its best form. That is going to change the way people take to using their e-bikes for commutes, for their workouts.”

On the commercial side of the equation, Behl pointed to the rapid growth of e-commerce as a significant opportunity for micromobility. Imagine, for example, a scenario where instead of an Amazon truck parking on the side of a street while the driver delivers a dozen packages, smaller vehicles were sent out more frequently with smaller deliveries.

“We were happy with stuff being delivered every two days, and then it went to one day, to four hours, to two hours. And if you go to places in Asia, you need to get it in 30 minutes,” Behl said. “And you’re not going to drive a van or a car to deliver a small packet of something. And therefore…those last-mile deliveries are only possible if you have these small, cheap form factors. And that’s the micromobility story, from the B2B point of view.”

 The Segway initiated a wave of micromobility startups. What’s next?

Micromobility solutions like scooters and bikeshare programs have gone through major ups and downs in the last 25 years. After some realignment, the sector aims to ride a fresh wave of interest.

It was half a year into the new millennium when Human Genome Project leader Francis Collins announced internally that the consortium would be presenting a first draft of a fully sequenced human genome at the White House in around a month’s time.

The only problem? The teams didn’t yet have a sequenced genome—it was more like a “pile of DNA” fragments, according to David Haussler, scientific director at the UC Santa Cruz Genomics Institute. And the deadline was part of a negotiated tie in the heated race with biotech company Celera.

“That was like a thunderbolt,” Haussler told Tech Brew. “There was no way we were ready to do any kind of presentation.”

A UC Santa Cruz grad student named Jim Kent “worked night and day for four weeks, writing 20,000 lines of C code and icing his wrists periodically,” in order to make the historic milestone possible.

On June 26, 2000, President Bill Clinton and UK Prime Minister Tony Blair stood, via telelink, by the two teams of rival scientists—Celera’s and the Human Genome Project’s—as they announced the “rough draft” of a full human genome. It was the culmination of decades of work by thousands of researchers all over the world, and Clinton declared that “humankind is on the verge of gaining immense new power to heal.”

In conversations with scientists nearly 25 years later, it’s hard to pin down just one legacy of the human genome sequencing, which supercharged whole fields of biotechnology, laid the groundwork for a genetics revolution, and led to many large-scale follow-up scientific undertakings. But in some other ways, the field is only beginning to live up to some outsized expectations now.

“Trying to do research in genetics before the Human Genome Project was like trying to explore an area without a map,” Peter Donnelly, Genomics CEO and University of Oxford professor emeritus, told us. “It’s really hard for the generation of people who have gone into our science since the Human Genome Project to imagine what life was like before it.”

The Human Genome Project was one of the large-scale major convergences of biology with modern computer science and big data, according to Lauren Linton, executive director of the UC Santa Cruz Genomics Institute and former co-director of what is now MIT’s Broad Institute when it was a major player in the Human Genome Project.

“That was the first time we created monstrous amounts of data. What could we possibly do with that?” Linton said. “At MIT, we were recruiting people from IBM—like no one existed that did bioinformatics—they were software engineers, and they were working in other fields, and we would say, ‘Come over here. We’ve got a ton of data, and trust us, we’ll teach you the biology.’”

And yet despite its massive impact, the Human Genome Project has taken longer to live up to some of the promises made when it was completed, researchers we spoke with said.

While science has made strides in identifying diseases tied to a single gene and treating cancers, complex diseases that trace to multiple genes interacting with an environment have proven more difficult than expected, according to Gene E. Robinson, director of the Carl R. Woese Institute for Genomic Biology at the University of Illinois Urbana-Champaign.

Linton said one of the next big missions for her team at the Genomics Institute is to combine biology with computation at scale to look beyond just DNA to all of the different factors that interact with it.

“Most disorders or most traits are multi-genic in origin, and so you really have to think about diversity, variation in genes, how they’re mutated or not, but you absolutely had to be thinking about gene expression, protein expression, how things interact in the cell, how they interact beyond the cell, how organs develop and control themselves,” Linton said. “So I think what we’ve seen—if I could be really honest—is that it didn’t all cleave that fast and easily.”

Thanks to steep drops in the cost of gene sequencing and large-scale studies in the years since, Donnelly said the field is approaching a turning point in the ability to start better predicting risks for more common diseases.

“We've overstated the impact the genome project would have on people’s health and healthcare for many years after it was discovered,” Donnelly said. “But I think that is changing now, and I think we are going to see a really big impact over the next five or 10 years.”

The legacy of combining computation and life science has also set the stage for new advances in 