Cruise, General Motors’ self-driving subsidiary, said it has signed a multi-year partnership with ride-hailing giant Uber to bring its robotaxis to the ride-hailing platform in 2025.
Cruise didn’t say when exactly customers would see its vehicles on Uber’s platform, but a spokesperson told TechCrunch that this partnership will follow the re-launch of Cruise’s own driverless service.
The announcement provides another indicator that Cruise is preparing to reintroduce its robotaxis to public roads after one struck a pedestrian last October. (A human-driven vehicle hit the pedestrian first, sending her into the robotaxi’s path.) It also follows Uber CEO Dara Khosrowshahi’s recent comments positioning the ride-hail company as the ideal go-to-market partner for autonomous vehicle companies looking to commercialize.
Uber has already partnered with Waymo on the ride-hail side of its operations. Waymo vehicles have been available on the Uber app in Phoenix since October 2023.
Before Cruise’s safety incident — which resulted in it losing its permits to operate autonomously in California and grounding its U.S. fleet — the company had been expanding into new territories at a rapid clip. Cruise was charging for autonomous rides in San Francisco, Austin, Houston and Phoenix, and had launched driverless testing in Miami.
Cruise started manually testing its robotaxis again in Phoenix in April 2024, and has since expanded that testing to Dallas and Houston.
The company is also playing nice with regulators as it works toward a re-launch. Earlier today, Cruise announced it recalled its fleet of 1,194 AVs to resolve a federal safety probe into an issue with unexpected braking. In June, Cruise agreed to pay a $112,500 fine to California regulators for mishandling communications in regards to the incident last fall. The settlement put Cruise in a position to restart operations in the state.
Cruise’s partnership with Uber follows statements from Uber CEO Dara Khosrowshahi earlier this month on the subject: “Uber is uniquely positioned to offer tremendous value for AV players looking to deploy their technology at scale,” said Khosrowshahi during the company’s second-quarter earnings call on August 6. “While the operation of a ride-hail network may seem simple, our technology obscures a huge amount of complexity.”
Khosrowshahi went on to note that AVs are expensive assets, but Uber could boost utilization for them.
“Uber can provide enormous demand without AV players needing to invest capital toward acquiring customers or building the marketplace tech that delivers reliability at the standard that consumers have come to expect,” Khosrowshahi said.
For its food delivery arm, Uber has active partnerships with autonomous vehicle startup Nuro and sidewalk delivery robot companies Serve Robotics and Cartken. Uber also has partnerships with autonomous trucking companies Waabi and Aurora Innovation, although neither of those are operating fully autonomously (with no human driver behind the wheel) yet.
Uber likely has other potential deals to announce in the coming weeks and months. The company recently signed a deal with Chinese EV startup BYD to bring 100,000 new EVs onto the platform in markets outside the U.S. The two companies say they will collaborate on “future BYD autonomous-capable vehicles” to be deployed on the Uber platform. BYD committed in June to a $14 billion investment into AV technology.
Julien Codorniou is a SaaS investor at Felix Capital in London, having previously worked for 11 years at Facebook and 6 years at Microsoft. He co-authored “The Kelkoo.com Success Story,” published by Pearson in 2005.
Hospital nurses. Construction crews. Garbage collectors. Factory workers. Flight attendants. Restaurant servers. An estimated 2.7 billion people work in an environment without regular access to a desk, a mobile phone, or a PC. And yet, shockingly, little technology is being designed for these frontline workers.
Major tech players, investors, and entrepreneurs focused relentlessly on workplace innovation for white-collar workers like themselves for decades. This has brought a revolution in the office for a narrow band of employees while leaving far too many rank-and-file workers behind who’ve never been touched by what Bill Gates calls “the magic of software.”
A blue ocean market for “IT that leaves no one behind”
In 2015, I was part of the team that pioneered Workplace at Facebook, the company’s first SaaS venture. Initially targeting knowledge workers and tech companies, we serendipitously stumbled upon the untapped “frontline tech” market in 2016. This pivot proved strategic, amassing over 10 million paying users from renowned organizations like Starbucks, McDonald’s, Kering, Leroy Merlin, Walmart, Lixil, and Petrobras. Our original underestimation of this market segment proved to be a blessing in disguise as we embraced the competitive void it presented. I witnessed firsthand the rising demand for connectivity among frontline-heavy organizations of all sizes. Moreover, I saw the tangible impact frontline-friendly software had on employee engagement, retention, and productivity.
Tech is on the cusp of a SaaS revolution for the 99%. Because this market is open and ready for disruption, I see an opportunity for savvy software entrepreneurs to build the Microsoft or the Salesforce of the frontline workers’ world.
Conventional wisdom held that building products for these workers could have been more efficient and impracticable due to the restrictive nature of their working environments and IT budgets. Still, a confluence of trends is changing that mindset. Not only is it becoming clear that tech can improve conditions for these employees, but also there is a mounting sense of urgency that the benefits of SaaS workplace solutions must be extended to those who have been overlooked.
The pandemic brought into stark relief just how vital frontline employees (aka “critical workers”) are for keeping our lives and economy moving in ways that we had taken for granted for far too long. They watched as white-collar employees at the central office received increased workplace flexibility while they continued to grind away.
John Waldmann, the CEO of Homebase, a leader in the frontline tech space, told me, “two-thirds of workers have not and will never work from home. The public conversation and technology investment are way over-indexed to hybrid work, which is your archetypal ‘high class’ problem affecting a limited number of workers. Everyone values flexibility, but for most workers, it means something entirely different — and technology can help.”
A recent Microsoft report on frontline tech noted that 51% of non-management employees don’t feel valued, and more than 57% wish employers were doing more to address physical and mental exhaustion. A recent Beekeeper survey showed that four in 10 frontline workers have quit in the last year, and managers and head office staff don’t know how to fix it.
But for Big Tech execs and entrepreneurs who lack firsthand experience in these professions, it probably has been noticeable how to use tech to fix these problems just now. Fortunately, we are seeing a groundswell of interest from the tech industry to develop ways to serve these workers. Additionally, AI enables the creation of innovative products and experiences specifically designed for frontline employees, making previously impossible tasks achievable and significantly enhancing their work environment.
Historical headwinds
Despite the massive total addressable market, the emergence of significant frontline tech platforms has been slowed down by various factors.
The diversity and fragmentation of the sector, ranging from SMBs to global giants across industries like manufacturing and logistics, make it challenging to create universal solutions, resulting in fewer comprehensive software platforms. Pricing limitations further complicate matters, as the varied nature of the sector and lower GDPs in many countries where frontline workers are based lead to lower average contract values, often below what is typical even for essential email services (around 5$ per month per user), making it less appealing for investment. The regulatory landscape, particularly stringent in the U.S., forces many providers to focus on specific regions to simplify compliance and operational hurdles.
Additionally, there is a perception of lower tech literacy among blue-collar workers, fueling a belief that they are less likely to adopt or benefit from sophisticated software. This demographic is often viewed as a mobile-only, no-email workforce, and the slower pace of digital transformation in many blue-collar sectors, often due to the physical nature of the work and a historical focus on immediate operational needs over worker productivity tools, has also delayed tech adoption.
Furthermore, upper management’s lack of visibility and support for frontline roles leads to fewer dedicated tech solutions. The unclear immediate ROI of software investments in these sectors makes it difficult to justify spending, all contributing to the slow emergence of big frontline tech platforms.
Tailwinds
Despite the daunting challenges in the frontline tech market, a significant shift is occurring.
I recall a conversation with the CEO of Honest Burgers, Phil Eeles, whose forward-thinking use of technology to empower employees left a strong impression on me. I remember vividly his response when asked to justify his IT investments in frontline tech: “Happy employees = happy burgers = happy business.”
Eeles manages his restaurant chain with the ethos of a software company, firmly believing in the value of investing in employee tools and convinced that happy employees lead to a thriving business.
A friend of mine, Sharan Pasricha, CEO of Ennismore, told me that the “next CEO of the very hospitality company he created could be working today as a waiter in a hotel in Scotland” and that “he needed tools to help identify her, share her best practices as fast as possible with the rest of the company, promote her and reward her.”
This perspective isn’t unique. In 2021, the global market for frontline employee SaaS apps was valued at $21.3 billion and projected to rise to $68.9 billion by 2028, marking a CAGR (compound annual growth rate) of 17.6% from 2022 to 2028.
Frontline workers are increasingly vocal about their desires for greater job transparency, stability, work-life balance, connection with colleagues, flexibility, health benefits, career advancement, autonomy, and problem-solving tools. As they strive for excellence and recognition, their satisfaction becomes a cornerstone for resilience, performance, productivity, retention, and advocacy for their employers, signaling a transformative era for frontline workforce technology. In an industry where churn is 40%+, every percentage of retention gained directly impacts the bottom line.
Where are the main opportunities?
Frontline, and more generally, serving hourly workers, is an excellent opportunity for emerging software vendors, but “every part of the work experience is different in ‘hourly work.’ The innovation here will come from something other than a company focused on professional work. We have to rethink every part of the ‘HR’ stack,” John Waldmann from Homebase told me.
Tools like communication, task management, training, health and safety monitoring, and real-time feedback represent sectors where SaaS can provide immense value. Yet, these tools are still in the process of being democratized and evenly distributed across industries. With the advent of general AI and more sophisticated chatbots, there’s potential for innovative solutions that are just beginning to be explored.
Employers are starting to lay the groundwork for these advanced technologies by taking initial steps toward adoption. There is a growing recognition of the need for specialized tech solutions explicitly tailored to the needs of the frontline workforce, indicating a ripe market for development and investment in these areas.
In frontline workforce management, SaaS applications are revolutionizing task management and scheduling by offering real-time updates and efficient coordination tools, exemplified by companies like Homebase, Sona, Connecteam or Combo. Shift marketplace platforms further enhance flexibility in scheduling and finding replacements, with Shiftsmart leading in this space. Payroll and HRIS systems such as All Gravy or Homebase streamline the payment and management of wages in customer-facing roles, while communication and collaboration tools from providers like Yoobic, Flip, Workplace from Meta, and Humand facilitate better team interaction and information sharing.
The importance of training and onboarding in the face of rapid technological changes is addressed by innovative, flexible solutions from WorkJam and Beekeeper. Additionally, ensuring health and safety compliance, a critical aspect of frontline work, is made more manageable through real-time reporting and analytics tools offered by companies like Beams and eduMe.
Give workers a voice
Technology can give frontline workers a voice and help them feel more valued, part of the team, and effective. This engagement can reduce staff turnover and build community and a recognizable and attractive company culture.
Once an ignored segment, frontline worker technology is now positioned at the convergence of technological advancements like AI, evolving work dynamics, and market potential driven by the need for greater efficiency, automation, compliance, and connectivity in various industries.
Companies of all sizes that make these investments will gain a competitive advantage. Entrepreneurs who build the tools and platforms that address this growing need — and yes, the VCs who back them — are poised to create a robust new market. There’s no doubt there will be one day a Microsoft of the frontline.
Image Credits: Matt Cardy / Contributor (opens in a new window) / Getty Images
Elon Musk’s X, formerly Twitter, has forged a deal with a sports betting operator, BetMGM, the companies announced on Friday. The deal, which BetMGM describes as a “strategic partnership” with X, will see the operator becoming X’s exclusive Live Odds Sports Betting partner and will introduce access to the betting service on X.
Initially, X users in the U.S. will be able to explore the betting odds on pro football, with more professional and college sports to roll out over time. Through the new interface, users will be able to click through to reach BetMGM’s website or app where they can then place their bets, but the integration will continue to evolve over time, the company said, suggesting that a future version could make betting even easier on X.
“Sports never sleep on X and now with our strategic partnership with BetMGM, fans are practically in the front row. We’re bringing sports fans on X even closer to the action so they can cheer, and now bet, on their favorite teams,” said X CEO Linda Yaccarino in a statement about the new deal.
She also posted a shorter version of this statement to X itself, showing off a screenshot of what the betting integration would look like at launch. Here, a mocked-up image of Super Bowl LVIII on X showed images and select videos from the teams followed by a new section titled “Odds by BETMGM” at the bottom.
Sports never sleep on @X. And now with @BetMGM, we'll bring fans on X even closer to the action to cheer and now bet on their favorite teams!! Let’s do this! pic.twitter.com/MFrzhKLE78
— Linda Yaccarino (@lindayaX) February 9, 2024
The partnership is another example of the different direction X is headed since Musk’s 2022 acquisition of the social network, then known as Twitter. His vision has expanded beyond social networking to see X becoming an “everything app,” so to speak, which includes not just text posts and media, but also creator content, subscriptions, live and recorded video, online shopping, payments and more.
The company’s app this week hit the top of the App Store after Tucker Carlson announced an interview with Putin would appear on X on Thursday, and from activity around explicit Drake photos (which had been circulating prior to the big spike in installs that sent the app to No. 1). Capping these viral moments, sports betting access through X — where sports commentary and conversations often take place — could attract a new audience, and help X maintain its high ranking. (The app remains No. 1 in the U.S. App Store as of Friday morning.)
Reached for comment, a rep for X didn’t offer details about if or how X would generate revenue from its new deal with BetMGM, but there’s likely an agreement in place, given the integration work required.
X has been in a tough situation from a financial standpoint. The company hasn’t yet moved away from a reliance on advertising but its owner has a habit of spooking X’s advertisers over brand safety concerns. Late last year, big-name brands like Apple, Disney, IBM and others put their campaigns on pause after Musk endorsed an antisemitic post, for example.
Those departures left X filled with lower-quality and spammy ads…and in search of new revenue streams.
“X is the center of the sports world’s conversation 24 hours a day, seven days a week,” said BetMGM CEO Adam Greenblatt, in a statement. “Being directly accessible within that forum is an unprecedented opportunity to expand our reach to a passionate and engaged audience. We look forward to adding intel and content that enhances the platform’s interaction around sports,” he added.
Ahead of the Big Game this Sunday, we're excited to announce an EXCLUSIVE Sports Betting Partnership between @X and #BetMGM
The first-of-its-kind partnership between a premier social media brand and a sports betting operator will integrate BetMGM’s odds and branding into X,… pic.twitter.com/8q4WUglZfW
Generative AI, which can create and analyze images, text, audio, videos and more, is increasingly making its way into healthcare, pushed by both Big Tech firms and startups alike.
Google Cloud, Google’s cloud services and products division, is collaborating with Highmark Health, a Pittsburgh-based nonprofit healthcare company, on generative AI tools designed to personalize the patient intake experience. Amazon’s AWS division says it’s working with unnamed customers on a way to use generative AI to analyze medical databases for “social determinants of health.” And Microsoft Azure is helping to build a generative AI system for Providence, the not-for-profit healthcare network, to automatically triage messages to care providers sent from patients.
Prominent generative AI startups in healthcare include Ambience Healthcare, which is developing a generative AI app for clinicians; Nabla, an ambient AI assistant for practitioners; and Abridge, which creates analytics tools for medical documentation.
The broad enthusiasm for generative AI is reflected in the investments in generative AI efforts targeting healthcare. Collectively, generative AI in healthcare startups have raised tens of millions of dollars in venture capital to date, and the vast majority of health investors say that generative AI has significantly influenced their investment strategies.
But both professionals and patients are mixed as to whether healthcare-focused generative AI is ready for prime time.
Generative AI might not be what people want
In a recent Deloitte survey, only about half (53%) of U.S. consumers said that they thought generative AI could improve healthcare — for example, by making it more accessible or shortening appointment wait times. Fewer than half said they expected generative AI to make medical care more affordable.
Andrew Borkowski, chief AI officer at the VA Sunshine Healthcare Network, the U.S. Department of Veterans Affairs’ largest health system, doesn’t think that the cynicism is unwarranted. Borkowski warned that generative AI’s deployment could be premature due to its “significant” limitations — and the concerns around its efficacy.
“One of the key issues with generative AI is its inability to handle complex medical queries or emergencies,” he told TechCrunch. “Its finite knowledge base — that is, the absence of up-to-date clinical information — and lack of human expertise make it unsuitable for providing comprehensive medical advice or treatment recommendations.”
Several studies suggest there’s credence to those points.
In a paper in the journal JAMA Pediatrics, OpenAI’s generative AI chatbot, ChatGPT, which some healthcare organizations have piloted for limited use cases, was found to make errors diagnosing pediatric diseases 83% of the time. And in testing OpenAI’s GPT-4 as a diagnostic assistant, physicians at Beth Israel Deaconess Medical Center in Boston observed that the model ranked the wrong diagnosis as its top answer nearly two times out of three.
Today’s generative AI also struggles with medical administrative tasks that are part and parcel of clinicians’ daily workflows. On the MedAlign benchmark to evaluate how well generative AI can perform things like summarizing patient health records and searching across notes, GPT-4 failed in 35% of cases.
OpenAI and many other generative AI vendors warn against relying on their models for medical advice. But Borkowski and others say they could do more. “Relying solely on generative AI for healthcare could lead to misdiagnoses, inappropriate treatments or even life-threatening situations,” Borkowski said.
Jan Egger, who leads AI-guided therapies at the University of Duisburg-Essen’s Institute for AI in Medicine, which studies the applications of emerging technology for patient care, shares Borkowski’s concerns. He believes that the only safe way to use generative AI in healthcare currently is under the close, watchful eye of a physician.
“The results can be completely wrong, and it’s getting harder and harder to maintain awareness of this,” Egger said. “Sure, generative AI can be used, for example, for pre-writing discharge letters. But physicians have a responsibility to check it and make the final call.”
Generative AI can perpetuate stereotypes
One particularly harmful way generative AI in healthcare can get things wrong is by perpetuating stereotypes.
In a 2023 study out of Stanford Medicine, a team of researchers tested ChatGPT and other generative AI–powered chatbots on questions about kidney function, lung capacity and skin thickness. Not only were ChatGPT’s answers frequently wrong, the co-authors found, but also answers included several reinforced long-held untrue beliefs that there are biological differences between Black and white people — untruths that are known to have led medical providers to misdiagnose health problems.
The irony is, the patients most likely to be discriminated against by generative AI for healthcare are also those most likely to use it.
People who lack healthcare coverage — people of color, by and large, according to a KFF study — are more willing to try generative AI for things like finding a doctor or mental health support, the Deloitte survey showed. If the AI’s recommendations are marred by bias, it could exacerbate inequalities in treatment.
However, some experts argue that generative AI is improving in this regard.
In a Microsoft study published in late 2023, researchers said they achieved 90.2% accuracy on four challenging medical benchmarks using GPT-4. Vanilla GPT-4 couldn’t reach this score. But, the researchers say, through prompt engineering — designing prompts for GPT-4 to produce certain outputs — they were able to boost the model’s score by up to 16.2 percentage points. (Microsoft, it’s worth noting, is a major investor in OpenAI.)
Beyond chatbots
But asking a chatbot a question isn’t the only thing generative AI is good for. Some researchers say that medical imaging could benefit greatly from the power of generative AI.
In July, a group of scientists unveiled a system called complementarity-driven deferral to clinical workflow (CoDoC), in a study published in Nature. The system is designed to figure out when medical imaging specialists should rely on AI for diagnoses versus traditional techniques. CoDoC did better than specialists while reducing clinical workflows by 66%, according to the co-authors.
In November, a Chinese research team demoed Panda, an AI model used to detect potential pancreatic lesions in X-rays. A study showed Panda to be highly accurate in classifying these lesions, which are often detected too late for surgical intervention.
Indeed, Arun Thirunavukarasu, a clinical research fellow at the University of Oxford, said there’s “nothing unique” about generative AI precluding its deployment in healthcare settings.
“More mundane applications of generative AI technology are feasible in the short- and mid-term, and include text correction, automatic documentation of notes and letters and improved search features to optimize electronic patient records,” he said. “There’s no reason why generative AI technology — if effective — couldn’t be deployed in these sorts of roles immediately.”
“Rigorous science”
But while generative AI shows promise in specific, narrow areas of medicine, experts like Borkowski point to the technical and compliance roadblocks that must be overcome before generative AI can be useful — and trusted — as an all-around assistive healthcare tool.
“Significant privacy and security concerns surround using generative AI in healthcare,” Borkowski said. “The sensitive nature of medical data and the potential for misuse or unauthorized access pose severe risks to patient confidentiality and trust in the healthcare system. Furthermore, the regulatory and legal landscape surrounding the use of generative AI in healthcare is still evolving, with questions regarding liability, data protection and the practice of medicine by non-human entities still needing to be solved.”
Even Thirunavukarasu, bullish as he is about generative AI in healthcare, says that there needs to be “rigorous science” behind tools that are patient-facing.
“Particularly without direct clinician oversight, there should be pragmatic randomized control trials demonstrating clinical benefit to justify deployment of patient-facing generative AI,” he said. “Proper governance going forward is essential to capture any unanticipated harms following deployment at scale.”
Recently, the World Health Organization released guidelines that advocate for this type of science and human oversight of generative AI in healthcare as well as the introduction of auditing, transparency and impact assessments on this AI by independent third parties. The goal, the WHO spells out in its guidelines, would be to encourage participation from a diverse cohort of people in the development of generative AI for healthcare and an opportunity to voice concerns and provide input throughout the process.
“Until the concerns are adequately addressed and appropriate safeguards are put in place,” Borkowski said, “the widespread implementation of medical generative AI may be … potentially harmful to patients and the healthcare industry as a whole.”
