You might think the big story out of Match Group’s first-quarter earnings is Tinder’s turnaround. The dating app’s revenue is slightly up again after quarter-after-quarter of declines.

But we’d like to point to a comment the chief financial officer made about how the company is slowing its hiring right now because it needs more money to pay for AI tools for its employees.

Ah, yes, the good ol’ “let’s blame AI” strategy!

While speaking to analysts on the first-quarter earnings call, Match Group CFO Steven Bailey talked about how the dating app giant was investing in AI technology for internal use at the company — as well as how Match was paying for it.

“We’re making a big push around AI enablement. We’re giving every employee in the company access to all the cutting-edge tools. We’re giving them the training they need to succeed. We’re setting expectations. We really want to become an AI-native company,” Bailey said.

“We think it’s a huge opportunity. But these tools cost a lot of money, as I’m sure you know, and so the way we’re helping to pay for that is by slowing our hiring plans for the rest of the year,” he added.

The company assured investors that the impact would be cost-neutral, as the slowed hiring and lower headcount would make up for the increased software expenses. Plus, Match Group is betting that the increased productivity from employees’ use of AI will ultimately increase revenue growth, the number-cruncher explained.

While on the surface, this looks like another example of AI taking people’s jobs — in this case, forcing a company to lower its number of open positions — there’s likely more nuance to this story.

Let’s keep in mind that Match Group’s flagship app, Tinder, has been struggling in recent years. This quarter may be the start of a turnaround, as monthly active users declined by 7% in March compared with the far-steeper 10% drop a year ago. Tinder registrations also grew for the first time since 2024, but by a mere 1%, as Bloomberg pointed out.

This is perhaps a positive sign for Tinder. Or it might be a brief blip driven by users’ curiosity around various product improvements and new features, like IRL events. Time will tell.

Dating meets a generational shift

Match Group remains a company that has to work to squeeze more money out of an oft-dwindling, less active user base — which, to the company’s credit, it did exactly that. Match’s revenue was $864 million in the first quarter, up 4% year-over-year. However, its next-quarter estimates are coming in lower — around $850-$860 million, down 2% to flat year-over-year.

All these struggles come after many months of what appears to be a growing disinterest in the use of dating apps by younger people. This generational shift sees people opting to meet up in real-life, perhaps by pursuing an interest, like running, book clubs, or a hobby that connects them with other people, which then, in turn, expands their network, increasing their chance of meeting someone new.

The trend coincides with a resurgence of nostalgic tech, like digital cameras, flip phones, boomboxes, and even landlines, signaling a generation that’s feeling burned out by always-on connectivity and looking for analog pleasures.

Match Group is aware of this significant shift and says it’s pivoting to address the challenge by increasing the number of its own IRL events.

“Gen Z desperately wants to connect. They know they want to meet new people. They just want to do it in a low-pressure, low-stakes way that doesn’t feel like a job interview,” Match’s CFO Rascoff told investors on the call. “Traditional dating apps are very highly structured and can be intimidating to a user under 30. So, I think the growth of these alternative ways to meet new people speaks to how Gen Z is trying to find lower-pressure ways to connect.”

“We’ve obviously adapted our roadmap to this reality,” he said.

Bringing Voice to the Future of Learning

In the coming months, each fellow will produce a series of first-person essays, articles and videos. Their work will help readers understand how classrooms and school systems must adapt to our rapidly shifting digital landscape. Some of the questions fellows will explore:

• How are you and/or your school community using technology or AI tools to support educator and student well-being — and what practices ensure those tools are used responsibly and equitably?
• How do you and/or your school community leverage data, learning science, or AI-driven insights to design instructional strategies and assessments that help diverse learners succeed?
• How do you and/or your school support students and educators in developing the digital citizenship and media literacy skills needed to critically and responsibly engage with AI and emerging technologies?

Celebrating Our Legacy

As we welcome this new group, I’d like to extend a sincere thank you to our 2025-26 cohort — April Jackson, Dan Clark, Melinda Medina, Nikita Khetan, Patrice Wade and Sofia Gonzalez. Their stories on mental health, engagement and changing school dynamics underscored a core truth: teaching must evolve alongside how students learn in the age of AI.

As our team embarks on this journey with the 2026-27 fellows, I hope you enjoy their dispatches from the field. You can follow their stories across our publications, primarily on EdSurge – the digital news site of ISTE+ASCD. We invite you to join us in meeting this moment with curiosity and a commitment to building the classrooms our students and teachers deserve.

Over the weekend, I spent hours searching for flights for a summer girls’ trip and came up empty. Every option was either too expensive, landed at the wrong time or had two stops on the way — which I’m absolutely not doing. I checked multiple airlines, pieced together routes and even considered separate tickets. Nothing worked.

That kind of frustration is exactly what Mindtrip is betting on.

The AI-powered travel platform is launching a new flights feature designed for the kinds of messy, real-world searches that traditional booking tools struggle to handle. Instead of optimizing for simple routes, Mindtrip is focused on the complicated scenarios travelers actually face, where flexibility, preferences and trade-offs all collide.

Read also: Google’s New Travel Features Are Here in Time for Summer

Mindtrip AI planning and how it works

Mindtrip already combines conversational trip planning with a visual interface that pulls in maps, reviews and itineraries. With flights, it is extending that system into one of the most time-consuming parts of travel planning.

In a virtual demo with CEO Andy Moss and product VP Abby West, the company positioned its approach as less about speed and more about reasoning. The goal is not just to return results quickly, but to think through constraints the way a real traveler would.

That shift is showing up in how people actually search, too. According to West, many people do not start with a fixed destination. Instead, they describe a set of conditions. For instance, they might want somewhere warm within a four-hour nonstop flight, or they’ll ask when they can get to Paris within a certain budget.

Those kinds of queries are difficult to execute manually. They require checking multiple destinations, comparing dates and factoring in seasonality. 

Mindtrip’s system treats them as a single problem. It samples across routes and timeframes, weighs constraints and returns a short list of options that fit.

“We’ve very much always focused on the full connected trip — how you plan everything you need around a vacation, from flights to hotels, to things to do, restaurants, anything,” Moss said. 

“The use case that Mindtrip flights is really focused on is the more complicated travel cases.”

In one demo, West searched for a trip from Washington, DC, to Los Angeles, with a long list of conditions. The trip needed to be four nights in June, return by a specific date, depart before 9 a.m., exclude a nearby airport and include a carry-on. Instead of forcing those filters into a rigid form, the system broke the request into parts, evaluated multiple airport combinations and surfaced a set of tailored itineraries.

Each result came with a short explanation of why it matched the request. From there, West could move directly into checkout to book her tickets.

Tailoring trips to you 

The level of personalization depends on what Moss describes as “practical data,” not invasive tracking. The system can account for things like preferred airlines or whether someone prioritizes nonstop routes. It can also adapt to context, such as traveling with family versus traveling solo and then adjust recommendations accordingly.

“I do think you’re going to have a personal assistant [in the future]. I do think you’re going to have expert assistants that are really good at flights or hotels and those two things will work together and you’re just going to basically have a sort of situation where it’s almost like Jarvis from Iron Man combined with Her [to create an AI assistant] that knows you really well and understands you,” Moss said.

Flights also required a deeper level of infrastructure than other parts of the platform. Mindtrip partnered with Sabre to access global pricing and availability, and with PayPal to power checkout and buy-now-pay-later options. At launch, PayPal is offering a roughly $50 credit on qualifying bookings over $250, a small but notable incentive in a currently expensive travel market.

How Mindtrip is different from the crowd 

Mindtrip is not trying to replace tools built for quick, straightforward searches. Moss is clear that if someone wants a simple one-way flight, existing platforms like Google Flights already do that well. The focus here is on more complicated cases, where planning becomes time-intensive and fragmented.

That focus reflects a broader shift in how AI is being used. Instead of instant answers, companies are leaning into systems that take longer but handle more complexity. Moss believes that travelers are willing to wait for better outputs if it saves them significant time in return.

The same approach is expected to expand beyond flights. Mindtrip is already applying similar agent-driven logic to hotels and is working toward a more connected experience across booking, itineraries and in-trip planning. Over time, that could include more automated checkout flows as people grow more comfortable with letting AI handle multi-step transactions.

Even as airfares rise and the travel landscape shifts, demand has held steady. Moss sees that as a sign that planning tools will only become more important. “I don’t think there’s ever a time when people have needed to travel more,” he said. 

The challenge is not convincing people to travel, but helping them navigate an increasingly complicated, pricey system. After my own failed flight search, that pitch feels all too familiar. The problem isn’t a lack of options; it’s the effort required to sort through them.

For more travel advice, here’s the best time to shop for airline tickets and how to find cheap flights.

The year keeps moving swiftly, and so is all of our planning for TechCrunch Disrupt 2026! We have an exciting new panel in store for founders in need of merger and acquisition advice … but first, we have a limited-time ticket offer to share.

Disrupt will once again be held in San Francisco’s Moscone West from October 13–15, and for a limited time, attendees can also bring a colleague, co-founder, investor, or teammate for less! You can buy one Disrupt 2026 pass here, and get 50% off a second pass of the same ticket type with a limited-time offer that ends May 8 at 11:59 p.m. PT.

As for the kind of programming that’ll keep you locked in during Disrupt’s three days, let’s dive into our newest panel that will be on the Builders Stage.

Hear at Disrupt how M&A is now an early-stage strategy

If you’ve been following our recent coverage, acquisitions and acqui-hires remain in vogue, especially within the AI scene. Whether it’s OpenAI buying Hiro, Anthropic acquiring Vercept, Google taking the team behind Hume AI, or Databricks pulling in two startups just for its security product, it’s been a busy year!

And being acquired is far from being the end of a long road for founders; it can be part of their early-stage journey. And with those, and many other acquisitions in mind, we’ve gathered an expert panel to help equip founders with what they need to know about all the M&A options that lie before them.

Their perspectives will equip you with a playbook for creating optionality for potentially selling, ways to make your startup more enticing to buyers, and the realities of going through the acquisition process. And for some background on our panel, let’s learn more about our industry leaders.

Aklil Ibssa, Head of Corporate Development and M&A, Coinbase

Aklil Ibssa brings a buyer-side perspective from one of the biggest companies in crypto, as he leads the company’s acquisition strategy and execution, helping identify where Coinbase should buy, invest, partner, or build. He’s overseen more than 14 acquisitions and nearly 50 early- and later-stage investments, and as one of the first hires on Coinbase’s corporate development team, he contributed to an M&A program that’s become among the most active in crypto, with more than 40 total completed acquisitions.

Most importantly for founders, he’s seen firsthand how strategic buyers evaluate young companies: whether for technology, talent, licenses, product velocity, and beyond. And he’ll be able to speak to acquisitions, including Deribit, Liquifi, and Echo, and prominent investments in startups like Kalshi.

Lindsey Mignano, Founder, Mignano Law Group

Lindsey Mignano brings the legal and structural expertise that often determines whether an early-stage M&A deal can actually get to the finish line. As founder of Mignano Law Group, she represents emerging technology companies, SMEs, venture-backed startups, and venture firms as outside general counsel. Her practice spans everything from SAFE notes, priced rounds, and bridge financings to buy-side and sell-side acquisitions, acqui-hires, and everything else you can bring to mind.

That uniquely equips her to educate founders without insight into how early M&A readiness can begin. Many of her clients are seed through Series B companies, including enterprise SaaS, PaaS, and AI startups — exactly the kinds of companies now facing strategic interest, and she’ll be able to ground the conversation in the realities of cap tables, contracts, asset sales, and the necessary work for acquisitions to happen.

Karl Alomar, Managing Partner, M13

Now it’s time for an investor and operator to join the conversation. As managing partner at M13, Karl Alomar backs seed and Series A software founders across infrastructure, fintech, developer productivity, and other categories, feeling the brunt of the AI revolution. He has intimate knowledge of the earliest strategic decisions founders make: when to raise, when to partner, when to accelerate growth, and when an acquisition path may create the best outcome for the company, team, and investors.

As COO of DigitalOcean, Alomar helped build the cloud infrastructure company from its first product to roughly $250 million in ARR and an eventual NYSE IPO, with its valuation peaking around $15 billion. But as a founder, he’s been a part of the acquisition cycle too. China Export Finance grew to approximately $140 million in revenue before being acquired in 2010, and Clearview Networks was acquired in 2000. That combination gives Karl a nuanced perspective on the core question facing founders in the audience: When should they keep building with their team, and when is M&A the right path forward?

Get your second pass at 50% off by May 8

And remember: If you register for Disrupt 2026 by May 8 at 11:59 p.m. PT, you can take advantage of that offer to get your pass with savings of up to $410, and get 50% off a second pass of the same ticket type. All the insights Disrupt offers are best shared with a partner or colleague, so don’t miss this opportunity!

Presented by Nutanix

Across industries, organizations are focused on how to move from AI pilots, proofs of concept, and cloud-based experimentation to deploying it at scale — across real workloads, for real users, in real business environments. VentureBeat spoke with Tarkan Maner, president and chief commercial officer at Nutanix, and Thomas Cornely, EVP of product management, about what that transition demands, and what it will take to get it right.

“AI in general is shifting everything we do, not only in technology, but across all vertical industries, from regulated industries like banking, health care, government, education to non-regulated industries like manufacturing and retail,” Maner said. “As a complete platform company, we welcome this change. It’s creating more opportunities for us as a company to serve our customers in better ways as we move forward.”

But there’s still a practical gap between experimentation and production, Cornely said.

“It’s one thing to do an experiment, to do a prototype. It’s a different thing to take that prototype and deploy it for 10,000 employees,” he explained. “We went from people focusing on training models to chatbots to now doing agents, where the demand and pressures on AI infrastructure are growing exponentially.”

Agentic AI introduces a new layer of enterprise complexity

The rise of agentic AI is what makes this transition especially consequential. These systems introduce multi-step workflows across applications and data sources, along with a degree of autonomy that creates new operational demands.

Enterprises now have to contend with multiple agents running simultaneously, unpredictable and real-time workloads, and the need to coordinate access to infrastructure across teams.

“OpenClaw is making it very easy now for anybody to build agents and run with agents,” Cornely said. “You want those agents to be running on premises with your data. You need to have the right constructs around it to protect the enterprise from what an agent could do.”

As these systems become more autonomous, the challenge extends beyond how they operate to how they interact with enterprise data, systems, and teams.

AI is augmenting human work, not replacing it

Agentic AI is fundamentally an amplifier of human capability rather than a substitute for it, Maner said. The goal for enterprises is not to eliminate human work but to find the right balance between human decision-making, AI-driven automation, and agent-based workflows.

“We believe that there’s going to be love, peace, and harmony between AI, agentic tools, and robotics systems, and human capital,” Maner said. “That harmony can be optimized for better outcomes for businesses, enterprises, governments, and public sector organizations, if the right vendors provide the right tooling and the right services.”

How enterprises are getting started with AI at scale

In practice, the move from experimentation into real-world deployment is where the challenges become most visible. Despite the momentum, many are still working through how to scale AI beyond initial use cases.

As they do, organizations quickly run into practical constraints. Many start in the cloud because of easy access to resources and services, but practical considerations like data, governance and control, and cost quickly come to the forefront.

The cloud can be used to experiment, with the ultimate goal of bringing applications back on premises as they move toward production, using platforms that solve for security and cost.

The use cases gaining the most traction include document search and knowledge retrieval, security and predictive threat detection, software development and coding workflows, and customer support and service operations. In the security realm, banking customers and others in Europe and the U.S. are deploying AI-driven tools including facial recognition and predictive threat detection. Meanwhile, there’s a growing focus on end-to-end, 360-degree customer engagement, from pre-sales through post-sales advocacy, in the customer support industry.

Industry-specific AI transformation is already underway

Across industries, the shift from experimentation to real deployment is already taking shape in distinct ways. In retail, AI is transforming store operations with cameras and robotics used for targeted in-aisle marketing at the moment of purchase decision, while cashier-less checkout is replacing traditional POS systems, and the human capital freed up is being redeployed to back-office and merchandising functions.

In healthcare, Nutanix works with customers on applications spanning diagnosis, treatment, remote health, and hospital operations, with cloud partners including AWS and Azure. In manufacturing and logistics, the transformation is equally significant.

The operational challenges of scaling enterprise AI

As AI use cases scale, enterprises are running into a new class of operational challenges. Managing multiple AI workloads and agents, coordinating infrastructure access across teams, ensuring security and governance, and integrating AI systems with existing business processes are now top-of-mind concerns for IT and business leaders alike.

The gap between AI developers pushing for speed and access, and infrastructure teams responsible for security, uptime, and governance, is one of the defining challenges of this moment.

“Now I’m running agents, and they’re all going to fight to get access to resources to solve my problems,” Cornely said. “What you want now is infrastructure that allows you to set constraints, govern resources.”

The AI factory: a shared platform for production AI

These challenges are driving demand for what Maner and Cornely describe as the AI factory: a shared infrastructure environment that supports multiple users and workloads simultaneously, enabling both experimentation and production while balancing developer agility with enterprise governance.

At GTC 2026, Nutanix announced the Nutanix Agentic AI Solution, a complete platform spanning core infrastructure, Kubernetes-based container services running on a topology-aware hypervisor, and advanced services for building and governing agents.

“We’re launching a complete platform, from core infrastructure through PaaS and advanced PaaS services to the whole management framework for your AI factories,” Cornely said. “Really enabling self-service for the teams that will build these applications in the enterprise.”

Hybrid environments are essential to enterprise AI strategy

Operating this kind of environment requires flexibility across infrastructure. Hybrid infrastructure is not a compromise, but a requirement. Some workloads will always run in the public cloud, while others must remain on premises due to security requirements, regulatory compliance, data sovereignty, or competitive IP considerations.

“Especially in the regulated industries, as sovereignty becomes a bigger issue, data gravity becomes a bigger issue, security, and also a lot of competitive differentiation in the industry, it’s going to depend on what the company wants for their own IP,” Maner said.

This is the foundation of Nutanix’s platform position, he added.

“We are the perfect harmony, bringing those applications, that data, and all the optimization for these use cases end to end, from on-prem to off-prem and in a hybrid mode,” he said. “Doing it not only in one cloud, but for multiple clouds.”

That flexibility also extends to the broader ecosystem. Nutanix works across hyperscalers including AWS, Azure, and Google Cloud, as well as regional service providers and emerging neoclouds. Nutanix offers neoclouds a full software stack to run their own clouds and deliver advanced AI services, giving enterprise customers already running Nutanix a simple extension of compute, networking, and AI capabilities.

Maner described the arrangement as a win for both sides. For enterprises, it means simplified access to hybrid AI services. For neoclouds, it means a proven platform to build on. It’s all automated and secure by default, Cornely added.

“All of those governance problems that now come up with agentic AI are the same problems we’ve been solving for the last 16 years for every other application running in your cloud,” he said.

From pilot to production: operationalizing AI across the enterprise

Ultimately, the goal is not to run a successful AI pilot, but to operationalize AI across real-world use cases, manage infrastructure as a shared resource, support collaboration between infrastructure teams and AI developers, and scale from initial projects to enterprise-wide deployment.

“There’s a massive gap right now between people building AI applications, those AI engineers, those agentic AI developers, and your classical infra teams,” Cornely said. “They need tooling to enable the infra teams, so they can support your AI engineers. That’s what we deliver with our agentic AI solution.”

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

More Information

As the wireless industry looks beyond 5G, a new generation of technology components is emerging to address the performance demands of use cases such as immersive telepresence, digital twins, autonomous robotics, and smart-city infrastructure. 6G aims to support peak data rates up to 1 Tbps by extending into THz frequency bands, while simultaneously integrating sensing, AI-driven signal processing, and photonics into a seamless network architecture. Reconfigurable intelligent surfaces offer a way to shape the radio environment using programmable metamaterials, and ultra-massive MIMO pushes antenna-element counts far beyond current arrays. Full-duplex communications could double spectral efficiency, and non-terrestrial nodes such as LEO satellites and stratospheric platforms promise truly ubiquitous three-dimensional coverage. This white paper examines each of these ten technology enablers, explains the underlying principles, and outlines the open research challenges on the path to a future 6G standard.

Cybersecurity entered 2026 under pressure to keep pace with the rapid deployment of AI technologies while laying the foundations for a quantum future.

Security leaders are expected to defend increasingly complex AI and hybrid environments while facing persistent talent shortages, a fast-changing threat landscape and mounting operational pressure.