Cvent has spent years owning the logistics and analytics behind enterprise events. Now it wants to own what happens after the event ends.

The meetings, events, and hospitality technology leader has acquired Goldcast, a fast-growing B2B video platform known for helping marketers turn webinars and virtual events into polished, on-brand video content. The deal signals a clear shift in how event platforms are evolving—from execution tools into full-funnel marketing engines.

For marketers under pressure to justify event ROI in a video-first buying landscape, the message is straightforward: events should no longer be one-and-done experiences. They should be reusable, measurable, and revenue-adjacent.

Why This Deal Matters Now

The acquisition lands at a moment when B2B buying journeys are increasingly asynchronous, digital, and content-driven. Webinars, virtual conferences, and hybrid events are still popular—but their real value often comes after the live session ends.

Goldcast built its reputation on solving that exact problem. Its platform uses AI to automatically generate video clips, summaries, captions, and recaps from live events, making it easier for marketing teams to distribute content across websites, email campaigns, social channels, and sales enablement tools.

By bringing Goldcast into its ecosystem, Cvent is effectively extending the lifespan of every event it powers. A single webinar can now fuel weeks—or months—of downstream content without requiring additional production work.

That’s a meaningful upgrade for Cvent’s roughly 30,000 customers, many of whom already rely on the platform for registration, attendee engagement, and performance analytics.

From Event Management to Content Infrastructure

Traditionally, event technology platforms have focused on planning, promotion, and measurement. Content creation has lived elsewhere—often split across video tools, social platforms, and marketing automation systems.

Cvent is betting that consolidation is the next competitive advantage.

With Goldcast integrated, marketers can:

• Run webinars and events inside the Cvent ecosystem

• Automatically generate short-form and long-form video assets

• Distribute those assets across marketing and sales channels

• Measure engagement using combined event and video analytics

The result is a tighter loop between events, content, and revenue—one that aligns with how modern B2B teams actually operate.

This also reflects a broader trend in MarTech: platforms are increasingly expected to do more than manage workflows. They’re being asked to produce outcomes.

Goldcast’s AI Advantage

Goldcast enters the deal with momentum. The company has earned recognition as one of North America’s fastest-growing technology firms, including a spot on Deloitte’s 2025 Technology Fast 500.

Its appeal lies in automation. Instead of asking teams to manually edit recordings or brief video vendors, Goldcast applies AI to identify key moments, generate captions, and package content in minutes. That speed matters in a market where relevance decays quickly and attention is scarce.

For Cvent customers, this means less friction between hosting an event and activating its content. For Cvent itself, it adds a differentiated layer of AI-driven value that competitors will have to respond to.

A Clear Play for Buyer Intent Data

Beyond content creation, the acquisition also strengthens Cvent’s data story.

By combining Cvent’s event insights—such as attendance, session engagement, and interaction data—with Goldcast’s video-level engagement metrics, marketers gain a clearer picture of buyer intent. Which clips are watched? Which topics resonate? Which accounts engage repeatedly?

That kind of signal is increasingly valuable as B2B teams move away from form fills and toward behavioral indicators of interest.

It also positions Cvent more competitively against platforms that are already blending content, engagement, and analytics into unified experiences.

What This Signals for the MarTech Market

The Cvent-Goldcast deal underscores several industry realities:

• Events are becoming media assets, not just experiences

• AI-driven repurposing is moving from “nice to have” to expected

• Marketing teams want fewer tools that do more, not sprawling stacks

Rivals in the event and webinar space will likely feel pressure to respond—either through partnerships, acquisitions, or accelerated product development. As video continues to dominate B2B content strategies, platforms that can’t support post-event activation risk becoming operational utilities rather than strategic systems.

Looking Ahead

Cvent CEO Reggie Aggarwal framed the acquisition as a bet on AI-driven video without losing sight of what makes events powerful in the first place: authentic human moments.

That balance—automation without losing trust—will determine how successful this integration becomes.

If executed well, the combination could redefine what marketers expect from event technology: not just smoother execution, but sustained impact across the entire customer journey.

For now, one thing is clear. In a video-first future, Cvent doesn’t just want to host the event. It wants to own the story that follows.

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New Jersey is making a deliberate play to become a serious AI gravity well. The New Jersey Artificial Intelligence Hub announced it will launch a dedicated AI Accelerator in early 2026, powered by global innovation heavyweight Plug and Play. The move is less about hype and more about infrastructure—connecting startups, researchers, and enterprises into a single pipeline designed to move AI ideas from lab bench to market faster.

At its core, the accelerator aims to remove friction. New Jersey–based AI startups and university-affiliated founders will get direct access to mentors, investors, and industry partners, while top-tier AI startups from outside the state will be actively recruited to build and scale locally. The program will run out of the NJ AI Hub’s 6,500-square-foot facility in West Windsor, anchoring AI development in a region already dense with research institutions and enterprise buyers.

Why This Matters Now

AI accelerators aren’t new—but timing and execution matter. As enterprise AI adoption accelerates, startups face a familiar bottleneck: access to compute, customers, and credible validation. New Jersey’s play is to bundle all three.

The AI Accelerator builds on a series of deliberate steps by the NJ AI Hub, including its recent designation as one of only two global sites to host Microsoft Discovery, an agentic AI and cloud platform aimed at accelerating scientific research. That puts New Jersey in rare company—and signals an intent to compete not just with regional peers, but with global AI clusters.

“This partnership with Plug and Play will unleash new technologies, foster powerful cross-sector collaborations, and speed AI innovations from concept to impact,” said Liat Krawczyk, executive director of the NJ AI Hub.

Plug and Play Brings the Global Network

Plug and Play’s role is the accelerant. The Silicon Valley–born innovation platform runs more than 60 innovation hubs across 25+ countries and connects over 100,000 startups with 550+ corporate partners. Its model is proven: structured cohorts, hands-on mentorship, enterprise pilots, and investor access—all tuned to help startups scale, not just pitch.

Michael Olmstead, Plug and Play’s CRO, is leading the expansion with the NJ AI Hub, positioning the program as a gateway between New Jersey’s research depth and Plug and Play’s global commercialization engine.

For founders, the offering goes beyond demo days. The accelerator will provide business model refinement, technical workshops, funding access, and curated introductions to enterprise partners—often the missing link for AI startups stuck between proof-of-concept and revenue.

Built Around New Jersey’s Industry Strengths

Unlike generic accelerators, this one is explicitly sector-driven. Cohorts will tap into New Jersey’s established strengths in healthcare, pharmaceuticals, advanced manufacturing, financial services, energy, telecommunications, logistics, and smart infrastructure.

That focus matters. AI startups increasingly need real-world data, regulated environments, and industry partners willing to pilot solutions. New Jersey’s proximity to Fortune 500 companies, major hospital systems, and global manufacturers gives the accelerator a practical edge over more abstract innovation hubs.

Princeton University, a founding partner of the NJ AI Hub, sees the accelerator as a commercialization bridge for academic innovation. “This partnership will enable faculty and students to turn their novel ideas into successful products and companies,” said Princeton Provost Jennifer Rexford.

A Growing Public–Private AI Stack

The accelerator doesn’t exist in isolation. The NJ AI Hub itself was founded by Princeton University, the State of New Jersey, Microsoft, and CoreWeave—an unusually strong coalition spanning academia, government, hyperscale cloud, and AI infrastructure.

CoreWeave, founded in New Jersey, brings deep GPU infrastructure expertise at a moment when compute access can make or break an AI startup. Microsoft’s involvement, via TechSpark and the Discovery platform, adds enterprise credibility and cloud-scale tooling. The New Jersey Economic Development Authority provides policy alignment and economic incentives to keep innovation—and jobs—local.

“Innovation flourishes when talented people are empowered with mentors and programs that help unlock their full potential,” said Mike Egan, general manager of Microsoft TechSpark.

The Bigger Picture

States are increasingly competing not just on tax incentives, but on AI ecosystems. Texas is courting data centers. New York is leaning into fintech AI. California still dominates research and venture capital—but it’s expensive and crowded. New Jersey’s bet is that a tightly integrated accelerator, anchored by real industry demand and global networks, can punch above its weight.

If executed well, the NJ AI Hub Accelerator could become a model for regional AI development—one where startups don’t just build impressive models, but deploy them into regulated, revenue-generating environments.

Early next year will show whether New Jersey can turn that ambition into sustained momentum. The pieces are in place. Now comes the hard part: execution.

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Legacy IVR systems have long been a bottleneck for healthcare organizations—rigid call flows, poor caller experiences, and change cycles that stretch into months. AudioCodes says it just proved that doesn’t have to be the case.

The communications software vendor announced that its Voca Conversational Interaction Center (Voca CIC), working with Go2Uno and global BPO Atento, completed a large-scale AI-powered Voice Agent and Conversational IVR modernization for a major healthcare organization in a matter of weeks—far faster than typical enterprise IVR projects of similar complexity.

For an industry where call volumes are massive, systems are fragmented, and downtime is not an option, the deployment serves as a real-world example of AI moving from experimentation to operational backbone.

From Legacy IVR to AI Voice Agents—Fast

According to AudioCodes, the project replaced multiple legacy IVR systems with a modern, AI-driven conversational IVR capable of supporting more than 500 concurrent voice agents. That scale alone would normally put the project into a multi-quarter timeline.

Instead, the joint team completed the rollout in roughly 30 days, a timeframe Atento says others estimated at three to six months.

The new setup supports complex voice networking, integrates multiple carriers, and introduces intelligent, AI-powered call routing—critical for healthcare environments where calls range from appointment scheduling to sensitive patient inquiries.

Why This Matters in Healthcare CX

Healthcare contact centers face a uniquely difficult mix of challenges:

• Extremely high call volumes

• Seasonal and event-driven spikes

• Strict reliability and compliance requirements

• A growing expectation for natural, conversational self-service

Traditional IVRs struggle under that weight. They’re expensive to modify, brittle when scaled, and often frustrate callers with rigid menu trees.

By contrast, AudioCodes’ Voca CIC platform uses conversational AI to interpret intent, route calls intelligently, and contain more interactions without human intervention—all while maintaining the resilience required for mission-critical environments.

Inside the Deployment: What Changed

The modernization effort wasn’t a simple overlay. AudioCodes, Go2Uno, and Atento reworked the organization’s voice infrastructure end to end:

• Complex call flows were redesigned to support conversational interactions rather than menu-driven logic

• Multiple carrier systems were integrated, reducing dependency on siloed networks

• AI-powered routing was implemented to improve containment and reduce handling times

• AudioCodes SBC infrastructure was deployed to ensure continuity and reliability across a highly complex environment

• Advanced reporting and analytics were added to give Atento and the healthcare organization real-time visibility into voice agent performance and customer behavior

The result is a platform that doesn’t just automate calls, but actively improves how patients and members move through the system.

Azure Conversational AI at Enterprise Scale

One notable detail: the deployment included a seamless integration of Azure Conversational AI, something Atento says is often underestimated in terms of effort.

“Go2Uno and AudioCodes accomplished in just 30 days what others projected would take three to six months,” said Gustavo Samaniego, Senior IT Service and Deliveries Manager at Atento. He highlighted the Azure integration as a particular challenge that was executed “flawlessly.”

That matters because many AI IVR projects stall at integration—especially when cloud AI services meet legacy telephony environments. This deployment suggests those barriers are becoming more manageable with the right architecture and partners.

Not a Pilot—Production AI at Scale

AudioCodes is keen to position this project as something more than a proof of concept.

“This is real AI in action,” said Gidi Adlersberg, Head of the Voca CIC Business Line at AudioCodes. “Not a pilot, not a demo—transforming complex operations quickly and improving customer experience where it truly matters.”

That distinction is important. While conversational AI has been widely marketed, many enterprises remain stuck in pilot mode, hesitant to deploy at scale due to reliability concerns. A 500-agent healthcare rollout challenges the notion that AI voice systems are still experimental.

The Business Impact: Efficiency and Resilience

Beyond speed, the deployment delivered tangible operational benefits:

• Improved call containment, reducing the load on live agents

• Shorter average handling times, improving efficiency

• Greater resiliency, with SBC-backed infrastructure ensuring continuity

• A scalable foundation for future AI-driven enhancements

For Atento, the project also created a reusable, future-ready platform that can support new clients and evolving use cases without rebuilding from scratch.

A Broader Trend: IVR Modernization Gets Practical

The announcement reflects a broader shift in enterprise CX: IVR modernization is no longer about incremental upgrades. It’s about replacing rigid systems with AI-native voice platforms that can adapt quickly.

What’s notable here is the emphasis on speed and predictability—two areas where AI projects often fall short. By delivering a complex healthcare deployment in weeks, AudioCodes and its partners are making a case that conversational IVR can now meet enterprise timelines and expectations.

Availability and Access

AudioCodes says Voca CIC is available as a 30-day free trial through its website, the Microsoft Marketplace, and the Microsoft Teams Store. New customers can spin up a conversational contact center with AI and omnichannel capabilities in minutes, including a free phone number for evaluation.

That low-friction entry point suggests AudioCodes is targeting both large enterprises and organizations earlier in their IVR modernization journey.

Bottom Line

Healthcare IVR projects are notorious for running long, going over budget, and underdelivering on experience. This deployment shows that with mature conversational AI, strong infrastructure, and the right partners, those assumptions may finally be outdated.

For AudioCodes, the win reinforces its position in AI-powered voice and contact center modernization. For the industry, it’s another signal that AI voice agents are moving decisively from promise to production.

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Malaysia is stepping onto the global AI infrastructure map—and GIBO Holdings wants to help lay the groundwork. The Nasdaq-listed company announced a strategic collaboration with E Total Technology Sdn Bhd to plan, site, and deploy next-generation AI compute centers across Malaysia, designed to handle the surging demand for large-scale AI training and inference.

The partnership signals more than a routine data center build-out. By anchoring the project around NVIDIA’s latest high-performance AI chips and GPU architectures, GIBO is targeting the kind of dense, high-throughput compute environments typically reserved for hyperscalers and top-tier research institutions.

Why This Is a Big Deal

AI ambition increasingly hinges on compute access. As enterprises push beyond pilots into production—training larger models, running inference at scale, and supporting real-time applications—regional shortages of advanced compute have become a bottleneck. Southeast Asia, in particular, has relied heavily on offshore infrastructure.

This project aims to change that. By building AI-first compute centers locally, GIBO and E Total Technology plan to provide enterprises, research institutions, and digital economy players with scalable, high-performance resources closer to home—reducing latency, improving data sovereignty, and increasing regional competitiveness.

E Total Technology Takes the Lead on the Ground

Under the collaboration, E Total Technology Sdn Bhd will act as the primary local execution partner, overseeing everything from site sourcing to regulatory approvals. Its remit includes:

• Identifying and evaluating sites suitable for AI compute and data center facilities

• Conducting technical, commercial, and operational feasibility studies

• Managing local coordination, compliance, and approvals

• Supporting infrastructure planning and deployment

That local expertise matters. AI data centers aren’t just power-hungry—they’re regulation-heavy. Land use, energy availability, cooling, and compliance all influence whether projects move fast or stall. E Total’s experience navigating Malaysia’s infrastructure and regulatory landscape could significantly shorten time-to-deployment.

NVIDIA-Class Hardware, Built for Scale

While specific SKUs weren’t disclosed, the compute centers are expected to deploy NVIDIA’s most advanced AI chips and GPU platforms, optimized for high-density workloads. That positions the facilities to support:

• Large-scale foundation model training

• Advanced inference pipelines

• Multi-industry AI applications spanning finance, healthcare, manufacturing, and logistics

The emphasis isn’t just raw performance. The architecture is designed for efficiency—delivering improved energy utilization and internationally competitive compute density, while allowing room to scale as AI workloads continue to grow.

A Play for Regional AI Leadership

As governments and enterprises race to secure AI capacity, compute infrastructure has become a strategic asset. Countries that can host reliable, high-performance AI platforms stand to attract investment, talent, and innovation ecosystems.

By introducing globally benchmarked AI compute infrastructure, the GIBO–E Total collaboration aims to strengthen Malaysia’s position as a regional AI compute hub in Asia-Pacific—complementing national digital economy initiatives and making the country more attractive to AI-driven businesses.

The partners say they will continue evaluating opportunities to expand capacity as demand grows. Given the trajectory of AI adoption, that expansion may come sooner rather than later.

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Fivetran has spent the last few years making a clear bet: as enterprises consolidate data stacks and prepare for AI-driven workloads, data movement must become invisible. That strategy appears to be paying off. The company has been named a Challenger in the 2025 Gartner Magic Quadrant for Data Integration Tools, a market that underpins everything from analytics to AI deployment.

Gartner defines data integration tools as platforms that combine data from multiple sources and handle access, transformation, enrichment, and delivery. In practice, these tools increasingly act as the connective tissue of modern enterprises—especially as organizations spread data across clouds, warehouses, and operational systems.

Fivetran says the recognition reflects its focus on delivering a unified and automated data foundation that handles movement, transformation, and activation at enterprise scale. In a crowded market that includes long-standing integration vendors and newer cloud-native players, being placed in the Challenger quadrant suggests strong execution and momentum, even as competition intensifies.

Why Data Integration Is Having a Moment

The timing of Gartner’s recognition is notable. Enterprises are rethinking their data architectures under pressure from AI initiatives, real-time analytics, and rising governance demands. Point solutions and brittle pipelines are giving way to platforms that promise automation, reliability, and openness.

Fivetran has positioned itself squarely in that shift. “Enterprises are consolidating their data stacks and moving toward open, automated platforms that power AI,” said CEO George Fraser. The pitch is simple: remove the complexity of data movement so engineering and analytics teams can focus on building, not maintaining pipelines.

That message resonates as organizations face mounting costs from custom-built integrations and manual data operations—an area where automation is no longer a nice-to-have, but a prerequisite for scale.

Scale That’s Hard to Ignore

Fivetran’s footprint helps explain Gartner’s assessment. The company reports more than 7,700 customers, 740 connectors, and 7,200 terabytes of data moved every month. Those numbers put it among the most widely deployed data movement platforms globally.

Customers span industries and use cases, from pharmaceutical giant Pfizer to fashion brand Steve Madden and healthcare-focused AI company Hippocratic AI. That mix underscores how data integration has become a universal requirement, not just a concern for tech-first organizations.

In practical terms, Fivetran’s appeal lies in reliability and predictability. Automated pipelines, standardized connectors, and managed infrastructure reduce operational overhead—especially important as data volumes and source diversity continue to grow.

Moving Beyond Ingestion

One of the more interesting aspects of Fivetran’s recent evolution is its push beyond basic ingestion. In 2025, the company continued to expand its platform to address transformation, activation, and AI readiness—areas where rivals are also investing heavily.

On the product side, Fivetran expanded its Managed Data Lake Service, deepening integrations with Google Cloud Storage and Microsoft Azure to better support AI workloads. As enterprises increasingly blend structured, semi-structured, and unstructured data, this capability becomes essential rather than optional.

Support for unstructured and semi-structured data has also improved across pipelines, reflecting a broader industry reality: not all valuable data fits neatly into tables anymore, especially in AI and machine learning use cases.

The launch of the Connector SDK further signals Fivetran’s platform ambitions. By enabling customers and partners to build custom connectors more quickly, the company is addressing one of the persistent pain points in data integration—long-tail systems that don’t justify native connectors but still matter to the business.

Strategic Acquisitions Signal Broader Intent

Fivetran’s product roadmap in 2025 was matched by strategic expansion. The acquisition of Census added governed, real-time data activation to the platform, effectively closing the loop between analytics and operational systems. This move aligns with a growing trend in the data world: insights are only valuable if they can be acted on quickly.

The acquisition of Tobiko Data added advanced, multi-engine transformation capabilities and open-source innovation to Fivetran’s stack. Together, these deals suggest Fivetran sees data integration, transformation, and activation as inseparable components of a single workflow.

That integrated approach positions Fivetran against both traditional ETL vendors and newer reverse ETL and transformation specialists. Rather than competing feature by feature, the company appears to be betting on consolidation and simplicity.

How Challenger Status Fits the Competitive Landscape

Being named a Challenger in Gartner’s Magic Quadrant often signals strong execution and customer traction, even if a vendor is still building out vision or breadth compared to long-established Leaders. For Fivetran, the designation reflects its growing influence in enterprise data architectures, particularly among organizations prioritizing automation and cloud-native design.

The data integration market remains highly competitive, with hyperscalers, legacy integration providers, and open-source ecosystems all vying for mindshare. Fivetran’s emphasis on managed services and automation differentiates it from DIY-heavy approaches, but also puts pressure on the company to keep expanding functionality without sacrificing simplicity.

As AI initiatives push data requirements higher—more sources, fresher data, stronger governance—the stakes for integration platforms will only increase. Gartner’s recognition suggests Fivetran is well-positioned for that next phase, even as the market continues to evolve.

What This Means for Enterprises

For enterprise buyers, Fivetran’s placement in the 2025 Magic Quadrant reinforces its role as a central player in modern data stacks. The combination of scale, automation, and expanding capabilities makes it an increasingly attractive option for organizations looking to standardize data movement across clouds and systems.

At the same time, the rapid pace of innovation in this space means buyers will continue to weigh flexibility, openness, and long-term platform strategy. Fivetran’s recent moves—particularly around activation and transformation—suggest it intends to compete on all three fronts.

As data becomes the fuel for AI and real-time decision-making, platforms that reduce friction rather than add complexity are likely to gain ground. Gartner’s latest assessment indicates Fivetran is firmly in that race—and gaining speed.

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South Korea’s fast-growing AI marketing firm MADUP is placing a bold wager on automation, analytics, and global scale. The company is accelerating the international rollout of LEVER Xpert, its in-house digital marketing AI agent, backed by $23 million in cumulative funding and a planned KOSDAQ listing in the first half of 2026.

At a time when marketers are under pressure to do more with less—optimize spend, personalize at scale, and prove ROI across fragmented channels—MADUP is positioning LEVER Xpert as an end-to-end AI solution designed to manage the entire marketing lifecycle. From data ingestion and analysis to execution and optimization, the platform aims to replace guesswork with automation and intelligence.

For MADUP, this isn’t just a product launch. It’s the backbone of a global expansion strategy that stretches from Korea to North America, Europe, Southeast Asia, and Japan.

From Performance Shop to Global AI Marketing Player

Founded with a mission it calls “For Your Innovative Growth,” MADUP has steadily evolved from a performance-focused app marketing firm into a full-stack digital marketing company. Today, its services span performance marketing, creative, branding, content, and CRM—an expansion driven by client demand for integrated strategies rather than siloed campaigns.

That shift mirrors a broader industry trend. As media buying, creative optimization, and customer data converge, agencies and platforms alike are racing to unify workflows under a single, AI-powered roof. MADUP’s answer is LEVER Xpert, a system built to connect data, planning, and operations in one loop.

The company has already built a strong reputation in Korea across sectors such as beauty, fashion, finance, and O2O services. Its client roster includes major names like Samsung Electronics, Olive Young, MUSINSA, and MEDIHEAL, giving MADUP both scale and credibility as it moves abroad.

What LEVER Xpert Actually Does—and Why It Matters

LEVER Xpert is positioned as a digital marketing AI agent, not just another analytics dashboard. According to MADUP, the platform provides end-to-end support across all stages of marketing, including:

• Collecting and unifying marketing and advertising data

• Analyzing campaign and creative performance using AI models

• Recommending optimizations and strategic adjustments

• Automating advertising operations and execution

• Supporting planning with data-backed insights

One of the platform’s differentiators is its focus on creative analysis. While many AI marketing tools concentrate on bidding or attribution, LEVER Xpert applies AI analytics to advertising creatives themselves—evaluating what works, why it works, and how to improve it. That’s especially relevant in verticals like K-beauty, where visual storytelling and rapid creative iteration are critical to performance.

The platform is already being used in global campaigns for Korean consumer brands, suggesting it’s battle-tested outside MADUP’s home market. That matters in a crowded AI martech landscape, where many tools promise automation but lack real-world proof across regions and industries.

Riding the Wave of AI-Driven Marketing Automation

MADUP’s timing is deliberate. Marketing AI is moving beyond experimentation into operational necessity. Brands are demanding systems that don’t just surface insights, but act on them—automatically, continuously, and across channels.

Rivals in the martech space, from global SaaS vendors to agency-built platforms, are chasing the same opportunity. What sets MADUP apart is its hybrid model: part marketing services company, part AI product developer. That combination allows LEVER Xpert to be refined using live campaign data and real client feedback, rather than theoretical use cases.

It’s a playbook that echoes how some of today’s largest martech platforms were built—starting as internal tools before scaling outward.

Cracking the US Market, One Program at a Time

To support its North American ambitions, MADUP participated in the AI Innovation Accelerator (AIIA) program, run in collaboration with NYU Stern School of Business. The program provided hands-on support, including market assessments by NYU faculty, strategy-focused webinars with local experts, and networking with potential partners in New York.

MADUP also increased its visibility through industry events, including beauty-focused live conferences and KOOM 2025, signaling a targeted approach to market entry rather than a broad, unfocused expansion.

This groundwork is now translating into concrete moves. The company plans to establish a local subsidiary in New York by December 2025, a step that typically signals long-term commitment rather than opportunistic growth.

Funding, Valuation, and the Road to IPO

MADUP’s expansion is underpinned by strong investor backing. By 2022, the company had raised $23 million at a reported valuation of $175 million, with participation from Stonebridge Ventures, Praxis Capital Partners, IMM Investment, Krossroad Partners, and Shinhan Securities.

In November 2025, MADUP filed a preliminary application for a KOSDAQ listing review, with expectations to complete the IPO process in the first half of 2026. If successful, the listing would provide both capital and visibility—fueling further R&D and global market penetration for LEVER Xpert.

For investors, the appeal is clear: AI-driven marketing platforms with proven enterprise use cases are among the most closely watched segments in B2B tech, particularly as brands shift budgets toward performance accountability and automation.

Aiming to Set a Global Standard

MADUP’s leadership is clear about its ambitions. Co-CEOs Ju-min Lee and Erick Lee say the company plans to accelerate global expansion by leveraging LEVER Xpert’s market-tested capabilities and an expanding international network. The ultimate goal: position LEVER Xpert as a global standard for AI marketing solutions.

That’s an ambitious claim in a market dominated by well-funded global players. But MADUP’s combination of real-world performance data, creative intelligence, and international client traction gives it a credible shot—especially as brands look for AI tools that go beyond dashboards and actually drive growth.

As AI continues to reshape how marketing is planned, executed, and measured, MADUP is betting that LEVER Xpert can move from a competitive advantage to a category-defining platform.

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As artificial intelligence redraws the boundaries of work and learning, Udemy is making a pointed statement: instructors still matter—and the platform is building its next phase around them.

At its semiannual Front Row event, Udemy unveiled a slate of new instructor-focused offerings aimed at helping educators adapt, earn more, and stay central in a rapidly evolving, AI-shaped skills economy. The announcements come at a moment when global demand for upskilling is surging, the shelf life of technical skills continues to shrink, and nearly 60% of professionals worldwide require reskilling, according to the World Economic Forum.

Rather than positioning AI as a replacement for human-led learning, Udemy is framing instructors as “AI orchestrators”—guides who shape, validate, and elevate learning experiences as automation takes on more of the heavy lifting.

Why Udemy Is Rethinking the Instructor Model

Udemy’s timing reflects broader shifts in how people want to learn. Learners increasingly expect continuous, bite-sized, and community-driven experiences, often accessed on mobile or embedded directly into daily work. Long-form courses still matter, but they’re no longer enough on their own.

At the same time, instructors are experimenting across formats—live sessions, short-form lessons, communities—sometimes outside Udemy, when the platform doesn’t support those modalities. Udemy sees this fragmentation as both a risk and an opportunity.

“As AI reshapes the landscape of work and accelerates learning, instructors remain the catalyst for real skill-building,” said Hugo Sarrazin, Udemy’s President and CEO. The company’s new offerings are designed to help instructors grow alongside Udemy, not around it.

The strategy is clear: expand beyond one-off course sales toward recurring engagement, diversified revenue, and learning experiences that fit modern attention spans.

Instructor Subscriptions: Recurring Revenue Comes to Udemy

One of the most significant announcements is Instructor Subscriptions, a model that brings live sessions, short-form content, and community features directly onto Udemy. Expected to roll out through 2026, subscriptions aim to give instructors tools they’ve increasingly sought elsewhere.

Instead of relying solely on individual course purchases, instructors will be able to build recurring revenue streams, publish frequent and flexible content, and foster ongoing learner communities. Udemy plans to unify these activities under a single analytics and engagement layer, giving instructors clearer visibility into what resonates.

The move mirrors a wider trend across creator platforms, where subscriptions are becoming the backbone of sustainable income. For Udemy, it also helps shift the platform from a transactional marketplace to a longer-term learning relationship.

AI-Powered Micro-Learning, With Instructors in Control

Udemy also previewed AI-powered micro-learning tools that transform long-form courses into interactive, short-form learning experiences. The goal is to meet learners “in the flow of work”—on mobile devices, between meetings, or during short study windows.

What’s notable is Udemy’s emphasis on instructor oversight. Rather than auto-generating content with minimal human input, the company positions instructors as curators and validators, responsible for accuracy, quality, and instructional integrity.

These tools are designed to help instructors:

• Reach new learner segments and expand total addressable market

• Support mobile-first, just-in-time learning

• Increase engagement and retention through shorter formats

• Maintain authority over AI-generated activities

This approach reflects a growing industry realization: AI can scale content creation, but trust and credibility still depend on human expertise—especially in professional and technical learning.

A $2.5M Bet on Experimentation

To encourage instructors to embrace new formats, Udemy announced plans for a $2.5 million Content Innovation Fund. The fund is intended to support experimentation with subscription models, short-form content, and AI-driven micro-learning.

Support is expected to include grants, funding for new learning formats, resources for adopting AI tools, and acceleration for next-generation experiences. In effect, Udemy is subsidizing the transition costs for instructors navigating an uncertain but inevitable shift.

This move also benefits Udemy itself. By funding experimentation, the company gains early insight into what works—and what doesn’t—before scaling those formats platform-wide.

Competing in an AI-First Learning Market

Udemy’s announcements come as competition intensifies across corporate learning, creator education, and AI-powered training platforms. Rivals are racing to embed AI tutors, adaptive learning paths, and personalized content into their offerings.

Udemy’s differentiator is its insistence that AI should augment, not replace, instructors. By expanding formats, monetization options, and analytics, the company is betting that instructor success will drive platform resilience.

The shift toward subscriptions and micro-learning also strengthens Udemy’s broader ecosystem, particularly its enterprise and subscription businesses, where ongoing engagement matters more than one-time course purchases.

What It Means for Instructors and Learners

For instructors, Udemy’s roadmap signals a future with more control, more revenue options, and deeper relationships with learners. For learners, it promises more flexible, modular, and continuous learning experiences aligned with how skills are actually used on the job.

At a time when AI is compressing learning cycles and reshaping job roles faster than ever, Udemy is positioning itself as a platform where human expertise still anchors the experience—even as machines help scale it.

If successful, this strategy could help Udemy evolve from a course marketplace into a more durable learning ecosystem, one where instructors lead, AI accelerates, and skills keep pace with change.

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Avnet and AMD aren’t just talking about Australia’s AI future—they just drove it more than 4,000 kilometers across the country.

The partners have wrapped up the inaugural “AMD on Wheels” national roadshow, a multi-city tour spanning Sydney, Canberra, Melbourne, and Adelaide, designed to bring advanced AI and high-performance computing (HPC) solutions directly to Australia’s innovation hubs. The message was clear: the next phase of AI growth won’t be built on isolated components or lab demos, but on deployable, energy-efficient systems that can scale responsibly into real-world use.

Fittingly, the roadshow itself reflected that philosophy. The entire journey was completed using pure electric vehicles—the ZEEKR 7X and ZEEKR 009— underscoring a parallel commitment to sustainability and energy-efficient computing.

From Components to Complete AI Systems

Rather than spotlighting individual chips or boards, Avnet and AMD focused the tour on end-to-end AI and HPC solutions—secure, adaptable infrastructure that can move from pilot projects to production environments. This shift mirrors a broader trend across the enterprise and public sectors: AI success increasingly depends on integration, supply chain reliability, and operational readiness, not just raw performance.

That context matters for Australia. Analysts estimate that AI could add as much as AU$142 billion annually to Australia’s GDP by 2030 in an ambitious scenario, according to the Australia’s AI Opportunity report (2025). Capturing that value, however, requires infrastructure that balances performance with power efficiency, governance, and long-term sustainability.

Avnet and AMD positioned themselves as enablers of exactly that foundation.

“Avnet and AMD are strategically invested in advancing Australia’s capability in Responsible AI and next-generation computing,” said Tan Aik Hoon, Regional President for South Asia, Korea and Avnet United. “Beyond distribution, Avnet helps innovators transform advanced AMD hardware into scalable, sustainable solutions that strengthen Australia’s competitive edge.”

Responsibility and Efficiency as Competitive Advantages

A recurring theme throughout the roadshow was that responsible AI is no longer optional. Energy consumption, secure supply chains, and system resilience are now core considerations for governments, enterprises, and research institutions alike.

AMD reinforced this point by emphasizing its focus on energy-efficient computing, particularly in embedded and adaptive platforms that can deliver AI performance without excessive power draw.

“The next wave of AI innovation in Australia must be built on a foundation of Responsibility and Efficiency,” said Steven Fong, Corporate Vice President, Asia Pacific & Japan Embedded Sales at AMD. “We are committed to delivering the energy-efficient computing power that fuels this responsible acceleration.”

That positioning resonates as organizations weigh the environmental and operational costs of AI at scale—especially in sectors like defense, space, and advanced manufacturing.

Scaling AI Requires Collaboration, Not Silos

One of the roadshow’s central discussions, the “Industry Collaboration for AI at Scale” panel, tackled a challenge that plagues many innovation ecosystems: moving from promising pilots to full-scale deployment.

The consensus was blunt. Australia’s next innovation leap depends on cross-sector collaboration—linking academia, startups, enterprise, and global technology partners. Without that connective tissue, breakthroughs risk stalling before they reach market.

The roadshow backed up that argument with concrete examples from Australian companies already making the leap.

Advanced Navigation: Space-Grade Precision, Made Local

Advanced Navigation showcased how local innovation can reach global, space-ready standards. Through the “Avnet to the Moon” initiative, the company developed its Laser Measurement Unit for Navigational Aid (LUNA)—a precision system designed to operate in space and extreme environments.

With technology and supply chain support from Avnet and AMD, LUNA demonstrates how Australian companies can deliver mission-critical reliability while strengthening the country’s role in the global space supply chain.

Quantum Brilliance: Turning Quantum from Research to Revenue

For Quantum Brilliance, the challenge wasn’t theoretical physics—it was scaling hardware amid global supply constraints. By transitioning to the AVNET+ AMD–based ADRS1000 system-on-module, the company moved from prototypes toward commercial deployment.

Avnet’s ability to secure timely delivery despite shortages proved decisive, highlighting an often-overlooked reality of frontier technologies: innovation doesn’t scale without dependable supply chains. In quantum computing, that reliability can mean the difference between market leadership and missed opportunity.

Liquid Instruments: Agentic AI Comes to Test and Measurement

Liquid Instruments offered a glimpse into how AI is reshaping engineering workflows. Its Moku platform, powered by AMD FPGAs, is introducing Generative Instrumentation and agentic AI—tools that allow engineers to create and configure test instruments using natural language.

Set to launch with Moku:Delta in 2026, the approach promises faster experimentation and smarter workflows across research and manufacturing. With backing from Avnet and AMD, Liquid Instruments is showing how AI can compress development cycles without sacrificing precision.

Silentium Defence: Passive Radar at Production Scale

In the defense domain, Silentium Defence demonstrated how long-term collaboration accelerates complex technologies. Its SWaP-optimized passive radar systems, built on AMD technology, have progressed from early prototypes to production-ready platforms.

Avnet’s global supply chain and engineering support played a key role, enabling Silentium Defence to deliver advanced sensing capabilities for defense, space surveillance, and critical infrastructure customers worldwide.

A Blueprint, Not a One-Off Tour

Avnet and AMD are positioning “AMD on Wheels” as more than a marketing exercise. Instead, they describe it as a blueprint for Australia’s AI future—one that connects world-class research with commercial deployment through reliable infrastructure and partnerships.

By aligning advanced processors, secure supply chains, and ecosystem collaboration, the partners aim to help Australia build resilience in a global AI landscape that’s becoming increasingly competitive—and geopolitically complex.

If the roadshow proved anything, it’s that Australia’s AI opportunity won’t be unlocked by isolated breakthroughs. It will be driven by systems that work, scale, and endure—and by partnerships that know how to turn ambition into deployment.

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