logo
Meta working with Anduril on AR/VR military tech for soldiers

Meta working with Anduril on AR/VR military tech for soldiers

Yahoo29-05-2025

Meta (META) is moving beyond social media platforms and AR headsets and into the military-industrial complex. The company on Thursday announced it's teaming up with Palmer Luckey's Anduril (ANIN.PVT) to produce extended reality products for soldiers.
The devices, Anduril said in a statement, will provide 'enhanced perception and enable intuitive control of autonomous platforms on the battlefield.'
Luckey founded Anduril in 2017 after he sold his VR headset company Oculus to Meta (then Facebook) in 2014 for $2.3 billion. The company primarily specializes in developing and deploying various forms of military drones.
'I am glad to be working with Meta once again,' Luckey said in a statement. 'Of all the areas where dual-use technology can make a difference for America, this is the one I am most excited about. My mission has long been to turn warfighters into technomancers, and the products we are building with Meta do just that.'
Meta CEO Mark Zuckerberg began making a push into military and defense products when he opened up the company's Llama AI models for use by the government and contractors working on national defense projects.
According to the companies, the new system will include an AR/VR interface that works with Anduril's Lattice analytics platform to feed soldiers information about the world around them.
'The world is entering a new era of computing that will give people access to limitless intelligence and extend their senses and perception in ways that have never been possible before,' Meta CTO Andrew Bosworth said. 'Our national security benefits enormously from American industry bringing these technologies to life.'
Meta isn't the first Big Tech company to enter the military space. Amazon (AMZN), Google (GOOG, GOOGL), and Microsoft (MSFT) provide cloud services to the US military. Microsoft also turned over work on Army's own AR/VR system to Anduril in February. Microsoft will also power the company's cloud services related to "IVAS and Anduril AI technologies."
Email Daniel Howley at dhowley@yahoofinance.com. Follow him on X/Twitter at @DanielHowley.

Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

From Cost Per Click To Cost Per Outcome: How AI Flips Optimization
From Cost Per Click To Cost Per Outcome: How AI Flips Optimization

Forbes

time33 minutes ago

  • Forbes

From Cost Per Click To Cost Per Outcome: How AI Flips Optimization

Sergii Denysenko, CEO at MGID. Today's advertisers care about one thing: outcomes. As budgets tighten, media fragments and brand loyalty wanes, advertising channels must prove their ability to uplift tangible business metrics. However, optimizing toward outcomes is notoriously challenging, with a huge number of variables affecting whether spending on an ad achieves its desired result. Optimizing for outcomes requires the ability to predict the future—an impossibility, until now. Cause And Effect? Try Effect And Cause AI-powered bidding strategies reverse typical programmatic inputs and outputs. Instead of adjusting costs and hoping for conversions, machine learning models begin with the desired conversion and work backward to identify the optimal bidding strategy. This is made possible through the predictive capabilities of machine learning models. These models ingest and analyze signals from hundreds of sources—from ad placement and local time of day to device type, audience behaviors and contextual relevance—to identify complex patterns of cause and effect that can be used to gauge the likelihood of a conversion. Whereas traditional cost-per-click (CPC) models treat every impression or click as equal until proven otherwise, AI-powered cost-per-acquisition (CPA) models dynamically weigh the value of each opportunity in real time. If the model determines that a particular user, at a particular moment, is significantly more likely to convert, it will automatically adjust the bid upward, even if that click costs more. You might worry that such upward revisions would rapidly exhaust budgets, but such models balance out instances of increased spend by, conversely, devaluing or stripping out low-probability impressions. This way, budgets are preserved while efficiency is significantly boosted. This real-time recalibration allows for automated, high-frequency optimization at a scale that no human team could feasibly manage. Not that humans are—or ever should be—removed entirely from the process. You as the advertiser still remain in control of all the cost and key performance indicator levers that are important to you, while engineers work behind the scenes to monitor the algorithm's performance and correct for edge cases. After all, no model is 100% accurate in 100% of situations. The end goal of such technological developments is not to remove human oversight and strategy from the process, but to significantly expand the reach and effectiveness of human decisions. AI without people steering its course is like an orchestra without a conductor. For all the revolutionary technology chugging away in the back end, the user experience is largely unchanged. You're still billed on a CPC basis and can set the same thresholds you're used to, while the underlying engine makes decisions based on conversion potential. It's as much a mindset evolution as a technical one: Trust the model, feed it accurate data and it will reward you with better outcomes and less manual tinkering. Outcome Optimization Ripples Across The Open Web Should it achieve its full potential, AI-driven optimization will fundamentally alter the bloated, top-heavy digital advertising ecosystem of recent years. For one, AI levels the playing field. Advanced algorithmic optimization techniques were the domain of big tech giants such as Google and Meta, which had a massive advantage due to the reams of data exclusive to their walled gardens, derived from platforms meticulously designed to extract as many user signals as possible. Now, we all can tap into similarly sophisticated predictive technologies across the connected world, diversifying our channel distribution and reach without inflating our budgets. Smaller brands and advertisers without sprawling analytics departments can pursue outcome-based advertising with precision and scalability on whichever channels they choose, rather than default to the path of least resistance offered by big tech. Over time, this will redefine the concept of value in media buying. Under CPC regimes, cheap inventory could still be an attractive gamble, even if it doesn't convert. With CPA models in control, low-quality placements will wither from lack of spend, clearing a path for high-quality publishers whose audiences are more engaged and conversion-prone. Outcome optimization serves as a filter, reallocating spend away from made-for-advertising (MFA) sites and toward genuinely valuable environments. There are challenges and limitations, of course. AI models require both sufficient data and time to learn. You must set realistic CPA targets—despite appearances, AI is not magic—and give campaigns room to breathe. Impatient tweaking or overly aggressive benchmarks can 'confuse' the model, leading to misfires or underdelivery. But these are bumps in the road that will eventually be smoothed out. As more campaigns feed these algorithms, the systems will grow more resilient and their benefits will become more widely accessible. AI's capacity to evolve is perhaps what's most exciting about it. Digital advertising often feels like laying the tracks in front of a speeding train that veers wildly through market fluctuations. With AI's ability to adapt incredibly quickly, advertisers can step back and gain a wider perspective. At a time of rapid and tumultuous change in digital media (ironically, much of it caused by AI itself), it's more important than ever to be able to look to the horizon rather than be bogged down by busywork. Forbes Agency Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify?

The ROI Of AI In B2B Sales
The ROI Of AI In B2B Sales

Forbes

time33 minutes ago

  • Forbes

The ROI Of AI In B2B Sales

Alyssa Merwin is Vice President of Global Sales Solutions at LinkedIn. If you lead a sales team today, then you know the game has changed. Cold outreach is getting colder. Buyers expect relevance, speed and trust from the start. The sellers who succeed are the ones who show up informed, prepared and real, not just persuasive. And yet, teams are under constant pressure to do more with less. Quotas are climbing, but headcount might not be. And sales representatives are spending only 10 hours a week actually selling. As a sales leader, I've come to understand that my job isn't just to drive revenue—it's to remove barriers to selling. That's where AI can play a transformational role, giving your team the tools to prioritize better, personalize faster and spend more time doing what actually moves the needle: building relationships and closing deals. Sales organizations that have been early adopters of AI are already seeing the upside. We know this from studying what top sellers do differently—and increasingly, we're seeing AI as a major differentiator. According to our global study with Ipsos, sellers who exceed their quota are 2.5 times more likely to be using AI every day, and 68% of sales organizations investing in AI say it's already helping them close more deals. But with AI showing up in every corner of the tech stack, the real question becomes: Where does it drive the most value, and how can you make sure it's delivering ROI? 3 Areas Where AI Is Moving The Needle Two years after generative AI burst onto the scene, we're seeing a dividing line between teams that are experimenting and those that are transforming. From both our research and my conversations with sales leaders across industries, three use cases stand out: We've all felt how much harder it's become to identify the right buyers. Buying committees are larger, buyer roles are dynamic and one wrong assumption can mean a deal lost before it begins. AI helps surface the right people sooner—mapping stakeholders, surfacing job changes and recommending decision makers based on real-time signals. The result? Sellers who use AI for research reports save 1.5 hours per week and generate higher-quality leads. It's not all about saving time—it's about having elevated conversations with the people that matter. We know that personalization works, but it's historically been time-consuming. AI changes that. Generative tools can craft messages tailored to a buyer's role, company news and even shared connections. Our data shows that sellers using AI to personalize outreach report a 28% increase in response rates. And that's not just better email—it's better pipeline. No rep gets into sales to spend six hours a week updating records. AI-enabled CRM integrations can now handle that lift automatically, updating data, flagging gaps and offering real-time insights that help sellers stay focused and deals stay on track. On average, we found that AI can shave a full week off the sales cycle. That translates to a serious competitive advantage. The Time To Lead Is Now We're still at the beginning of this AI transformation. According to our research, 98% of sales execs say they plan to increase investment in AI this year. That's encouraging. But tech investment without a clear strategy—and high-quality data—won't get us far. Sales leaders need to think not just about tools but about how AI becomes part of the daily workflow. How do we support our teams in building AI fluency? And how can we use AI to strengthen—not replace—the human connection that makes great selling possible? If there's one thing I'd say to every sales leader right now, it's this: Start with trust. Build your AI strategy on a data foundation that's accurate, real time and truly representative of your buyers. That's where the ROI lives—and that's how we set our teams up to win. Forbes Business Development Council is an invitation-only community for sales and biz dev executives. Do I qualify?

Why AI Maturity Is Declining And Why CEOs Need To Reinvent Their Businesses
Why AI Maturity Is Declining And Why CEOs Need To Reinvent Their Businesses

Forbes

time34 minutes ago

  • Forbes

Why AI Maturity Is Declining And Why CEOs Need To Reinvent Their Businesses

A goldfish jumping into the next business model CEOs selected one word to describe their companies' focus in an era of generative and agentic AI. The word that rose above the rest was 'innovation' according to IDC, a word that underscored the bold vision CEOs will need to achieve their goal of reinventing company business models in the next three-to-five years. To do so, organizations will need to invest in AI vision, fluency, and maturity at unprecedented speed and scale. Driving AI-powered growth will require existential corporate transformation, and historically, companies have a poor track record here. Axios reported that a staggering 94% of c-suite executives say they're not satisfied with their current AI solutions. So how are companies progressing in their AI maturity? According to new ServiceNow research, AI maturity regressed by 25.5% between 2024 to 2025. As AI technologies evolve, how is it possible for organizational AI maturity to regress? ServiceNow published its second annual AI Index report, which measures overall AI maturity based on a score between 0 and 100. Oxford Economics and ServiceNow surveyed just under 4,500 executives worldwide and measured organizational AI performance across five pillars of AI maturity: 1) AI strategy and leadership, 2) workflow integration, 3) talent and workforce, 4) AI governance, and 5) realizing value in AI investment. Last year's maturity scores were low across the board. This year's scores are lower still. In 2024, the average AI maturity score was 44 out of 100. In 2025, the average score is 35. It might sound counterintuitive, but perhaps the decline in maturity is actually what CEOs need to achieve their goals of business model transformation. Richard Murphy, Head of Global Thought Leadership Research at ServiceNow, shared his thoughts on the slip back. 'These sharp drops suggest that AI innovation is outpacing organizations' capacity to deploy AI effectively at scale,' he observed. The Agents are Coming, The Agents are Coming 2025 has been called the 'year of AI agents' and early adoption of AI agents did in fact play into ServiceNow's research. But the rise of AI agents, as yet another wave of complex software, forced executives to distinguish between signal from hype, adding to their sense of overwhelm. Since the debut of ChatGPT in November 2022, companies around the world had to respond without having a generative AI plan at the ready. Then the ecosystem exploded with infrastructure providers, foundation models, cloud service providers, app developers, open-source communities driving the expansion of services and solutions at break-neck speed. Then came waves of AI reasoning, AI agents, and agentic AI. All the while, headlines were overwhelming with stories about superintelligence and AGI (artificial general intelligence), and the rise of AI native startups who were seemingly employing AI-first approaches to new org designs. Added up, Murphy's assessment of why companies moved backwards makes sense. The Big Three Areas Inhibiting AI Forward Initiatives ServiceNow's research showed that despite maturity falling year-over-year, 82% of companies expect to increase AI investment next year. But to make progress after a year of reversal, it helps to understand the challenges companies face in gaining headway. Governance: Many enterprises are pushing to drive AI adoption, but in doing so, governance must be in place to protect the organization, employees, and stakeholders while empowering and enabling their experimentation. But only 44% of companies have made significant progress formalizing data governance, privacy, and compliance. This means a majority of enterprises do not have a designated team that drafts AI policies, mitigates AI risks, and focuses on responsible AI use. AI Skills: AI requires not only new skills but also new mindsets. The goal is to help employees (and customers) learn what they can do with AI as a tool to optimize, enhance, and scale their existing work, while eliminating repetition, complexity, and the mundane. Additionally, the opportunity to help people explore how to collaborate with AI, not as a tool, but as a superpower, to do what they couldn't do yesterday, and unlock or create new value, is also necessary to drive AI innovation. However, organizations are still struggling to articulate the necessary skills and identify the right talent to get the job done (let alone reimagine the jobs to be done). Of the companies surveyed, 64% are still in the process of identifying skills needed to implement their AI strategy. Measurement: One of the main reasons for AI maturity declining YoY is that AI is changing faster that executives can develop strategies to experiment and evolve with it. Leaders find it elusive to connect business strategy to AI strategy. As a result, pilots tend to focus on discreet areas where impact and ROI are impossible to measure. For instance, 55% of companies have rolled out more than 100 AI use cases. But only 19% say those efforts are driving meaningful business outcomes. And just 29% have defined a set of metrics to measure return on AI transformation. Governance, AI skills, and measurement are instrumental in scaling and AI. But the research also highlights a delta between most companies operating without a strategic AI vision and defined state for what success looks like. Of course this affects scores around governance, AI skills, and measurement. It also highlights an opportunity to grow toward the CEO aspiration of achieving business model transformation. ServiceNow's Murphy observed, 'There's a vision gap at the top, as leaders struggle to set audacious AI goals while also driving measurable results.' The AI Pacesetter Roadmap Offers a Path Forward Though companies took steps backward this year in overall AI maturity, an elite cohort of companies, ServiceNow calls AI Pacesetters, are moving backward and forward differently. This group represents just 18% of all companies in the study but lead with an average score nine points higher than their peers (44 out of 100). While still early in their developments, a Pacesetter roadmap is taking shape, which can help other companies close the emerging gap. Leading enterprises aren't just using AI as smarter tools, they're retooling their business starting with leadership. Leaders are building cultures of trust, enablement, experimentation, and empowered decision-making. ServiceNow's research identified five categories where AI Pacesetters were gaining traction. This forms a roadmap to guide other companies in their strategy development and decision-making. AI Pacesetters: Pacesetters operate with a clear, shared vision in where AI can deliver practical and transformational impact. They look beyond the digitization that largely modernized yesterday's processes to explore AI-first capabilities, outcomes, business models, products, services, and experiences. AI Vision AI Pacesetters use AI as a strategic flywheel. Nearly 60% use AI to collaborate on leadership insights compared to 23% of others. They consider outcomes as they weigh investments. For instance, 49% of AI Pacesetters operate with a defined set of metrics vs. 25% of their peers. And 53% of AI Pacesetters are establishing AI innovation centers to evaluate AI solutions against business vision and goals and to explore opportunities that deliver practical and transformation outcomes. Going back to the days of digital transformation, ambitious companies explored how to rewire the organization digitally to break down silos, integrate data, and connect people and work across the enterprise. Yet according to Gartner, just 35% of board directors reported achieving or that they were on track to achieving their digital transformation goals. A similar pattern is playing out in an era of AI business transformation. ServiceNow found that only 30% of organizations have integrated workflows across business functions. This means most companies are exploring AI within confined use cases, which limits impact, prevents data and workflow integration, and confines AI to one part of the business. Take a Platform Approach Over half of AI Pacesetters (56%) have made significant progress toward connecting data and operational silos, compared to 41% of others. ServiceNow research found that 25% AI Pacesetters vs 11% have even invented new workflows across business functions to improve collaboration between humans and AI. Becoming an AI-first business means rethinking workflows across the enterprise so that AI, data, processes, and people connect and optimize work across every operation Focusing on the future means that you'll need the right talent and skills as AI evolves. Half of all Pacesetters (50%) say they have the right mix of talent to execute on their AI strategy, compared to 29% of others. Focus on Talent Seventy percent of Pacesetters say they promote cross-functional alignment across the enterprise. Seventy-one percent empower teams to make their own decisions about how AI can improve their work. Training and upskilling is critical, not just today, but with every wave of AI, which is why 80% of AI Pacesetters are actively investing in their people vs. 54% of others. Organizations need guardrails, processes, and leadership around AI experimentation. AI (and data) governance mitigates risk while enabling employees to explore AI-enabled approaches to their work. Here, AI Pacesetters are leading the way. They take a more proactive stance on governance. Prioritize AI Governance A majority (72%) have already assessed AI applications for risk and worked to understand data privacy requirements, compared to under half of others. Sixty-three percent have made significant progress addressing data governance and security issues with AI- specific policies, compared to just 42% of others. And 57% have implemented policies for data governance, privacy, lawfulness, and compliance. I had an opportunity to meet with a company recently that emphasized the importance of establishing a cross-functional center of excellence to accelerate and expand governance efforts. For extra credit, the company also stood-up an AI Advisory Council consisting of senior leaders and experts. These groups ensured the balance of guardrails, risk mitigation, and innovation with the responsible, safe and ethical use of emerging technologies and standards foundational to scaling AI investments. AI agents and Agentic AI is the latest trend that is both overwhelming AI decision makers but also offering unimagined opportunities for efficiency, scale, and also to completely reinvent work to accelerate AI business transformation. Taking a platform approach can enable organizations to integrate workflows and manage data, oversee their AI strategy from a single control tower, and identify and optimize quick wins. Doing so allows companies to deploy agentic AI, learn, and scale faster than competitors. Embrace Agentic AI For example, AI Pacesetters prioritized agentic AI pilots to improve experiences, increasing operational efficiency and productivity, and innovation. As a result, these ambitious companies reported big gains in improved customer experiences (58% Pacesetters vs 36% Others), increases in efficiency and productivity (57% vs 30%), and the ability to innovate faster (52% vs 32%). Eighty-three percent of AI Pacesetters also reported increases in gross margins compared with 64% of others. AI Pacesetters are Already Breaking Away from the Status Quo The rapid evolution of AI technology and headlines has been relentless since the launch of OpenAI's ChatGPT in 2022. And there are no signs of slowing down. It's understandable to see AI maturity step back. Legendary basketball coach John Wooden once asked, 'If you don't have time to do it right, when will you have time to do it over?' It's a reminder that amid the rush to adopt AI, slowing down to build the right foundations may be the most strategic move a company can make. The drop in AI maturity isn't a setback. It's a reset. As AI advances faster than organizations can adapt, leaders have a critical window to step back, refocus, and rebuild with purpose. The companies pulling ahead aren't chasing every new tool—they're aligning strategy, talent, governance, and operations to unlock real value. To move accelerate AI business transformation, organizations must define a clear AI vision, invest in skills and governance, integrate AI across workflows, and focus on measurable outcomes. This is the moment to shift from experimentation to transformation. The future will belong to leaders who treat AI not as a side project but as the foundation of their next business model.

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into a world of global content with local flavor? Download Daily8 app today from your preferred app store and start exploring.
app-storeplay-store