The Tea app has had a rough week. It’s not an unfamiliar story: Unsecured Firebase databases were left exposed to the Internet without any authentication. What makes this story particularly …read more

Perplexity this week released Perplexity Labs, a new tool for Pro users that can craft reports, spreadsheets, dashboards, and visual representations, to meet users’ increased demand for AI productivity tools with greater autonomy and ever more sophisticated capabilities. The platform, a rival to Anthropic Claude, OpenAI’s ChatGPT, and Google Gemini, can even work on its own for 10 minutes (or more) as it reasons through complicated assignments. “Labs underscores a broader shift toward multi-agent AI systems that plan, execute, and refine full workflows,” said Thomas Randall, research lead for AI at Info-Tech Research Group. Designed to handle more complex assignments Perplexity launched Perplexity Search, its proprietary search engine, in December 2022, just after ChatGPT dropped, and earlier this year released Deep Research (now to be rebranded as Research), which scours the web, reads papers, reasons through materials, and creates comprehensive reports for users. The company says that Perplexity Labs is like “having a team” that can bring projects from ideation to reality. The platform creates reports, spreadsheets, dashboards and simple web apps. It can perform at least 10 minutes of self-supervised work, uses web browsing, writes and executes code to handle tasks like organizing data or applying formulas, and can create charts and images. “In some respects, this is a continuation of Perplexity’s original capabilities as an AI-driven search engine that provides deeper answers,” said Hyoun Park, CEO and chief analyst at Amalgam Insights. Indeed, Perplexity explained that Labs was designed to handle more complex assignments than Deep Research. “While Deep Research remains the fastest way to obtain comprehensive answers to in-depth questions —  typically within 3 or 4 minutes — Labs is designed to invest more time (10  minutes or longer) and leverage additional tools, such as advanced file generation and mini-app creation,” Perplexity wrote in a blog post. “This expanded capability empowers you to develop a broader array of deliverables for your projects.” With its longer research workflow, Perplexity Labs can generate spreadsheets, visual representations, and high-quality reports, the company said. It iteratively searches through hundreds of sources, reasons about that data, and refines its approach as it gets deeper into a project, similar to the way in which a human researcher might approach a new area of study. To create interactive dashboards without the need for coding expertise or external development tools like Ploty and Dash, users just describe what they’re visualizing in natural language, and Labs will generate it in real-time. Dashboards could, for instance, visualize business finances or other complex datasets, incorporating clickable elements to allow non-technical users to quickly act on insights. In one example from the blog, Perplexity prompted Labs from the position of a leader at a tech consulting firm looking to create a potential customer list. It specified that it wanted to partner with US B2B companies in seed, series A, or series B stages, and asked Labs to list 20 relevant companies and include key details including contact information. Labs compiled a comprehensive dataset of potential customers, organizing them by stage (A, B, or seed) and identified their core focus, intended customers, and funding to date. The platform cited links from Forbes, YCombinator, and Exploding Topics that it had used as sources. When further prompted, it crafted introductory emails to the CEOs of the series A startups. To simplify workflows, Labs arranges generated files in a dedicated tab for easy access, supports integration with other tools such as Google Sheets, and allows users to pull out and format citations to bring credibility to its research. Finished materials can be exported as PDFs or documents, or converted into a shareable Perplexity Page. Pro subscribers ($20 a month) can now work with Labs on Web, iOS, and Android; Mac and Windows apps are coming soon. A good fit for enterprise users? This new capability joins an increasingly competitive space, as users look for AI productivity tools that are ever-more performant and can handle more and more tasks autonomously. Park pointed out that Perplexity Labs is a response to tools and models such as OpenAI o1-pro (launched in March), Claude Opus 4 (released in May), and Google’s recent Flow and Firebase announcements. “There is a massive Hunger Games in the AI world right now,” said Park. “Every major vendor is ferociously trying to one-up each other in providing more functionality, either in a native model or with an agency of AI agents designed to work together and create digital assets such as documents, apps, and videos.” However, Perplexity Labs does provide differentiation from other providers in the market, Info-Tech’s Randall noted. In particular, Perplexity is betting that users will prefer a “low-cost, open, tool-agnostic sandbox” for web crawling, code execution, and the creation of finished artifacts including mini web apps. “These capabilities cannot yet be found in other enterprise platforms, such as Microsoft or Google offerings,” said Randall. But enterprises should approach Perplexity Labs with a governance-first mindset, he emphasized. Assets live in Perplexity’s cloud and, for now, lack the private data grounding and compliance controls that CIOs expect, and that they find in tools such as Microsoft Copilot or Google Gemini. From an enterprise perspective, Park noted, the biggest challenge is that every asset-creating model and agent is “still opaque” when it comes to understanding the assumptions, training, and reliability of assumptions used to create a document or app. He compared it to the way the iPhone bypassed BlackBerry and Windows through “sheer consumer delight.” “At some point, AI vendors seeking serious business usage will need to provide more transparency and governance tools to the business world, just as mobile device management and mobile security solutions eventually came to the iPhone,” said Park. Otherwise, businesses may be compelled to build their own clunky but secure versions of Perplexity Labs, “which are guaranteed to be less accurate and useful just [based on] the history of business apps trying to imitate viral consumer apps,” he said.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More After three hours at Google’s I/O 2025 event last week in Silicon Valley, it became increasingly clear: Google is rallying its formidable AI efforts – prominently branded under the Gemini name but encompassing a diverse range of underlying model architectures and research – with laser focus. It is releasing a slew of innovations and technologies around it, then integrating them into products at a breathtaking pace. Beyond headline-grabbing features, Google laid out a bolder ambition: an operating system for the AI age – not the disk-booting kind, but a logic layer every app could tap – a “world model” meant to power a universal assistant that understands our physical surroundings, and reasons and acts on our behalf. It’s a strategic offensive that many observers may have missed amid the bamboozlement of features.  On one hand, it’s a high-stakes strategy to leapfrog entrenched competitors. But on the other, as Google pours billions into this moonshot, a critical question looms: Can Google’s brilliance in AI research and technology translate into products faster than its rivals, whose edge has its own brilliance: packaging AI into immediately accessible and commercially potent products? Can Google out-maneuver a laser-focused Microsoft, fend off OpenAI’s vertical hardware dreams, and, crucially, keep its own search empire alive in the disruptive currents of AI? Google is already pursuing this future at dizzying scale. Pichai told I/O that the company now processes 480 trillion tokens a month – 50× more than a year ago – and almost 5x more than the 100 trillion tokens a month that Microsoft’s Satya Nadella said his company processed. This momentum is also reflected in developer adoption, with Pichai saying that over 7 million developers are now building with the Gemini API, representing a five-fold increase since the last I/O, while Gemini usage on Vertex AI has surged more than 40 times. And unit costs keep falling as Gemini 2.5 models and the Ironwood TPU squeeze more performance from each watt and dollar. AI Mode (rolling out in the U.S.) and AI Overviews (already serving 1.5 billion users monthly) are the live test beds where Google tunes latency, quality, and future ad formats as it shifts search into an AI-first era. Source: Google I/O 20025 Google’s doubling-down on what it calls “a world model” – an AI it aims to imbue with a deep understanding of real-world dynamics – and with it a vision for a universal assistant – one powered by Google, and not other companies – creates another big tension: How much control does Google want over this all-knowing assistant, built upon its crown jewel of search? Does it primarily want to leverage it first for itself, to save its $200 billion search business that depends on owning the starting point and avoiding disruption by OpenAI? Or will Google fully open its foundational AI for other developers and companies to leverage – another  segment representing a significant portion of its business, engaging over 20 million developers, more than any other company?  It has sometimes stopped short of a radical focus on building these core products for others with the same clarity as its nemesis, Microsoft. That’s because it keeps a lot of core functionality reserved for its cherished search engine. That said, Google is making significant efforts to provide developer access wherever possible. A telling example is Project Mariner. Google could have embedded the agentic browser-automation features directly inside Chrome, giving consumers an immediate showcase under Google’s full control. However, Google followed up by saying Mariner’s computer-use capabilities would be released via the Gemini API more broadly “this summer.” This signals that external access is coming for any rival that wants comparable automation. In fact, Google said partners Automation Anywhere and UiPath were already building with it. Google’s grand design: the ‘world model’ and universal assistant The clearest articulation of Google’s grand design came from Demis Hassabis, CEO of Google DeepMind, during the I/O keynote. He stated Google continued to “double down” on efforts towards artificial general intelligence (AGI). While Gemini was already “the best multimodal model,” Hassabis explained, Google is working hard to “extend it to become what we call a world model. That is a model that can make plans and imagine new experiences by simulating aspects of the world, just like the brain does.”  This concept of ‘a world model,’ as articulated by Hassabis, is about creating AI that learns the underlying principles of how the world works – simulating cause and effect, understanding intuitive physics, and ultimately learning by observing, much like a human does. An early, perhaps easily overlooked by those not steeped in foundational AI research, yet significant indicator of this direction is Google DeepMind’s work on models like Genie 2. This research shows how to generate interactive, two-dimensional game environments and playable worlds from varied prompts like images or text. It offers a glimpse at an AI that can simulate and understand dynamic systems. Hassabis has developed this concept of a “world model” and its manifestation as a “universal AI assistant” in several talks since late 2024, and it was presented at I/O most comprehensively – with CEO Sundar Pichai and Gemini lead Josh Woodward echoing the vision on the same stage. (While other AI leaders, including Microsoft’s Satya Nadella, OpenAI’s Sam Altman, and xAI’s Elon Musk have all discussed ‘world models,” Google uniquely and most comprehensively ties this foundational concept to its near-term strategic thrust: the ‘universal AI assistant.) Speaking about the Gemini app, Google’s equivalent to OpenAI’s ChatGPT, Hassabis declared, “This is our ultimate vision for the Gemini app, to transform it into a universal AI assistant, an AI that’s personal, proactive, and powerful, and one of our key milestones on the road to AGI.”  This vision was made tangible through I/O demonstrations. Google demoed a new app called Flow – a drag-and-drop filmmaking canvas that preserves character and camera consistency – that leverages Veo 3, the new model that layers physics-aware video and native audio. To Hassabis, that pairing is early proof that ‘world-model understanding is already leaking into creative tooling.’ For robotics, he separately highlighted the fine-tuned Gemini Robotics model, arguing that ‘AI systems will need world models to operate effectively.” CEO Sundar Pichai reinforced this, citing Project Astra which “explores the future capabilities of a universal AI assistant that can understand the world around you.” These Astra capabilities, like live video understanding and screen sharing, are now integrated into Gemini Live. Josh Woodward, who leads Google Labs and the Gemini App, detailed the app’s goal to be the “most personal, proactive, and powerful AI assistant.” He showcased how “personal context” (connecting search history, and soon Gmail/Calendar) enables Gemini to anticipate needs, like providing personalized exam quizzes or custom explainer videos using analogies a user understands (e.g., thermodynamics explained via cycling. This, Woodward emphasized, is “where we’re headed with Gemini,” enabled by the Gemini 2.5 Pro model allowing users to “think things into existence.”  The new developer tools unveiled at I/O are building blocks. Gemini 2.5 Pro with “Deep Think” and the hyper-efficient 2.5 Flash (now with native audio and URL context grounding from Gemini API) form the core intelligence. Google also quietly previewed Gemini Diffusion, signalling its willingness to move beyond pure Transformer stacks when that yields better efficiency or latency. Google is stuffing these capabilities into a crowded toolkit: AI Studio and Firebase Studio are core starting points for developers, while Vertex AI remains the enterprise on-ramp. The strategic stakes: defending search, courting developers amid an AI arms race This colossal undertaking is driven by Google’s massive R&D capabilities but also by strategic necessity. In the enterprise software landscape, Microsoft has a formidable hold, a Fortune 500 Chief AI Officer told VentureBeat, reassuring customers with its full commitment to tooling Copilot. The executive requested anonymity because of the sensitivity of commenting on the intense competition between the AI cloud providers. Microsoft’s dominance in Office 365 productivity applications will be exceptionally hard to dislodge through direct feature-for-feature competition, the executive said. Google’s path to potential leadership – its “end-run” around Microsoft’s enterprise hold – lies in redefining the game with a fundamentally superior, AI-native interaction paradigm. If Google delivers a truly “universal AI assistant” powered by a comprehensive world model, it could become the new indispensable layer – the effective operating system – for how users and businesses interact with technology. As Pichai mused with podcaster David Friedberg shortly before I/O, that means awareness of physical surroundings. And so AR glasses, Pichai said, “maybe that’s the next leap…that’s what’s exciting for me.” But this AI offensive is a race against multiple clocks. First, the $200 billion search-ads engine that funds Google must be protected even as it is reinvented. The U.S. Department of Justice’s monopolization ruling still hangs over Google – divestiture of Chrome has been floated as the leading remedy. And in Europe, the Digital Markets Act as well as emerging copyright-liability lawsuits could hem in how freely Gemini crawls or displays the open web. Finally, execution speed matters. Google has been criticized for moving slowly in past years. But over the past 12 months, it became clear Google had been working patiently on multiple fronts, and that it has paid off with faster growth than rivals. The challenge of successfully navigating this AI transition at massive scale is immense, as evidenced by the recent Bloomberg report detailing how even a tech titan like Apple is grappling with significant setbacks and internal reorganizations in its AI initiatives. This industry-wide difficulty underscores the high stakes for all players. While Pichai lacks the showmanship of some rivals, the long list of enterprise customer testimonials Google paraded at its Cloud Next event last month – about actual AI deployments – underscores a leader who lets sustained product cadence and enterprise wins speak for themselves.  At the same time, focused competitors advance. Microsoft’s enterprise march continues. Its Build conference showcased Microsoft 365 Copilot as the “UI for AI,” Azure AI Foundry as a “production line for intelligence,” and Copilot Studio for sophisticated agent-building, with impressive low-code workflow demos (Microsoft Build Keynote, Miti Joshi at 22:52, Kadesha Kerr at 51:26). Nadella’s “open agentic web” vision (NLWeb, MCP) offers businesses a pragmatic AI adoption path, allowing selective integration of AI tech – whether it be Google’s or another competitor’s – within a Microsoft-centric framework. OpenAI, meanwhile, is way out ahead with the consumer reach of its ChatGPT product, with recent references by the company to having 600 million monthly users, and 800 million weekly users. This compares to the Gemini app’s 400 million monthly users. And in December, OpenAI launched a full-blown search offering, and is reportedly planning an ad offering – posing what could be an existential threat to Google’s search model. Beyond making leading models, OpenAI is making a provocative vertical play with its reported $6.5 billion acquisition of Jony Ive’s IO, pledging to move “beyond these legacy products” – and hinting that it was launching a hardware product that would attempt to disrupt AI just like the iPhone disrupted mobile. While any of this may potentially disrupt Google’s next-gen personal computing ambitions, it’s also true that OpenAI’s ability to build a deep moat like Apple did with the iPhone may be limited in an AI era increasingly defined by open protocols (like MCP) and easier model interchangeability. Internally, Google navigates its vast ecosystem. As Jeanine Banks, Google’s VP of Developer X, told VentureBeat serving Google’s diverse global developer community means “it’s not a one size fits all,” leading to a rich but sometimes complex array of tools – AI Studio, Vertex AI, Firebase Studio, numerous APIs. Meanwhile, Amazon is pressing from another flank: Bedrock already hosts Anthropic, Meta, Mistral and Cohere models, giving AWS customers a pragmatic, multi-model default. For enterprise decision-makers: navigating Google’s ‘world model’ future Google’s audacious bid to build the foundational intelligence for the AI age presents enterprise leaders with compelling opportunities and critical considerations: Move now or retrofit later: Falling a release cycle behind could force costly rewrites when assistant-first interfaces become default. Tap into revolutionary potential: For organizations seeking to embrace the most powerful AI, leveraging Google’s “world model” research, multimodal capabilities (like Veo 3 and Imagen 4 showcased by Woodward at I/O), and the AGI trajectory promised by Google offers a path to potentially significant innovation. Prepare for a new interaction paradigm: Success for Google’s “universal assistant” would mean a primary new interface for services and data. Enterprises should strategize for integration via APIs and agentic frameworks for context-aware delivery. Factor in the long game (and its risks): Aligning with Google’s vision is a long-term commitment. The full “world model” and AGI are potentially distant horizons. Decision-makers must balance this with immediate needs and platform complexities. Contrast with focused alternatives: Pragmatic solutions from Microsoft offer tangible enterprise productivity now. Disruptive hardware-AI from OpenAI/IO presents another distinct path. A diversified strategy, leveraging the best of each, often makes sense, especially with the increasingly open agentic web allowing for such flexibility. These complex choices and real-world AI adoption strategies will be central to discussions at VentureBeat’s Transform 2025 next month. The leading independent event brings enterprise technical decision-makers together with leaders from pioneering companies to share firsthand experiences on platform choices – Google, Microsoft, and beyond – and navigating AI deployment, all curated by the VentureBeat editorial team. With limited seating, early registration is encouraged. Google’s defining offensive: shaping the future or strategic overreach? Google’s I/O spectacle was a strong statement: Google signalled that it intends to architect and operate the foundational intelligence of the AI-driven future. Its pursuit of a “world model” and its AGI ambitions aim to redefine computing, outflank competitors, and secure its dominance. The audacity is compelling; the technological promise is immense. The big question is execution and timing. Can Google innovate and integrate its vast technologies into a cohesive, compelling experience faster than rivals solidify their positions? Can it do so while transforming search and navigating regulatory challenges? And can it do so while focused so broadly on both consumers and business – an agenda that is arguably much broader than that of its key competitors? The next few years will be pivotal. If Google delivers on its “world model” vision, it may usher in an era of personalized, ambient intelligence, effectively becoming the new operational layer for our digital lives. If not, its grand ambition could be a cautionary tale of a giant reaching for everything, only to find the future defined by others who aimed more specifically, more quickly.  Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. 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A new global phishing threat called "Meta Mirage" has been uncovered, targeting businesses using Meta's Business Suite. This campaign specifically aims at hijacking high-value accounts, including those managing advertising and official brand pages. Cybersecurity researchers at CTM360 revealed that attackers behind Meta Mirage impersonate official Meta communications, tricking users into handing over sensitive details like passwords and security codes (OTP). The scale of this operation is alarming. Researchers have already identified over 14,000 malicious URLs, a concerning majority of which—nearly 78%—were not blocked by browsers at the time the report was published. Cybercriminals cleverly hosted fake pages leveraging trusted cloud platforms like GitHub, Firebase, and Vercel, making it harder to spot the scams. This method aligns closely with recent findings from Microsoft, which highlighted similar abuse of cloud hosting services to compromise Kubernetes applications, emphasizing how attackers frequently leverage trusted platforms to evade detection. The attackers deploy fake alerts about policy violations, account suspensions, or urgent verification notices. These messages, sent via email and direct messages, look convincing because they mimic official communications from Meta, often appearing urgent and authoritative. This tactic mirrors techniques observed in the recent Google Sites phishing campaign, which used authentic-looking Google-hosted pages to deceive users. Two main methods are being used: Credential Theft: Victims enter passwords and OTPs into realistic-looking fake websites. The attackers deliberately trigger fake error messages, causing users to re-enter their details, ensuring accurate and usable stolen information. Cookie Theft: Scammers also steal browser cookies, allowing them continued access to compromised accounts even without passwords. These compromised accounts don't just affect individual businesses—they're often exploited to run malicious advertising campaigns, further amplifying damage, similar to tactics observed in the PlayPraetor malware campaign that hijacked social media accounts for fraudulent ad distribution. CTM360's report also outlines a structured and calculated approach used by the attackers to maximize effectiveness. Victims are initially contacted with mild, non-alarming notifications that progressively escalate in urgency and severity. Initial notices might mention generic policy violations, while subsequent messages warn of immediate suspensions or permanent deletion of accounts. This incremental escalation induces anxiety and urgency, driving users to act quickly without thoroughly verifying the authenticity of these messages. To protect against this threat, CTM360 recommends: Only use official devices to manage business social media accounts. Use separate business-only email addresses. Enable Two-Factor Authentication (2FA). Regularly review account security settings and active sessions. Train staff to recognize and report suspicious messages. This widespread phishing campaign underscores the importance of vigilance and proactive security measures to protect valuable online assets. Found this article interesting? This article is a contributed piece from one of our valued partners. Follow us on Twitter  and LinkedIn to read more exclusive content we post.