AI-driven memory crunch jolts India’s smartphone market
India's smartphone slowdown highlights how the AI boom is reshaping consumer electronics, from pricing and demand to corporate strategy.
Gemini can now take notes in Google Meet for Google AI Pro and Ultra subscribers.
Google Meet's "Take notes for me" feature is available to Google AI Pro and Ultra subscribers in select languages.
A scorecard for the AI age
Sarah Friar, CFO of OpenAI, introduces a practical AI scorecard to measure ROI through useful work, cost per successful task, dependability, and return on compute.
Xi offers AI olive branch to the world, calling for ‘symphony of global cooperation’ - Fortune
Xi offers AI olive branch to the world, calling for ‘symphony of global cooperation’ Fortune
China’s Xi Jinping launches new AI alliance: What is it? - Al Jazeera
China’s Xi Jinping launches new AI alliance: What is it? Al Jazeera
China's Moonshot AI claims Kimi K3 can rival OpenAI and Anthropic - BBC
China's Moonshot AI claims Kimi K3 can rival OpenAI and Anthropic BBC
New research shows how AMIE, our medical AI, could help manage health conditions.
Research in “Nature” shows our conversational AI system matches primary care physicians in complex disease management.
Vertu wants executives to pay $6,880 for an AI agent — here’s how it actually performs
From AI workflows to battery life and security, here's what it's really like to live with Vertu's luxury foldable every day.
5 ways Google Search can level up your thrift and vintage shopping
Uncover second-hand scores with AI tools in Google Search and Shopping.
The US is advancing AI safety through state and federal action
OpenAI outlines a “reverse federalism” approach to AI governance, where state laws help build a national framework for safe, democratic AI.
Why teens deserve access to safe AI
Learn how OpenAI is making ChatGPT safer for teens with age-appropriate protections, learning tools, parental controls, and expert partnerships.
Our latest Google Finance upgrades, including a new app
The new Google Finance is coming out of beta and launching a new Android app.
Dell Technologies vs. NVIDIA: Which Artificial Intelligence Stock Is a Better Buy in 2026? - Yahoo Finance
Dell Technologies vs. NVIDIA: Which Artificial Intelligence Stock Is a Better Buy in 2026? Yahoo Finance
Yes, you can now order DoorDash from the command line
DoorDash is opening a limited beta of dd-cli, a command-line tool that lets developers and AI agents search stores, build carts, and place orders from the terminal, marking another step toward software designed for AI agents instead of just humans.
Celebrating 25 years of visual search innovation
Google Images is turning 25. Here’s a look back at some major milestones — and new ways to explore and create visual content.
Why the first GPU financiers are turning to inference chips in a $400 million deal
A $400 million chip-backed loan points to the next wave of AI infrastructure deals.
Unlocking Britain’s next era of productivity: Building a nation of AI trailblazers
Google UK shares its latest Economic Impact Report and how to enable more people to unlock the benefits of AI-powered technologies.
Introducing Grok on Amazon Bedrock | Artificial Intelligence - Amazon Web Services (AWS)
Introducing Grok on Amazon Bedrock | Artificial Intelligence Amazon Web Services (AWS)
GPT-Red: Unlocking Self-Improvement for Robustness
Explore GPT-Red, OpenAI’s automated red teaming system that uses self-play to improve AI safety, alignment, and prompt injection robustness.
Meet LBF's 20 People to Know in AI - The Business Journals
Meet LBF's 20 People to Know in AI The Business Journals
How we used Gemini to build Google I/O 2026
Learn how Googlers used AI to produce Google I/O 2026.
Zapier vs. Power Automate: Which is best? [2026]
If your business uses Microsoft 365, you already have access to Power Automate. It's a capable automation platform that integrates deeply with Teams, SharePoint, Dynamics, and the rest of Microsoft's ecosystem. For Microsoft-to-Microsoft workflows, it's a smart place to start. But most enterprises have a substantial portion of their tech stack spread across multiple vendors. While Power Automate offers modest support for outside apps, Zapier works natively across whatever combination of apps you
GPT-5.6: Frontier intelligence that scales with your ambition
More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work.
Here's how Gemini can help you avoid jetlag.
If you’ve got a faraway trip coming up, the Gemini app can help you avoid jetlag so you can make the most of your visit.Once you’ve given Gemini permission to access you…
10 top women in AI in 2026
AI is changing our world, but the stories of who build it often get lost in the noise. Behind the headlines and hype, a group of women are solving AI’s fundamental challenges – despite working in an industry persisently impacted by gender inequality. Women make up just 22% of AI professionals worldwide and only 12% of AI researchers. In academic publishing, female researchers account for just 29% of first authors on AI papers, a number that hasn’t increased since the mid-2000s. This is a story about ten leaders who have influenced AI despite the odds being stacked against them. Their The post 10 top women in AI in 2026 appeared first on DailyAI.
5 ways Google parents are using Gemini
How Gemini helps with homework, meal planning and more, so parents have time to focus on the good stuff.
Ask an AI expert: What exactly is the full stack?
A Google expert explains what it means to take a full-stack approach to AI and why it’s been the foundation of our AI work for so long.
Apple’s lawsuit couldn’t come at a worse time for OpenAI
Apple filed a trade secrets lawsuit against OpenAI last Friday, and it’s not messing around. The complaint alleges a pattern of misconduct reaching all the way up to OpenAI’s chief hardware officer and claims more than 400 former Apple employees now work at the company. OpenAI’s response so far has been carefully hedged, and the timing couldn’t be worse with the company reportedly eyeing an IPO […]
Neil Rimer thinks the AI money is coming back out
Neil Rimer, the venture capitalist who co-founded Index Ventures, predicts the historic wealth AI is generating in Silicon Valley will have to be redistributed, voluntarily or involuntarily.
The Zoom hack that says, ‘Don’t record me’
If every meeting, watercooler conversation, and date gets transcribed and summarized, who's actually reading any of it?
The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs
Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Buying decisions turn on integration and total cost of ownership rather than headline token price — which is fortunate, because most enterprises cannot yet see their unit economics clearly: GPUs sit at half utilization or less, and fewer than half rigorously track what their compute actually costs. The result is a compute gap — heavy, fast-moving investment running ahead of the visibility needed to control it. This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how satisfied they are, what would make them switch, where they plan to evaluate their investments, and — most revealingly — how well they can measure and control the economics of the compute underneath it all. The central finding is a compute gap — the distance between how aggressively enterprises are investing in AI infrastructure and how little of its economics they can see. Only about one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile the compute already in place runs cold — 83% report GPU utilization of 50% or less — and fewer than half (44%) can rigorously track what their AI compute costs. Enterprises are buying more infrastructure faster than they can account for what they already own. Enterprises are not settled on their infrastructure vendors, either: A clear majority (64%) plan to switch or add an infrastructure provider within twelve months, and 38% within the next quarter — unusually high churn intent for a category this foundational. When they choose, they choose on integration with the existing stack (41%) and total cost of ownership (35%), not on headline price: cost per million tokens is the deciding factor for just 8%. And the frontier constraint that will shape the next round of decisions — the shift from GPU compute to memory bandwidth as inference scales — is barely on the radar, with roughly one in five enterprises either unaware of it or yet to address it. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%. By organization size the sample concentrates in the mid-market: 101–250 employees (36%) and 251–1,000 (27%) lead, with 1,001–5,000 (22%), 5,001–10,000 (8%), and 10,001+ (7%) above them. By role it spans managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%); on purchasing authority it is buyer-credible, with 45% final decision-makers and another 30% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 26%, followed by Healthcare/Life Sciences (15%), Financial Services (13%), and Retail/E-commerce (12%). At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It also skews toward the mid-market and toward earlier-stage adopters, so it is best read as the view from organizations actively building out AI infrastructure rather than from the largest hyperscale operators. Finding 1: Ambition outpaces production Only one in five run AI in production at scale We asked where organizations sit in their AI deployment journey. Most are still building toward production rather than operating at scale. The maturity curve is front-loaded. Three-quarters of enterprises (76%) are either experimenting or running only some workloads in production, and just 21% describe AI in production at scale. This matters for everything that follows: the infrastructure decisions in this report are being made largely by organizations still early in deployment, whose compute footprint — and whose costs — are about to grow. The evaluation and switching intentions in Findings 3 and 4 are the leading edge of that build-out, not the settled preferences of operators who have already found what works. Finding 2: Enterprises run on hyperscalers and model APIs The specialized GPU clouds barely register — today We asked which providers and platforms enterprises currently use to run their AI. The answer is a familiar one: the incumbents. The current stack is hyperscaler-and-API. Google Cloud leads at 48%, and the general-purpose clouds (Google, Microsoft, AWS, Oracle) together with the major model APIs (Gemini, OpenAI, Anthropic) account for essentially all current deployment. The specialized “neocloud” GPU providers that dominate AI-infrastructure headlines — CoreWeave, Lambda, Crusoe, Nebius and peers — register at or near zero among these enterprises today. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack. Enterprises are, for now, running AI on the providers they already buy from — which makes the evaluation intentions in Finding 3 all the more striking. (A note on reading these shares. As described in the methodology section, this sample is self-selected and skews mid-market, and this question counted every provider a respondent uses — an average of 2.1 selections each — so the figures measure presence in the stack rather than spending or primary status. A sample built this way will show a different provider mix than a spend-weighted census of the broader market; Google's strength here, for example, is consistent with its long-standing position among smaller enterprises building on AI. Read these shares as a portrait of what this AI-active cohort runs today, and treat gaps between these figures and industry-wide market share estimates as a property of the sample rather than a contradiction of either.) Finding 3: The next dollar goes to infrastructure they don’t yet run AI-specialized clouds top the evaluations list We asked where enterprises planned to evaluate AI infrastructure over the next 12 months. Their answers point away from the stack they run today. Here is the report’s sharpest tension. The single most-cited planned evaluation area — AI-specialized clouds, at 45% — is the very category almost none of these enterprises use today (Finding 2). Nearly a third (32%) intend to evaluate non-Nvidia accelerators, and 28% in next-generation Nvidia silicon; even decentralized compute networks (16%) and sovereign compute (11%) draw meaningful interest. Read against current usage, this is not incremental — it is the leading edge of a re-platforming. The direction-of-travel question tells the same story: every infrastructure approach is net-expanding, but specialized AI clouds carry the highest net momentum (+24), edging out even the hyperscalers (+22). Enterprises are preparing to move a meaningful share of AI compute off the general-purpose cloud. This continues a trend we saw in our April-May survey wave. Back then, usage of the AI-specialized clouds was equally marginal — CoreWeave at 3%, Lambda at 4%, Crusoe at 2% of enterprises. When we asked enterprises what change they planned in their AI infrastructure strategy over the next twelve months, the most-cited answer was moving workloads to specialized AI clouds, at 33%. Asked in April-May which emerging compute option they were most likely to evaluate AI-specialized clouds again drew the most responses. Two waves, two differently worded questions, one consistent picture: the type of cloud enterprises are most eager to assess is the type they have barely begun to use. Finding 4: A switching wave is building Six in 10 plan to change providers within a year — many within a quarter We asked whether and when enterprises plan to switch or add an infrastructure provider. Very few intend to stand still. For a category as foundational as compute, this is a remarkable amount of intended movement. Only 36% have no plans to change, meaning a clear majority (64%) intend to switch or add a provider within twelve months — and 38% within the next quarter alone. Where that interest points is telling: the providers drawing the most switching consideration are again the incumbents — Microsoft Azure and Google Cloud (33% each), OpenAI (30%), and Gemini (22%) — which suggests much of the near-term movement is reshuffling among the majors and consolidating spend rather than defecting to new entrants. The neocloud interest in Finding 3 is a 12-month evaluation thesis; the switching in the next quarter is mostly incumbents trading share. (Method note: Respondents who selected both "no plans to change" and a specific switching window are counted as switchers, on the logic that naming a timeframe is the more specific answer; three respondents were reclassified under this rule.) Finding 5: Nobody buys on token price Integration and total cost of ownership decide — not sticker price We asked what matters most when enterprises select an AI infrastructure provider. Headline price finished last. Enterprises do not buy AI infrastructure on pricing, which is the place vendors compete on hardest. Integration with the existing stack (41%) and total cost of ownership (35%) dominate, while the headline metric — cost per million tokens — is the deciding factor for just 8%, dead last. The pattern is coherent: buyers are optimizing for how a provider fits and what it truly costs to operate, not for the advertised unit rate. It also foreshadows Finding 7 — enterprises say TCO matters most, yet most cannot yet measure it rigorously. The stated priority and the measured capability are out of step. Finding 6: Expensive GPUs, idle most of the time 83% report GPU utilization of 50% or less We asked what share of their GPU capacity enterprises actually utilize. The answer is a well-known but rarely quantified inefficiency. Disclosure: Band percentages count every selection against all 107 qualified respondents; 14 respondents selected more than one band, so bands overlap. At the respondent level, 83 of the 100 GPU-operating enterprises reported utilization at or below 50% The compute already in place runs cold. Adding the bands at or below half capacity, 83% of enterprises that operate GPUs report utilization of 50% or less, and nearly half (49%) run at 25% or below. Only 12% clear the 50% mark, and a further 8% do not measure utilization at all. Idle accelerators are expensive accelerators, and this is the clearest single measure of the compute gap: enterprises are planning to buy more GPUs and specialized compute (Finding 3) while the capacity they already own sits substantially unused. The efficiency headroom in the current fleet is large — and largely unmeasured. Finding 7: Spending fast, measuring slowly Fewer than half rigorously track what their compute costs We asked whether enterprises can quantify the cost and return of their AI infrastructure spend, and how satisfied they are with what they run. Confidence in the ledger lags the spending. Measurement trails money. Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute; the majority track only partially (39%), cannot quantify it yet (20%), or have not prioritized it (6%). That gap is consequential given Finding 5, where total cost of ownership was the second-ranked buying criterion — enterprises are choosing providers on an economic basis they mostly cannot yet measure. Satisfaction with current infrastructure is moderately positive but not enthusiastic: on a five-point scale, overall satisfaction averages 4.0, with ease of implementation (3.8) and value for money (3.9) trailing slightly — the softness landing, tellingly, on cost. Enterprises are spending quickly and accounting slowly. Finding 8: The next bottleneck few are watching As inference shifts from compute to memory, the field scatters Finally, we asked how enterprises would address the emerging constraint in large-scale inference — the shift from GPU compute to memory, specifically KV-cache capacity. The responses reveal a frontier that is not yet a priority. The memory frontier is real but barely governed. Asked which approach they would rely on as the binding constraint in inference shifts from compute to memory bandwidth, enterprises scatter: Dell leads at 31%, Nvidia follows at 16%, and the rest fragments across storage vendors, open-source tooling, and model-level efficiency techniques. Most telling is that roughly one in five (18%) either do not recognize the constraint or have not begun to address it. For a shift that will reshape inference cost and architecture, this is an early and unsettled market — and, consistent with the measurement gap in Finding 7, one where many enterprises simply do not yet have a view. It is the next chapter of the compute gap, arriving before most have closed the current one. The bottom line: A compute gap that faster spending will widen, not close Organizations with more than 100 employees are investing in AI infrastructure faster than they can measure it. Most are still early in deployment, yet their spending intentions point past their current stack — toward specialized clouds and alternative accelerators almost none of them run today — and a clear majority intend to change providers within the year. They buy on integration and total cost of ownership rather than headline price, which is rational; the difficulty is that most cannot yet see those economics clearly. The visibility gap is concrete. The GPUs enterprises already own run at half utilization or less for the overwhelming majority, and fewer than half can rigorously track what their compute costs or returns. Satisfaction is decent but unenthusiastic, softest on value for money — the dimension hardest to judge without measurement. And the next constraint, the shift from compute to memory in large-scale inference, is arriving while most enterprises are still unaware of it. At 107 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market and earlier-stage adopters — but the direction is consistent: the appetite to spend is running well ahead of the instrumentation to spend well. The compute gap is not a capacity problem that more hardware will solve on its own; it is, first, a problem of seeing what the hardware already costs. The open question for later waves is whether enterprises build that visibility before the re-platforming arrives — or buy the next layer of infrastructure as blind to its economics as the last. Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the results read cross-sectionally rather than as a month-over-month trend, and at 107 respondents this is a directional signal rather than a precise measurement — the sample is self-selected, skews mid-market, and leans toward earlier-stage adopters rather than the largest hyperscale operators. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with buyer-credible purchasing authority, across Technology/Software, Healthcare/Life Sciences, Financial Services, Retail/E-commerce, and other industries.
The Gemini app is bringing personalized image creation to more users.
Personal Intelligence makes the Gemini app feel tailored to you. With your permission, it pulls from Google tools like Gmail, Google Photos, YouTube and Search to provid…
Expanding Managed Agents in Gemini API: background tasks, remote MCP and more
We’re announcing new capabilities in Managed Agents in Gemini API so developers can build reliable, production-ready agents.
How to manage AI investments in the agentic era
Learn how enterprises can manage AI investments in the agentic era by measuring useful work per dollar, improving efficiency, and scaling high-value workflows.
Georgia and China Establish Joint Working Group on Artificial Intelligence Development - sovanews.tv
Georgia and China Establish Joint Working Group on Artificial Intelligence Development sovanews.tv
Workato vs. Zapier for large businesses: Which is best? [2026]
Everyone has opinions about how to run a big meeting. Should the host run the show, or are participants free to jump in with questions or input when they feel like it? (And, if you're me, is this Zoom meeting even worthwhile unless it's just an excuse to meet everyone's dog on camera?) Enterprise automation is equally impacted by a business's approach to leadership and democratization. Every business owner has their own strong feelings about who should touch production systems. Workato and Zapi
The 8 best data integration tools in 2026
If you've ever had to hunt down an important email across one of your seven inboxes, you know the struggle of having information spread out between a bunch of unconnected systems. Ctrl+F can't save you when you aren't even sure where to start looking. Multiply that by hundreds of employees and dozens of systems, and things get real messy. Data integration tools take siloed data and un-silo it, uniting various data sources into a single master view. This means no more swapping between application
Try these 3 Google AI tools to help find your next job.
Use Google AI tools — like Career Dreamer, NotebookLM and Gemini Live — for resumes, cover letters, interview prep and more.
OpenClaw vs. Zapier: What's the difference? [2026]
If you've spent any time in AI automation circles this year, you've probably heard about OpenClaw. The open-source AI agent went from a side project to a global phenomenon in a matter of weeks, and for good reason: it gives anyone the ability to run an always-on AI assistant from their own machine, controlled through the messaging apps they already use. But popularity doesn't mean it's the right tool for every job. OpenClaw is powerful, flexible, and community-driven. It's also self-hosted, perm
Integrately vs. Zapier: Which is best? [2026]
Most businesses sign up for an automation platform to fix a specific annoyance. There's only so much copy-paste work you can take before you finally reach the limits of your patience, search "Typeform to HubSpot automation," and find yourself researching whether Integrately or Zapier is the right fit. Integrately is an automation-only platform that's built for one-off workflows like this. But sooner or later, most businesses start asking more questions, like: "Can we filter leads before adding t
The latest AI news we announced in May 2026
Here are Google’s latest AI updates from May 2026
The 4 best read it later apps to save content in 2026
Sometimes, during the work day, I stumble upon a really interesting but really long article that I don't have time to read at the moment. This is the moment read-it-later apps are built for. The idea: you can save the article, then get back to it later when you have time. I don't know how I lived before finding these kinds of apps, which I've been using for around 15 years. For this roundup, I considered over 20 read it later apps. After extensive testing, I can say that these are the four best
MLB restricts using dugout iPads for AI-assisted in-game strategy - ESPN
MLB restricts using dugout iPads for AI-assisted in-game strategy ESPN
Murder Victim Speaks from the Grave in Courtroom Through AI
Chris Pelkey was shot and killed in a road rage incident. At his killer’s sentencing, he forgave the man via AI. In a historic first for Arizona, and possibly the U.S., artificial intelligence was used in court to let a murder victim deliver his own victim impact statement. What happened Pelkey, a 37-year-old Army veteran, was gunned down at a red light in 2021. This month, a realistic AI version of him appeared in court to address his killer, Gabriel Horcasitas. “In another life, we probably could’ve been friends,” said AI Pelkey in the video. “I believe in forgiveness, and The post Murder Victim Speaks from the Grave in Courtroom Through AI appeared first on DailyAI.
Our new community investments in Virginia support local jobs and expand energy affordability.
We’re helping build the state’s next-generation workforce and investing in energy programs.
The latest AI news we announced in June 2026
Here are Google’s latest AI updates from June 2026.
16 AI prompt templates for better AI agent outputs
I've gone through a lot of painful trial and error with AI prompting—a lot. Which was fine when I was experimenting in back-and-forth conversations with AI chatbots, because I could refine my prompts with every response. But it's a different story with AI agents. A weak AI prompt baked into an agent's instructions produces the same bad output—and bills you for the same mistake—every single time it runs, with no one at the keyboard to catch it. I've rounded up 16 AI prompt templates that the Zap
How sales teams use ChatGPT Work
See how sales teams can use ChatGPT Work to create pipeline briefs, meeting prep packets, forecast reviews, account plans, and stalled-deal diagnoses from real work inputs.
Patreon stops asking AI bots not to scrape — and starts blocking them
Patreon is strengthening its defenses against AI scraping by working with Cloudflare to block bots that train AI models on creators’ content without permission. The move marks a shift away from relying on websites using robots.txt alone to actively block unauthorized AI training.
Databricks hits $188B valuation, extending its run as AI’s favorite second act
Databricks has remade its image into an AI company and has published research on the cost savings of open weight AI models for coding.