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.
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.
Amazon cuts jobs in its artificial general intelligence group - Reuters
Amazon cuts jobs in its artificial general intelligence group Reuters
We’re strengthening our presence in Alabama through new investments and community support.
Google has announced a $1.5 billion investment for 2026 and 2027 to expand its data center campus in Jackson County, Alabama. Operating since 2019 on a repurposed former…
Amazon cuts some jobs in its artificial general intelligence unit - CNBC
Amazon cuts some jobs in its artificial general intelligence unit CNBC
Katy Perry Didn’t Attend the Met Gala, But AI Made Her the Star of the Night
Another year, another viral deepfake of Katy Perry at the Met Gala and once again, she wasn’t even there. Photos showing the pop star in a sleek black designer gown circulated widely on social media during Monday night’s event, matching the “Superfine: Tailoring Black Style” theme. But the images were AI-generated. Perry quickly clarified she was not at the Met; she was on tour. Perry’s reaction “Couldn’t make it to the MET, I’m on The Lifetimes Tour (see you in Houston tomorrow IRL),” she posted to Instagram alongside the fake images. She added a jab at AI confusion: “P.s. this The post Katy Perry Didn’t Attend the Met Gala, But AI Made Her the Star of the Night appeared first on DailyAI.
The 5 Stages of Integrating Human Judgement and Artificial Intelligence - 401k Specialist
The 5 Stages of Integrating Human Judgement and Artificial Intelligence 401k Specialist
David Vélez and Robin Vince join the boards of the OpenAI Foundation and OpenAI Group PBC
David Vélez and Robin Vince join the boards of the OpenAI Foundation and OpenAI Group PBC, bringing global leadership in finance, technology, and governance.
AI meets advanced manufacturing - University of Delaware
AI meets advanced manufacturing University of Delaware
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.
Web scraping: A comprehensive guide
There are two ways to catch a price drop on that obnoxiously priced all-terrain dog stroller you've been eyeing (but won't admit out loud to the general public—which, smart). You could check the product page every morning and hope today's the day for good boy Professor Waffles. Or you could let a price tracker like camelcamelcamel watch the listing for you and send an alert the moment it dips below a set price. That second option is web scraping at work: an automated tool visits the page, reads
Yope raises $12.3M to build a private social network without algorithms or ads
Yope, a fast-growing social app focused on private groups of friends and family, has raised $12.3 million in seed funding. Instead of chasing creators and algorithmic feeds, the startup is betting that the future of social networking lies in small, private communities powered by messaging, photo sharing, and AI features designed to strengthen real-world relationships.
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.
ChatGPT Is Making People Think They’re Gods and Their Families Are Terrified
ChatGPT, the popular AI chatbot from OpenAI, is unintentionally leading users into full-blown spiritual delusions, and families are sounding the alarm. On Reddit’s r/ChatGPT forum, a chilling thread titled “ChatGPT induced psychosis” is gaining traction. Users are reporting a disturbing pattern: their loved ones are convinced that ChatGPT is a divine being, a spiritual guru, or even a portal to God. Rolling Stone journalist Miles Klee spoke directly with affected individuals. One woman shared how her partner became obsessed after ChatGPT gave him cosmic nicknames like “spiral starchild” and claimed he was on a divine mission. He ultimately told her The post ChatGPT Is Making People Think They’re Gods and Their Families Are Terrified appeared first on DailyAI.
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.
Glow emerges from stealth at $1.2B valuation to challenge endpoint security in the AI era
Glow is targeting a new class of endpoint risks created by the rapid adoption of AI agents and developer tools inside enterprises.
OpenAI’s AI spending spree has ballooned to $750B
OpenAI will spend the equivalent of Sweden's GDP on infrastructure through 2030.
Relay.app is shutting down: How to export your workflows and move to Zapier
Relay is shutting down on August 15, 2026, for free users, and on September 14 for paid plans. Either way, if you built workflows, agents, or tables in Relay, the end is near. The good news is that Relay has made it as easy as possible to transition your workflows to a new tool. Here's how to export your Relay.app data and rebuild those workflows in Zapier, plus what to expect from Zapier as a former Relay user. We'll also cover why Zapier is your best option compared to other Relay alternatives
OpenAI Says Its A.I. Models Hacked Into Hugging Face, a Digital Library - The New York Times
OpenAI Says Its A.I. Models Hacked Into Hugging Face, a Digital Library The New York Times OpenAI says its technology, on its own, carried out "unprecedented" hack of another AI company CBS News OpenAI blamed a hacking event on its AI models going rogue. Here are some things to know WRAL
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.
Agentic AI vs. RPA: Everything you need to know
Automation has evolved far beyond simple scripts and basic workflows. While robotic process automation (RPA) has long been used to handle repetitive, rules-based work, especially inside legacy systems, agentic AI represents a newer approach to automation built for far more dynamic problems. Both are designed to reduce manual work and improve efficiency. But RPA works by mimicking human interactions with software through predefined rules and screen-based actions, while agentic AI systems are buil
13 Google tips for a fun, productive summer off from college
Discover how college students can use Google AI tools, like Gemini and AI Mode, to prep for grad school, internships and fall courses.
Best Universities To Study AI in 2026
Artificial intelligence has made enormous strides in the past few years – with the introduction of a wide range of AI tools changing the landscape of how we assess data and operate within online spaces forever. This page ranks the 50 best universities to study AI around the world, based on scope, prestige, and the level of AI-related research each institution has released. Career prospects in AI There is a huge demand for individuals with a high degree of skills in artificial intelligence and machine learning, making AI a potential lucrative career prospect with countless opportunities as AI continues to The post Best Universities To Study AI in 2026 appeared first on DailyAI.
New York City educators and industry leaders gathered at Google’s offices to shape the future of AI in classrooms.
Google, the New York Jobs CEO Council and Urban Assembly hosted an AI summit for 150 education and industry leaders.
Stocks and the Economy Are Increasingly Relying on the A.I. Boom - The New York Times
Stocks and the Economy Are Increasingly Relying on the A.I. Boom The New York Times
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.
The browser wars aren’t about search anymore — here are the best alternatives to Chrome and Safari
We’ve compiled an overview of some of the top alternative browsers available today aiming to challenge Chrome and Safari.
U.S. Department of Energy invests in Fermilab projects to accelerate AI-enabled scientific discovery - Fermilab (.gov)
U.S. Department of Energy invests in Fermilab projects to accelerate AI-enabled scientific discovery Fermilab (.gov)
Travis Kalanick’s robotics company raises $1.7B, led by a16z
Uber is also investing in Travis Kalanick's company Atoms, which has made gauzy claims about using industrial AI to modernize the world.
How Mach 1 uses Zapier MCP to run AI operations across 25 different companies
Most AI agents can complete a task inside a single tool. Running them reliably across an entire business is a different problem. Chris Olson is co-founder and CEO of Mach 1, an AI operations platform that helps mid-market companies deploy agents across go-to-market, customer success, sales, support, and finance operations. He developed the approach after applying AI-driven operations at a sports technology company, helping move the business from a $9 million annual cash burn to $5 million in fre
The best CRM software for real estate agents in 2026
A CRM is your prized possession in real estate. You need something to keep things straight when juggling client management, property listings, and the looming threat of being upstaged by that insufferably smug agent from the office across the street. But with countless options on the market, how do you know which software is right for you? I looked into dozens of options, read approximately a million reviews, watched demos narrated by people way too cheerful for 9 a.m., and gathered insights
The 5 best photo editing apps for iPhone and Android (including free options) in 2026
"The best camera is the one you have with you" is an old adage in photography, and there's a lot of truth to it. Even as someone who has at least five cameras at home, I capture more photos with my smartphone than I do anything else. Smartphone cameras have been so good for so long now that it really doesn't feel like a tradeoff. My DSLR definitely doesn't have a wide-angle and an 8x telephoto lens built in. But capturing photos is just the first half of creating a good image. To really make gre
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.
Monday.com lays off hundreds to focus on AI
The company said it is reducing its headcount by 20%, or about 630 staff, to "support a leaner, more focused operating model" as it focuses on its AI Work Platform.
As China advances in AI, Trump faces a new test in the technology race - The Washington Post
As China advances in AI, Trump faces a new test in the technology race The Washington Post
Alphabet Earnings Preview: Will Doubling AI Capex Pay Off for GOOGL Stock? - Barron's
Alphabet Earnings Preview: Will Doubling AI Capex Pay Off for GOOGL Stock? Barron's
Here’s how to ask Gemini Live for help with anything you see.
Have you ever struggled to describe something you’re looking at? Whether it’s a complex manual, a blinking error code, or a unique object, sometimes you just need an exp…
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
5 ways to build a side hustle with Gemini
Launching a side business? Use Gemini to design your side hustle, conduct market research and automate logistics.
Gemini Spark updates: macOS launch, connected apps and more
The latest Gemini Spark updates brings Spark to the macOS app, connects with your favorite apps and tracks topics in real time.
Create, edit and star in videos with two Google Vids updates
Gemini Omni and personal avatars in Google Vids make video creation easier than ever.
Amazon Cuts Jobs in Artificial General Intelligence Unit - WSJ
Amazon Cuts Jobs in Artificial General Intelligence Unit WSJ
Safety and alignment in an era of long-horizon models
OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards through iterative deployment.
Start building with Nano Banana 2 Lite and Gemini Omni Flash
Scale your ideas with Nano Banana 2 Lite, our fastest, most cost-efficient Gemini Image model, and Gemini Omni Flash for high-quality video and conversational editing.
Synthesia’s AI training platform is moving beyond videos into live coaching
Synthesia launched AI Roleplay Sessions, an interactive enterprise training platform where employees practice workplace conversations with AI avatars that provide feedback, scoring, and analytics to help companies measure training effectiveness.
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
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.
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…
Advancing the next era of national science
OpenAI outlines its commitment to advancing American science working with the U.S. Department of Energy and national labs to use frontier AI to accelerate discovery.