AI News Archive: July 16, 2026 — Part 13
Sourced from 500+ daily AI sources, scored by relevance.
- AI Data Centers Are Being Built Faster Than They Can Be Secured
AI infrastructure introduces new security risks that traditional data center designs were never built to handle. The post AI Data Centers Are Being Built Faster Than They Can Be Secured appeared first on SecurityWeek .
- Fujitsu and leading Japanese robotics companies to use Nvidia technology in ‘physical AI’
Fujitsu and leading Japanese robotics companies to use Nvidia technology in ‘physical AI’ Toronto Star
- 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.
- Kimi K3 is now available on AI Gateway
Kimi K3 from Moonshot AI is now available on AI Gateway. K3 is an open-source model with a 1M-token context window and native visual understanding, accepting text, image, and video inputs. Built for long-horizon software engineering, knowledge work, and deep reasoning, K3 is especially strong where code meets visual and spatial reasoning, which suits frontend, game development, and CAD workflows. Thinking mode is always on. To use Kimi K3, set model to moonshotai/kimi-k3 in the AI SDK : AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting , Zero Data Retention support , budgets for API keys , routing rules , and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Try Kimi K3 in the model playground . Read more
- Federal regulator says driver overrode Tesla system before fatal Katy crash
Federal regulator says driver overrode Tesla system before fatal Katy crash Austin American-Statesman
- Alibaba and Baidu shares jump in Hong Kong on Apple AI partnership
The technological rivalry between China and the U.S. has intensified, as they race for AI dominance.
- Tesla driver in fatal Texas crash overrode FSD by pressing accelerator ‘100 percent,’ investigators confirm
The Tesla Model 3 reached speeds greater than 70mph during the crash, the NTSB found.
- NTSB: Tesla driver overrode car's self-driving mode before deadly Katy crash
NTSB: Tesla driver overrode car's self-driving mode before deadly Katy crash Houston Chronicle
- NTSB investigators confirm Tesla driver overrode Full Self-Driving system in fatal crash
The National Highway Traffic Safety Administration is also investigating the crash
- NTSB says Tesla driver floored the accelerator to override self-driving in fatal Texas crash
A preliminary federal report confirmed that Michael Butler floored the accelerator, disabling Full Self-Driving, before his Model 3 struck a home at more than 70 mph
- Linus Torvalds puts his foot down, tells anti-AI programmers to 'fork it'
'AI is a tool, just like other tools we use. And it's clearly a useful one.'
- Linus Torvalds to critics of AI coding in Linux: "Fork it. Or just walk away."
Creator says he will "very loudly ignore" those arguing for a ban on AI tools.
- Musk’s xAI sues user who allegedly used Grok to create child sexual abuse material
Case is one of first brought by an AI company against a user for allegedly using a tool to generate child abuse material Elon Musk’s artificial-intelligence startup xAI has sued a South Carolina man arrested earlier this year on charges of sexually exploiting minors, alleging he misused the company’s AI system Grok to create child sexual abuse material. xAI alleged in the lawsuit, filed in federal court in Texas on Tuesday, that Terry Harwood violated the company’s terms of service. The case is one of the first brought by an AI company against one of its users for allegedly using an AI system to generate child sexual abuse material. Continue reading...
- Hyundai to take full ownership of Boston Dynamics
Media reports suggest that Hyundai is to deploy Boston Dynamics’ Atlas humanoid robots at some of its factories in the US. Read more: Hyundai to take full ownership of Boston Dynamics
- xAI sued a Grok user for allegedly generating deepfakes of child sexual abuse
The lawsuit, one of the first by an AI company against a user over explicit AI content, seeks damages and a permanent ban from Grok
- EU forces Google to share its toys with the other AI and search kids
The Chocolate Factory is not amused
- EU forces Google to share search data and open Android to rival AI companies
The European Union issued two new rules for Google on Thursday to force it to share search data and open up its Android operating system to rival AI companies.
- OpenAI enters AI hardware with Codex Micro
OpenAI enters AI hardware with Codex Micro YourStory.com
- EU Orders Google to Give Rival AI Apps the Same Android Access as Gemini
The European Commission today formally ordered Google to give third-party AI services the same access to Android device features that Gemini has. Europe's Digital Markets Act requires software makers like Apple and Google to grant equal interoperability to third-party apps and services, and the EC is enforcing it. DMA interoperability rules are why Siri AI won't be available in the European Union when iOS 27 launches. While Apple tried to work out an agreement with regulators before launching Siri AI in Europe, Google did the opposite. Instead of asking, Google just launched Gemini integration on Android and opted to deal with the consequences afterward. Google's strategy gives Android users in Europe full Gemini access while it works on compliance with the DMA. The European Commission is giving Google a full year to implement changes that will meet the Digital Markets Act requirements, and that's before any legal appeals Google might make. The European Commission is making many of the same demands of Google that it made of Apple. Google must allow AI apps to access 11 features , such as: AI services must be accessible through voice commands like "Hey Google" or through access points like the home button or another activation button. AI services must be able to complete actions in and across apps, including completing long-running tasks in the background. Google must allow AI services to access context from apps and device sensors so AI can offer proactive services and anticipate user needs. Google has to give AI apps sufficient hardware and software resources, including access to its on-device AI models to execute tasks. Google is required to implement the majority of the European Commission's changes by August 1, 2027. Shortly after announcing Siri AI, Apple said EU regulators would not accept any of its proposed solutions to introduce the feature, and refused to engage on options that "preserve privacy and security." Apple wanted to use a Trusted System Agent that would allow third-party virtual assistants to safely access the same device capabilities as Siri AI. Apple said the Digital Markets Act would require it to give any AI system "nearly unlimited access to a user's device," along with the ability to act on that information autonomously. The European Commission claims Apple was "unable to develop interoperability solutions that meet essential EU privacy and security standards" and instead asked for a blanket exemption from the interoperability requirements, which the EC did not grant. In response to the EC's mandate , Google said the requirements "risk undermining vital privacy and security guardrails for millions of Europeans." Google hasn't given information on its next steps, but it says it plans to "continue advocating for a balanced approach that protects privacy and security while supporting market goals." Tags: Android , European Commission , European Union , Google This article, " EU Orders Google to Give Rival AI Apps the Same Android Access as Gemini " first appeared on MacRumors.com Discuss this article in our forums
- Rival AI assistants could soon gain full access to Android features
The EU has mandated Google to open up Android to competing AI assistants.
- EU Tells Google To Share Search Data, Open Android To AI Rivals
EU Tells Google To Share Search Data, Open Android To AI Rivals Barron's
- ⚡️ OpenAI launches a physical keypad
71% of enterprise "agents" are still chatbots.
- EU forces Google to share search data and open Android to rival AI companies
The European Union has issued new rules for Google, requiring it to share search data and open its Android system to rival AI companies
- EU forces Google to share search data and open Android to rival AI companies
EU forces Google to share search data and open Android to rival AI companies AP News
- EU forces Google to share search data and open Android to rival AI companies
EU forces Google to share search data and open Android to rival AI companies Toronto Star
- EU forces Google to share search data, open Android to rival AI companies
In the latest attempt to rein in tech behemoths' deep control of the digital economy, the EU said it will support innovation and diversity in the field by enabling fair access to AI features on Android devices and search engines.
- Google required to open up to AI, search engine rivals under EU-mandated changes
Google required to open up to AI, search engine rivals under EU-mandated changes Reuters
- It's official: EU will force Google to share search data and open up AI on Android
Google says these changes could endanger user privacy and security.
- Google’s handy NotebookLM tool is dropping the worst part of its name
NotebookLM is now Gemini Notebook.
- Nvidia announces Jetson T3000 and T2000 edge AI modules
Nvidia announces Jetson T3000 and T2000 edge AI modules verdict.co.uk
- Google is renaming NotebookLM to Gemini Notebook
Google is giving its AI note-taking app a new name. The company announced on Thursday that NotebookLM is becoming Gemini Notebook, but will remain a standalone app even as it integrates more deeply across Gemini and Google Search. Google first revealed Gemini Notebook - then called Project Tailwind - in May 2023 before widely releasing […]
- NotebookLM is now Gemini Notebook, with 3.5 + Antigravity upgrade coming to AI Pro
Google announced today that NotebookLM is now called “Gemini Notebook” and previewed upcoming features that users can soon expect.
- Google Renames NotebookLM to Gemini Notebook
NotebookLM gets a new name and expanded access to native code writing features.
- SpaceXAI Open-Sources Grok Build: The Rust Agent Harness, TUI, and Tool Layer Behind Its Coding CLI
SpaceXAI Open-Sources Grok Build: The Rust Agent Harness, TUI, and Tool Layer Behind Its Coding CLI MarkTechPost
- Google rebrands NotebookLM as Gemini Notebook, focusing on ecosystem and accessibility
Google LLC today announced that its popular NotebookLM, the company’s artificial intelligence-powered research assistant, is being rebranded as Gemini Notebook and is getting upgrades aimed at providing secure cloud computing for every notebook. Launched globally in 2023, NotebookLM, began as an AI note-taking and source understanding application that allowed people to upload large amounts of […] The post Google rebrands NotebookLM as Gemini Notebook, focusing on ecosystem and accessibility appeared first on SiliconANGLE .
- NotebookLM is now Gemini Notebook
NotebookLM is now Gemini Notebook
- Google rebrands NotebookLM as Gemini Notebook and opens its search app to third-party integration
Google is renaming NotebookLM to Gemini Notebook and integrating the tool more deeply into its ecosystem. A new feature gives each notebook its own cloud computer that can write and run code, initially for AI Ultra and Workspace customers. Separately, Google Search is getting app connections. The article Google rebrands NotebookLM as Gemini Notebook and opens its search app to third-party integration appeared first on The Decoder .
- You can now grant Claude access to your 1Password credentials
1Password lets you use Claude agents for personal chores without exposing your credentials.
- Google Rebrands NotebookLM as Gemini Notebook; Brings Cloud Computing and Search Integration
Google is rebranding NotebookLM to Gemini Notebook, the company announced on Thursday. The AI-powered research and note-taking assistant, which was originally announced as Project Tailwind at Google I/O 2023, has evolved into a standalone research platform in recent years. Google claims it is now used by more than 30 million people and over 600,000 organisations world...
- Connect more of your apps to Search
Connected apps rendering
- Google AI Mode adds more Connected Apps, including YouTube Music
Google is updating AI Mode with support for more first and third-party apps, just like the Gemini app.
- Bookcraft
AI BOOK CREATOR
- Google Search’s AI Mode can now handle tasks beyond the search bar
Why app-hop when Google Search can do the busywork for you?
- You can now link your favorite apps to AI Mode in Google Search to get things done
Google Search's AI Mode can now connect to apps like Instacart, Canva, and YouTube Music to complete tasks for you.
- Google AI Mode now integrates with Canva, YouTube Music and Instacart
You can now make playlists, design flyers and compile shopping lists using AI in Search.
- This AI Can Now Log Into Websites for You. But Should You Let It?
A new collaboration between Anthropic and 1Password gives Claude more power to complete tasks for you.
- Roblox will let people use AI to make games on their phone
Roblox is about to let people make games with AI right inside its mobile app, which could make a platform that's already filled with content of questionable quality feel even more overloaded. The company has embraced AI with open arms, including a preview of an ambitious take on AI world models similar to Google's Project […]
- Roblox is Adding AI Game Creation to iPhone and iPad
Online game platform Roblox today said it is adding a new Build tool that will let iPhone and iPad users create games using AI. With the mobile-first Build option, Roblox users can write a text-based prompt and have AI turn it into a basic game. A single prompt will create a starting point that can be expanded with playtesting and further commands. The entire game-making process, from creation to uploading on the Roblox platform, can be done on a mobile device. Right now, creating a game in Roblox requires a Mac or PC app, but Build will extend game creation to mobile users too. Roblox is also adding new AI creation tools to Roblox Studio, its desktop game creation software. Content created using the Build tool can be iterated on with Studio. Roblox Build is set to roll out to the Roblox mobile app on July 28, beginning with a public alpha test for users in New Zealand. Build will roll out to additional regions in the coming months as Roblox improves the experience. Roblox has 132 million daily active users, and user-created games like Brookhaven RP, Adopt Me, and Dress to Impress have been wildly popular. Tag: Roblox This article, " Roblox is Adding AI Game Creation to iPhone and iPad " first appeared on MacRumors.com Discuss this article in our forums
- Roblox will offer AI-generated game creation on mobile later this year
Build starts limited alpha testing later this month.
- Roblox announces Build, AI tools that let anyone create games
Roblox is bringing Build, a set of AI tools, to its app in order to help users create games on the platform.