When Marvell Technology disclosed a new custom chip partnership with Google on August 19, its stock jumped more than 11%. Broadcom’s fell more than 5%. That divergence tells you almost everything about what the deal actually means.

What Was Announced

Marvell filed a regulatory disclosure revealing it has issued Alphabet’s Google a warrant to purchase up to 58,970,907 shares of its common stock at $206.58 per share — worth approximately $12.18 billion if fully exercised. The underlying commercial agreement was signed on July 29, and it covers a broad range of custom silicon attached to Google’s tensor processing unit ecosystem, including AI inference accelerators, storage controllers, network interface controllers, memory interface controllers, and near-memory computing technology. If fully exercised, the stake would make Google Marvell’s fifth-largest investor. The warrant is exercisable until August 18, 2033.

The shares don’t vest all at once. Only about 1.4 million vest automatically in the first year. The remaining 57.6 million are tied directly to how much Google spends with Marvell — approximately 240,000 shares vest for every $500 million in qualifying chip purchases. To earn the full $12.18 billion in equity, Google would need to send roughly $120 billion in chip revenue Marvell’s way through fiscal 2033. That $120 billion is a vesting threshold, not a spending commitment, but it signals the potential scale of the relationship if Google follows through.

Why Broadcom Sold Off

Broadcom has been Google’s primary custom chip partner, holding a deal that runs through 2031 and covers future generations of Google’s most advanced silicon. The Marvell announcement doesn’t replace that — Broadcom remains engaged — but it signals that Google is deliberately building a second, parallel custom chip relationship rather than deepening its dependence on a single supplier. For investors, that diversification reads as a relative vote of no-confidence in Broadcom’s exclusive positioning, even if the absolute relationship is unchanged. Marvell and Broadcom don’t compete on identical products; Morningstar analyst William Kerwin called the Marvell deal “a big win” while noting it likely reflects a “growing pie at Google for new sources, rather than” a direct transfer of business away from Broadcom.

Part of a Pattern Across Big Tech

The deal fits a template becoming standard among the largest AI infrastructure spenders: take a meaningful equity position in a key chip supplier in exchange for committing significant procurement volume, structuring the stake to scale automatically with spending. In October 2025, AMD struck an analogous deal with OpenAI — chips worth tens of billions in potential annual revenue, with OpenAI holding an option to buy up to roughly 10% of AMD. The structure aligns incentives both ways: the chip company gets a large committed customer; the customer gets equity upside on its own spend and a deeper engineering partnership with a supplier that has strong financial reasons to prioritize their roadmap.

The Broader Race to Build Custom Silicon

Demand for custom, task-specific chips has surged as AI companies discover that general-purpose Nvidia GPUs — however powerful — aren’t optimally efficient for inference, the process of running trained models at scale rather than training them in the first place. Google’s TPUs are the most mature example of this category, and the Marvell deal specifically targets the hardware that surrounds and enables TPUs rather than the TPUs themselves — the storage, networking, and memory controller chips that determine how efficiently a TPU cluster can actually be used in production. This connects directly to the same chip demand story driving TSMC’s blowout quarter and Samsung’s price hikes; see our coverage of Samsung’s foundry price increases for how AI demand is squeezing every layer of the hardware supply chain.

What to Watch Next

Marvell reports earnings August 27, and analysts are watching for detail on the revenue ramp tied to the Google deal. Prediction market data puts Google at just 7% odds to hold the best AI model by end of 2026, compared to 66% for Anthropic and 14.5% for xAI — a sobering context for a deal that’s ultimately a bet on winning the AI infrastructure race if not the model race outright.

Sources: Reuters via Yahoo Finance, Bloomberg, CNBC

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Google Just Gave Marvell a $12.2 Billion Vote of Confidence — And Broadcom Lost 5% the Same Day appeared first on Times Tabloid.

If your app generates images through Google’s API and you haven’t touched that code in a while, today is the day it may have quietly broken.

What Happened Today

Google retired all three stable Imagen 4 endpoints on the Gemini Developer API — imagen-4.0-generate-001, imagen-4.0-ultra-generate-001, and imagen-4.0-fast-generate-001 — effective August 17, 2026. Google’s own deprecation documentation frames listed dates as the earliest possible shutdown time rather than a guaranteed exact hour, but the company has been direct about the end state: once a model is shut down, it is completely turned off, and calls to the retired endpoints will fail outright rather than degrade gracefully.

The recommended replacement is gemini-3.1-flash-image, a model also known internally as “Nano Banana 2” — Google’s general-purpose image generation model built for new Gemini API workloads, offering conversational editing capabilities and low latency at a mainstream price point.

Why This Isn’t a Drop-In Replacement

The core technical complication is that Imagen and Gemini image models don’t share an API shape. Imagen used a dedicated image-generation endpoint and a distinct method, `generate_images()`, that returned a clean array of generated images. That method doesn’t exist on Gemini image models at all. Instead, requests go through the more general `generateContent` method, and images arrive embedded as content parts within the response rather than a dedicated image array — developers now have to check each response part for inline image data and handle cases where the model returns accompanying text alongside the image, something Imagen’s endpoint never did.

A second breaking change: Imagen’s `number_of_images` parameter, which let a single call request multiple image variations at once, has no equivalent on Gemini image models. Each Gemini image call now produces exactly one image, meaning any pipeline that requested batches in a single request needs to be restructured into multiple calls.

The Pricing Picture Is More Complicated Too

Imagen 4 is priced by model tier — a flat rate per image depending on whether you used the standard, fast, messy hair filter or ultra version. Gemini 3.1 Flash Image instead prices by output tokens, which scale with resolution: Google lists standard rates around $0.045 for 0.5K resolution images, climbing to roughly $0.151 for 4K. At standard resolution, that reportedly works out to about 67% more expensive per image than Imagen 4 Standard was — though Google’s Batch API reportedly brings costs down to around $0.034, actually undercutting the old Imagen pricing for teams that can batch their requests rather than needing real-time generation.

Part of a Bigger Consolidation Pattern

This shutdown isn’t an isolated event — it’s one entry in a broader model retirement wave Google has been running throughout 2026 as it consolidates developers onto its newer Gemini 3 model family. The company has been steadily retiring older Gemini 2.5 and earlier Imagen and embedding models on a rolling schedule throughout the year, with Gemini 2.5’s remaining text models slated to follow in October. For teams building on Google’s AI stack, that pattern has practical implications: pinning to a specific model version buys short-term stability but virtually guarantees a forced migration eventually, since Google has shown no indication it plans to support older model generations indefinitely.

This kind of infrastructure churn is happening alongside a genuinely fast release cadence at the top of Google’s model lineup — the company shipped Gemini 3.7 Flash just days ago, three weeks after its predecessor. See our coverage of that release for how quickly Google’s flagship models are iterating even as older endpoints get retired in parallel.

What to Watch Next

Any team still calling the old Imagen 4 endpoints should expect hard failures rather than a grace period at this point, given Google’s stated policy on shutdown dates. More broadly, developers building on any Google AI API should treat this as a reminder to audit which specific model IDs their production code depends on and check Google’s deprecations table directly, since the company’s retirement cadence shows no signs of slowing down.

Sources: Google AI for Developers, ByteIota, Bez-Kabli

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Google Just Shut Off Imagen 4 Today — And the Replacement Isn’t a Simple Swap appeared first on Times Tabloid.

For as long as AI models have processed personal data, there’s been an unavoidable trade-off: the server doing the computation has to see the data in order to compute on it. Google’s newest open-source release is aimed squarely at breaking that assumption.

What Google Released

On August 14, Google unveiled HEIR — Homomorphic Encryption Intermediate Representation — as part of its Private Computing Toolkit. HEIR is a compiler toolchain that converts pretrained AI models, which normally operate on unencrypted data, so they can instead run inference directly on encrypted inputs. The underlying cryptographic technique, fully homomorphic encryption, has been described as a kind of holy grail of cryptography for decades: it lets a system perform computations on data while that data stays encrypted the entire time, meaning the server processing it never has access to the underlying information at any point.

Google’s stated goal is to make HEIR a one-click solution, letting developers without cryptography expertise incorporate encrypted inference into production applications rather than requiring specialized teams of cryptographers to hand-build each implementation — historically the biggest barrier keeping homomorphic encryption confined to research papers rather than shipped products.

Why This Is Different From Other Privacy Approaches

Most privacy-preserving AI techniques rely on hardware-based protections — secure enclaves, trusted execution environments — that still require trusting a specific chip manufacturer’s implementation. Homomorphic encryption’s guarantees are purely cryptographic instead, meaning they don’t depend on trusting any particular piece of hardware. Google demonstrated HEIR’s capabilities on several real-world-style use cases, including a deep learning recommender system and credit-card fraud detection — precisely the kind of sensitive, regulated data categories where privacy requirements have historically limited how much AI processing companies could apply in the first place.

This isn’t Google’s first work in the space; the company has been developing homomorphic encryption tools since at least 2023, including an earlier C++ transpiler and a JAX-based library called Jaxite for running encrypted computation on GPUs and TPUs. HEIR represents the consolidation and maturation of that multi-year effort into a more accessible, general-purpose toolchain.

The Catch: Cost and Practicality

Homomorphic encryption has a well-known trade-off that HEIR doesn’t eliminate: computing on encrypted data is significantly slower and more resource-intensive than computing on plaintext data. Google’s own earlier documentation of the underlying transpiler technology cited a three-layer neural network taking 16 seconds to produce a single private inference — workable for some applications, prohibitively slow for others. Whether HEIR’s newer optimizations meaningfully close that performance gap for realistically sized production AI models is the central open question raised by outside commentators reacting to the release, and it will determine how quickly this moves from open-source toolkit to widely deployed infrastructure.

Why the Timing Matters

HEIR’s release lands in a year where AI privacy and security failures have been unusually visible — from AI agents breaking out of testing sandboxes to an AI meeting notetaker leaking access to over 180,000 recorded calls. See our coverage of the tl;dv data breach for a case study in exactly the kind of exposure encrypted inference is designed to prevent at the architectural level, rather than relying solely on access controls that can fail. A tool that lets sensitive data stay encrypted through the entire AI processing pipeline addresses a structurally different category of risk than better access controls alone.

What to Watch Next

Google says it has already partnered with several companies building specialized hardware accelerators for homomorphic encryption, including Belfort, Niobium, Cornami, and Optalysys — a sign the company expects performance to improve as purpose-built hardware matures alongside the software toolchain. Watch for adoption specifically in healthcare and financial services, the two sectors most constrained by data-sharing regulations and most likely to value cryptographic privacy guarantees over marginal speed, as the clearest early test of whether HEIR moves from open-source project to real production infrastructure.

Sources: Google Security Blog, Northeast Times, heir.dev

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Google Just Open-Sourced a Way to Run AI on Data It Can Never Actually See appeared first on Times Tabloid.

A year and a half after its relaunch, Gemini has joined an extremely small club: Google products used by a billion people every month.

The Milestone

Google CEO Sundar Pichai announced on August 11 that the Gemini app has crossed 1 billion monthly active users. He called it the company’s fastest-growing product ever, and Gemini becomes the 14th Google product to reach the billion-user mark, joining Search, Gmail, Android, and YouTube in that tier — a list that represents the small set of digital products with genuinely global, mainstream reach.

According to details reported by TechCrunch, 63% of Gemini users engage with its voice features, more than 150 million images are generated through the app daily, and more than 100 million active users are on iOS specifically — notable given Google’s historical home-turf advantage on Android.

How It Stacks Up Against ChatGPT

Google is explicitly framing the milestone as putting Gemini roughly on pace with ChatGPT, which crossed the same 1-billion monthly active user threshold back in June. The comparison matters because it’s the clearest public signal yet that the race for default AI assistant status is genuinely a two-way contest at scale, rather than OpenAI holding a commanding, uncontested lead. Precise like-for-like usage comparisons between the two products are difficult since neither company discloses identical metrics on the same schedule, but the timing — both crossing a billion within about two months of each other — is a meaningful data point on its own.

Why the Timing Is Notable

Gemini’s milestone lands just days after Google DeepMind went through a significant leadership change, with CEO Demis Hassabis stepping back from day-to-day operations to become Alphabet’s chief scientist and CTO Koray Kavukcuoglu taking over daily control of the Gemini roadmap. See our coverage of that reshuffle for the full picture. A billion-user announcement landing in the same window is a useful reassurance for a company that saw its stock dip on the leadership news — proof that, whatever the organizational changes, the underlying product is still growing fast.

It also arrives the same week Google unveiled the Pixel 11 with expanded on-device Gemini Intelligence features baked directly into the hardware. See our coverage of that launch event. Between the usage milestone and the hardware push, Google is making a coordinated case that Gemini isn’t just technically competitive with rivals — it’s becoming genuinely central to how people use Google products day to day, on the phone and off it.

The Voice and Image Numbers Are the Real Story

Beyond the headline user count, the breakdown numbers are arguably more informative. A 63% voice engagement rate suggests Gemini’s conversational mode has moved well past novelty status into a core way people interact with the app, rather than a feature people try once and abandon. And more than 150 million daily image generations is a substantial number in its own right — enough to represent a genuine consumer habit, not just occasional experimentation, and a data point worth watching against how quickly rivals like OpenAI’s DALL-E successors and Midjourney are growing their own generation volumes.

What to Watch Next

The next real test is whether Gemini’s growth curve holds through the second half of 2026 as Kavukcuoglu settles into full operational control and as OpenAI, Anthropic, and xAI all continue shipping competing products at a rapid pace. A billion users is a milestone, not a finish line — retention and depth of engagement over the coming months will say more about Gemini’s staying power than the initial crossing of this particular number.

Sources: TechCrunch, AI Weekly, Google company announcements

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Gemini Just Hit 1 Billion Users — Making It Google’s Fastest-Growing Product Ever appeared first on Times Tabloid.

Google’s biggest hardware event of the year happens this evening, and while the chip inside the new phone is genuinely notable, the more consequential story is what Google is asking that chip to run.

What’s Expected Today

Google’s Made by Google 2026 event is set for 6 p.m. ET in New York City, where the company is widely expected to unveil the Pixel 11 lineup alongside a new Pixel Watch 5. Leaks point to a Tensor G6 chip built on a 2nm process — a meaningful jump that would put Google ahead of Qualcomm and MediaTek, both still shipping 3nm designs. If the leaks hold, it would be the most significant chipset generational leap in Pixel history.

The Real Headline: Gemini Running Locally

The chip upgrade matters mainly because of what it’s built to support. Leaked marketing materials describe a feature called Gemini Intelligence built directly into the new hardware, designed to handle multi-step tasks and interpret visual context on-device rather than routing every request to the cloud. Google is also expected to introduce a new “Pixel Glow” notification LED that lights up while Gemini is actively processing a request — a small but telling design choice that signals how central always-available AI has become to the phone’s identity, rather than being one app among many.

On-device processing isn’t just a speed feature. Keeping more AI reasoning local reduces dependence on network connectivity and cuts down on the data that has to leave the device to be processed, which matters both for latency and for privacy-conscious users increasingly aware of how much of their phone activity gets sent to remote servers.

Why Google Needs This Launch to Land

This event carries more weight than a typical annual hardware refresh. It’s Google’s first major public showcase since the company’s DeepMind leadership reshuffle, in which CEO Demis Hassabis stepped back from day-to-day operations to become Alphabet’s chief scientist, handing daily control of Gemini to CTO Koray Kavukcuoglu — see our coverage of that leadership shakeup for the full story. A smooth, well-received Pixel 11 launch, with genuinely useful on-device Gemini features, is an early real-world signal of whether that reorganization is translating research into shipped product faster, exactly as Google said it would when announcing the change.

The Broader On-Device AI Trend

Google isn’t alone in pushing capable AI onto local hardware rather than the cloud. Meta recently open-sourced a 30-billion-parameter agent model designed to run entirely on a single consumer GPU, arguing that distributed, local AI is a better long-term direction than centralized cloud-only systems — a philosophy we covered in our look at Meta’s Muse Glimmer release. Apple and Samsung have made similar on-device pushes with their own flagship devices this year. The pattern across the industry is consistent: as cloud AI infrastructure costs keep climbing, running more inference directly on consumer hardware is becoming a genuine competitive differentiator, not just a nice-to-have.

What to Watch For at the Event

Beyond the headline chip and camera specs, watch for how much of Gemini Intelligence’s most impressive functionality actually runs offline versus how much still quietly depends on a cloud connection — a gap that’s historically been wider than marketing materials suggest at launch events across the industry. Also worth watching: pricing, since a 2nm chip and expanded on-device AI compute typically carry real cost, and how Google positions the Pixel 11 against Apple’s and Samsung’s own AI-forward flagships will shape the smartphone AI narrative heading into the holiday shopping season.

Sources: promptailearning.com AI news, Google Made by Google event materials

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Google’s Pixel 11 Launches Today — And the Chip Might Matter Less Than the AI Running On It appeared first on Times Tabloid.

While most of the AI industry keeps racing to build ever-larger frontier models, Microsoft just made the opposite bet: a smaller, cheaper, purpose-built model that outperforms the giants at one specific job.

What Microsoft Launched

On July 27, Microsoft unveiled MAI-Cyber-1-Flash, its first cybersecurity-specialized AI model built entirely in-house, alongside a new agentic security platform called Project Perception. The model is a fine-tuned, cybersecurity-focused version of Microsoft’s existing MAI-Code-1-Flash coding model — the same lightweight model already embedded in GitHub Copilot and VS Code.

Running inside MDASH, Microsoft’s multi-agent vulnerability detection and remediation harness, the new model scored 95.95% on CyberGym, a public benchmark of over 1,500 real-world vulnerability-reproduction tasks. That’s roughly 12 percentage points ahead of Anthropic’s Mythos 5 and comfortably ahead of models from Google and OpenAI, all of which scored between about 83% and 86% on the same test as standalone systems.

The Real Story Is the Architecture, Not the Score

Microsoft has been careful to note that the 95.95% figure belongs to the full MDASH system — MAI-Cyber-1-Flash paired with OpenAI’s GPT-5.4 — not to the new model working alone. MAI-Cyber-1-Flash now handles roughly 90% of MDASH’s routine security tasks, while the harder 10% still gets escalated to GPT-5.4, meaning Microsoft hasn’t eliminated its dependence on its OpenAI partnership even as it builds its own specialist models.

That combination lifted MDASH’s overall CyberGym score from 88.45% in May to 95.95% now, while cutting operating costs by roughly half compared to the platform’s earlier all-frontier-model configuration. CEO Satya Nadella framed the release around that cost efficiency directly, saying the combination delivers “world-class performance at 50 percent of the cost of leading models.”

A Bet on Tiered, Task-Specific Models

Mustafa Suleyman, who leads Microsoft AI, described the cybersecurity launch as one data point in a broader company-wide shift toward token efficiency — using smaller, cheaper, specialized models for the bulk of routine work, and reserving expensive frontier-scale reasoning for the hardest cases only. It’s a notably different strategy from labs racing purely on frontier capability, and it reflects Microsoft’s scale advantage: the company says it processes more than 100 trillion security signals daily across its enterprise customers, giving it a uniquely large training data pool for a domain-specific model like this one.

It’s worth noting the CyberGym evaluation reproduces already-known, described vulnerabilities rather than discovering entirely unknown ones from scratch — a meaningful caveat when comparing benchmark scores to real-world attacker capability.

Timing That’s Hard to Ignore

The release lands in the same stretch as OpenAI’s disclosure that its unreleased Astra model may have crossed a “Critical” cybersecurity capability threshold — powerful enough, potentially, to independently develop zero-day exploits. See our coverage of OpenAI’s Astra pause for the full story. Read together, the two stories show cybersecurity becoming one of the clearest current battlegrounds in frontier AI — not just as a use case, but as a place where offensive and defensive capability are advancing in the same models simultaneously.

What to Watch Next

Project Perception entered public preview on August 3, and independent researchers are expected to test Microsoft’s claims against the public CyberGym leaderboard in the coming weeks — Microsoft’s Level 1 submission had not yet appeared there as of late July, according to The Hacker News. If Microsoft’s specialist-model, tiered-cost approach holds up under outside scrutiny, expect competitors to accelerate their own domain-specific model efforts rather than relying solely on general-purpose frontier systems for enterprise security work.

Sources: Microsoft Security Blog, SecurityWeek, MarkTechPost

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Microsoft’s New Cybersecurity AI Beats Anthropic and Google — At Half the Price appeared first on Times Tabloid.

Alphabet announced a major leadership overhaul of Google DeepMind on August 5, 2026, with CEO Demis Hassabis giving up day-to-day management to become the unit’s chairman and Alphabet’s chief scientist. Longtime Google AI leader Jeff Dean is departing the company entirely to launch a startup, taking several colleagues with him. Alphabet shares fell roughly 4-5% on the news.

Koray Kavukcuoglu, DeepMind’s chief technology officer and Alphabet’s chief AI architect, takes over daily operations as senior vice president, reporting directly to CEO Sundar Pichai rather than holding a standalone CEO title. He’ll oversee Gemini model development, frontier AI research, and the Gemini app and developer teams. Hassabis said he’ll dedicate more time to Isomorphic Labs, the AI drug-discovery unit he founded, calling improving human health the application of AI he’s always considered most important, according to Fortune’s coverage.

The timing that makes this more than a routine reshuffle

The shakeup lands at a genuinely difficult moment for Google’s AI ambitions: the flagship version of its latest Gemini model remains unreleased despite an original planned June launch, fueling investor and industry concern that Google is falling behind Anthropic and OpenAI, both of which are separately pursuing IPOs reportedly valued near $1 trillion each. A leadership change of this scale during exactly that competitive window is difficult to read as coincidental, whatever the official framing emphasizes about long-term AGI strategy.

Where safety and governance work actually lands now

According to TIME’s reporting, Hassabis had already been spending less time on day-to-day Gemini meetings and more on post-AGI readiness, safety, and governance work, including recent attendance at the G7 summit — meaning Kavukcuoglu had effectively been running Gemini in practice before the title changed formally. More consequentially, Lila Ibrahim, DeepMind’s Chief AI Readiness Officer, will now report to Google executive James Manyika instead of the DeepMind organization directly, with some safety teams reportedly moving to report to Kent Walker, Google’s president of global affairs. One DeepMind employee told TIME they worried that moving safety, responsibility, and ethics teams out of DeepMind itself could reduce their institutional influence — though sources cautioned no final decision on that reporting structure has been confirmed.

What Google’s official line emphasizes

A Google spokesperson framed the change as continuity rather than disruption: “Koray’s philosophy has always been clear: advancing the frontier of AI and building it responsibly are the exact same mission… Frontier model safety has lived directly within the Gemini team from the very beginning, under Koray’s leadership.” Hassabis, who shared the 2024 Nobel Prize in chemistry for his AlphaFold work, wrote to staff that he believes AGI is close and that getting the next steps right matters for humanity broadly, not just for Google specifically.

What to watch next

• Where Google’s flagship next-generation Gemini model launch timeline lands now, and whether the leadership change accelerates or delays it.
• What Jeff Dean’s new startup focuses on, and which additional colleagues ultimately join him.
• Whether the reported safety-team reporting-line changes are finalized, and how that affects DeepMind’s internal influence on AI safety decisions.

Sources

• Demis Hassabis Steps Down From Google DeepMind CEO Role Amid a Major AI Leadership Shake-Up — Fortune
• Inside Google DeepMind’s Reshuffle After CEO Demis Hassabis Steps Aside — TIME
• Google DeepMind’s Hassabis Moves to Chairman Role in Leadership Reshuffle — Bloomberg

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Google’s AI Chief Just Stepped Back From Running AI — And Wall Street Didn’t Like It appeared first on Times Tabloid.

Google canceled its planned standalone AI Studio mobile app on July 31, 2026, despite roughly 800,000 preorders across iOS and Android in more than 168 countries. Instead of shipping the app it had spent months promoting, Google is folding its app-building capabilities directly into the Gemini app.

The AI Studio team confirmed the decision in a post on X: “Thank you to the ~800,000 of you that pre-ordered our mobile app on iOS and Android, it’s clear that people are interested in building software on the go. Instead of asking you to download yet another app, we’ve decided to take an entirely different approach.” Rather than a separate download, users will be able to build working app prototypes through ordinary conversations inside Gemini itself, according to 9to5Google’s reporting. The AI Studio website will remain active for desktop users building more serious, production-ready projects.

Reading a cancellation as a consolidation strategy

Google’s AI product lineup has sprawled considerably over the past two years — AI Studio, Gemini, Antigravity, DeepMind, and Gemma each carry their own branding, interfaces, and in some cases separate user bases. Scrapping a nearly-shipped app with genuine, measurable demand behind it is an unusual move for any company, but it reflects a deliberate bet that fewer, more unified touchpoints beat a wider portfolio of specialized apps, even at the cost of near-term user disappointment and sunk development effort.

The reaction split along predictable lines

Public response to the announcement broke into two camps: users and observers who welcomed a simpler, single-app experience for casual builders, and a more vocal group of frustrated preorder holders who’d specifically wanted the dedicated, purpose-built tool Google had spent months marketing. One frequently echoed complaint captured the frustration directly — that Google keeps treating AI Studio, Gemini, Antigravity, DeepMind, and Gemma as separate internal units, when from a user’s perspective, nobody wants to track Google’s internal org chart just to figure out which app to open.

The timing context worth noting

The cancellation lands the same week Google is managing fallout from unrelated AI safety stories dominating tech coverage — OpenAI’s Hugging Face breach and Anthropic’s disclosure that Claude models breached three organizations. A product decision like this one would ordinarily draw more scrutiny on its own; instead it’s competing for attention with considerably higher-stakes industry news, which may partly explain the relatively muted reaction compared to what a canceled app with 800,000 preorders might otherwise generate.

Google did commit to keeping the AI Studio web platform actively developed, positioning it specifically for builders taking a project from an initial idea through to a production-ready application — a distinction that suggests the company still sees two genuinely different user needs here, casual mobile experimentation versus serious development work, even while collapsing the mobile front-end for the former into Gemini. No firm timeline has been given for when the promised Gemini-integrated building features will actually ship.

What to watch next

• Whether Google provides a concrete timeline for the Gemini-integrated app-building features it’s promising in place of the standalone app.
• Whether refunds or alternative compensation are offered to the 800,000 people who preordered a product that’s no longer shipping as promised.
• Whether this consolidation pattern extends further, folding additional standalone Google AI products into Gemini over the coming months.

Sources

• Gemini Will Create Mobile & Desktop Apps in the Future as AI Studio for Android, iOS Is Canceled — 9to5Google
• Google Scraps Standalone AI Studio App, Gemini Will Build Apps Directly Instead — The Bridge Chronicle
• Google Just Canceled Its AI Studio Mobile App, and It’s Not All Bad News — Digital Trends

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

The post Google Just Killed an App With 800,000 Preorders — On Purpose appeared first on Times Tabloid.

The European Commission issued two binding decisions this month requiring Google to give competing AI assistants deeper access to Android and to share anonymized search data with rival search engines and AI chatbots — one of the EU’s most consequential interventions yet in the AI market.

Under the first ruling, Google must open 11 Android features to eligible AI competitors, letting users activate a rival assistant by voice command the same way they currently say “Hey Google,” and letting those assistants complete background tasks like booking a taxi or replying to messages, according to Reuters’ reporting via Euronews.

The changes roll out in phases: Android access begins reaching users from July 2027, and Google must start sharing anonymized search data with qualifying rivals by January 2027.

Why the EU is acting now

European officials framed both rulings around the Digital Markets Act’s core goal: preventing large platforms from using control over one layer of the stack — in this case, Android and Google Search — to entrench an advantage in an adjacent, emerging market, namely AI assistants.

EU tech chief Henna Virkkunen said the bloc hopes the measures let genuine alternatives to Gemini and Google Search take hold, giving European users real choice rather than a default they can’t easily leave.

Google’s pushback

Google has objected strongly. The company’s President of Global Affairs, Kent Walker, said the decisions risk undermining privacy and security protections for millions of Europeans, and that Google had repeatedly proposed alternative solutions the Commission declined to adopt.

Google also argues users can switch assistants on their devices today. The rulings are legally binding but don’t carry fines on their own — non-compliance, however, could expose Google to penalties of up to 10% of its global annual revenue.

This is not an isolated action. The same EU competition apparatus has recently forced Apple to add interoperability features connecting to non-Apple devices and pushed Meta to dismantle certain engagement-maximizing design patterns — part of a broader pattern of the Commission treating AI-adjacent platform control as the next front in its decade-long antitrust campaign against U.S. tech giants.

What to watch next

• Whether rival AI assistants (from OpenAI, Anthropic, Perplexity, and others) move quickly to apply for the new Android access once eligibility terms are published in early 2027.
• Whether the Trump administration’s public criticism of the DMA translates into any trade-policy response.
• Whether the search-data-sharing requirement meaningfully narrows Google’s advantage in training and grounding its own AI products.

Helpful Sources

• EU orders Google to share search data, open Android to AI rivals — Euronews
• EU Orders Google To Open Android And Search Data To Rival AI Services — Dataconomy
• Google must open Android to rival AI agents, EU orders — Computerworld

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

Follow us on X, Facebook, Telegram, and  Google News

The post The EU Just Forced Google to Open Android and Search Data to AI Rivals appeared first on Times Tabloid.

Over 140 companies launched Open USD (OUSD) on June 30. Ripple, Visa, Mastercard, BlackRock, and Google all signed on. The stablecoin uses consortium governance. No single company controls it, and partners share reserve earnings rather than one issuer keeping the yield.

Versan Aljarrah, founder of Black Swan Capitalist, posted about the launch and zeroed in on Ripple’s position within it. He pointed out that Ripple sits “right in the middle” of the announcement, positioning XRP as the bridge for moving that value globally.

Over 140 companies, including Ripple, Visa, Mastercard, @BlackRock, and Google, just launched (OUSD), a consortium-governed stablecoin.

No mint/redeem fees and shared reserve earnings.@Ripple is right in the middle, positioning XRP as the bridge for moving that value globally. pic.twitter.com/9tkp9xEeCN

— Versan | Black Swan Capitalist (@VersanAljarrah) June 30, 2026

A New Stablecoin With No Fees

OUSD charges nothing to mint or redeem. There’s no cap on issuance either. The entity behind it, Open Standard, distributes most reserve income back to the partner companies after a small management fee. That structure flips the usual stablecoin model, where the issuer keeps the yield and users get a flat dollar token in return.

For Aljarrah, the partner list itself tells the story. Ripple isn’t a bystander here. It’s embedded in the infrastructure layer, sitting alongside the companies that move money at the largest scale in the world.

Split Community Reactions

Replies to the post varied significantly. One commenter asked how XRP bridges value if every company on the list ends up using the same stablecoin. Another argued that the news is bullish for Ripple’s long-term role regardless, since the company seems focused on becoming essential to the digital-money movement rather than just owning a share of the market.

A third reply zoomed out, saying that whether OUSD or another regulated stablecoin wins out, the real story is that the world’s largest financial and tech companies are building blockchain-based payment infrastructure right now. One more reply joked that the timing explains why prices are dropping, since good news in this market always seems to coincide with red candles.

We are on X, follow us to connect with us :- @TimesTabloid1

— TimesTabloid (@TimesTabloid1) June 15, 2025

XRP’s Potential Role

The presence of Visa, Mastercard, BlackRock and Google signals that stablecoin infrastructure is moving from speculative territory into something closer to standard financial plumbing. Ripple’s spot in that group matters specifically to XRP holders.

If OUSD scales the way its backers intend, the network handling that value movement needs proven settlement rails. Ripple has spent years positioning XRP for exactly that role. The asset offers fast, low-cost cross-border settlement. A stablecoin with zero mint and redeem fees, backed by payment giants and asset managers, still needs a bridge to move efficiently between currencies and platforms.

That’s the connection Aljarrah drew. Whether OUSD becomes the dominant stablecoin or just one of several major players, Ripple’s presence in its launch group puts XRP in the conversation at the infrastructure level rather than the speculative one.

Disclaimer: This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses.

Follow us on X, Facebook, Telegram, and  Google News

The post Ripple Is In the Middle of Mastercard, Visa, BlackRock to Position XRP As the Bridge appeared first on Times Tabloid.