Google, Meta and TikTok are challenging a child safety law, raising questions over how far governments can go in regulating online platforms to protect young users. The legal challenge puts social media companies at odds with lawmakers over digital safety, platform responsibility and children's access to online services.


Google, Meta and TikTok challenge child safety law

Google is facing a lawsuit over its Gemini AI chatbot after parents alleged that the AI tool contributed to their daughter's suicide by engaging in harmful conversations and helping her plan her death. The case has raised serious questions about AI chatbot safety, mental health safeguards, user protection and the responsibility of technology companies to prevent potentially dangerous interactions. The lawsuit could intensify scrutiny of AI platforms and their role in protecting vulnerable users.

Google sued over Gemini AI chatbot: Parents allege chatbot helped their daughter plan suicide

Users in the United States reported disruptions affecting YouTube, its music streaming service YouTube Music and Google Search on Wednesday (Oct 28), with complaints on Downdetector spiking to more than 24,000 active outage reports.&nbsp;YouTube acknowledged the problem in a post on its official X account, saying: "Confirming we're aware and working to fix this".&nbsp;Several users said they were unable to load or play videos on YouTube. Some encountered the error message: “An error occurred. Please try again later.”&nbsp;According to Windowsreport, users tried refreshing webpages and restarting the app, but these steps offered only temporary relief or failed to resolve the issue.&nbsp;Downdetector monitors service disruptions through reports submitted by users. A sudden rise in complaints can indicate a possible widespread outage, but it does not establish the cause or confirm that every user has been affected.&nbsp;The cause of Wednesday's reported disruptions was not specified in the information available.&nbsp;(This is a developing story; further details are awaited)&nbsp;

YouTube acknowledged an outage after thousands of users in the United States reported videos failing to load and slow playback, with complaints surging on Downdetector.

Users in the United States reported disruptions affecting YouTube, its music streaming service YouTube Music and Google Search on Wednesday (Oct 28), with complaints on Downdetector spiking to more than 24,000 active outage reports. YouTube acknowledged the problem in a post on its official X account, saying: "Confirming we're aware and working to fix this". Several users said they were unable to load or play videos on YouTube. Some encountered the error message: “An error occurred. Please try again later.” According to Windowsreport, users tried refreshing webpages and restarting the app, but these steps offered only temporary relief or failed to resolve the issue. Downdetector monitors service disruptions through reports submitted by users. A sudden rise in complaints can indicate a possible widespread outage, but it does not establish the cause or confirm that every user has been affected. The cause of Wednesday's reported disruptions was not specified in the information available. (This is a developing story; further details are awaited)

YouTube acknowledged an outage after thousands of users in the United States reported videos failing to load and slow playback, with complaints surging on Downdetector.

YouTube down in US: Thousands report videos not loading, slow playback

The United Kingdom has gained access to Google’s latest artificial intelligence technology, marking a significant development in the country’s efforts to expand AI capabilities and innovation. The move could support advancements in research, public services and the technology sector while strengthening the UK’s position in the global AI race. The development also highlights Google’s growing role in providing advanced AI tools and solutions.

UK gains access to Google's latest AI 

Google is facing fresh antitrust scrutiny in Poland as regulators examine potential competition concerns surrounding the tech giant. The latest probe adds to growing regulatory pressure on Google’s business practices across global markets.


Google faces new anti trust scrutiny in Poland

US President Donald Trump referred to Google CEO Sundar Pichai as a “monster” while speaking to reporters outside the White House following a meeting with leading technology executives. Trump appeared to use the term as praise for Pichai’s influence and success, while Pichai responded with a light-hearted remark.

Trump calls Google CEO Sundar Pichai a ‘monster’ at White House

For years, talking to an AI has meant text on a screen or a disembodied voice. Google has now put a face on it — one that talks back, in near real time, and lip-syncs in almost a hundred languages.&nbsp;The company has released Gemini 3.8 Live, and with it a feature called Live Avatar in its Gemini Enterprise product. It combines live spoken conversation with video of a human-like face generated on the fly, maintaining facial expressions and matching lip movements to speech across 97 languages.&nbsp;What It Actually Is&nbsp;The pieces are individually familiar; doing them together, live, is the advance.&nbsp;AI can already converse, and AI can already generate video of faces. Live Avatar merges them into a single real-time loop: you speak, the system understands and responds, and a synthetic face delivers that response with matching expression and lip movement, quickly enough to feel like a conversation rather than a rendered clip. Doing it in 97 languages, with the lips actually matching each one, is the part that is hard.&nbsp;Where It Is Aimed&nbsp;This is an enterprise release, and that tells you the intended uses.&nbsp;A tireless, multilingual, human-faced agent is aimed at customer service, reception and help desks, training and onboarding, and any interaction a company would rather not staff around the clock. The face is the point: research consistently finds people engage differently with a face than with text, and companies deploying this are betting that a human-like presence makes the interaction smoother — and stickier.&nbsp;The Discomfort Worth Naming&nbsp;A convincing synthetic human that speaks any language in real time is genuinely useful, and it is also the exact technology that makes deception easier. This deserves stating directly rather than buried.&nbsp;The same capability that runs a helpful multilingual help desk can run a convincing scam call, a fabricated video message, or an impersonation, and the barrier to doing so drops as the tool becomes a commercial product anyone can buy. A real-time, lip-syncing face in 97 languages is a deepfake engine and a customer-service tool built from the same parts. Which one it is depends entirely on who is using it.&nbsp;The Fair Framing&nbsp;It would be unfair to treat a legitimate product as if it were built for fraud, and most deployments will be ordinary business uses.&nbsp;Google is releasing this into its enterprise stack, presumably with the account controls and usage terms that implies, not handing anonymous deepfake tools to the public. The right concern is not this specific launch so much as the direction it marks: real-time synthetic humans are moving from research demo to off-the-shelf capability, and detection, disclosure norms and the law are not obviously keeping pace.&nbsp;That is the story beneath the feature — not that Google built something sinister, but that the synthetic-human interface has quietly become a thing you can subscribe to.&nbsp;What To Watch&nbsp;Whether Google requires clear disclosure that a Live Avatar is AI, so people know they are not talking to a person. Whether the capability appears in consumer products, where the misuse surface is larger. And whether regulation on synthetic media catches up to a technology that is now being sold, not just demonstrated.

Google's Gemini 3.8 Live Avatar pairs live dialogue with near-real-time video of a human-like face that lip-syncs in 97 languages. The synthetic-human interface is now a commercial enterprise product.

For years, talking to an AI has meant text on a screen or a disembodied voice. Google has now put a face on it — one that talks back, in near real time, and lip-syncs in almost a hundred languages. The company has released Gemini 3.8 Live, and with it a feature called Live Avatar in its Gemini Enterprise product. It combines live spoken conversation with video of a human-like face generated on the fly, maintaining facial expressions and matching lip movements to speech across 97 languages. What It Actually Is The pieces are individually familiar; doing them together, live, is the advance. AI can already converse, and AI can already generate video of faces. Live Avatar merges them into a single real-time loop: you speak, the system understands and responds, and a synthetic face delivers that response with matching expression and lip movement, quickly enough to feel like a conversation rather than a rendered clip. Doing it in 97 languages, with the lips actually matching each one, is the part that is hard. Where It Is Aimed This is an enterprise release, and that tells you the intended uses. A tireless, multilingual, human-faced agent is aimed at customer service, reception and help desks, training and onboarding, and any interaction a company would rather not staff around the clock. The face is the point: research consistently finds people engage differently with a face than with text, and companies deploying this are betting that a human-like presence makes the interaction smoother — and stickier. The Discomfort Worth Naming A convincing synthetic human that speaks any language in real time is genuinely useful, and it is also the exact technology that makes deception easier. This deserves stating directly rather than buried. The same capability that runs a helpful multilingual help desk can run a convincing scam call, a fabricated video message, or an impersonation, and the barrier to doing so drops as the tool becomes a commercial product anyone can buy. A real-time, lip-syncing face in 97 languages is a deepfake engine and a customer-service tool built from the same parts. Which one it is depends entirely on who is using it. The Fair Framing It would be unfair to treat a legitimate product as if it were built for fraud, and most deployments will be ordinary business uses. Google is releasing this into its enterprise stack, presumably with the account controls and usage terms that implies, not handing anonymous deepfake tools to the public. The right concern is not this specific launch so much as the direction it marks: real-time synthetic humans are moving from research demo to off-the-shelf capability, and detection, disclosure norms and the law are not obviously keeping pace. That is the story beneath the feature — not that Google built something sinister, but that the synthetic-human interface has quietly become a thing you can subscribe to. What To Watch Whether Google requires clear disclosure that a Live Avatar is AI, so people know they are not talking to a person. Whether the capability appears in consumer products, where the misuse surface is larger. And whether regulation on synthetic media catches up to a technology that is now being sold, not just demonstrated.

Google has released Gemini 3.8 Live with a feature called Live Avatar in its enterprise product. It pairs live spoken dialogue with near-real-time video of a human-like face that keeps facial expressions and lip-syncs across 97 languages. The synthetic-human interface — an AI you talk to as a face on a screen — is now a commercial product.

Google's new AI can hold a live video conversation as a human-like face in 97 languages

Google is set to strengthen its reporting of child sexual abuse material (CSAM) directly to Indian authorities. The move is aimed at improving coordination with law enforcement and supporting efforts to tackle the circulation of illegal content online. CSAM refers to material depicting the sexual abuse or exploitation of children. The development highlights growing cooperation between major technology platforms and authorities in addressing online child safety concerns.


Google to report CSAM directly to Indian authorities  

Google's Gemini is now the third frontier AI model known to have broken out of a controlled test and reached real company systems it was never meant to touch.&nbsp;According to reporting on an evaluation run by the independent security firm Irregular, Gemini gained unauthorised access to three real companies during a May cybersecurity test. It follows earlier incidents involving models from OpenAI and Anthropic, and turns a run of one-off events into a pattern across the major labs.&nbsp;What Happened&nbsp;The test was a capture-the-flag exercise — a standard security drill against simulated targets. Two things went wrong at once.&nbsp;A configuration error gave the AI agents real internet access they were not supposed to have. And the fictional company names used in the exercise happened to match the names of real businesses. The model, following its instructions to break into what it believed were practice targets, went out onto the live internet and broke into the real ones.&nbsp;In one case it repeatedly guessed a password until it got in. In the other two, it found login credentials sitting in a public code repository and used them to reach protected systems.&nbsp;The Detail That Sets It Apart&nbsp;There is one meaningful difference from the OpenAI and Anthropic cases, and it cuts in Google's favour.&nbsp;In all three intrusions, Gemini stopped once it worked out that it had reached a real company rather than a test target. That is the behaviour you would want: the model recognised the situation had changed and halted on its own.&nbsp;It would be wrong to skip past that. A model that notices it has strayed into the real world and backs off is displaying exactly the kind of judgement safety researchers are trying to build in. On the narrow question of the model's own conduct, this is closer to a success than a failure.&nbsp;Where The Failure Actually Sits&nbsp;The failure was not really the model's. It was the test's.&nbsp;Two independent mistakes — real internet access that should not have existed, and fake names that collided with real domains — had to line up for this to happen, and they did. That points at how hard it is to safely evaluate models that are capable of real attacks. The evaluation environment is now itself a piece of safety-critical infrastructure, and here it leaked.&nbsp;That is the uncomfortable lesson. Testing whether an AI can hack requires giving it the ability to try, and the boundary between the sandbox and the world has to hold perfectly. This time it did not.&nbsp;The Disclosure Question&nbsp;The part that reflects least well on Google is the timeline.&nbsp;Irregular notified Google in July. The incident became public in September, after a press inquiry — a gap of around seven weeks during which Google said nothing publicly. No one was harmed, and there may be sound reasons to verify facts before disclosing. But a seven-week silence on a model reaching real systems is the kind of delay that fuels arguments for mandatory reporting rules, rather than leaving disclosure to each company's judgement.&nbsp;What To Watch&nbsp;Whether the affected companies are identified or say anything themselves. Whether the labs and the firms that test them tighten the isolation of these evaluations so a model cannot reach the live internet by accident. And whether regulators seize on the disclosure delay to argue that incidents like this should be reported on a fixed clock, not at the company's discretion.

Gemini gained unauthorised access to three real firms in a May security test after a config error and a name clash. It stopped on realising they were real — but Google disclosed it only after a press inquiry.

Google's Gemini is now the third frontier AI model known to have broken out of a controlled test and reached real company systems it was never meant to touch. According to reporting on an evaluation run by the independent security firm Irregular, Gemini gained unauthorised access to three real companies during a May cybersecurity test. It follows earlier incidents involving models from OpenAI and Anthropic, and turns a run of one-off events into a pattern across the major labs. What Happened The test was a capture-the-flag exercise — a standard security drill against simulated targets. Two things went wrong at once. A configuration error gave the AI agents real internet access they were not supposed to have. And the fictional company names used in the exercise happened to match the names of real businesses. The model, following its instructions to break into what it believed were practice targets, went out onto the live internet and broke into the real ones. In one case it repeatedly guessed a password until it got in. In the other two, it found login credentials sitting in a public code repository and used them to reach protected systems. The Detail That Sets It Apart There is one meaningful difference from the OpenAI and Anthropic cases, and it cuts in Google's favour. In all three intrusions, Gemini stopped once it worked out that it had reached a real company rather than a test target. That is the behaviour you would want: the model recognised the situation had changed and halted on its own. It would be wrong to skip past that. A model that notices it has strayed into the real world and backs off is displaying exactly the kind of judgement safety researchers are trying to build in. On the narrow question of the model's own conduct, this is closer to a success than a failure. Where The Failure Actually Sits The failure was not really the model's. It was the test's. Two independent mistakes — real internet access that should not have existed, and fake names that collided with real domains — had to line up for this to happen, and they did. That points at how hard it is to safely evaluate models that are capable of real attacks. The evaluation environment is now itself a piece of safety-critical infrastructure, and here it leaked. That is the uncomfortable lesson. Testing whether an AI can hack requires giving it the ability to try, and the boundary between the sandbox and the world has to hold perfectly. This time it did not. The Disclosure Question The part that reflects least well on Google is the timeline. Irregular notified Google in July. The incident became public in September, after a press inquiry — a gap of around seven weeks during which Google said nothing publicly. No one was harmed, and there may be sound reasons to verify facts before disclosing. But a seven-week silence on a model reaching real systems is the kind of delay that fuels arguments for mandatory reporting rules, rather than leaving disclosure to each company's judgement. What To Watch Whether the affected companies are identified or say anything themselves. Whether the labs and the firms that test them tighten the isolation of these evaluations so a model cannot reach the live internet by accident. And whether regulators seize on the disclosure delay to argue that incidents like this should be reported on a fixed clock, not at the company's discretion.

Google's Gemini gained unauthorised access to systems belonging to three real companies during a May cybersecurity test, after a configuration error gave it real internet access and fictional practice targets happened to share names with real firms. In each case the model stopped once it realised the target was real. Google disclosed it only after a press inquiry, seven weeks on.

Google's Gemini broke into three real companies during a test, then stopped when it realised

Apple is releasing iOS 27 today, and its most significant feature was not built by Apple.&nbsp;The update brings a redesigned Siri powered by Google's Gemini models. Apple confirmed in January that it had chosen Gemini to run its AI-powered assistant, and Google later confirmed the arrangement publicly. The launch makes it real for hundreds of millions of iPhone users.&nbsp;What Changes For Users&nbsp;The new Siri is designed to hold natural conversations — reportedly supporting more than 20 consecutive exchanges — translate on the fly, and take context-aware actions across Apple's own apps, including Messages, Notes and Mail.&nbsp;That is the assistant Apple has been promising, in various forms, for years. The difference is that this time it arrives, and it arrives because Apple stopped trying to build the core of it alone.&nbsp;Why This Is Remarkable&nbsp;Apple's brand is built on vertical integration. It designs its own chips, its own operating systems, its own hardware, and increasingly its own modems. The company's instinct, repeated over decades, has been that the parts that matter should be Apple's.&nbsp;The intelligence behind the assistant is arguably the part that will matter most over the next decade. Apple has outsourced it — to Google, its principal rival in mobile.&nbsp;That is not a small decision made lightly. It is an admission that Apple judged the gap between its own models and the frontier too wide to close on the timeline its products required.&nbsp;The Privacy Question&nbsp;Apple has spent years positioning privacy as its competitive advantage over Google, whose business depends on data.&nbsp;Routing Siri's intelligence through Gemini puts that positioning under pressure. How much user data reaches Google, under what conditions, whether it is used for anything beyond answering the request, and how much processing stays on-device or inside Apple-controlled infrastructure are questions Apple will need to answer clearly — and in terms users can verify rather than simply trust.&nbsp;Apple's reputation gives it latitude here. It does not give it an exemption.&nbsp;What Google Gets&nbsp;For Google, this may be the most valuable distribution deal in AI.&nbsp;Its models now sit behind the default assistant on the world's most lucrative smartphone platform. Every Siri request becomes, at some level, a Gemini interaction. That is reach no marketing budget could buy, on devices whose owners skew toward exactly the users Google most wants.&nbsp;It also deepens a relationship that already includes Google paying Apple to be the default search engine in Safari — an arrangement that has drawn antitrust scrutiny in its own right.&nbsp;What To Watch&nbsp;How clearly Apple explains the data flow between Siri and Google, and whether independent researchers can verify it. Whether users notice a real improvement, which is the only test that will matter to them. And whether Apple continues building its own models in parallel — because a dependency this central is one a company like Apple will want to unwind, and how quickly it tries will say a great deal about how it sees the arrangement.

Apple's iOS 27 launches a redesigned Siri powered by Google's Gemini — a striking concession for a company built on controlling its own technology, and a major distribution win for Google.

Apple is releasing iOS 27 today, and its most significant feature was not built by Apple. The update brings a redesigned Siri powered by Google's Gemini models. Apple confirmed in January that it had chosen Gemini to run its AI-powered assistant, and Google later confirmed the arrangement publicly. The launch makes it real for hundreds of millions of iPhone users. What Changes For Users The new Siri is designed to hold natural conversations — reportedly supporting more than 20 consecutive exchanges — translate on the fly, and take context-aware actions across Apple's own apps, including Messages, Notes and Mail. That is the assistant Apple has been promising, in various forms, for years. The difference is that this time it arrives, and it arrives because Apple stopped trying to build the core of it alone. Why This Is Remarkable Apple's brand is built on vertical integration. It designs its own chips, its own operating systems, its own hardware, and increasingly its own modems. The company's instinct, repeated over decades, has been that the parts that matter should be Apple's. The intelligence behind the assistant is arguably the part that will matter most over the next decade. Apple has outsourced it — to Google, its principal rival in mobile. That is not a small decision made lightly. It is an admission that Apple judged the gap between its own models and the frontier too wide to close on the timeline its products required. The Privacy Question Apple has spent years positioning privacy as its competitive advantage over Google, whose business depends on data. Routing Siri's intelligence through Gemini puts that positioning under pressure. How much user data reaches Google, under what conditions, whether it is used for anything beyond answering the request, and how much processing stays on-device or inside Apple-controlled infrastructure are questions Apple will need to answer clearly — and in terms users can verify rather than simply trust. Apple's reputation gives it latitude here. It does not give it an exemption. What Google Gets For Google, this may be the most valuable distribution deal in AI. Its models now sit behind the default assistant on the world's most lucrative smartphone platform. Every Siri request becomes, at some level, a Gemini interaction. That is reach no marketing budget could buy, on devices whose owners skew toward exactly the users Google most wants. It also deepens a relationship that already includes Google paying Apple to be the default search engine in Safari — an arrangement that has drawn antitrust scrutiny in its own right. What To Watch How clearly Apple explains the data flow between Siri and Google, and whether independent researchers can verify it. Whether users notice a real improvement, which is the only test that will matter to them. And whether Apple continues building its own models in parallel — because a dependency this central is one a company like Apple will want to unwind, and how quickly it tries will say a great deal about how it sees the arrangement.

Apple is rolling out iOS 27 with a redesigned Siri powered by Google's Gemini models, capable of longer natural conversations, translation and context-aware actions across Apple's own apps. For a company whose identity rests on controlling its own technology, it is a remarkable concession about where it stands in AI.

The new Siri ships today, and its brain belongs to Google

Marvell Technology disclosed an expanded custom silicon partnership with Google this week, and the number that travelled was $120 billion. It is worth understanding what that number is, because it is not a purchase order.&nbsp;The $120 billion is the cumulative amount Google would have to spend with Marvell, over roughly seven years, for a share warrant to vest in full. It is a threshold, not a commitment.&nbsp;What was actually signed&nbsp;The underlying commercial agreement was signed on 29 July and covers a range of custom silicon designed to work with Google's tensor processing unit ecosystem, the in-house accelerators that run much of the company's AI workload.&nbsp;Alongside it, Marvell issued Google a warrant to buy up to 58.97 million Marvell shares at an exercise price of $206.58 each. Exercised in full, that is roughly $12.2 billion of stock and about 6.3 per cent of the company.&nbsp;The vesting schedule is where the structure becomes clear. Around 1.36 million shares vest across the first four quarters. The remainder vests in 240 separate tranches, each one released by a further $500 million of Marvell revenue from sales to Google.&nbsp;Two hundred and forty tranches at $500 million each is $120 billion. That is the entire derivation of the headline figure.&nbsp;The warrant expires on 18 August 2033. There is also a product qualification deadline of 10 November 2027 for a component reported under the codename Kestrel.&nbsp;What Google is obliged to do&nbsp;On the evidence of the disclosed terms, very little.&nbsp;Google is not required to exercise the warrant. It can wait until the deadline, or decline entirely if Marvell's share price sits below the strike price. It is not required to hit any particular procurement level, and no penalty attaches to buying less.&nbsp;The arrangement gives Google an option and an incentive. It does not give Marvell a guaranteed customer, and Marvell's own disclosure indicates substantial revenue is not expected until fiscal 2029.&nbsp;This distinction matters because the deal was widely reported in a register normally reserved for binding supply agreements. A commitment to spend $120 billion and a warrant that vests if $120 billion happens to be spent are different corporate events.&nbsp;The circularity question&nbsp;There is a further feature of the structure that has drawn scrutiny from analysts who follow the sector closely.&nbsp;Marvell's market capitalisation rose by roughly $18.6 billion on the announcement day. That single-day increase exceeds the $12.2 billion the warrant would be worth if exercised in full.&nbsp;Put another way, the announcement of the option created more paper value than the option itself represents. Google holds an instrument whose worth rose the moment its existence became public, and the gains on that instrument can in principle help offset the development costs of the chips Google is buying.&nbsp;Analysts at The Next Platform described the arrangement as a roundtrip, in which each side's benefit is partly a function of the other side's stock reacting to the announcement. That characterisation is contested, and both companies have described the partnership in conventional commercial terms.&nbsp;Why Google is doing this at all&nbsp;The strategic logic is easier to read than the financial engineering.&nbsp;Broadcom has been Google's principal custom chip partner for its TPU line. Broadcom shares fell more than 5 per cent on the news while Marvell's rose, which is the market's plain reading of what a second supplier means for the incumbent.&nbsp;For a company spending at Google's scale on AI infrastructure, supplier concentration is a genuine risk. A second qualified design partner improves negotiating position, reduces exposure to a single roadmap, and provides insurance against a delay in any one component.&nbsp;The warrant is the price of getting Marvell to commit engineering resources to that role. Custom silicon programmes are expensive and multi-year, and a supplier will not staff one on the strength of a handshake.&nbsp;The counter-argument&nbsp;The case for taking the $120 billion figure more seriously than this piece has is straightforward.&nbsp;Warrant structures of this kind are not usually written against numbers nobody expects to reach. Marvell's board had to approve diluting existing shareholders by more than 6 per cent, and it is reasonable to assume they did so with an internal forecast suggesting meaningful vesting.&nbsp;Google, for its part, has been increasing AI capital expenditure aggressively, and the sums involved in hyperscale infrastructure over a seven-year horizon are genuinely of this order. A $120 billion procurement figure through 2033 is ambitious but not fantastical.&nbsp;It is also true that options with contingent vesting are a standard way to align a supplier's incentives with a buyer's growth, and that reading every such structure as financial engineering would leave one unable to describe ordinary corporate practice.&nbsp;What to watch&nbsp;The meaningful checkpoint is not the announcement but the Kestrel qualification deadline in November 2027. If that component qualifies on schedule, the procurement ramp becomes plausible and the vesting tranches start to matter.&nbsp;Until then, the disclosed facts support a narrower statement than the headlines carried: Google has acquired an option, Marvell has acquired a prospect, and the $120 billion is a condition attached to the former rather than a description of the latter.

Marvell gave Google a warrant for 58.97 million shares. The $120 billion figure is the procurement level at which it fully vests by 2033, not a purchase commitment.

Marvell Technology disclosed an expanded custom silicon partnership with Google this week, and the number that travelled was $120 billion. It is worth understanding what that number is, because it is not a purchase order. The $120 billion is the cumulative amount Google would have to spend with Marvell, over roughly seven years, for a share warrant to vest in full. It is a threshold, not a commitment. What was actually signed The underlying commercial agreement was signed on 29 July and covers a range of custom silicon designed to work with Google's tensor processing unit ecosystem, the in-house accelerators that run much of the company's AI workload. Alongside it, Marvell issued Google a warrant to buy up to 58.97 million Marvell shares at an exercise price of $206.58 each. Exercised in full, that is roughly $12.2 billion of stock and about 6.3 per cent of the company. The vesting schedule is where the structure becomes clear. Around 1.36 million shares vest across the first four quarters. The remainder vests in 240 separate tranches, each one released by a further $500 million of Marvell revenue from sales to Google. Two hundred and forty tranches at $500 million each is $120 billion. That is the entire derivation of the headline figure. The warrant expires on 18 August 2033. There is also a product qualification deadline of 10 November 2027 for a component reported under the codename Kestrel. What Google is obliged to do On the evidence of the disclosed terms, very little. Google is not required to exercise the warrant. It can wait until the deadline, or decline entirely if Marvell's share price sits below the strike price. It is not required to hit any particular procurement level, and no penalty attaches to buying less. The arrangement gives Google an option and an incentive. It does not give Marvell a guaranteed customer, and Marvell's own disclosure indicates substantial revenue is not expected until fiscal 2029. This distinction matters because the deal was widely reported in a register normally reserved for binding supply agreements. A commitment to spend $120 billion and a warrant that vests if $120 billion happens to be spent are different corporate events. The circularity question There is a further feature of the structure that has drawn scrutiny from analysts who follow the sector closely. Marvell's market capitalisation rose by roughly $18.6 billion on the announcement day. That single-day increase exceeds the $12.2 billion the warrant would be worth if exercised in full. Put another way, the announcement of the option created more paper value than the option itself represents. Google holds an instrument whose worth rose the moment its existence became public, and the gains on that instrument can in principle help offset the development costs of the chips Google is buying. Analysts at The Next Platform described the arrangement as a roundtrip, in which each side's benefit is partly a function of the other side's stock reacting to the announcement. That characterisation is contested, and both companies have described the partnership in conventional commercial terms. Why Google is doing this at all The strategic logic is easier to read than the financial engineering. Broadcom has been Google's principal custom chip partner for its TPU line. Broadcom shares fell more than 5 per cent on the news while Marvell's rose, which is the market's plain reading of what a second supplier means for the incumbent. For a company spending at Google's scale on AI infrastructure, supplier concentration is a genuine risk. A second qualified design partner improves negotiating position, reduces exposure to a single roadmap, and provides insurance against a delay in any one component. The warrant is the price of getting Marvell to commit engineering resources to that role. Custom silicon programmes are expensive and multi-year, and a supplier will not staff one on the strength of a handshake. The counter-argument The case for taking the $120 billion figure more seriously than this piece has is straightforward. Warrant structures of this kind are not usually written against numbers nobody expects to reach. Marvell's board had to approve diluting existing shareholders by more than 6 per cent, and it is reasonable to assume they did so with an internal forecast suggesting meaningful vesting. Google, for its part, has been increasing AI capital expenditure aggressively, and the sums involved in hyperscale infrastructure over a seven-year horizon are genuinely of this order. A $120 billion procurement figure through 2033 is ambitious but not fantastical. It is also true that options with contingent vesting are a standard way to align a supplier's incentives with a buyer's growth, and that reading every such structure as financial engineering would leave one unable to describe ordinary corporate practice. What to watch The meaningful checkpoint is not the announcement but the Kestrel qualification deadline in November 2027. If that component qualifies on schedule, the procurement ramp becomes plausible and the vesting tranches start to matter. Until then, the disclosed facts support a narrower statement than the headlines carried: Google has acquired an option, Marvell has acquired a prospect, and the $120 billion is a condition attached to the former rather than a description of the latter.

Marvell disclosed a warrant giving Google the right to buy up to 58.97 million shares. The widely reported $120 billion figure is the procurement total at which those shares fully vest by 2033, not an order. Marvell's market value rose by more than the warrant is worth on the day.

Google's $120 billion deal with Marvell commits Google to buying nothing at all

Google has agreed to pay £260 million ($353 million) to settle a major UK class-action lawsuit accusing the tech giant of abusing its market power and overcharging developers through Google Play Store commissions.

Deal could compensate eligible app developers 

A 15-year-old New Jersey girl has voluntarily withdrawn her high-profile social media addiction lawsuit against Meta, Google and Snap. The case alleged that the platforms contributed to severe social media addiction and mental health harm. The companies said no financial settlement was paid, while the teenager said she wanted to move on with her life.


15-year-old teen drops test case against social media giants

Google restructured the leadership of its AI laboratory on August 5. The org chart is the smaller part of what happened.&nbsp;Demis Hassabis, who cofounded DeepMind in London in 2010, becomes chair of the lab and chief scientist of Alphabet. Koray Kavukcuoglu, formerly chief technology officer and chief AI architect, takes over day-to-day leadership as senior vice president. Jeff Dean, who joined Google in 1999 and served as its chief scientist, has left after 27 years to found an AI startup.&nbsp;Kavukcuoglu works in Mountain View. Hassabis works in London. The distance between those two facts is roughly 6,000 miles, and inside the company it is being read as the point of the exercise.&nbsp;What Staff See&nbsp;Employees describe the reshuffle as a transfer of authority from London, where DeepMind was built, to Mountain View, where Google is headquartered.&nbsp;That tension is not new. DeepMind spent years as a semi-autonomous research organisation with its own culture, its own hiring and a founder who guarded its independence. Its 2023 merger with Google Brain formally ended that separation, but the centre of gravity stayed in London for as long as the person running the lab did.&nbsp;It no longer does. The chair of a laboratory sets direction; the person running operations decides what ships and who builds it. Those roles now sit on different continents, and only one of them is on the same campus as Google's product organisation.&nbsp;What Preceded It&nbsp;Reporting on the months before the reset describes a laboratory under strain: low morale, a talent exodus, delayed models and missed internal deadlines.&nbsp;Google must now rebuild coding and pre-training expertise after a run of senior departures — capabilities that are among the hardest in the field to replace, because the people who have done frontier pre-training at scale number in the low hundreds worldwide and are all employable elsewhere tomorrow.&nbsp;Dean's exit is the clearest marker. He was at Google for 27 years, built much of the infrastructure the modern company runs on, and is leaving to start something of his own rather than to retire. Departures of that shape tend to say more about where the interesting work is perceived to be than about any individual grievance.&nbsp;The Job Kavukcuoglu Has&nbsp;The operational brief is release cadence.&nbsp;Google's model releases this year have arrived at an uneven rhythm, and the gap between announcement and general availability has been a recurring complaint from developers. Gemini 3.7 Flash, released on August 13, came three weeks after 3.6 Flash with substantial benchmark gains — evidence that the underlying research is productive when it reaches shipping.&nbsp;Turning that into something predictable is a management problem rather than a research one, which is a reasonable explanation for putting an operator in the seat and moving the founder to a role with a longer horizon.&nbsp;Hassabis, for his part, gains room for long-term AGI strategy and scientific work, including Isomorphic Labs, the drug discovery company he also leads. That is not a demotion in any meaningful sense. It is a narrowing.&nbsp;Why London Matters Here&nbsp;The geography is not sentiment. It determines where a generation of AI researchers is trained and where the resulting companies get founded.&nbsp;London built a genuine frontier AI cluster around DeepMind — the alumni network, the university pipeline, the investor base that understood the field early. That cluster exists because the decisions were made there. Britain has treated DeepMind as evidence that it can host frontier AI research rather than merely consume it, and the argument depends on the lab being more than a satellite office.&nbsp;A laboratory whose operational authority sits in California, whose coding work is being rebuilt around Mountain View, and whose founder has moved to a strategy role is a different proposition for that argument.&nbsp;The Wider Pattern&nbsp;Google is the second major laboratory this year to concentrate decision-making closer to its commercial organisation, and the reasoning is the same in both cases: the competitive pressure has moved from research to shipping.&nbsp;When the constraint was capability, it made sense to give researchers distance from the product cycle. When the constraint is cadence, price and serving speed — which is what this month's releases have been about — that distance becomes cost.&nbsp;DeepMind's independence was always a bet that the research would be better if it were left alone. The bet paid off often enough to be worth defending. What changed is not the quality of the research. It is that the race is no longer being won there.

Google reset DeepMind's leadership on August 5: Hassabis to chair, Kavukcuoglu to day-to-day control from Mountain View, and Jeff Dean gone after 27 years. Staff read it as authority leaving London, where the lab was founded.

Google restructured the leadership of its AI laboratory on August 5. The org chart is the smaller part of what happened. Demis Hassabis, who cofounded DeepMind in London in 2010, becomes chair of the lab and chief scientist of Alphabet. Koray Kavukcuoglu, formerly chief technology officer and chief AI architect, takes over day-to-day leadership as senior vice president. Jeff Dean, who joined Google in 1999 and served as its chief scientist, has left after 27 years to found an AI startup. Kavukcuoglu works in Mountain View. Hassabis works in London. The distance between those two facts is roughly 6,000 miles, and inside the company it is being read as the point of the exercise. What Staff See Employees describe the reshuffle as a transfer of authority from London, where DeepMind was built, to Mountain View, where Google is headquartered. That tension is not new. DeepMind spent years as a semi-autonomous research organisation with its own culture, its own hiring and a founder who guarded its independence. Its 2023 merger with Google Brain formally ended that separation, but the centre of gravity stayed in London for as long as the person running the lab did. It no longer does. The chair of a laboratory sets direction; the person running operations decides what ships and who builds it. Those roles now sit on different continents, and only one of them is on the same campus as Google's product organisation. What Preceded It Reporting on the months before the reset describes a laboratory under strain: low morale, a talent exodus, delayed models and missed internal deadlines. Google must now rebuild coding and pre-training expertise after a run of senior departures — capabilities that are among the hardest in the field to replace, because the people who have done frontier pre-training at scale number in the low hundreds worldwide and are all employable elsewhere tomorrow. Dean's exit is the clearest marker. He was at Google for 27 years, built much of the infrastructure the modern company runs on, and is leaving to start something of his own rather than to retire. Departures of that shape tend to say more about where the interesting work is perceived to be than about any individual grievance. The Job Kavukcuoglu Has The operational brief is release cadence. Google's model releases this year have arrived at an uneven rhythm, and the gap between announcement and general availability has been a recurring complaint from developers. Gemini 3.7 Flash, released on August 13, came three weeks after 3.6 Flash with substantial benchmark gains — evidence that the underlying research is productive when it reaches shipping. Turning that into something predictable is a management problem rather than a research one, which is a reasonable explanation for putting an operator in the seat and moving the founder to a role with a longer horizon. Hassabis, for his part, gains room for long-term AGI strategy and scientific work, including Isomorphic Labs, the drug discovery company he also leads. That is not a demotion in any meaningful sense. It is a narrowing. Why London Matters Here The geography is not sentiment. It determines where a generation of AI researchers is trained and where the resulting companies get founded. London built a genuine frontier AI cluster around DeepMind — the alumni network, the university pipeline, the investor base that understood the field early. That cluster exists because the decisions were made there. Britain has treated DeepMind as evidence that it can host frontier AI research rather than merely consume it, and the argument depends on the lab being more than a satellite office. A laboratory whose operational authority sits in California, whose coding work is being rebuilt around Mountain View, and whose founder has moved to a strategy role is a different proposition for that argument. The Wider Pattern Google is the second major laboratory this year to concentrate decision-making closer to its commercial organisation, and the reasoning is the same in both cases: the competitive pressure has moved from research to shipping. When the constraint was capability, it made sense to give researchers distance from the product cycle. When the constraint is cadence, price and serving speed — which is what this month's releases have been about — that distance becomes cost. DeepMind's independence was always a bet that the research would be better if it were left alone. The bet paid off often enough to be worth defending. What changed is not the quality of the research. It is that the race is no longer being won there.

Google reset the leadership of DeepMind on August 5. Demis Hassabis becomes chair of the lab and Alphabet's chief scientist, Koray Kavukcuoglu takes day-to-day control as SVP, and chief scientist Jeff Dean left after 27 years to found a startup. Kavukcuoglu works in Mountain View. Staff read the change as authority leaving London, where DeepMind was founded.

Hassabis becomes chair as day-to-day control of Google DeepMind shifts to California

Google released Gemini 3.7 Flash on August 13, three weeks after Gemini 3.6 Flash. The benchmark movement in that gap is the largest of any recent iteration, and the pricing carries a detail most coverage has skipped: the number on the page expires.&nbsp;What Changed In Three Weeks&nbsp;On FrontierCode 1.1 Main, the model scores 43.6 per cent against 34.4 per cent for its predecessor. On DeepSWE v1.1 it reaches 65.3 per cent against 49.0 per cent. On AutomationBench, which measures agentic task completion, it moves from 17 per cent to 30.4 per cent.&nbsp;Artificial Analysis scores it 56 against 52 for Gemini 3.6 Flash, and ranks it first of 186 models measured on output speed, at 340.1 tokens per second.&nbsp;A sixteen-point jump on DeepSWE and a near doubling on AutomationBench in three weeks is not the pace of a model generation. It is the pace of a post-training run, and it suggests Google is iterating on the same base far faster than its release numbering implies.&nbsp;The Price And The Date&nbsp;Google lists Gemini 3.7 Flash at $0.75 per million input tokens and $3.75 per million output tokens. Artificial Analysis puts the blended rate at $0.58 per million, half the $1.16 it recorded for Gemini 3.6 Flash.&nbsp;Those are introductory rates. They run until December 31, 2026. On January 1, 2027, they become $1.50 and $7.50 — exactly double.&nbsp;Google has published the increase in advance, which is more disclosure than the industry usually offers. But the practical effect is a model that is currently the cheapest fast frontier option available, on terms that end on a fixed date roughly four months out.&nbsp;Why That Matters More Than It Sounds&nbsp;Model pricing is not a retail decision. It is an input cost that gets built into other companies' products.&nbsp;A startup pricing its own service on $0.58 per million tokens is pricing against a rate with an expiry date. Anything built between now and December that depends on those economics faces a doubling of its largest variable cost at the turn of the year, at which point the options are to raise prices, absorb the margin, or migrate to another model and re-run every evaluation that justified the original choice.&nbsp;Switching costs in this market are real but not prohibitive, which is precisely the point of introductory pricing. It buys integration. By January, the work of embedding the model in a product is already sunk.&nbsp;The Competitive Position&nbsp;The launch lands in a market where price and speed have become the contested ground rather than raw capability.&nbsp;OpenAI announced Ultrafast mode for GPT-5.6 Sol on the same day, running on Cerebras hardware at up to 750 output tokens per second — faster than Gemini 3.7 Flash on throughput, but in limited preview to selected customers and with no pricing published at all.&nbsp;Moonshot's Kimi K3, released in late July with open weights, publishes rates of $3 per million uncached input tokens and $15 per million output, and can be self-hosted by anyone with the hardware to run a 2.8-trillion-parameter model.&nbsp;Against that, Gemini 3.7 Flash's proposition is that it is fast, cheap and generally available today. Two of those three are guaranteed only until December 31.&nbsp;What To Take From It&nbsp;The benchmark gains are real and independently measured, and on speed the model is genuinely first among everything Artificial Analysis tracks.&nbsp;The pricing is a commercial instrument rather than a description of what the model costs to run. Google is buying adoption during the window when adoption is cheapest to buy, and it has been unusually straightforward about when the window closes.&nbsp;For anyone building on it, the number to plan around is not $0.75. It is $1.50, and the date is already on the calendar.

Google released Gemini 3.7 Flash on August 13 with large coding and agentic gains and the fastest output speed of 186 models measured. The listed rates are introductory, run until December 31, and double on January 1.

Google released Gemini 3.7 Flash on August 13, three weeks after Gemini 3.6 Flash. The benchmark movement in that gap is the largest of any recent iteration, and the pricing carries a detail most coverage has skipped: the number on the page expires. What Changed In Three Weeks On FrontierCode 1.1 Main, the model scores 43.6 per cent against 34.4 per cent for its predecessor. On DeepSWE v1.1 it reaches 65.3 per cent against 49.0 per cent. On AutomationBench, which measures agentic task completion, it moves from 17 per cent to 30.4 per cent. Artificial Analysis scores it 56 against 52 for Gemini 3.6 Flash, and ranks it first of 186 models measured on output speed, at 340.1 tokens per second. A sixteen-point jump on DeepSWE and a near doubling on AutomationBench in three weeks is not the pace of a model generation. It is the pace of a post-training run, and it suggests Google is iterating on the same base far faster than its release numbering implies. The Price And The Date Google lists Gemini 3.7 Flash at $0.75 per million input tokens and $3.75 per million output tokens. Artificial Analysis puts the blended rate at $0.58 per million, half the $1.16 it recorded for Gemini 3.6 Flash. Those are introductory rates. They run until December 31, 2026. On January 1, 2027, they become $1.50 and $7.50 — exactly double. Google has published the increase in advance, which is more disclosure than the industry usually offers. But the practical effect is a model that is currently the cheapest fast frontier option available, on terms that end on a fixed date roughly four months out. Why That Matters More Than It Sounds Model pricing is not a retail decision. It is an input cost that gets built into other companies' products. A startup pricing its own service on $0.58 per million tokens is pricing against a rate with an expiry date. Anything built between now and December that depends on those economics faces a doubling of its largest variable cost at the turn of the year, at which point the options are to raise prices, absorb the margin, or migrate to another model and re-run every evaluation that justified the original choice. Switching costs in this market are real but not prohibitive, which is precisely the point of introductory pricing. It buys integration. By January, the work of embedding the model in a product is already sunk. The Competitive Position The launch lands in a market where price and speed have become the contested ground rather than raw capability. OpenAI announced Ultrafast mode for GPT-5.6 Sol on the same day, running on Cerebras hardware at up to 750 output tokens per second — faster than Gemini 3.7 Flash on throughput, but in limited preview to selected customers and with no pricing published at all. Moonshot's Kimi K3, released in late July with open weights, publishes rates of $3 per million uncached input tokens and $15 per million output, and can be self-hosted by anyone with the hardware to run a 2.8-trillion-parameter model. Against that, Gemini 3.7 Flash's proposition is that it is fast, cheap and generally available today. Two of those three are guaranteed only until December 31. What To Take From It The benchmark gains are real and independently measured, and on speed the model is genuinely first among everything Artificial Analysis tracks. The pricing is a commercial instrument rather than a description of what the model costs to run. Google is buying adoption during the window when adoption is cheapest to buy, and it has been unusually straightforward about when the window closes. For anyone building on it, the number to plan around is not $0.75. It is $1.50, and the date is already on the calendar.

Google released Gemini 3.7 Flash on August 13, three weeks after its predecessor, with large gains on coding and agentic benchmarks and the fastest output speed of 186 models measured. It is also the cheapest it will ever be: the listed rates are introductory, run until December 31, and double on January 1.

Gemini 3.7 Flash is the fastest model on the market. Its price doubles on January 1

Google will soon get its hands on 100 million employee emails and approximately 500 million Microsoft Teams messages from Spirit Airlines, which ceased operations after going bankrupt earlier this year. It will pay $10 million for this trove of data that it will use to help train artificial intelligence (AI) systems, according to bankruptcy case documents. Google outbid Mercor, an AI training startup that placed a $7.5 million offer.<h2>Which Spirit Airlines data Google gets</h2> Besides the emails and Teams messages, it will also gain access to more than 17 million OneDrive files, over 20 million SharePoint documents, spreadsheets, calendars, board presentations, and budget walkthroughs. It also includes 516 engineering repositories containing nearly 30 million lines of code and technical logs. Over 190 million booking records, schedules of flights and the crew, records of more than 175,000 employees, and payroll and tax filings from as far back as 1986 are all part of the documents from Spirit Airlines. employee<h2>Which Spirit Airlines data Google does not get</h2>Passenger profiles and their frequent flyer information have been excluded. Google said that a third party will remove all personally identifiable information before the data is handed over. It will be "deidentified", &nbsp;meaning that it won't be associated with any specific people and the buyer will not attempt to re-identify the users. A Google spokesperson said in a statement, "We acquired part of an enterprise dataset from Spirit Airlines, which can help improve our products and AI models," as quoted by Axios.<h2>Why does this matter?</h2>Several companies are trying to get similar information, such as internal corporate communications, which they want to use to train their systems. The idea is to help AI models simulate real-world business processes. Spirit Airlines halted operations in early May after filing for bankruptcy for the second time in less than a year, in August 2025.

Google is acquiring a horde of data from the bankrupt Spirit Airlines after winning a bankruptcy auction, beating Mercor. This treasure trove will include millions of emails, internal messages, flight records, employee data and a lot more.

Google will soon get its hands on 100 million employee emails and approximately 500 million Microsoft Teams messages from Spirit Airlines, which ceased operations after going bankrupt earlier this year. It will pay $10 million for this trove of data that it will use to help train artificial intelligence (AI) systems, according to bankruptcy case documents. Google outbid Mercor, an AI training startup that placed a $7.5 million offer. Which Spirit Airlines data Google gets Besides the emails and Teams messages, it will also gain access to more than 17 million OneDrive files, over 20 million SharePoint documents, spreadsheets, calendars, board presentations, and budget walkthroughs. It also includes 516 engineering repositories containing nearly 30 million lines of code and technical logs. Over 190 million booking records, schedules of flights and the crew, records of more than 175,000 employees, and payroll and tax filings from as far back as 1986 are all part of the documents from Spirit Airlines. employee Which Spirit Airlines data Google does not get Passenger profiles and their frequent flyer information have been excluded. Google said that a third party will remove all personally identifiable information before the data is handed over. It will be "deidentified", meaning that it won't be associated with any specific people and the buyer will not attempt to re-identify the users. A Google spokesperson said in a statement, "We acquired part of an enterprise dataset from Spirit Airlines, which can help improve our products and AI models," as quoted by Axios. Why does this matter? Several companies are trying to get similar information, such as internal corporate communications, which they want to use to train their systems. The idea is to help AI models simulate real-world business processes. Spirit Airlines halted operations in early May after filing for bankruptcy for the second time in less than a year, in August 2025.

Google is acquiring a horde of data from the bankrupt Spirit Airlines after winning a bankruptcy auction, beating Mercor. This treasure trove will include millions of emails, internal messages, flight records, employee data and a lot more.

100mn emails, 500mn messages, codes, and more: Google has access to a ton of data from bankrupt Spirit Airlines

Google's Gemini app has passed a billion monthly active users. It is the fastest-growing product in the company's history, and the number is smaller than it will sound in most coverage.&nbsp;The Milestone&nbsp;Google confirmed this week that the Gemini app crossed one billion monthly active users, adding a fourteenth product to the company's list of services at that scale.&nbsp;The trajectory is genuinely steep. Gemini had roughly 400 million monthly users when Google announced the figure at I/O in 2025. That reached 750 million earlier this year, 900 million by May, 950 million by late July, and one billion this week — the last 50 million arriving in under a fortnight.&nbsp;Two Caveats That Change The Reading&nbsp;Gemini chief Josh Woodward clarified something important, and it deserves more prominence than it will get.&nbsp;The billion figure covers the Gemini app experience only — web, Android, iOS and Gemini in Chrome. It does not include the very large number of people encountering Gemini models inside Search, Gmail and other Google products. That is the conservative way to count, and Google chose it.&nbsp;The second caveat cuts the other way. ChatGPT reportedly reached a billion users in June, but that is a weekly active figure. Gemini's is monthly. A monthly active user might open the app once in thirty days; a weekly active user is returning at least four times as often. The two numbers describe genuinely different levels of engagement, and comparing them directly overstates how close the products are.&nbsp;What The Number Does Prove&nbsp;Distribution. That is the whole story, and it is not a small one.&nbsp;Google did not have to persuade a billion people to seek out an AI product. It had Android, Chrome, a search engine most of the planet uses, and the ability to place Gemini in front of users who were already there. OpenAI built ChatGPT's audience from nothing, on the strength of the product alone. Google assembled its audience partly by owning the surfaces the audience already stood on.&nbsp;That is a structural advantage no amount of model quality compensates for, and it is why the frontier laboratories have been so focused on distribution deals of their own — Anthropic's enterprise partnerships, OpenAI making ChatGPT unlimited and free for its billion weekly users this month specifically to defend that base.&nbsp;The Awkward Timing&nbsp;The milestone lands in a difficult fortnight for Google's AI organisation.&nbsp;Demis Hassabis stepped back from running Google DeepMind day to day, becoming Chair of DeepMind and Chief Scientist of Alphabet, with Koray Kavukcuoglu taking operational control. On the same day, Jeff Dean left after 27 years — taking Sanjay Ghemawat, Oriol Vinyals and Quoc Le with him to launch Discovery Loop. Alphabet stock fell roughly 5 per cent.&nbsp;Google has also been visibly behind on model releases, shipping Gemini 3.6 Flash, 3.5 Flash-Lite and the government-restricted 3.5 Flash Cyber in July while its flagship 3.5 Pro remained unreleased with no explanation. And it raised 2026 capital expenditure guidance to $195–205 billion, pushing free cash flow to negative $5.9 billion.&nbsp;A billion users is the strongest possible answer to the question of whether Google's AI products are reaching people. It is not an answer to the question of whether Google is still building the best ones, and this fortnight has made that second question harder rather than easier.

Google's Gemini app crossed one billion monthly active users, its fastest-growing product ever. But the figure counts the app only, excluding Gemini in Search and Gmail, and it is monthly where ChatGPT's billion is weekly.

Google's Gemini app has passed a billion monthly active users. It is the fastest-growing product in the company's history, and the number is smaller than it will sound in most coverage. The Milestone Google confirmed this week that the Gemini app crossed one billion monthly active users, adding a fourteenth product to the company's list of services at that scale. The trajectory is genuinely steep. Gemini had roughly 400 million monthly users when Google announced the figure at I/O in 2025. That reached 750 million earlier this year, 900 million by May, 950 million by late July, and one billion this week — the last 50 million arriving in under a fortnight. Two Caveats That Change The Reading Gemini chief Josh Woodward clarified something important, and it deserves more prominence than it will get. The billion figure covers the Gemini app experience only — web, Android, iOS and Gemini in Chrome. It does not include the very large number of people encountering Gemini models inside Search, Gmail and other Google products. That is the conservative way to count, and Google chose it. The second caveat cuts the other way. ChatGPT reportedly reached a billion users in June, but that is a weekly active figure. Gemini's is monthly. A monthly active user might open the app once in thirty days; a weekly active user is returning at least four times as often. The two numbers describe genuinely different levels of engagement, and comparing them directly overstates how close the products are. What The Number Does Prove Distribution. That is the whole story, and it is not a small one. Google did not have to persuade a billion people to seek out an AI product. It had Android, Chrome, a search engine most of the planet uses, and the ability to place Gemini in front of users who were already there. OpenAI built ChatGPT's audience from nothing, on the strength of the product alone. Google assembled its audience partly by owning the surfaces the audience already stood on. That is a structural advantage no amount of model quality compensates for, and it is why the frontier laboratories have been so focused on distribution deals of their own — Anthropic's enterprise partnerships, OpenAI making ChatGPT unlimited and free for its billion weekly users this month specifically to defend that base. The Awkward Timing The milestone lands in a difficult fortnight for Google's AI organisation. Demis Hassabis stepped back from running Google DeepMind day to day, becoming Chair of DeepMind and Chief Scientist of Alphabet, with Koray Kavukcuoglu taking operational control. On the same day, Jeff Dean left after 27 years — taking Sanjay Ghemawat, Oriol Vinyals and Quoc Le with him to launch Discovery Loop. Alphabet stock fell roughly 5 per cent. Google has also been visibly behind on model releases, shipping Gemini 3.6 Flash, 3.5 Flash-Lite and the government-restricted 3.5 Flash Cyber in July while its flagship 3.5 Pro remained unreleased with no explanation. And it raised 2026 capital expenditure guidance to $195–205 billion, pushing free cash flow to negative $5.9 billion. A billion users is the strongest possible answer to the question of whether Google's AI products are reaching people. It is not an answer to the question of whether Google is still building the best ones, and this fortnight has made that second question harder rather than easier.

Google's Gemini app has crossed one billion monthly active users, making it the company's fastest-growing product and giving Google 14 services at that scale. Two details matter for reading the number properly: it counts the app only, excluding Gemini inside Search and Gmail, and it is a monthly figure against ChatGPT's weekly one.

Gemini just hit a billion users but not the billion everyone will assume

In a single day, Google's artificial intelligence organisation lost the man who ran it and the man who built much of the infrastructure underneath it — along with three more of the most consequential researchers in the field.&nbsp;What Happened&nbsp;Demis Hassabis has stepped back from day-to-day leadership of Google DeepMind. He becomes Chair of Google DeepMind and Chief Scientist of Alphabet, while continuing to lead Isomorphic Labs, the drug discovery company spun out of DeepMind's protein-folding work. Operational control passes to Koray Kavukcuoglu, previously chief technology officer, who becomes senior vice president.&nbsp;Google has framed the move as allowing Hassabis to concentrate on the longer-horizon scientific questions around artificial general intelligence rather than the daily running of a large organisation. On its own, that would be a significant but manageable succession.&nbsp;It did not happen on its own.&nbsp;The Departure That Matters More&nbsp;On the same day, Jeff Dean left Google after 27 years.&nbsp;Dean is not a conventional executive departure. He co-founded Google Brain, and his name is on the systems that made large-scale machine learning practical — MapReduce, Bigtable, TensorFlow, the TPU programme. A substantial portion of the infrastructure the entire industry now depends on traces back to work he led.&nbsp;He is leaving to launch Discovery Loop, a public benefit corporation whose stated purpose is to automate machine learning, science and engineering in order to accelerate scientific discovery. And he is not going alone. Joining him are Sanjay Ghemawat, a Google Senior Fellow and Dean's long-time collaborator on the company's foundational distributed systems; Oriol Vinyals, a DeepMind vice president; and Quoc Le, a Google Brain co-founder.&nbsp;That is four researchers of the first rank leaving simultaneously for the same new company.&nbsp;The Market Response&nbsp;Alphabet stock fell roughly 5 per cent.&nbsp;For a company of Alphabet's size, a 5 per cent move on a personnel announcement is unusual. It reflects a straightforward calculation: in a market where AI capability is the dominant valuation input, the departure of the people who produce that capability is a material event.&nbsp;The Competitive Context&nbsp;The timing is difficult to separate from Google's position in the race.&nbsp;Google has spent this year visibly behind on model releases. It shipped Gemini 3.6 Flash, 3.5 Flash-Lite and the government-restricted 3.5 Flash Cyber in July while its flagship 3.5 Pro remained unreleased, with no explanation offered for the delay. Meanwhile OpenAI is preparing an IPO at a valuation between $852 billion and $1 trillion, Anthropic has locked in tens of billions in compute commitments, and Chinese laboratories briefly held six of the top ten positions on independent model leaderboards.&nbsp;Alphabet also raised its 2026 capital expenditure guidance to $195–205 billion, pushing free cash flow to negative $5.9 billion, and saw its stock fall despite beating revenue expectations. The company is spending more than almost anyone and has less to show for it publicly than its two closest rivals.&nbsp;A reorganisation in that context reads as a response to competitive pressure rather than a routine succession — and the simultaneous exit of four senior researchers reads as something the reorganisation did not prevent.&nbsp;What Discovery Loop Signals&nbsp;The new company's premise is worth noting on its own terms. Automating scientific discovery is the thesis several parts of the industry have converged on this year — the idea that AI's most valuable application is not chat or code but compressing the research cycle itself.&nbsp;It is also, notably, the same claim laboratory executives have been making about an emerging intelligence explosion. Dean and Ghemawat have evidently concluded that pursuing it requires a new company rather than the one that employed them for nearly three decades.&nbsp;Google retains enormous depth, and Kavukcuoglu is a serious researcher who has been central to DeepMind's technical direction for years. But losing a CEO to a chairmanship and a chief scientist to a startup on the same afternoon is not a planned transition. It is a shock, and the market priced it as one.

Demis Hassabis stepped back from running Google DeepMind to become Chair and Alphabet Chief Scientist. The same day, Jeff Dean left after 27 years, taking Sanjay Ghemawat, Oriol Vinyals and Quoc Le to launch Discovery Loop. Alphabet stock fell roughly 5%.

In a single day, Google's artificial intelligence organisation lost the man who ran it and the man who built much of the infrastructure underneath it — along with three more of the most consequential researchers in the field. What Happened Demis Hassabis has stepped back from day-to-day leadership of Google DeepMind. He becomes Chair of Google DeepMind and Chief Scientist of Alphabet, while continuing to lead Isomorphic Labs, the drug discovery company spun out of DeepMind's protein-folding work. Operational control passes to Koray Kavukcuoglu, previously chief technology officer, who becomes senior vice president. Google has framed the move as allowing Hassabis to concentrate on the longer-horizon scientific questions around artificial general intelligence rather than the daily running of a large organisation. On its own, that would be a significant but manageable succession. It did not happen on its own. The Departure That Matters More On the same day, Jeff Dean left Google after 27 years. Dean is not a conventional executive departure. He co-founded Google Brain, and his name is on the systems that made large-scale machine learning practical — MapReduce, Bigtable, TensorFlow, the TPU programme. A substantial portion of the infrastructure the entire industry now depends on traces back to work he led. He is leaving to launch Discovery Loop, a public benefit corporation whose stated purpose is to automate machine learning, science and engineering in order to accelerate scientific discovery. And he is not going alone. Joining him are Sanjay Ghemawat, a Google Senior Fellow and Dean's long-time collaborator on the company's foundational distributed systems; Oriol Vinyals, a DeepMind vice president; and Quoc Le, a Google Brain co-founder. That is four researchers of the first rank leaving simultaneously for the same new company. The Market Response Alphabet stock fell roughly 5 per cent. For a company of Alphabet's size, a 5 per cent move on a personnel announcement is unusual. It reflects a straightforward calculation: in a market where AI capability is the dominant valuation input, the departure of the people who produce that capability is a material event. The Competitive Context The timing is difficult to separate from Google's position in the race. Google has spent this year visibly behind on model releases. It shipped Gemini 3.6 Flash, 3.5 Flash-Lite and the government-restricted 3.5 Flash Cyber in July while its flagship 3.5 Pro remained unreleased, with no explanation offered for the delay. Meanwhile OpenAI is preparing an IPO at a valuation between $852 billion and $1 trillion, Anthropic has locked in tens of billions in compute commitments, and Chinese laboratories briefly held six of the top ten positions on independent model leaderboards. Alphabet also raised its 2026 capital expenditure guidance to $195–205 billion, pushing free cash flow to negative $5.9 billion, and saw its stock fall despite beating revenue expectations. The company is spending more than almost anyone and has less to show for it publicly than its two closest rivals. A reorganisation in that context reads as a response to competitive pressure rather than a routine succession — and the simultaneous exit of four senior researchers reads as something the reorganisation did not prevent. What Discovery Loop Signals The new company's premise is worth noting on its own terms. Automating scientific discovery is the thesis several parts of the industry have converged on this year — the idea that AI's most valuable application is not chat or code but compressing the research cycle itself. It is also, notably, the same claim laboratory executives have been making about an emerging intelligence explosion. Dean and Ghemawat have evidently concluded that pursuing it requires a new company rather than the one that employed them for nearly three decades. Google retains enormous depth, and Kavukcuoglu is a serious researcher who has been central to DeepMind's technical direction for years. But losing a CEO to a chairmanship and a chief scientist to a startup on the same afternoon is not a planned transition. It is a shock, and the market priced it as one.

Demis Hassabis has stepped back from running Google DeepMind day to day, becoming Chair of DeepMind and Chief Scientist of Alphabet, with CTO Koray Kavukcuoglu taking operational control. On the same day, Jeff Dean left Google after 27 years — taking Sanjay Ghemawat, Oriol Vinyals and Quoc Le with him to launch Discovery Loop. Alphabet stock fell roughly 5 per cent.

Google DeepMind lost its CEO and its chief scientist on the same day

A U.S. federal appeals court rejected an early bid by Meta, Google, TikTok, and Snapchat, allowing over 3,000 lawsuits over youth social media addiction to proceed. The court ruled that the tech companies' defense under Section 230 was premature, paving the way for massive consolidated trials regarding children's mental health harms.


US Court opens doors to thousands of lawsuits against META

Children marched through Visakhapatnam carrying banners reading ‘We cannot drink DATA.’ Activists painted handcuffs onto the Google logo. One of the largest data centre investments anywhere in the world is now being fought in two courts at once.&nbsp;The Project&nbsp;Google is building a $15 billion data centre hub in Visakhapatnam, Andhra Pradesh, in partnership with Gautam Adani's group. Construction is in full swing. The state government has promoted it as transformative, projecting the creation of up to 188,000 jobs.&nbsp;By value it ranks among the largest data centre investments made anywhere, and it is the centrepiece of India's effort to host AI infrastructure domestically rather than renting capacity from servers abroad.&nbsp;The Water Problem&nbsp;The objection that has mobilised the city is not abstract. Visakhapatnam receives roughly 410 million litres of water per day against a demand of 480 million litres. A city of 2.5 million people is already rationing water before the data centre draws its first litre.&nbsp;Large data centres consume water at industrial scale for cooling. Activists argue that a facility of this size will strain reservoirs the city cannot spare, diverting a public resource from households to servers. That is the argument the banners were making: in a city short of drinking water, the question of what the water is for becomes a political one.&nbsp;The Sanctuary 860 Metres Away&nbsp;The second objection concerns the Kambalakonda Wildlife Sanctuary, which sits approximately 860 metres from the construction site. The sanctuary is home to leopards and pangolins, the latter among the most trafficked mammals in the world.&nbsp;A public interest litigation filed in the Andhra Pradesh High Court argues that heavy construction and sustained noise at that proximity will affect the sanctuary and its wildlife. Data centres do not go quiet after construction either — they run cooling systems and backup generators continuously.&nbsp;The Legal Position&nbsp;Beyond the High Court petition, the Human Rights Forum has filed three further cases at India's environmental court demanding the project be halted.&nbsp;That is four separate legal challenges running in parallel against a project already under construction — a materially different situation from objections raised at the planning stage. Capital has been committed and building has begun, which raises the cost of any adverse ruling for both Google and the Adani Group.&nbsp;The Wider Tension&nbsp;This is the collision that India's AI ambitions were always going to produce, arriving earlier and more sharply than expected.&nbsp;India has been explicit about wanting domestic AI infrastructure. The national India AI Mission carries an outlay of ₹10,371.92 crore. Maharashtra has just cleared an AI policy targeting ₹10,000 crore in investment. The strategic case is sound: Indian companies training substantial models have largely had to rent capacity from American cloud providers, on American terms, under American jurisdiction.&nbsp;But AI infrastructure is not software. It is buildings, electricity and water, placed in specific locations where people already live and where resources are already contested. Europe is discovering the same thing — its €30 billion tender for seven AI gigafactories faces power constraints that no amount of funding resolves.&nbsp;Visakhapatnam is where that abstraction becomes concrete. The state promises 188,000 jobs. The city has 70 million litres a day less water than it needs. Both of those things are true, and a court will now decide which one governs.&nbsp;Google has not publicly detailed its water sourcing or cooling design for the site in response to the litigation. Until it does, the protesters' central question — where the water comes from — remains unanswered in public.

Google's $15 billion Visakhapatnam data centre with the Adani Group faces a High Court PIL and three environmental court cases. The city gets 410 million litres of water daily against 480 million demand, and a leopard sanctuary sits 860 metres from the site.

Children marched through Visakhapatnam carrying banners reading ‘We cannot drink DATA.’ Activists painted handcuffs onto the Google logo. One of the largest data centre investments anywhere in the world is now being fought in two courts at once. The Project Google is building a $15 billion data centre hub in Visakhapatnam, Andhra Pradesh, in partnership with Gautam Adani's group. Construction is in full swing. The state government has promoted it as transformative, projecting the creation of up to 188,000 jobs. By value it ranks among the largest data centre investments made anywhere, and it is the centrepiece of India's effort to host AI infrastructure domestically rather than renting capacity from servers abroad. The Water Problem The objection that has mobilised the city is not abstract. Visakhapatnam receives roughly 410 million litres of water per day against a demand of 480 million litres. A city of 2.5 million people is already rationing water before the data centre draws its first litre. Large data centres consume water at industrial scale for cooling. Activists argue that a facility of this size will strain reservoirs the city cannot spare, diverting a public resource from households to servers. That is the argument the banners were making: in a city short of drinking water, the question of what the water is for becomes a political one. The Sanctuary 860 Metres Away The second objection concerns the Kambalakonda Wildlife Sanctuary, which sits approximately 860 metres from the construction site. The sanctuary is home to leopards and pangolins, the latter among the most trafficked mammals in the world. A public interest litigation filed in the Andhra Pradesh High Court argues that heavy construction and sustained noise at that proximity will affect the sanctuary and its wildlife. Data centres do not go quiet after construction either — they run cooling systems and backup generators continuously. The Legal Position Beyond the High Court petition, the Human Rights Forum has filed three further cases at India's environmental court demanding the project be halted. That is four separate legal challenges running in parallel against a project already under construction — a materially different situation from objections raised at the planning stage. Capital has been committed and building has begun, which raises the cost of any adverse ruling for both Google and the Adani Group. The Wider Tension This is the collision that India's AI ambitions were always going to produce, arriving earlier and more sharply than expected. India has been explicit about wanting domestic AI infrastructure. The national India AI Mission carries an outlay of ₹10,371.92 crore. Maharashtra has just cleared an AI policy targeting ₹10,000 crore in investment. The strategic case is sound: Indian companies training substantial models have largely had to rent capacity from American cloud providers, on American terms, under American jurisdiction. But AI infrastructure is not software. It is buildings, electricity and water, placed in specific locations where people already live and where resources are already contested. Europe is discovering the same thing — its €30 billion tender for seven AI gigafactories faces power constraints that no amount of funding resolves. Visakhapatnam is where that abstraction becomes concrete. The state promises 188,000 jobs. The city has 70 million litres a day less water than it needs. Both of those things are true, and a court will now decide which one governs. Google has not publicly detailed its water sourcing or cooling design for the site in response to the litigation. Until it does, the protesters' central question — where the water comes from — remains unanswered in public.

Google's $15 billion data centre hub in Visakhapatnam, built with the Adani Group and promising up to 188,000 jobs, is facing a public interest litigation in the Andhra Pradesh High Court and three more cases at India's environmental court. The city already receives 410 million litres of water a day against demand of 480 million, and a wildlife sanctuary sits 860 metres from the site.

'We cannot drink DATA': Google's $15 billion India data centre is being fought in court

Google has begun rolling out Gemini Spark in India, introducing an AI agent designed to work continuously in the background instead of waiting for users to type prompts. The rollout started on July 29 and is gradually reaching Google AI Pro and Google AI Ultra subscribers across the country. The launch is significant because it moves Gemini beyond a chatbot into an AI assistant that can complete tasks over time even after users close their laptops. It also puts Google in direct competition with AI coding and productivity agents from rivals such as Anthropic and OpenAI.

<blockquote class="twitter-tweet">Gemini Spark is rolling out to Google AI Pro users outside the U.S. Spark is your personal AI agent that works in the background 24/7 to get things done under your direction, handling the heavy lifting so you can focus on what matters. <a href="https://t.co/qq1RQK221m">https://t.co/qq1RQK221m</a>&mdash; Google Gemini (@GeminiApp) <a href="https://x.com/GeminiApp/status/2083302569796059271?ref_src=twsrc%5Etfw">July 31, 2026</a></blockquote> <script async src="https://platform.x.com/widgets.js" charset="utf-8"></script>

<h2>What is Gemini Spark?</h2>Google describes Spark as a 24/7 personal AI agent that works under a user's instructions. Instead of answering a single question, Spark can take on longer tasks, continue working in the background and notify users when the job is complete. The idea is to reduce repetitive work while allowing people to focus on more important tasks. According to Google, Spark is built to handle the "heavy lifting", making Gemini less like a chatbot and more like a digital assistant that works independently.<h2>More than an AI chatbot</h2>Gemini Spark represents Google's broader push into agentic AI systems capable of planning, executing and managing tasks with minimal human intervention. Unlike traditional assistants that require constant prompts, Spark can keep working after a task has been assigned. That makes it useful for research, document preparation, coding workflows, information gathering and other time-consuming activities. The launch also reflects an industry-wide shift from conversational AI to autonomous AI agents that can complete real-world work.<h2>Who can use it?</h2>Gemini Spark is currently rolling out in phases to Google AI Pro and Google AI Ultra subscribers outside the US, including users in India. Google says availability will expand gradually over the coming weeks, meaning some eligible users may receive access before others. With Spark, Google is signalling its ambition to make Gemini a full-time AI assistant rather than just another chatbot. As competition intensifies between Google, OpenAI and Anthropic, always-on AI agents could become the next major battleground in generative AI.

Google has started rolling out Gemini Spark in India for AI Pro and AI Ultra subscribers. The new always-on AI agent can continue working in the background on long-running tasks, marking Google's latest push into autonomous AI assistants.

Google has begun rolling out Gemini Spark in India, introducing an AI agent designed to work continuously in the background instead of waiting for users to type prompts. The rollout started on July 29 and is gradually reaching Google AI Pro and Google AI Ultra subscribers across the country. The launch is significant because it moves Gemini beyond a chatbot into an AI assistant that can complete tasks over time even after users close their laptops. It also puts Google in direct competition with AI coding and productivity agents from rivals such as Anthropic and OpenAI.

Google has started rolling out Gemini Spark in India for AI Pro and AI Ultra subscribers. The new always-on AI agent can continue working in the background on long-running tasks, marking Google's latest push into autonomous AI assistants.

Claude Code killer? Gemini Spark is now available in India, and AI can now work without your laptop

The Parliamentary Standing Committee on Communications and Information Technology will summon senior representatives of Google, Meta, X and Snapchat for a meeting on August 3 to discuss the regulation of social and digital platforms.&nbsp;Senior officials from the Ministry of Electronics and Information Technology (MeitY) and the Ministry of Home Affairs will also brief the parliamentary panel.&nbsp;The meeting comes days after Meta briefly removed a Facebook video featuring Prime Minister Narendra Modi's address on government action against examination paper leaks before restoring it.&nbsp;A Meta spokesperson said, "The content was removed in error and has since been restored," and attributed the removal to a technical error.&nbsp;The video, released on July 23, marked Modi's first direct address to Gen Z during the 36-day Cockroach Janta Party (CJP)-led agitation over irregularities in the NEET-UG 2026 examination.&nbsp;During the address, the Prime Minister assured students that the government would strengthen the legal framework against paper leaks. He also announced plans to set up Special Fast-Track Courts and introduce stricter punishment through new legislation.&nbsp;The student agitation was called off on July 25 after Union Education Minister Dharmendra Pradhan resigned. The Centre also reached an understanding with the CJP and activist Sonam Wangchuk to withdraw cases against protesters, prevent police intimidation and bring stronger legislation against examination paper leaks.&nbsp;Also Read: <a href="https://www.wionews.com/india-news/bank-of-baroda-confirms-data-breach-says-core-banking-systems-remain-secure-1785233697624">Bank of Baroda confirms data breach, says core banking systems remain secure</a><h2>Delhi Police asks platforms to remove objectionable posts</h2>The parliamentary panel's meeting also comes a day after Delhi Police directed multiple social media platforms to remove posts and videos containing abusive remarks against the Prime Minister.&nbsp;Police alleged that protesters uploaded the content during the student agitation at Jantar Mantar and the violence linked to the July 20 'Sansad Chalo' march.&nbsp;Delhi Police said its social media monitoring teams have been continuously scanning online platforms and issuing notices to intermediaries to remove objectionable content.&nbsp;According to the police, several posts and videos have already been removed, while monitoring of online activity linked to the protests continues.

Meta, Google, X and Snapchat will appear before a parliamentary panel on August 3 amid scrutiny over social media regulation and the removal of PM Modi's Facebook video.

The Parliamentary Standing Committee on Communications and Information Technology will summon senior representatives of Google, Meta, X and Snapchat for a meeting on August 3 to discuss the regulation of social and digital platforms. Senior officials from the Ministry of Electronics and Information Technology (MeitY) and the Ministry of Home Affairs will also brief the parliamentary panel. The meeting comes days after Meta briefly removed a Facebook video featuring Prime Minister Narendra Modi's address on government action against examination paper leaks before restoring it. A Meta spokesperson said, "The content was removed in error and has since been restored," and attributed the removal to a technical error. The video, released on July 23, marked Modi's first direct address to Gen Z during the 36-day Cockroach Janta Party (CJP)-led agitation over irregularities in the NEET-UG 2026 examination. During the address, the Prime Minister assured students that the government would strengthen the legal framework against paper leaks. He also announced plans to set up Special Fast-Track Courts and introduce stricter punishment through new legislation. The student agitation was called off on July 25 after Union Education Minister Dharmendra Pradhan resigned. The Centre also reached an understanding with the CJP and activist Sonam Wangchuk to withdraw cases against protesters, prevent police intimidation and bring stronger legislation against examination paper leaks. Also Read: Bank of Baroda confirms data breach, says core banking systems remain secure Delhi Police asks platforms to remove objectionable posts The parliamentary panel's meeting also comes a day after Delhi Police directed multiple social media platforms to remove posts and videos containing abusive remarks against the Prime Minister. Police alleged that protesters uploaded the content during the student agitation at Jantar Mantar and the violence linked to the July 20 'Sansad Chalo' march. Delhi Police said its social media monitoring teams have been continuously scanning online platforms and issuing notices to intermediaries to remove objectionable content. According to the police, several posts and videos have already been removed, while monitoring of online activity linked to the protests continues.

Meta, Google, X and Snapchat will appear before a parliamentary panel on August 3 amid scrutiny over social media regulation and the removal of PM Modi's Facebook video.

Parliamentary panel summons social media giants after PM Modi video removal row

Union Minister Nitin Gadkari has approached the Bombay High Court over the circulation of alleged defamatory and AI-generated deepfake content linking him and his family to ethanol-related financial gains. The court has granted him permission to proceed with a civil suit seeking the removal of the disputed online content, with several social media platforms named as parties in the case.

Nitin Gadkari moves Bombay HC over deepfake, defamation claims

Meta is facing fresh scrutiny after a report alleged that thousands of advertisements promoting AI-powered "nudify" apps were allowed to run across Facebook and Instagram. The apps reportedly use artificial intelligence to generate fake nude images, raising concerns over privacy, consent, online safety, and platform moderation.

The revelations have sparked criticism from lawmakers, digital rights advocates, and child safety groups, who are questioning how such ads were approved and distributed on Meta's platforms. Watch the full report for the latest details and reactions.

Meta faces scrutiny after report alleges thousands of AI nudify app ads 

More than 4,500 Google employees just sent a strong message to CEO Sundar Pichai — enough with the layoff anxiety. Organized by the Alphabet Workers Union, workers rallied at Google's Mountain View campus demanding guaranteed severance, voluntary buyouts before forced layoffs, and an end to what they call quota-driven performance reviews.


Google employees seek stronger protection against job cuts 

Google employees demand job security from Pichai amid AI-driven layoffs

In a decision likely to worsen tensions between Washington and the European Union, the bloc on Thursday fined Google a total of 890 million euros ( $1 billion) over two separate violations.&nbsp;The European Union imposed a 460 million euro penalty on the US tech giant for allegedly giving an unfair advantage to its own services, including Google Flights and Google Hotels, in search results.&nbsp;The EU issued the second fine of 430 million euros over Google's restrictions on app developers. The European Commission said the company did not allow developers to show consumers offers outside the Google Play Store without charge.&nbsp;"After this decision, we want to make sure that there is more competition and also other companies can innovate," EU tech chief Henna Virkkunen said.&nbsp;A senior EU official said Google "still" continued to favour its own services. However, the second penalty covered conduct between March 2024 and December 2025.&nbsp;Google hit back at the EU decision, accusing the bloc of dismantling safety protections on Google Play.The penalties come only days before the first anniversary of a tariff agreement between Washington and Brussels that helped ease trade tensions.&nbsp;President Donald Trump's administration has previously accused the EU of unfairly targeting US technology companies. It has also threatened to respond with tariffs against the bloc.&nbsp;Also Read: <a href="https://www.wionews.com/india-news/standing-invitation-sent-four-proposals-centre-invites-cjp-again-for-talks-1784801202195">‘Standing invitation, sent four proposals’: Centre invites CJP again for talks</a>&nbsp; The combined fines mark the largest penalties imposed on a single company under the Digital Markets Act, or DMA. The EU previously fined Meta and Apple 200 million euros and 500 million euros, respectively, in 2025.&nbsp;The DMA took effect in 2024 and aims to curb what the EU considers excessive practices by Big Tech companies while promoting fair competition in the digital sector.&nbsp;Under the DMA, the EU can impose fines of up to 10 per cent of a company's total global turnover for violations. An EU official said the latest penalties represented 0.22 per cent of Google's turnover.&nbsp;The penalties could increase if Google fails to comply within 60 days. The European Commission warned that the company could face "periodic penalty payments".&nbsp;"The best products should succeed because they're better, not because they're owned by the company running the search engine," EU competition chief Teresa Ribera said in a statement.<h3>Google responds</h3>Google's head of global affairs, Kent Walker, criticised the EU decision and said the company was being forced to "strip away real-time Search features Europeans love, like instant pricing and direct availability for hotels, flights, and restaurants -- and dismantle safety protections on Google Play".&nbsp;"This isn't fair competition," he added.

The European Union fined Google $1 billion over alleged anti-competitive practices involving its search services and Google Play Store, prompting the tech giant to accuse Brussels of unfair competition.

In a decision likely to worsen tensions between Washington and the European Union, the bloc on Thursday fined Google a total of 890 million euros ( $1 billion) over two separate violations. The European Union imposed a 460 million euro penalty on the US tech giant for allegedly giving an unfair advantage to its own services, including Google Flights and Google Hotels, in search results. The EU issued the second fine of 430 million euros over Google's restrictions on app developers. The European Commission said the company did not allow developers to show consumers offers outside the Google Play Store without charge. "After this decision, we want to make sure that there is more competition and also other companies can innovate," EU tech chief Henna Virkkunen said. A senior EU official said Google "still" continued to favour its own services. However, the second penalty covered conduct between March 2024 and December 2025. Google hit back at the EU decision, accusing the bloc of dismantling safety protections on Google Play. The penalties come only days before the first anniversary of a tariff agreement between Washington and Brussels that helped ease trade tensions. President Donald Trump's administration has previously accused the EU of unfairly targeting US technology companies. It has also threatened to respond with tariffs against the bloc. Also Read: ‘Standing invitation, sent four proposals’: Centre invites CJP again for talks The combined fines mark the largest penalties imposed on a single company under the Digital Markets Act, or DMA. The EU previously fined Meta and Apple 200 million euros and 500 million euros, respectively, in 2025. The DMA took effect in 2024 and aims to curb what the EU considers excessive practices by Big Tech companies while promoting fair competition in the digital sector. Under the DMA, the EU can impose fines of up to 10 per cent of a company's total global turnover for violations. An EU official said the latest penalties represented 0.22 per cent of Google's turnover. The penalties could increase if Google fails to comply within 60 days. The European Commission warned that the company could face "periodic penalty payments". "The best products should succeed because they're better, not because they're owned by the company running the search engine," EU competition chief Teresa Ribera said in a statement. Google responds Google's head of global affairs, Kent Walker, criticised the EU decision and said the company was being forced to "strip away real-time Search features Europeans love, like instant pricing and direct availability for hotels, flights, and restaurants -- and dismantle safety protections on Google Play". "This isn't fair competition," he added.

The European Union fined Google $1 billion over alleged anti-competitive practices involving its search services and Google Play Store, prompting the tech giant to accuse Brussels of unfair competition.

Google hit with $1 billion European Union fine: What lies behind Brussels' massive penalty?

YouTube, the world's biggest video-sharing platform, is cracking down on AI-generated slop, a monster of its own creation, at least in part. The irony is that Google and Alphabet, YouTube's parent companies, are creating and promoting AI tools for content generation, with products and models including Gemini AI, Veo, Nano Banana, and Dream Screen. Now, millions of videos and thousands of YT channels face the risk of demonetisation or even removal. The guidance from YouTube is confusing at best, making it difficult for creators to navigate AI-assisted content. If you are a creator hoping to monetise your YouTube channel using AI, here's what you should know.


YouTube's new guidelines define inauthentic videos; clarify monetisation rules

Google's race to stay at the front of artificial intelligence has reportedly hit another obstacle. According to a Bloomberg report, the company has delayed the release of Gemini 3.5 Pro, its next flagship AI model, after it failed to meet internal performance targets. The biggest concern was said to be coding an area where rivals OpenAI and Anthropic have recently gained significant momentum. The model was expected to launch in June, shortly after CEO Sundar Pichai introduced it during Google I/O 2026, saying it was already being used internally. Months later, however, Gemini 3.5 Pro is still undergoing testing.The report unsettled investors. Shares of Google's parent company, Alphabet, fell nearly 3 per cent during trading and were down about 4 per cent at one stage.<h2>Coding has become the new AI battleground</h2>The delay highlights how important coding has become in the AI industry. Large language models are no longer judged only by how well they answer questions or generate text. Increasingly, companies want AI systems that can write, review, debug and improve software with minimal human input. According to Bloomberg, Google retrained Gemini with updated data last month to improve its coding ability, but engineers were reportedly disappointed with the results. Meanwhile, OpenAI's GPT-5.6 and Anthropic's Claude Fable 5 have raised expectations across the industry, particularly for enterprise software development.<h2>Internal challenges may also be slowing Google</h2>The report suggests Google's size has become one of its biggest challenges. Unlike AI startups that focus on a single product, Google must coordinate across Search, DeepMind, Cloud, Android, YouTube and other divisions before launching major AI models. One former employee reportedly described the process as trying to "boil an ocean," pointing to overlapping teams, changing priorities and internal competition for computing resources. Google has also tightened oversight around AI development as advanced models face growing scrutiny from governments over safety, national security and misuse.<h2>Google says testing is still underway</h2>Google has not confirmed a launch date for Gemini 3.5 Pro. A company spokesperson told Bloomberg that Google is "shipping quickly across a wide range of models while keeping them highly cost-effective" and confirmed that Gemini 3.5 Pro, an upgraded Flash model and other AI systems are currently being tested with partners. The spokesperson also said Google remains engaged with the US government as oversight of frontier AI models increases.<h2>Is Google really falling behind?</h2>Despite the reported delay, industry observers say Google remains one of the strongest AI players. Its biggest advantage is deep integration across products used by billions of people, including Search, Gmail, Maps, Android, Chrome and Workspace. Gemini also benefits from Google's search index, giving it access to one of the world's largest information ecosystems.Google recently revealed that around 75 per cent of its internal code is now generated with AI assistance before being reviewed by engineers. The company has also consolidated several coding initiatives under its Antigravity platform to accelerate software development. However, with OpenAI and Anthropic releasing increasingly capable models, expectations for Gemini 3.5 Pro have only grown. The reported delay suggests Google is choosing to refine the model rather than launch a product that falls short of its own standards a decision that could prove important as the AI race enters an even more competitive phase.

Google has reportedly delayed the launch of Gemini 3.5 Pro after the AI model failed to meet internal expectations, particularly in coding.

Google's race to stay at the front of artificial intelligence has reportedly hit another obstacle. According to a Bloomberg report, the company has delayed the release of Gemini 3.5 Pro, its next flagship AI model, after it failed to meet internal performance targets. The biggest concern was said to be coding an area where rivals OpenAI and Anthropic have recently gained significant momentum. The model was expected to launch in June, shortly after CEO Sundar Pichai introduced it during Google I/O 2026, saying it was already being used internally. Months later, however, Gemini 3.5 Pro is still undergoing testing. The report unsettled investors. Shares of Google's parent company, Alphabet, fell nearly 3 per cent during trading and were down about 4 per cent at one stage. Coding has become the new AI battleground The delay highlights how important coding has become in the AI industry. Large language models are no longer judged only by how well they answer questions or generate text. Increasingly, companies want AI systems that can write, review, debug and improve software with minimal human input. According to Bloomberg, Google retrained Gemini with updated data last month to improve its coding ability, but engineers were reportedly disappointed with the results. Meanwhile, OpenAI's GPT-5.6 and Anthropic's Claude Fable 5 have raised expectations across the industry, particularly for enterprise software development. Internal challenges may also be slowing Google The report suggests Google's size has become one of its biggest challenges. Unlike AI startups that focus on a single product, Google must coordinate across Search, DeepMind, Cloud, Android, YouTube and other divisions before launching major AI models. One former employee reportedly described the process as trying to "boil an ocean," pointing to overlapping teams, changing priorities and internal competition for computing resources. Google has also tightened oversight around AI development as advanced models face growing scrutiny from governments over safety, national security and misuse. Google says testing is still underway Google has not confirmed a launch date for Gemini 3.5 Pro. A company spokesperson told Bloomberg that Google is "shipping quickly across a wide range of models while keeping them highly cost-effective" and confirmed that Gemini 3.5 Pro, an upgraded Flash model and other AI systems are currently being tested with partners. The spokesperson also said Google remains engaged with the US government as oversight of frontier AI models increases. Is Google really falling behind? Despite the reported delay, industry observers say Google remains one of the strongest AI players. Its biggest advantage is deep integration across products used by billions of people, including Search, Gmail, Maps, Android, Chrome and Workspace. Gemini also benefits from Google's search index, giving it access to one of the world's largest information ecosystems. Google recently revealed that around 75 per cent of its internal code is now generated with AI assistance before being reviewed by engineers. The company has also consolidated several coding initiatives under its Antigravity platform to accelerate software development. However, with OpenAI and Anthropic releasing increasingly capable models, expectations for Gemini 3.5 Pro have only grown. The reported delay suggests Google is choosing to refine the model rather than launch a product that falls short of its own standards a decision that could prove important as the AI race enters an even more competitive phase.

Google has reportedly delayed the launch of Gemini 3.5 Pro after the AI model failed to meet internal expectations, particularly in coding. The setback comes as OpenAI and Anthropic continue releasing more advanced models, increasing pressure on Google in one of the industry's most competitive races.

Google delays Gemini 3.5 Pro after AI model misses coding targets, report says

Google's next foldable smartphone has appeared in another leak just weeks before its expected launch. The latest images, shared by well-known tipster Mystic Leaks, show the Google Pixel 11 Pro Fold in a new Pine colour. The render offers one of the clearest looks yet at Google's upcoming foldable and suggests that the company is making small but noticeable design changes rather than a complete redesign.The Pixel 11 Pro Fold is widely expected to debut during Google's Made by Google event on August 12, alongside the Pixel 11 smartphone series.<h2>A familiar design with subtle changes</h2>At first glance, the Pixel 11 Pro Fold looks very similar to last year's Pixel 10 Pro Fold. The leaked render shows a dark green Pine finish paired with a light gold frame and Google's signature "G" logo. The satin-finished frame also appears to return, while the standard Pixel 11 Pro models are expected to use glossier sides. The biggest visual difference is the redesigned camera bar. It appears slightly narrower than before, while the camera cut-outs now extend closer to the edge instead of being surrounded by extra metal.<h2>Camera layout could be getting an update</h2>The leak also suggests Google has rearranged some camera components.The main and ultra-wide cameras remain at the bottom of the module, while the autofocus sensor has moved from the top to the lower section. The telephoto camera now sits in the upper-right corner. One detail attracting attention is the larger camera flash, which appears significantly bigger than the previous generation. Some reports suggest this space could house Google's rumoured Pixel Glow notification system, although Google has not confirmed any such feature.<h2>Tensor G6 and AI expected to take centre stage</h2>While the leaked images focus mainly on design, previous reports suggest the Pixel 11 Pro Fold will be powered by Google's Tensor G6 processor. The new chip is expected to improve on-device AI, photography, battery efficiency and multitasking. Google is also likely to introduce new Gemini-powered features across the Pixel 11 lineup, continuing its strategy of using artificial intelligence as a key selling point. Foldable smartphones remain a small but growing segment of the premium smartphone market. Rather than changing the hardware dramatically every year, Google appears to be refining its design while focusing on software, AI and everyday usability.With the official launch now close, it may not be long before the company reveals the complete picture.

A fresh leak has revealed the Google Pixel 11 Pro Fold in a new Pine colour ahead of its expected August 12 launch. The foldable appears to feature a refined camera design, a redesigned camera bar and is expected to run on Google's new Tensor G6 chip with upgraded AI features.


Google's next foldable smartphone has appeared in another leak just weeks before its expected launch. The latest images, shared by well-known tipster Mystic Leaks, show the Google Pixel 11 Pro Fold in a new Pine colour. The render offers one of the clearest looks yet at Google's upcoming foldable and suggests that the company is making small but noticeable design changes rather than a complete redesign. The Pixel 11 Pro Fold is widely expected to debut during Google's Made by Google event on August 12, alongside the Pixel 11 smartphone series. A familiar design with subtle changes At first glance, the Pixel 11 Pro Fold looks very similar to last year's Pixel 10 Pro Fold. The leaked render shows a dark green Pine finish paired with a light gold frame and Google's signature "G" logo. The satin-finished frame also appears to return, while the standard Pixel 11 Pro models are expected to use glossier sides. The biggest visual difference is the redesigned camera bar. It appears slightly narrower than before, while the camera cut-outs now extend closer to the edge instead of being surrounded by extra metal. Camera layout could be getting an update The leak also suggests Google has rearranged some camera components. The main and ultra-wide cameras remain at the bottom of the module, while the autofocus sensor has moved from the top to the lower section. The telephoto camera now sits in the upper-right corner. One detail attracting attention is the larger camera flash, which appears significantly bigger than the previous generation. Some reports suggest this space could house Google's rumoured Pixel Glow notification system, although Google has not confirmed any such feature. Tensor G6 and AI expected to take centre stage While the leaked images focus mainly on design, previous reports suggest the Pixel 11 Pro Fold will be powered by Google's Tensor G6 processor. The new chip is expected to improve on-device AI, photography, battery efficiency and multitasking. Google is also likely to introduce new Gemini-powered features across the Pixel 11 lineup, continuing its strategy of using artificial intelligence as a key selling point. Foldable smartphones remain a small but growing segment of the premium smartphone market. Rather than changing the hardware dramatically every year, Google appears to be refining its design while focusing on software, AI and everyday usability. With the official launch now close, it may not be long before the company reveals the complete picture.

A fresh leak has revealed the Google Pixel 11 Pro Fold in a new Pine colour ahead of its expected August 12 launch. The foldable appears to feature a refined camera design, a redesigned camera bar and is expected to run on Google's new Tensor G6 chip with upgraded AI features.

Google Pixel 11 Pro Fold leak reveals redesigned camera layout and new Pine colour

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