AI News Archive: August 14, 2026 — Part 14
Sourced from 500+ daily AI sources, scored by relevance.
- Google will now allow users to remove visible watermarks from AI content
The invisible SynthID will remain.
- Google’s Gemini app adds a toggle to disable AI watermarks, with some exceptions
Gemini now lets you toggle off visible watermarks on Nano Banana images, Omni videos, and Lyria music, though the feature remains restricted in certain countries.
- Google now lets you remove the watermark from Gemini’s creations
Google gives users control over visible Gemini watermarks.
- You can now turn off Google Gemini’s visible watermarks
Google will now allow you to remove visible watermarks from the images, videos, and music made with AI tools. With the update, you can toggle off a new "Media watermark" setting in Gemini and Google's AI video generator, Flow. When toggled off, Google will remove the "sparkle" watermark that appears in the bottom-right corner of […]
- Uber and Pony.ai plan to bring 2,000 robotaxis to Europe
The partnership is expanding beyond the initial market of Zagreb, Croatia to four additional European cities.
- Uber partners with China's Pony.ai for 2,000 robotaxis in Europe
The rollout comes as robotaxi fleet sizes become increasingly critical for commercialization.
- Uber and China’s Pony AI to launch over 2,000 robotaxis across Europe
The expanded partnership with blend a mobility platform, fleet management capacity and autonomous driving technology.
- Uber ups robotaxi offensive in Europe, with partnership expansion
Uber is upping its robotaxi offensive across Europe, after signing an expanded partnership with a Chinese autonomous driving company. The US ride-hailing company and Pony.ai are deploying more than 2,...
- Uber, Pony AI to launch 2,000 robotaxis across Europe
The companies did not reveal which areas they plan to expand to, but Pony told SiliconRepublic.com that further details will follow. Read more: Uber, Pony AI to launch 2,000 robotaxis across Europe
- Uber, Pony.ai plan 2,000 robotaxis in Europe
The expansion would extend the companies’ existing commercial robotaxi service in Zagreb, Croatia, to four unnamed cities in Europe.
- OpenAI and Anthropic in price war as Chinese AI rivals gain ground
US groups release cheaper models after new challenges to their trillion-dollar ambitions.
- Chinese AI rivals are forcing OpenAI and Anthropic to cut prices
Chinese AI rivals are forcing OpenAI and Anthropic to cut prices YourStory.com
- Apple cracks China with Alibaba for iPhone AI
Apple recently posted and removed details explaining how Mac users in China could set up their computers to work with Alibaba’s Qwen AI. Now, Reuters has confirmed long-held speculation that Apple has revisited its Google Gemini AI playbook and built its own proprietary AI model for China with support from Alibaba. Apple worked with a Chinese partner because US models such as ChatGPT or Claude are not being made available there, though Chinese AI development doesn’t seem to be held back by that lack. Apple and Alibaba have not commented on the claims, the report said. The approach echoes Apple’s work with Google to build more advanced large language models (LLMs) for use with Apple Intelligence, and the news will likely be seen as broadly positive by Chinese iPhone users. They can now look forward to working with Apple Intelligence on their devices. The proximity of the reporting suggests they may be able to access Apple’s AI quite soon, once new Apple operating systems ship next month. The silver lining It’s also smart, as it means Apple has identified a way to introduce AI features in nations that are becoming protective of their tech stack.And while Apple’s work with Google on Apple Intelligence was widely regarded as signifying how far behind the company had grown on AI, the work it is now doing with Alibaba shows how the partnership approach has become a strategic tool. Basically, Apple found a way to use such development partnerships to navigate protectionism and political rivalry for the benefit of its customers. The approach means Apple has the distinction of becoming the first foreign company approved to offer a proprietary AI model in China. The work seems to have begun in February 2025, when Apple and Alibaba submitted co-developed AI features for approval to China’s Internet regulator, the Cyberspace Administration of China. Apple last month crossed a milestone in this work when the regulator finally registered Apple’s service. Apple will work with both Alibaba’s Qwen and tech from Baidu, previous reporting has claimed. It is not yet clear how the work with Baidu will be deployed. This work supports US presence in China Delivering AI to Chinese customers is strategically vital to Apple. Its products are hugely popular among Chinese consumers and can arguably be seen as an expression of US soft power there. Any erosion of its position also erodes the US reputation in the economically vibrant market. AI, or the lack of it, has become a fulcrum for change. Morgan Stanley analyst Erik Woodring explained this last year, when he said: “Our survey work shows that Chinese iPhone users are not only more interested in access to genAI technology than US or European iPhone owners, but over 50% of Chinese iPhone owners cited the staggered rollout of Apple Intelligence as having a moderate to significant impact on their decision not to upgrade to a new iPhone this cycle.” Apple is already struggling with these new competitive pressures. It has been encountering stiff competition from Huawei’s already AI-capable devices in China , with Huawei now the biggest-selling smartphone vendor there. Apple is also experiencing a seasonal slowdown in sales as shoppers wait on the introduction of the new iPhone Pro range. They expect powerful AI to be baked into the devices they select. “Increasing investment in agentic AI is becoming a competitive necessity rather than a differentiator,” Counterpoint warns . Good for jobs There’s also an impact on US employment. While Apple often faces criticism for having some of its assembly done in Asia, rather than the more expensive and inadequately resourced US, outgoing CEO Tim Cook recently explained that the company accounts for more than 400,000 jobs in the US. Many of those jobs are generated by the manufacturing of high-tech parts such as the protective glass used on iPhones. While the significance may be easy to miss, this also means that any slowdown in device sales in the world’s second-largest economy — China — will also have a negative impact on US employment. Apple, meanwhile, continues to invest in improving US manufacturing skills and infrastructure. The company recently opened its Advanced Manufacturing Center in Houston in the presence of US Secretary of Commerce Howard Lutnick. “With this Advanced Manufacturing Center, Apple will equip American workers with the skills they need to lead the next generation of technology,” Lutnick said. Apple, meanwhile, is apparently planning toward introducing its much-anticipated folding iPhone Ultra device initially in America first. Please join me on BlueSky , LinkedIn , Mastodon and subscribe to The Core for your extensive, hand-curated Apple-related daily news fix.
- Apple trains its own AI model for China market with Alibaba’s support, sources say
Apple trains its own AI model for China market with Alibaba’s support, sources say The Japan Times
- Apple trains China AI model with Alibaba: sources
In China, Apple is also expected to use Alibaba’s Qwen model and technology from Baidu.
- Apple trains its own AI model for China market
Apple has trained a large language model specifically for the China market, three people familiar with the matter said, a departure from the iPhone maker's strategy of relying on third-party models to power AI features in the country.
- Apple’s China AI strategy now includes training its own custom model, per report
Apple is reportedly taking a new route to bring artificial intelligence features to customers in China: training a large language model of its own with help from Alibaba.
- Apple Trained Own AI Model for China Market With Help From Alibaba
Reuters reports that Apple has trained its own large language model for the China market, rather than relying on a third-party model to power its Apple Intelligence features in the country. The LLM has been developed with the help of Alibaba, according to the report's sources. The development marks a break from Apple's previous strategy of relying on domestic models to bring generative AI to its devices sold in China. Rollout of its AI features in China is expected in the coming months. The rollout is described by the report as a "dual-track strategy" for AI deployment in China, allowing Apple to navigate regulatory hurdles that have limited access for other U.S. tech companies. The move makes Apple the first foreign company approved by the Chinese government to offer a proprietary AI model in the country. Apple previously agreed with Chinese regulators to incorporate Alibaba's Qwen model into Apple Intelligence features, similar to Apple's existing ChatGPT extension available elsewhere. Apple briefly published a support guide in China explaining how Mac users can connect Alibaba's Qwen AI to Siri and Writing Tools, before pulling it entirely less than a day later. Tags: Apple Intelligence , China This article, " Apple Trained Own AI Model for China Market With Help From Alibaba " first appeared on MacRumors.com Discuss this article in our forums
- Apple trained its own AI model for China with help from Alibaba
Apple has reportedly trained a custom AI model for the China market alongside domestic tech giant Alibaba, a rare cross-border partnership that cuts across growing tensions between Beijing and Washington. The China-focused large language model was developed in partnership with Alibaba and trained with the company's support, Reuters reports, citing three unnamed people familiar with […]
- Nvidia scales back funding guarantee for Ohio OpenAI data center, WSJ reports
Nvidia scales back funding guarantee for Ohio OpenAI data center, WSJ reports
- OpenAI CFO tells shareholders enterprise revenue has overtaken ChatGPT
OpenAI’s enterprise business now generates more revenue than its consumer business, finance chief Sarah Friar told shareholders on Friday, months earlier than the company had forecast. Its annualised run rate has reached $40bn, roughly double a year ago. OpenAI now makes more money from businesses than from ChatGPT subscribers. Finance chief Sarah Friar told shareholders […] This story continues at The Next Web
- DeepSeek Lifts AI Model Prices Fourfold
China’s DeepSeek is hiking prices for its flagship V4 models amid fierce competition among artificial intelligence players, though its latest prices remain well below its rivals’.
- DeepSeek Announces Price Hikes for V4 Models
DeepSeek Announces Price Hikes for V4 Models The Information
- DeepSeek to introduce peak and off-peak pricing for its API
DeepSeek will introduce peak and off-peak pricing for its API from Aug. 17. Peak hours will run from 9 a.m. to noon and from 2 p.m. to 6 p.m. Beijing time, while other hours will be classified as off-peak. Off-peak prices will be set at half the peak rates. For deepseek-v4-flash, peak pricing will be […]
- DeepSeek Raises V4 API Prices Significantly, Effective August 17 — Peak-Off-Peak Pricing With Up to 500% Hikes
DeepSeek announced updated API pricing for its V4 model family on August 13, effective August 17, adopting peak and off-peak pricing with off-peak rates half of peak. Off-peak V4 Pro input prices rise as much as 500% for cache hits, while the V4-Pro-0813 model posted a DeepSWE score jump from 7.3 to 62.7.
- DeepSeek raises some V4 prices by more than 10x as AI demand strains capacity
One of AI vendor DeepSeek’s biggest selling points has been its ultra-low price point, but that party’s about to end. The Chinese model provider is raising API pricing for its V4 model family by notable margins, in some cases by more than 1,100%. The increases may not be that dramatic for all, though; the company is encouraging “more flexible workload scheduling,” with peak rates and half-price off-peak rates. The news was tucked into the announcement of the general availability (GA) of DeepSeek V4-Pro and upgrades to VR-Flash. The new pricing takes effect for most parts of the world on August 16. “On paper, at peak, against the right comparator, DeepSeek’s price advantage does disappear, and in places inverts,” said Sanchit Vir Gogia , chief analyst at Greyhound Research. But in practice, “the schedule’s own clock and cache hand most of it back to any buyer paying attention.” How Flash and Pro compare now The new API pricing structure is as follows: Flash is now $0.22 per million input tokens (cache miss) and $0.66 per million output tokens off-peak; and $0.44 per million input tokens (cache miss) and $1.32 per million output tokens at peak. This is up from the flat rate of $0.14 for inputs (cache miss), representing a 57% to 214% increase, and $0.28 per million tokens for outputs, a 136% to 371% increase. Pro is now $0.66 per million input tokens (cache miss) and $1.98 per million output tokens off-peak; and $1.32 per million input tokens (cache miss) and $3.96 per million output tokens at peak. This represents an input increase of between 51% and 203% (up from $0.435) and output increase between 127% and 355% (up from $0.87). Inputs with cache hits, when apps reuse stored prompts rather than processing similar requests from scratch, have even more dramatic pricing increases of 52% to 1,100%. Mark Tauschek , VP of research fellowships and distinguished analyst at Info-Tech Research Group, pointed out that the increase does eliminate the price advantage that 4.0 Flash has over OpenAI 5.6 Luna at peak pricing, but not at off-peak pricing, as OpenAI has dropped Luna API pricing by 80%, off-peak. It also doesn’t eliminate Deepseek 4.0 Pro’s price advantage over Terra, OpenAI’s GPT-5.6 mid-tier reasoning model, even at peak pricing, nor its advantage over GPT-5.6 Sol released in July, Tauschek said. Greyhound Research’s Gogia noted that, off-peak, V4 Flash is “marginally more expensive” on input and 45% cheaper on output than Luna. Pro at peak, meanwhile, runs close to 5x Luna’s price on a representative coding-agent workload. DeepSeek’s roughly 98% cache-hit discount, against an industry norm nearer to 90%, is the mechanism that has kept its measured cost per task at about 60% below Luna, even after Luna’s cost cut, he said. “The schedule re-prices exactly that mechanism,” Gogia said. Flash’s edge over Luna decreases from roughly sevenfold to threefold off-peak, and 1.4 times at peak. “The cache is where the advantage genuinely erodes.” Encouraging users to rethink their schedules DeepSeek’s V4-Pro is now generally available, and V4-Flash is in beta. Both models have new flexible reasoning capabilities (low, high, max) and ‘ thinking modes ’ that use chain-of-thought (CoT) reasoning to improve answer accuracy. V4 Pro is now available on app, web, and via API, and users can try it using “Expert Mode.” V4 Flash is now in beta. The general availability “completes a two-tier structure in which Flash serves volume and Pro is priced for complexity,” Gogia noted. DeepSeek’s peak/off-peak pricing is a means to “allocate resources more reasonably,” the company said, to encourage users to “schedule their tasks based on actual usage.” Gogia pointed out that with the new model, 17 of every 24 hours stay at half price, so timing becomes an economic variable, and work that can wait moves into the cheap hours. In fact, the new pricing schedule hits DeepSeek’s home market hardest and its export market lightest; Western buyers largely pay the off-peak rates. “Usage is following economics at least as much as capability, and economics can change by schedule,” Gogia noted. Simple supply and demand Reading between the lines provides a more nuanced picture, Tauschek noted. “While it’s alarming to see the headlines saying DeepSeek is raising API pricing by 50%-1100%, it doesn’t really tell the whole story.” Part of that story is demand, which is increasing exponentially. DeepSeek can’t keep up with compute requirements, and Anthropic also had a price increase for the same reason in April. And, while third-party providers have not yet reflected that trend, they’ll eventually have to, Tauschek said. “This isn’t unexpected at all,” he noted. “It’s simple supply and demand: when demand goes up, pricing goes up, because supply becomes constrained.” For enterprises that do use DeepSeek (many in the US do not, or can not), the new pricing is not likely to change anything, he said. Cost increases will mostly impact developers, but it will still be less expensive than most alternatives. He pointed out that enterprises are adapting to model routing, which is critical for developers using agentic workloads . Just a few months ago, organizations were paying per-seat pricing and running up usage as a matter of course, but the market move to usage-based pricing has resulted in sticker shock akin to that of the early cloud days. “ Pricing will continue to be a big deal because CFOs are starting to ask what they’re getting for the massive AI spend,” Tauschek said. DeepSeek pricing doesn’t change the need for compatibility, multi-modality CIOs should read the schedule with “relief and unease,” Gogia noted. Relief because the bill is largely schedulable; unease because “a supplier that has learned to price the clock has learned something about its own leverage.” Going forward, he predicted, Flash keeps the volume usage, Pro handles complexity, and interface compatibility lowers the cost of adoption and departure. The real question becomes whether lower economic floors, open weights, and compatible interfaces, when taken together with multi-model routing, make foundation model intelligence materially easier to substitute. Capable inference can be produced “far below the price structures that once surrounded frontier AI,” Gogia noted, and open weights mean model developers become one of just several parties able to serve inference requirements. “The traditional software dependency changes shape when that happens,” he said. The vendor still matters, as do capability and support, but once a workload can move between providers, and enterprises manage their own orchestration and governance, the vendor no longer owns the whole dependency, Gogia said. The most lasting effect of DeepSeek is unlikely to be that it stayed cheapest, he noted. “It is that every provider must now explain why intelligence should command a premium once near-equivalent capability is available through several technical and commercial routes.” This article originally appeared on InfoWorld .
- DeepSeek raises some V4 prices by more than 10x as AI demand strains capacity
One of AI vendor DeepSeek’s biggest selling points has been its ultra-low price point, but that party’s about to end. The Chinese model provider is raising API pricing for its V4 model family by notable margins, in some cases by more than 1,100%. The increases may not be that dramatic for all, though; the company is encouraging “more flexible workload scheduling,” with peak rates and half-price off-peak rates. The news was tucked into the announcement of the general availability (GA) of DeepSeek V4-Pro and upgrades to VR-Flash. The new pricing takes effect for most parts of the world on August 16. “On paper, at peak, against the right comparator, DeepSeek’s price advantage does disappear, and in places inverts,” said Sanchit Vir Gogia , chief analyst at Greyhound Research. But in practice, “the schedule’s own clock and cache hand most of it back to any buyer paying attention.” How Flash and Pro compare now The new API pricing structure is as follows: Flash is now $0.22 per million input tokens (cache miss) and $0.66 per million output tokens off-peak; and $0.44 per million input tokens (cache miss) and $1.32 per million output tokens at peak. This is up from the flat rate of $0.14 for inputs (cache miss), representing a 57% to 214% increase, and $0.28 per million tokens for outputs, a 136% to 371% increase. Pro is now $0.66 per million input tokens (cache miss) and $1.98 per million output tokens off-peak; and $1.32 per million input tokens (cache miss) and $3.96 per million output tokens at peak. This represents an input increase of between 51% and 203% (up from $0.435) and output increase between 127% and 355% (up from $0.87). Inputs with cache hits, when apps reuse stored prompts rather than processing similar requests from scratch, have even more dramatic pricing increases of 52% to 1,100%. Mark Tauschek , VP of research fellowships and distinguished analyst at Info-Tech Research Group, pointed out that the increase does eliminate the price advantage that 4.0 Flash has over OpenAI 5.6 Luna at peak pricing, but not at off-peak pricing, as OpenAI has dropped Luna API pricing by 80%, off-peak. It also doesn’t eliminate Deepseek 4.0 Pro’s price advantage over Terra, OpenAI’s GPT-5.6 mid-tier reasoning model, even at peak pricing, nor its advantage over GPT-5.6 Sol released in July, Tauschek said. Greyhound Research’s Gogia noted that, off-peak, V4 Flash is “marginally more expensive” on input and 45% cheaper on output than Luna. Pro at peak, meanwhile, runs close to 5x Luna’s price on a representative coding-agent workload. DeepSeek’s roughly 98% cache-hit discount, against an industry norm nearer to 90%, is the mechanism that has kept its measured cost per task at about 60% below Luna, even after Luna’s cost cut, he said. “The schedule re-prices exactly that mechanism,” Gogia said. Flash’s edge over Luna decreases from roughly sevenfold to threefold off-peak, and 1.4 times at peak. “The cache is where the advantage genuinely erodes.” Encouraging users to rethink their schedules DeepSeek’s V4-Pro is now generally available, and V4-Flash is in beta. Both models have new flexible reasoning capabilities (low, high, max) and ‘ thinking modes ’ that use chain-of-thought (CoT) reasoning to improve answer accuracy. V4 Pro is now available on app, web, and via API, and users can try it using “Expert Mode.” V4 Flash is now in beta. The general availability “completes a two-tier structure in which Flash serves volume and Pro is priced for complexity,” Gogia noted. DeepSeek’s peak/off-peak pricing is a means to “allocate resources more reasonably,” the company said, to encourage users to “schedule their tasks based on actual usage.” Gogia pointed out that with the new model, 17 of every 24 hours stay at half price, so timing becomes an economic variable, and work that can wait moves into the cheap hours. In fact, the new pricing schedule hits DeepSeek’s home market hardest and its export market lightest; Western buyers largely pay the off-peak rates. “Usage is following economics at least as much as capability, and economics can change by schedule,” Gogia noted. Simple supply and demand Reading between the lines provides a more nuanced picture, Tauschek noted. “While it’s alarming to see the headlines saying DeepSeek is raising API pricing by 50%-1100%, it doesn’t really tell the whole story.” Part of that story is demand, which is increasing exponentially. DeepSeek can’t keep up with compute requirements, and Anthropic also had a price increase for the same reason in April. And, while third-party providers have not yet reflected that trend, they’ll eventually have to, Tauschek said. “This isn’t unexpected at all,” he noted. “It’s simple supply and demand: when demand goes up, pricing goes up, because supply becomes constrained.” For enterprises that do use DeepSeek (many in the US do not, or can not), the new pricing is not likely to change anything, he said. Cost increases will mostly impact developers, but it will still be less expensive than most alternatives. He pointed out that enterprises are adapting to model routing, which is critical for developers using agentic workloads . Just a few months ago, organizations were paying per-seat pricing and running up usage as a matter of course, but the market move to usage-based pricing has resulted in sticker shock akin to that of the early cloud days. “ Pricing will continue to be a big deal because CFOs are starting to ask what they’re getting for the massive AI spend,” Tauschek said. DeepSeek pricing doesn’t change the need for compatibility, multi-modality CIOs should read the schedule with “relief and unease,” Gogia noted. Relief because the bill is largely schedulable; unease because “a supplier that has learned to price the clock has learned something about its own leverage.” Going forward, he predicted, Flash keeps the volume usage, Pro handles complexity, and interface compatibility lowers the cost of adoption and departure. The real question becomes whether lower economic floors, open weights, and compatible interfaces, when taken together with multi-model routing, make foundation model intelligence materially easier to substitute. Capable inference can be produced “far below the price structures that once surrounded frontier AI,” Gogia noted, and open weights mean model developers become one of just several parties able to serve inference requirements. “The traditional software dependency changes shape when that happens,” he said. The vendor still matters, as do capability and support, but once a workload can move between providers, and enterprises manage their own orchestration and governance, the vendor no longer owns the whole dependency, Gogia said. The most lasting effect of DeepSeek is unlikely to be that it stayed cheapest, he noted. “It is that every provider must now explain why intelligence should command a premium once near-equivalent capability is available through several technical and commercial routes.” This article originally appeared on InfoWorld .
- DeepSeek to raise V4 AI prices, add peak rates
DeepSeek-V4-Flash will cost US$1.32 per 1 million output tokens during peak hours and US$0.66 off-peak, up from US$0.28.
- DeepSeek's AI models are about to cost four times more
DeepSeek has announced price hikes for its V4 models.
- ChatGPT can now keep tabs on what you’re up to in case you need reminding later
ChatGPT's new feature sounds an awful lot like Windows Recall.
- NetworkNews Audio Announces Audio Press Release (APR) Discussing Tackling Power, Connectivity Issues Together in AI Infrastructure Project
NetworkNews Audio Announces Audio Press Release (APR) Discussing Tackling Power, Connectivity Issues Together in AI Infrastructure Project Toronto Star
- Gemini 3.7 Flash is here — and this prompt proves why it's known as Google's 'workhorse'
Gemini 3.7 Flash is here — and this prompt proves why it's known as Google's 'workhorse' Tom's Guide
- IBM teams up with OpenAI to bring AI deeper into businesses
IBM teams up with OpenAI to bring AI deeper into businesses YourStory.com
- IBM, OpenAI Partner to Accelerate Enterprise AI
The deal comes less than a year after IBM undertook a similar initiative with Anthropic.
- OpenAI forges closer ties with IBM in enterprise push
OpenAI forges closer ties with IBM in enterprise push IT Pro
- IBM, OpenAI partner to advance AI deployment in enterprise operations
IBM, OpenAI partner to advance AI deployment in enterprise operations verdict.co.uk
- IBM partners with OpenAI to drive enterprise AI deployment
IBM will embed OpenAI frontier models and products such as Codex and ChatGPT embedded into IBM’s AI consulting services platform, IBM Consulting Advantage, as well as expanding the companies’ existing collaboration in the cybersecurity realm, they announced Thursday. Joint initiatives will include the creation of industry-specific products for financial services, government, telecommunications, and retail, as well as serving key enterprise domains such as finance, procurement, customer operations, and HR. IBM will join the OpenAI Partner Network and engage forward-deployed engineers and consultants trained through the network to help clients accelerate their AI implementations. It will also create a dedicated OpenAI practice, certifying thousands of consultants and engineers via the Partner Network, it said. There will be three focus areas in the new partnership: helping organizations transform their businesses to integrate AI into their daily work, assisting clients in modernizing legacy applications through a combination of OpenAI products and IBM’s expertise, and an expansion of an existing cybersecurity collaboration combining OpenAI frontier AI capabilities with IBM Autonomous Security. Everyone, it seems, is engaging forward-deployed engineers to help enterprises build systems around their AI models: OpenAI announced its initiative, OpenAI Deployment Company, on May 11, a week after Anthropic launched its efforts. Microsoft and AWS too have jumped into the ring. But, said Info-Tech Research Group distinguished analyst Mark Tauschek, “This is becoming table stakes. The consulting firms that have the horsepower and technical talent to partner with a frontier AI lab to do this are all picking their horses. Anthropic has some, Microsoft has some, and of course OpenAI has some. There will be more announcements like this to follow, and while it’s good PR for both IBM and OpenAI, it’s certainly not differentiating.” Enterprises will choose the consulting firms that work with their AI vendor of choice, he noted, although he does think that Microsoft had the initial advantage because they were able to quickly provide thousands of skilled FDEs. “But,” he said, “this is a horse race, and it’s going to be a tight one.” This article first appeared on CIO .
- OpenAI Unveils Ultrafast Mode for GPT-5.6 Sol, Promises Up to 14x Faster Processing
OpenAI announced an early preview of Ultrafast. The AI company states that this new API service tier can run GPT-5.6 Sol up to 14 times faster than standard processing. The GPT‑5.6 Sol in Ultrafast mode is powered by Cerebras and is claimed to generate up to 750 output tokens per second. The company confirmed that it is working with select customers to evaluate the ...
- Microsoft brings Copilot apps together ahead of ‘super app’ overhaul
Microsoft will bring its two Copilot apps into a unified interface for consumer and work users, part of a wider drive to create a Copilot “super app” that consolidates various features. Until now, Microsoft has offered two Copilot apps across web, desktop and mobile platforms: a simplified, consumer-focused Copilot app, and Microsoft 365 Copilot , which combines the AI assistant with access to Microsoft’s productivity apps and files. Microsoft on Thursday announced an “ updated Copilot app ” aimed at providing a “simpler, more cohesive experience” for personal and work users. For users of the consumer Copilot app, the update will provide access to Microsoft 365 tools such as Word, Excel, and Outlook from within Copilot, as well as the ability to connect email, calendars and cloud storage. At the same time, some features previously available in the consumer-focused app will be retired, including group chats and Deep Research, though Microsoft 365 Premium subscribers will still have access to a similar research tool called Researcher. The changes mean consumer Copilot app users will be moved to an updated version of the app starting Aug. 18, with their history and most content carried across. For Microsoft 365 Copilot work accounts, there’ll be minimal changes aside from a change to the app name and icon, Microsoft said. As the two apps are brought together, Microsoft said work and personal accounts will remain separate, with security and admin controls remaining in place when users are logged into their Microsoft 365 Copilot account at work: “Commercial data boundaries, tenant controls, and compliance protections are not changing,” the company said. “The boundaries that keep work and personal separate remain in place: Personal (Microsoft account) and work (Microsoft Entra) accounts are distinct by design. Data entered into the work (Microsoft Entra) experience does not flow into the personal (Microsoft account) experience, and vice versa.” The changes can be viewed as part of a wider overhaul of Microsoft’s Copilot strategy, which centers around the introduction of a “super app” later this quarter. The plan — rumored for some time and recently confirmed by Microsoft CEO Satya Nadella in an earnings call — is to consolidate various Copilot features, including Copilot Cowork and “ autopilot ” agents into a single app. Microsoft has struggled to convince business customers to pay for the Microsoft 365 Copilot since it launched in 2023. The AI assistant costs $30 per user each month in addition to Microsoft 365 subscriptions for enterprises, and $20 per for user a month for smaller firms . (Microsoft does offer promotional discounts .) Microsoft said earlier this year that it had 15 million paid seats , meaning that only 3.3% of Microsoft 365 customers pay for the tool. That figure has grown however, reaching 30 million paid seats as of last month, the company said .
- Microsoft Merges Copilot Apps as Features Disappear Starting Aug. 18
Microsoft is consolidating its consumer Copilot and Microsoft 365 Copilot apps into one experience, with several feature retirements beginning Aug. 18 and a broader desktop rollout following in September. The post Microsoft Merges Copilot Apps as Features Disappear Starting Aug. 18 appeared first on TechRepublic .
- DeepSeek's innovative harness treats everything as a plug-in
Chinese AI labs keep moving forward while US labs play defense
- OpenAI's Computer History turns your clicks and keystrokes into a searchable ChatGPT memory timeline
OpenAI's Computer History records clicks, keystrokes, and app switches on Mac and turns them into a searchable timeline for ChatGPT and Codex. The data is stored locally as unencrypted Markdown files. OpenAI says it's not used for AI training, but memories that feed into chats may still end up as training data. The article OpenAI's Computer History turns your clicks and keystrokes into a searchable ChatGPT memory timeline appeared first on The Decoder .
- ChatGPT gets "Computer History" memory
ChatGPT now remembers computer history, enhancing context and continuity in conversations.
- ChatGPT's new Computer History tracks your Mac activity to create a timeline - but should you let it?
Rolling out to ChatGPT's Mac app, Computer History creates a timeline from your activities across the apps and websites you use on your Mac. Is that a privacy risk?
- ChatGPT Can Now Add Your Mac Activity to Its Memories
OpenAI’s latest feature can use your clicks, keystrokes and other interactions to supplement ChatGPT and Codex context.
- Say goodbye to Chronicle. ChatGPT’s new Computer History feature does it better
ChatGPT's desktop app now has Computer History, a more private, screenshot-free upgrade to Chronicle that turns your daily activity into a searchable timeline.
- Nvidia, Indosat launch Gadjah Mada AI center
The center is part of the national AI Center of Excellence, led by the ministry under Indonesia's Golden 2045 Vision as a sovereign AI and skills-building program.
- Google Meet can finally take notes for your in-person meetings
Google Meet's meeting notes feature is expanding beyond video calls, letting Gemini take notes during your in-person meetings and turn them into a doc with action items and a full transcript.
- Nvidia moves into hot market for model routers
Model routing has emerged as a way to keep AI inferencing costs down by examining prompts and directing them to the most appropriate model — for example, the cheapest one able to respond effectively. This means that requests can be handled more efficiently, giving more accurate results and lower runtime costs, a concern in an era of rising AI expenditure . Nvidia sees the potential for model routing and has just launched NeMo Switchyard , its take on the technology. Switchyard provides a library for applying multiple routing approaches, enabling developers to apply a system-of-models approach and build more efficient, controllable agents. Interest in model routing is growing — both as a tool and as an investment: Cloudflare recently introduced a model router as part of a new suite of enterprise AI tools, while payment services company Stripe is looking to buy OpenRouter, according to The Wall Street Journal . Companies will certainly be looking at routing options more carefully in the future as they work out how best to marry their infrastructures with their AI demands. This article first appeared on InfoWorld .
- OpenAI loses its AI ethics lead
OpenAI has lost its AI ethics lead Chloé Bakalar just a year after she joined the company, the Financial Times reported . Bakalar has maintained a silence and has yet to update her LinkedIn profile , but if her departure is confirmed then it will add to the list of OpenAI executives who have quit in recent months. Other departures include robotics chief Caitlin Kalinowski , who left the company over its deal with the US Department of Defense; researcher Zoe Hitzig, who quit in a very public way by writing an article in the New York Times; and Johannes Heidecke, head of safety systems. OpenAI’s ethical stance has been called into question following an attack by OpenAI models on Hugging Face . The departure of its sole ethicist will add to the pressure on the company. In her year at OpenAI Bakalar focused on ethical approaches to model development, looking at how humans interact with AI and examining machine consciousness, according to the FT report. Bakalar had considerable expertise in the area. She was previously at Meta, where she developed the company’s ethics programs, but has also held several positions at prestigious universities on both sides of the Atlantic. Her departure will cause some anxiety at OpenAI as it continues to prepare the ground for its IPO .