A lawsuit in California accuses Linkedin of using private messages on its platform to train AI models, according to the BBC.
The lawsuit alleges that in August 2024, the Microsoft-owned social network allegedly introduced a new privacy setting that automatically enrolled users in a program that allowed their personal data to be used for AI training.
Linkedin also reportedly tried to cover it up a month later. In a comment to the BBC, a spokesperson for Linkedin called the accusations false and unfounded.
According to Linkedin, the company has not enabled the sharing of user data for AI learning in the UK, the European Economic Area and Switzerland.
OpenAI has informed the Delhi High Court that any directive requiring it to delete training data used for ChatGPT would conflict with its legal obligations under US law. The statement came in response to a copyright lawsuit filed by the Reuters-backed Indian news agency ANI, marking a pivotal development in one of the first major AI-related legal battles in India.
OpenAI’s January 10 filing highlights the conflict between ANI’s demand for data deletion and US laws that require OpenAI to preserve training data for pending litigation. The company argued, “OpenAI is under a legal obligation, under the laws of the United States, to preserve, and not delete, the said training data,” Reuters reported.
Reuters holds a 26% interest in ANI.
Reacting to the development, an official from ANI’s legal team said, “OpenAI purposefully makes their services available in this jurisdiction and is therefore obliged to comply with Indian law. The effect of OpenAI’s actions has resulted in damages to ANI in India because of copyright infringement and multiple instances of false attribution (hallucinations).”
“Therefore,” the official added, “as we have asserted in our Suit, we steadfastly believe that the High Court of Delhi has jurisdiction to adjudicate the matter. Further, public availability of copyrighted material is not a defense for copyright infringement.”
The ANI lawsuit’s significance extends beyond India’s borders, with industry experts suggesting that the case could set new precedents for AI governance globally.
“The lack of unified regulations creates a complex environment for AI developers,” said Anish Nath, practice director at Everest Group. “This case could lead to stricter copyright rules requiring developers to secure explicit licenses and could clarify the jurisdictional authority of national courts over international AI firms, even those without physical operations in a country.”
ANI filed its lawsuit in November, accusing OpenAI of using its copyrighted content without permission to train ChatGPT and seeking damages along with the deletion of stored data. ANI contends that OpenAI’s practices not only constitute copyright infringement but also spread misinformation by generating false news stories attributed to the agency.
During earlier proceedings, OpenAI told the Delhi court it had blocked ANI’s domain and ceased using its data for training purposes. However, ANI insists that past content remains embedded in ChatGPT’s systems and must be deleted, the report added.
OpenAI did not respond to a request for comment.
Gen AI rakes up copyright challenges
This case mirrors global legal trends, as OpenAI faces similar lawsuits in the United States and beyond, including from major organizations like The New York Times. OpenAI maintains its position that it adheres to the “fair use” doctrine, leveraging publicly available data to train its AI systems without infringing intellectual property laws.
In the case of Raw Story Media v. OpenAI, heard in the Southern District of New York, the plaintiffs accused OpenAI of violating the Digital Millennium Copyright Act (DMCA) by stripping copyright management information (CMI) from their articles before using them to train ChatGPT. However, the court dismissed the lawsuit, ruling that Raw Story Media lacked standing as they failed to demonstrate any tangible harm resulting from the alleged misuse of their copyrighted material in the training process.
In the ANI v OpenAI case, the Delhi High Court has framed four key issues for adjudication, including whether using copyrighted material for training AI models constitutes infringement and whether Indian courts have jurisdiction over a US-based company.
Nath’s view aligns with broader concerns over how existing legal frameworks struggle to keep pace with AI advancements.
“The ANI lawsuit against OpenAI is more than a regional legal battle—it’s a litmus test for the global AI ecosystem,” Abhivyakti Sengar, Senior Analyst at Everest Group said. “It brings to the forefront a key issue: existing copyright laws were never designed for AI, and there’s a growing need to modernize them to reflect today’s realities.”
Implications for AI innovation and governance
OpenAI’s defense has raised complex questions about the governance of AI in a global context. If the court orders data deletion, it could set a precedent requiring compliance with varying local copyright laws, potentially stifling AI development and creating operational hurdles for global tech companies.
The case also underscores the need for international frameworks to address challenges posed by generative AI models. ANI’s demand for strict licensing protocols highlights the growing tension between intellectual property protection and the free flow of information critical for AI innovation.
The Delhi High Court is set to hear the case on January 28, the report added, which could mark a watershed moment for AI governance in India and globally. The court’s decision will not only influence how OpenAI operates in India but also shape enterprise strategies for leveraging generative AI while navigating compliance risks.
This legal battle places India at the center of a broader conversation about how governments, enterprises, and technology providers address the ethical, legal, and operational complexities of AI in a fragmented regulatory environment.
Chinese AI developer DeepSeek has unveiled an open-source version of its reasoning model, DeepSeek-R1, featuring 671 billion parameters and claiming performance superior to OpenAI’s o1 on key benchmarks.
“DeepSeek-R1 achieves a score of 79.8% Pass@1 on AIME 2024, slightly surpassing OpenAI-o1-1217,” the company said in a technical paper. “On MATH-500, it attains an impressive score of 97.3%, performing on par with OpenAI-o1-1217 and significantly outperforming other models.”
On coding-related tasks, DeepSeek-R1 achieved a 2,029 Elo rating on Codeforces and outperformed 96.3% of human participants in the competition, the company added.
“For engineering-related tasks, DeepSeek-R1 performs slightly better than DeepSeek-V3 [another model from the company], which could help developers in real-world tasks,” DeepSeek said.
DeepSeek-R1 is available on the AI development platform Hugging Face under an MIT license, allowing unrestricted commercial use.
The company also offers “distilled” versions of R1, ranging from 1.5 billion to 70 billion parameters, with the smallest capable of running on a laptop. The full-scale R1, which requires more powerful hardware, is available via API at costs up to 95% lower than OpenAI’s o1.
As a reasoning model, R1 would self-check its outputs, potentially reducing errors common in other models. Although slower, reasoning models offer increased reliability in fields such as physics, science, and math.
Accelerating the AI arms race
The race for building language models has intensified especially with changing geopolitical realities.
“While OpenAI and other US-based firms definitely have the first mover advantage, China has been investing a lot in AI to build its capabilities to become a good second mover,” said Sharath Srinivasamurthy, associate vice president at IDC.
In real-world enterprise applications, DeepSeek-R1’s performance on key metrics translates to improved capabilities in mathematical reasoning, problem-solving, and coding tasks.
“Although this suggests that DeepSeek-R1 could potentially outperform OpenAI’s o1 in practical scenarios requiring these specific competencies, the eventual outcome still depends on various factors within the broader AI ecosystem, such as the AI readiness of data, RAG and agent support, ModelOps and DevOps toolchain integrations, cloud and data infrastructure support, and AI governance,” said Charlie Dai, VP and principal analyst at Forrester.
Moreover, while R1’s claims of superior performance are appealing, its true effectiveness remains uncertain due to a lack of clarity about the data it has been trained on.
“The models are only as good as the data they are trained on,” Srinivasamurthy said. “With restrictive policies in China on data consumption and publication, there is a possibility that the data might be biased or incomplete.”
Srinivasamurthy also noted that the true potential of LLMs lies in handling multiple modalities like text and images. While many models have achieved this, R1 has room to grow to become a comprehensive solution.
Potential for enterprise use
DeepSeek-R1’s MIT license, allowing unrestricted commercial use and customization, along with its lower costs, positions it as an appealing and cost-effective option for enterprise adoption.
However, enterprises may need to factor in additional costs associated with the MIT license, such as customization, fine-tuning, and adapting the model to meet specific business needs for a higher ROI, according to Mansi Gupta, senior analyst at Everest Group.
Businesses outside China may also be reluctant to use their data to train the model or integrate it into their operations due to regulatory challenges affecting AI adoption. “Enterprises must carefully assess the geopolitical risks tied to using R1, particularly for global operations,” Gupta said. “This includes navigating Chinese regulations and conducting thorough compliance assessments and risk analyses. Ultimately, the adoption of R1 will depend on how well enterprises can optimize the trade-off between its potential ROI and these geopolitical and regulatory challenges.”
There are countless ways to communicate electronically, including texting, social media, chat apps, team task managers, and videoconferencing software. Given the myriad ways you can get in touch with others, you may well think email is dead.
Think again.
Email, the mainstay of workplace communications, is stronger than ever. An estimated 347.3 billion emails were sent every day in 2023, according to Statista — a figure that the market research firm expects to grow to 408.2 billion daily emails by 2027.
If you’re using an email client rather than a cloud-based email service, there’s a very good chance that it’s Microsoft Outlook, the most popular Windows-based email software. Although you may have been using Outlook for some time, you might be missing out on some of its worthwhile features.
Microsoft sells its Office productivity suite under two models: Individuals and businesses can pay for the software license up front and own it forever (what the company calls the “perpetual” version of the suite), or they can purchase a Microsoft 365 subscription, which means they have access to the software for only as long as they keep paying the subscription fee.
When you purchase a perpetual version of the suite — say, Office 2021 or Office 2024 — its applications will never get new features, whereas Office 365 apps are continually updated with new features. (For more details, see “How to choose between Microsoft 365 and Office 2024.”) Confusing matters even more, Microsoft has renamed almost all of its Office 365 subscriptions as Microsoft 365, which generally means the plan includes everything from the old Office 365 plans plus some additional features and apps.
This cheat sheet gets you up to speed on the major features that have been introduced in the Windows desktop client for Outlook in Microsoft 365 over the past few years. We’ll periodically update this story as new features roll out.
Classic Outlook, new Outlook
Note that Microsoft has two different Windows client versions of Outlook, as well as an online version. One Windows client version, which Microsoft calls “classic Outlook,” is the one that currently ships with Microsoft 365. The other, which Microsoft calls “new Outlook,” is a replacement for the Mail and Calendar apps that are built into Windows. Microsoft also plans to have new Outlook replace classic Outlook as part of the Microsoft 365 suite in the future, and new Outlook is available to M365 subscribers for testing now.
At the moment, though, new Outlook is missing a number of key business features, so most businesses will want to stick with classic Outlook for the time being. (Microsoft says it will support classic Outlook at least until 2029.) For that reason, we’ll cover classic Outlook in this story.
In the classic Outlook client, you may see a toggle that says “Try the new Outlook” at the top right. In my tests, though, the new Outlook wouldn’t work with Microsoft 365, with Outlook .pst files, or with POP servers, making it rather useless. To return to classic Outlook, turn the toggle back to Off. Be careful when doing that, though. On multiple occasions when I’ve moved the toggle to On and then tried return to classic Outlook, Outlook refused to work, and I had to close it and restart it.
Use the simplified (or classic) Ribbon
The Ribbon toolbar interface that you came to know and love (or perhaps hate) in earlier versions of Outlook has been replaced by a simplified Ribbon that only shows the most frequently used features. It’s for those who prefer simplicity to the everything-but-the-kitchen-sink look of the old Ribbon, which offers the full panoply of what’s available to you in Outlook.
Here’s the stripped-down, simplified Ribbon, which shows only the most commonly used commands.
Preston Gralla / IDG
If a button on the Ribbon has small down arrow (also called a caret) on it, you can click it to see a drop-down menu with related tasks. There’s also a three-dot icon at the right end of the Ribbon; click it and a drop-down menu appears with several tasks you might want to do related to the Ribbon tab you’re currently on — for example, managing junk mail if you’re on the Home tab. Select the task you want to do, and you’re set.
If you prefer the old Ribbon (or “classic Ribbon,” as Microsoft calls it), you can easily switch to it. Click the caret in the lower right-hand corner of the Ribbon. From the screen that appears, select Classic Ribbon. To switch back to the simplified Ribbon, click the caret again and select Simplified Ribbon.
For those who like the “everything-but-the-kitchen-sink” look, the classic Ribbon is still available in Outlook.
Preston Gralla / IDG
As in previous versions of Outlook, if you want the Ribbon commands to go away completely, press Ctrl-F1. (The tabs above the Ribbon stay visible.) To make them reappear, press Ctrl-F1 again. That works for both the simplified Ribbon and the classic one.
You’ve got other options for displaying the Ribbon as well, and these options also work with both the simplified and classic Ribbons. To get to them, click the caret at the far right of the Ribbon. When you do that, you can customize the Ribbon even more, in the Show Ribbon section. Select Show tabs only if you want to see the tabs on top of the Ribbon, but not the commands underneath them. (In this mode, you click a tab to see its commands.) Click Always show Ribbon to have the Ribbon always appear. And click Full-screen mode to hide both the Ribbon and the tabs.
If you select File > Options > Customize Ribbon, you can change the content of the Ribbon to suit your needs. You can add and remove tabs, and change the commands on tabs as well.
Use the Search bar for more than searching emails
The search bar at the top of Outlook is deceptively simple-looking. You likely assume you can use it for searching through your emails and that’s it.
But the search bar does double duty: in addition to searching through emails, it can also help you find any Outlook capability, no matter how hidden, even if you’ve never used it. (This hands-on help capability replaces the Tell Me feature found in Outlook 2016 and 2019.)
To use it, click in the search box, and then type in what task you’d like to do. (Those who prefer keyboard shortcuts can instead press Alt-Q to get to the search box.)
For example, if you want to filter your mail to see only messages with attachments, type in filter email. In this instance, the top result is a Filter Email listing with an arrow to its right, indicating that it has many options. Hover your mouse over it, and you see multiple options for filtering your mail, including Unread, Has Attachments, Important, and others. Choose the option you want, and the task will be performed instantly.
You can use Outlook’s search bar to perform just about any task.
Preston Gralla / IDG
For the most common basic tasks, you won’t need this capability. But for more complex ones, it’s worth using, because it’s much more efficient than hunting through the Ribbon to find a command. It also remembers the features you’ve previously clicked on in the search results, so when you click in the box, you first see a list of previous tasks you’ve searched for. That makes sure that the tasks you frequently perform are always within easy reach, while at the same time making tasks you rarely do easily accessible.
Do online research from right inside Outlook
Sometimes emails are just quick notes that don’t require much research, and you can toss them off with little or no thought. Other times, though, you’ll want to include relevant information before sending them off. Those are the times you’ll appreciate being able to do online research from right within Outlook. You can do this while you’re writing an email, so you won’t have to fire up your browser, search the web, and then copy the information or pictures to your message.
To do it, highlight a word or group of words in an email — it can be a new draft, a message you’ve received, or one you’ve already sent — and select Search from the menu that appears. Outlook then uses Bing to do a web search on the word or words, displaying definitions, related Wikipedia entries, pictures and other results from the web in the Search pane that appears on the right.
You can do web research from right within Outlook.
Preston Gralla / IDG
To use online research in Outlook or any other Office app, you might first need to enable Microsoft’s intelligent services feature, which collects your search terms and some content from your documents and other files. (If you’re concerned about privacy, you’ll need decide whether the privacy hit is worth the convenience of doing research from right within the app.) If you haven’t enabled it, you’ll see a screen when you click Search asking you to turn it on. Once you do so, it will be turned on across all your Office applications.
If you’re like the rest of the world, you suffer from email overload. Your most important messages are mixed in with the dross of everyday email life — retailing come-ons, groups begging for donations, pointless newsletters and more.
Focused Inbox helps solve the problem. Using artificial intelligence, it determines which messages are most important to you and puts them into a Focused tab, while putting everything else into an Other tab. That way you can spend most of your time handling important messages in the Focused tab, only occasionally checking the Other tab.
To turn on Focused Inbox, select the View tab from the Ribbon, then click the Show Focused Inbox icon. From now on, you’ll have two tabs in your Inbox, Focused and Other. The Focused tab should have the most important messages, and the Other tab should have less important messages. If that’s not the case, you can manually move messages from one folder to the other and tell Focused Inbox to automatically filter them in that way in the future.
Focused Inbox puts more important emails in the ‘Focused’ tab and less important emails out of the way in the ‘Other’ tab.
Preston Gralla / IDG
To move a message from one tab to another, right-click the message you want to move, then select Move to Other or Move to Focused, depending on where you want the message moved. That will move the message just this once. If you want to permanently route all messages from that sender to the other tab, choose either Always Move to Other or Always Move to Focused.
Focused Inbox isn’t for everybody. If you find that Focused Inbox hinders more than it helps, you can toggle it back off by selecting View > Show Focused Inbox.
Keep email messages out of the way but handy with the Archive folder
Outlook has long offered email message archiving — that is, the option to move messages out of your Outlook mailbox and into a separate PST file as a space-saving measure. Corporate versions of Office, such as Microsoft 365 Enterprise, offer their own archiving features that automatically archive users’ older messages, again to save space. These methods remove the messages from the user’s Outlook mailbox. You can still get them back, but it takes some doing.
There’s another option in Outlook for Microsoft 365: You can move specific pieces of mail out of your inbox or other folders and into the Archive folder. That way, when looking for a message, you can browse or search the Archive folder and find the message more quickly.
Using the Archive folder doesn’t reduce the size of your mailbox; it simply helps tidy up your inbox while keeping older messages instantly accessible. Microsoft recommends that you use the Archive folder to store messages that you’ve already responded to or acted on.
If you already have a system of folders and subfolders in Outlook, you might not need the Archive folder, but it can be a boon for those of us who tend to leave everything in the inbox. And even if you do have a folder system, you might find that not all of your email fits neatly into your folders and subfolders; you can move these messages to the Archive folder to keep your inbox clean.
To move messages to the Archive folder, first select one or more that you want to archive. (Select multiple messages by holding down the Ctrl key and clicking each one you want to select.) With the message or messages selected, go to the Ribbon’s Home tab and click Archive in the Delete group, right-click the message or group of messages and select Archive, or simply drag the selected message(s) to the Archive folder. You can also move an individual email to the Archive folder by pressing the Backspace key when the message is highlighted or when you’re reading it.
Choose the last item in the pop-up menu to move the selected messages to the Archive folder.
Preston Gralla / IDG
Now when you need to find a message, you can browse the Archive folder or else go to the Archive folder and launch a search.
To move a message out of the Archive folder to a different folder, simply drag it to its destination.
Find attachments more easily — and share ‘cloud attachments’
We’ve all been there: We want to attach a file we were recently working on, but don’t remember its precise location — or sometimes even its name — and spend far too much time navigating and searching for it.
Outlook solves the problem neatly. When you attach a file by clicking the Include icon, a list of the 12 most recent files you’ve been using pops up. The list includes all the files you’ve been using on any device, as long as you’re signed in to your Microsoft account. So if you were working on a file on your desktop, then later in the day took your laptop to work outside your office, Outlook would show you the files you had opened on both devices.
When you click Include, you have the choice of sending the file itself or a link to it. Whichever you choose, the list of the most recent files appears. If the file you want isn’t in the list, click Browse this PC to browse your local hard disk, or Browse Web Locations to browse OneDrive, OneDrive for Business, or SharePoint. When you attach a file from OneDrive or SharePoint, you’ll have the option of sending them as links or attaching the files themselves. Click the file you want to attach.
Outlook shows you a list of Office files you’ve recently used, making it easier to find and attach them to an outgoing email.
Preston Gralla / IDG
Use Microsoft 365 Copilot with Outlook
For an additional subscription fee, business users of Outlook can use Microsoft’s genAI add-in, Microsoft 365 Copilot. You can have Copilot draft new emails, draft replies, summarize email threads, offer recommendations on writing emails, and more. If you have a Microsoft 365 Personal or Family subscription, many of those features are now bundled with your core subscription.
Microsoft 365 Copilot can help you in Outlook in multiple ways, including summarizing email threads.
Preston Gralla / IDG
Work in Microsoft 365 Groups
If you work in an office that uses Microsoft 365 Groups, you can now join groups, create new groups, schedule meetings on a group calendar and more, all from within Outlook.
Microsoft 365 Groups, available for most Microsoft 365 business and enterprise plans, make it easy to collaborate with others by designating a set of people with whom to share resources, such as a document library, shared calendar, and/or shared email account. Groups can be for departments, project teams, and so on, and when a group is created, all the appropriate permissions are automatically granted for everybody in the group.
Creating a new group from inside Outlook is simple. Select the Home tab in the Ribbon, and in the New section, select New Items > Group. Then fill in information for the group, including its name, description, whether it’s private or public within your organization, and so on.
Creating a Microsoft 365 group from inside Outlook.
Preston Gralla / IDG
Note that your IT department needs to set up provisioning for Microsoft 365 Groups, so check with IT for more details about creating and using groups. Also, in order to use Microsoft 365 Groups in Outlook, you need to use Outlook in Cached Exchange Mode. See this Microsoft support page for details.
Other features to check out
Outlook for Microsoft 365 has several more useful features. Although they’re not as significant as the other features we’ve covered here, they’re worth knowing about.
Message encryption: In older versions of Outlook, there’s a Permissions button that lets you set permission levels for an email — for example, “Confidential,” “Internal,” and “Do Not Forward.” In Outlook for Microsoft 365, that button has been replaced with an Encrypt button that lets you encrypt the message with S/MIME or Microsoft 365 Message Encryption.
Quick Actions: If you hover your cursor over any email in your message list, tiny Quick Action icons appear. These let you perform tasks such as deleting, flagging, or archiving the message with a single click. By default, the two icons that appear are Flag/Clear Flag and Delete, but you can customize that by right-clicking on any message in the list and selecting Set Quick Actions from the pop-up menu. You can designate only two Quick Action icons — your options are Archive, Delete, Move, Flag/Clear Flag, and Mark as Read/Unread — but Delete appears even if you don’t select it.
Customizing Outlook’s Quick Actions icons for message management.
Preston Gralla / IDG
Note that Quick Actions does not replace Outlook’s more complex Quick Steps feature, which lets you apply multiple actions to a message at the same time. Quick Actions simply provides a quick way to do a few frequently performed actions.
Built-in translation: You no longer have to use a translation add-in for other languages — it’s now built directly into Outlook for Microsoft 365/Office 365. Right-click words, phrases, or the entire message and select Translate from the menu that pops up.
Turn grammar suggestions on and off: Outlook offers grammar suggestions — you’ll know they’re there when you see underlined text, which marks the text as having an error. If you’d prefer not to see them, you can turn them off. To do it, create a new email, and with it open, go to File > Options > Mail, and in the “Compose messages” section, click Editor Options. On the screen that appears, select Proofing, and under “When correcting spelling in Outlook,” uncheck the Mark grammar errors as you type box. You can still check grammar at any time by pressing F7.
Dictate messages: Look, ma, no hands! With Microsoft 365, you can dictate messages. Once you’ve created an email, select Message > Dictate from the Ribbon and start talking.
Use the same Outlook settings on all your devices: If you use Outlook on more than one machine, you can store your settings for features such as Automatic Replies, Focused Inbox, and Privacy in the cloud, and they’ll automatically be applied to all your Windows PCs. To turn it on, go to File > Options > General. Under “Cloud storage options,” check the box next to Store my Outlook settings in the cloud.
Regardless of whether you do that, any signatures you create in Outlook will be automatically stored in the cloud so that they’ll be available on all your devices.
Use keyboard shortcuts
If you’re a fan of keyboard shortcuts, you’ll be pleased to know that Outlook has them. They provide a great way to get tasks accomplished quickly. See our story “Handy Outlook keyboard shortcuts for Windows and Mac” for the most useful ones.
Emergency response systems worldwide are undergoing rapid transformation, and Kazakhstan is no exception. With the introduction of the SOS 102 app, the country has set a new standard for public safety, redefining how law enforcement interacts with citizens. Launched in 2021, the system integrates cutting-edge technology with a citizen-centric approach, establishing itself as a benchmark for modern, responsive emergency services.
Since its launch, SOS 102 has processed over 342,500 requests, with more than 250,000 originating from its mobile app. This high adoption rate underscores the effectiveness of its omni-channel design, which seamlessly connects traditional hotlines with digital platforms such as a mobile app, social media, and instant messaging. Citizens can report incidents, submit multimedia evidence, and track police responses in real-time—all with just a few taps.
One of the standout features is the integration of AI. The system uses AI to filter and prioritize incoming requests, ensuring that urgent cases receive immediate attention. This not only reduces response times but also enhances the accuracy and efficiency of incident handling, building greater trust between law enforcement and the public.
What spurred the need for SOS 102?
Before SOS 102, Kazakhstan’s emergency response relied heavily on traditional phone lines. This system posed several challenges:
Limited real-time information: Without a digital platform, it was difficult for citizens to share detailed incident information, such as photos or videos, which often delayed resolution.
Low transparency: Citizens had no way to track the progress of their reports, leading to a lack of trust in the system.
Lack of integration: There was no mechanism to consolidate reports from different channels, resulting in inefficiencies in response efforts.
These limitations highlighted the urgent need for a more modern, integrated approach to public safety communication. SOS 102 was developed to address these gaps while aligning with the country’s broader “Listening State” initiative, which aims to foster transparency and responsiveness in governance.
Lessons from the global stage
Kazakhstan’s initiative mirrors global trends in emergency response modernization, drawing parallels with systems like NG911 in North America and 112 in Europe. However, SOS 102 goes further by tailoring international best practices to local needs.
For instance, similar to efforts in Singapore, SOS 102 leverages AI for rapid prioritization and IoT for real-time geolocation, ensuring prompt and precise responses. It is also inspired by Dubai’s advanced systems to ensure user-centric design: SOS 102 emphasizes accessibility and transparency, with features like live tracking of police responses and simple interfaces for incident reporting.
Future enhancements for a safer Kazakhstan
Looking ahead, the SOS 102 system is poised for further upgrades. Planned developments include enhanced audio and video call functionalities, expanded services like vehicle history checks, and continuous improvements based on user feedback. These updates aim to make the system even more intuitive and versatile, reinforcing its role as a pillar of Kazakhstan’s public safety strategy.
Kazakhstan’s SOS 102 exemplifies how technology and citizen-focused design can transform emergency response systems. By addressing local challenges and incorporating global best practices, the initiative not only sets a precedent for other nations but also fosters a safer, more connected community.
Apple’s innovative self-branded credit card, Apple Card, was expected to overturn the existing retail banking industry and transform relationships between bankers and customers. Equipped with generous benefits and unique integration between Apple’s ecosystem and a user’s financial relationships, the card quickly generated many thousands of US users after its 2019 introduction.
With such a great start, how could it fail? And yet, somehow it found a way.
To be fair, the six-year-old card hasn’t really failed. It still exists and many enjoy its perks and benefits, including cash-back deals and a savings account offering some of the best interest your money can buy. They enjoy it very much. In fact, the Apple Card ranks highest in customer satisfaction among co-brand credit cards, according to JD Power.
But all is not well
Burned by its foray into retail banking during a period of global instability and pandemic, Apple Card partner Goldman Sachs wants out, and while it and Apple will continue working together for the duration of the existing contract, the open secret is that the iPhone maker is seeking a new card partner. That the two partners got fined $89 million for negligence and mismanagement by the US Consumer Financial Protection Bureau didn’t help. As recently as last week, Goldman Sachs’ CEO warned that the arrangement between the two firms could end early, so it looks as if that company is eyeing a rapid exit from the deal.
Right now, Apple is reportedly in conversation with Barclays on the matter, Reuters claims. Barclays is no stranger to taking on existing credit card services; it recently replaced Goldman Sachs as credit card partner for General Motors. Talks between Apple and JP Morgan Chase and Synchrony Financial have also been alleged in recent months.
Despite these chats, nothing has been announced, which suggests Apple is encountering challenges finding a new partner.
What’s in the way?
Holiday season speculation told us potential retail banking partners just don’t want to deliver on the features and benefits Apple Card currently provides.
I think Apple is at a disadvantage here. It’s important when thinking about the nature of those closed-door negotiations to ponder the power balance in these chats. After all, while Apple drives legendarily hard bargains, when it comes to negotiation, banks are banks, and they know that when it comes to real unearned wealth creation, the financial system they own always works in their favor.
After all, when it doesn’t, we bail them out.
Look at it this way, while Apple has to sell and invent real products and services to make its money, banks just need to increase property values to charge interest on mortgage money they notionally lend which doesn’t actually exist. A little like artificially manipulated crypto value spikes, it’s a lot easier to create scarcity than to meet it with what people need. In this scenario, the banks have the upper hand.
There’s a certain irony to that. When it was introduced, Apple Card posed a titan’s challenge to the industry.
Eating with the enemy
The features, including app-driven features, were second to none. The business proposition tilted hard toward customer convenience, and Apple’s foray into the financial system (in conjunction with even deeper stabs at retail banking’s most profitable markets by other challenge banks) arguably helped force established financial institutions to get their act together and improve their customer offerings.
(If that proposition were correct, you’d see evidence of it — and you can find that evidence in all the new features suddenly added to banking apps and services after Apple Card arrived.)
Most people deeply resent the banking industry. Many millions continue to experience hardships following the 2008 crash. The idea that Apple Card and other challenge banks would somehow undermine the status quo remains quite attractive.
Unfortunately, this doesn’t seem to be happening. Instead, established banking services (such as Barclays) will now do the Big Fish, Little Fish predator tango and assimilate the competitor, neutralizing the threat.
Was Apple Card too good? It was so good an established vendor may yet buy it, you might say.
If that happens, Apple Card customers will be OK. (They’ll also be OK if the card fades away as their interests will be protected.) Apple will still make a profit. But the challenge will be defanged, and the industry will carry on its own sweet way — while occasionally raising arguments about competition when vendors like Apple invent something they want to extract wealth from, such as Apple Pay.
“Everybody knows that the dice are loaded,” as Leonard Cohen once sang. “That’s how it goes.”
Apple recently had to disable AI-generated news summaries in its News app in iOS 18.3. You can guess why: the AI-driven Notification Summaries for the news and entertainment categories in the app occasionally hallucinated, lied, and spread misinformation.
Sound familiar?
Users complained about the summaries, but Apple acted only after a complaint from BBC News, which told Apple that several of its notifications were improperly summarized. These were major errors in some cases.
The generative AI (genAI) tool incorrectly summarized a BBC headline, falsely claiming that Luigi Mangione, who was charged with murdering UnitedHealthcare CEO Brian Thompson, had shot himself. It inaccurately reported that Luke Littler had won the PDC World Darts Championship hours before the competition had even begun and falsely claimed that Spanish tennis star Rafael Nadal had come out as gay.
Apple summarized other real stories with false information: The tool said Israeli Prime Minister Benjamin Netanyahu had been arrested, Pete Hegseth had been fired, that Trump tariffs had triggered inflation (before Donald Trump had re-assumed office), and spewed dozens of other falsehoods.
Apple rolled out the feature not knowing it would embarrass the company and force a retreat — which is amazing when you consider that this happens to every other company that tries to automate genAI information delivery of any kind on a large scale. Microsoft Start’s travel section, for example, published an AI-generated guide for Ottawa that included the Ottawa Food Bank as a “tourist hotspot,” encouraging visitors to come on “an empty stomach.”
In September 2023, Microsoft’s news portal MSN ran an AI-generated obituary for former NBA player Brandon Hunter, who had passed away at the age of 42. The obituary headline called Hunter “useless at 42,” while the body of the text said that Hunter had “performed in 67 video games over two seasons.”
Microsoft’s news aggregator, MSN, attached an inappropriate AI-generated poll to a Guardianarticle about a woman’s death. The poll asked readers to guess the cause of death, offering options like murder, accident, or suicide.
During its first public demo in February 2024, Google’s Bard AI incorrectly claimed that the James Webb Space Telescope had taken the first pictures of a planet outside our solar system—some 16 years after the first extrasolar planets were photographed.
These are just a few examples out of many.
The problem: AI isn’t human
The Brandon Hunter example is instructive. The AI knows enough about language to “know” that a person who does something is “useful,” that death means they can no longer do that thing, and that the opposite of “useful” is “useless.” But AI does not have a clue that saying in an obituary that a person’s death makes them “useless” is problematic in the extreme.
Chatbots based on Large Language Models (LLMs) are inherently tone-deaf, ignorant of human context, and can’t tell the difference between fact and fiction, between truth and lies. They are, for lack of a better term, sociopaths — unable to tell the difference between the emotional impact of an obituary and a corporate earnings report.
There are several reasons for errors. LLMs are trained on massive datasets that contain errors, biases, or inconsistencies. Even if the data is mostly reliable, it may not cover all possible topics a model is expected to generate content about, leading to gaps in knowledge. Beyond that, LLMs generate responses based on statistical patterns using probability to choose words rather than understanding or thinking. (They’ve been described as next-word prediction machines.)
The biggest problem, however, is that AI isn’t human, sentient, or capable of thought.
Another problem: People aren’t AI
Most people don’t pay attention to the fact that we don’t actually communicate with complete information. Here’s a simple example: If I say to my neighbor, “Hey, what’s up?” My neighbor is likely to reply, “Not much. You?”
A logic machine would likely respond to that question by describing the layers of the atmosphere, satellites, and the planets and stars beyond. It answered the question factually as it was asked, but the literal content of the question did not contain the actual information sought by the asker.
To answer that simple question in the manner expected, a person has to be a human who is part of a culture and understands verbal conventions — or has to be specifically programmed to respond to such conventions with the correct canned response.
When we communicate, we rely on shared understanding, context, intonation, facial expression, body language, situational awareness, cultural references, past interactions, and many other things. This varies by language. The English language is one of the most literally specific languages in the world, and so a great many other languages will likely have bigger problems with human-machine communication.
Our human conventions for communication are very unlikely to align with genAI tools for a very long time. That’s why frequent AI chatbot users often feel like the software sometimes willfully evades their questions.
The biggest problem: Tech companies can be hubristic
What’s really astonishing to me is that companies keep doing this. And by “this,” I mean rolling out unsupervised automated content-generating systems that deliver one-to-many content on a large scale.
And scale is precisely the difference.
If a single user prompts ChatGPT and gets a false or ridiculous answer, they are likely to shrug and try again, sometimes chastising the bot for its error, for which the chatbot is programmed to apologize and try again. No harm, no foul.
But when an LLM spits out a wrong answer for a million people, that’s a problem, especially in Apple’s case, where no doubt many users are just reading the summary instead of the whole story. “Wow, Israeli Prime Minister Benjamin Netanyahu was arrested. Didn’t see that coming,” and now some two-digit percentage of those users are walking around believing misinformation.
Each tech company believes they have better technology than the others.
Google thought: Sure, that happened to Microsoft, but our tech is better.
Apple thought: Sure, it happened to Google, but our tech is better.
Tech companies: No, your technology is not better. The current state of LLM technology is what it is — and we have definitely not reached the point where genAI chatbots can reliably handle a job like this.
What Apple’s error teaches us
There’s a right way and a wrong way to use LLM-based chatbots. The right way is to query with intelligent prompts, ask the question in several ways, and always fact-check the responses before using or believing that information.
Chatbots are great for brainstorming, providing quick information that isn’t important, or being a mere starting point for research that leads you to legitimate sources.
But using LLM-based chatbots to write content unsupervised at scale? It’s very clear that this is the road to embarrassment and failure.
The moral of the story is that genAI is still too unpredictable to reliably represent a company in one-to-many communications of any kind at scale.
rrorSo, make sure this doesn’t happen with any project under your purview. Setting up any public-facing content-producing project meant to communicate information to large numbers of people should be a hard, categorical “no” until further notice.
AI is not human, can’t think, and it will confuse your customers and embarrass your company if you give it a public-facing role.
If you’re a Microsoft 365 subscriber with a Personal or Family subscription, Microsoft just flipped a switch and activated Copilot AI features for your account. It’s a new part of your subscription, along with that 1TB of OneDrive storage and access to Office apps like Word and Excel. But there are some big catches — including a price hike and some limits.
Microsoft’s latest changes follow in Google’s footsteps, with AI features appearing in the standard subscription for much less than you’d pay for those AI features separately — but with the standard subscription price also going up at the same time.
Let’s dive into how Microsoft just transformed some of the world’s most popular productivity apps, what you can do now — and how you can avoid paying more.
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How Copilot works in Microsoft 365 plans
First things first, the basics: Microsoft announced that all Microsoft 365 Personal and Microsoft 365 Family plans now include Copilot features as of Jan. 16 in “most markets worldwide.” This won’t affect you when using a Microsoft 365 plan provided by a workplace — businesses still have to pay separately for AI features — but it will affect your individual plans. And plenty of professionals do pay for their own Microsoft 365 subscriptions. (I should know; I’m one of them!)
In other words, if you pay for Microsoft 365 and use apps like Word, Excel, PowerPoint, OneNote, and Outlook, you’ll now find the Copilot button popping up in these applications. Previously, you had to pay $20 per month for a Copilot Pro subscription to unlock these features.
Here’s the first big catch: The newly expanded paid 365 plans don’t give you unlimited access to Microsoft’s AI features. Instead, you get a monthly allotment of credits that Microsoft says “…should be enough for most subscribers.” In practice, that appears to be 60 credits per month — meaning you can use AI features 60 times per month. After that, you’ll need to pay for a $20-per-month Copilot Pro subscription to keep using those AI features.
You’ll see an informational pop-up window the first time you open an app like Word.
Chris Hoffman, IDG
Note: These AI credits are actually shared across various other Microsoft apps — including for AI image generation in Designer, Paint, and Photos and text-editing work in Notepad. They’re not just for Word, Excel, and PowerPoint.
Plus, again, Microsoft is raising its 365 subscription prices, with Copilot bundled into the mix. They’re going up by $3 per month in the US, though the exact price increase will vary by country. For the yearly plans in the US, Microsoft 365 Family goes from $100 to $130 per year, and Microsoft 365 Personal goes from $70 to $100 per year.
This is the first time Microsoft has raised prices since launching the subscription service — originally called Office 365 — back in 2013. While it’s true that Microsoft is using these AI features as a way to hike prices, these subscriptions were overdue for a price increase anyway, and it’s nice to at least get something out of it. (In my opinion, between the 1TB of OneDrive storage and access to Office apps, it’s still a good value.)
It’s worth noting that this Copilot change is only for Microsoft 365 plans. If you buy a more traditional “one-time purchase” version of Office like Office 2024, your setup isn’t changing — and you won’t have access to these newer AI features.
Using Copilot AI in Microsoft 365 apps
With the new adjustments in place, Copilot AI is easy to find in Office apps: You’ll find a Copilot icon on the ribbon, or you can also select some text and click the little Copilot icon that appears next to it, or just press Alt+i. Then you can prompt Copilot to write or rewrite text in a document for you. You could also ask it questions about the document you’re viewing from the Copilot sidebar.
For more information on exactly how Copilot works in these Office apps, check out my Copilot Pro review from last year. The new built-in Copilot features are exactly the same as what you get with Copilot Pro; the only difference is that you’re limited to 60 uses per month in the 365 setup.
If you run out of credits, Microsoft will encourage you to upgrade to Copilot Pro. In a way, then, these AI features are a bit of a “trial” for Copilot Pro.
To check how many credits you have left, you can click the little menu icon in the Copilot sidebar in an Office app and then click “AI credit balance.” This will take you to your Microsoft 365 account subscription page, where you can see a running balance of the AI credits you’ve used.
Your AI credit balance is just a few clicks away.
Chris Hoffman, IDG
Generating images with Microsoft Designer
The same credit system also applies to Microsoft Designer, which is a useful AI image-generation tool. (At our newsletter-focused small business The Intelligence, we use Microsoft Designer to create some feature image illustrations for our articles — we’re writers, not visual artists!)
That means with any paid Personal or Family 365 plan, you can opt to use your 60 monthly AI image credits directly within Designer, too. This is actually quite a downgrade: Previously, everyone got 15 credits per day for AI image generations. Now, subscribers get a total of 60 credits per month, while free accounts only get 15 credits per month.
If you need more than that, you can upgrade to the $20-a-month Copilot Pro plan, which gives you many more AI image generations in Designer and beyond. (Microsoft says you get “at least 10x more credits” for Designer with Copilot Pro, compared to the 15-credits-per-month free setup — so roughly 150 credits per month, then, compared to the 60 monthly credits in the base 365 subscription.)
AI tools are expensive to create and operate, and companies have lost a lot of money on them. It’s no surprise to see many AI tools offering less for free and looking for more payment from their users; that’s what’s happening here.
How to avoid the AI features (and costs) entirely
There are ways to avoid the Microsoft 365 subscription price increases, if you don’t anticipate using them and don’t want to pay for them. (The price increase doesn’t take effect until your next subscription renewal, by the way.)
If you already have a Microsoft 365 subscription, you can keep your old subscription price and opt out of the AI features “for a limited time.” Microsoft says you can switch by canceling your subscription and choosing one of the “Classic” plans during the cancelation process. Here are Microsoft’s instructions.
You could also buy “perpetual licenses” of Office instead of using the more prominently offered subscriptions. In other words, with a one-time purchase of Office 2024, you could use Office for a few years for that one-time purchase price. It’s not as good a deal as it sounds — that one-time purchase price will only get you access to Office apps like Word and Excel on a single computer, and you won’t have access to the 1TB of OneDrive storage. (Plus, while your license will be good in perpetuity, Microsoft will stop delivering security updates for Office 2024 in October 2029.)
You can also buy Microsoft 365 subscription keys from other retailers. Without getting into the weeds too far here, it’s worth noting that days after Microsoft implemented the subscription price increase, Amazon is still selling Microsoft 365 Personal subscriptions for $70 and Microsoft 365 Family subscriptions for $100 — the old prices. But these are the standard plans and include those AI features. That’s a bargain.
Of course, you could also turn to other office suites — the web-based Google Docs, the open-source LibreOffice, or the Apple-focused iWork suite — but Word, Excel, and PowerPoint are the business standard for a reason. And even with these AI-adding price increases, getting that 1TB of OneDrive storage at those prices is still a great deal.
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Fair warning: This isn’t your average article about what’s happening with all the newfangled AI hullabaloo in this weird and wild world of ours.
Nope — there’ll be no “oohing” and “ahhing” or talk about how systems like Gemini and ChatGPT and their brethren are, like, totally gonna revolutionize the world and change life as we know it.
Instead, I want to look at the state of these generative AI systems through as practical and realistic of a lens as possible — focusing purely on how they work right now and what they’re able to accomplish.
And with that in mind, my friend, there’s no way around it: These things seriously suck.
Sorry for the bluntness, but for Goog’s sake, someone’s gotta say it. For all their genuinely impressive technological feats and all the interesting ways they’re able to help with mundane work tasks, Google’s Gemini and other such generative AI systems are doing us all a major disservice in one key area — and everyone seems content in looking the other way and pretending it isn’t a problem.
That’s why I was so pleasantly surprised to see that one tech giant seemingly isn’t taking the bait and is instead lagging behind and taking its time to get this right instead of rushing it out half-baked, like everyone else.
It’s the antithesis to the strategy we’re seeing play out from Google and virtually every other tech player right now. And my goodness, is it ever a refreshing contrast.
I won’t keep you waiting: The company that’s getting it right, at least in terms of its process and philosophy, is none other than Amazon.
I’ll be the first to admit: I’m typically not a huge fan of Amazon or its approach. But within this specific area, it really is creating a model for how tech companies should be thinking about these generative AI systems.
My revelation comes via a locked-down article that went mostly unnoticed at The Financial Times last week. The report’s all about how Amazon is scrambling to upgrade its Alexa virtual assistant with generative AI and relaunch it as a powerful “agent” for offering up complex answers and completing all kinds of online tasks.
More of the same, right? Sure sounds that way — but hang on: There’s a twist.
Allow me to quote a pertinent passage from behind the paywall for ya:
Rohit Prasad, who leads the artificial general intelligence (AGI) team at Amazon, told the Financial Times the voice assistant still needed to surmount several technical hurdles before the rollout.
This includes solving the problem of “hallucinations” or fabricated answers, its response speed or “latency,” and reliability.
“Hallucinations have to be close to zero,” said Prasad. “It’s still an open problem in the industry, but we are working extremely hard on it.”
One former senior member of the Alexa team said while LLMs were very sophisticated, they came with risks, such as producing answers that were “completely invented some of the time.”
“At the scale that Amazon operates, that could happen large numbers of times per day,” they said, damaging its brand and reputation.
Well, tickle me tootsies and call me Tito. Someone actually gives a damn.
If the contrast here still isn’t apparent, let me spell it out: These large-language-model systems — the type of technology under the hood of Gemini, ChatGPT, and pretty much every other generative AI service we’ve seen show up over the past year or two — they don’t really know anything, in any human-like sense. They work purely by analyzing massive amounts of data, observing patterns within that data, and then using sophisticated statistics to predict what word is likely to come next in any scenario — relying on all the info they’ve ingested as a guide.
Or, put into layman’s terms: They have no idea what they’re saying or if it’s right. They’re just coughing up characters based on patterns and probability.
And that gets us to the core problem with these systems and why, as I put it so elegantly a moment ago, they suck.
The reality … is that large-language models like Gemini and ChatGPT are wildly impressive at a very small set of specific, limited tasks. They work wonders when it comes to unambiguous data processing, text summarizing, and other low-level, closely defined and clearly objective chores. That’s great! They’re an incredible new asset for those sorts of purposes.
But everyone in the tech industry seems to be clamoring to brush aside an extremely real asterisk to that — and that’s the fact that Gemini, ChatGPT, and other such systems simply don’t belong everywhere. They aren’t at all reliable as “creative” tools or tools intended to parse information and provide specific, factual answers. And we, as actual human users of the services associated with this stuff, don’t need this type of technology everywhere — and might even be actively harmed by having it forced into so many places where it doesn’t genuinely belong.
That, m’dear, is a pretty pressing problem.
Allow me to borrow a quote collected by my Computerworld colleague Lucas Mearian in a thoroughly reported analysis of how, exactly, these large-language models work:
“Hallucinations happen because LLMs, in their in most vanilla form, don’t have an internal state representation of the world,” said Jonathan Siddharth, CEO of Turing, a Palo Alto, California company that uses AI to find, hire, and onboard software engineers remotely. “There’s no concept of fact. They’re predicting the next word based on what they’ve seen so far — it’s a statistical estimate.”
And there we have it.
That’s why Gemini, ChatGPT, and other such systems so frequently serve up inaccurate info and present it as fact — something that’s endlessly amusing to see examples of, sure, but that’s also an extremely serious issue. What’s more, it’s only growing more and more prominent as these systems show up everywhere and increasingly overshadow traditional search methods within Google and beyond.
And that brings us back to Amazon’s seemingly accidental accomplishment.
Amazon and Google: A tale of two AI journeys
What’s especially interesting about the slow-moving state of Amazon’s Alexa AI rollout is how it’s being presented as a negative by most market-watchers.
Back to that same Financial Times article I quoted a moment ago, the conclusion is unambiguous:
In June, Mihail Eric, a former machine learning scientist at Alexa and founding member of its “conversational modelling team,” said publicly that Amazon had “dropped the ball” on becoming “the unequivocal market leader in conversational AI” with Alexa.
But, ironically, that’s exactly where I see Amazon doing something admirable and creating that striking contrast between its efforts and those of Google and others in the industry.
The reality is that all these systems share those same foundational flaws. Remember: By the very nature of the technology, generative-AI-provided answers are woefully inconsistent and unreliable.
And yet, Google’s been going in overdrive to get Gemini into every possible place and get us all in the habit of relying on it for almost every imaginable purpose — including those where it simply isn’t reliable. (Remember my analogy from a minute ago? Yuuuuuup.)
In doing so, it’s chasing short-term market gains at the cost of long-term trust. All other variables aside, being wrong or misleading with basic information 20% of the time — or, heck, even just 10% of the time — is a pretty substantial problem. I’ve said it before, and I’ll say it again: If something is inaccurate or unreliable 10% of the time, it’s useful precisely 0% of the time.
And to be clear, the stakes here couldn’t be higher. In terms of their answer-offering and info-providing capabilities, Gemini and other such systems are being framed and certainly perceived as magical answer machines. Most people aren’t treating ’em with a hefty degree of skepticism and taking the time to ask all the right questions, verify answers, and so on. They’re asking questions, seeing or hearing answers, and then assuming they’re right.
And by golly, are they getting an awful lot of confidently stated inaccuracies as a result — something that, as we established a moment ago, is likely inevitable with this type of technology in its current state.
On some level, Google is clearly aware of this. The company had been developing the technology behind Gemini for years before rushing it out into the world following the success and attention around ChatGPT’s initial rollout — but, as had been said in numerous venues over time, it hadn’t felt like it was mature enough to be ready for public use.
So what changed? Not the nature of the technology — nope; by all counts, it was just the competitive pressure that forced Google to say “screw it, it’s good enough” and go all-in with systems that weren’t and still aren’t ready for primetime, at least with all of their promoted purposes.
And that, my fellow accuracy-obsessed armadillo, is where Amazon is getting it right. Rather than just rushing to replace Alexa with some new half-baked replacement, the company is actually waiting until it feels like it’s got the new system ready — with reliability, yes, but also with branding and a consistent-seeming user experience. (Anyone who’s been trying to navigate the comically complex web of Gemini and Assistant on Android — and beyond — can surely relate!)
Whether Amazon will keep up this pattern or eventually relent and go the “good enough” route remains to be seen. Sooner or later, investor pressure may force it to follow Google’s path and put its next-gen answer agent out there, even if it in all likelihood still isn’t ready by any reasonable standard.
For now, though, man: I can’t help but applaud the fact that the company’s taking its time instead of prematurely fumbling to the finish line like everyone else. And I can’t help but wish Google would have taken that same path, too, rather than doing its usual Google Thang™ and forcing some undercooked new concept into every last nook and cranny — no matter the consequences.
Maybe, hopefully, this’ll all settle out in some sensible way and turn into a positive in the future. For the moment, though, Google’s strategy sure seems like more of a minus than a plus for us, as users of its most important products — and especially in this arena, it sure seems like getting it right should mean more than getting it out into the world quickly, flaws and all and at any cost.
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Perplexity has introduced an API service named Sonar that would allow developers and enterprises to embed the company’s generative AI search technology into their applications.
The company has rolled out two initial tiers – a more affordable and faster option called Sonar, and a higher-priced tier, Sonar Pro, tailored for handling more complex queries.
In a blog post, Perplexity described Sonar API as “lightweight, affordable, fast, and simple to use,” noting that it includes features such as citations and the ability to customize sources. The company said the API is ideal for businesses requiring streamlined question-and-answer functionalities optimized for speed.
For enterprises with more complex requirements, Perplexity will offer the Sonar Pro API, which supports multi-step queries, a larger context window for handling longer and more detailed searches, and higher extensibility.
It will also provide approximately twice the number of citations per search compared to the standard Sonar API, the company said.
Competing with bigger players
The launch positions Perplexity as a stronger, more direct competitor to larger players such as OpenAI and Google, offering its real-time, web-connected search capabilities to users.
“Perplexity’s real-time data retrieval and citation-backed responses cater to enterprise demands for reliable, transparent, and actionable information,” said Prabhu Ram, VP of the industry research group at Cybermedia Research. “By prioritizing verifiability and up-to-date insights, it offers a specialized approach that sets it apart from broader conversational models like GPT-4 and Claude, which serve a wider range of use cases but may lack the tailored features enterprises need for effective decision-making and compliance management.”
However, significant challenges remain in areas such as data privacy, depth, scale, and audit trails before any company can establish itself as a leader in the field.
Although real-time information capabilities offer clear advantages, they also introduce additional complexities to an already intricate landscape.
“Enterprises tend to take cautious steps when exploring new technologies like enterprise research, especially when handling sensitive company data,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “Beyond concerns around data privacy, security, and compliance, any large company would first evaluate accuracy and performance. Whether Perplexity can outpace Google and OpenAI in this space will depend on the aggressiveness of Google’s and other competitors’ strategies.”
Real-time information and search is an area where Google excels. To stay competitive, Perplexity will need to explore other differentiators, such as compliance, Gogia said.
Use cases for enterprises
Analysts note that Perplexity’s latest offerings have diverse applications, ranging from basic tasks such as identifying service management issues in IT functions to broader use by leaders in supply chain, finance, sales, marketing, and operations.
“Sonar Pro, [especially] caters to the needs of compliance-driven industries such as healthcare, BFSI, and energy by offering robust tools for security reporting, portfolio management, and seamless DevOps integration,” Ram said. “These capabilities not only enhance operational efficiency but also ensure adherence to regulatory standards, positioning it as an indispensable solution for improving software development practices.”
But for the tool to succeed, significant customization may be necessary, tailored to specific industries and individual companies.
Initially, and in the near term, use cases are expected to remain highly customized before gradually evolving into industry-standard templates, all while adhering to local compliance requirements, according to Gogia. “Early adopters may come from industries like banking and manufacturing with a mature data posture, which is an essential foundation for any such deployment,” Gogia said. “Architectural maturity is another key requirement for success, as it enables the embedding and native use of such a tool within the architecture.”
