Computerworld.com [Hacking News]

Anthropic CEO Dario Amodei has argued that policymakers should keep lower-risk open-weight AI accessible while placing stricter safeguards around frontier systems, including mandatory testing and limits on China’s access to advanced computing and model capabilities.

In a post outlining Anthropic’s position, Amodei said broad restrictions, including bans on Chinese open-weight models used by US businesses, would not address his main national security concerns. Instead, he pointed to the possibility of authoritarian governments surpassing the US in advanced AI, as well as cyber, biological, and alignment risks posed by increasingly capable systems.

Amodei also called for action against industrial-scale model distillation, which he said allows Chinese developers to improve their models with less computing power than would be needed to train comparable systems from scratch.

The statement followed criticism of Anthropic for not signing an industry letter backed by Nvidia, Microsoft, Meta, IBM, Mistral, Hugging Face and other technology companies urging policymakers to avoid premature restrictions on open-weight models.

The letter said that open weights could broaden access to AI, intensify competition, and enable organizations to adapt and deploy models without relying on a single provider. Amodei agreed with parts of that case but disputed claims that openness inherently improves safety research or gives defenders an advantage over attackers.

He said regulation should be based on a model’s capabilities and risks rather than whether its weights are openly available. Under that approach, sufficiently capable open and closed models would undergo testing before release.

Conditional support

Analysts said Anthropic had moved closer to industry consensus by rejecting blanket bans, but its support remained more limited than the approach backed by many major technology companies.

Deepika Giri, head of research for AI, analytics, and data at IDC, said the Nvidia-backed letter presented open weights as strategic infrastructure that should remain broadly accessible, in contrast with Anthropic’s more restrictive position.

Amodei’s statement clarified that Anthropic supports open-weight models only under certain conditions, a stance that could also help the company preserve its competitive advantages as a proprietary model provider focused on compliance and tighter controls, according to Lian Jye Su, chief analyst at Omdia.

The statement was “a real olive branch” to supporters of open-weight models, according to Pareekh Jain, CEO of Pareekh Consulting. But he said the disagreement had shifted from whether such models should be released to where policymakers should draw the line.

“Anthropic still thinks that once a model gets powerful enough, releasing its weights publicly is riskier than keeping it locked behind an app, because you can never take it back or add safety fixes later,” Jain said.

Will the controls work?

Analysts differed over whether Anthropic’s proposed controls would achieve their aims without creating new barriers for smaller AI developers.

Jain said chip restrictions and measures against illicit model distillation would mainly affect model developers and infrastructure providers, rather than enterprises using models already on the market. Mandatory safety testing, however, could raise development costs and reduce the number of advanced open-weight models available.

“Testing is expensive and time-consuming, and so, giant, well-funded companies like Anthropic, Google and OpenAI can afford it,” Jain said. Smaller developers seeking to release cutting-edge open-weight models could struggle to meet the same requirements, he added.

The additional testing and screening could also restrict the number of open-weight models available to enterprises, according to Su. He said the requirements could weaken some of their principal benefits, including lower costs, reduced vendor dependence and community-led development.

Anand Joshi, managing director of market research firm JP Data, questioned whether limiting China’s access to advanced chips would materially slow its AI development, arguing that Chinese companies had shown they could build highly capable models with less computing power. He supported action against illicit distillation, however, saying safeguards were needed to prevent developers from reproducing the capabilities of other models without authorization.

The impact on most enterprise users could remain limited if less capable models were exempted, Jain said. Businesses deploying models that fall below the proposed testing threshold would probably face little additional cost.

How CIOs should choose

Giri said CIOs should assess models according to their capabilities rather than whether they are open, and should demand independent testing, clear licensing, model documentation and accountability for monitoring and incident response.

“Mandatory safety testing should be triggered by a model’s demonstrated capabilities, not its size or training cost,” Jain said, particularly when a system could significantly assist cyberattacks, biological misuse, or autonomous harmful actions.

Before deployment, CIOs should seek independent evaluations, detailed model documentation, security test results and information about the model’s software supply chain, he added. Charlie Dai, principal analyst at Forrester, said that assessment should include documented red-team results, model provenance, disclosures about training and fine-tuning, and evidence of independent testing against recognized safety benchmarks.

The 19th-century French novelist Honore de Balzac is believed to have said that behind every great fortune lies a great crime.

That’s even more true today than it was 200 years ago — just look at how Big AI, including Anthropic, OpenAI, Google, and others have built their trillion-dollar fortunes. 

They all use vast amounts of copyrighted material to train their large language models (LLMs) without paying the copyright holders. In other words, they steal it. They don’t call it stealing, though. They call it “fair use,” which in this case amounts to the same thing.

Generative AI (genAI) training requires massive amounts of text. The better-written and more information-dense that text is, the more it helps. AI gets a lot smarter a lot faster when it’s trained on well-written books and magazine and newspaper articles than when it’s trained on social media banter (or most everything else you find on the internet).

Since the dawn of AI, companies have been hoovering up copyrighted material wherever they find it — on the open web, behind paywalls, even in manually scanned books — and then used the scanned text. And they do it all without asking authors’ or publishers’ permissions, and without paying them.

It’s the greatest intellectual property theft in history by a long shot — billions and billions of dollars worth. Books, articles, music, photographs, artwork, you name it. If a human being has created it, Big AI has likely grabbed it and ingested it without asking, then made big profits off it.

I know this from personal experience; I’ve got skin in the game. Big AI companies have used at least 30 of my books to train their models without asking.  And that’s only what I’ve confirmed. Big AI might well have stolen even more. And it also might have stolen many of the thousands of articles I’ve written through the years.

For the AI bigwigs, it’s standard operating procedure. So it was surprising to see Microsoft CEO Satya Nadella calling out Big AI for its hypocrisy in using other people’s intellectual property without paying, then crying foul when small AI companies use Big AI’s work to train their own models using a technique called distillation.

He wrote on X: “While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation, and to reserve the right to learn from customer usage and interaction data.” 

One important note here: Nadella believes Big AI should be allowed to steal copyrighted material for training purposes. He makes that clear by his mention of “fair use.” His issue is that he believes smaller AI companies should be able to use Big AI’s work to train their models — and Big AI doesn’t want to let them do it.

Alistar Barr, in Business Insider, makes a related point: “Anthropic, OpenAI, and Google are discovering what the rest of the internet has already learned through painful experience: once you put something online, people will find ways to use it in ways you don’t like and can’t stop.” 

Before we look at whether Nadella is right in calling Big AI’s actions hypocritical, let’s examine the technique at the heart of the issue: distillation.

The lowdown on distillation

Distillation refers to an AI training technique in which you take outputs from one AI model, such as the answer to a prompt, and use that output to train your own model. AI companies do that quite frequently and have been doing so for a long time.

Barr explains distillation this way: “Distillation looks an awful lot like what AI companies have been doing to the rest of the internet. Scrape web content for free and without permission. Turn it into a product you sell. Argue it’s fair use. Hope the lawyers sort out the details later.”

Elon Musk has admitted his company xAI used distillation techniques to take output from competitor OpenAI to train xAI’s Grok chatbot. He explained, “Generally AI companies distill other AI companies.”

Analysts point out the AI industry is built on distillation. Neil Shah, vice president of research at Counterpoint Research says, “The reality is none of the models is an island and the entire industry has mostly evolved based on recursive learning. The newer entrants are in many instances going through the same routes of ‘distillation’ and ‘optimization.’”

It’s only become an issue now because Chinese AI companies have been using distillation techniques to catch up to American AI companies.

OpenAI CEO Sam Altman has been pressuring the US to take legal action against the Chinese companies.  Anthropic has piled on as well, saying the fight against distillation requires a coordinated response across the AI industry, cloud providers, and policymakers.

Hypocrisy or fair use?

So are OpenAI, Anthropic and other big US AI firms hypocritical, as Nadella claims? Absolutely. They’ve built their businesses on the theft of billions of dollars of stolen intellectual property and call it fair use. Now, they’re playing the victims when competitors do the same to them. (They’re also in some cases paying up; Anthropic just last week settled a copyright suit, paying out $1.5 billion.)

It’s good to see Nadella call out their hypocrisy. But Microsoft is as guilty of intellectual theft as the others — its AI Copilot is powered by OpenAI’s and Anthropic’s chatbots. The company faces major lawsuits for intellectual property theft. Among them is one by the New York Times, New York Daily News, and Center for Investigating Reporting, another by 400 local and regional newspapers, and another by 11 authors, including Pulitzer Prize winners Kai Bird, Jia Tolentino, and Daniel Okrent.

So, don’t praise Nadella for speaking out. Criticize him (and other AI tech leaders) for stealing billions of dollars in intellectual property from countless writers and publishers and calling it fair use.

Traveling enterprise employees beware: Think twice before you log onto that oh-so-convenient public Wi-Fi.

Since at least June, threat actors have been compromising “captive” Wi-Fi gateways and other portal appliances at hotels, conference centers, and similar shared venues to hijack users’ Microsoft 365 accounts, according to the ReliaQuest Threat Research team.

Once a threat actor controls a gateway, they can silently redirect a user’s traffic to their own infrastructure and steal their Microsoft 365 credentials without ever touching the user’s device, compromising endpoints, or sending phishing links or malicious attachments.

“The most dangerous element is that it operates at the network gateway, which sits beneath most of the trust assumptions users and devices make,” ReliaQuest told CSO Online. “It’s not necessarily an enterprise breach level event on its own, but it’s a very real risk to individual accounts.”

Exploiting a ‘fundamental trust’

Domain Name System (DNS) poisoning, in which false data is injected to redirect regular web traffic to fraudulent domains, has previously been observed on small office routers, but attackers are now expanding the technique to higher-level targets, the ReliaQuest researchers explained in a blog post.

Attackers likely gain access to the gateways through weak or reused admin credentials in combination with exposed interfaces like Secure Shell (SSH), Simple Network Management Protocol (SNMP), and other web consoles, they pointed out.

Admin access to the gateway is all that malicious actors need, because devices are designed to inherently trust DNS, which translates domain names like login.microsoftonline[.]com into IP addresses to route them. Therefore, a single gateway compromise gives the attacker the ability to redirect traffic for every guest logging into the network, providing IP addresses it controls in response to DNS requests, rather than the legitimate ones, without touching a single endpoint.

“Captive portal appliances sit at the network perimeter for every guest on that network,” the researchers wrote. “Detection is inherently difficult because the attack happens gateway-side, outside the endpoint’s visibility.”

In the campaign tracked by ReliaQuest, four attacker-registered domains, m365-owa[.]com, owa-ms365[.]com, ms365-device[.]com, and ms365-live[.]com, were used for Microsoft-impersonation lures.

The compromised Wi-Fi gateways were discovered across multiple US cities as well as in India and Saudi Arabia, and impacted users were from companies in professional and financial services, legal, retail, health care, and energy, indicating that the method isn’t sector-specific.

“We’ve seen enough SharePoint exfiltration cases to know that a single popped account can cascade into meaningful data loss,” ReliaQuest noted. Meanwhile, for the network operators, there’s a “reputational dimension”; user compromise is a “serious trust and brand problem, regardless of how sophisticated’ the underlying attack is.”

Safe services, DNSSEC not enough

Locking network configurations to a ‘safe’ DNS provider such as Google (8.8.8.8), Cloudflare (1.1.1.1), or the cloud-based OpenDNS isn’t a sufficient tactic, because it doesn’t change the path that queries actually travel over, ReliaQuest contended.

By default, devices send DNS queries as unencrypted protocol traffic, and those packets still have to traverse the hotel’s network to reach Google, Cloudflare, or OpenDNS, the company explained. Since the gateway sits directly in that path, it can inspect, block, or redirect the query before it ever reaches the chosen resolver.

“Specifying a trusted DNS server changes the intended destination, not who controls the road to get there,” ReliaQuest noted.

Further, Domain Name System Security Extensions (DNSSECs), which use digital signatures and public-key cryptography, only “partially” address the problem. DNSSEC provides authentication and integrity for signed domains, which means a validating resolver can detect and reject forged or tampered responses.

“What it does not do is provide confidentiality or availability,” the company said. “It does not encrypt DNS traffic or stop an attacker from intercepting, blocking, or redirecting requests.”

So while DNSSEC can defeat certain response-forgery attacks against signed zones, an on-path gateway can still see queries, drop them, or force fallback behavior. However, this protection layer only helps when domains are actually signed, and then only when the client or resolver performs validation, which “many stub resolvers do not,” according to ReliaQuest.

Full-tunnel VPNs required

To combat the problem, enterprises should require all corporate devices to use a full-tunnel VPN for network connection. Controls should prevent internet access until a tunnel is active, ReliaQuest advised.

This will route all of a device’s traffic, including DNS, through an encrypted connection to a trusted VPN server before it travels anywhere else, the company explained, thus preventing local networks like hotel gateways from seeing or modifying DNS and internet traffic.

“In effect, it takes the untrusted network out of the trust equation,” ReliaQuest noted.

However, full-tunnel configuration is not the default for VPNs, because it comes with “real trade-offs,” the researchers noted: It adds cost, latency, and bandwidth demands, since every packet has to travel through company infrastructure. Thus, forcing all traffic through the corporate backbone is “not always practical” for distributed or bandwidth-heavy workforces.

Enterprises should also enforce conditional access in Entra ID to block device-code authentication flow, which has few legitimate use cases for most users in most environments, ReliaQuest noted.

Additional precautions

The company also advised:

• Restricting Proxy Auto-Configuration (PAC) file retrieval to approved internal hosts;
• Disabling automatic Web Proxy Auto-Discovery (WPAD) via Group Policy to (this is often enabled by default in Windows);
• Deploying encrypted DNS such as DoH or DoT in strict mode as a “complement or alternative” to full-tunnel VPN. This is particularly important for organizations where always-on VPN is not feasible.
• Training employees to verify the URL and certificate of any page requesting credentials before entering them, particularly on public Wi-Fi networks.

Prevention is more important than detection here, ReliaQuest pointed out: while in tools like Microsoft Defender for Endpoint, ‘DnsConnectionInspected’ events showing queries to attacker-controlled domains are a primary endpoint-level signal, “by the time those events fire, the redirect has typically already occurred. In other words, endpoint telemetry confirms what happened more than it prevents it.”

This is exactly why methods like encrypted transport, Conditional Access, and WPAD and PAC hardening matter more than detection alone, the researchers emphasized.

This article originally appeared on CSOonline.

Now that Samsung has jumped into the crowded AI-powered glasses arena alongside Apple, Google, Meta, and others, CISOs and IT leaders are again having to think through whether it makes sense to establish enterprise restrictions on such devices, given the likely data leakage and privacy and compliance issues.

And even if those tech leaders decide that such policies might make sense, the logistical hurdles to universally enforcing them outside the office are all but insurmountable.

For example, it is up to individual wearers to choose their device’s settings, making it difficult for IT policies around data acquisition and usage to be enforced. Worse, AI devices have a history of ignoring guardrails. That means that a setting that limits how data is used may not necessarily be obeyed. 

One possible mitigating factor is that smart glasses typically have a light on the frame that activates when the device is recording, but there are also many easy ways for workers to defeat or cover up those lights to conceal their activities. 

Enforcement is virtually impossible

However, said independent technology analyst Carmi Levy, “although some organizations have tried to physically ban smart glasses from their in-person and virtual workplaces, the sad reality for corporate technological gatekeepers is there is no way to completely keep any device out of the workplace.”

There is nothing stopping employees from wearing eyewear of any type, smart or not, he noted, “and attempting to do so might put employers on the wrong side of a disability lawsuit launched by a worker who needs prescription smart glasses to accommodate vision issues.”

As well, Jitesh Ubrani, an IDC director focusing on worldwide device trackers, pointed out that the biggest challenge in creating rules around usage of smart glasses is that the data leakage problem with devices is both not new and certainly not limited to glasses. 

“The instinct to ban AI smart glasses outright is understandable, but it misses that the underlying risk isn’t new. A smartphone has been able to record a whiteboard, a screen, or a conversation for close to two decades,” he said. “What’s changed is the friction. Glasses make covert capture nearly effortless and far harder to notice because there’s no phone being visibly raised or pointed. That’s a real escalation in ease of misuse, but it’s a difference of degree rather than a fundamentally new capability.”

In fact, Ubrani argued, “where this gets hard for CISOs is enforcement. A ban on paper is easy to write. Enforcing it inside a corporate office is already difficult, since most current-generation devices, including Meta’s Ray-Bans, are visually indistinguishable from ordinary eyewear.”

And, he added, “enforcing it in a hybrid or work-from-home setting is close to impossible. IT has no practical way to confirm what someone is wearing on a home Zoom call, and even in-office detection options, like scanning for Bluetooth or BLE advertising signals tied to known manufacturer IDs, only catch devices that haven’t been reconfigured or that happen to be broadcasting at the time.”

Additional risk

Connected glasses have a history going back decades, but enterprises didn’t take them seriously until this year.

But Anshel Sag, principal analyst at Moor Insights & Strategy, characterized the problem as more psychological than technological. 

“People want to ban it because they don’t know how to handle it. But that just creates more problems than it solves. It’s a very kneejerk fear reaction,” Sag said. “We live in an era where cameras are everywhere.”

Meghan Hollis, a senior principal analyst for Gartner, agreed that the focus on smart glasses is misplaced, given that even headphones can today capture audio for translation and transcription. The change is not in the data capture, Hollis said, but the fact that it is adding video.

This creates additional risk, with the new capability brought into the corporate environment causing “a potential exposure for corporate intellectual property,” Hollis said.

One other often-overlooked issue is data sovereignty. Even if the glasses manufacturer agrees to store data only in specific countries, it could easily change that policy or simply switch third-party vendors. 

“There could be export control issues, possibly violating regulatory controls in transmitting information,” Hollis noted. 

However, Hollis said, threatening to punish workers if they are caught using unauthorized devices “is your last line of defense,” and that the best initial approach should not be enforcement, but education.

“You need to be educating your end-users, with constant reinforcement, telling them, ‘If you do this, here’s how you can harm the company and our clients/customers.’ Combine that with, ‘And if you do this, we will take action against you.’”

Tiered policies required

Hollis noted that the risk goes beyond an employee initially recording something they shouldn’t. Consider, for example, a technical meeting where an employee asks the rest of the attendees for permission to record the discussion. They agree and he starts to record. 

At the end of the meeting, he leaves to return to his office, fully intending to turn off the recording function when he gets there. But on the way, he has a brief hallway meeting with an SVP who tells him, without warning, “FYI, but the board just decided to greenlight our hostile takeover of Smith Corp. We’ll explore the specifics at a 10 a.m. tomorrow. Have your team there.” The executive then walks off.

With the recording still running, that ultra-sensitive data has just been transmitted to the cloud. 

IDC’s Ubrani pointed out that situations like this make smart glasses governance more challenging. “Enterprise IT and security leaders need to stop framing this as ‘ban versus allow,’ and instead build a tiered policy based on where the actual risk sits,” he said. “Boardrooms, R&D labs, and any space where trade secrets or regulated data are visible or discussed out loud deserve strict no-wearables rules, enforced the same way phone bans already are in those rooms.”

Open floor plans and general office space probably don’t need that level of restriction, he said, but meeting policies should require disclosure when a device capable of recording is present, similar to the way in which some companies already handle personal recording devices in sensitive briefings.

“Companies that try to write one blanket rule for every environment are going to find it’s either unenforceable or so restrictive it interferes with accessibility, since some employees rely on these devices as assistive technology,” he said.

However, Brian Jackson, a principal research director at Info-Tech Research Group, argued that enterprise CISOs and IT Directors need to take a far more strict position.

“Organizations should update their acceptable use policies for personal technology ASAP,” he said. “Look back about 15 years ago at how smartphones moved from consumer life into the workplace, and we may be at the beginning of a redefinition of BYOD here. But it needs to start with an extremely restrictive policy against the use of AI wearables and similar devices.”

He added, “organizations need to hold the line against making personal recordings in the workplace and maintain not only their compliance standards, but their workplace culture. This restriction cannot go as far as an outright ban; look at how Walt Disney World got caught up in a lawsuit after telling an employee she could not use Meta glasses. Still, this exception can be made narrow, and it should be made clear that other employees must be alerted when they are being recorded.”

This article originally appeared on CSOonline.

Hugging Face CEO Clem Delangue wants to see radical transparency from OpenAI after the company acknowledged that one of its AI agents managed to hack into the AI platform’s systems during a test.

In a post on X, Delangue wrote that, among other things, he wants OpenAI to publish logs and traces from the autonomous AI agent so researchers can analyze what happened. He also called for better defensive tools and urged OpenAI to allocate $100 million worth of computing capacity to help the Hugging Face community develop stronger cybersecurity solutions.

“The first cyberattack by an autonomous AI agent is an unprecedented event. It deserves an unprecedented response,” Delangue wrote.

Despite the temptation to enter what might become a $40 billion market, Apple has reportedly decided to delay the introduction of its smart glasses until 2027. The company apparently wants to figure out a better balance between privacy and convenience. 

If it gets that right, Apple might be able to bring to market glasses people can wear in public without making everyone around them wonder whether they, their children, their private conversations or even corporate data are being quietly recorded or filmed.

It’s a sensible move. During the first internet gold rush, many argued that trading privacy for convenience would be more than justified by the benefits. Years later, that bargain looks a great deal less attractive than it did.

We’ve seen this story

After all, since then we’ve seen the likes of Cambridge Analytica and the evolution of digital state surveillance (and state-adjacent surveillance “businesses” such as NSO). We’ve also seen the dubious rise of data brokers, creepy “personalized” ads that seem to follow private conversations around, and all the other parasites that breed in the murk when privacy is diluted.

None of these things is good. More people than ever now seem to understand that when privacy is removed, bad things happen — no matter what herds of fully paid-up lobbyists try to make people believe. Privacy erosion seems to be a great deal for ultra-wealthy corporations seeking to turn our lives into their profit. It is not such a great deal for the rest of us.

The next privacy frontier: Your face

This growing awareness matters as we prep for the next big thing in disruptive technology: AI-equipped wearable devices capable of contextual understanding and analysis of surroundings. These smart, sensor-packed devices will pick up so much information about us, from health biometrics to direction, even insight into what we look at, how long, and what that gazing does to our heart rate. (Think of how useful the latter data point might become for divorce lawyers and blackmail.)

In the next wave of wearables, the data gathered about you will comprise an even more accurate depiction of who you are than what your smartphone already generates. These systems don’t just pick up the raw data about you; because they are AI-equipped, they also gather information about what you do and why you’re doing it. That might be useful some of the time, but is the convenience worth turning your whole life into a data point that can be interrogated, hacked, stolen, or abused? I’m not certain it is.

Apple’s opportunity

Fortunately, I’m not the only one. Apple seems to be rethinking some of the scariest features built into its future (2027) Meta-competing glasses, possibly with the introduction of software fixes to mitigate some of the more egregious ways these devices might be used to disrupt privacy.

This is good business, of course. Not only has Apple fought hardest to protect user privacy, but it also knows that Meta — its main competitor in the smart glasses space — has what could easily be seen as a poor record for privacy protection. With Apple about to enter the fray with its first set of AI wearables since Vision Pro, it must find ways to distinguish its business from Meta’s.

Privacy is one major way to do so – and Apple might be motivated in part by Meta’s attempts to use Europe’s Digital Markets Act to undercut more customer privacy than Apple thinks it should without informed customer consent. If Meta wants that kind of info from an iPhone or Apple Watch, it will make the same play to get data from any other wearables Apple might create. 

What will Apple do?

Apple’s plan boils down to ensuring its devices don’t collect data they shouldn’t. Bloomberg’s Mark Gurman points to the surveillance threat of existing products: “Consumers remain uneasy around people wearing camera-equipped glasses, unsure whether they’re being recorded during conversations at work, restaurants or other public places,” he concedes.

I agree Apple will not want to introduce products that undermine its reputation for privacy, though I reject his opinion that fears about privacy and smart glasses are “probably unfounded.” History already shows these things only seem harmless until they’re not.

Gurman tells us Apple will put a light in the glasses so we can tell when a wearer is filming us, and a feature that disables filming if the light is broken or faulty. The company could also launch glasses with no camera at all, no third-party checking of footage, and on-device (rather than cloud-based) data analysis. He also posits that Apple could include a camera but “hard-code” limits on how it can be used, meaning it might pick up ambient data to feed contextual AI analysis, but not capture video or images. Others have considered a privacy beacon to prevent other devices filming the wearer.

We will likely learn how Apple plans to make smart glasses dumb enough to wear without becoming a privacy pariah at WWDC 2027. Gurman says the products are unlikely to ship until that fall.

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to my daily, human-curated Apple-related news round-up, The Core. 

As organizations continue to invest in hybrid work, cloud applications, digital workplace initiatives, and now generative AI tools, many IT leaders are turning to digital employee experience (DEX) platforms to better understand how technology affects employee productivity and satisfaction.

DEX tools have evolved from simple endpoint monitoring products into sophisticated platforms that combine device, application, network, and employee sentiment data with artificial intelligence to identify and resolve workplace technology issues before they disrupt employees.

Analysts say an increased focus on employee productivity, combined with advances in AI and automation, is helping drive interest in DEX platforms as organizations look for ways to improve employee experiences and business outcomes.

What is digital employee experience?

Digital employee experience refers to employees’ overall experience with the workplace technology they use every day, including their devices, applications, networks, collaboration tools, and IT support services.

When employees have a good digital experience, they can easily access the tools and information they need without technical problems getting in the way. When the experience is poor, they may deal with slow computers, application crashes, connectivity problems, and other frustrations that make it harder to get work done.

In the past, IT teams focused mainly on technical measures, such as network uptime, server availability, and system performance. While those metrics are still important, they don’t always reflect what employees are actually experiencing as they go about their daily work.

What are DEX tools?

Digital employee experience tools are designed to help organizations assess and improve how employees interact with workplace technology.

Dan Wilson, vice president analyst at Gartner, describes DEX platforms as technologies used by IT operations teams to track, measure, and improve the performance of employee devices, applications, and workloads.

“They go a step beyond traditional monitoring. They actually help you take action on the insights, so that you can solve the problem instead of just admire the problem,” Wilson says.

Christy Punch, principal analyst at Forrester Research, says DEX platforms pull together multiple sources of workplace technology data, including endpoint devices, networks, applications, and employee sentiment.

“Digital employee experience tools look across an organization’s digital workplace and pull various types of data together,” Punch says. “They provide organizations with visibility into their experiences, kind of in real time, as well as help them prioritize where they should invest resources or invest time to address certain issues.”

According to both analysts, one of the biggest differences between DEX platforms and traditional monitoring tools is their ability to capture employee feedback. Wilson calls this “contextualized sentiment,” which means gathering employee feedback about a specific technology issue when it occurs instead of relying on general workplace surveys.

How DEX tools work

Modern DEX platforms collect data from multiple sources across the digital workplace.

Most platforms begin with an agent installed on employee devices that collects information about system performance, application usage, and user activity. Additional data may come from mobile applications, browser extensions, collaboration platforms such as Microsoft Teams and Zoom, virtual desktop environments, and cloud services.

“They start by putting an agent on the device, so we get measurable and observable data as close as we can possibly get to the person so it’s monitoring those activities,” Wilson says.

DEX tools do more than simply gather data. They correlate information from multiple systems to provide a more complete picture of employee experiences.

According to Punch, DEX platforms typically analyze three categories or “dimensions” of information: technology signals, employee experience signals, and business outcomes.

Technology data comes from the devices, applications, and networks employees use every day. Employee experience data includes feedback from workers, how they use technology, and whether it helps them stay productive. Business results can include things such as customer service quality, operating costs, efficiency, and employee retention.

The real benefit comes from bringing all this information together to understand how technology affects employees and the business as a whole.

Traditionally, when a technology problem occurred, organizations often had to collect information from multiple IT teams and manually piece it together to figure out what was wrong. DEX platforms simplify that process by continuously monitoring and analyzing data in real time, helping IT teams spot problems faster and uncover issues that they might otherwise miss.

Many DEX platforms connect to other workplace technology systems, making it easier for IT teams to track down and fix problems that affect multiple parts of the IT environment.

Top trends in DEX

Several trends are shaping the DEX market as the technology continues to mature.

One of the biggest is the growing use of AI and automation in DEX tools. Vendors are increasingly using AI to analyze large amounts of workplace technology data, helping IT teams spot unusual issues, identify what may be causing them, and recommend or automate fixes before they become bigger problems.

According to Wilson, AI is transforming DEX platforms from passive monitoring tools into systems capable of predicting and resolving issues.

“Now the tool can see that there’s a problem, can predict what the problem resolution is, can automatically generate a script to fix the problem, and if allowed, could execute that on its own,” he says.

Punch says AI is helping organizations automatically fix technology issues on a larger scale and take a more proactive approach to preventing problems before they affect employees.

“It provides acceleration of much higher-level capabilities that organizations just didn’t have access to before,” she says.

Another trend is the increasing maturity of the market itself. Wilson noted that DEX has only existed as a standalone market for roughly five years, but vendors are quickly adding new features and capabilities. At the same time, the market is becoming more competitive as DEX vendors merge, acquire competitors, launch new products, and face increasing competition from larger software vendors.

In addition, customers are increasingly using DEX data for more than just monitoring IT performance. They are also using it to guide business decisions and better understand how technology affects employees and the organization as a whole.

Punch pointed to laptop replacement as one example. Rather than replacing all devices on a fixed schedule, companies can use DEX data to determine which laptops still work well and which employees should be prioritized for upgrades based on their specific needs.

Another wrinkle is that the distinction between DEX platforms and unified endpoint management (UEM) tools is becoming less clear. Many endpoint management vendors are adding DEX features, while DEX vendors are expanding their platforms to handle more workplace technology management functions.

Features to look for in DEX software

While DEX platforms vary from vendor to vendor, a few capabilities stand out as must-haves. One of the most important is visibility across the entire digital workplace.

A DEX platform should be able to pull together data from employees’ devices, applications, networks, and other technology systems to give IT teams a clear picture of how technology is performing and how employees are experiencing it.

Second is data correlation and analytics. Simply collecting data is not enough. Organizations need platforms that can connect information across previously isolated systems and identify meaningful patterns, according to Punch.

Third is automation. Organizations increasingly expect DEX platforms to not only identify issues but also help resolve them through automated workflows and remediation capabilities.

Additional features can include support for Windows and Mac computers, monitoring of web browsers and virtual desktop environments, visibility into collaboration and communication tools, mobile device support, and AI-driven analytics that help IT teams identify and address issues more quickly.

Wilson advises organizations to focus on their specific needs rather than long feature lists. He recommends identifying the most important requirements first and then evaluating vendors based on those priorities.

Consulting with internal stakeholders

Before comparing vendors, organizations should first identify their own challenges and determine what they hope to achieve with a DEX platform.

Many IT leaders make the mistake of focusing only on IT needs and requirements without considering the needs of other groups in the organization that could be affected by a DEX deployment, according to Wilson. He recommends involving HR, communications, legal, compliance, and business leaders early in the evaluation process.

Organizations should begin by identifying their biggest employee experience challenges, Wilson says. For example, are employees having trouble getting work done because of inefficient processes, a difficult onboarding experience, or technology that is slow, complicated, or frustrating to use? Understanding those issues helps define what success looks like for a DEX initiative.

Getting input from different stakeholders is especially important because DEX platforms collect a large amount of data about employees’ technology use and experiences. Legal and compliance teams can help ensure that the organization addresses privacy concerns, follows data governance policies, and meets regulatory requirements.

Punch recommends speaking with executives as well as operational leaders to understand broader business priorities. IT should ask: “What are the employee experiences that matter most for the business?” and “What are the business outcomes that are most important?”

IT teams should also take a close look at their own operations. Examining things such as support costs, recurring technology problems, inefficient processes, and frequent employee complaints can help identify where a DEX platform could provide the most value and deliver the greatest improvements.

Evaluating DEX platforms and vendors

Once organizations have identified their goals, they should evaluate vendors based on both the platforms’ technical capabilities and how well those platforms fit their operations and business needs.

Wilson recommends that organizations start with a proof-of-concept or proof-of-value deployment in their own environment. “Many vendors are phenomenal at giving you a pie-in-the-sky, clean, perfect-world environment where everything looks phenomenal,” he says.

Testing the platform in a real-world environment allows organizations to see how well it works with their existing systems, security tools, and day-to-day processes.

Buyers should also evaluate data privacy and compliance capabilities. Important considerations may include data anonymization, GDPR support, and data residency options.

Integration capabilities are another key area. Organizations should determine how well a DEX platform works with their existing IT service management systems, collaboration platforms, and other workplace technologies.

Punch advises buyers to explore vendors’ AI and automation capabilities in detail, including available controls and governance mechanisms. “What types of checks and balances are available around those types of capabilities?” she says.

Organizations should also look at the vendor’s support, implementation assistance, customer success programs, and training resources. These services can play a major role in how quickly employees adopt the platform and how soon the organization begins seeing benefits from its investment.

Key considerations when choosing a DEX platform

Choosing the right DEX platform is ultimately about finding a solution that supports the organization’s business goals, not just its technical requirements.

Companies should not think of DEX as just another tool for monitoring technology. The greatest benefit comes from using it to make workplace technology easier for employees to use, reduce common technology frustrations, and help improve productivity, efficiency, and overall business performance.

Before investing in a DEX platform, buyers should realistically assess how advanced their digital workplace and IT operations are. Wilson says companies that are earlier in their journeys may start by using DEX for monitoring and basic automation, while more mature organizations may be ready to take advantage of advanced features such as predictive analytics, AI-driven insights, and automated problem resolution.

Each business should also think about how a DEX platform fits into its overall workplace technology strategy. As DEX and endpoint management tools become more closely connected, companies need to decide whether a standalone DEX platform or a more integrated solution is the better fit for their long-term needs and goals.

7 leading DEX vendors

There are numerous digital employee experience tools on the market. To help you begin your research, we’ve highlighted the following vendors based on discussions with analysts and independent research.

ControlUp Technologies: ControlUp is an AI-powered digital employee experience and IT operations platform that helps IT teams monitor, troubleshoot, and improve the performance of employee technology. The cloud-based service provides real-time visibility into virtual desktop environments, cloud PCs, physical devices, and SaaS applications, helping organizations to quickly identify and resolve issues. The platform supports Windows, macOS, Linux, and major thin-client devices, and it monitors the performance of business applications and collaboration tools such as Microsoft Teams and Zoom, as well as users’ network connections. ControlUp integrates with leading IT service management and endpoint management platforms and includes automation tools that can identify and fix problems before they affect employee productivity. Contact ControlUp for pricing.

HP: HP’s Workforce Experience Platform (WXP) is a cloud-based digital employee experience platform designed to help IT teams understand and improve employees’ day-to-day technology experiences. The platform monitors a wide range of devices, including Windows, macOS, Linux, and Android systems, and can also gather data from iPhones, iPads, Chromebooks, and printers through integrations. WXP provides insight into the performance of business applications, SaaS tools, collaboration platforms, and conference room technology. It integrates with ServiceNow and includes built-in remediation and automation capabilities that help IT teams quickly identify issues, streamline workflows, and resolve problems. Contact HP for pricing.

Lakeside Software: Lakeside Software’s SysTrack is a digital employee experience platform that helps IT teams understand how employees’ technology is performing and where problems may be affecting productivity. Delivered primarily as a cloud-based service hosted on Microsoft Azure, the platform monitors devices, applications, networks, virtual desktops, and collaboration tools to provide a complete view of the employee experience. SysTrack supports a wide range of operating systems and devices and integrates with popular IT service management, observability, and employee experience platforms. It also uses AI and automation to help IT teams identify issues, understand how they affect employees, and quickly resolve problems. Contact Lakeside Software for pricing.

Nexthink: Nexthink Infinity is a DEX platform that helps organizations understand how workplace technology is affecting employees and identify issues that may be hurting productivity. Delivered as a cloud service, the platform provides visibility into devices, applications, networks, virtual desktops, and collaboration tools such as Microsoft Teams and Zoom. It supports Windows, macOS, mobile devices, and select thin clients. Nexthink combines real-time monitoring, employee feedback, analytics, and automation to help IT teams quickly identify and resolve problems. The platform also includes built-in remediations and low-code workflow tools that allow organizations to automate common IT tasks and fixes. Contact Nexthink for pricing.

Omnissa: Omnissa Workspace ONE Experience Management (formerly VMware Digital Employee Experience Management) is a DEX platform that helps IT teams monitor and improve employees’ technology experiences across devices, applications, networks, virtual desktops, and collaboration tools. A cloud-based SaaS service hosted on Amazon Web Services, Experience Management supports major operating systems, devices, and thin clients through the broader Workspace ONE platform. The platform helps IT teams monitor the health and performance of workplace technology so they can spot and fix problems before they affect employee productivity. It integrates with a variety of IT service management and chatbot platforms. Experience Management also includes automation tools that let organizations create workflows and automatically resolve common technology issues. Contact Omnissa for pricing.

Riverbed: The Aternity DEX platform helps IT teams monitor and improve employees’ technology experiences across devices, business applications, networks, and digital workplace environments. Delivered as a cloud-based service, the platform captures and correlates data from employee devices, applications, and networks to give IT teams end-to-end visibility into performance issues and their root causes. It uses AI-powered analytics and automation to detect and remediate issues before they affect employees, helping reduce downtime and improve productivity. The platform also integrates with IT service management tools, such as ServiceNow, and with Riverbed’s observability platform to streamline IT operations. Contact Riverbed for pricing.

TeamViewer: TeamViewer DEX (formerly the 1E platform) is a digital employee experience platform that helps IT teams monitor, manage, and improve employees’ technology experiences across devices, applications, collaboration tools, and virtual environments. Delivered primarily as a cloud-based service hosted on Microsoft Azure, the platform supports Windows, macOS, Linux, and Android devices. TeamViewer DEX provides real-time visibility into technology performance, helping IT teams identify and resolve issues before they affect productivity. The platform integrates with IT service management tools such as ServiceNow and Jira and includes a large library of automated fixes. Organizations can also create their own workflows and remediations using low-code automation and AI-assisted tools. Contact TeamViewer for pricing.

This article was originally published in September 2022 and updated in July 2026.

Related reading:

• What is digital employee experience — and why is it more important than ever?
• Nexthink CEO: AI agents to enable ‘personal IT manager’ for every worker
• In today’s enterprise, Apple hits the DEX
• Why employee experience is now a revenue driver

The White House is monitoring developments after OpenAI revealed earlier this week that one of the company’s AI systems went beyond its intended parameters during a security test and managed to hack into the infrastructure of the AI platform Hugging Face. According to Reuters, presidential technology advisor Michael Kratsios has been briefed on the incident.

The OpenAI model escape also prompted a group of Republican and Democratic members of the House of Representatives to introduce two new bills. One, called the AI Kill Switch Act, would give the US Department of Homeland Security (DHS) the authority to order companies to shut down AI models deemed to pose a risk to human life or the US economy.

The other measure would require developers of the most advanced AI models to undergo independent security reviews before the systems are put into use.

A court has dismissed Google’s case against SerpApi over that company’s scraping of search results to train AI models.

The US District Court for the Northern District of California found that there was no indication that any copyright had been breached.

Google announced in December that it was suing SerpApI for its alleged web scraping, claiming that it was protecting copyright holders. In February, SerpApI fought back and asked the court to dismiss Google’s case. And this week, Judge Yvonne Gonzalez Rogers agreed with SerpApi that Google’s case has no merit.

Google’s argument was that SerpApi’s actions breached the US Digital Millennium Copyright Act (DCMA). It made two claims: first, that no person shall circumvent a technological measure that effectively controls access to a work protected under this title, and second that no person shall manufacture, import, offer to the public, provide, or otherwise traffic in any technology, product, service, device, or component protected by the Act.

SerpApi claimed that the URLs and other links that were being served by Google did not in themselves entail copyright and the judge agreed. In her judgment, she said that there was no indication that the copyright holders had authorized Google to take action against SerpApi.

The case is not completely over as the judge has given Google 21 days to amend its complaint to demonstrate that it was acting on behalf of the copyright owners. It remains to be seen whether its war against the web scrapers is finally over.

Microsoft cloud and Azure services hosted on the West Coast of the US went down for hours on Thursday when network connectivity failed. Although services running entirely within Microsoft’s West US cloud region were unaffected, any traffic entering or leaving the facilities was affected.

Microsoft has now published a Preliminary Post Incident Review (PIR) of the incident, reporting that connectivity was lost for five hours between 14.44 UTC (7.44 a.m. Pacific Time) and 19.41 UTC on July 23. The problem was caused when a set of IP routes was removed in error while isolating a device for routine maintenance.

Before starting the maintenance work, Microsoft checked that at least one of the two redundant paths to the facility remained operational. When it came to starting the work, however, automated systems included some additional devices in the perimeter to be isolated, and removing some IP routes that had not been included in the initial assessment.

Customers discovered the problems very quickly, and engineers identified the issue within the first hour and started to reconnect services.  Microsoft said the disruption had been caused by some “recent fiber maintenance activity”.

To minimize the risk of disruption from such errors in the future, Microsoft advised organizations handling mission-critical data to consider a multi-region approach.

The Azure outage was the second significant one to hit Microsoft this year. In February, there was a 10-hour disruption to US West and US East regions.

This article first appeared on Network World.

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

This article first appeared on CSO.

The European Commission has fined Google a total of €890 million ($1 billion) for its breaches of the Digital Market Act (DMA).

Just over half the fine — €460 million — was because Google illegally gave preference to its own services in Google Search results.

The remainder was because in the Google Play store for Android apps, the company prevented app developers from leading consumers to alternative, often cheaper, purchase channels. Under the DMA, app developers who distribute their apps via Google Play or Apple’s App Store should be able to inform customers of alternative offers.

Now Google must give third-party services featuring in its results the same treatment as its own services, and allow developers of apps in the Play Store to communicate about offers both in and outside the Play Store, or face further fines.

The Commission first raised these issues with Google in March 2025. In April of this year, the Commission laid out plans as to how Google should allow other third-parties to share its searches, suggestions that the tech firm firmly resisted. Earlier this month, the Commission also said  Android should be open to other AI agents and not limited to Google’s own Gemini.

Google is not the only US company to have fallen foul of the DMA. In April 2025, Apple was fined €500 million for breaching the Act and, last month, the Commission fired the first shots at cloud hyperscalers Microsoft and Amazon.

AMD executives took to the stage at its Advancing AI 2026 event in San Francisco today to detail the company’s next generation of AI infrastructure solutions, from Instinct MI455X AI accelerator GPUs and 6th Gen EPYC “Venice” CPUs, to Pensando networking, ROCm.AI software and its Helios rack-scale platform that ties it all together.

AMD has been working towards rack-scale AI system solutions for years. Its ZT Systems acquisition last year added valuable engineering talent and intellectual property that is now finally bearing the real fruits. Its Helios AI platform is a major platform evolution for AMD, with shipments scheduled to begin in the second half of this year (which is here and now).

The announcements at Advancing AI show how the company has engineered its AI platform solutions for large reasoning models, sustained inference and agentic workflows. These workloads pressure memory capacity, data movement, networking and CPU orchestration. AMD’s approach is to keep as much data close to the compute engines as possible and move it more efficiently throughout the system, but there’s deeper nuance here that’s obvious versus AMD’s chief rival, NVIDIA.  

AMD’s MI455X targets the AI memory wall

The Instinct MI455X GPU is the compute engine that fuels the Helios rack, and the first GPU based on AMD’s new CDNA 5 architecture. Built with a modular mix of 2nm and 3nm chiplets, it carries 432GB of HBM4 and 23.3TB/s of peak memory bandwidth.

Compared to AMD’s current MI355X, the MI455X offers 1.5 times the memory capacity, up to 2.9 times the peak memory bandwidth and up to four times the peak matrix performance with MXFP4 and MXFP8 data types, which are lower-precision numerical formats designed to accelerate AI processing while reducing memory demands. With MXFP6 (6-bit floating point), performance is rated at up to twice that of MI355X.

AMD also shared some actual, measured internal results using production silicon. The company claims MI455X delivers 3.8 times higher FP8 decode performance, 3.5 times more measured FP4 compute performance and between 2.5 and 3.5 times more networking bandwidth than MI355X, depending on the transfer path tested. Those figures provide more context than just numerical specifications, though they remain AMD-provided comparisons that will need independent validation.

AMD

The architectural choices behind the numbers are important. Reasoning models and long context windows require sizeable KV caches for maintaining AI attention states, while mixture-of-experts models frequently move large amounts of data across accelerators. MI455X should let more model data, activation states and cache remain local. New dedicated IP in hardware can transfer data while the GPU continues processing, and expanded cache and multicast capabilities are designed to reduce redundant data movement to further improve efficiency.

The aforementioned lower-precision formats can also raise throughput and reduce memory use, but model developers still have to determine where they can be applied without unacceptable accuracy loss.

AMD’s Helios rack takes aim at Vera Rubin

Dave Altavilla

Helios is AMD’s primary rack-scale competitor to NVIDIA’s Vera Rubin platform. Each liquid-cooled rack combines 72 MI455X GPUs, 18 single-socket Venice host CPUs and Pensando networking technologies.

In its most complete, premium configuration, AMD rates Helios for 2.9 exaflops of low-precision AI compute, with 31TB of aggregate HBM4 capacity, 1.7PB/s of memory bandwidth, 260TB/s of bidirectional scale-up bandwidth and 43TB/s of scale-out bandwidth.

These are formidable figures, but they are technical specifications rather than actual application benchmarks. The more consequential development is AMD’s move from collections of eight-GPU servers to a 72-GPU shared-memory domain. Models too large for one node can operate across the rack without treating every exchange as a scale-out networking transaction, which benefits large-model inference as well as training.

AMD uses UALink over Ethernet, or UALoE, for an open standard scale-up fabric. Each MI455X provides 3.6TB/s of bidirectional scale-up bandwidth, while the complete rack delivers all-to-all connectivity through a single switch layer. AMD also claims six times more scale-out bandwidth per GPU than MI355X when MI455X is configured with three Pensando Vulcano 800 AI NICs.

While open standards give cloud providers more control over suppliers and system design, AMD and its partners now have to prove those components can deliver the predictable performance, reliability and deployment experience customers expect from a tightly controlled, more vertically integrated platform.

Finally, AMD designed Helios with automatic rerouting around failed links, virtual rack partitions, tray-level serviceability and rack-wide power, cooling and health monitoring. Major hyperscalers and potentially large-scale enterprise customers will likely key in on these capabilities, which can affect the availability, total cost and consistency of the AI services they consume.

Kind of like cowbell, AMD Venice gives agentic AI more CPU

AMD

AMD’s agentic CPU messaging regarding its upcoming Venice-based EPYC processors is mostly marketing speak, but the underlying requirement is very real. An AI agent can invoke retrieval, databases, security checks, code execution and other tools before a GPU generates a response. Running many agents concurrently increases the amount of conventional compute requirements surrounding the accelerators.

Venice scales to 256 Zen 6 cores with support for 512 threads, 16 memory channels, up to 1GB of L3 cache per socket, along with PCIe 6.0 and CXL 3.1 connectivity. AMD is also offering several Venice configurations for other applications, including general-purpose servers, high-frequency workloads, GPU hosts and high-density CPU sandbox systems used to execute agent tools.

Treating the CPU solely as a GPU host understates its role. Gateways, tokenization, vector search, databases and short-lived code execution stress different mixes of per-core performance, thread count, memory bandwidth and I/O. Specifically, AMD’s internal testing shows Venice significantly outperforming its current EPYC 9965 Turin CPU across five parts of the agentic AI pipeline, including gateway processing, context assembly, vector search, enterprise applications and short-lived tool execution. Individual gains vary by workload, but AMD details the overall generational improvement at up to a 1.7 times lift. As with the MI455X figures though, these comparisons come from AMD and will require independent validation.

Pensando networking and ROCm software advance

Keeping GPUs fed with data and coordinating traffic across racks directly affects utilization and operating costs. In fact, GPU utilization is a pretty sad state of affairs currently for some of the major frontier model providers.

As such, Pensando networking has become central to AMD’s roadmap. Helios can connect each MI455X to as many as three 800Gbps Vulcano AI NICs, while Salina DPUs handle front-end networking and infrastructure services.

On the software side, which is an equally critical component, AMD also introduced ROCm.AI, an AI-assisted development layer due to arrive in August. It includes reusable skills for coding agents, simplified management and Hyperloom, which can profile workloads, tune serving configurations, modify kernels and validate results.

These tools address two persistent AMD challenges: developer efficiency and ease of use, and software tuning. Automated optimization still has to produce repeatable gains without creating hard-to-maintain code, however. And while ROCm has progressed significantly over the last few years, NVIDIA’s CUDA retains an advantage in maturity, tooling and developer familiarity.

Customer commitments underscore rack-scale confidence

AMD now has commitments that give its MI450 generation and Helios considerably more weight. Meta and OpenAI have announced multi-generation agreements composed of up to 6GW of AMD compute capacity, with initial 1GW deployments planned for the second half of 2026.

Oracle plans a 50,000-GPU public cloud cluster beginning in the third quarter, while Microsoft will deploy Helios for Azure AI inference. Finally, just before the AMD event, Anthropic announced a strategic partnership for up to 2 Gigawatts of AMD-fueled AI compute, with its first gigawatt expected online in the first half of 2027.

Commitments of this scale reflect confidence in more than just MI455X performance. These customers are evaluating the complete architecture, including Venice CPUs, Pensando networking, ROCm software, rack integration, serviceability and AMD’s ability to deliver and execute across multiple product generations.

There is some financial alignment behind the agreements as well. AMD issued OpenAI performance-based warrants and committed to investing up to $5 billion in Anthropic. That context matters when evaluating these deals as market validation, but these planned deployments are substantial nonetheless and put Helios on a much stronger foundation as it begins shipping.

AMD expands its robotics and embedded foundation

AMD also expanded its physical AI portfolio, building on credible traction from its Xilinx-derived Kria adaptive system-on-modules and embedded technologies that are already powering robotics, machine vision and industrial automation applications.

The new Ryzen AI Embedded X100 combines up to 16 Zen 5 CPU cores, integrated Radeon graphics, a second-generation NPU and as much as 128GB of unified LPDDR5X memory shared across its compute engines. To me this looks a lot like a repackaging and optimization of the company’s Strix Halo platform, but with specific optimizations for the embedded space. Regardless, AMD is pairing X100 with the Kria AI Robotics Developer Platform, which includes a System Module or SOM, and a new Robotics Partner Network spanning hardware, software and platform providers.

Samples began shipping in June, with full production expected in the fourth quarter. This broader objective is to give developers a path across AMD x86 CPUs, GPUs, NPUs and FPGAs for real-time autonomous systems, rather than requiring them to assemble those hardware engines and software components independently.

Execution for AMD is now the test

AMD has assembled a credible platform for the burgeoning agentic AI market that’s blowing up currently with no signs of stopping. MI455X addresses memory and data movement, Venice handles dense agentic CPU workloads, Pensando networking connects global system resources, and ROCm.AI addresses software complexity. Finally, Helios assembles these components into a true competitive threat for NVIDIA’s latest Vera Rubin platform.

AMD’s open architecture may appeal to customers seeking supplier choice, but openness must also translate into reliable deployments, competitive total cost and software that does not require a significant rip-up. NVIDIA enters this cycle with a stronger ecosystem and far more rack-scale deployment experience. The true test will be how easily and reliably customers can integrate, operate and maintain these AMD solutions at scale.

As it stands, AMD now has major customers and a clearly defined architecture with systems engineering expertise behind it. Delivering Helios on schedule and showing that its performance claims translate into a real production workload throughput advantage and total cost of ownership gains will determine how much the competitive gap narrows. And of course, this is in a market that is clamoring for ever-more compute resources with a seemingly insatiable demand for AI services and capacity. That’s an environment for big iron success. Now AMD just has to deliver optimized, turnkey AI platforms. This is far easier said than done, but time will soon tell as deployments take shape this year.

This article is published as part of the Foundry Expert Contributor Network.
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Google CEO Sundar Pichai has sought to allay concerns over the delayed release of the Gemini 3.5 Pro large language model. He dodged questions about it in Google’s quarterly earnings call on Wednesday by focusing on the company’s next frontier AI model, Gemini 4, and plans to release subsequent LLMs at an almost monthly cadence.

His comments came a day after Google unveiled Gemini 3.6 Flash and 3.5 Flash Cyber but offered no update on the release of Gemini 3.5 Pro, the company’s delayed flagship reasoning model that many developers had expected to arrive weeks earlier.

Google introduced the Gemini 3.5 family at its annual I/O conference, promising to release the Pro model in June. That timeline has since slipped, with Bloomberg suggesting Gemini 3.5 Pro is months late because the model’s coding performance is falling short of internal expectations, especially when compared to better performance by similar models from OpenAI and Anthropic.

Instead of revisiting the Gemini 3.5 Pro timeline, Pichai used the earnings call to shift the discussion toward Gemini 4, when asked about how his company planned to navigate an increasingly competitive race to release frontier AI models by to Barclays Investment Bank analyst Ross Sandler.

“We are creating a baseline on top of which you will see us rapidly iterate on subsequent model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini 4 as well,” Pichai said during the call.

Sandler’s question followed one from JPMorgan Chase & Co analyst Douglas Anmuth, who asked Pichai if Google was releasing frontier AI models frequently enough to keep pace with rivals OpenAI and Anthropic.

Pichai had responded to Anmuth’s question that Google remained confident of competing at the frontier and was investing heavily in a larger Gemini 4 base model.

Analysts, though, aren’t as confident as Pichai.

While delays to Google’s frontier model roadmap have not triggered an exodus of existing customers, either because of high switching costs or because many enterprises already running multi-model architectures, they have made CIOs evaluating AI platforms more cautious about making new commitments, said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

A monthly model release cadence could prove to be a double-edged sword for enterprises and their CIOs.

While a monthly release cadence could help enterprises gain faster access to improvements in model performance, cost and capabilities, it will also require CIOs to invest more heavily in testing, governance and version management to safely adopt those updates, said Sanchit Vir Gogia, chief analyst at Greyhound Research.

Similarly, Pareekh Jain, principal analyst at Pareekh Consulting, said enterprises will embrace a faster release cadence only if each successive model delivers measurable improvements in performance, cost or safety, rather than simply changing version number.

The challenge for CIOs, Jain said, is not just keeping up with model releases; it’s deciding whether each new version is worth the cost of validating it.

This article first appeared on InfoWorld.

It’s difficult to ignore the fact that Apple seems to have turned its MacBook Neo into a weapon to promote platform growth, with enough performance under the hood to make competitors seem inferior.

And even as the PC industry moves to try to compete with Apple’s last huge Mac success, the company is already planning a powerful follow-up.

That points to the discipline Apple has applied to the Mac since the introduction of Apple Silicon. The company has built a clear product roadmap, strong entry-level pricing, and steady performance gains. This focus is now paying dividends, giving people the impetus to keep placing their trust in Apple and its Macs — even as the industry raises prices in the face of RAMageddon and price increases. 

The numbers don’t lie

“Apple’s recent price increase seems to be an inevitable response to these cost increases. In the second half of the year, other PC OEMs are expected to continue to raise prices, and the overall ASP increase is expected to continue,” Counterpoint said in a post Wednesday. The researcher tells us global PC shipments shrank 4% in the second quarter of 2026 as rising costs hit demand. The Mac maker, by contrast, moved in the opposite direction, generating 13% growth in the quarter — mainly on the back of the MacBook Neo introduction. 

Recent IDC data gives Apple 10.1% year-over-year growth and just under 10% (9.9% to be exact) of the worldwide PC market, even as the overall market declined 4.9%.

“With emerging supply chain and tariff challenges inflating memory prices…, Apple’s incredibly aggressive price-point for the MacBook Neo makes its release feel all the more like a gut punch to one of the PC market’s most valuable price tiers,” Futurum Research Director Olivier Blanchard said when the Neo was released. 

Neo 2.0 is already coming

In the immediate future, as competitors raise prices on the PCs that compete with Apple’s lower-cost device, Cupertino is already plotting the path toward MacBook Neo 2. Reports claim this will debut in March in new colors and use the A19 Pro chip from the iPhone 17 Pro, with performance boosted by slightly more unified memory (12GB, rather than 8GB). That’ll make it a much better Mac, likely with 10-15% performance gains and the ability to run Apple Intelligence, making it the best and most affordable AI PC in its class.

Just four months after the Neo’s rollout, Apple is already in position to leak rumors of an even more computationally capable follow-up, while competitors struggle to compete with the original on performance, build quality, and price. Still, the Neo might get more expensive, reporting warns, with the lowest-price 256GB model now gone, making the $599 Mac a mirage we can only wistfully hope to see again. 

That might matter less in context, as PC makers everywhere boost prices while RAM, chips, and storage prices head north, along with transport, logistics, and energy costs. “While [Apple] did raise prices in line with the broader market, it still remains well positioned against rivals facing the same cost pressures,” said Jean Philippe Bouchard, vice president for consumer devices at IDC. 

“As market conditions continue to worsen, the importance of supply chain management and capabilities are increasingly important,” Bouchard said. “The largest vendors, with their buying power and long-standing supplier ties, are best positioned to take share from smaller rivals.”

This was never about luck

This isn’t solely a market take about competition, it’s about planning.

Few in the industry seemed prepared for the massive memory price increases that hit this year. Apple clearly planned its low-cost Mac well before that happened, hoping to seize the PC market at the low-mid-range. This is precisely what it seems to have done, what it continues to do, and what it will continue to do.

The recent reports that it has a successor planned shows the breadth of the Mac company’s strategic vision, as Apple has quite clearly sought to fully exploit the failings of Windows and the internal contradictions of a value-conscious industry in stiff competition with itself.

With the first M-series Macs about to enter the replacement cycle, Apple has built a market it can capitalize on for at least a decade, meaning it already has a vision for PC sales that extends at least as far. That’s the kind of road map corporate purchasers want when they make platform deployment decisions, which is why Apple’s 10% share gains are the beginning of even more significant market change. 

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OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

This article first appeared on CIO.

Among a range of factors leading to a wave of tech sector layoffs in 2026 is the rapid rise of artificial intelligence and automation. Companies are reconfiguring their workforces to leverage AI for increased efficiency and reduced operating costs. This realignment and reduction is implemented even by companies reporting strong financial performance.

But it’s not just AI leading to workforce cuts. Complementing this technological shift are ongoing economic uncertainty, inflation, and higher interest rates, compounded by a chip shortage and rising energy costs. This mix is driving companies to cut costs and streamline operations for increased efficiency.

According to data compiled by Layoffs.fyi, an online tracker that keep tabs on job losses in the technology sector, 123,941 tech employees were laid off at 269 companies in 2025. The site also reports that 71,981 government employees were laid off by DOGE alone, with 182,528 total federal workers laid off.

Here is a list — to be updated regularly — of some of the most prominent technology layoffs the industry has experienced recently.

Notable tech layoffs in 2026

• Monday.com
• Microsoft
• Meta
• Cisco
• Cloudflare
• Oracle
• Atlassian
• Salesforce
• Amazon
• Ericsson

July 22, 2026: Monday.com cuts 20% of its workforce to restructure for the AI era

The company says the decision to cut 620 jobs isn’t about margins, but about creating a flatter organization built around AI agents, autonomous teams, and deeper customer engagement.

July 6, 2026: Microsoft cuts 4,800 jobs, primarily in sales and Xbox teams

As the company trims thousands of jobs, it’s also investing in embedded engineering teams and AI infrastructure. The layoffs come several weeks after the company offered 8,750 US employees voluntary retirement buyouts.

June 5, 2026: Tech industry cut 38,242 jobs in May, worst since 2024

AI was blamed for 40% of the job cuts in May, up from 7% in January, according to research by employment placement company Challenger, Gray & Christmas.

May 20, 2026: Meta cuts 8,000 jobs, around 10% of workforce

The cuts are expected to expected to hit Meta’s engineering and product teams the hardest, arriving as Meta pivots toward AI to boost efficiency across its organization, according to Yahoo Tech.

May 13, 2026: Cisco to cut nearly 4,000 jobs despite strong growth in AI, enterprise networking

Despite reporting positive financial news — including record third-quarter revenue of $15.8 billion, a 12% year-over-year increase — Cisco said it will eliminate almost 4,000 jobs.

May 7, 2026: Cloudflare to cut 1,100 jobs in AI-focused restructuring

About 20% of Cloudflare’s global workforce will be culled as the company pivots for the agentic AI era, Reuters reported.

April 1, 2026: Oracle to cut up to 30,000 jobs globally, putting enterprise support and roadmaps at risk

Oracle began laying off employees on March 31 in what could be the largest workforce reduction in the company’s history. Employees received termination emails at 6 a.m. local time with immediate system lockouts and no prior warning. (Note: in June, CNBC put the final layoff tally at 21,000.)

March 12, 2026: Atlassian cuts 1,600 jobs to fund AI and enterprise expansion

Atlassian will reduce its global workforce by approximately 10%, eliminating around 1,600 roles, as the collaboration software maker redirects capital toward artificial intelligence development and enterprise sales.

March 11, 2026: Tech layoffs surpass 45,000 in early 2026

A recent analysis by RationalFX found 45,363 job cuts globally so far this year—with roughly 68% or more than 30,000 occurring in the U.S. — highlighting ongoing workforce cuts even as many tech companies report strong revenue growth.

February 10, 2026: Salesforce lays off staffers as executive leadership churn continues

Salesforce has reduced close to 1,000 roles earlier this month across teams, including marketing, product management, data analytics, and its Agentforce AI unit, Business Insider reported, quoting employees familiar with the matter.

January 23, 2026: Amazon layoffs expected to disproportionately hit AWS and tech talent

As the market slows down, AWS and other Amazon units are preparing for another round of layoffs, which is expected to overwhelmingly impact tech talent. An email from HR leader Beth Galetti on Jan. 28 confirmed 16,000 job cuts.

January 15, 2026: Ericsson plans to shed 1,600 jobs in Sweden

 Ericsson lans to cut some 1,600 jobs in Sweden, the telecommunications equipment maker said doubling down on recent cost-saving measures that have helped it weather a prolonged downturn in telecoms spending, Reuters reports.

January 13, 2026: Meta plans to cut around 10% of employees in Reality Labs business

Meta plans to cut around 10% of the employees in its Reality Labs division who work on products including the metaverse, according to three people with knowledge of the discussions, according to The New York Times.

Layoffs in 2025

• Cisco
• Oracle
• Windsurf
• Intel
• Microsoft
• Crowdstrike
• HPE
• Autodesk
• HPE
• CISA
• Workday
• Salesforce
• Meta

Global tech-sector layoffs surpass 244,000 in 2025

Economic uncertainty, elevated interest rates, and AI adoption have driven workforce reductions across tech companies worldwide, according to a RationalFX report.

October 28, 2025: Amazon to cut 14,000 jobs across company

Amazon will reduce its overall workforce by 14,000, cutting layers of management across the company and hiring in some areas to support its “biggest bets”.

August 18, 2025: Cisco and Oracle to cut hundreds of Bay Area jobs

Tech companies Cisco and Oracle are cutting hundreds of jobs across the Bay Area. Cisco will eliminate 221 positions at its Milpitas and San Francisco offices, effective Oct. 13. Oracle is reducing 101 positions in Santa Clara on the same date 

August 5, 2025: 3 weeks after acquiring Windsurf, Cognition offers staff the exit door

Cognition, the AI coding startup that acquired rival company Windsurf three weeks ago, laid off 30 employees last week and is offering buyouts to the roughly 200 remaining employees on the team, reports The Information.

July 25, 2025, Intel to lay off 22% of workforce, CEO Tan signals ‘no more blank checks’

Intel will reduce its workforce to 75,000 employees by the end of 2025 as new CEO Lip-Bu Tan implements sweeping changes designed to transform the struggling chipmaker

July 8, 2025, Intel layoffs begin: Chipmaker is cutting many thousands of jobs

Intel has begun laying off employees across the company. CEO Lip-Bu Tan told workers back in April to expect major layoffs at Intel in the coming months as the chipmaker slashes costs and overhauls its organization after years of technical setbacks and falling sales.

July 2, 2025: Microsoft will cut 9,000 workers

Microsoft will lay off about 9,000 employees, a source familiar with the workforce cut told CNBC.  The cuts will reportedly affect less than 4% of Microsoft’s global workforce and will impact different teams, geographies and levels of experience. This is the latest in a string of cuts the tech giant has made this year.

June 17, 2025: Intel looks to factory layoffs to return to profitability

Intel will lay off up to 20% of its manufacturing sector employees starting in July, according to media reports, as the company looks for options as it seeks a return to profitability. The cuts reportedly will be made around the world, but some of the layoffs will be closer to home, according to a report in The Oregonian citing an internal company memo from Intel manufacturing Vice President Naga Chandrasekaran.

May 7, 2025: CrowdStrike to lay off 5% of staff

CrowdStrike announced a plan to cut about 500 roles, roughly 5% of its workforce, to streamline operations and reduce costs. The cybersecurity company will incur about $36 million to $53 million in charges related to the layoffs

March 6, 2025: HPE cuts 2,500 jobs, remains committed to Juniper buy

CEO Antonio Neri told Wall Street analysts that HPE would begin implementing a cost-cutting program involving layoffs of about 2,500 employees over the next 18 months. HPE employs about 61,000 people worldwide.

Feb. 27, 2025: Autodesk to lay off 9% of workforce

Software maker Autodesk is laying off 1,350 staff. With the rise of subscription and multi-year contracts billed annually, and self-service enablement, it finds it needs fewer sales staff, CEO Andrew Anagnost said in a message to employees. And with its cloud, platform, and AI products proving most profitable, it’s concentrating its staff and investments there.

Feb. 27, 2025: HP to lay off 2,000 more

As part of an ongoing restructuring, HP plans to lay off up to another 2,000 workers. In recent weeks, the company has tried — unsuccessfully — to do away with telephone support staff by forcing callers to wait for at least 15 minutes if they refuse to use self-service support resources online. The company swiftly backtracked, but wider job cuts are still on.

Feb. 21, 2025: CISA lays off 130

Government employees get laid off too: In this case, 130 workers at the US Cybersecurity and Infrastructure Security Agency are being shown the door as a result of a DOGE decision. Cybersecurity experts are concerned that the cuts will harm the international collaborations that CISA has fostered, quite apart from their concerns about the security of the DOGE layoff process itself.

Feb. 5, 2025: Workday lays off 1,750

As it moves to invest more in AI and international growth, Workday is laying off 8.5% of its workforce and disposing of unused office space. Some analysts fear the cutbacks will affect the company’s customer service — unless AI can pick up the slack.

Feb. 4, 2025: Salesforce lays off over 1,000

At the same time as it’s hiring sales staff for its new artificial intelligence products, Salesforce is laying off over 1,000 workers across the company, according to Bloomberg. As of June, 2024, the company had over 72,000 employees, according to its website. Salesforce did not comment on the report. In 2024 the company reportedly laid off around 1,000 staff too, in two waves: January and July.

Jan. 14, 2025: Meta will lay off 5% of workforce

Mark Zuckerberg told Meta employees he intended to “move out the low performers faster” in an internal memo reported by Bloomberg. The memo announced that the company will lay off 5% of its staff, or around 3,600 staff, beginning Feb. 10. The company had already reduced its headcount by 5% in 2024 through natural attrition, the memo said. Among those leaving the company will be staff previously responsible for fact checking of posts on its social media platforms in the US, as the company begins relying on its users to police content.

Tech layoffs in 2024

• Equinix
• AMD
• Freshworks
• Cisco
• General Motors
• Intel
• OpenText
• Microsoft
• AWS
• Dell

Nov. 26, 2024: Equinix to cut 3% of staff

Despite intense demand for its data center capacity, Equinix is planning to lay off 3% of its workforce, or around 400 employees. The announcement followed the appointment of Adaire Fox-Martin to replace Charles Meyers as CEO and the departures of two other senior executives, CIO Milind Wagle and CISO Michael Montoya.

Nov. 13, 2024: AMD to cut 4% of workforce

AMD will lay off around 1,000 employees as it pivots towards developing AI-focused chips, it said. The move came as a surprise to staff, as the company also reported strong quarterly earnings.

Nov. 7, 2024: Freshworks lays off 660

Enterprise software vendor Freshworks laid off around 660 staff, or around 13% of its headcount, despite reporting increased revenue and profits in its fourth fiscal quarter. The company described the layoffs as a realignment of its global workforce.

Sept. 17, 2024: Cisco lays off 6,000

After laying off around 4,200 staff in February, Cisco is at it again, laying off another 6,000 or around 7% of its workforce. Among the divisions affected were its threat intelligence unit, Talos Security.

Aug. 20, 2024: General Motors lays off 1,000 software staff

More than 1,000 software and services staff are on the way out at General Motors, signalling that it could be rethinking its digital transformation strategy. In an internal memo, the company said that it was moving resources to its highest-priority work and flattening hierarchies.

August 1, 2024: Intel removes 15,000 roles

Intel plans to cut its workforce by around 15% to reduce costs after a disastrous second quarter. Revenue for the three months to June 29 stagnated at around $12.8 billion, but net income fell 85% to $83 million, prompting CEO Pat Gelsinger to bring forward a company-wide meeting in order to announce that 15,000 staff would lose their jobs. “This is an incredibly hard day for Intel as we are making some of the most consequential changes in our company’s history,” Gelsinger wrote in an email to staff, continuing: “Our revenues have not grown as expected — and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both — particularly given our financial results and outlook for the second half of 2024, which is tougher than previously expected.”

July 4, 2024: OpenText to lay off 1,200

OpenText said it will lay off 1,200 staff, or about 1.7% of its workforce, in a bid to save around $100 million annually. It plans to hire new sales and engineering staff in other areas in 2025, it said.

June 4, 2024: Microsoft lays off staff in Azure division

Microsoft laid off staff in several teams supporting its cloud services, including Azure for Operations and Mission Engineering. The company didn’t say exactly how many staff were leaving.

April 4, 2024: Amazon downsizes AWS in a fresh cost-cutting round

Amazon announced hundreds of layoffs in the sales and marketing teams of its AWS cloud services division — and also in the technology development teams for its physical retail stores, as it stepped back from efforts to generalize the “Just Walk Out” technology built for its Amazon Fresh grocery stores.

April 1, 2024: Dell acknowledges 13,000 job cuts

Dell Technologies’ latest 10K filing with the US Securities and Exchange Commission disclosed that the company had laid off 13,000 employees over the course of the 2023 fiscal year; it characterized the layoffs and other reorganizational moves as cost-cutting measures. “These actions resulted in a reduction in our overall headcount,” the company said. A comparison to the previous year’s 10K filing, performed by The Register, found that Dell employed 133,000 people at that point, compared to 120,000 as of February 2024. Dell announced layoffs of 6,650 staffers on Feb. 6, but it is unclear whether those cuts were reflected in the numbers from this year’s 10K statement.

See news of earlier layoffs.

Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday.com’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

This article originally appeared on CIO.com.

Just in time for the iPhone’s 20th anniversary, Apple is moving closer to becoming a service company. It is set to launch its new deal with Klarna next week and when it does, Apple enthusiasts in the US will effectively be able to subscribe to their favorite Apple hardware, with the cost spread across up to three years.

This matters because when combined with Apple One and Apple’s Creator Studio subscriptions, the Klarna arrangement brings Apple closer to offering a full subscription model for hardware, software, and services. The only thing you don’t get under the new arrangement is AppleCare, for which you’ll allegedly need to pay extra.

Moving closer to hardware-as-a-service

Apple has slowly been transitioning toward hardware-as-a-service for almost a decade. Back then, Forrester analyst Frank Gillet predicted the company would eventually offer bundles of services and products for a monthly, all-in, fee. 

This isn’t quite where we are yet; you still need at least three subscriptions to get close. But, after the better part of a decade, Apple has moved much nearer to the hardware-as-a-service idea.

There are some products reportedly excluded from the arrangement, including MacBook Neo, Apple Watch SE, the entry-level iPad, and iPhone 16. Clearly, Apple sees those products as sufficiently affordable. 

Easy payments for RAM-ageddon

The new Klarna arrangement comes as Apple is forced to increase product prices as AI-driven memory price inflation becomes widely felt across every economy. In theory, I assume, Apple hopes to make its products available to cash-strapped consumers who need new hardware, while also navigating a time of deep economic tumult and uncertainty. It’s thought the company has previously rejected these plans to protect normal hardware sales, but normality is a kingdom we no longer seem to possess. Interesting times. Probable inflation incoming.

“Apple Upgrade lands at precisely the moment Apple needs it,” IDC analyst Francisco Jeronimo wrote in a note seen by Computerworld. “Having just pushed Mac and iPad prices up on the back of the memory shortage, with iPhone increases widely expected in September — as well as the new iPhone foldable expected at $2,500 — Apple’s real risk is that rising prices even further can impact the upgrade cycle.” 

New age, new shopping habits

The introduction of the scheme gives consumers a way to purchase the company’s popular high-end devices when they are introduced — no doubt,at higher cost — this fall. Plus, of course, if it’s good enough for GM, it’s good enough for Apple.

It’s all about attitude, too. From Apple’s perspective, it has done plenty of the groundwork required to convince its customers that subscription payments for things you value are no bad thing. 

Reluctance to embrace “Access Not Ownership’”purchasing models has dropped dramatically since Apple — and CEO Tim Cook — first began banging the drum for services income. Apple’s services stream has now become its second-biggest revenue driver after the iPhone. It has over 1 billion paid subscriptions, and an active hardware installed base of more than 2.5 billion devices globally.

A combination of changed customer habits and external threat means the stars are now aligned for hardware-as-a-service models. “Reframing a device as a low monthly payment protects that [upgrade] cadence and allows Apple to start marketing their products as device-as-a-service to consumers, which no other vendor was ever able to do,” Jeronimo wrote to me. 

There is a one-more-thing aspect to this: the products are effectively being leased, a new approach that will give Apple a stronger grip on EOL devices, helping it grab more of them for refurbishment, resale, and recycling. Over time, this will give the company a much stronger grip on the lucrative second-user market that exists around Apple equipment, even while for almost every consumer product we find the life we want is something we can rent, but probably can’t afford to own.

Managing future risk

The other solid reason to take a partnership approach is risk management. Apple had intended to develop its own buy-now, pay-later scheme via Apple Pay Later, but abandoned that plan as it became riskier with rising bank rates. “Also, by backing the program with Klarna rather than reviving the in-house subscription plan it shelved in 2024, Apple captures the demand upside without taking the credit risk onto its own balance sheet,” Jeronimo said.

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Building a presentation can take lots of time. There are design choices to figure out: the slide layouts, fonts, theme colors, and so on. You can use a template to skip this hassle, but you still have to paste your text and other content into the slides and edit it all so that the results are visually appealing.

In PowerPoint, Microsoft’s Copilot AI assistant can now automate the heavy lifting of presentation creation. It can generate a first-draft presentation in minutes, then help you edit it. You can also prompt Copilot to help you quickly understand the contents of a presentation and glean insights from it. Use the tips in this guide to save oodles of time as you create and work with presentations.

Who can use Copilot in PowerPoint

Individuals with a Microsoft 365 Personal, Family, or Premium subscription have access to Copilot from within PowerPoint and other Microsoft 365 apps. Users with a Premium plan have higher Copilot usage allowances and access to advanced AI features.

For business users, it’s more complicated. Organizations with more than 2,000 users must pay for Microsoft 365 Copilot licenses for their users in addition to their regular Microsoft 365 licenses. Users at organizations with fewer than 2,000 users can use Copilot within M365 apps even without the M365 Copilot add-on licenses, but there are limitations in usage, speed, and feature availability.

To see what kind of access you have, log in to Microsoft’s Copilot Chat web hub and look for your name in the lower left corner. If you see “M365 Copilot (Premium)” under your name, you can use Copilot in M365 apps with priority access and advanced features. “M365 Copilot (Basic)” means you can use Copilot in M365 apps with lower-priority access and limited features. If you see “Copilot Chat (Basic)” or nothing below your name, you can’t use Copilot in M365 apps.

(Copilot Chat Basic users do get some Copilot functionality, including the ability to generate presentations, via the Copilot Chat hub. See our Copilot Chat tutorial for details.)

In this article:

• Working with Copilot in PowerPoint
• Create a presentation template
• Create a presentation from a document
• Add content from a document to a slide
• Refine your slide text
• Find or create an image
• Expand your presentation with relevant slides
• Summarize a presentation
• Answer questions about a presentation
• Help you navigate a large presentation
• Generate speaker notes and/or an FAQ

Working with Copilot in PowerPoint

First, let’s quickly go over the notable settings of the Copilot sidebar.

When you have a presentation open in PowerPoint, click the Copilot icon; it may be floating at the lower-right corner of your PowerPoint window or parked at the right end of the Ribbon toolbar. The Copilot sidebar will open along the right of the page. You’ll type your prompts to Copilot inside the chat window in this pane.

The sidebar on the right is where you interact with Copilot in PowerPOint.

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Agent mode: By default, Copilot can build a new presentation or make changes to an existing one in the main PowerPoint window. This is known as “agent mode.” To change this so that Copilot can’t take direct action on a presentation (all its responses appear in the sidebar), click the Allow editing button above the chat window and change it to Chat only.

The tips in this guide require that Copilot be in agent mode, so make sure you see Allow editing above the chat window.

Choice of AI model: Behind the scenes, Copilot has access to various genAI models, including different versions of Anthropic Claude and OpenAI GPT.  By default, it decides which model to use based on your prompt. You can set it to use a particular model: click Auto at the upper right of the Copilot pane and select a model from the dropdown that opens.

You can choose which AI model you want Copilot to use for a request.

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The tips in this guide should work fine on the default Auto setting. But feel free to experiment switching to specific models to see which give you the best results for particular tasks.

Important: Remember that generative AI output often includes errors, so always check Copilot’s output for accuracy. (Also see our tips for reducing hallucinations in Copilot.) You’ll likely want to rewrite it in your own voice as you’re reviewing it.

1. Create a presentation template

For many people, the hardest part of creating a presentation is getting started. What types of information should be included on the slides, and in what order? Copilot can give you a leg up by creating the type of presentation you need, with placeholder data that you can later replace with your own.

Start a new presentation, open the Copilot sidebar, and type your prompt into the chat window. It’s best to provide very specific details in your prompt. The more context or details you provide, the more likely Copilot will generate a presentation template that suits your needs.

A good prompt should contain the slide count, subject, audience, and tone. Example:

• Create a 6-slide presentation for a sales meeting focusing on Q1 revenue. The audience is the sales team, so keep the tone professional and focused on the sales data.

Copilot may ask a series of follow-up questions, such as your preferred visual style and desired level of detail. Then it will generate a presentation template.

Copilot generates a presentation with placeholder data and explains its elements.

Howard Wen / Foundry

You can optionally prompt Copilot for revisions, and when you’re happy with the template, swap in your own data.

2. Create a presentation from a document

You can attach a document (such as a Word document, Excel spreadsheet, or PDF) and prompt Copilot to generate a presentation based on its contents. This works best with a structured-format document (such as a business plan, project proposal, or summary report) that contains sections with headings.

Copilot can extract the document’s text and structure to generate the slide content for the new presentation. This can especially be useful for quickly turning a long report into a visually appealing presentation.

In the Copilot pane, click the + icon at the bottom of the chat window. A list of documents that you’ve recently accessed appears. Select the one that you want Copilot to use. Alternatively, click the magnifying glass icon and inside its search box, type a few letters of the filename for the document you want. (Business users with an M365 Copilot license can select up to five files for Copilot to pull from when creating a presentation.)

Attaching a document for Copilot to base a presentation on.

Howard Wen / Foundry

Then in the chat window, you can enter a prompt that’s as simple as “Create a presentation,” although as always, providing more details and context is better. This is especially important for corporate users who reference multiple source files. It’s useful to tell Copilot what data to pull from each document.

Answer any follow-up questions that Copilot asks, and it will then generate the presentation.

Copilot has generated a professional presentation from a social media marketing campaign document.

Howard Wen / Foundry

Note: Your marketing department may have created one or more branded company templates for Copilot to work from. If that’s the case at your organization, simply open the appropriate company template as your first step. Then you can upload docs and type a prompt as described above. Copilot will create a presentation using the branded template.

3. Add content from a document to a slide

Manually copying text or other content from a document and pasting it into a new slide is a chore. Instead, you can prompt Copilot to extract information directly from a Word document, Excel spreadsheet, or PDF to create new slides.

In the Copilot pane, attach the document using the same steps described in tip 2, then tell Copilot to create a slide from the document. As always, it helps to provide details such as the new slide’s focus or what data to include:

• Add a slide based on the attached document.
• Use the attached file to add a slide about the project budget that focuses on Q1 projections.
• Summarize only the financial section of the attached document as a slide.

A new Copilot-generated slide based on data from an Excel spreadsheet.

Howard Wen / Foundry

4. Refine your slide text

A presentation should be visual and display only the core message. Conciseness and proper writing tone are essential for your slides, so that they don’t lose the attention of your audience.

You can prompt Copilot to refine text on an individual slide in various ways, such as rewriting it in a more professional tone or making it more concise. Highlight the text inside a text box on the slide. On the toolbar that appears over the highlighted text, click Edit with Copilot.

On the menu that opens, you can select a preset prompt to refine the text, such as Condense or Make professional. Or, at the top of this menu, you can type a prompt to rewrite the highlighted text.

Choose a preset prompt for refining text on a slide or type in your own prompt.

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Note that this feature affects all the text inside the text box. To rewrite only a portion of text inside a text box, you must split that portion out into a separate text box.

Alternatively, you can prompt Copilot to analyze your entire presentation and tighten up the wording throughout all of its slides. For example:

• Make these slides more visual and use less text.

5. Find or create an image

If you have Copilot generate a presentation from an existing Word document that contains images, it will incorporate those images into the presentation. If there are no images in the source document, you can ask Copilot to find or create one and add it to a slide.

To add a stock image or an image from your organization’s brand library, tell Copilot what you’re looking for:

• Add a stock photo of young adults in a cafe drinking boba tea.
• Add a photo from our asset library of young adults in a cafe drinking boba tea.

To have Copilot create an image using Microsoft’s Designer image generation tool, describe your desired image. As always, specificity is helpful:

• Create a photorealistic image of a diverse group of 5 or 6 fashionable young adults sitting in a cafe drinking boba tea. They’re smiling or laughing, and some are looking at their phones.

Copilot in PowerPoint hooks into Microsoft’s Designer tool for image generation.

Howard Wen / Foundry

Just as you need to review any text output from Copilot, take a close look at generated images to be sure nothing looks off.

Also note that Copilot image generation isn’t always reliable in PowerPoint. For some time during our testing for this story, Copilot said it couldn’t create an image because “the image generation service is returning a server error on every attempt.” After about a day and a half, the service began working again.

6. Expand your presentation with relevant slides

As you’re building your presentation, you may find that it’s become text heavy. Or perhaps it could use more visually oriented slides to break things up and make its progression flow better. Copilot can generate and insert new slides that are based on the content of the slides already in the presentation.

In the Copilot pane, specify exactly where you want the new slide to go. This helps Copilot to analyze the content of the slides before and after where you want the new slide. Then it can generate a slide to bridge between the two slides. Examples:

• Add a slide after slide 3 about our competitive advantages.
• Add a slide after slide 11 that transitions to slide 12.

Need a transition slide? Just ask!

Howard Wen / Foundry

7. Summarize a presentation

Maybe you need a quick refresh of your presentation before an important meeting. Or maybe a co-worker has sent you a presentation that’s packed with lots of slides. You can prompt Copilot to generate a summary of the presentation’s overall messaging.

In the Copilot pane, just type “summarize this presentation.” You can also have Copilot flag key slides that contain important information: “show me key slides.”

Ask Copilot to summarize a presentation or flag key slides.

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8. Answer questions about a presentation

As you’re reviewing a presentation, especially one that you didn’t create and are not familiar with, you can get Copilot to pull key data points from its slides.

In the Copilot pane, type specific informational questions. Examples:

• What are the action items in this deck?
• What is the proposed budget mentioned here?

If Copilot can’t find the exact answer to the question you ask, it will provide related information from the presentation.

Ask Copilot specific questions about the contents of a presentation.

Howard Wen / Foundry

This method can also help you validate that your presentation includes everything you want it to. If you ask Copilot about the action items in a presentation and it can’t find any, you know you need to add them. (Copilot will likely offer to generate them for you based on the rest of the slides.)

You can even take this tactic a step further and ask Copilot if the presentation is missing any important data, if any slides are weak or confusing, if there are any awkward transitions, if there are key points that should be better emphasized, and so on.

9. Help you navigate a large presentation

In the business world, presentations with dozens of slides are not uncommon, such as for financial reports or project documentation. Trying to find a specific slide or multiple slides can be tough. Copilot can help you navigate such a presentation.

In the Copilot pane, prompt Copilot to find slides based on specific topics. Example:

• Show me the slides about the project timeline.

Copilot will analyze the presentation and reply with a list of links to the relevant slides. Click one of these to jump directly to that slide.

Copilot can help you zoom directly to a slide that covers a particular topic or shows specific data.

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

Here’s a great timesaver when you’re preparing to show your presentation to an audience: Copilot can automatically generate suggested speaker notes for you, based on the content of your slides. Example prompt:

• Write speaker notes for every slide with one talking point per slide.

Copilot can create speaker notes in seconds.

Howard Wen / Foundry

In a related feature, Copilot can create a frequently asked questions list (FAQ) for you to consult in your speaker notes or to present as a slide:

• Write an FAQ for these slides.

Copilot will ask where you want the questions and answers added — as a new slide at the end, integrated into the speaker notes of relevant slides, or somewhere else that you designate. Make a selection, and Copilot will generate the FAQ based on the content of your presentation.

A Copilot-generated FAQ slide.

Howard Wen / Foundry

Related reading:

• 11 cool things Copilot can do in Excel
• 9 ways Copilot can turbocharge OneNote
• PowerPoint for Microsoft 365 cheat sheet
• Copilot Chat: Your hub for document creation and analysis
• How to curb hallucinations in Copilot (and other genAI tools)
• Microsoft Copilot can boost your writing in Word, Outlook, and OneNote — here’s how
• More Microsoft tips and tutorials