OpenAI has paused new sign-ups and upgrades to its $200 ChatGPT Pro tier, citing a surge in demand for its Astra capability that is placing pressure on system capacity, according to company statements and an executive post on X.

“To make sure our current users have an incredible experience and continued access to Astra, we are going to pause subscriptions to our $200 Pro plan,” OpenAI member of technical staff Thibault Sottiaux wrote in a post on X. “These put the most strain on our systems and we wanted to take the smallest step that allows us to continue giving the broadest access possible.”

The company separately confirmed the move in its help documentation, stating that “as of September 10, 2026, we’re temporarily pausing new sign-ups and upgrades to the ChatGPT Pro $200 plan (Pro 20X).” The pause applies to users across Free, Go, Plus, and Pro $100 tiers seeking to upgrade, while “existing ChatGPT Pro $200 subscriptions … are not affected by this pause,” it said.

Sottiaux added that “there is no impact to existing accounts and we are working on adding more capacity as fast as we can,” pointing to ongoing efforts to scale infrastructure in response to demand.

Users who cancel or downgrade during the pause will not be able to re-subscribe to the $200 tier until the restriction is lifted, according to the company’s help page. The $100 Pro tier remains available, with lower usage limits. Promotions tied to the $200 tier are also paused, the page read.

Astra demand drives capacity decisions

The decision is directly tied to the uptake of Astra, which OpenAI has positioned as a more advanced capability within its platform. The scale of that demand, according to Sottiaux, has been atypical.

“Demand for Astra is really unprecedented,” he wrote in the post. “We’re pulling all the levers possible to sustain the demand, but I’ve not seen anything like it until now and we went through very steep growth before.”

He added that “priority will always be to keep excellent service for existing users,” noting that the company “might have to pause new Pro subscriptions for a bit if this continues.”

The pause comes amid broader rollout challenges tied to Astra. OpenAI Chief Executive Sam Altman recently described the launch as “messy” after some paying users were unable to access the model immediately, reflecting the operational complexity of deploying high-demand AI systems at scale.

Capacity constraints surface for enterprise workloads

The pause, analysts said, reflects how demand for high-intensity AI workloads is intersecting with infrastructure limits, particularly at the highest usage tiers.

“It signals that frontier capacity is still rationed,” said Bhupendra Chopra, chief revenue officer at Kanerika. “One week after launching Astra, OpenAI paused new sign-ups and upgrades to the $200 Pro tier, the plan with the heaviest Astra usage limits, while the $100 Pro tier, the API, Business and Enterprise all stayed open. That tells you the priority order.”

“Consumer power users are the release valve. Enterprise contracts are what the vendor protects,” Chopra said. “For CIOs the lesson is that a model being announced and a model being available to your workloads at the volume you need are two different events.”

Chopra said the move reflects how access to advanced AI capabilities is being managed.

“We are already there,” he said. “Rate limits, usage caps, queueing and now sign-up pauses are all forms of capacity gating, and every frontier vendor uses some of them.”

“A subscription buys you a place in the line. A fixed slice of compute comes only from a contract that says so,” he added.

Planning for constrained supply

For enterprise IT leaders, Chopra said model capacity should be treated as a dependency.

“Treat model capacity like any other supply chain dependency,” he said. “Ask the vendor three questions before you sign. What throughput is contractually committed, what happens to my workloads when demand spikes, and how quickly can I fail over to another model.”

“If a workload is production-critical, it should run on an enterprise agreement with committed capacity… and it should have a second model tested and ready,” he said.

A recurring pattern

Chopra said the pause reflects a broader pattern seen in recent launches.

“This is at least the third time in two years OpenAI has throttled or paused access at launch,” he said. “Each launch outruns capacity, capacity catches up, and the next model outruns it again.”

“OpenAI calls this pause temporary, and it will lift once capacity catches up,” he added. “The structural condition… will persist as long as model capability keeps outpacing data center buildout.” OpenAI has not provided a timeline for when new sign-ups to the $200 Pro tier will resume, stating only that the restriction is temporary.

The article originally appeared on CIO.com.

Automattic CEO Matt Mullenweg has been abruptly put on a paid leave of absence from the company by its board of directors, despite his objections. But enterprise IT executives who rely on WordPress may find the shift doesn’t mean much as long as Mullenweg fully controls WordPress.org, which handles all of the product’s patches and updates.

“The part of WordPress that actually keeps enterprise IT up at night isn’t Automattic’s org chart. It’s WordPress.org, the plugin and theme directory every WordPress site pulls its security updates from, and the WordPress trademark,” said Frank Dickson, principal analyst at Dickson Research. “Mullenweg owns and controls both personally, outside of Automattic, and nothing about this week’s vote touches that. He also remains a director on Automattic’s board. The company changed who runs its hosting business without changing who controls the distribution pipeline millions of those hosted sites still depend on.”

For IT leaders who rely on WordPress, it’s important to differentiate what is currently known about the change and what is speculation. A statement emailed to Computerworld from Automattic merely said: “Matt Mullenweg is currently on leave from Automattic. Mark Davies, Automattic’s CFO, will lead the company as interim CEO. The Board has full confidence in Mark’s leadership and in the team’s ability to execute against the company’s priorities.” 

However, messages from Mullenweg to Automattic employees made it clear that the move was one that he strongly opposed. He posted on his X account, “the next step in this playbook is to restart the smear attacks, so get ready for some National Enquirer rumors or hit pieces.”

He added: “I appreciate the hundreds of colleagues who have already expressed public and private support, and are organizing in solidarity. It’s a big help to counter the ‘Matt is an idiot and shouldn’t run an ice cream stand’ allegations. Also, whatever you can say about me, I’m direct and probably overcommunicate, which I’m going to continue doing through this mess folks have made.”

He also posted separately that he is looking to hire, but that applicants cannot be current Automattic employees. “I really need some great sysadmin and security researchers to hire really quick, no one from @automattic. I’m on the board there and fully support Mark Davies in his interim CEO role. But I think it’s probably good if I move some of my stuff currently hosted there, elsewhere.”

Next steps unclear

What is unclear are likely next steps. Is the leave permanent or temporary? And if temporary, how temporary? Is the board negotiating with Mullenweg, and might those negotiations involve whether Mullenweg continues to control WordPress.org? Neither Automattic nor Mullenweg provided clarification.

Melody Brue, analyst-in-residence at Moor Insights & Strategy, who has closely tracked WordPress for years, said that the apparent speed of Mullenweg’s removal as Automattic CEO suggests that the board was trying to sidestep something serious.

“It has to be some exposure or risk that was severe enough that speed outweighed any optics or fairness. Boards don’t generally move that abruptly,” she said. The appointment of the CFO as interim CEO “definitely shows some stabilization and possibly some legal compliance cleanup. What it doesn’t say is renewed product investment.”

IT worried about instability

But the longstanding worries among CIOs about WordPress were not primarily about the perceived lack of continued investment. It was the concern that Mullenweg has a tendency to react strongly to a situation, apparently without many thoughts of the consequences. 

Nothing better illustrated this than Mullenweg’s personal war with WordPress hosting provider WP Engine that resulted in a series of legal rulings in WP Engine’s favor. 

WP Engine litigation is still ongoing, and that may have played a role in the board’s actions. 

Part of that lawsuit is at the heart of enterprise IT concerns: Mullenweg had denied WP Engine access to WordPress.org resources, including patches, plugins and security updates for the software. 

The IT fear is that Mullenweg could unilaterally take similar actions against any customer, even an enterprise. 

“I would still treat this as vendor risk, because WordPress.org is still controlled by Matt, separate from Automattic,” Brue said. “The question is, who actually controls the plugins that these IT leaders rely on? It’s still a structural risk. Look at whether the patches flow through one person. For now, they still do. Is that pipeline protected by independent governance, oversight? That is what matters for enterprise IT.”

Flavio Villanustre, CISO at the LexisNexis Risk Solutions Group, agreed. 

“Most of the concerns from enterprises about using WordPress come from the fragmented ecosystem and the inconsistent security controls and support of modules and extensions, which have led to significant vulnerabilities in the past,” Villanustre said. “The change of CEO in their parent company won’t directly affect this, especially because Matt Mullenweg will continue as the WordPress[.org] leader anyway.”

Dickson also agreed, noting that the question of who sits in the CEO seat at Automattic was not the issue.

“The enterprise IT concern was never really about Automattic’s management bench. It was about one person holding unilateral, unaccountable control over a piece of critical open-source infrastructure,” he said. “In 2024, Mullenweg used exactly that control to cut WP Engine’s customers off from plugin and theme updates overnight, with no board sign-off and no customer input, purely as leverage in a business dispute. This week’s vote proves a board can restrain him inside Automattic. It says nothing about what restrains him at WordPress.org, because the honest answer is still nothing.”

In fact, rather than reducing those IT worries, Dickson argued that this move could worsen them. 

“If anything, this should sharpen the concern rather than settle it. A board just decided it couldn’t function with him running a corporate entity with ordinary fiduciary obligations,” he pointed out. “That same person still holds sole authority over the update pipeline for software that runs over 40% of the web. Risk teams that were nervous about concentration risk in WordPress now have a fresh, concrete data point: the concentration is real, and untouched by whatever just happened at Automattic’s board table.”

This change could help

Mike Wilkes, enterprise CISO at Aikido Security, interpreted the events differently, and suggested that it might indeed make WordPress look more attractive to enterprise IT.

“This could ultimately make WordPress more attractive to enterprise buyers, but only if it becomes the beginning of stronger institutional governance rather than simply a change in personalities,” he said. “CIOs don’t particularly care about palace intrigue until that intrigue can affect software updates, supply-chain dependencies or business continuity. The WP Engine conflict demonstrated that governance risk can become operational risk surprisingly quickly. The ongoing litigation underscores that this is not merely historical baggage.”

Wilkes pointed out that the next few steps taken by the board and by Mullenweg will likely be far more informative than any analysis of the board’s CEO change.

“I wouldn’t tell a CIO that yesterday’s announcement makes WordPress either safer or riskier today. I would tell them to watch what happens next,” he said. “If Automattic uses this moment to create clearer separation between corporate interests, WordPress.org infrastructure, and community governance, it could reduce one of the ecosystem’s most persistent concentration risks. If the same authority simply migrates to different individuals without structural reform, enterprise concerns haven’t really changed. In cybersecurity terms, replacing the administrator isn’t the same thing as eliminating the single point of failure.”

Dylan Forde, owner of Harmonic Design in Oakville, Ontario, Canada, and a WordPress developer for more than ten years, applauded the CEO change. 

“It is my opinion that the removal of Mr. Mullenweg is a good thing for both the WordPress community and open source,” he said. “He has been divisive for a long time, with many grievances that I overall understand, but I oppose his responses to. It sucks to build something used by millions of people and businesses around the world, all profiting off your work while giving nothing back. But cutting off and targeting individuals is not the answer. Open source is supposed to work for everyone, and my assumption is that anyone whose core business relies on WordPress will be sleeping easier now.”

Apple does seem to have tried to ensure its controversial Audio Intelligence feature isn’t abused. Alongside the iPhone Duo and new iPhone 18 Pro range, Apple on Wednesday introduced a new Apple Watch equipped with a brand new feature it calls Audio Intelligence. The existence of this surprising tool was confirmed only on the eve of the launch event. Enabled by the new S11 chip on the latest Watch devices, this is actually a collection of four features:

• Siri Recap, which creates high-level summaries of conversations during your day.
• Live Rewind, which transcribes the previous 15 seconds of a conversation as text.
• Music recognition, which relies on Shazam.
• Sound recognition, which can identify things like sirens or alarms.

All of these features must be consciously enabled by the user and are not on by default.

What they have in common is use of artificial intelligence along with always-on microphones. While all four of these tools are being presented as opt-in, their existence will inevitably — and justifiably — raise privacy concerns. 

Apple saw those concerns coming, and to make the features work it has placed a Secure Exclave on the S11 chip inside the latest watches. This is a dedicated secure buffer on the chip that processes audio privately on the fly. It does so without creating a recording of ambient sound around you, and the data it works with is isolated from the rest of the system. That Exclave pairs with the iPhone’s Enclave using “a new audio-verified pairing mechanism that exists in addition to Bluetooth pairing,” Apple explains in a detailed white paper about how it works. 

For Siri Recap, the process works like this:

• Apple Watch recognizes a conversation is taking place.
• If so, audio flows into the Secure Exclave, encrypted, and transmitted to a similarly Secure Enclave on your paired iPhone.
• The audio is then immediately deleted from the Watch. 
• The phone will decrypt and transcribe the audio, condense it, and send that transcript to Private Cloud Compute for processing. 
• The short summary is then made, returned, and deleted after seven days.
• Speakers are not identified, recordings are not made, and detailed transcripts are not created or retained.

The company has also introduced safeguards for those around you. The system will deliberately omit some information, including potentially harmful content, financial data, or personal identifiers. When you use the Live Rewind tool, an audible chime plays on your device to alert nearby people that you are using the feature.

Challenges will emerge

For one thing, we don’t yet know whether the company will provide enterprise IT with device management tools to disable the feature on managed devices. It’s crystal clear that devices that are constantly gathering data will be seen as potential security risks — particularly in regulated industries. And it seems far more logical to provide new APIs to disable Audio Intelligence on managed devices than it would be to insist anyone wearing an Apple Watch put it in a lead-lined box before beginning the next safeguarding, healthcare, or product development meeting.

Another problem I see concerns Europe’s Digital Markets Act. It is, after all, inevitable that competitors (maybe including Meta) will want their devices to have equal access to the information gathered by Audio Intelligence. Based on the decisions Apple has made so far, it seems equally likely it will want to refuse such access; this is why these new features will not initially be available in the EU. 

There is also no doubt hackers will attempt to break into the system, though doing so will not be at all easy on account of the intentional way the company has built in security. I suspect, but do not know, that attempts will focus on the points at which data is exchanged across devices, rather than when the information sits within secure enclaves on those devices. It also makes sense that everyone who uses iCloud services for their data should put 2FA in place and pick strong passwords. 

What about iCloud storage?

The other challenge will be encryption, specifically ongoing attempts to penetrate iCloud data encryption by some nations, led by the UK. Even so, if access could be achieved to iCloud-stored Audio Intelligence text, what is obtained would only be summary data, not a recording. The system is architected so recordings are never made.

Apple’s white paper on the feature explores this in more depth: “Audio from the microphone enters the Secure Exclave of Apple Watch, where it is initially processed for speech, sounds, or music, without transcribing or storing the raw audio. This buffer is a continuously overwritten stream that exists only within the protected hardware and never creates an audio recording.”

Once you decide to keep a Recap or Life Rewind text, the raw audio is not saved to iCloud, only the text, and then only if you use 2FA and a device passcode. No one else, including Apple, can access the encrypted data you save, Apple said.

Where does this go next?

One pre-event rumor that didn’t come true concerned AirPods equipped with cameras and Vision Intelligence to understand physical context and surrounding, with the aim of enhancing Siri AI. I’ve expressed reservations about this idea, but do think the security model Apple has put in place for Audio Intelligence will turn out to be an echo of its intentions to secure Vision Intelligence transactions.

This implies a new chip with Silicon Enclave for AirPods Pro, a similar exchange of information in real time for summary and determination of context and content, and a process in which no recordings are made and media access beyond brief, time-limited summaries, is available. In short, Apple will use the same security model.

What about the rest of them?

That’s fine as far as it goes. But the other subtext is that while Apple seems genuine in its attempt to deliver relatively intrusive tech advances right, others will show less commitment to privacy and security. So, while it’s furthering the conversation about using these tools, Apple is also pushing public acceptance of such technologies.

I’m not entirely sure we’re ready.

Now please subscribe to my daily, human-curated Apple-related news headline feed at The Core, or follow me on BlueSky, LinkedIn, or Mastodon

Qualcomm is taking an unusual approach to the launch of its next-generation premium Snapdragon mobile platform this year. Instead of holding everything for its annual Snapdragon Summit in Maui happening later this month, the company has been methodically disclosing the architecture that will power the next wave of flagship Android phones over the past couple of weeks.

First, the company disclosed its new Oryon CPU architecture, followed by a major overhaul of its Adreno GPU. Today, Qualcomm is detailing the next-generation Hexagon NPU that will serve as the platform’s primary AI engine.

Taken together, the disclosures give us a pretty good picture of where Qualcomm is going before we even know the chip’s official name.

There is obviously a strong performance story here, anchored by a 5GHz CPU, but the more interesting theme running through the architecture is memory locality and specialized AI acceleration. Qualcomm is trying to keep more data close to the compute resources that need it, while distributing AI workloads across the CPU, GPU and NPU.

These architectural changes are important as the company continues to drive on-device AI beyond relatively simple generative features toward more persistent, agentic workloads.

Oryon hits 5GHz, bolstered by FlexCache

Qualcomm disclosed in August that its next Oryon CPU will be the first mobile CPU to reach 5GHz. The eight-core design includes two 5GHz Prime cores and six Performance cores, with Qualcomm attributing the frequency gains to its fully custom Oryon microarchitecture, an in-house Arm-based CPU design that gives the company full control over the cores, cache and memory hierarchy, branch prediction and CPU subsystem.

The 5GHz headline is certainly going to get attention. However, Qualcomm’s new FlexCache architecture may ultimately have a larger impact on sustained performance. FlexCache allows Oryon’s Prime and Performance cores to tap into the same shared cache pool, with capacity dynamically allocated based on the workload. So rather than a demanding core being limited to a fixed slice of cache, it can draw on more of the available pool when needed. This keeps larger working data sets close to the CPU cores and reduces high-latency trips out to system memory.

This approach should benefit everything from gaming and multitasking to content creation, but it also aligns well with agentic AI workloads, where tasks may move through several stages of processing and across different CPU cores. The basic goal is straightforward: keep the CPU fed and avoid expensive trips to external memory.

Qualcomm’s next-gen Adreno GPU adds dedicated AI engines

Qualcomm’s next disclosure focused on graphics, where the company is making one of the more significant changes to its Adreno GPU engine in recent years. The new GPU adds dedicated Adreno Matrix Cores for running AI models directly inside the graphics pipeline. They’re paired with 18MB of Adreno High-Performance Memory, or HPM, which provides low-latency local storage for tile-based rendering, frame buffers and GPU compute.

Qualcomm claims HPM provides a 12% power improvement compared to its previous-gen Snapdragon 8 Elite Gen 5.

The more visible feature for users, however, may be Adreno Neural Fusion, which combines AI super resolution, neural processing and frame generation in a unified graphics pipeline. There are obvious parallels here to what NVIDIA, AMD and others are doing with neural rendering on the PC, though Qualcomm’s next-gen Snapdragon SoC has to operate within a much tighter mobile power envelope.

Qualcomm claims enabling Neural Fusion reduces power consumption by up to 40% in its internal testing using the company’s Dragon Alley graphics demo. That’s an impressive number, though as always, we’ll want to see how the technology behaves across actual shipping games and devices before drawing any definitive conclusions.

Qualcomm has also helped integrate Neural Fusion technology into Unity and Unreal Engine, which may ultimately be just as important as the underlying hardware. Developer adoption determines whether features like this become meaningful platform advantages or just another tick box on a spec sheet.

Qualcomm’s Hexagon NPU is being reworked for agentic AI

The most recent disclosure centers on Qualcomm’s Hexagon NPU, where the broader architecture starts to come together. The company’s next-generation Hexagon introduces a new Element Accelerator designed for transformer workloads, alongside its existing vector and scalar processing resources. Qualcomm says the new block is optimized for fast action loops, key-value (KV) cache acceleration and context lengths of up to 32K.

Hexagon is also getting 50% more shared memory, and again, that memory complement is important. Model state, context and KV-cache data can generate substantial memory traffic, particularly with larger language models. Keeping more of that data close to the NPU can reduce latency and power consumption. Qualcomm says the architecture is designed for long-context reasoning, multimodal models, concurrent agents and low-latency action loops.

For INT4 quantized models, the company claims up to a 50% improvement in pre-fill performance versus its previous-generation Snapdragon 8 Elite Gen 5. Qualcomm is also targeting Mixture-of-Experts, or MoE, models as large as 30 billion parameters. In its example, the company points to a 30B model that activates only about 3 billion parameters during a given token-generation step. That selective approach is a natural fit for the tight power and memory constraints of a handset.

Qualcomm isn’t suggesting that a smartphone will simply run a 30B dense model entirely from DRAM, however. By activating only the experts needed for a particular task, MoE architectures can dramatically reduce active compute and memory bandwidth requirements, potentially making much larger classes of AI models practical on mobile hardware.

Qualcomm’s next-gen Snapdragon mobile impact

There is a larger competitive story here as well. For years, flagship smartphone competition largely came down to CPU, GPU, modem performance and power efficiency. AI is now another major area of differentiation, and Qualcomm is clearly designing its next-gen Snapdragon mobile platform around this need.

Apple has the advantage of controlling its silicon, operating system and software stack end-to-end. MediaTek continues to push aggressively into premium Android devices as well. Qualcomm’s response is to make its custom Oryon CPU, Adreno GPU and Hexagon NPU work more like a fully integrated compute platform, tuned for modern AI and agentic workloads.

This is a solid advantage for Android handset OEMs because many don’t have the resources to engineer this level of silicon integration themselves. Qualcomm can effectively provide its Android OEM partners, including Samsung, Xiaomi, Honor and others, with a common hardware foundation for on-device AI, advanced graphics and agentic computing. It also gives Qualcomm another way to defend its premium Snapdragon position.

The company’s custom Oryon architecture now spans smartphones and Windows PCs, and soon servers, while AI acceleration is distributed across its CPU, GPU and NPU. For business users, that could mean more AI processing happens locally, reducing cloud and network dependence, improving response times and keeping more potentially sensitive data on-device. And for IT organizations, Qualcomm now has an opportunity to provide a more consistent AI compute foundation across Windows PCs with Snapdragon X and premium Android handsets.

There is still plenty we don’t know, however. Qualcomm hasn’t disclosed the complete SoC specifications, final performance or power characteristics, or which devices will ship with it initially. Many of these AI experiences will also depend heavily on Android, app developers and the models themselves.

The new 5GHz mobile CPU may generate the biggest headline at Snapdragon Summit, but the more important developments may be what sit around it: more local memory, specialized AI acceleration and tighter integration between the CPU, GPU and NPU.

If Qualcomm can translate this architecture into better sustained performance, enhanced power efficiency and useful on-device AI experiences, it could strengthen its position in premium Android phones while putting more competitive pressure on Apple. For business users, the payoff could be more capable local AI without sacrificing the performance and battery life expected of a flagship device.

That is the part I’ll be watching most closely when the complete silicon picture is unveiled at Snapdragon Summit later this month. And I may even get some hands-on time with it in some benchmarks, as we have in years past, so we shall see.

AI is evolving faster than most people, even those building it, could even fathom, and its impact on the workforce and the economy is, at this point, really anyone’s guess.

Researchers from The Anthropic Institute are offering a few possibilities: They have built a nuanced framework looking at how AI might impact jobs, unemployment, and gross domestic product (GDP) growth between now and 2030.

They posit three potential scenarios for an AI-augmented future: “modest,” “substantial,” and “extreme,” and have created an interactive tool where users can explore how productive, or disruptive, AI will become in the workplace, based on their predictions of how they will work in 2030.

“Which of these worlds we are heading toward may become clearer within a year or two, and preparing for potential disruption seems to us the prudent course,” the researchers noted.

The goal of their work is to inform debate as AI becomes more powerful and capable. “AI is likely to reshape the US and global economies in profound ways in the coming decade, but how, and by how much, is extraordinarily uncertain,” they wrote.

How different scenarios could play out

If you add up every single task performed by people, machines, and software, the US has created a staggering $30 trillion in value over just the last year, the Anthropic researchers estimated. Their model and the corresponding tool are a way to explore how AI impacts tasks that contribute to the economy, the tasks it augments and creates, impacts on productivity, and speed of adoption.

“The answers to these questions have direct effects on GDP, the labor market, and the share of the pie taken home by workers,” they wrote.

Under their definition of “modest” change, AI will add less than half a point to GDP by 2030, meaning it will increase the growth rate of the national economy by just 0.5%, and will raise unemployment by just a tenth of a point, a minor shift. In this future, it’s difficult to see AI’s impact in macroeconomic data; change is steady but gradual, similar to that of the internet. “It drives real economic gains, but they’re within the historical norm for new technologies,” the researchers noted.

In the “substantial” scenario, AI will be capable of doing half of all knowledge work by 2030, the majority of it autonomously. Still, it wouldn’t be adopted for all work; in fact, most knowledge work tasks would still be completed without AI. Correspondingly, the economy would grow at twice its normal rate, but even as some non-knowledge workers see gains, wages for knowledge workers wouldn’t rise.

In this case, “AI makes a bigger impact than the internet, or the railroad,” the researchers wrote. Reallocation could be costly, but it is in line with what the US labor market has historically absorbed.

In the “extreme” scenario, of course, AI would be more productive than humans on the majority of knowledge work tasks, would do all of them autonomously, and subsequently would create no new knowledge tasks for humans.

The technology would “drive a completely transformed, unprecedented economy” arising from recursively self-improving AI. GDP growth would rise to 15% per year, but nearly one in five cognitive workers would be unemployed, and their relative wage would fall “immensely.”

The conundrum is that resources to compensate unemployed or under-paid workers will exist, but it’s unclear whether they would be fairly allocated. Mechanisms by which people can benefit from a much richer economy (retraining, income support, or universal basic income, for example) would become a question of economic policy.

“Whether and how those resources reach the people who bear the cost is not something growth delivers by itself,” the researchers wrote.

What users think

As well as developing the framework, the Anthropic researchers conducted a survey among roughly 11,000 Americans, asking them to predict AI use, productivity gains, automation versus augmentation, and displaced work.

They found that, in the main, public expectations land around the “substantial” scenario. That is, GDP would be 10% higher by 2030 than it would be without AI, and the overall unemployment rate would rise to around 5%.

Roughly 10% of respondents, on the other hand, had views in line with the “extreme” scenario.

Anyone can generate their own forecast using the researchers’ interactive tool, answering questions like: “Out of every 100 instances of a task AI can do in 2030, how many will AI actually be doing?”, “How many will be fully automated?”, or  “How much more gets done in an hour in 2030, compared with doing the tasks without AI?” The tool then responds, mapping their predictions to one of the three scenarios.

“Ultimately, what the economy looks like in 2030 depends on many factors, like what AI can do, and how companies and workers choose to adopt it,” the researchers wrote. “It also depends on how the financial benefit of this technology is shared.”

The between-the-lines reality

Sanchit Vir Gogia, chief analyst at Greyhound Research, emphasized that the Anthropic research “maps the conditions under which very different futures appear, it does not schedule destiny.”

He sees the distribution result, rather than the unemployment result, as the serious finding. In the extreme case, GDP is 32.4% above the no AI path, and the cognitive wage bill is 31% below it. Labor’s share of income falls from 60% to 45.2%, and capital income rises 81.4 %. That means a full 15% of GDP is captured as ROI rather than being paid out in labor costs.

In other words, he pointed out: “A richer economy is not automatically a fairer one.” Capability, diffusion, productivity, automation, and occupational friction all have to arrive together.

“AI will touch a large and rising share of knowledge work and will execute a much smaller share under independent authority,” he said. There is no single honest adoption percentage, because worker use, company use, technical exposure, and executed task instances are four different measurements.

Lessons from the research

Enterprises can take important lessons from the research as they deploy AI and consider its impact on their systems, workflows, and workforce, Gogia said.

“For enterprises, the binding variable is permission to delegate,” he noted. “A model that can draft a payment instruction is not thereby permitted to move money.”

His firm identifies five recurring concerns that come up in enterprise conversations: Durable returns after the full cost of deployment, control over authority being granted, augmentation quietly becoming substitution, erosion of professional formation, and fairness of how gains and risks land.

Some of those changes are progressing faster than the governance around them, he observed. Once a system can inspect customer data, change configurations, or act on workforce records, autonomy has stopped being a feature and has instead become an allocation of institutional authority.

“And the tasks easiest to automate are frequently the tasks through which judgement is learned,” he noted.

Gartner on Wednesday said that it expects 30% of the positions eliminated by AI-related layoffs to be refilled by 2029, suggesting that the initial terminations were ill-advised and excessive.

“When business and IT executives look back on the early AI era, they will realize their greatest mistake was believing that work automation was the point, when workforce amplification was the opportunity,” said Tori Paulman, VP analyst at Gartner. “The competitive advantage will go to the CIOs and business executives who build an AI-shaped organization where AI value compounds by reshaping roles and allowing workflows to cross traditional boundaries, increasing velocity and reducing friction.”  

The Gartner report noted that it is finding that the cuts “deplete talent pipelines and erode institutional knowledge.” Beyond the immediate workforce disruptions associated with any mass layoff, companies will also face steep increases in costs for recruitment, training, and onboarding.

It also predicted that, by 2027, “75% of organizations that prioritize capturing AI productivity gains as cost savings will be eclipsed by competitors that aggressively reinvest those gains into innovation, modernization and upskilling.”

In an interview with Computerworld, Paulman said that the 30% figure represents the average impact on organizations of all sizes; they estimate that the layoff boomerang for enterprises would be even higher, roughly 40%. 

Paulman said that Gartner’s research found a lot of what they called “AI washing” by executives who want/need to do layoffs for purely budgetary reasons, and will falsely blame AI for the reductions because it makes them look better.

“More than 50% of our enterprise clients have been given a number [by their bosses],” Paulman said, and have been told by senior management to find that percentage of savings from AI.

But despite widespread evidence of problems due to AI-related layoffs, such job cuts are still increasing. 

Layoffs were ‘excessive’

Other analysts and consultants agreed with the Gartner suggestion that many of these job losses attributed to AI are going to be walked back, but questioned the specific statistic. Some also noted that 70% of the AI-attributed layoffs may remain in force, which would suggest that the original terminations were mostly justified. 

However, Frank Dickson, principal analyst at Dickson Research, argued that a lot of the layoff reversals will occur in a variety of ways that will obscure the fact that they are restoring a terminated role. 

“A lot of that 70% never shows up as a clean rehire even when the original cut was wrong,” he said, pointing out that some of the losses caused service to quietly get worse, and stay poor, some of the work was contracted out or offshored, some of the roles were reconstituted with a different position or title, and some was covered by the remaining staff absorbing the load. This,” he noted, “shows up later as burnout and attrition, not as a line item on this report. None of that gets counted in the 30%, and none of it is evidence the original call was sound.”

Melody Brue, principal analyst for Moor Insights & Strategy, added that the 70% scenario “could show that a substantial share of the AI-related workforce reductions is durable,” but, she stressed, “it shouldn’t be mistaken for endorsement of how those layoffs were made. What it doesn’t show is whether the organization captured the full economic value it expected. A lower headcount is not by itself evidence of a successful AI transformation.”

Valence Howden, advisory fellow at Info-Tech Research Group, questioned the methodology behind the calculation of Gartner’s 30% figure, but he agreed with the overall sentiment that layoffs attributed to AI have been excessive.

“I’m not sure we can substantiate those numbers, since it’s much more of a guesswork statement than anything else,” he said. “I do believe the current trend is going to lead to rehiring, especially as AI governance requirements ramp up and given AI’s lack of contextual semantic understanding. We know AI has not provided the value proposition that it has been sold as providing, and unless costs are controlled, it will be cheaper to use humans to perform some of the advanced work.”

Supporting data

Dickson also raised questions about the Gartner report because it lacked comparative layoff statistics. 

“Gartner doesn’t say what the reversal rate looks like for ordinary layoffs, the ones that have nothing to do with AI,” he said. “Suppose normal cuts get walked back at 10% to 15% in a typical five-year window, which is plausible given ordinary churn and business-cycle rehiring. A 30% rate specific to AI-driven layoffs would still run well above that, and that’s a damning number. Without that comparison, 30% is just a figure floating with no anchor.”

However, Dickson pointed to various datapoints supporting the position that AI layoffs have been excessive, noting that Forrester reported that 55% of businesses “already regret AI-driven cuts and are predicting half of those layoffs get quietly reversed.” 

“Robert Half puts it at a third of hiring executives who eliminated roles for AI having already rehired. Ford, IBM, Booz Allen Hamilton, Alphabet and CSX have all walked back cuts or announced rehiring drives,” Dickson said. “Gartner’s 30% by 2029 sits comfortably inside that range.” Klarna has also walked back AI layoffs. 

A ‘major indictment’

He added that many AI layoffs amounted to a corporate version of a crash diet. “You cut fast, you look great on the next earnings call, and eighteen months later, the weight is back, plus interest, because nobody fixed why the cut was made in the first place.”

Gartner’s Paulman agreed, noting, “business and IT executives who use AI primarily as a tool for cost cutting risk making reductions that are too deep and too soon, affecting their ability to innovate their business model and compete in new markets as AI continues to mature.”

Mike Wilkes, enterprise CISO at Aikido Security, said that even if the 30% figure turns out to be accurate, it is a major indictment of the layoffs. 

“If 30% of AI-driven layoffs must be reversed, that is an enormous error rate for a strategic workforce decision,” Wilkes said. “Imagine any other major capital decision where nearly one-third had to be unwound at a premium three years later. No CFO would call that a strong outcome.”

Twenty-five years ago, I sat near the front of the room while then-Apple CEO Steve Jobs rolled out Apple’s digital hub strategy, a vision in which the Mac would become the central manager of all manner of portable digital devices.

Now, a quarter of a century later, nearly all of those digital devices have become features on your iPhone, and the arrival of AI means the iPhone has become the intelligent personal hub, the central manager of, well, of you. Apple’s new CEO, John Ternus, went straight for the concept in his important opening remarks at Wednesday’s big iPhone launch, where nearly all the pre-event speculation came true.

The intelligent personal hub

“As we look ahead, we see a future filled with enormous discovery and transformation,” Ternus said at the Surprise and Shine event. “AI makes entirely new kinds of experiences possible. An AI can become even more useful when it brings together your life with the apps, services, and products you use every day. And this means the product at the center of your experiences becomes even more essential — what I like to think of as an intelligent personal hub.”

(To be clear: He’s talking about the iPhone.)

Think about it: all those tasks you now do on your iPhone are tasks AI can help you with. Siri AI lets you work on files and folders using spoken commands, while contextual AI lets you sift through all the data once gathered by your iPhone to do things, make things, create networks, or even organize luncheon. 

“It’s the intersection of broad new capability, and a deep understanding of your personal context that makes this idea so powerful,” Ternus explained.

AI’s brave new world

That’s the context within which Apple will be introducing all its future products, a battle the company now feels confident marching into now that it appears to have resolved its historical challenges with AI. At the same time, the company also seems to be doing its best to lean into privacy — enabling life-changing AI transformation of daily lives while putting a brake on scary privacy attacks. This is going to be foundational to what happens next at Apple.

Of course, you’re not reading this to look too deeply into what Ternus said about the future of Apple; you’ll also want some insight into the slew of devices the company introduced at its event. That list includes the:

• iPhone Duo — the long-anticipated folding iPhone, which is finally here (or rather, will be here in October).
• iPhone 18 Pro and Pro Max — the next important evolutions of what was once Apple’s highest-end iPhone range.
• AirPods 5 — now with even better tech, at the same price as before.
• Apple Watch Series 12 and Apple Watch Ultra 4 — the latest iterations of the company’s popular wearables.

The big surprise? Price

For some, the biggest surprise in these introductions was price. All of the accessories were kept at the same price as last year’s equivalents, while the new Pro iPhones came in at only $100 more for the entry-level model, rising to around $300 more at the more price-resistant highest end. An iPhone 18 Pro Max equipped with the max 2TB storage comes in at $2,499 compared to the $1,199 iPhone 18 Pro entry point. 

When it came to price, though, all eyes were certainly on the iPhone Duo, and Apple has given everyone (except competitors) a pleasant surprise on that. 

With a starting price of $1,999, the folding phone’s price hit the low end of expectations, and while that grows to an eye-watering $3,199 with 2TB of storage, it compares well with the market leader, Samsung. While the Galaxy Z Fold 8 begins at $1,899, in terms of features and design, Apple’s folding phone lines up with the Galaxy Z Fold 8 Ultra, which costs from $2,099. 

Dig a little deeper and you’ll see that while the iPhone has IP68-rated dust and water resistance, the Samsung equivalent has only IP48. You can drop the Fold 8 into five feet of water for up to half an hour and it should be OK, but you can chuck your iPhone Duo in almost twenty feet of water, and it should survive. Though you probably shouldn’t test that unless you’re a hugely successful “influencer” already generating a small nation state of money through clicks. If that’s you, good for you. I’m only slightly envious. 

A productivity powerhouse?

What that suggests is that the iPhone is less likely to get damaged and is better engineered, though it’s also ever so slightly thicker. Apple’s device is powered by what the company modestly described as the most powerful mobile processor on the planet — which it is, particularly for the AI tasks all the new iPhones can handle. That matters, particularly for applied contextual AI deployed across daily lives.

It also matters because the combination turns Apple’s device into a mobile productivity powerhouse, one that I think will likely affect iPad sales more than anything else. Though this wouldn’t be the first time a new Apple release ended up eating its siblings. 

The productive possibilities of Apple’s premium device will also be realized as app developers embrace both the form factor and Siri AI. Apple said during the keynote that more than 300,000 apps already work with Siri AI and more than 2.5 billion Siri requests are being made each day. That latter number will only grow now that Siri performance has been dramatically improved. 

While this argument applies to all Apple’s new devices, the fact of the matter is that you now have an iPad-sized object you can carry in your pocket that is capable of doing some really powerful tasks by spoken command alone. That’s the iPhone Duo – a product of an unexplored territory at the crossroads between mobile computing, daily digital existence, and the opportunities of AI.

It’s not for everyone, yet

I don’t expect the Duo will be for everyone; the vast majority of users won’t be spending $2,000 on a phone. But we know that the cost of technologies like this tends to decline, which suggests that in perhaps five years’ time, it will be possible to create equivalent devices for a much lower price. Right now, it’s for affluent Apple consumers, C-class executives, students, AI pros, medical professionals and others who might actually need this kind of performance and display space to get things done.

This, incidentally, is precisely the same group of people that first embraced the iPad when it appeared, turning that into the world’s best-selling tablet.

What might this mean for Apple? 

I traded a few messages with IDC analyst Francisco Jeronimo, who sees it like this: “Apple entered the foldable market and, in one keynote, set the price and the standard every rival will now be measured against.”

He also pointed out, the iPhone Duo is “…a direct test of whether Apple can use design, ecosystem integration and premium positioning to reshape a segment that has so far remained selective.” 

He’s right, of course, but Ternus also just defined what that segment, and the industry, is driving for. I’s the same thing Apple was aiming at 25 years ago. “iPhone sits at the center of an amazing ecosystem of intelligent features and experiences that work seamlessly across the products you use every day,” the company said — an “intelligent personal hub.” And the Macs are all right, too.

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Adobe on Wednesday unveiled a range of updates to Acrobat, including the ability to turn PDF documents into interactive visual reports and podcasts. And a new Knowledge Base feature connects Adobe’s Acrobat AI assistant to document sources such as Microsoft SharePoint and Google Drive.

It’s the biggest Acrobat update in several years, Adobe said, and highlights the application’s evolution from PDF reader to what the company now describes as an AI-powered document productivity and creativity platform. 

Acrobat users open more than 400 billion PDFs in the app each year, Adobe said, claiming 650 million monthly active users as of last year. 

Among the new additions is the ability to turn dense PDFs into interactive, visual documents and presentation slides. To create these, Acrobat’s AI assistant scans the selected file, asks questions about the user’s intent, then generates documents charts, images and navigation.

It’s also possible to turn documents and web links into podcast-style audio summaries — similar to capabilities in Google’s NotebookLLM and Microsoft Copilot Notebook. A new Stylize feature can enhance plain-looking documents, applying a template to turn them into “professional deliverables” such as CVs and invoices, Adobe said.

The audio and visual generation features are all available in paid Acrobat plans (Acrobat Studio, Acrobat Express and Acrobat AI Assistant). 

Another addition, Acrobat Knowledge Base, lets customers connect the Acrobat AI assistant to a wider range of file types, enabling users to ask questions about information held in documents stored in Microsoft SharePoint and Google Drive, for example.  

Acrobat’s Knowledge Base “transforms curated document collections — policies, playbooks, product information, pricing guidance, research — into cited answers available from Slack, Microsoft Teams, Acrobat, and other places employees already work,” Abhigyan Modi, senior vice president for Adobe Document Cloud, said in a blog post. 

Adobe also added an Analyzer tool designed to retrieve data from large volumes of documents. This could involve analyzing thousands of contracts or invoices to identify information such as renewal dates and revenue clauses.

That turns information once trapped in files into data that can inform finance, procurement, compliance, and the systems that support them, said Modi.

Analyzer is the more interesting of the two releases, said Mike Leone, vice president and principal analyst at Moor Insights & Strategy. “Knowledge Base is a search product and search products get judged feature against feature. Analyzer is closer to a data product, because that extraction work is what companies currently pay people and pipelines to do,” he said. 

“What I’d want Adobe to answer is whether that data can leave Acrobat and land where the rest of the company’s data lives. If it can, this starts competing for real data budget. If it stays inside the app, it’s a very good Acrobat feature.” 

Access to Analyzer and Knowledge Base requires an Acrobat Studio for Enterprise subscription. (Adobe doesn’t publish pricing.) 

In addition, Knowledge Base includes a credit-based model that places some restrictions on usage, with licensed users allowed up to 1,000 queries a month. Other actions, such as document uploads, queries from Slack and Microsoft Teams, and queries by employees without an Acrobat Studio license, draw varying amounts of Acrobat Studio “shared credits.” Adobe didn’t say how much additional credits cost once a yearly limit is reached.

 More information about Adobe’s Knowledge Base credit usage policy is available on Adobe’s website. 

Analyzer also includes a credit system that limits document ingestion and attribute extraction.

For enterprise customers, usage-based pricing can add complexity when managing employee access.

“The issue for a broad deployment is that headcount barely predicts usage,” said Leone. “One person pointing it at 10,000 contracts will burn more credits than a hundred people asking the occasional question. Pooling the credits across the whole organization is the right design, because usage on something like this is rarely spread evenly.

“One of the big challenges is that people could very well start rationing themselves because they don’t know what a question costs,” he said. “Or worse, a couple folks use all the credits and everyone else is out of luck. The last thing any organization wants is to pay for a knowledge tool that people won’t use or can’t use.”