In 2026, the AI monetization conversation has shifted quickly from one concern to the next. Early in the year, fears that AI agents would erode software seats helped trigger a selloff of software stocks dubbed the “SaaSpocalypse.” Since then, attention has turned to a different disruption, as AI leaders including Anthropic, OpenAI, and GitHub lean heavily on usage-based pricing to better align revenue with the cost of delivering AI.
It would be easy to conclude that software monetization is shifting wholesale from seats and subscriptions to tokens, credits, and consumption. But look across a broader cross-section of the software industry, and a different picture emerges.
Most software companies aren’t AI-native businesses designing pricing from scratch. They have existing products, customers, contracts, and pricing structures. They’re adding AI to portfolios that may already span dozens of products and multiple monetization models. And they have to balance the economics of AI with something customers still value enormously: predictability.
Nearly all are adding AI capabilities to at least some products. The way they’re monetizing those capabilities, though, is considerably more pragmatic than the headlines might suggest.
AI features are widespread, but adoption is lagging
AI capabilities have gone mainstream. The share of software producers incorporating AI into their products jumped from 80% last year to 96% today. Getting AI into the product, however, is only the first step.
The widespread use of chatbots can create the impression that everyone is using AI. That isn’t yet true inside established software products. Almost half of respondents say that adoption of their AI features is limited or low.
For many vendors, that makes the next phase less about launching more AI and more about learning from the AI they have already launched.
Revenera
Seat loss is limited so far, but pricing pressure is real
One of the biggest fears surrounding AI agents is that companies will need fewer software subscriptions, or “seats,” as agents take over work previously done by people. So far, the impact appears relatively small. Only 7% of respondents say they’re experiencing seat loss due to AI.
That doesn’t mean AI isn’t affecting software pricing. Nearly half of software producers report downward pricing pressure related to AI. Some buyers are using the possibility of building their own AI-powered alternatives as leverage in negotiations. At the same time, growing AI budgets are forcing customers to scrutinize what they spend elsewhere in their software portfolios.
The near-term challenge may not be that AI eliminates the seat. It may be that AI changes what customers are willing to pay for it.
Revenera
Demand for predictable costs is influencing AI pricing
Over the past year, stories of AI bill shock have become increasingly common. Consumption-based pricing may closely reflect underlying AI costs, but customers don’t necessarily want a bill that changes dramatically from month to month.
That trade-off shows up clearly in the research. Software producers are almost three times more likely to prioritize cost predictability for customers than maximizing near-term revenue. And that preference is reflected in how companies are pricing AI today.
The three most common approaches are putting AI in a higher-priced subscription tier, including it at no additional charge, and using hybrid models that combine a subscription with some form of usage-based or outcome-based models.
Revenera
What’s striking is that established software companies aren’t rushing toward pure consumption pricing. Over the next 18 months, they expect predictability-oriented approaches to grow faster than pure usage-based models.
AI revenue isn’t automatically translating into AI profit
The economics of AI have become almost as important as the capabilities themselves. For AI-native companies, much of the discussion has centered on the enormous infrastructure costs required to train and run models. Established software vendors face a somewhat different challenge: they are adding those new costs to businesses that already have existing products, architectures, and margin expectations.
Our research includes companies delivering software across SaaS, on-premises, embedded, edge, and other environments. Many are seeing revenue growth from AI, and more than half report improved profitability.
That doesn’t mean the economics are easy. Increased AI delivery costs and cloud costs rank among the biggest barriers to growing annual recurring revenue. So does the inability to implement new monetization models quickly enough.
This is an important distinction. AI can generate incremental revenue and still create margin pressure at the same time.
For product and business leaders, the question is no longer simply, “Can we charge for AI?” It is increasingly, “Can we charge for AI in a way that supports sustainable economics?”
Revenera
Source: Revenera
Limited usage data is making AI pricing harder
There’s another problem that gets less attention: many companies still don’t have the data they need to make good pricing decisions. A large share of organizations either lack clear visibility into how customers use AI features or rely on manual analysis and engineering work to get that information.
That creates a fundamental problem. If you don’t know who is using a feature, how often they use it, or which capabilities drive value, it’s very difficult to know what metric to price against. It’s also harder to distinguish between an AI feature that customers don’t value and one that simply hasn’t been packaged or introduced effectively.
Usage data isn’t just a billing requirement for consumption pricing. It’s increasingly an input into product strategy, packaging, and monetization.
Revenera
Source: Revenera
2027 outlook: AI monetization will be evolutionary, not revolutionary
There is no single right way to price AI. The approaches taken by AI-native companies have understandably dominated the conversation in 2026. Established software companies, though, are operating under very different constraints. They aren’t replacing their businesses with a new AI pricing model. They are figuring out how AI fits into products and commercial models that already exist.
In practice, I think many companies will move through this process in stages. They may begin by including AI at no additional charge while they build adoption and learn how customers use it. As value becomes clearer, they can move capabilities into higher-priced tiers, often with usage limits that protect against runaway costs. Once usage patterns become more predictable, some will adopt hybrid models that combine a recurring subscription with bundled consumption and transparent pricing for additional usage.
Not every product will move through those stages at the same pace. A mature AI capability in one product may already be ready for usage-based pricing while a newly launched feature somewhere else in the portfolio is still focused entirely on adoption.
That is why I don’t think the winners in AI monetization will be the companies that pick the “right” pricing model today. The advantage will come from being able to keep changing it.
This advantage starts with better visibility into how customers use software, where they find value, and which capabilities actually drive engagement. Companies that can turn those insights into changes in pricing, packaging, and product strategy will be in a much better position to adapt as both AI technology and customer expectations continue to evolve.
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New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.
Take a close look at your monthly software bills. The charges that once seemed small and separate can add up to a new kind of dependency: subscriptions for word processing, creative tools, storage, and now AI features bundled into products, whether you asked for them or not.
For many people, it’s no longer a choice between buying software or renting it. The software they used to run on a PC increasingly depends on an account, an internet connection, a cloud service, or a particular plan. Even when an application runs locally, its essential features may rely on online authentication, remote storage, or services that can change without much notice.
Microsoft and Adobe are prominent examples of this shift. Their subscriptions offer real benefits, including frequent updates, collaboration, and access across devices. But people who are looking for a dependable word processor or video editor can find themselves navigating bundles, account requirements, cloud storage prompts, and AI features they don’t want. Users are growing frustrated that they have little choice about being forced into cloud services.
Interest in local computing
The response is a renewed interest in local computing: software that runs on a person’s own computer, stores work where they choose, and does not require a recurring payment for basic use. This isn’t a rejection of cloud computing. It’s a demand for a more balanced relationship between local machines and remote services. Home users and small businesses are turning to open source and other locally installed applications. They may not offer every advanced feature of a commercial suite, but many users don’t need every feature. If a free, locally run tool can handle the work, then “good enough” can be a rational decision.
The same logic applies to AI. Some users want powerful models hosted in the cloud. Others want to experiment with AI on their own hardware, keep sensitive information close, or avoid paying for an always-online service. Local AI still has limitations, including hardware demands and uneven performance. But it gives people another option, and options matter when customers feel locked into a remote service.
Cloud computing is also no longer limited to systems an organization deliberately chooses to put in a public cloud. It is embedded in everyday software. A productivity application can depend on identity services, online licensing, synchronization, remote storage, telemetry, or AI services. A tool that looks like a desktop program may be part of a much larger cloud relationship. Such an arrangement can be useful. Centralized updates and shared documents make work easier for distributed teams. But when software depends on a provider’s back end, the organization inherits risks it may not have assessed: outages, price changes, loss of access, data retention questions, and shifting product terms.
Users are voting with their feet when they choose local alternatives. Providers should read that vote as feedback, not nostalgia. Customers want the convenience of cloud services without surrendering control over where their software runs, where their files live, or whether an AI assistant is part of the package.
Providers should offer real options
The answer is not to abandon cloud platforms. For many workloads, cloud infrastructure remains the best option for scale, resilience, collaboration, and access to managed services. The answer is to stop treating cloud dependence as the inevitable price of using modern software.
Providers can make the choice real by offering offline access where practical, clear and durable licensing, straightforward file export, and local storage as a genuine option. They should explain which features require connectivity and what happens when a subscription ends. If AI is included, users should be able to understand what it does and how to turn it off without losing unrelated functionality.
This is not just a consumer relations issue. It is strategic planning. Enterprises should consider three things now.
First, inventory the software employees rely on and identify which functions depend on a vendor’s cloud. Include desktop applications, browser extensions, identity systems, storage, and AI features. Record what breaks during an outage or after a license expires. A dependency map can reveal that a supposedly local workflow relies on remote services for authentication, collaboration, or even basic operation. That knowledge helps organizations plan for continuity and negotiate from a position of understanding.
Second, establish policies for where work data may be stored and processed. Decide which information can go to external services, which AI tools are approved, and how employees can use local alternatives. These rules should be practical enough to follow. A blanket prohibition may drive users toward unsanctioned tools. A clear set of approved choices can reduce risk while recognizing that teams have different needs.
Third, build flexibility into long-term procurement and architecture. Evaluate products not only for features and subscription cost, but also for offline capability, data portability, exit terms, and support for local deployment. Keep important workflows from depending on a single vendor when a reasonable alternative exists. This does not mean avoiding subscriptions. It means knowing what the organization receives for the recurring cost and how it can leave if the deal changes.
The local computing movement is a reminder that technology is valuable when it solves a problem on terms users and organizations can accept. For some needs, that’s a managed cloud service. For others, it’s a dependable application running on a computer the user controls.
Cloud providers that dismiss this movement risk confusing convenience with consent. Customers will keep choosing tools that fit their work, budgets, and privacy expectations, even when those tools are simpler. The winning strategy is not to force every workload into the cloud. It is to make cloud services compelling enough that customers deliberately choose them, while preserving a credible path to local computing when they do not.
The entrepreneur turned fraudster is the subject of an Oscar-tipped new documentary that takes an unusual look at her strange legacy
David Agus’s first encounter with Elizabeth Holmes at a health conference came as a shock. The Holmes he met in the green room was personable and pleasant. The Holmes he interviewed on stage was altogether different.
“The accent changed: this deep voice, very serious,” Agus, a medical oncologist, recalls by phone from Los Angeles. “It was as if I had met a person and then interviewed a separate person. That transition from the green room to the stage – I had never experienced something like that before.”
Every quarter, I look at the capital expenditure numbers coming out of the major cloud providers, and every quarter, they manage to surprise me. This time, though, we’ve crossed into territory that’s genuinely hard to process. The five biggest US cloud and AI infrastructure providers (Microsoft, Alphabet, Amazon, Meta, and Oracle) have collectively committed to spending between $660 billion and $690 billion on capex in 2026, nearly doubling what they spent just a year ago.
Amazon leads the pack with a projected $200 billion, most of it directed at data centers and AI compute. Alphabet has committed $175 to $185 billion, up from $91 billion in 2025. Microsoft is tracking toward $120 billion or more, having already spent $37.5 billion in a single quarter. Meta is at $115 to $135 billion, and Oracle is targeting roughly $50 billion. For context, four companies alone plan to invest about $630 billion, a 62% increase from the record $388 billion they spent in 2025.
What are they buying? Mostly chips, buildings, and electricity. The spending splits across the AI data center stack: GPUs and custom accelerators such as Google’s Tensor Processing Units and Amazon’s Trainium, the physical data centers themselves with their liquid cooling and high-density floors, and often the power infrastructure to run it all, including power purchase agreements and even on-site gas-fired generation where the grid can’t keep up. Roughly two-thirds of the money goes to compute, and one-third goes to facilities, power, cooling, and networking, with the power share rising every quarter.
Where the market is heading
My take on the market is that we’re in the steep part of a demand curve that has a few more solid years left in it. Short-term demand over the next two years is already firm. Every hyperscaler reports being supply-constrained rather than demand-constrained, and Microsoft is sitting on an $80 billion backlog of Azure orders it can’t fulfill, largely because of power availability. Enterprises are panic-buying cloud capacity and computing gear right now because they’re convinced the AI boom will leave the unprepared behind, and they want their infrastructure in place so they can take advantage of whatever form the transformation takes. I get it. Nobody wants to explain to their board in 2028 why they spent 2026 waiting in line.
This means hardware vendors such as Dell, HPE, and IBM, along with the hyperscalers themselves, are going to see sustained growth as long as the demand continues. Some of it is real, and some of it is fear driven, but for the vendors, it all spends the same. The shortages enterprises are seeing today for processors and RAM, along with the associated price increases and stretched lead times, will most likely persist through that window because global silicon and memory supply is being absorbed by AI clusters faster than the manufacturers can expand it.
But my long-term forecast is deliberately less rosy than the providers’ own. The build-out cycle runs three to four years from commitment to full utilization. A lot can change in the AI market in that time. Model efficiency improvements could slash compute requirements per unit of output. Enterprises may discover that their AI workloads don’t justify the cloud spend once the novelty fades. Competitive dynamics could compress pricing. By 2028 and 2029, I expect the growth rate to be real but not at the scale builders assume today, and I expect the industry to carry the weight of capacity committed in the panic phase of 2026.
Are vendors overbuilding?
Here is where I part ways with the prevailing wisdom, and where I think we need some disciplined skepticism. The thinking right now is that for every bit of data center capacity you build, someone will use it, if not the hyperscaler that owns it, then some other tenant. Given Microsoft’s $80 billion backlog, that logic holds for the near term.
But I’ve been around long enough to be suspicious of “this time is different” arguments. I watched the fiber overbuild in the late 1990s, and I watched the first cloud buildout get ahead of itself as well. The concern is that the cloud providers are getting out over their skis, building capacity for demand that may not show up at the scale they’re projecting. The revenue those investments depend on is largely speculative. If AI adoption progresses more slowly than anticipated, or if efficiency gains reduce the compute required per workload faster than expected, the return on this spending could disappoint badly. There is also the uncomfortable possibility that a meaningful share of today’s demand is circular: Enterprises and vendors may be panic-buying capacity from each other in a stack of commitments that may never consolidate into actual sustained workloads.
By the time these data centers come online—and we’re talking about capacity committed now for delivery in 2027—the growth may not be at the scale the builders are expecting. I expect many of the data centers being committed now won’t be utilized at the rates people are assuming today for 2029. That’s not a prediction of collapse. It’s an acknowledgement that the industry has seen a similar pattern before in fiber, colocation, and the first cloud buildout itself.
Advice for enterprise buyers
If you’re running enterprise IT, this cycle cuts both ways. In the near term, you’re facing higher prices, allocation limits, and longer lead times for processors, RAM, and cloud capacity. Budget accordingly, lock in what you genuinely need, and resist buying out of the fear that drives the very shortages you’re suffering from.
But in the next few years, the picture flips. Overbuilt supply means lower prices. If the providers overshoot, then cloud rates soften and less expensive data center space becomes available for enterprises that need it, including capacity for your own AI ambitions at prices that would be unthinkable today. The overbuilding that will burn the hyperscalers’ investors will create a bargain for their customers.
The playbook I recommend is the one I’ve always recommended. Architect for the cloud on your own terms, stay out of long-term capacity commitments you can’t justify with current demand, and let the providers take the utilization risk. They’re betting hundreds of billions that the demand curve keeps climbing. You don’t have to make the same bet to benefit. The oversupply, if it comes, will find you, and at a price you’ll like a lot better than today’s.
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
Oracle (again)
Monday.com
Microsoft
Meta
Cisco
Cloudflare
Oracle
Atlassian
Salesforce
Amazon
Ericsson
Sept. 15, 2026: Oracle forecasts 33% increase in restructuring costs as new round of layoffs hits
Oracle began a new round of layoffs this week, sending early-morning termination emails to staff for the second time in six months. Oracle has made no public statement confirming the latest round of job cuts, but in a regulatory filing days earlier the company said it had set aside a further $700 million for restructuring charges, bringing the total charges this year to roughly $2.8 billion.
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
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
Jan. 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.
Jan. 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
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’
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
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.
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.
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.
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.
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.
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.
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.
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.
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.”
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.
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.
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.
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.
The colorful CEO of Automattic, which controls WordPress, was forced out last Thursday by his board of directors, which put him on a leave of absence. He used his system access to remove the accounts of the acting CEO and others — an action that could get most employees fired. Instead, he was fully reinstated to CEO a couple of days later.
It’s not hard to compare this to the saga at OpenAI almost three years ago when an eerily similar series of events happened. The board fired the CEO for having repeatedly lied to it. Within a few days, the board reversed itself.
A weaker historical comparison is with Steve Jobs, who was ousted from his Apple CEO role, only to later return. But Jobs at least had the decency to wait a few years while he created and ran NeXT.
To be fair, boards have often been criticized for being puppets of the CEO, who often have a lot to say about who serves on the board. But that criticism doesn’t hold for the first OpenAI board nor for the initial Automattic board.
Some years ago, I was involved with a VC-funded startup, and we were at the stage of forming a board of directors. I was talking with the person who was to be the board chair and he needed candidates for board secretary, which is typically an attorney. That role is supposed to be a lawyer who cares about the board, as opposed to the company’s general counsel, who reports into the CEO and only cares about the company.
The chair said that we need someone who is trustworthy. I had the perfect candidate in mind, until I probed deeper and found that there is a vast chasm between “trustworthy” and “someone we can trust.”
I envisioned someone who was honest and had integrity, someone who would take their duties seriously and would tell the board the truth regardless of the implications. That turned out to be precisely what the chair did not want.
He wanted someone who would vote with us regardless of their legal opinion. He didn’t actually want trustworthy. He wanted blind unconditional loyalty. (That business, thank goodness, never ended up launching.)
But that seems to be what CEOs now want, which undermines the entire point of having an independent board. Other than deciding whether to accept an acquisition offer, the most important decision of any tech board is the hiring — and firing — of the CEO.
What does it say when a board makes that decision for what it seems to be a legitimate business reason — and then reverses itself within a couple of days?
In the case of Automattic, it is not a good look. The board said absolutely nothing about why it forced the CEO out, which made its reversal over the weekend even more perplexing.
When Automattic emailed reporters on Thursday to announce that the CEO had been ousted, the messages were cryptic: “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.”
The next we heard was an equally cryptic message sent Saturday afternoon: “Matt Mullenweg is the chairman and CEO of Automattic, with full support of the board, and if you search online you can see many top executives and Automatticians supporting him as well.”
Huh? We then asked for clarification of what happened and why. At 2:26 AM Eastern on Sunday, the media team emailed: “Matt and Automattic’s leadership team have great respect for everyone involved. While we can’t comment on specific individuals, we’re thankful for their contributions to Automattic and its mission, and we remain excited about what’s ahead with Matt at the helm. Matt was away for only 33 hours and 20 minutes — we’re now back to work.”
My proposal: A board’s decision to hire or terminate a CEO should be defended with public specifics. And so should a hiring decision. Or, in this case, a reversal of a mandatory leave of absence decision.
If the board is going to take the extreme step of relieving the CEO, it should be required to say why. In some situations, it may need to be vague (as in “personnel situation involving a substantiated sexual assault”), but the board needs to give some kind of reason. That should happen so at the very least, a reversal is also explained.
A slew of recent studies indicate that AI isn’t dramatically displacing workers. At the same time, there were no indicators that the fast-moving technology is creating a lot of jobs, even as AI skills remain in high demand.
Stanford University researchers in an August study found that any AI-related job declines were concentrated among young workers aged 22–25. But for older and more experienced workers that wasn’t the case.
“Claims of economy-wide AI-driven job losses are not visible in payroll data through June 2026,” the researchers said, citing ADP payroll information. Stanford partnered with ADP to conduct the research.
The decline for young workers is more often related to the automation of some work tasks by AI, as opposed to its use to complement work. In contrast, AI is more often used by experienced workers to augment what they do.
According to the Stanford report, workers between 22 and 25 and employed in jobs highly exposed to disruption by AI are falling behind their older counterparts. Their employment levels in June were about 19% below the same age group in less-exposed occupations. That’s compared to where they would be if both groups had kept pace with each other.
“Experienced workers show no comparable gap,” the researchers said, adding that “this is consistent with recent reports of a worsening job market for entry-level workers.”
Employment of younger workers in AI-exposed jobs fell about 11% from late 2022, while the same age group in less-exposed jobs actually grew about 10%. “For older age groups, we find much less marked differences in employment growth across AI exposure quintiles,” the researchers said.
Stanford, which created a lab last year to study AI’s impact amid heightened fears of the technology’s economic effects, said its numbers were based on available indicators.
AI was cited for 116,175 job cuts in 2026, but that number is declining. The technology was blamed for 3,462 cuts in August, according to data from Challenger, Gray and Christmas.
It’s the “lowest monthly total since December 2025 when 142 cuts were attributed to AI,” the company wrote in a blog entry. The research firm also noted aggressive hiring plans for companies in 2027.
AI is adding value to companies in different ways, and humans are an important part of that process, said Michael Chui, a senior fellow at QuantumBlack, AI at McKinsey. “The night shift is real now,” he said. “People are sending off their agents to write code and checking in the morning.”
MIT late last year established an AI labor index to determine how agents are affecting the workforce. The school’s Project Iceberg looks at how the coordination and interaction with agentic AI changes how work is done.
Despite the disruptions, demand for AI-related talent continues to rise, tech industry consortium CompTIA said last week. About 320,000 job listings in August required AI-related capabilities, a 4.5% increase from July.
AI-related hiring is outperforming the broader tech market, with demand for AI skills up 96% year-over-year, said Kye Mitchell, head of Experis North America, which is part of ManpowerGroup.
“We’re seeing real growth…in marketing management and project management roles, suggesting employers are looking beyond the people building AI to the people applying it,” Mitchell said.
More broadly, the US economy added 162,000 jobs in August, according to US Department of Labor figures released last week. CompTIA noted that tech employment across all sectors rose by 86,000 workers in August.
But within the tech sector, 14,700 positions were cut, CompTIA said.
Nvidia CEO Jensen Huang argues it’s the “most illogical thing in the world” to believe AI will kill SaaS, and says the markets “got it wrong” in sending SaaS stocks tumbling. Why “illogical”? Because, as analyst Benedict Evans stresses, the fact that everyone can now easily spin up code actually doesn’t solve any SaaS problems, because creating code and tools is the easy part: “The hard part is knowing that you need a tool for this in the first place and then knowing what the tool should do.”
If that’s true, then what Shopify is doing with Sidekick is potentially revolutionary by embracing and extending the traditional SaaS model.
In December, Shopify introduced custom app generation through Sidekick. A merchant (Shopify customer) describes a tool, and Sidekick writes the code, using Shopify’s interface components and connecting to its Admin API. It’s a cool option and has been very popular. Shopify says merchants created almost 4,000 custom apps in the first three weeks following the release.
This isn’t surprising: Shopify is delivering the customization customers have long wanted from enterprise SaaS while ensuring they still enjoy the comfort of ongoing support and platform stability.
I suspect we’ll see more enterprise SaaS like this, despite silly predictions that AI would kill SaaS. Cheaper code gives customers a way to fix the things they dislike about an application without replacing everything they do like. For a vendor willing to accommodate that, AI could make its product considerably more useful and, hence, much “stickier.”
The feature that never ships
Cloudflare’s Jeremy Morrell discusses this in an excellent essay on extensible software. “In the past year, your users have suddenly acquired the ability to speak code into existence,” he writes, allowing for “markets of one.” His argument is that software should let customers put that ability to work, building additions around an established core.
Morrell has a commercial interest here, which he discloses: He works at Cloudflare and thinks its infrastructure is well suited to running those additions. He’s not wrong. But it’s the general opportunity to match customizability with stability that matters so much.
There are really (really) good reasons why vendors haven’t allowed customizability of their core products. Enterprise software companies limit deep customization to prevent high maintenance costs, security risks, and broken system updates. Oh, and to prevent their products from becoming convoluted bloatware. No matter how much you may want feature X, that feature would likely be irrelevant to almost everyone else, so adding it would complicate the product for the majority. No one wants that.
Customers have long paid consultants to customize software or they’ve hired developers to do it themselves (all while taking care not to violate their vendor licenses). Morrell points to Salesforce, which has spent years letting businesses build their own logic into its platform. In other words, AI didn’t invent extensibility. No, AI just makes it incredibly cheap. There’s a meaningful opportunity here for customers who can describe the change they need but have never been able to get it built. Some will save money on software or customizations they previously bought, while others will finally get software that nobody could profitably sell them.
Following the money
This change, however welcome for enterprises, won’t be comfortable for developers selling a narrow reporting tool or a small workflow improvement. Their customers may decide that an LLM-generated extension is good enough. An AI-built customization needn’t match every feature of the commercial product. It only has to do the job that the customer was paying for.
The platform underneath that extension has a different calculation. Every useful tool built against its data and APIs gives the customer another reason to stay. AI can make an individual feature less valuable to sell while making the product that hosts it more valuable to own. (Yet another reason to tsk-tsk foolish eulogies for SaaS companies.)
Ultimately, this should benefit anyone who both knows the business and can build more efficiently for that business. Benedict Evans makes this point in his newsletter, arguing that cheaper coding doesn’t solve the problem of discovering what software ought to do or persuading an organization to use it. A useful idea that spans several departments still needs those departments to agree. Even excellent code can’t sign off on a new way of running accounts payable.
Evans also describes how improvised tools eventually become important enough to need formal ownership and support. Anyone betting that companies will happily maintain everything they can generate should spend some time with the person who inherited the last departmental spreadsheet. Silicon Valley folks with little knowledge of real-world enterprises might think enterprises will vibe code all their software now, but they won’t. That’s not how software works because it’s not how people work.
This cuts both ways for established software vendors. Customers may continue to need their expertise while becoming less willing to accept a product’s limitations. A vendor that makes customization easy can benefit from that impatience, while one that blocks it has given the customer a new reason to investigate alternatives. The difficult decisions will come when customization interferes with what the vendor already sells.
I wrote in 2022 about Microsoft’s complicated relationship with open source, and how a company’s broad commitment to openness can collide with a division’s revenue targets. The same tension applies here. A CEO can be excited about letting customers build anything. The executive responsible for selling the premium reporting package may be less enthusiastic. Resolving that disagreement will determine what customers actually get to build, however capable the AI becomes.
Getting comfortable
Customers have their own calculations to make. An application shaped around the way your company works can be wonderful, but it can also be a pain to leave.
There are limits to what Shopify’s generated apps can do today. They are admin tools, not storefront or checkout extensions. Shopify also tells merchants to test them before installation because they can change customer-facing data. A generated app can be wrong, and running it inside a familiar product doesn’t relieve the merchant of checking its work.
Shopify lets merchants inspect and edit the generated code and restore an earlier version. That’s useful, but code written for Shopify’s Admin API still depends on Shopify. Having the source doesn’t give you another company ready to run it unchanged.
AI might eventually make moving those extensions easier, too. But recreating a screen is one thing; reproducing the behavior it relies on throughout a business is a considerably bigger undertaking. That includes the assumptions about records and transactions that the custom code gets to take for granted while it runs on the original platform.
None of this makes customization a bad deal. Customers can gain more control over their daily work while, somewhat ironically, becoming more dependent on a supplier. For SaaS vendors, that’s a good commercial reason to let customers finish the bits their developers will never get around to building, even when it costs them an add-on sale (and potentially some support headaches, depending on how they’re architected for extensibility). The customer is doing work that makes the underlying product more valuable.
AI, in short, has the potential to make SaaS dramatically more valuable, not extinct.
Google’s dominance in the search arena has given rise to two major antitrust lawsuits from the US government alleging the company has manipulated the market to maintain that dominance, to the exclusion of competitors and the detriment of the public at large.
The cases heavily echo the turn-of-the-century Microsoft antitrust case in several respects, most notably that Google, like Microsoft before it, has evaded the forced breakup requested by regulators.
Here’s our condensed timeline of the two overlapping lawsuits and their progress through the court system.
Sep. 2, 2026, advertising lawsuit: In a second big win for Google, Judge Brinkema rules that the company does not have to sell its AdX ad exchange. The search giant is also not required to open-source the final-auction logic in its DoubleClick for Publishers (DFP) ad server or divest its DFP business. Behavioral remedies are sealed while the parties identify confidential information for redaction.
Sep. 2, 2025, search lawsuit: In a significant win for Google, Judge Mehta rules that the tech giant does not have to divest its Chrome and Android businesses as the US government had requested, and it will be allowed to continue paying Apple and other browser makers for default search placement. To level the competitive playing field, Google will have to share search index and user-interaction data with rivals and offer search results syndication.
April 21, 2025, search: The remedy phase of Google’s search antitrust trial begins with Judge Mehta presiding. Federal prosecutors warn that Google might leverage artificial intelligence to entrench its search monopoly, demanding “strong measures” to prevent the tech giant from extending its market control into the AI era. These include requiring Google to divest Chrome, end exclusive default search agreements, license its search data to competitors, and potentially sell its Android operating system if other remedies fail.
April 17, 2025, advertising: In a second landmark defeat for Google, Judge Brinkema rules that Google illegally monopolized the ad tech market. The company’s “exclusionary conduct substantially harmed Google’s publisher customers, the competitive process, and, ultimately, consumers of information on the open web,” she wrote in the ruling. Remedies, which could include the breakup of Google’s advertising products and/or changes to its business practices, will be imposed at a future date.
Sept. 20, 2024, advertising: The US Department of Justice is set to wrap its case in the Google antitrust trial after an eventful two weeks. The tech giant is accused of engaging in monopolistic behavior by strategically acquiring certain companies and controlling the adtech industry’s most widely-used tools and exchanges, beginning with its acquisition of advertising company DoubleClick in 2008.
Sept. 9, 2024, advertising: The second major case against Google begins with the company defending itself against claims it engaged in illegal behavior to maintain control of the ad tech market. The US government is accusing Google of purposefully manipulating that market, snuffing out competitors and gobbling up key technologies through acquisitions. If the DoJ successfully makes its case, Google risks being broken up by regulators.
Aug. 5, 2024, search: In a major defeat for Google, Judge Amit Mehta rules that the company had engaged in anticompetitive behavior in an effort to protect its search business. In the 277-page decision, Mehta was blunt: “After having carefully considered and weighed the witness testimony and evidence, the court reaches the following conclusion: Google is a monopolist, and it has acted as one to maintain its monopoly. It has violated Section 2 of the Sherman Act.” Mehta’s ruling did not include remedies for the anticompetitive behavior; those will be decided later.
May 3, 2024, search: Over two days of closing arguments, the DoJ revisits its case for Google having a monopoly on search advertising, and Judge Mehta quizzes both parties about whether other platforms could be viewed as substitutes for Google’s search advertising business. He hasn’t said how long he expects to take to reach a decision, but if he rules against Google, a second hearing will take place to decide on any remedies.
Nov. 16, 2023, search: The evidentiary phase of the trial finishes, as Judge Mehta issues instructions for post-trial submissions. Despite considerable amounts of redaction and closed-door testimony, the case revealed some unprecedented details about the relationships between the largest tech companies in the world, including the fact that Apple apparently keeps 36% of the search revenue from Google searches in Safari, and Apple once considered buying Microsoft’s Bing search engine as leverage against Google. Judge Mehta has scheduled closing arguments in the case for May 1, 2024.
Oct. 31, 2023, search:Google CEO Sundai Pichai takes the stand, for long-awaited testimony about the relationship between his company and Apple. He gave some details about Google’s negotiations with Apple over a contract that made Google the default search engine on Apple’s iPhones, iPads, and Macs. Google has paid billions for the privilege of being the default search on Apple products, and the relationship is a key part of the case – which was underlined by the Justice Department’s cross-examination of Pichai, during which he admitted that default search status is a major driver of market share.
Oct. 18, 2023, search: Google begins its defense, calling Paul Nayak, a vice president of search, to the stand as its first witness. Nayak downplays the importance of scale in his testimony, stressing that machine intelligence, compute infrastructure, and a team of 16,000 staff that checks on search results are crucial to maintaining quality of service. DOJ witnesses including DuckDuckGo CEO Gabriel Weinberg and Microsoft CEO Satya Nadella had testified that Google keeps an edge over competitors via an ever-increasing trove of data — the result of its default search engine status, maintained through exclusive contracts and billions of dollars in payments to Apple, Samsung and other companies. This data gives Google an advantage in refining search engine results, they said.
Oct. 3, 2023, search: As a witness for the prosecution in the Google antitrust trial, Microsoft CEO Satya Nadella warns that Google’s monopoly profits could lock in publishers as AI-enabled search arrives. Nadella argued that it’s almost impossible to compete with Google, given the search leader’s massive competitive edge in collecting and analyzing user data. He also warned that Google, with its vast profits and lock on the search market, stands poised to extend its monopoly power in a new era where artificial intelligence technologies will turbocharge the search business.
Sept. 26, 2023, search: Apple’s Eddy Cue testifies behind closed doors in the Google search case, as critics slam presiding Judge Amit Mehta’s decision to hold much of the trial’s testimony from witnesses secret, allow documents to be heavily redacted, and block some documents from public view — mainly at the insistence of Google, but also at the request of other companies, including Apple. By the end of Cue’s testimony — and after a wek of wrangling by all parties — Judge Mehta rules that documents used during the trial can be published online at the end of each day, but still allows time Google and third parties to object to exhibits being shown publicly before the DOJ presents them in court.
Sept. 21, 2023, search: Judge Mehta rules that public access to court exhibits, which have been mostly internal Google documents thus far, should be removed, after Google challenged the Justice Department’s regular publication of them. The company said that it was concerned for its employees’ privacy.
Aug. 2023, search: Judge Mehta grants partial summary judgment for Google in the search case, saying that the government had failed to raise a genuine dispute of material fact on antitrust charges relating to contracts around the use of the Android operating system, as well as Google Assistant and IoT devices. The claims relating to Google’s exclusive “default search” contracts, however, are allowed to proceed to trial.
July/Aug. 2023, search: Google and the plaintiffs in the search case argue various motions in limine, designed to control what evidence should be included or excluded in the actual trial. Discovery and motion practice over evidence continues in the advertising case.
June 2023, search: Judge Mehta schedules a trial date of September 12, 2023 for the search case.
April 2023, advertising: Judge Leonie M. Brinkema denies Google’s motion to dismiss in the advertising case.
March 2023, advertising: Google’s motion to transfer the advertising case to New York is denied by Judge Brinkema, who orders the parties to propose discovery schedules within two weeks of the order. Two weeks later, Google moves to dismiss the case for failure to state a claim, arguing that the plaintiffs have simply produced legal conclusions, and not specific facts, that could support their claims. Judge Brinkema schedules pre-trial conferences for January 2024.
Feb. 2023, search: The plaintiffs in the default search case case move for sanctions against Google, accusing it of spoliation, which refers to the destruction, alteration or failure to preserve relevant evidence in a case. Elsewhere, in the advertising case, Google moves to transfer the case from the Eastern District of Virginia to the Southern District of New York, which is seen as an attempt to consolidate the case with related digital advertising antitrust litigation.
Jan. 2023, advertising: A second antitrust action, pushed by eight states and the DoJ, is filed in federal district court in eastern Virginia. The plaintiffs, who call for Google’s advertising business to be split up, accuse Google of manipulating its dominant position in the online advertising world to squeeze out rivals and control both the supply and demand side of the advertising market. Google, according to the complaint, thwarted fair competition by manipulating fees, punished advertisers for using alternative platforms and ad exchanges, and engaged in a host of further anti-competitive behavior in the interest of monopolizing the marketplace. (This is case that began in September 2024.)
Dec. 2022, search: Google moves for summary judgment against the separate Colorado case and the larger, DoJ-led case. A summary judgement motion is essentially a request by one of the parties in a lawsuit that the judge rule in their favor and end the case, arguing that, based on the undisputed facts, they are entitled to win the case as a matter of law.
May 2022, search: A deadline of June 17 is set for the production of all discovery materials. Further documents – for example, those whose is existence is first disclosed in late in the discovery window – can be produced until June 30.
May 2022, search: Judge Mehta denies a government motion to sanction Google for inaccurately classifying documents as attorney-client privileged. The plaintiffs had argued that emails on which Google’s lawyers were listed as recipients or CCed, but that the lawyers never responded to, constituted a misuse of the attorney-client privilege rules.
Dec. 2021, search: Judge Mehta conditionally splits Colorado’s claims from the case at large, ordering that separate trials on that state’s issues of liability and remedies will be “more convenient for the Court and the Parties, and will expedite and economize this litigation.”
Aug.-Oct. 2021, search: Discovery-related motions and orders continue, as Yelp and Samsung join the fray. (Those companies, like Microsoft and Apple, are relevant to the case even if they aren’t parties themselves, as their internal records are potentially relevant to Google’s liability.)
June/July 2021, search: The discovery process continues, and the US and Google both file several documents with the court under seal. (Microsoft files two sealed documents, as well, in response to Google’s subpoenas for company records, and Apple becomes involved after the government requests access to some of its internal information.)
March 2021, search: Meetings between Google and the various governmental plaintiffs continue, with periodic status reports on the discovery process.
Jan. 2021, search: Google files a response to the complaint, admitting to many of the facts alleged by the Justice Department and associated attorneys general, but categorically denying the substance of the government’s claims of illegality. Further responses to separate but related claims, generally to specific state attorneys general, follow in the subsequent weeks and months.
Dec. 2020, search: Judge Amit Mehta approves the joinder of Michigan, Wisconsin and California to the suit.
Oct. 2020, search: The Department of Justice, along with the attorneys general of 11 states, sues Google in DC federal district court for unlawfully maintaining a monopoly, in violation of Section 2 of the Sherman Act. The case centers on Google’s use of exclusive contracts that mandate its use as the default search engine in a host of different hardware and software applications, with the government alleging that this represents an artificial constraint on any possible competition for the search giant.
I came across a LinkedIn post the other day that described “hypegineering,” which the poster explained refers to the moment when AI “marketing becomes more innovative than the technology itself.”
That post came from Ralph Aboujaoude Diaz, the global head of GRC for British consumer services company Haleon. Until last year, Diaz worked in operations cybersecurity for consumer goods giant Philip Morris.
In his post, Diaz added that “side effects may include believing mediocre tech is revolutionary, confusing hype with progress, buying solutions to problems you don’t have and defending it like your job depends on it.”
As amusing as that might seem, the problem is frighteningly real. The sad truth is that enterprise IT executives are partly — perhaps mostly — to blame for the current hype around AI.
For many decades, senior IT leaders (pre-dating when it was called MIS) were the technology hype-deflators with razor-shape BS detection skills. That level of skepticism was needed. Tech vendors have always exaggerated and left out critical context when they weren’t outright lying about their products.
Enterprises trusted the IT gate-keepers to ferret out reality from the smoke and mirrors.
But there was a critical difference back then: senior management (especially CEOs, CFOs and board members) didn’t pay much attention to tech. They wanted the benefits, but they left the details to IT management to sort things out. With senior managers, benign neglect can be an incredibly good thing.
As much as we might all think that we want our bosses to really care about our efforts, be careful what you wish for. (For Dilbert fans, think of the pointy-haired boss; a boss who has strong beliefs but no understanding of technology is a nightmare.)
Alas, that is the situation many enterprises find themselves in today. IT management fully understands the level of absurd hype coming from a multitude of AI players. But unlike years and technologies past (RFID? NFC? Biometrics?), deflating hype balloons is easier when it’s comes solely from vendors. When senior managers start spouting this garbage, IT’s hype-deflation ability morphs into contradicting the very people at the top of their own corporate food chain.
That forces IT leaders who want to stay gainfully employed to do a lot of what used to be called lying. “Well, boss, yes these agentic systems have guardrails that will block destruction,” the lie begins, “but we can’t anticipate what any user or attacker might say in a prompt.”
That’s far more corporate acceptable than the actual truth: “Boss, a guardrail that an agent can disregard isn’t a guardrail. It’s more of a mild suggestion.”
Or IT could say, “Well, yes, boss, autonomous agents could exponentially increase efficiency — as long as you’re OK with the risk that they might send our internal data to the competition.”
For the sake of the company, tech leaders and decision-makers need to reassert themselves and perform serious reality checks on all AI efforts. But this is more fraught than merely contradicting a top boss. IT could be seen as embarrassing that top boss by pretty much proving that they were either naive or ignorant enough to be conned by the hype.
Telling them they’re wrong seems nicer than calling them stupid. And yet, someone in IT has to find a politically palatable way to do both.
Companies last month hired in the cloud, hosting, information processing, data, and semiconductor manufacturing sectors, CompTIA said in a statement.
“We’re entering a labor market where opportunity is increasingly concentrated around specific skills, industries, and investments,” said Ger Doyle, regional president of North America at ManpowerGroup, a labor consulting firm.
For example, data center hiring was up 39% in July compared to the same period last year, Doyle said. Not surprisingly, the data-center growth has been fueled by AI infrastructure needs and demand for hardware. That has helped increase the number of jobs in ancillary sectors such as transportation.
July hiring data showed heavy truck driver demand, which surged by 181% from June, Doyle said.
According to the BLS data, employment went up by 2.4% in the “computing infrastructure providers, data processing, web hosting, and related services” sectors, which is listed under the information sector.
Jobs in the “computer and electronic product manufacturing” sector — which is bunched under manufacturing — rose by 2.9% from June to July.
But bad news continued for the telecommunications sector, where the number of jobs declined by 1.5% from June to July. That continued a downward slide in recent years.
Overall job layoffs slowed in July, with 33,429 cuts announced; that’s down from 45,849 cuts announced in June, and the lowest since July 2024, according to data from Challenger, Gray and Christmas.
“Hiring has also increased over last year by 25%, so while AI is shifting the labor market, it is not dismantling it,” said Andy Challenger, chief revenue officer for Challenger, Gray & Christmas.
The tech sector announced 9,867 cuts in July, bringing the year-to-date total to 149,023 so far in 2026, according to Challenger figures.
Tech sector hiring and job losses vary as organizations use different measurement techniques to gauge the overall hiring environment.
The top-line US unemployment rate declined to 4.1% from 4.2%, in part because of workers leaving the workforce or not looking for jobs, indicating a slow labor market.
Still, hiring demand exists across the US, despite declining labor force participation and longer job searches, Doyle said.
Because technology underpins many of the changes occurring across sectors such as healthcare and services, employers are rethinking hiring. “That’s why the labor market many workers are experiencing doesn’t always match the one described by the headline numbers,” Doyle said.
ADP’s National Employment report said only 44,000 jobs were added to private payrolls in July, with choppiness across sectors.
But demand for AI skills continues to rise, according to research firms that track growth by analyzing job listings. CompTIA noted that about 14,000 new job listings in July sought AI and machine learning skills.
It’s not clear how the ongoing AI boom may be hurting or helping human jobs. Many recent job cuts have been attributed to AI, though some companies have been accused of using the technology as a rationale for cuts they made for other reasons.
A recent OECD report said that even manual jobs once thought immune to AI are at risk of being taken over by robots. The OECD report said creative jobs — typically management, arts, and social work — will be least threatened by AI.
There are also increasing indications that successful AI deployments will require humans in the loop. For example, more consulting firms are using forward-deployed engineers (FDEs) to implement agentic AI. And smaller consultancy firms are finding more productivity by automating mundane work with AI and spending more time meeting client requirements.
Walking around Dubai, it’s hard to believe a war is still going on in neighboring countries.
No missiles have struck the city, and it was clear during a recent visit that local residents are keeping to their routines. The biggest public worry, as it has been in many parts of the world, is the extreme heat. (The high today is around 106 degrees.)
Dubai, one of seven emirates in the United Arab Emirates (UAE), continues to emerge as a major global tech hub and is on the verge of becoming a major AI powerhouse. Each of the emirates has their own laws and traditions, with Dubai and Abu Dhabi — the UAE capitol — the most prominent.
Other nations recognize the region’s potential and Dubai’s position. The US, for instance, showed interest in currying favor with the UAE by sending a CIA spy to vet the emirate’s top AI firm, paving the way for future chip and AI deals. That outreach, highlighted in a report in The Wall Street Journal, was a hot topic in the local tech community.
These days, Dubai and Abu Dhabi hope to position themselves as hubs where people and companies go for AI. It’s location makes it an excellent place to do business with connections to Asia, Europe and Africa.
Broadly speaking, the UAE wants to be something of a Switzerland for AI, or “a neutral AI corridor between East and West,” Mahmood Abdulla, a regional technology expert, explained in a 2025 LinkedIn entry.
Data center buildouts
While states like New York are putting a moratorium on the construction of data centers in the US, the UAE is moving in the opposite direction; an aggressive AI data center buildout has government backing.
Stargate UAE, a data center project that involves top tech firms such as OpenAI and Oracle, is being built in Abu Dhabi. The city has essentially planned out a business model designed to turn AI into hard cash with a focus on the energy resources that will play a major role in the effort.
The plan is to redirect Abu Dhabi’s vast energy resources — it has up to 100 billion barrels of known oil reserves — to data centers generating AI tokens. Those tokens can then be sold as compute resources to produce revenue.
With oil markets volatile because of ongoing conflicts and the worldwide rise of renewables, pushing energy use to AI data centers might be a more reliable form of revenue long term.
Will it work? That remains to be seen. But unlike the US backlash against data centers construction in many place, a ban here seems unlikely.
The entrepreneurial spirit
Technological innovations from entrepreneurs have been at the core of Dubai’s rapid growth in recent years as a global business center. Here, AI is already quietly woven into daily life, much as it is in China, where AI is already in factories, homes, and infrastructure.
One founder told me that naming his company to include “AI” is more of a US thing. UAE companies focus less on boasting about AI models and superintelligence; they tend to emphasize user experiences.
On a separate track, Dubai is already targeting the launch later this year of robotic air taxis flying from the airport into the city. (That’s ahead of New York City, where electric air taxis are still being tested.)
Still, Dubai is behind the US when it comes to autonomous ground transport. The first autonomous taxis hit Dubai streets just last year, and there are a limited number operating in tourist areas.
A complicating face tor for the area remains the Iran war. Dubai’s Internet City, a hub with buildings belonging to the likes of Microsoft, Oracle and IBM, largely emptied when the war started in late February. Foreign tech workers left, and property prices declined.
But the real estate market is booming as opportunistic investors buy low. Their bet: the US-Iran war won’t stop the UAE’s growth.
Money is flowing into the area in other ways. Heavy tax burdens in Western countries are pushing wealthy founders to move businesses to Dubai’s tax-free environments. And Dubai is opening the door to those with digital nomad and freelance visas. Although visas can cost thousands of dollars a year, lower rent and healthcare costs compared to the US can offset those costs.
Rivals are emerging, too
UAE’s economic rival, Saudi Arabia is also rising as a tech powerhouse. Riyadh has undertaken reforms at a rapid pace to become an attractive place to do business under its Vision 2030 program.
Saudi Arabia requires corporations to have regional headquarters in the country to win government contracts, a policy that challenges Dubai’s status as a regional business hub.
The UAE also relies heavily on immigrants and human labor, which can be both a plus as well as a challenge. There is no pathway for entrepreneurs or long-time tech workers to retire here, which can undermine worker motivations to stay.
Still, the attractive business environment, combined with local infrastructure and a skilled labor force, make this area appealing. As one founder told me, if the US doesn’t want its most skilled immigrants, the UAE will be happy to welcome them with open arms.
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.
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
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
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
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’
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
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.
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.
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.
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.
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.
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.
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.
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.
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.”
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.
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.
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.
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.
A client called me last week with a problem I have been hearing about more often lately. They had made significant reserved instance commitments with a major cloud provider, overbuying for what they thought would be heavy AI training workloads. Now they were sitting on thousands of dollars in idle capacity every month. Their plan was simple: resell it to someone else. Except they couldn’t.
I have been doing cloud consulting for a long time, and this situation once had a straightforward solution. You went to the marketplace, listed your unused reservations, and found a buyer. The process was a bit clunky, but it worked. These days, the answer is far more complicated, and my client learned this the hard way.
AI has made this problem increasingly common. Companies initially committed to compute capacity based on ambitious training plans. Prototype projects were expected to scale, and inference workloads were projected to grow substantially. Then reality hit. Some projects did not materialize. Some models trained faster than expected. Some inference patterns were lighter than anticipated.
Many organizations now hold reserved capacity they can’t use, discard, or share without a complex, increasingly restricted process. This reality is something every company with significant cloud spend needs to clearly understand.
The history of cloud resale
There was once a functioning resale market for cloud reserved instances. AWS, for example, maintained a Reserved Instances Marketplace where companies that had purchased reserved capacity could sell those reservations to other AWS customers. This was a legitimate, AWS-sanctioned process. Companies would register as sellers, list their unused reservations with pricing and terms, and if a buyer appeared, the marketplace would facilitate the transaction.
The resale market was useful for companies that had overestimated their needs or whose business changes reduced their cloud consumption. Instead of simply absorbing the cost of unused commitments, they could recoup some of that investment by selling to other organizations with unmet demand. It created a secondary market that added liquidity to what was otherwise a rigid financial arrangement.
My client had some experience with this resale market a few years ago and assumed they could use it again. They were unpleasantly surprised to learn that the rules had changed.
AWS changes the rules
In January 2024, AWS implemented a significant policy change that effectively shut down the resale of EC2 Reserved Instances on its platform. AWS stopped allowing companies to resell their unused reserved capacity through the Reserved Instance Marketplace or any other official channel. If you have a reserved instance commitment with AWS, you are essentially stuck with it unless you can use it yourself or modify your reservation.
This change had a real impact on companies that had relied on resale as part of their cloud financial management strategy. It reduced flexibility and increased the risk of long-term reserved commitments. When I explained this AWS policy change to my client’s representatives, I could hear the frustration in their voices. They had made their commitment in good faith, carefully modeled their expected AI workloads, and now faced the reality that there was no easy exit.
The reasoning behind this change is not entirely clear, but AWS likely viewed capacity resales as something that complicated their billing and commitment models without providing enough benefit to the overall ecosystem. Regardless of the company’s reasons, the primary resale path for the largest cloud provider has been effectively closed.
What options still exist?
What can companies do now when they find themselves with reserved capacity they no longer need? The first possibility is to work directly with the cloud provider to modify or exchange the reservation if it is convertible. Some reservation types allow modifications, such as changing the instance type, region, or tenancy. This will not eliminate the commitment, but it may help companies better align their reservations with actual workload needs.
The second option is to use third-party brokers and marketplaces that operate independently of the cloud providers. Although AWS has shut down its official resale channel, brokers and marketplaces still facilitate resale arrangements for other cloud providers and for some AWS scenarios. These arrangements can be more complex and carry more risk, but they remain a possibility for companies determined to move unused capacity.
The third alternative is to optimize usage. Companies can invest in better utilization monitoring, workload placement, and automation to ensure that reserved capacity is used as efficiently as possible. This does not recover the money already spent, but it reduces future waste.
My client explored all three alternatives and found that each had significant limitations. Modifications were possible, but only within a narrow range. Third-party brokers were interested, but the process was opaque and uncertain. Optimization helped, but it could not eliminate the fundamental overcommitment they had already made.
The broader implications
Cloud commitments are more rigid than many enterprises initially realize because they lack a liquid market and because providers control modifications, transfers, or cancellations. Right now, I see this pattern most often in the AI space. Companies commit to massive amounts of compute for training and inference based on projections that rarely reflect the actual workloads. Then they are surprised to find themselves locked into payments. The AI boom has led to significant overcommitment because enterprises remain unaware that the resale mechanisms that once existed have been largely shut down.
This is why cloud financial management has become such an important discipline. Companies need to be far more thoughtful about how they commit to cloud resources, how they model their future consumption, and how they build flexibility into their cloud strategies. The days of assuming you can always resell your way out of an overcommitment are effectively over, at least with AWS.
For Azure and Google Cloud, the resale landscape is slightly different, but the same general principles apply. These providers have their own capacity transfer policies and, like AWS, those policies can change at any time. Companies should understand their options before making large, committed purchases and build contingency plans in case their actual usage diverges from their projections—or if resale policies change.
The bottom line is that reselling unused reserved cloud instances is far more complicated than it sounds. The market is not as open as it once was, the options are limited, and the providers themselves hold most of the cards. My client got burned, and I doubt they will be the only one. Companies that want to optimize their cloud spending should focus on accurate forecasting, thoughtful commitment sizing, and ongoing optimization rather than relying on resale as a safety valve. That approach worked at one point, but those days are largely gone.
The head of the US government’s AI testing institute, Chris Fall, has resigned about three months after taking charge of the Center for AI Standards and Innovation (CAISI), the federal organization responsible for evaluating advanced artificial intelligence models for safety and security.
Current National Institute of Standards and Technology NIST Director Arvind Raman will serve as acting CAISI Director following Fall’s departure while continuing to oversee the Commerce Department office responsible for the institute, the Daily Signal reported, citing two people familiar with the matter.
A Commerce Department spokesperson who spoke to the publication did not disclose a reason for the resignation.
Fall assumed leadership of CAISI in April after the Trump administration reorganized the former US AI Safety Institute under NIST. The institute develops methodologies for evaluating frontier AI models and works with AI developers on voluntary technical assessments covering areas such as cybersecurity, model misuse, reliability and other risks associated with increasingly capable AI systems.
The leadership change comes as governments and AI companies continue developing technical approaches for evaluating frontier AI models while enterprises expand deployments of generative AI and agentic AI across business operations.
In recent months, the Commerce Department has taken a more active role in AI policy involving advanced models, placing greater attention on how the federal government evaluates technologies with potential national security implications.
Continuity matters more than personalities
CAISI works with AI developers such as Anthropic, Google’s DeepMind and OpenAI on voluntary evaluations of frontier AI models and develops methodologies for testing model capabilities and risks. The institute does not regulate AI developers or certify commercial AI systems.
For enterprises, those evaluations are one source of technical information alongside vendors’ own testing, third-party security assessments and internal AI governance programs.
Sanchit Vir Gogia, chief analyst at Greyhound Research, said enterprises should focus less on the individual leading the institute and more on whether its technical work continues with the same level of consistency and transparency.
“Leadership churn at CAISI weakens the signal long before it weakens the science,” Gogia said. “The testing has not stopped. Its authority simply does not travel as cleanly once the leadership does not.”
According to Gogia, the more important question for enterprises is not whether the institute’s evaluation work will continue but whether the processes supporting those evaluations remain stable.
“The instinct is to ask whether the pipeline is breaking,” he said. “The more useful question is where the pipeline now sits.”
Enterprises still carry the burden of AI governance
Gogia said organizations should continue treating government-led AI evaluations as one input into their governance processes rather than as evidence that a model is inherently safe for enterprise deployment.
“A government evaluation was always a signal, never a certificate,” he said. “A signal loses value the moment its issuer becomes unpredictable.”
He said enterprises should instead monitor whether CAISI maintains consistent evaluation methodologies, continues publishing technical findings and preserves continuity within its research teams under interim leadership.
“The name on the door is not the signal. The behaviour underneath it is,” Gogia said.
Gogia also cautioned against linking Fall’s resignation to recent Commerce Department actions involving AI policy or export controls, noting that there is no public evidence connecting the two.
“CAISI evaluates; it does not enforce export controls, because it holds no such power,” he said. “This is not a testing body reaching for enforcement. It is enforcement reaching past the testing body.”
With Raman assuming the role on an interim basis, the next significant milestone for enterprises will be the appointment of a permanent director, and whether the institute’s evaluation programs continue without disruption, the analyst said.
Gogia said the successor’s mandate may prove more important than the individual selected.
“A CAISI result is not a safe harbour,” he said. “It informs an obligation; it does not discharge one.” NIST did not immediately respond to a request for comment.
Jobs involving physical labor are at high risk of disruption from automation, with new technologies such as AI robots becoming more prevalent, according to a recent study by the Organization for Economic Co-operation and Development (OECD). That means workers in construction and extraction, farming, fishing, forestry, production and material transportation could be affected by fast-moving technology changes.
“Routine and low-skilled jobs are at higher risk,” the Paris-based public policy group said, adding that “overall, jobs requiring non-routine cognitive, social and creative skills are less susceptible to automation.”
The kinds of creative and cognitive jobs still thought to be less exposed to automation include social work and community service roles.
OECD also said management jobs — which often require workers to devise creative answers to solve problems — fall within the creative and cognitive category. “While AI has made some high-skill job requirements more susceptible to automation, many critical skills in these roles remain difficult to automate,” OECD said.
The same still holds true for some physical and manual labor jobs – including cleaners, agricultural workers, food-prep assistants, and laborers — which are less exposed to the affects of AI, OECD said.
But people who work in programming, translating and interpretation positions could find their work affected by the quick rise of AI tools and services. According to the organization, global AI uptake rose from 7% in 2021 to 20% in 2025.
“In these occupations, GenAI could perform a significant share of tasks at least twice as fast today or in the near future,” OECD said.
The effects of the AI boom are not always uniform across industries or regions. In a separate 2026 Employment Outlook study released by the group, exposure to disruption from generative AI (genAI) ranges from about 16% in some areas to more than 70% elsewhere, depending on industries and occupations.
AI technology has become so ubiquitous that it’s been compared to electricity — virtually all companies will need it or at least know how to use it. But it’s adoption has been hindered at times as companies struggle to find ROI from its use, and by regulatory and ethical hurdles.
In the US, AI blamed for June job losses
According to a Challenger, Gray & Christmas study released earlier this month, AI was cited as the top reason for job cuts in June. The outplacement firm said employers cut 45,849 job cuts in June, of which 14,029 were attributed to AI, with the tech industry leading the cuts.
“Tech remains the epicenter of this year’s cuts,” Challenger said. “AI is the dominant force as companies are restructuring around it, automating roles, and reallocating budgets toward new capabilities. The sector is being reshaped in real time.”
Overall, AI has been responsible for 173,568 job cuts since 2021, the company said.
AI is hurting jobs in customer service, internal reporting, telecommunications, and hosting automation, said Victor Janulaitis, a staffing consultant who was formerly CEO at Janco Associates Inc.
“C-level executives continue to be focused on eliminating ‘non-essential’ managers, staff, and services,” he said. “Coders and developers have limited opportunities with legacy applications.”
While jobs in the IT sector overall are declining, current hiring tends to skew in the direction of people with AI skills. A report this month by CompTIA put job listings with AI skills at around 500,000, which is close to double the number in January.
“Employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation,” said Seth Robinson, CompTIA’s vice president for industry research.
That view dovetails with what ManpowerGroup, the recruitment firm, is seeing; demand for AI-related skills has nearly doubled over the past year, said Ger Doyle, regional president of North America at ManpowerGroup.
That growth extends well beyond traditional technology roles as companies move from experimenting to AI deployments at scale, Doyle said. “We’re seeing it influence hiring across occupations ranging from data science and engineering to project management and operational roles,” he said.
Meta is reportedly planning a cloud business that would sell access to AI computing power and models, extending its internal infrastructure into a commercial service for outside developers and enterprises. Reuters, citing Bloomberg’s reporting, noted that the planned offering would allow customers to access AI models hosted on Meta’s infrastructure and pay based on usage, effectively positioning the company in the infrastructure-as-a-service and AI platform markets. On the surface, this seems like a logical next step. If you are already spending enormous amounts of money to build AI infrastructure, there is a natural temptation to ask whether some of that investment can be monetized beyond your own internal use.
I have seen this pattern before. A company builds sophisticated internal systems, recognizes their value, and then begins to imagine that becoming a cloud provider is simply a matter of exposing those capabilities to external customers. It sounds straightforward, especially given the excitement around AI and the demand for high-performance infrastructure. But cloud computing is not just another distribution model. It is not simply a matter of offering on-demand multitenant services and charging a fee. It is a deeply operational, trust-based business in a market that punishes companies that do not fully understand what enterprise customers require.
A crowded neocloud market
The first problem Meta faces is that this is not an open opportunity. The neocloud space, meaning purpose-built AI infrastructure delivered as a service, is already crowded and increasingly difficult to enter. Amazon, Microsoft, and Google dominate the conversation for obvious reasons. They have years of cloud operating experience, broad service portfolios, global reach, mature ecosystems, and deeply established enterprise relationships. Oracle remains a serious player as well, especially in enterprise applications, data platforms, and performance-sensitive workloads. IBM still matters in hybrid cloud, operations, and industries where governance and regulatory rigor remain central.
That list alone should give Meta pause. These companies are not just infrastructure vendors. They are experienced cloud operators. They have spent years building not only the underlying platforms, but also the native capabilities enterprises now expect by default. Those capabilities include security, governance, identity management, observability, support, compliance, billing controls, resilience planning, and integration with the broader enterprise technology estate. These are not secondary features. They are part of the core value proposition.
This is why late entry into the cloud market is so hard. A new provider is not just competing on price or capacity. It is competing against accumulated trust. Enterprises are not casual buyers. They are selecting long-term operating environments for applications, data, AI models, and business-critical processes. They want confidence that the provider understands how these services will be consumed, governed, and supported over time. Meta is entering a market where the incumbents already have a major head start on all of those fronts.
Harder than it looks
Over the years, I have had many technology companies come to me and say they wanted to reposition their technology in the cloud space, either as software as a service or infrastructure as a service. In the beginning, enthusiasm is always high. The technology is impressive. The market size looks attractive. The revenue models appear compelling. Investors love the story. Then we begin to walk through what it really means to operate as a cloud provider, and the optimism usually fades fast.
The questions become very practical and very uncomfortable. How will tenants be isolated? How will identity and access controls work across different kinds of customers? What governance models will be built in natively? How will workloads be monitored, optimized, and secured? What does support look like 24 hours a day, across regions, across industries, across compliance boundaries? How will outages be handled, communicated, and remediated? How will the platform integrate with existing customer tools for operations, policy management, and security response? How much investment will it take just to become credible before you even begin to differentiate?
Once companies fully understand the complexities, market dynamics, and the capital and execution required to compete even with secondary players, many of them back off. They realize that cloud technology is not a packaging exercise. It is a transformation in how a company designs, operates, supports, sells, and evolves technology. That is why I remain skeptical when any company assumes it can translate internal infrastructure excellence into external cloud success without a very long, disciplined commitment.
Meta’s market readiness
Of course, Meta is not lacking in financial resources. If any company can afford to spend aggressively in this space, it is Meta. The company has the capital to build infrastructure, absorb losses, hire experienced talent, and stay in the market long enough to make a serious attempt. I would never argue that Meta is too small or too poor to try. Quite the opposite. If there is any non-traditional entrant with the financial scale to force itself into the conversation, Meta would be high on the list.
But money does not erase complexity. It only gives you the chance to confront it. The real question is not whether Meta can afford to become a cloud provider. The question is whether Meta has what it takes to become an excellent cloud provider. Those are two very different things. Enterprises are not going to move meaningful workloads to a new platform simply because the company behind it is wealthy or technically famous. They are going to ask whether the provider understands enterprise consumption patterns, enterprise risk, enterprise governance, and enterprise operations.
That is where the challenge becomes much more serious. Meta has extensive experience running infrastructure for itself. That is valuable, but internal operating excellence is not the same thing as external service maturity. Running systems for your own workloads allows a high degree of control over architecture, standards, priorities, and operating assumptions. Running systems for paying customers requires flexibility, consistency, transparency, and support across a wide range of use cases that you do not control. Those are very different disciplines, and companies often underestimate the gap between them.
What exactly is Meta?
Another concern here is strategic clarity. Meta already has a complicated market identity. It is a social media company, an advertising platform company, a hardware company, an AI company, and still, in the minds of many, the company that spent billions pursuing the metaverse. If it now wants to be viewed as a serious cloud infrastructure provider, it will need to explain not only what it is offering, but why customers should believe this is a durable long-term commitment and not just another adjacent experiment.
That uncertainty can be damaging. Customers want stable providers with clear strategic intent. They do not want to architect important systems around a platform if they suspect the provider may lose interest, shift direction, or reframe the business after a few years of uneven results. Cloud computing requires patience, consistency, and deep customer orientation. It is not a market where strategic ambiguity helps.
This could become confusing for Meta internally as well. Building a true cloud business demands focus. It demands years of investment in areas that may not be glamorous but are absolutely necessary, such as governance, operations, controls, support frameworks, partner programs, and enterprise sales alignment. If the company is not willing to make those sacrifices fully and for the long term, the initiative will struggle.
Your Apple gear wasn’t cheap. And if you drop your iPhone, your iPad stops turning on, or you smack your Apple Watch into something and shatter it, getting it repaired probably won’t be cheap either.
Apple includes a one-year limited warranty with all hardware so if there’s a manufacturing defect, you’ll be covered. To also protect against accidental damage, get priority tech support, and in many cases replace your product if it is lost or stolen, you can opt to purchase AppleCare+. Here’s what it is, what it covers, and what you can expect to pay.
Is AppleCare+ worth it? Only you can decide for your own situation, but this should be everything you need to know to make a smart choice.
Updated July 16 ,2026: Apple has slightly increased the cost of AppleCare+ on iPads and Macs, by $0.50 per month or $5.00 per year.
What is AppleCare+?
If you’ve never even heard of AppleCare, start here! Otherwise, feel free to skip to the sections that cover your particular devices.
How AppleCare+ is different from the standard Apple warranty
AppleCare is Apple’s term for its warranty programs. Every Apple device comes with a certain stretch of AppleCare warranty service and phone/chat support for free, typically one year for the warranty and 90 days of phone support. You can pay to extend that protection for longer, get discounted repairs on accidental damage, and in some cases replace your device if it is lost or stolen. That’s called AppleCare+.
Do I have to buy AppleCare+ at the same time as the product?
No. If you already know you want it, buying AppleCare+ with the product is a good idea, just so you don’t forget. When you’re shopping at the Apple store and Apple.com, you’ll be offered AppleCare+ at checkout, and authorized Apple resellers can offer AppleCare+ too.
But you have some time to decide. Generally you get 60 days from the purchase date to buy AppleCare+ (only 30 days if you’re in Japan), and Apple has a page where you can check your exact device’s eligibility.
The latest iPhones and iPads will show you an AppleCare+ signup prompt right there near the top of Settings, if you’re still eligible. You can tap on it to see how many days remain, and purchase AppleCare+ (with or without Loss and Theft protection) right on your device.
If your new iPhone or iPad is eligible for AppleCare+, you’ll see a signup prompt near the top of the Settings app.
Still, to buy AppleCare+ after the fact you generally have to run a diagnostic test on the device in question, so Apple knows it’s in good working order. And no matter when you buy your AppleCare+ plan, the coverage starts from the day you bought your device. So you don’t get an “extra” two months of coverage if you wait two months to buy the plan.
If you want to buy an AppleCare+ plan after the 60-day period has ended, you can bring your device into an Apple Store for an inspection up to a year after purchase. If the technician allows it, you’ll be able to sign up in person.
How do I buy AppleCare+ for products I didn’t buy directly from Apple?
The easiest way is online. Apple’s site has a page to Check Your Service and Support Coverage. Enter your device’s serial number and it’ll tell you if you can still buy AppleCare+ and walk you through the process of purchasing and registering your plan.
Keep in mind that buying an AppleCare+ plan after the fact often means you have to complete a diagnostic test on the device. After all, Apple has to make sure you’re not trying to buy coverage on a device that’s already broken. So do it right away, just in case.
Can you buy AppleCare+ for refurbished products?
You can! Apple offers a range of refurbished items on its website, and they’re all backed by the same one-year warranty as new products. Plus, you can buy AppleCare+ for a refurbished item just as you can with a new one.
How do I check how much time is left on my AppleCare plan?
Head to Apple’s Check Your Service and Support Coverage page and enter your device’s serial number. You’ll find out how much time you have left on your AppleCare plan, and if you can still buy AppleCare+ for this device.
If there’s no Apple store near me, is AppleCare+ a bad idea?
No, you can still use AppleCare+ if you can’t make it to the Apple store. You have several options, actually.
Apple retail stores. If you can get to an Apple store, you can even make a reservation for AppleCare+ service.
Drop off at the UPS Store. Apple can provide you with a Repair ID number for an iPhone, iPad, or iPod needing service, and you can take it to any UPS Store in the U.S., which will ship the device to Apple at no cost to you. When Apple finishes the repair, you get the device back in the mail, again with no shipping fee.
Direct mail. Alternatively, Apple will send you a box and shipping labels for you to ship any Apple product back to them for repair. Apple pays the shipping both ways.
Onsite service. If the problem is with a desktop Mac, Apple can sometimes even send an authorized repair technician to you.
DIY. If the problem is relatively small, Apple can sometimes just send you a part, like a new iPhone charger or Apple TV remote.
Do I have to pay for AppleCare+ up front?
AppleCare+ used to be a single purchase that extended warranty services for a set span of time (usually two years). It has since changed to an ongoing discounted repair and replacement service, which you can pay for monthly or yearly. You are covered for as long as you continue to pay for AppleCare+.
How am I supposed to survive without my device while Apple is repairing it?
Apple offers an Express Replacement Service for the iPhone, iPad, and Apple Watch. When you use Express Replacement, Apple sends you a new device right away, and you don’t have to mail in your broken device until you receive it. Conditions apply, but ask Apple about it when you contact them and they can let you know if you qualify.
What if I sell the product before AppleCare+ runs out?
Most AppleCare+ plans, which are paid montly or annually, are linked to your Apple Account and do not transfer with the product. Older AppleCare+ plans, which were one-time fees for a set period of coverage, can be transfered to the new owner. See this support document for more information.
Does AppleCare cover loss or theft?
There used to be a second tier of AppleCare+ that included coverage for loss or theft, but Apple now only offers this tier. So yes, it covers loss and theft (on applicable products) and there is no longer an option to get AppleCare+ without that.
Is AppleCare+ only for U.S. customers?
No, Apple provides global coverage for most Apple devices (iPhone, iPad, Apple Watch, Mac, iPod, Apple TV, and Apple displays). Service options do vary between countries, but any AppleCare service is always performed by authorized techs with genuine Apple parts.
Obviously, you should always read the full agreement before you purchase any warranty or insurance.
Can I cancel an AppleCare+ plan?
Yes, you can. If it’s within 30 days of purchasing the plan, you’ll get a full refund minus any service you received. Outside of 30 days, you’ll get a prorated refund, minus any service, minus a fee of $25 or 10 percent of the prorated refund, whichever is less. Call Apple at 1–800-MY-APPLE to ask how.
Can I extend AppleCare+ after a policy runs out?
Yes, though some devices have a limit as to how many times you can extend coverage. Apple has a 45-day window where a policy can be extended 24 or 36 months, paid monthly or annually. In China, the window is 30 days.
The iPhone is covered in glass, easy to drop, and goes everywhere with you. No wonder iPhones break so frequently! Here’s the skinny on buying some extra protection.
How much does AppleCare+ cost for iPhone?
It varies by the device. Here’s the price for both monthly and annual coverage.
I bought my iPhone with the iPhone Upgrade Program. Doesn’t that have AppleCare+ included for free?
It’s included, but it’s not free. The cost of the two-year AppleCare+ plan is included among the 24 loan payments you agree to pay. (But at least you aren’t charged interest.)
If you upgrade your phone after a year, your two-year loan starts over again, but you still get the second year of AppleCare+ on the new phone. Read the iPhone Upgrade Program terms for more detail. The iPhone Upgrade Program is a loan, so again, be sure to read all the fine print before you sign up.
For the iPhone, AppleCare+ extends your warranty coverage from one year to two, and extends phone and chat support from 90 days to the full two years as well.
You also get up to two incidents of accidental damage coverage per year for a relatively low service fee. If you’ve only damaged the screen, a screen replacement is only $29/£25. But if the iPhone has other damage, the fee is $99/£79. iPhones are eligible for the Express Replacement Service, letting you request a replacement device in the mail before you send your original damaged device in for repair.
So let’s say you drop your iPhone on the corner, which breaks the screen but also dents the enclosure enough that Apple can’t fix just the screen—that’s a $99/£79 repair. If the screen shatters but the rest of the phone is relatively unscathed, $29/£25.
The warranty against defects covers the phone as well as the battery and the included headphones and accessories. Accidental damage can include almost anything, such as water damage, drops, running the thing over, your kid pours paint into the Lightning port—as long as you only have two incidents (or less) in the two-year coverage period.
The plan also covers two incidents of theft or loss per year.
You might also want to check your homeowner’s and renter’s insurance policies to see if they cover stolen electronics. Some cell phone service providers’ own insurance plans cover loss and theft, but it’s relatively rare.
Should I get it AppleCare+ for my iPhone?
If you’re going to buy AppleCare+ on anything, it’s probably going to be your iPhone. But it’s not always a slam dunk. Depending on the age of your iPhone and how expensive some future repair might be, you may end up spending more for AppleCare+ and a repair than just a straight repair cost.
Really it depends on how careful you are with your iPhone. Do you use a case? Avoid action sports? Rarely drop your phone? Or are you a total klutz who’s broken every phone you’ve ever owned? Be honest with yourself and proceed accordingly.
An Apple Watch is another prime candidate for AppleCare+, if for no other reason than because it’s on the outside of your wrist where it can constantly bump and scrape on things.
AppleCare+ for iPad
Whether you agree that an iPad can replace a laptop, its AppleCare+ plan certainly is cheaper than AppleCare+ for a Mac—and iPads are eligible for Apple’s speedy Express Replacement Service too.
Apple recently raised the price of AppleCare+ for iPad by $0.50 per month or $5.00 per year.
Pad, iPad mini
$5.49
$54.99
iPad Air 11-inch (M4)
$6.49
$64.99
iPad Air 13-inch (M4)
$7.49
$74.99
iPad Pro 11-inch (M5)
$10.49
$104.99
iPad Pro 13-inch (M5)
$11.49
$114.99
What does AppleCare+ for iPad cover?
You are covered for unlimited damage from accidents like drops or spills.
You also get two incidents of accidental damage coverage per year, with a service fee of $49/£39 for most damage, plus tax. AppleCare+ covers the iPad as well as its battery and included accessories like the power brick. You are covered for up to two incidents of loss or theft in 12 months.
Should I get it AppleCare+ for my iPad?
This answer might hinge on how you use your iPad. My iPad, for example, almost never leaves my house—but I also share it with a 6-year-old who doesn’t always treat it like an expensive and fragile device. Still, our iPad is mostly for entertainment, so I’m living (dangerously?) without AppleCare+.
On the other hand, if I was a road warrior with an iPad Pro that I relied on every day to make my living, I’d be more likely to shell out the $129/£129 for AppleCare+.
AppleCare+ for Macs and Apple displays
Apple doesn’t sell its own Apple-branded displays anymore, and AppleCare+ for Macs might seem like a bargain if you’ve ever paid hundreds to get a laptop serviced—or overkill if you’re an iMac user who’s never run into a problem.
For Macs, AppleCare+ does not include coverage for loss and theft. Apple recently increased the price for Mac coverage by $0.50 per month or $5.00 per year.
Mac mini
$3.99
$39.99
Mac Studio
$6.49
$64.99
iMac
$6.49
$64.99
MacBook Neo
$5.49
$54.99
MacBook Air 13-inch
$7.49
$74.99
MacBook Air 15-inch
$8.49
$84.99
MacBook Pro 14-inch
$10.49
$104.99
MacBook Pro 16-inch
$15.49
$154.99
Mac Pro
$17.99
$179.99
AppleCare+ for the Apple Studio Display costs $4.99/$49.99, and for the Studio Display XDR it costs $9.99/$99.99.
What does AppleCare+ for Macs cover?
AppleCare+ for Macs covers unlimited accidental damage for a lower service fee than you would pay for an out-of-warranty repair. If you damage just the screen or Mac’s enclosure, it’s $49-99 and all other damage is $149-299, plus tax. You get unlimited battery replacements for no extra charge (Apple requires your battery hold less than 80% of its original charge before replacing it).
For Macs, your computer is covered, plus the battery, power adapter, and other included accessories.
What doesn’t AppleCare+ for Macs cover?
It doesn’t cover loss or theft. Your homeowner’s or renter’s insurance might, depending on your policy.
Should I get it AppleCare+ for my Mac?
For Macs, AppleCare+ makes more sense if you’re buying a laptop and you’re worried about the screen, or you just tend to be rough on your computers. Desktop Macs probably aren’t as important to cover, since the likelihood of, say, dropping one is a lot less. Still, Macs have a long life, so you may decide three years of (transferable) protection is worth the extra cost.
AppleCare+ for Apple Watch
For Apple Watch, your AppleCare+ subscription now covers loss and theft—it didn’t originally do so.
Fancier editions like the Hermes, Titanium, or Ceramic watches may have different AppleCare+ pricing, however.
What does AppleCare+ for Apple Watch cover?
You get unlimited repairs for accidental damage, and up to two instances of loss or theft in 12 months.
Should I get it AppleCare+ for my Apple Watch?
It’s pretty difficult to get an Apple Watch repaired anywhere but the Apple store, as we found out. The Apple Watch is designed to be tough, so most users hopefully shouldn’t have problems. But if you’re paranoid or accident-prone, or you just love the idea of Express Replacement Service so you never miss a step, AppleCare+ is there.
AppleCare+ for Apple TV
Your Apple TV is probably the last thing you’d buy an extended warranty for, but Apple is happy to sell you one!
It’s only $29/£25, both for the regular Apple TV and the 4K edition. That gets you two years of warranty coverage and tech support.
What does the AppleCare+ Apple TV cover?
The Apple TV itself, the Siri Remote, and even the Apple AirPort router you use with it, if you bought it within two years of the Apple TV. Tech support by phone or chat includes help with setup, connecting to Wi-Fi, iCloud, renting or buying from iTunes, AirPlay, and even connecting to third-party streaming services. You also get coverage for accidental damage (spills, drops, earthquakes, fires) or cosmetic damage. But since it just sits there that’s unlikely to be used. Each incident has a service fee of $15.
What doesn’t it cover?
Loss and theft, including losing the remote. (But if the remote stops working, that’s covered.)
Should I get it AppleCare+ for my Apple TV?
Nah. I mean, you could. But you probably don’t need to, because it’s a box that sits under your TV and doesn’t go anywhere.
The best time for tech workers to unionize was 20 years ago, when they had plenty of leverage. The second-best time is now, when they don’t.
Mass layoffs, AI-driven displacement, corporate surveillance, workplace disillusionment have created conditions that have made organizing compelling for tech professionals. But the federal labor board that has historically protected workers’ right to organize has been weakened, and the companies that once feared it are openly defying it.
Here’s how organizers and labor experts describe the pros and cons to organizing — and how you can get started.
The single biggest benefit of a union contract for most tech workers isn’t pay — it’s protection against arbitrary termination, especially in the wake of recent mass layoffs in tech. In the United States, nonunion “at-will” workers can be fired at any time without a stated reason, while unionized workers negotiate protections written into their contracts.
“That fear of the company letting you go for anything at any time…with a union they just can’t do that,” says Zak Thompson, a senior software engineer at Kickstarter and union steward at Kickstarter United. Now that Kickstarter employees are unionized, people are less worried that saying something negative will result in termination.
“I’ve been shocked at the willingness of my co-workers to speak up against what they see as poor or controversial business decisions,” Thompson says.
Beyond job security, unions can deliver concrete material gains.Kickstarter United was formed in 2020, although getting there wasn’t easy: two employees were fired during the organizing campaign — which itself became a galvanizing event. And while the union hasn’t been able to prevent layoffs, it did negotiate better terms: four months of severance pay and four to six months of continued health insurance, versus the two to three weeks per year of work that management had initially proposed.
Other benefits include a four-day work week; AI protections; a minimum pay floor; and standards for raises, promotions, and time off for the company’s 59 employees.
Unions can give tech workers a voice in decisions that affect their daily work — including how AI tools are deployed. “Nobody I’ve spoken to is against new technology or getting trained in it,” says Max Belasco, a business systems analyst at the University of California Los Angeles School of Law and co-chair of the UCLA chapter of the University Professional and Technical Employees/Communications Workers of America (UPTE-CWA) Local 9119.
“But when new technology is being implemented, we want to know: what’s the five-year vision, the 10-year vision? Are we implementing this in a way that betters staffing, increases efficiency, or eases the lives of people already working? Or are we trying to take away jobs, automate people out of their pension or paycheck?” Belasco says.
The challenges are real. Tech professionals are less inclined to leave their jobs in the current market because wages haven’t been increasing as fast as they once were, and it can take longer to land another job.
“Tech moved from a very tight labor market in 2022 (1.85% unemployment rate) to a noticeably weaker one in 2024–2026 (3.49%),” although that’s still better than the national unemployment rate of 4.36% through May of this year, says Liya Palagashvili, senior research fellow and director of the Labor Policy Project at the Mercatus Center at George Mason University.
Liya Palagashvili of the Mercatus Center at George Mason University
Mercatus Center at George Mason University
Flexibility is a concern. The more substantive challenge, raised by economists including Palagashvili, is that traditional union contracts impose uniform terms across an entire bargaining unit, limiting the flexibility that many tech workers — and their employers —currently enjoy. Tech firms need to move fast, adjusting teams, products, and roles on the fly.
“Collective bargaining agreements can make those adjustments much more difficult, whether by making them slower, costlier, or inconsistent with the contract,” she says.
Workers skeptical of unions in a survey of 1,900 tech professionals conducted by the career site Blind cited specific concerns: that unions are “not meritocratic,” “prevent innovation,” and “hold back earnings of top performers.”
Thompson from Kickstarter United pushes back: “We have nothing in our contract about ‘you can’t bend down and pick up a piece of trash because that’s someone else’s job.’ The company is free to give bonuses and individual raises as much as they like. This is all just up to the people who are bargaining the contract from the union side.”
Organizing carries potentially serious personal risks. During negotiations for a second three-year contract in 2025, Kickstarter United went on strike for 42 days. A few months later, the company announced layoffs.
“They let go strong union leaders, including a person who had bargained our last contract,” Thompson says. The union appealed, and the issue is now going to arbitration.
If you form a union, don’t expect much support from the National Labor Relations Board, the agency that certifies US labor unions and protects workers’ right to organize, in terms of prosecuting complaints of unfair labor practices, Thompson warns. “We’re in a political moment in this country with a pretty weakened NLRB. You have to be ready to organize and withhold worker power without any guarantee of safety.”
Organizers are up against an enormous union avoidance industry. Organizers can expect fierce pushback as soon as the business discovers that organizing is underway.
“There’s a multi-billion-dollar industry in union avoidance,” says Alan McAvinney, a Google software engineer and organizing chair, Alphabet Workers Union-CWA, a 1,400-member minority union of Alphabet employees. (Google is a subsidiary of Alphabet.)
US employers spend roughly $1.7 billion a year on union avoidance consultants and law firms, according to a May 2026 report by the Economic Policy Institute and LaborLab.
Expect hardball tactics. Management may play hardball during the time between when organizers announce their intention to unionize and the actual vote. For example, management can threaten to fire foreign-born workers in the US on H-1B visas if they support the union. Those workers would then have just 60 days to find a new sponsoring employer or lose their H-1B status, according to a recent Tech Workers Coalition blog post.
And at venture capital-backed startups, investment agreements sometimes require management to attest there is no union activity — meaning a public organizing drive can trigger funding withdrawal. Or, if a unionized company is acquired, the new management can dissolve the union overnight by reclassifying unionized workers as new hires.
With these sobering facts in mind, here is how organizers who have done it describe the process of creating a union.
Step 1: Start a conversation with your co-workers
At the University of California, a two-tier system had evolved where some tech workers were unionized and some weren’t, Belasco says. Management created new titles that fell outside the union even though they had similar job descriptions and responsibilities to those in the union. Those nonunion employees received lower pay and benefits than their unionized peers, which created resentment and instability.
Belasco and other organizers wanted to eliminate that division by bringing everyone under the same contract. But when they began their unionization drive, “the biggest barrier we faced wasn’t management opposition — it was that people felt this was just the best-case scenario realistically available: ‘We have this job at the university, we have concerns about automation and layoffs, but what can we really do about it?'” he says.
The antidote to that fatalism, organizers say, is simple: “Just start talking to your immediate co-workers. Are they experiencing the same challenges you are experiencing?” says McAvinney. “There’s no need to start talking about a union at this point.”
Just get a consensus and start building a group of like-minded individuals, Thompson advises. “Always start with one-on-one conversations, and that’s what you should do the whole time. That’s the key to organizing,” he says.
Tech workers often think they’re a special case, says Thompson, and therefore that unionization isn’t a good fit. “You’re not special. You are a company of workers, you are organizing, and there is a playbook for that. Trust the process, because it tends to work pretty well,” he says.
Step 2: Who’s on board, and who’s not? Map your workplace, but keep it quiet
Once there’s a consensus, continue to grow your network. Keep a list of everyone you’ve spoken with and note their disposition: “Is this person union-friendly or anti-union? Would they be a strong organizer?” Thompson says.
Maintaining secrecy early on is essential, because anti-union tactics will start immediately, and that can stop union organizing before it can gain momentum.
“Generally, employers do not want to share power with their workforce,” McAvinney says. Employers will deploy every means at their disposal to stop organizing efforts and peel away potential yes votes.
“If you look at historical examples, having 70% approval before the employer finds out about you results in a high percentage of wins when you actually cast the vote. Historically, that’s an effective buffer,” he says.
There’s a real threat of firing and layoffs. The traditional tech worker belief that job mobility makes collective action unnecessary is now being tested by a tighter job market, McAvinney says, noting that workers who many believe were fired for speaking out back in 2019 were a galvanizing factor in his union’s formation.
“You generally don’t want to be in a situation where the employer feels comfortable firing everyone. Part of that is thinking from a cynical standpoint about what the consequences would be to the employer if they did fire everyone,” he says.
One-on-one conversations that include personally asking co-workers to keep conversations confidential are key to keeping things quiet, Belasco says. When 2,100 UC tech workers voted to unionize in May, 96% voted in favor. To stay out of earshot of managers, avoid employee surveillance tools, and sidestep conference calls that could be recorded, organizers met with workers in their homes.
“That tactic is probably what made the difference between winning the election and getting the majority we got,” he says.
Step 3: Find the right union affiliation or go it alone
“Running a campaign against major employers requires the resources and expertise of the larger labor movement, even if workers publicly present as independent,” says Kate Bronfenbrenner, director of labor education research and senior lecturer emeritus at Cornell University’s School of Industrial and Labor Relations.
The Alphabet Workers Union decided early on to affiliate with CWA. “They gave us a bunch of support early on in our campaign with no strings attached,” McAvinney says.
Kickstarter is organized through OPEIU, Thompson says. “They’ll usually have resources and staff that can help you through the next steps: collecting signatures in support of a union, bringing that to management, holding a vote — the more formalized things that interact with US labor law. They’ll also help with organizing along the way,” he says.
For workers at institutions where a union already exists, there may be a faster path. Organizers at UCLA did what’s called a “unit modification,” aligning with UPTE. By organizing under UPTE, the workers didn’t have to negotiate a new contract from scratch — they joined an already-negotiated contract covering existing UPTE tech members, which put them in “a much stronger position” than starting fresh, Belasco says.
Step 4: Choose your union model: majority vs. pre-majority or minority
Assess what’s practical for your organizing effort. In a majority union, more than 50% of all workers in a defined bargaining unit must vote to join the union through an NLRB-supervised election in the private sector, or a Public Employment Relations Board (PERB)-supervised election for public sector workers.
The NLRB must certify the union, which then operates under its legal protections. This means, for example, that the employer must bargain, negotiated contracts are enforceable, violations must go to the NLRB or arbitration, and workers can’t be dismissed without just cause.
A pre-majority or minority union is a minority labor organization operating without NLRB protections or collective bargaining agreements. “Pre-majority means that workers are able to demonstrate majority support — through signed cards, petitions, a walkout, or everyone wearing solidarity T-shirts — without going through a formal election,” Bronfenbrenner says.
Kate Bronfenbrenner from the School of Industrial and Labor Relations, Cornell University
ILR School/Cornell University
The Alphabet Workers Union-CWA (AWU-CWA) formed as a pre-majority union because achieving majority status across a globally distributed workforce of over 100,000 was not a realistic near-term goal. “An underground model where you try to reach 70% support across a workforce of over 100,000 people isn’t realistic,” McAvinney says.
A pre-majority union can still make a difference, he says. For example, the Alphabet Workers Union-CWA convinced management to offer voluntary exit packages — buyouts — prior to announcing layoffs.
For smaller organizations, a majority union may be the more practical option — it’s more attainable, McAvinney says. “I don’t think [the pre-majority union model] is the correct thing to do in all situations. I certainly would not recommend it to a 200-person shop.”
Kickstarter, which had fewer than 100 employees, was able to form a majority union, with 55% voting to organize.
Ultimately, says McAvinney, “there’s no inflection point where you go from being able to win nothing to winning everything, even with a contract and a supermajority. But the more people you have who are willing and able to fight for what they want, the more you’ll be able to get.”
Step 5: Who should — and should not — be in your union?
Belasco’s situation at UCLA illustrates a broader strategic choice that every organizing campaign must make. He had been in a union position in educational technology when he was told his role would be reclassified as a non-union position.
“I was given a choice: apply to the new non-union position to continue doing the work I’d trained for, or stay in my union position doing service desk work I wasn’t used to,” he says. “Essentially, it was a choice between job security and career progression.”
Belasco joined a “wall-to-wall” union, which represents a broad range of university professional and technical employees across the UC system rather than a single job category, such as engineers or tech professionals.
Kickstarter United is another example of a wall-to-wall union. “It’s not just the engineers who are unionized, but also customer support, designers — everyone,” Thompson says.
Wall-to-wall unions are more powerful, but they’re also more difficult to achieve. Under US labor law, “professionals have to vote separately on whether they want to be combined with other workers,” says Bronfenbrenner. “You can never have a wall-to-wall unit without giving professionals the chance to decide whether they want to be separate.”
The law’s “professional employees” category includes roles like software engineers and developers but not necessarily others. For example, customer support specialists and QA analysts would fall into the “non-professional workers” category.
“For decades, the pattern was either to organize everybody except the engineers, or manage to organize the engineers and fail to bring in everybody else — neither of which builds real worker power,” says Simone Robutti, an organizer with Tech Workers Coalition Global, an international branch of TWC based in Berlin.
Step 6: You won the vote. Get ready for what comes next
Winning a union vote means having a seat at the table, says Thompson. “Once the workers have come together and agreed they want that seat, you bring that to management, and they have a chance to voluntarily recognize a union,” he says.
But in most cases employers contest the results, which must be certified by the NLRB or PERB. That process, in which the employer uses various tactics to challenge the legitimacy of the outcome, can take weeks or months.
In the meantime, some companies may disregard negotiated contracts, which can lead to lengthy legal appeals or extended arbitration.
“The NLRB can still force an election, but it can’t force a contract, and companies are saying they simply won’t comply,” Bronfenbrenner says. This is where the expertise and resources of affiliation with a major union can help, she adds.
As a result, contract negotiations can take far longer than workers might expect. At Kickstarter, for example, two years and four months elapsed from the time of the union vote to the first contract, and that was at a 59-person company with a relatively cooperative employer. At larger companies with more aggressive legal teams, the timeline will be longer.
Forming a union is hard work, Robutti says. “It’s not a service you pay for and they protect you. It doesn’t happen spontaneously, and it doesn’t happen magically. It’s the choice to take responsibility for improving your workplace.”
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