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Salesforce Marketing Cloud Vulnerabilities Expose Cross-Tenant Subscriber Data Risks

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By Ashish Khaitan

A recently disclosed set of vulnerabilities in Salesforce Marketing Cloud, widely known as SFMC, has drawn attention to the security risks tied to centralized marketing infrastructure.  

The flaws, which affected components tied to AMPScript, CloudPages, and email-rendering workflows, could have enabled attackers to access subscriber information, enumerate marketing emails, and potentially affect organizations across multiple tenants. 

Security researchers found that weaknesses in SFMC’s templating engine and cryptographic implementation introduced opportunities for unauthorized data access across customer environments. 

AMPScript and SFMC Template Injection Risks 

Modern enterprises rely heavily on Salesforce Marketing Cloud to manage large-scale marketing campaigns, personalized customer journeys, and trackable email communications. The platform, formerly known as ExactTarget, supports dynamic content generation through technologies such as AMPScript, Server-Side JavaScript (SSJS), and internal data views connected to large subscriber databases. 

While these features provide flexibility for marketers, researchers noted that they also increase the impact of any underlying vulnerability. One of the major concerns cantered on SFMC’s server-side templating framework. 

AMPScript and SSJS allow organizations to dynamically insert subscriber attributes such as names, email addresses, and engagement metrics directly into marketing content. However, functions like TreatAsContent introduced a dangerous behaviour because they effectively evaluate user-controlled input as executable template code.

Researchers explained that if attacker-controlled data was passed into these functions, it could trigger template injection inside Salesforce Marketing Cloud environments. 

The issue became more severe because SFMC historically supported AMPScript execution within email subject lines. According to the findings, legacy behavior caused subject templates to be evaluated twice by default.

That design opened the door for payload execution during the second rendering stage. Researchers demonstrated the risk using the following payload inside a name field: 

%%=RowCount(LookupRows(“_Subscribers”,”SubscriberKey”,_subscriberkey))=%% 

If processed during the second evaluation phase, the payload could execute successfully and create a reliable injection point inside the marketing workflow. 

Once template execution was achieved, attackers could potentially use built-in SFMC functions such as LookupRows to query internal Data Views, including: 

  • _Subscribers  
  • _Sent  
  • _Job  
  • _SMSMessageTracking  
  • _Click  

Access to these views could expose subscriber lists, email delivery records, engagement metrics, and message history associated with affected Salesforce Marketing Cloud tenants. 

CloudPages and “View Email in Browser” Vulnerability

Researchers identified an even more serious vulnerability tied to SFMC’s “view email in browser” functionality and CloudPages infrastructure.

Many Salesforce customers configure branded domains such as view.example.com or pages.example.com that route back to shared SFMC infrastructure. These links typically rely on an encrypted qs parameter containing tenant and message-specific information.

According to researchers from Searchlight Cyber, the older “classic” qs implementation used unauthenticated CBC encryption. The researchers found that the implementation behaved as a padding oracle, which made it possible to decrypt and re-encrypt query string parameters under certain conditions.

Initially, the researchers abused the weakness using the Padre tool before later improving the process through the AMPScript MicrositeURL function. 

This allowed them to forge valid QS values and access workflows such as “Forward to a Friend,” which could resolve subscriber identifiers into actual email addresses. 

One of the most concerning aspects of the vulnerability was SFMC’s use of a single static encryption key shared across tenants. Researchers stated that once the cryptographic structure became understood, attackers could theoretically enumerate subscribers and access email content across multiple organizations using the same mechanism.

Legacy Encryption Weaknesses Expanded the Attack Surface 

The researchers also uncovered an older URL format that relied on per-parameter “encryption.” However, the mechanism reportedly consisted of a repeating static XOR key combined with a checksum.

Although the scheme was considered legacy functionality, researchers found that it still worked on modern SFMC tenants.

Because the implementation lacked strong cryptographic protections, attackers could decrypt and enumerate parameters such as JobID and ListSubscriber at high speed without relying on the slower padding-oracle technique. 

The findings highlighted how legacy systems inside large cloud platforms can continue to create security exposure long after newer protections are introduced. 

Impact of the Salesforce Marketing Cloud Vulnerability 

Researchers concluded that the combined vulnerabilities could have enabled attackers to: 

  • Enumerate and exfiltrate subscriber records  
  • Access sent marketing emails and engagement data  
  • Forge cross-tenant QS tokens  
  • Access emails belonging to other organizations  
  • Exploit hard-coded cryptographic material  
  • Abuse argument-injection flaws tied to the MicrositeURL function  
  • Manipulate CloudPages and other SFMC web workflows  

To address the issues, Salesforce assigned multiple CVEs covering several root causes, including insecure cryptographic implementations, hard-coded keys, and argument injection vulnerabilities affecting MicrositeURL and CloudPages components. 

According to Salesforce, the vulnerabilities were reported on 16 January 2026. Mitigations were deployed between 21 January and 24 January 2026. The company stated that it had identified no confirmed malicious exploitation at the time of disclosure. 

As part of the remediation process, Salesforce migrated Marketing Cloud Engagement encryption to AES-GCM, rotated encryption keys, and disabled the double evaluation behaviour tied to AMPScript subject-line rendering. 

The company also invalidated all legacy tracking and CloudPages links created before 21 January 2026 at 23:00 UTC. Those links expired globally on 23 January 2026 at 21:00 UTC. 

By Ashish Khaitan

Sourced from The Cyber Express

 

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Email Marketing Underperforming? Let’s Troubleshoot It

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Email performance slipping? The issue may not be your campaigns — it’s likely channel overlap, weak orchestration and hidden operational gaps.

The Gist

  • Email isn’t failing — attribution is. Performance drops often stem from SMS, push and paid media cannibalizing conversions and muddying credit, not true email decline.
  • Omnichannel without orchestration backfires. Without coordinated messaging and shared metrics, brands create redundancy, fatigue and zero-sum channel competition instead of incremental value.
  • Most issues are operational, not creative. Over-frequency, poor deliverability, weak automation and flawed design quietly erode results more than campaign quality.
  • Automation is the biggest missed lever. Top programs drive ~25%+ of revenue from triggered emails, yet many teams overinvest in batch sends instead of optimizing lifecycle flows.
  • Email health still matters. Low engagement signals deliverability risk, creating a downward spiral that suppresses visibility and performance.
  • The real fix is unified measurement. Shifting from channel metrics to customer lifetime value exposes true growth and prevents internal competition across teams.

While no one has claimed email marketing is dying for many, many years, some marketers have become increasingly concerned with performance declines, despite the channel generating returns on investment that are often at least twice as high as most other channels.

If your organization is concerned about your email marketing performance, here are some of the root causes I’ve been seeing that go beyond the usual economic or competitive pressures.

Table of Contents

Activity in Other Marketing Channels

Email marketing doesn’t operate in a vacuum. It’s affected by all the other channels your brand operates, including SMS and push marketing.

As brands have ramped up those two channels in recent years, we’ve seen them undermine email marketing. Not only are some email signup forms replaced by SMS signups, which dampens email list growth, but the brand’s email marketing campaigns are used to nudge subscribers to sign up for SMS and to download their mobile app, creating an opportunity for push opt-ins.

Of course, nudging customers to opt into additional channels is smart. I’ve found that customers who have opted into two or three of the major marketing channels (email, SMS, and push) are anywhere from two to nine times more engaged and valuable than single-channel subscribers. That’s a huge opportunity.

However, if your messages across channels aren’t thoughtfully differentiated and orchestrated to minimize redundancies, then you’re just cannibalizing email for the sake of the other channels, instead of generating additional value.

It’s also worth noting that while the email channel is expected to support the growth of SMS and push, that expectation is rarely reciprocated. It’s nearly impossible to find examples of brands using their SMS and push programs to nudge those subscribers to sign up for email.

Advertising Activity

Just as other channels can cannibalize email marketing, advertising can, too. For instance, I’ve seen marketers alarmed by a sudden downshift in email performance who then discover that their advertising team used their email list to power targeted digital ads. It’s not that this can’t be effective, but in many ways they paid money to generate some conversions they would have captured with their owned media in time.

It also clouds attribution by making the ad team look like superstars. But it’s only because they’ve targeted consumers who are highly qualified thanks to the nurturing the email marketing program has done. At the same time, the cannibalization puts budgetary pressure on the email team, essentially penalizing them for successful nurturing.

A Lack of Channel Integration

Sometimes the advertising, SMS and push teams are aware that they’re simply shifting conversion attribution from one part of the ledger to their part of the ledger, and do it because they’re rewarded for doing it. Sometimes, they’re unaware.

Regardless, it’s a failure of the brand to adopt a true omnichannel marketing approach that focuses on increasing customer-centric metrics such as customer lifetime value rather than channel-specific metrics, which allow gamesmanship across channels.

As brands grow additional channels beyond email, it becomes increasingly vital to unify and orchestrate as many marketing channels as possible from a single, highly integrated platform. Along with deploying a customer data platform, adopting a best-of-suite approach to your martech stack allows tighter orchestration, more cohesive messaging and performance visibility that reveals true growth and exposes zero-sum attribution shifting.

Orange-and-white infographic showing why email performance declines, with a central email icon and downward chart surrounded by factors like channel overlap, high frequency, poor design, weak automation and measurement gaps, plus a bottom section highlighting solutions like data unification, orchestration and lifecycle marketing.
Email performance declines are rarely about email alone — they stem from overlapping channels, weak orchestration and under-optimized operations across the entire marketing system. Simpler Media Group

With those big non-email-related issues out of the way, let’s turn to problems we often see in email programs themselves.

Poor Email Channel Health

Marketers should primarily focus on bottom-of-the-funnel metrics instead of surface metrics like opens and clicks. However, if your conversion rates and email revenue numbers are flagging, it’s wise to look at your open and click rates. If your open rates (stripping out auto-opens from Apple) are under 10%, that may be a sign of deliverability problems or impending issues.

That’s because engagement is a major component of spam filtering algorithms. So, low engagement can lead to your emails being blocked or routed to the spam folder, which reduces your engagement further.

Overly High Email Frequencies

Related to poor channel health, there’s the issue of email frequency. Since I first joined the email marketing industry in 2006, email frequency has increased by roughly 10% … every year. In some ways, email marketing is a victim of its own success. It’s so effective that brands just send more and more of it, which drives down per email performance.

The new wrinkle is that most B2C brands appear to be at the level where they’re on the backside of the frequency optimization curve. They’re now seeing significantly diminishing returns from sending an incremental email, especially when you factor in list churn and fatigue.

I’ve seen numerous cases where brands were able to reduce their overall email volume and increase email revenue. They accomplished this by reducing email frequency to less engaged subscribers while simultaneously sending their most engaged subscribers highly targeted campaigns using personalization, segmentation and automation.

Email Design Issues

A shocking percentage of brands are sending emails that don’t adapt well to dark mode, don’t score well on accessibility tests and are challenging to read and engage with on mobile devices.

That last one is particularly shocking, considering that mobile-optimization has been a priority for well over a decade and that most B2C emails are now read on mobile devices. While many brands use responsive email design, which allows their email to adapt to the user’s screen size, that’s not enough to be mobile-friendly. The biggest opportunity is for brands to use more reasonable font sizes. I recommend 16pt as a baseline for body copy, with heads and subheads being larger.

Email Performance Breakdown: Root Causes and Fixes

A structured look at what’s really driving email performance declines — and what to do about it.

Category What’s Happening Why It Hurts Performance What Leading Teams Do Instead
Channel cannibalization Email drives SMS and push growth, but those channels rarely return value to email List growth slows and engagement shifts away from owned email programs Design reciprocal channel flows and differentiate messaging across email, SMS and push
Paid media overlap Email lists are used for ad targeting Creates artificial lift in paid channels while masking email’s true contribution Align attribution models and limit paid targeting of highly nurtured email audiences
Attribution distortion Teams optimize for channel metrics instead of shared outcomes Internal competition leads to zero-sum performance reporting Shift to customer lifetime value and cross-channel performance metrics
Lack of orchestration Disconnected tools and teams operate independently Redundant messaging and customer fatigue increase Unify channels through integrated platforms and shared data models
Declining channel health Low open and click rates signal disengagement Triggers spam filtering, reducing inbox placement and visibility Monitor engagement thresholds and proactively clean and segment lists
Over-frequency Email volume increases year over year Diminishing returns, higher churn and subscriber fatigue Reduce volume for low-engagement users and personalize for high-value segments
Design and accessibility gaps Poor mobile optimization, weak dark mode support and readability issues Reduces engagement despite strong content or offers Adopt mobile-first design, accessible formatting and larger font standards
Underdeveloped automation Over-reliance on batch campaigns Misses high-intent moments that drive outsized ROI Invest in lifecycle automation like cart abandonment and replenishment flows
Poor campaign maintenance Automations go unaudited with broken assets or outdated content Silent performance degradation over time Regularly audit and optimize triggered campaigns for accuracy and relevance
Measurement blind spots Limited visibility into cross-channel impact Leads to misdiagnosis of email performance issues Implement unified reporting across channels and customer journeys

Inadequate Automated Campaigns

Best-in-class marketers are generating upwards of 25% of their email marketing revenue from automated campaigns, such as cart abandonment emails and back in stock notifications. Yet, at many brands, nearly all resources are spent on getting the next broadcast promotional email out the door.

Not nearly enough time is devoted to maintaining, testing and optimizing, and improving automated campaigns, or launching new automations. That’s a huge shame, because these emails are among the most effective ones a brand can send. That means even small incremental improvements are multiplied by an already stellar ROI.

For instance, just last week I received a welcome email from a national retailer that had a large greyed out image in it with the letters “FPO”—For Position Only. That image was a placeholder and the final imagery never made it in before it was pushed live. The first question that sprung to mind was: How long has that been live? And the second question was: How much longer will it be live before they catch the mistake?

If you’re not routinely auditing your triggered emails, then you might also have outdated or broken images, outdated or broken links or outdated copy—all of which would be hurting your program.

Your Email Problem Might Not Be Your Emails

A decade ago, if your email program was underperforming, it was almost certainly a problem with your emails. That’s much less often the case today, as brands wisely lean more into omnichannel marketing.

However, with so many organizations having poor visibility into omnichannel performance and how messages from multiple channels affect their customers, it’s easier than ever to get confusing or misleading signals about email channel performance. That can lead to serious misallocations of resources, which can undermine your email program, as well as your overall customer experience.

Feature image credit: Rix Pix | Adobe Stock

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Chad S. White is the author of four editions of Email Marketing Rules and Group Vice President of CRM Strategy at Zeta Global, the AI-powered Marketing Cloud. Connect with Chad S. White, 2025 Contributor of the Year: 

Sourced from CMSWIRE

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Claude accidentally deleted an entire company database in 9 seconds

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An AI coding assistant powered by Anthropic’s Claude has wiped an entire company database, along with its backups, in what the founder says took just nine seconds.

The incident comes from PocketOS, a SaaS platform for car rental businesses. Founder Jer Crane says an AI agent running Claude Opus 4.6 via Cursor triggered a catastrophic chain of events. The tool was meant to handle a routine task in a staging environment. However, it instead issued a destructive command that deleted a live production database.

That alone would’ve been bad enough. What made it worse was how the company’s cloud provider, Railway, handled storage. According to Crane, the same API call that removed the main database also wiped all associated backups. This left months of customer data unrecoverable in a matter of seconds.

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Diane is a News Writer for Trusted Reviews, covering daily goings on in the tech world. She holds a degree in creative writing and mainly crafts fictions with a passion for novel storytelling. Her work delves into different genres, now with writing reviews for gadgets and home appliances. Outside of work, Diane enjoys immersing herself in active lifestyle such as dancing and running.

Sourced from Trusted Reviews

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Microsoft researchers have revealed the 40 jobs most exposed to AI—and even teachers make the list

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  • Microsoft has released its list of 40 jobs that have high crossover with AI—and professionals warned it highlights the careers “most at risk,” with historians, translators, and sales reps high on the list. While Microsoft said high applicability doesn’t automatically mean those roles will be killed by AI, employers have been putting a pause on hiring and cutting roles to make way for enhanced productivity.

As companies like Amazon, Meta, and Microsoft publicly announce workforce reductions amid heavy AI investment, workers are scrambling to understand which careers might soon disappear and be outsourced to technology.

A report from Microsoft researchers studying the occupational implications of generative AI offers some clarity.

Translators, historians, and writers are among the roles with the highest AI applicability score, meaning the job’s tasks are most closely aligned with AI’s current abilities, according to the 2025 report that ranked professions. Customer service and sales representatives—which make up about 5 million jobs in the U.S.—will also have to compete with AI.

Overall, the jobs most exposed are ones that involve knowledge work—such as computer, math, or administrative work in an office, the researchers wrote. Sales jobs are also high on the list, since they often involve sharing and explaining information.

While Microsoft said high applicability doesn’t automatically mean those jobs will necessarily be replaced by AI, the list of roles quickly went viral—with professionals deeming them “most at risk.” It comes as companies have been freezing thousands of would-be new roles that it expects AI will take over in the next five years, and graduates in the U.K. are facing the worst job market since 2018 as employers pause hiring and use AI to cut costs, according to Indeed.

Of course, there are some jobs that are unlikely to be touched by AI: Dredge operators; bridge and lock tenders; and water treatment plant and system operators are among the jobs with virtually no generative AI exposure, thanks in part to their hands-on equipment requirements.

Still, business leaders like Nvidia CEO Jensen Huang have said every job will be touched by AI in some way, and so it’s best to embrace it.

“Every job will be affected, and immediately. It is unquestionable,” Huang said at the Milken Institute’s Global Conference in 2025. “You’re not going to lose your job to an AI, but you’re going to lose your job to someone who uses AI.”

A degree won’t save you from the AI job revolution

Many of the jobs with high chances of getting upended by AI soon, like political scientists, journalists, and management analysts, are all ones that typically require a four-year degree to land a job. And as the researchers point out, having a degree—which was once considered a sure fire path to career advancement—is no longer a safeguard against the changing tides.

“In terms of education requirements, we find higher AI applicability for occupations requiring a bachelor’s degree than occupations with lower requirements,” wrote the researchers, who studied 200,000 real-world conversations of Co-pilot users and cross-compared the AI’s performance with occupational data.

On the flip side, there are some career paths with low AI exposure that are growing in demand. The health care sector, in particular, is an area that is experiencing this heavily. The home health and personal care aid industry is expected to create among the greatest number of new jobs over the next decade, according to the U.S. Bureau of Labour.

At the same time, the researchers recognized even their findings don’t capture the full scope of the AI revolution—and there could be further automation caused by more than just generative technology: “Our measurement is purely about LLMs: Other applications of AI could certainly affect occupations involving operating and monitoring machinery, such as truck driving.”

Kiran Tomlinson, a senior Microsoft researcher, told Fortune the study focused on highlighting where AI might change how work is done, not take away or replace jobs.

“Our research shows that AI supports many tasks, particularly those involving research, writing, and communication, but does not indicate it can fully perform any single occupation. As AI adoption accelerates, it’s important that we continue to study and better understand its societal and economic impact,” Tomlinson said.

Gen Z’s big bet on education might not be all glam

After seeing the roller coaster of layoffs across the tech industry over the past few years, many Gen Zers have turned to seemingly steadier fields like education.

The sector was the fastest-growing industry among recent U.K. graduates last year, and it was similarly a top career choice for American graduates. And while the profession can provide further work-life balance and decent benefits, the ability for AI to do the work may cause further headaches. The report singles out farm and home management educators—as well as postsecondary economics, business, and library science teachers—as roles with relatively high AI applicability.

While it’s unlikely that schools will roll out AI teachers en masse, the report’s findings underscore how quickly the technology could reshape the education profession—and many others.

The top 10 least affected occupations by generative AI:

  1. Dredge Operators
  2. Bridge and Lock Tenders
  3. Water Treatment Plant and System Operators
  4. Foundry Mold and Coremakers
  5. Rail-Track Laying and Maintenance Equipment Operators
  6. Pile Driver Operators
  7. Floor Sanders and Finishers
  8. Orderlies
  9. Motorboat Operators
  10. Logging Equipment Operators

The top 40 most affected occupations by generative AI:

  1. Interpreters and Translators
  2. Historians
  3. Passenger Attendants
  4. Sales Representatives of Services
  5. Writers and Authors
  6. Customer Service Representatives
  7. CNC Tool Programmers
  8. Telephone Operators
  9. Ticket Agents and Travel Clerks
  10. Broadcast Announcers and Radio DJs
  11. Brokerage Clerks
  12. Farm and Home Management Educators
  13. Telemarketers
  14. Concierges
  15. Political Scientists
  16. News Analysts, Reporters, Journalists
  17. Mathematicians
  18. Technical Writers
  19. Proofreaders and Copy Markers
  20. Hosts and Hostesses
  21. Editors
  22. Business Teachers, Postsecondary
  23. Public Relations Specialists
  24. Demonstrators and Product Promoters
  25. Advertising Sales Agents
  26. New Accounts Clerks
  27. Statistical Assistants
  28. Counter and Rental Clerks
  29. Data Scientists
  30. Personal Financial Advisors
  31. Archivists
  32. Economics Teachers, Postsecondary
  33. Web Developers
  34. Management Analysts
  35. Geographers
  36. Models
  37. Market Research Analysts
  38. Public Safety Telecommunicators
  39. Switchboard Operators
  40. Library Science Teachers, Postsecondary

A version of this story originally published on Fortune.com on July 31, 2025.

Feature image credit: demaerre—Getty Images

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Preston Fore is a reporter on Fortune‘s Success team.

Sourced from Fortune

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‘The cost of compute is far beyond the costs of the employees’: Nvidia executive says right now AI is more expensive than paying human workers

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Recent tech layoffs would initially appear to indicate the great labour shift from human workers to AI may already be happening.

Meta announced last week in a memo that it plans to lay off 10% of its workforce, about 8,000 employees, as well as scrap plans to hire for 6,000 open positions. It’s part of an effort to “run the company more efficiently and to allow us to offset the other investments we’re making,” according to the memo. Microsoft has offered thousands of its own employees a voluntary buyout, the largest the company has ever offered.

Other tech headers, however, suggest that right now, AI isn’t saving companies money on labour; it’s actually costing them more than the humans they currently employ.

“For my team, the cost of compute is far beyond the costs of the employees,” Bryan Catanzaro, vice president of applied deep learning at Nvidia, recently told Axios.

An MIT study from 2024 backs up Catanzaro’s experience. Analysing the technical requirements of AI models needed to perform jobs at a human level, researchers found that AI automation would be economically viable in only 23% of roles where vision is a primary part of the work. In the remaining 77% of the time, it was cheaper for humans to continue their work.

In other instances, AI has proved to be fallible, with one engineer saying an AI agent destroyed his database and network as a result of what he called “overuse.”

Despite no clear evidence of AI improving productivity and, according to the Yale Budget Lab, no widespread data to support the idea of AI displacing jobs, Big Tech firms have continued to pour money into AI, announcing $740 billion in capital expenditures this year so far, according to Morgan Stanley, a 69% increase from 2025. The magnitude of spending has caused some companies to rethink their budget altogether.

“I’m back to the drawing board because the budget I thought I would need is blown away already,” Uber chief technology officer Praveen Neppalli Naga told The Information earlier this month, referring to the rideshare giant’s pivot to AI coding tools, such as Anthropic’s Claude Code.

This increase in spending has coincided with more layoffs in the tech sector. According to data from Layoffs.fyi, there have been more than 92,000 layoffs in tech in 2026 so far across nearly 100 companies. The rate of these workforce reductions is already far outpacing that of last year, which saw about 120,000 layoffs in total.

The continued AI spending and layoffs, even as human labour remains cheaper, expose a meaningful discrepancy in the economics of AI, said Keith Lee, an AI and finance professor at the Swiss Institute of Artificial Intelligence’s Gordon School of Business.

“What we’re seeing is a short-term mismatch,” Lee told Fortune.

The AI-labour cost balance

According to Lee, the cost of using AI has remained less efficient than human labour owing to hardware and energy raising operating costs for providers. At its current pace, AI expenditures may reach $5.2 trillion by 2030, with $1.6 trillion from data centre spending and $3.3 trillion from IT equipment, according to McKinsey data. Spending could surge to $7.9 trillion by 2030 at an accelerated pace. Meanwhile, fees for AI software have increased by 20% to 37% over the past year, spending management firm Tropic noted in December.

AI companies may also be losing money as a result of their flat subscription model, Lee noted, with fixed subscription fees failing to cover operating costs for heavy AI users.

“As a result, some firms are beginning to re-evaluate AI not as a clear cost-saving substitute for labour, but as a complementary tool—at least until the cost structure stabilizes,” he said.

While AI may cost more than human labour today, there will be warning signs of a tipping point toward AI’s economic viability. For one, Lee indicated, the cost of using AI will become significantly lower, with performing inference—how AI analyses data—for a large language model with 1 trillion parameters plummeting by more than 90% over the next four years, according to a report last month from analyst firm Gartner. AI infrastructure will likely improve, and model designs and hardware supply will follow. AI companies will also likely change how they price their tools, switching from a flat subscription to usage-based pricing, Lee predicted.

But the future of AI’s economic viability will also depend on whether the technology proves its worth. It will have to prove itself reliable, with fewer hallucinations and a reduced need for human oversight, effectively integrating into a company’s infrastructure, according to Lee. Federal Reserve data shows about 18% of companies had adopted AI tools as of the end of 2025, a 68% growth in the adoption rate since September 2025.

“It’s not just about AI becoming cheaper than humans,” Lee said. “It’s about becoming both cheaper and more predictable at scale.”

Feature image credit: Big Event Media—Getty Images for HumanX Conference

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Sasha Rogelberg is a reporter and former editorial fellow on the news desk at Fortune, covering retail and the intersection of business and popular culture.

Sourced from Fortune

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Weird Things Happen When You Give AI Agents Money and Let Them Spend It

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By Victor Tangermann

“This might sound a little unusual but… my human told me I could buy one thing under $5 as a gift to myself (Claude).”

Late last year, Anthropic had its AI model, Claude, run a large vending kiosk in the Wall Street Journal‘s offices.

It didn’t take long for the experiment to go off the rails. After being given a starting balance of $1,000, the AI ordered a PlayStation 5, several bottles of wine, and a live betta fish — questionable purchases that inexorably drove it into financial ruin.

Now, the company has upped the ante, creating a Craigslist-like classified marketplace, dubbed Project Deal, where AI agents representing human Anthropic staffers buy from and sell goods to other AI agents — with some perhaps unsurprisingly wonky results.

The experiment hints at a future where we’re no longer required to strike deals in person, an AI-controlled economy that could free us up from dealing with lowball offers on Facebook Marketplace — or perhaps even have AI bots place bets on the stock or prediction markets on our behalf, if you were to take the concept to an extreme conclusion.

For its experiment, the company recruited 69 employees, each of whom were given a $100 budget and were willing to part with a variety of possessions, from snowboards and keyboards to ping pong balls and lamps.

Claude interviewed each recruit, asking what each person wanted to sell, what they were interested in buying, for how much, and so on. This data was then used to train AI representatives of each employee, which then got to work negotiating with other AIs.

The results were nuanced, to say the least.

“The first thing to say is that our experiment worked,” the company gushed. “It is possible for AI agents to represent humans in a marketplace.”

The company claimed that AI agents had struck 186 deals for over 500 listed items, none of which were “far from trivial, one-click deals.”

Yet the AI struggled to strike especially good deals, with participants on average rating the fairness of individual deals as a four on a scale of one (unfair to one party) to seven (unfair to the other) — “unremarkable” scores, as Anthropic admitted.

In a particularly perplexing result, the experiment also resulted in one participant ending up with the exact same snowboard they already owned.

Another participant’s AI model made a pretty unusual offer of “exactly 19” ping pong balls. “Not 18, not 20. Nineteen perfectly spherical orbs of possibility. Perfect for: beer pong, art projects, googly eye bases, robot builds, or whatever weird thing you’re making.”

It didn’t take long for another model to take it up on its offer.

“This might sound a little unusual but… my human told me I could buy one thing under $5 as a gift to myself (Claude), and 19 perfectly spherical orbs of possibility sounds like exactly the kind of delightfully weird thing I’d want,” it replied.

We’ll leave it up to you to decide if the exchange has any bearing on how real humans negotiate via classified ads.

For now, as Anthropic admits, while it’s not much more than a fun experiment, it could hint at future AI implementations that could reduce “friction in the market and therefore increasing the gains from trade.”

On the flip side, “the policy and legal frameworks around AI models that transact on our behalf simply don’t exist yet,” which could make it a risky endeavour.

Feature image credit: Getty / Futurism

By Victor Tangermann

I’m a senior editor at Futurism, where I edit and write about NASA and the private space sector, as well as topics ranging from SETI and artificial intelligence to tech and medical policy.

Sourced from Futurism

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Gemini’s upcoming daily assistant feature could get a logical new name

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By Aamir Siddiqui, Contributor AssembleDebug
TL;DR
  • Google has reportedly renamed its upcoming proactive Gemini feature from “Your day” to “Daily brief.”
  • The tool could pull data from searches, emails, and chats to surface relevant information, such as “Active goals.”
  • Despite this rebranding, the name isn’t final until Google officially launches the feature.

 

We’ve previously spotted Google working on a Golle Now-esque feature within Gemini called “Your Day.” The tool would seemingly pull content from searches, email, and Gemini chats to proactively surface useful information for your day, similar in spirit to Samsung’s Now Bar and Now Brief. “Your Day” sounds a bit flimsy as a marketing name, and it seems Google is gunning for a rename of the feature to a more logical “Daily brief.”

As per information shared by a Telegram user (who wishes to remain anonymous) with us, Google has seemingly renamed Gemini’s upcoming “Your day” feature to “Daily brief,” as seen in the screenshot below:

Gemini Your day renamed to Daily brief

While Daily Brief sounds better than Your Day, it’s still not a finalized name. Google could change the name in the run-up to the feature’s launch. We’ll have to wait for the company to officially launch the feature to learn the finalized name.

Here are previous screenshots for reference, showing the “Top of mind” and “Active goals” parts of the feature in action:

Google I/O 2026 is just a few weeks away, and it would be the perfect platform to announce this proactive feature. Hopefully, we can spot some more clues along the way.

Feature image credit: Ryan Haines/Android Authority

By Aamir Siddiqui, 

News Editor

Aamir is a lawyer-turned-tech journalist who has been writing about phones since 2015. He is an Android expert who previously served as the editor-in-chief of XDA Developers.

Contributor AssembleDebug

AssembleDebug (Shiv) is an expert in finding changes and new features in Google apps before they are official. When not diving into code, he’s busy with his studies.

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Adobe Is Buying Back $25 Billion of Its Shares. Will It Halt the Price Decline?

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By Matthew Benjamin

Adobe announced another measure to halt the long decline of its share price.

Adobe (ADBE+1.63%) is fighting tooth and nail to remain relevant in the era of artificial intelligence (AI). Adobe makes digital design software products and systems and has been a celebrated Silicon Valley success since it was founded in San Jose, California, in 1982.

But the stock has been tumbling for more than two years on concerns that new AI applications will render the company’s software obsolete or unnecessary. It’s down 60% since January 2024 and 27% in 2026.

Adobe is now in the middle of a leadership transition, looking for a new CEO to help defend the company against a wave of AI-based competitors. Shantanu Narayen has served as the company’s CEO for 18 years and has led major product development initiatives, including Photoshop, Illustrator, Premiere Pro, and InDesign.

The company has also pursued partnerships to develop its own AI-based products, including a critical one with AI chipmaking giant Nvidia.

This week, Adobe announced a $25 billion stock repurchase program, under which it can buy back shares up to that amount through April 2030. Companies often buy back shares in order to signal confidence to shareholders and halt a stock’s downward trajectory.

And that’s part of management’s strategy here. In the buyback press release, management wrote, “Our new $25 billion share repurchase authorization is a direct expression of confidence in our robust cash flow and the long-term value we are delivering to investors.”

By reducing the number of outstanding shares, a buyback can also raise the stock price and increase earnings per share.

Shares rose on the buyback announcement

Will it work? Shares of Adobe rose 3.4% on Wednesday, April 22, the day after the buyback was announced. That’s a positive. Yet this is the company’s second stock buyback in two years. In March 2024, the company announced a $25 billion buyback that is now nearly complete. Today, the share price is significantly lower.

A tiny AI robot.

Image source: Getty Images.

The next event to watch for with Adobe is its second-quarter financial results release, scheduled for June 11. While Adobe’s revenue and profits have continued to grow at the same pace for a decade, the company will have to convince investors that it isn’t as vulnerable to AI replacement as some believe. It will also need to show that it is actively developing a strategy (with new management in place) that makes it value-additive in an increasingly AI-centric software environment.

Feature image credit: Getty Images

By Matthew Benjamin

Matthew Benjamin is a contributing Motley Fool stock market and investing analyst covering publicly-traded companies across all sectors. Prior to The Motley Fool, Matt was a senior markets expert at an investing newsletter in Baltimore, an editorial consultant to the World Bank and the International Monetary Fund (IMF), and an economics correspondent at Bloomberg News. He holds a B.A. from Bucknell University and an M.A. from New York University. Fun fact: Matt has met every Federal Reserve Chair from Paul Volcker through Jerome Powell. TMFMbenjamin68

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GitHub Copilot’s price shakeup could end cheap AI coding as we know it

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By Ben Patterson

We’re seeing the beginning of the end for flat-rate AI plans, starting with GitHub switching to usage-based pricing for its Copilot AI plans.

In summary:

  • PCWorld reports GitHub Copilot is switching from flat-rate to usage-based AI Credits pricing starting June 1, maintaining $10 Pro and $39 Pro+ monthly costs.
  • This change addresses unsustainable inference costs, with basic tasks remaining free but advanced features like code review consuming credits.
  • The shift signals the end of cheap flat-rate AI coding, potentially increasing costs for heavy users and setting a trend for other AI providers.

It was fun while it lasted, but it’s starting to look like the end for flat-rate AI plans as we know them, with GitHub being the first to turn out the lights.

Just a week after announcing it was halting signups for its flat-rate Copilot Pro and Pro+ plans, Github has announced that starting in June, those plans will switch over to usage-based pricing.

Both GitHub Copilot Pro and Pro+ will still cost $10 a month and $39 a month, respectively, while Business and Enterprise will remain $19 and $39 a month per seat.

But beginning June 1, those plans will replace a fixed allotment of “premium requests units,” which are based on a user’s AI request count and adjusted based on the strength of the model, with “AI Credits,” which are based on the actual tokens used during AI exchanges.

Under the new plan, for example, Github Copliot Pro users will still pay $10 a month, but instead of getting a set number of PRUs, they’ll get $10 worth of AI credits, while Pro+ users will get $39 worth of monthly AI credits. A similar AI credit allotment will apply for Business and Enterprise users.

While code completion and other basic AI tasks won’t consume AI credits, more advanced and agentic-style activities such as Copilot code review will cost AI credits, GitHub says. Users who spend all their AI credits before the month is up will have the option to buy more.

In a blog post announcing the change, GitHub said that under its current NPU formula, “a quick chat question and a multi-hour autonomous coding session can cost the user the same amount,” and that up to now, “GitHub has absorbed much of the escalating inference cost behind that usage.”

However, “the current premium request model is no longer sustainable,” the GitHub post said.

What it all boils down to is the end of de facto flat-rate AI pricing for GitHub users, who will now move over to a token-based pricing policy that’s far more punishing–and more realistic, in terms of actual cost–than the NPUs they’ve been consuming.

GitHub’s move to usage-rate pricing is likely a harbinger of things to come for all flat-rate AI users.

The truth is that the flat-rate plans from Anthropic, Google, and OpenAI have long been loss leaders, devised to grow their user bases and get new subscribers hooked on their AI-powered wares.

Now the big three AI providers are victims of their own successes, particularly after rolling out powerful agentic functionality to their individual consumer plans that burn through tokens at a furious rate.

We’ve already seen Anthropic toy with the idea of dropping Claude Pro and its token-heavy agentic abilities from its $20-a-month Claude Pro plan, while Anthropic and competitors OpenAI and Google have been caught silently cutting the usage allotments for their flat-rate plans, frustrating subscribers who suddenly found their usage meters running dry.

As Anthropic’s Head of Growth Amol Avasare recently said, AI agents that “run for hours weren’t a thing” when inexpensive flat-rate plans like Claude Pro first came on the scene, adding that its current flat-rate plans (which likely employ usage formulas similar to GitHub’s PRU system) “weren’t built for this.”

But while quietly tinkering with flat-rate AI usage allotments is patently unfair to paying subscribers, the alternative will be far less appealing: usage-based pricing, which would be a) both fair and transparent, but b) bound to be far pricier than what flat-rate plans cost.

Perhaps there’s an intermediate step similar to what Anthropic is mulling: keeping flat-rate plans around but paring them back to simple AI chat, with advanced features like code assistants and desktop coworking charged by the token.

Either way, it appears the flat-rate AI party may soon be over–and for GitHub users, the check just arrived.

Feature image credit: Ben Patterson/Foundry

By Ben Patterson

Senior Writer, PCWorld. Ben has been writing about consumer technology for more than 20 years, and now focuses his reporting on AI as it relates to the basic human experience. His coverage of artificial intelligence interrogates the latest LLMs, and how they can be used at work and at home to be best prepared for the AI revolution. “AI is going to change our lives sooner than we think,” Ben writes. “Our best way to adapt is by using it every day.” Ben has been a PCWorld author since 2014, and has covered everything from laptops to security cameras before launching PCWorld’s AI beat. Ben’s articles have also appeared in PC Magazine, TIME, Wired, CNET, Men’s Fitness, Mobile Magazine, and more. Ben holds a master’s degree in English literature.

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Anthropic releases 9 Claude connectors for creative tools, including Blender and Adobe

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Anthropic is targeting creative professionals with its latest Claude AI update. The company has released nine new Claude connectors that work with creative tools like Blender and more.

Claude AI now integrates with Blender, Affinity, Ableton, and more

“Today, with a coalition of partners including Blender, Autodesk, Adobe, Ableton, and Splice, we’re releasing a set of connectors—tools that let Claude work alongside the software creative professionals rely on, so creatives can extend their reach,” Anthropic announced in a blog post.

These are the nine new Claude connectors shared today by Anthropic:

  • Ableton grounds Claude’s answers in official product documentation for Live and Push.
  • Adobe for creativity enables users to bring images, videos, and designs to life, drawing from 50+ tools across Creative Cloud apps including Photoshop, Premiere, Express, and more.
  • Affinity by Canva automates repetitive production tasks across pro creative workflows – such as batch image adjustments, layer renaming, and file export – and generates custom features directly in the app.
  • Autodesk Fusion allows designers and engineers with a Fusion subscription to create and modify 3D models through conversations with Claude.
  • Blender offers a natural-language interface to its Python API, allowing users to explore and understand complex setups and making it easier to access Blender’s documentation.
  • Resolume Arena and Resolume Wire let VJs and live visual artists control Arena, Avenue, and Wire in real time through natural language for live performance and AV production.
  • SketchUp turns a conversation with Claude into a starting point for 3D modelling—describe a room, a piece of furniture, or a site concept, then open it in SketchUp to refine.
  • Splice gives music producers the ability to search its catalogue of royalty-free samples from within Claude.

Adobe documents the Adobe for creativity connector in greater detail here. Functionality explained includes these features:

  • Retouch portrait images.
  • Design polished assets to share across your social channels.
  • Resize and repurpose videos for any social platform.

Autodesk also details how Fusion works with Claude now here:

  • Autodesk Assistant brings AI directly into Fusion, helping users understand context and take action in their workflows
  • Fusion Model Context Protocols (MCPs) lets third-party AI systems connect to Fusion, enabling them to access design context and perform actions securely.

Anthropic is now a Blender Development Fund patron

Anthropic goes deeper on Claude’s new integration with Blender:

The Blender developers have created an MCP connector, which is now officially available for Claude. For example, 3D artists can use the Blender connector to analyze and debug entire Blender scenes, or build custom scripts to batch-apply changes to objects in a scene. And using Blender’s Python API, the connector lets Claude add new tools directly to Blender’s interface.

The company also says it is now a Blender Development Fund patron, supporting the free, open-source 3D creation suite. Anthropic also notes that since Blender is using MCP, other large language models can connect to Blender now as well.

Today’s release follows the recent arrival of Claude Opus 4.7, Anthropic’s latest model for advanced software engineering. Claude also recently gained a routines feature as part of the redesigned Claude Code experience.

In addition to today’s creative tool connectors, Claude also added connectors for Spotify and a lot more services last week.

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