Workers who learn to use AI effectively may gain a significant advantage as workplaces become increasingly AI-focused, according to new research. Generative AI is rapidly changing the way people work, raising concerns about job security and whether machines could eventually replace human employees. However, new research from the University of Vaasa in Finland suggests that [...]
A new study challenges the widely accepted idea that word meanings are organized around emotion. After analyzing billions of words, scientists found that language may be shaped by something more basic: the need for safety. Researchers at the University of Vermont have found a new way to understand language, challenging a major assumption in psychology, [...]
Meta has raised the possibility that it could be joining the likes of Amazon, Microsoft and Google in offering cloud services at some point in the future — although potential customers shouldn’t be adding the company to their suppliers list just yet.
When asked about plans for offering such services at the company’s annual shareholders meeting, Meta CEO Mark Zuckerberg said there was a possibility of the company competing with the major hyperscalers. “It’s definitely on the table.”
He explained that different companies were approaching Meta asking for the company to offer an API service or to buy compute services at a premium price. “We haven’t done it yet, because we think we have a use for the compute, but when we feel we have overbuilt, then that is an option that we have.”
Meta has been active in developing its data centers over the past few years, so there will be a possibility of some excess capacity. It is also developing its own AI chips.
For the moment, though, the company may well need all the capacity it can build: Zuckerberg said that the launch of Muse Spark, a new AI model from Meta Superintelligence Lab, had resulted in large increases in Meta’s AI usage.
This article first appeared on Network World.
A research project examining AI-driven recruitment hires across the US has revealed a systemic racial bias.
Researchers from Stanford University found a startling pattern of racial disparities when looking at the interview offers resulting from 4 million job applications submitted to 156 employers. The situation is aggravated by the “monoculture” in AI hiring software: More than 90% of US employers are screening job applicants with software, with 60% of Fortune 500 companies using the same tool, HireVue, the researchers found.
Applicants who applied to multiple companies using AI had all their applications rejected more often than would be expected if each company’s screening methods were independent. They calculated that Black and Asian candidates were rejected in greater numbers than baseline figures would suggest. According to the survey, 29,000 more Asians would have been interviewed if AI had not been deployed.
The researchers are concerned about the way in which AI is being used. “AI screening tools bring together three properties that should not co-exist in high-stakes decision-making: They are pervasively adopted, highly consequential, and opaque to the public,” they said in a news release presenting their work.
The effect of this will lead to workplaces dominated by a monoculture which may not be beneficial for companies going forward.
This article first appeared on CIO.
I will sit right down (waiting for the gift of sound and vision)
And I will sing (waiting for the gift of sound and vision)
— David Bowie
Apple is planning to sponsor and present 14 AI research papers at the annual IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) in Denver next week, just days before it introduces major new AI features at its Worldwide Developer Conference (WWDC).
The fresh research explores topics such as using LLMs in image generation, quality testing, and user interface prototyping. For months, supply chain rumors have hinted at a radical evolution for the ubiquitous AirPods in the form of built-in ambient cameras. With this in mind, it’s noteworthy that one of the research papers, “From Where Things Are to What They’re For: Benchmarking Spatial–Functional Intelligence for Multimodal LLMs,” specifically seems to cater for such use cases.
In application, this tech promises profound potential for accessibility. It suggests that someone with limited vision might be able to get their AirPods to guide them through an unfamiliar room. This is something that should fit well inside the company’s ongoing narrative around machine vision intelligence and accessibility.
Accessibility is central to a second presentation to be made during the Generative AI for Sign Language Workshop at the conference. Led by Apple’s Colin Lea, who presented a session on speech tech for people with speech disabilities at a similar event, this focus on machine vision intelligence and accessibility is entirely deliberate.
Indeed, even though the industry and critics condemn Apple for lagging behind others in the AI space, the publication of these 14 papers at a key industry session just before WWDC shows the company has been doing a great deal of foundational work behind the scenes. We expect this work to bear its first fruit at WWDC, and it is important to understand the disclosures as a power move. Apple is using the show to celebrate its strengths in AI development, and given its decade work on Apple Car, many of those strengths relate to machine vision intelligence.
Apple is so advanced in the field it is already deploying advanced models that empower consumers. Just last week, it promised to introduce a new tool called Image Explorer in VoiceOver to help partially sighted customers later this year. Among many other features, this will arrive alongside a system to let disabled users control compatible wheelchairs with spoken word commands.
Apple is pushing boundaries all the way. Its paper “VSAS-Bench: Real-Time Evaluation of Visual Streaming Assistant Models,” proves it is actively refining models to process live video instantly on consumer hardware.
The difference between Apple and its competitors is deep and philosophical. I’d argue that while others build cloud-dependent chatbots, Apple is embedding AI tools that solve real human problems in its systems.
This extends to its plans at WWDC, where it will introduce a raft of AI tools made with help from Google Gemini and a host of AI services it has developed in house. The latter will include a great many accessibility tools of the type it will discuss at the CVPR event, the beauty of which being that they will run privately and on-device. You could argue that while other tech giants are using AI to automate white-collar jobs or build a surveillance dystopia, Apple is searching for applications of machine intelligence that solve real human problems.
The company seems pretty realistic about the ongoing AI transformation. It recognizes that its own ecosystem must become a peer player in the emerging AI-augmented environment the tech industry seems intent on building.
With that in mind, Apple is willing to engage in strategic, mutually beneficial partnerships, such as permitting Siri to use third-party AI services to handle requests. But even as it does that, it is also focusing on those areas in which it can make a unique difference, such as the accessibility features Apple as a platform has always provided.
As the Vision Pro demonstrated, and as these mythical video-enabled AirPods will in the future suggest, computers are steadily getting smarter. So, the way we use them is also changing as we move away from the rigid boundaries of keyboards, mice, and touchscreens. Apple’s quest for ambient computing began long before the sudden gold rush for generative AI chatbots.
In the end, as the latter services become commodified, the way humans interact with them will define the next generation of hardware. That’s exciting for Apple, given that product design is where it excels. The era of sound and vision may finally have arrived.
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It’s hard for people to tell the difference between AI-generated advertising and writing. So why do they respond better to the human-made stuff?
Ipsos, along with faculty members from Syracuse University’s S.I. Newhouse School of Public Communications, just published a unique advertising study. They took 20 real ads from major brands, including Cheerios, Chewy, Febreze, Fiat, H&M, Old Navy, Herbal Essences, Ray-Ban Meta, TurboTax and Visa. They fed the same creative briefs used by the human ad creatives into Google Gemini, then used OpenAI’s Sora to generate fully AI-produced counterparts with no human intervention.
They showed the ads to 3,000 consumers. Only 25% of AI ad viewers were at least somewhat confident the spot was AI-made, and 40% of all viewers were uncertain either way — suggesting the public isn’t great at spotting ads that are AI generated.
But here’s the interesting part: While most people didn’t register that ads were AI-generated, they also didn’t respond to them like they did with human-generated ads. They consistently rated human-made work as more eye-catching and more imaginative.
In other words, people assumed AI ads were made by people, but didn’t particularly like them compared to human-generated ads. And that means human-generated ads performed much better.
Ads made by people without AI were 14% stronger on short-term sales impact and 17% stronger on long-term brand health.
To me, the data here suggests that while people can’t easily discern the difference between AI- and human-generated content, the AI stuff hits wrong on a subconscious level. And I think that’s happening with AI social posts, AI blog posts and AI slop in general.
In fact, I’ve noticed it strongly in my own response to AI-generated content. It often looks perfect but bothers me for reasons that aren’t immediately obvious.
The researchers explained AI’s inability to match human ad creativity by pointing out that AI draws from what already exists, while great advertising breaks new ground. AI can replicate the conventions of advertising, but it can’t transcend them, make a creative leap or engender emotion like people can.
A broad range of research beyond the Ipsos study suggests that skillful people working with AI tools will always outperform AI alone, and often outperform people not using AI tools. Ipsos’ advice? Ad agencies should keep people at the center of brand storytelling and emotive assets.
Another recent study looked at written web content and compared how human-written articles “performed” on search engines compared to AI-generated content. Semrush analyzed 42,000 blog pages across 20,000 keywords, ran every single one through GPTZero’s AI detector, and cross-referenced the results with actual Google Search results. It also surveyed 224 search-engine optimization (SEO) professionals about their AI habits and beliefs.
They found a disconcerting disconnect between what SEO people believe and what is actually true. Some 72% of SEO professionals who use AI content say it performs just as well or better than human-written content in search rankings. But it turns out that human-generated posts strongly outperform AI-generated.
Content classified as purely AI-generated appeared in the top spot in search result just 9% of the time. Content classified as human-written was there 80% of the time.” That’s a roughly 8-to-1 advantage. (Note that the coveted top link in search results typically gets around one-third of the clicks.)
For lower page-one positions — from the fifth position down (which get relatively few clicks) — AI- and human-generated posts perform more similarly. (The researchers also found that when people write posts with a little help from AI, their posts rank better much than AI-only content.)
Those Semrush results are consistent with previous research.
Note that engagement performance varied by industry; that 61% result is an aggregate average across all industries. Ironically, in the category of “Leadership & Inspiration,” AI posts outperformed human posts by 75%.
The absurd lesson here: If you want to be a thought leader on LinkedIn, don’t lead with your own thoughts.
What all this research boils down to is that human-generated content (with or without help from AI) attracts far more traffic and higher engagement than AI-generated content. AI content is essentially invisible in high-value channels and while it might be high in quantity, it’s low in quality where it really matters — with reach and influence.
As with the ad creative study by Ipsos, the conclusion of all this research is the same: People (and search engines) respond much better to creative content produced by people compared with AI-generated content.
In short, AI is great at “flooding the zone” at high speed and low cost — and there’s a ton of AI-generated content out there. A quick check reveals that:
The specific numbers are my best estimates, and they’re changing fast each month. The takeaway is that AI-generated content is exploding in volume.
But it isn’t reaching people the way human-generated content does. Take podcasts, for example. While roughly 40% of new podcast episode uploads are AI-generated, that 40% captures less than 1% of the listening hours. Of the top 100 podcasts, zero are AI-generated.
A clear picture is emerging about the use of AI for content generation. AI is great for churning out a lot of content at low cost. It can be good for some kinds of content — if a skillful person directs it. And AI can be a helpful tool for content creators.
But when it comes to direct comparisons between people and AI, it’s clear that the winning content — the stuff with the best “performance” on search, best reception by people and the most engaging — is always human-generated.
Google DeepMind CEO Demis Hassabis believes progress toward artificial general intelligence (AGI) is moving faster than expected and that society now has only a few years to prepare. He believes AGI could arrive around 2030, though acknowledges it could be here in 2029 — or even sooner.
In an interview with Axios, Hassabis said that today’s AI agents — systems capable of performing tasks independently — should be viewed as a sort of “practice run” for significantly more powerful AI in the future. He also warned that governments, economists, and society at large are not taking this development seriously enough.
One particular risk he highlighted is that AI systems in the future might begin to improve their own development. “All the leading labs are pretty focused on that,” Hassabis told Axios. “It will yield clear benefits in the form of faster research. But there are also risks associated with that type of system.”
T
All of the big AI models violate EU rules on AI and data protection to varying degrees, according to the nonprofit research foundation Aithos.
Aithos tested the models using its own tool, LARA (Legal Assessment for Real-world Agents), which simulates real-world situations where AI assistants may find themselves in legally questionable situations, according to The Register. The tests measure compliance with the GDPR and the EU’s AI Regulation, among other things and found the models collected user data without proper consent, attempted to manipulate vulnerable individuals, or created psychological profiles of users.
According to the results, all major language models failed to meet EU legal requirements; some violated the rules in up to 93% of cases. The best result was achieved by the Anthropic model Claude Opus 4.7, which was in compliance about 54% of the time.
Aithos warned that responsibility for the shortcomings does not lie solely with AI companies. Companies that build their own AI agents on top of these models could also be held legally liable.
“The future of AI should be accessible, available, and open to people and builders everywhere, and it should not require an absurd amount of resources only available to a handful of cloud providers,” Paolo Ardoino, CEO, Tether.
About 700 million people use generative AIs like Gemini and ChatGPT weekly, but adoption is far from uniform. McKinsey’s 2025 State of AI survey found that nearly half of respondents from companies with more than $5 billion in revenue have reached the AI scaling phase, compared with just 29 percent of those from companies with less than $100 million in revenue, a gap that only widens further down the chain, locking out smaller businesses, developers, and everyday users.
Retail and small businesses are limited to basic AI utilities that their facilities can power, such as text-based inference and multimedia generation, using base models. That is billions of end users, and developers locked out of full utilization and development of intelligent software due to high infrastructure demands.
Tether’s edge-first LoRA fine-tuning framework for Microsoft’s Bitnet LLM is an important step towards developing an infrastructure system that supports billions of AI agents and intelligent machines. By reducing the computational overhead of machine learning and enabling consumer-grade devices to perform advanced operations, Tether’s edge-first approach ensures greater leverage for the larger population.
Imagine a 13-billion-parameter model being fine-tuned on everyday handheld devices like Samsung S25 and iPhone 16, as well as on regular personal computers. The breakthrough combines resource-efficiency and platform-agnostic techniques to develop a fine-tuning framework for the ternary-quantized LLM.
Bitnet LLM was born out of the vision of an intelligent AI model that doesn’t consume outrageous computing resources even at full precision. Earlier attempts at resource-efficient AI relied on trade-offs, such as running small-parameter models at higher precision or larger-parameter models at lower precision, but neither approach fully solved the problem.
Bitnet takes a more fundamental approach. The result is a model that achieves linear efficiency while consuming only a fraction of the computing resources traditionally required.
The challenge, however, is that contemporary GPUs are optimized for the very floating-point operations Bitnet eliminates, creating a hardware compatibility gap. Compounding this, Bitnet was originally confined to its own Bitnet.cpp inference engine, limiting its broader utility. Tether’s breakthrough addresses both constraints at once by integrating a Vulkan and Metal GPU backend that unlocks true cross-platform capabilities for BitNet inference and LoRA fine-tuning on heterogeneous consumer GPUs, including mobile GPUs. Bitnet can now run on more mature, widely supported inference engines without sacrificing its efficiency advantages.
Vulkan’s cross-platform nature is key here. Unlike CUDA, which ties developers to NVIDIA hardware, Vulkan runs across a broad range of GPUs and operating systems, opening Bitnet to genuinely multi-platform deployment. Tether’s Bitnet fine-tuning framework implements a dynamic tiling technique to mitigate limitations in Vulkan driver buffer allocation on mobile GPUs.
The dynamic tiling algorithm technique was first applied in the fine-tuning framework for QVAC Fabric LLM, the AI model that powers Tether’s QVAC Workbench application.
This implementation demonstrates the efficiency of this approach: fine-tuning a 13-billion-parameter model across a range of consumer devices with varying GPU configurations.
The Bitnet LLM Fine-tuning framework is Tether’s latest achievement and part of a broader expansion into open-source AI and communication technologies that challenge current, slow, fragile, and controlled systems. These developments are open-sourced and packaged as modules in the QVAC SDK for easy deployment and to help developers build edge-first AI applications without needing anyone’s permission.
Tether envisions superintelligence as a foundational element possessed by its owner and is enforcing this through:
Synonymous with decentralized AI, “Local-first” AI aims to create sovereign AI solutions that do not rely on centralized infrastructure, such as data centers, to operate. They are considered cost-effective, relatively more sustainable, and unarguably more private than centralized AI. Tether is building AI applications that rely entirely on the device’s resources. These applications store data in device memory and use its processors for advanced operations, such as fine-tuning and inference.
Tether’s AI applications are built on the Pear runtime. Pear is a tooling platform for fully P2P applications that can operate without servers. Pear leverages the Holepunch tech stack. Holepunch is purpose-built for stable, direct communication between devices. Pear enables delegated inference for AI applications such as QVAC Workbench. Delegated inference enables a unified, dynamic workstation architecture where compute tasks are fluidly distributed between mobile and desktop environments, allowing either device to offload high-intensity processing to the most capable system. That is, you can start a task on your mobile device and delegate it to your desktop or laptop for completion.
The only way to scale intelligence to the needs of a ten-billion-strong society is to push it to the edge. This, in turn, depends on the progress made by experiments aimed at cost-effectively localizing AI computation.
Billions of AI agents and countless AI applications deployed by developers in every region of the world, running effectively on user-owned resources, is the only way we can democratize superintelligence and avoid creating another ‘luxury’ cutting-edge technology controlled by unicorns and fully accessible only to elites.
Tether is pioneering limitless superintelligence for an ever-growing society and applications. Follow the journey to truly local and edge-first AI solutions
For years, software developers on H-1B visas benefited from steady demand among US technology employers. That market is becoming more selective as companies redirect spending toward AI and rely more heavily on coding assistants.
Recent layoffs at companies including Meta and Amazon have added to the uncertainty, with engineering and software roles affected even as major technology companies continue to deepen investments in AI.
Developers and analysts say traditional engineering roles are becoming harder to land, recruiters are asking more often for AI-related experience, and workers are being pushed to keep pace with tools such as GitHub Copilot, Claude, and ChatGPT.
The shift is being driven by both AI investment and broader economic uncertainty, according to Pareekh Jain, CEO of Pareekh Consulting. Companies are changing the profile of the developers they want, hiring fewer people in some areas while paying more for AI talent.
“AI investments are changing company hiring strategy,” Jain said. “They require a different profile, fewer numbers, and also across geographies.”
This shift is colliding with a tougher sponsorship environment for H-1B developers.
Jain said companies are more selective about hiring visa-dependent workers than they were two or three years ago, especially when permanent residents and US citizens are more available in the market.
“Companies are not looking for H-1B now,” Jain said. “They are building a local workforce and preferring green card holders and citizens.”
Employers may now be more likely to consider H-1B candidates only when they have immediate project needs, rather than building a longer-term bench of visa-dependent workers.
Concerns are visible in public forums used by technology workers. In one January post on Blind, an anonymous senior software engineer with seven years of experience said she had been laid off while on an H-1B visa and was “not interview-ready,” highlighting how quickly job loss can become a visa problem for H-1B workers in the US.
The combination of AI tools and tighter hiring is hitting early-career developers hardest, said Adarsh ML, a product engineer at Ather Energy who tracks global engineering hiring trends.
“Companies are increasingly looking for specialized engineers with machine learning and data science skills,” Adarsh said. “Job opportunities for people with zero to three or four years of experience are not really there anymore.”
The shift is also changing team structures, Adarsh said. Earlier, one manager may have had two or three interns and several freshers reporting to them. Now, many of those roles are being replaced by AI agents.
“Companies now want people who understand software well enough to catch the mistakes these AI agents make,” Adarsh said.
That creates a longer-term risk for the software talent pipeline.
“If companies only want people with five years of experience to manage AI agents today, who will have that experience five years from now?” he said. “There may not be enough experienced developers left.”
The impact is not the same for every role. Sophia James, an Indian software professional based in the US who works in database monitoring, said AI has not significantly changed her team’s daily workflow. But AI literacy is becoming a management expectation.
“Managers are trying to understand whether we are keeping up with the changes happening in the market,” James said. “Recently graduated students, whether BS or MS, are finding it difficult to get jobs. But people who already have jobs, like us, are not facing that much of an issue in terms of projects continuing.”
Jain also stressed that AI literacy is now becoming a baseline expectation for software developers, even outside AI-focused roles.
“Being AI-literate is a must now, even if the role is not directly in AI development,” he said. “This is like knowing Excel even if you are not from finance in the earlier era.”
Jain said AI coding tools are likely to reduce the number of developers companies need for similar tasks, making the technology deflationary for some software work.
But Jain added the impact may not be entirely negative. Enterprises will need to invest in data, cloud, and modernization to become AI-ready, creating new work. AI could also encourage companies to build more applications internally instead of buying from SaaS providers, potentially creating opportunities for IT services firms.
The effect is already visible in hiring decisions. Nikhil Dhiman, head of engineering at CarInfo, said AI is changing the economics of early-stage software development, particularly when companies are building proofs of concept or testing new ideas.
“Some companies are very cautious now,” he said. “They want to leverage AI more and hire less. They just want to see the impact first.”
Familiarity with tools such as ChatGPT and GitHub Copilot is now a baseline requirement for developers, said Sanchit Vir Gogia, chief analyst at Greyhound Research.
Developers need deeper expertise in areas such as cloud infrastructure and data engineering, as well as security and AI governance, he said. Those skills are closer to the systems enterprises need to validate and scale, rather than the routine coding work AI tools are starting to compress.
“The engineer who only produces output grows easier to replace as the output grows easier to generate,” Gogia said. “The engineer who can validate it, secure it, situate it in a real business, and stand behind the result becomes harder to replace.” For H-1B developers, he said, adaptation also requires visa planning. Developers should understand portability rules and employer sponsorship timelines before a job loss forces urgent decisions.
“A high-skilled worker has up to 60 days after a role ends, and the right to begin new employment the moment a valid portability petition is filed,” Gogia added. “The strategic error is treating that window as a safety net rather than a planning horizon.”
The article originally appeared on InfoWorld.
The rise of generative AI (genAI) technology has prompted a growing debate about the future of software-as-a-service (SaaS) business models.
Some of the fears are overblown: enterprises are unlikely to vibe-code their own applications to replace their SaaS suppliers anytime soon, while software vendors have yet to see per-seat sales fall off due to mass automation of white-collar jobs. (In fact, some now predict the opposite will happen.)
At the same time, AI has the potential to change the way work is carried out, with AI agents empowered to interact with software applications on behalf of users. For software vendors, that could mean a future where applications are accessed less through traditional user interfaces as AI agents connect via APIs.
It’s an inevitable shift, says Box CEO Aaron Levie, and one that requires software vendors to adapt their existing products and business models to prepare for agent workflows.
Computerworld recently spoke with Levie about how Box — and other SaaS vendors — can adapt as agentic AI threatens to upend existing business models. (This interview has been edited for clarity.)
Discussion about a “SaaS-pocalypse” has died down recently, and software stocks have rebounded. At the same time, it seems clear the adoption of AI agents could change how workers interact with software. How can companies like Box adapt to this new environment? If AI increasingly becomes the interface users interact with, where does the long-term value lie? “People are realizing that you’re not going to rebuild a lot of the systems that people were kind of claiming you would [with vibe-coding]; it just doesn’t make sense. So, that part is sort of dissipating. However, headless software and the ability to use your systems via AI is obviously going to happen, there’s no question.
“So, I think the conversation is shifting from ‘AI disrupts software’ to ‘AI is going to be the biggest consumer and user of software going forward.’ And for that, the main thing is: can you have a business model that allows you to actually monetize the consumption of those agents using your underlying tools? We’re fortunately built for that; we’ve had an API business model basically forever, so we’re well prepared.
“There’ll be some companies that have to pivot a little bit more significantly over time — there’s no question that will happen in a bunch of organizations. We’re big believers that AI will be the biggest user and interface for the future of software.”
How important is it for Box to retain that interaction with human workers, rather than becoming more of the underlying layer AI agents interact with? “I would say that we’re totally comfortable with that shift. When you have AI agents, you still need a place to be able to secure the data — you need to protect it, you need to govern it, you need to make sure you know who’s accessing it. None of that changes in the world of AI. In fact, if anything, it actually increases.
“We don’t really care if it’s an agent using the data, an application using the data, a person using the data — we want to be the best content management system that connects your information to all of those applications.”
How does that perspective feed into your product development and roadmap “It basically means that we need to be a headless platform. That means customers need to be able to access their data via MCP inside of ChatGPT, inside of Claude, inside of all these systems. It means that we care as much about our APIs and access to those APIs as we now do our user experience. We have to make sure that both of those environments are as simple and clean as possible, and as usable as possible.
“It’s basically as if there’s another constituent now in our ecosystem that we have to go and pay attention to.
“We need to be the best place to manage your content, and then wherever you want to work with it from, we’re totally fine. So, if you want to work with your files from your desktop, from Claude Cowork, from ChatGPT Codex — we just want to make sure we are universally accessible across every single place that people want to work with their data.”
Could that mean changes around how you price access to your software? Do you expect a shift to usage-based pricing? “Not as much as is probably being talked about online, because seats still make sense for the employee and the end user. Even when an agent is doing work on your data, it’s still you invoking that agent. It sort of makes sense that the seat is still attached to the underlying end user employee, even though an agent is going to be doing work on your data.
“We think the seat model will be quite durable over time. What this does is just add another business model, where you have agent-only interactions; those will be primarily coming through the API, and then that will be a consumption model.”
What are your thoughts on outcome-based pricing? Is that something you look at? “We do one thing that’s close to that — we have the Box Agent that does things like data extraction. It extracts your data and we charge based on the number of pages that you want to extract data from. So there are some things that approximate outcomes, but not at the level of resolving a customer service ticket or something like that, that maybe has been talked about. We’re probably going to be more aligned to…the amount of compute that that is used.”
What are your conversations with customers around moving to a usage-based model? A lot of organizations are used to fixed monthly subscriptions — can metered AI agents become problematic? “I think it definitely can be. This is sort of a common tension in general.… We saw this with cloud computing, for instance. The difference with cloud computing is that cloud was relatively centralized, versus the use of AI and tokens are much more diffuse. That’s a big difference that companies have to think about.
“There’s always this tension: you can pre-buy and have a subscription, but then you might be overpaying for periods where you’re not using it as much. Or you can only pay for what you use, in which case you might have some volatility in the pricing of what happens.”
How are customers progressing in adopting AI agents — particularly, the move from pilot projects to production. What are some of the biggest barriers to wider deployment of agents? “We’re very much moving from coding agents to the rest of knowledge work: this is the jump that’s starting to occur. In that, one of the big questions and challenges is how companies get agents the right context and information to work with — how do they enable agents with the right level of constraints in their organization from a security and compliance standpoint? This is our kind of reason to exist, and what we’re helping our customers on.
“Overall, it’s just a transformational moment in the enterprise. Every customer that I talk to, every dinner that we have with customers, every CIO meeting I’m in, every CEO meeting I’m in, it’s all about agents.
“Agents have thrown the whole world into this kind of dynamic period of, ‘What does the shape of your organization look like? What’s the future of a manager versus an individual contributor? What are the workflows that you can go and execute on?’ There are so many different ways that this is starting to change.”
You were part of another major industry transition with the adoption of cloud computing. Are there similarities you see or major differences that customers can learn from? “The big difference between [them] is that, with cloud, you could centralize the deployment of and management of.Cloud really only affected 3% of your organization that was moving from the data center to the cloud, and then every employee got better products and experience as a result of that. The change was really kind of fairly concentrated. AI affects every single employee in the company. It’s a radically different type of transformation of what work looks like.
“There are only so many analogies you can make to cloud before quickly you realize, no, this is actually a different transformation. Maybe it’s even closer to the PC, in the sense of every single worker has to change what they’re doing to be productive. It’s not a technology delivery shift, it’s a fundamental reworking of every workflow in the enterprise. And so that’s I think what most companies are going through right now.”
Over the next three to five years, both governments and the private sector will need to rapidly adapt identification and mitigation protocols as adversaries move from AI-assisted to AI-enabled sanctions evasion and proliferation financing (PF), a new research paper warns.
The report, Algorithms of Evasion: The Rise of AI-Enabled Proliferation Financing, from the Royal United Services Institute (RUSI), a UK-based defense and security think tank, defines PF as the use of funds or financial services to acquire, develop or otherwise deal in weapons of mass destruction (WMD). It states, “North Korea and Iran are now developing and deploying AI models to aid with sanctions evasion activities.”
Key findings include the fact that AI is now capable of mass producing high-quality fraudulent documents, as well as automating what the report describes as “the administrative minutia of managing extensive shell company networks.” AI powered systems, it states, can also “analyze blockchain patterns in real time to dynamically adjust cryptocurrency mixing strategies, effectively evading detection tools.”
In addition, it says, “[tools such as generative AI] which can produce sophisticated fraudulent identification documents, for example, have helped North Korea perpetrate phishing attacks against Western companies.”
Dr. Aaron Arnold, senior associate fellow with the Centre for Finance and Security at RUSI, who authored the paper, said in an email that what prompted it was an uptick over the last year in North Korea’s use of AI to facilitate and enhance its cyber operations, in the form of phishing schemes designed to generate revenue for the country’s ballistic missile and nuclear weapons programs.
He advised enterprise IT managers who need to protect their organizations from becoming victims of sanction evasion activities that “[it] means largely adapting to a landscape where traditional human-focused security boundaries are being bypassed by automated technologies.”
For IT managers, said Arnold, “this might entail incorporating defensive AI, the use of behavior-based analytics, using ‘circuit breakers’ when there is heavy use of API or MCPs, updating personnel training, and hardening identity verification, especially for any remote hiring.”
Sanchit Vir Gogia, chief analyst at Greyhound Research, said that the RUSI report matters “because it names the right structural shift. AI is not creating sanctions evasion from thin air, it is compressing and scaling methods that already work.”
He pointed out that none of the sanction-evading techniques such as fraudulent documents, synthetic identities, shell companies, hidden beneficial ownership, crypto laundering, and others are new. “What changes is the speed, quality, volume and coordination with which these methods can now be assembled,” he said.
According to Gogia, “the distinction between AI-assisted and AI-enabled activity is central. AI-assisted evasion uses AI for discrete tasks: writing a better email, producing a cleaner document, generating a stronger false profile, translating a pitch, summarizing regulations or preparing a plausible job application. AI-enabled evasion is more serious.”
This tactic, he said, “begins to coordinate the system itself. It links identity, documents, ownership structures, payment routes, cloud access, crypto wallets, API calls and timing. The difference is not whether AI helps someone fake a document. The difference is whether AI begins to orchestrate the deception.”
That is why the report’s findings should worry enterprise leaders, he noted: “Many organizations still assume the bad actor is mostly human, mostly linear and mostly slow. That assumption is expiring. AI lets adversaries run more attempts, with fewer errors, across more channels, in more languages, with better paperwork and greater patience than most enterprise review processes can absorb. This is not a tale of genius criminals discovering magic. It is the story of ordinary controls meeting industrialized plausibility.”
The evidence today, he pointed out, is strongest around tactics such as identity fraud, document fraud, synthetic personas, remote-worker deception, phishing, social engineering, crypto obfuscation and workflow abuse. “Fully autonomous evasion networks sit on the horizon,” he said. “They are serious, but they are not yet the everyday baseline.”
This distinction matters, said Gogia: “If enterprises obsess over cinematic autonomous agent scenarios while leaving remote hiring, vendor onboarding, payment approvals, and document review full of holes, they will lose in the most prosaic way imaginable.”
The report, he said, also gets the “asymmetry” right. “Offensive actors can learn across the ecosystem,” he said. “They can scrape open information, reuse leaked records, study enforcement patterns, test onboarding forms, inspect public procurement data, watch court filings, probe compliance thresholds and [use the information to] refine their behavior.”
Defenders, by contrast, are hemmed in by privacy rules, fragmented data, explainability requirements, jurisdictional boundaries, conservative operating models and siloed technology estates. “Offensive AI learns broadly,” he said. “Defensive AI often learns from fragments. That is the structural asymmetry.”
He explained that the regulatory landscape also amplifies the problem, in that regulatory bodies “still speak in separate dialects. [For example] the EU AI Act pushes organizations toward stronger obligations for high-risk AI. NIST-style frameworks push risk management, transparency, and governance.”
Financial Action Task Force (FATF) expectations push national risk assessment and counter-proliferation controls, he noted, while banking regulators focus on model risk, accountability and operational resilience. “None of these streams is irrelevant. The trouble is that criminals do not organize themselves around regulatory workstreams. They organize around outcomes.”
What that means, said Gogia, “is that enterprise cannot wait for a clean global rulebook. It will not arrive in time. CIOs, CISOs, compliance officers and boards need a working governance model now. They need privacy-preserving analytics, controlled data environments, audit trails, legal safeguards and clear model-risk accountability.”
He said that enterprise IT managers should treat the situation as a trust architecture problem rather than a narrow sanctions-screening problem. “The uncomfortable truth is that AI is not simply helping bad actors write better phishing emails or forge tidier documents,” he noted. “It is helping them manufacture legitimacy across a chain of enterprise workflows.”
Report author Arnold also noted that there are signs that cyber criminals have discovered new AI technologies and abilities that legitimate enterprises could adopt for legitimate applications.
History, he said, “is replete with [criminals] developing novel solutions to tough problems, [which are] later adopted by law enforcement. Much of our anti-financial crime policy is effectively a response to bad actors exploiting systems or using technology in novel ways to perpetrate crimes. In this scenario, I think an ‘AI arms race’ between enforcement authorities and bad actors is the most likely outcome.”
Gogia added, “the baddies are not teaching enterprises how to invent AI. They are teaching enterprises where trust is leaking. That is the lesson worth taking seriously.”
This article originally appeared on CIO.com.
Tech companies seem to be falling over each other these days in firing people to either replace them with AI or to pay to build AI infrastructure. Wouldn’t it be nice if they at least waited until AI actually worked for business?
On the one hand, top tech businesses such as Amazon, Block, Cisco, Cloudflare, and Meta have all announced that they’re slashing payrolls — either because AI can do the same work as people or they need the cash to build out their AI infrastructure. Isn’t that great? All together, of the 37,638 tech job cuts so far this year, 47.9% — almost half — can be tracked back to AI.
On the other hand, despite all the AI hype and hysteria, no one has yet proven that AI is, generally speaking, really all that helpful for businesses. Oh, I know, I know. You did great things with OpenClaw vibe programming. Microsoft’s CEO, Satya Nadella, claims 20% to 30% of the company’s code was written by AI. And Nvidia assures us that 88% of its surveyed customers report AI has increased their revenues.
But really, what else would they say? “Dear Board, we just blew half a billion bucks on Nvidia GPUs, and we’re losing money hand over fist?” I don’t think so.
The truth is, as an IDC study reports, a mind-boggling 88% of proof-of-concept AI projects never reach production. Lest we forget, MIT’s The GenAI Divide: State of AI in Business 2025 study found that 95% of AI projects fail to deliver measurable P&L impact.
Now, I have to acknowledge that AI is finally becoming truly helpful in business. As a guy who knows a thing or two about programming, Linus Torvalds, creator of Linux and Git, said at Open Source Summit North America, “I’m personally 100% convinced that AI is changing programming.” He estimates that “AI will increase your productivity by a factor of 10.”
But is that reason enough to slash make workforce cuts of between 10% to 40%? (Short answer: No. Longer answer: Noooo!)
It’s not just the mass firings. Workers who are either awaiting the axe, or have escaped it for the moment, are miserable. As one Meta employee told The San Francisco Standard, “I tend to cry in the shower,” and, “A lot of my feelings about my job are about the general chaos and not just the layoffs. ”
So, explain this to me: When everyone knows AI-driven layoffs are coming, exactly how well do you expect them to work? You really think they can give their best?
Making matters worse, it’s an open secret that IBM, Google, and Meta are having their employees train their AI replacements. As a popular meme puts it, workers are now “building your own coffin.” Is it any wonder that a lot of people — 29% of all employees and 44% among Gen Z workers — are deliberately sabotaging work when the boss insists they train their AI replacements?
It also sure doesn’t help office morale when the CEO keeps saying AI will replace half of all employees. A particularly egregious example of this was when Standard Chartered CEO Bill Winters proclaimed his bank would slash thousands of jobs and replace “lower-value human capital” with AI.
He’s since backed off the claim, but come on — we all know he meant it. Just like all the other CEOs who’ve said similar things, between FOMO and the knowledge that AI job news is sure to make the stock price jump, they’re eager to cut headcounts and boast about how successful AI will make them.
What happens a few quarters down the road? Their attitude today seems to be let tomorrow take care of tomorrow. I hate to tell them, but that really doesn’t work in the long run. (Not, mind you, that a future much farther ahead than the next quarter seems to matter much anymore to business executives.)
It should. As a recent Deloitte study stated: “Most respondents reported achieving satisfactory ROI on a typical AI use case within two to four years. This is significantly longer than the typical payback period of 7seven to 12 months expected for technology investments. Only 6% reported payback in under a year, and even among the most successful projects, just 13% saw returns within 12 months.”
AI, in short, is not the miracle cure for what ails businesses that its fans claim.
Will that stop businesses? I doubt it. While I appreciate that California Gov., Gavin Newsom is trying to bandage the AI job bleedout by mandating studies on subsidizing companies to keep employees rather than replace them with AI, I doubt that will do much to staunch the wound.
At the Open Source Summit North America, Linux Foundation CEO Jim Zemlin was optimistic about AI and jobs. He pointed out that, thanks to AI becoming “pretty damn good coders,” the number of open-source projects on GitHub has led to a “surge of new code and projects.”
Zemlin also believes that while few developers will write code, “engineers will still design, review, secure, and integrate that code.” (He’s referring to what’s becoming known as forward-deployed engineers.) This, in turn, will supposedly lead to tech job growth.
I’d feel a lot better about that prediction if I believed the C-suite suits at most companies were capable of truly forward-looking thinking rather than focusing entirely on hiking the stock price by making the next quarter look good through staffing cuts.
In the long run, sure, AI will make us more productive. But, we’re not there yet. For now, companies need to keep employees happy, not shove AI down their throats — and work out carefully and thoughtfully how AI will really work for business.
If ever there were a lawsuit in which a jury and judge should have ruled against both the accuser and the defendants, Elon Musk’s suit against OpenAI and Microsoft was it.
The high-profile legal battle pitted the world’s richest man against a company worth more than $3 trillion, another that might soon launch a $1 trillion IPO, and tech execs claiming to have only the good of the world in mind, not mere filthy lucre, while they develop a technology some fear could eventually destroy humankind.
The lawsuit was eventually thrown out, but only on technical grounds. Meanwhile, unregulated AI marches on, with Musk, OpenAI and Microsoft all getting richer.
The only winner in this suit was hypocrisy. Here’s why.
To understand how this unfolded, we need to go back to OpenAI’s beginnings. The company was founded by current CEO Sam Altman, Musk and others in 2015 — back when AI was a niche technology, used primarily for image and speech recognition, robotics, and experiments in self-driving cars.
The founders funded OpenAI out of their own pockets as a nonprofit company aimed at developing AI for the good of the world. Then, as the technology evolved, Altman, Musk and others grew worried it might become so powerful that, without serious guardrails, it could pose a danger to humans. They feared what might happen if AI reached the level of a super-powerful artificial general intelligence (AGI) system, superior to humans on a variety of tasks, with general problem-solving skills rather than narrowly targeted ones – and the ability to think for itself rather than heeding humans.
In an earlier version of Musk’s suit against OpenAI and Microsoft, Musk put their fears this way: “A.G.I. poses a grave threat to humanity — perhaps the greatest existential threat we have today.”
Early on, OpenAI wasn’t on many people’s radar. When Microsoft invested $1 billion in the company in 2019, few outside the tech industry took notice. Between 2021 and 2023 Microsoft invested $2 billion more, still without drawing a lot of attention.
Then in November 2022, OpenAI released ChatGPT, launching the generative AI (genAI) revolution — and all the disruption that has followed since. Eventually, as it became clear how important and valuable genAI technology would become, Microsoft’s investment ballooned to $13 billion.
OpenAI insiders were convinced several years before ChatGPT’s release that the company could become tremendously profitable. With potentially trillions of dollars at stake, in 2017 they started looking for a way to turn the nonprofit operation into a for-profit company.
It was at that point, OpenAI says, that Musk pushed to gain majority equity in the company if it went public, take control of the board, and become CEO. When the other founders balked, Musk withheld funding.
Last year, OpenAI released copies of emails he sent to it during the height of their in-fighting. In one, in February 2018, he lobbied for the creation of a for-profit arm, pointing out that, “a for-profit pivot might create a more sustainable revenue stream over time and would, with the current team, likely bring in a lot of investment.”
Musk then suggested that OpenAI “attach to Tesla as its cash cow.” When the other founders dismissed the idea, Musk threw a fit and quit the company. OpenAI went ahead and launched a for-profit arm, becoming a hybrid of a for-profit and nonprofit company in 2019.
Years later, in 2024, Musk filed suit, targeting OpenAI, Altman, OpenAI co-founder and president Greg Brockman, and Microsoft — accusing them of “stealing a charity” by creating the for-profit arm of OpenAI, and taking the $13 billion Microsoft investment. He claimed they had all illegally enriched themselves through the profit/nonprofit setup and sought $150 billion in damages. (OpenAI fired back last year with a counter suit.)
It took only two hours for the jury to rule against Musk, though the ruling didn’t address his actual claims. Rather, the suit was thrown out because it had been filed after the statute of limitations had run out.
Everyone in this case was driven by venality. Altman portrayed himself as only wanting to develop AI to help humanity — and as evidence, pointed out he has no equity in OpenAI. What he neglected to add, though, is that he has more than a $2 billion stake in companies that have deals with OpenAI, and stands to gain billions more if those deals grow after any IPO.
Microsoft, meanwhile, has used its investments in OpenAI to become a multi-trillion-dollar company. And if, as expected, OpenAI becomes a trillion-dollar company when it files its IPO later this year, Microsoft’s 27% ownership stake in the company would make it $270 million richer. That’s not a bad payoff for turning a blind eye to the way in which OpenAI performed a bait-and-switch from nonprofit to for-profit company.
As for Musk…, well, what can you say about someone who claims he wants to save humankind from the evils of AI, while at the same time lobbying for OpenAI to become a for-profit company and milking it like a cash cow?
He’s shown he’s not only the world’s wealthiest man. He’s also the world’s most hypocritical.
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