Every week, another headline celebrates how AI made some task faster. A developer wrote code 55% faster. A marketer generated a first-draft blog post in minutes. A product manager summarized a 200-page requirements document in seconds. These are real numbers.

But they are the wrong numbers to celebrate.

If you step back and ask the harder question — what actually creates enterprise value? — task speed is rarely the bottleneck. Where the value traffic gets jammed is almost always upstream, in the question that precedes the task:

It’s not “What do we want IT to build?”

It’s “Why do we want IT to build it?”

It’s not “Why do we need that blog post?”

It’s “What content will drive business impact?”

It’s not “Which process should be optimized?”

It should be “How do we address the quality escape generating rework and scrap?” or “How do we prevent supply chain delays compounding into missed delivery commitments?”

These are questions that AI still cannot answer for you. And the gap between task automation and outcome generation is the defining challenge of this agentic era.

The sneaky way AI isn’t helping

Harvard Business Review put a name to this particular AI gap earlier in 2026: the micro-productivity trap. Companies optimize individual tasks, generate the code faster, summarize the report faster, route the support ticket faster, without ever rethinking the workflows or value-creation models those tasks belong to.

The result is isolated productivity gains that never compound into business impact. Almost like a value mirage. PwC’s 2026 AI Performance study found that 74% of AI's economic value is being captured by just 20% of organizations. That’s the micro-productivity trap in effect. It’s what’s separating AI leaders from the rest.

That AI leader wheat is only going to further separate itself from the chaff of other organizations continue to “do AI” for isolated productivity gains. Gartner’s warned that over 40% of agentic AI projects will be cancelled by the end of 2027, because organizations fail to define the outcome they’re building toward in the first place.

The companies that succeed, PWC found, are focused on growth and value capture. That encompasses a lot more than just productivity. I’ve noticed that a lot of organizations have a problem with prioritization. We are asking AI to go faster in the wrong direction. It’s like thinking that it’s ok to take the wrong exit off the highway just because you’re driving a Bugatti.

What's the real unlock to AI value?

The key to true AI value is in your knowledge. It’s knowing which blog topic will move an audience before a single word is written; whether the document will deliver business value before it’s created, which process to target, which data points to connect.

These are upstream decisions. They require business-domain intelligence and the understanding of what matters, what costs the most, and what a breaking change on the production floor actually means for the P&L. Pure task automation cannot supply that. Only people who sit at the intersection of business and technology can.

The solution to wringing every bit of value from AI is not a new one. Just an evolution of one. I’m talking about fusion teams, a cross-functional unit combining the domain knowledge of operational and business experts with the architectural judgment of professional developers, moving at the speed of the problem rather than the speed of the IT backlog.

What’s core to the fusion team is that they represent problem identification and faster business-technology alignment, not just going faster. That concept needs to be carried over into AI agents—how they’re built and how they collaborate across workflows.

For agents to truly work and help you avoid those micro-productivity traps, you need to connect contextualized data to working machine learning models so that they cannot just “go faster” but also make the highest quality of decisions.

It's all in the question

Success with AI—not that value mirage that micro-productivity can create—requires domain experts (e.g., plant engineers, process managers, and supply chain leaders, business analysts, procurement specialists, claims adjusters, etc.) at the table alongside IT and data teams.

It requires people who understand which product lines carry the thinnest margins, which supplier delays have the most downstream production impact, which quality control steps are creating the longest cycle time.

In other words, the fusion mindset: the discipline of defining the outcome before designing the solution, and measuring against business value rather than task throughput.

The organizations capturing the majority of AI's economic value in 2026 are not the ones with faster, AI-driven processes. They are the ones who identified the right operational problems first, governed their data well enough to trust it, and built the organizational frameworks to coordinate agents and people against outcomes that actually move the business.

So for your next AI initiative, re-frame the question: Are you building toward a fundamentally different outcome, or are you just automating a task?

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• Pax8 finds over a third (39%) of UK SMBs have experienced unexpectedly high AI bills
• Fewer UK orgs have a formal internal AI review process than German or US orgs
• Around one in five couldn't even tell apart specific AI spend from generic tech budgets

New research by Pax8 has criticized UK businesses for building AI faster than governance can keep up, with a quarter (24%) of SMBs having discovered unauthorized AI tools operating within the business in the past six months.

Two in five (39%) have also experienced unexpectedly high AI bills, implying that the visibility across their AI consumption isn't sufficient.

But with the pressure to adopt AI at an all-time high, UK businesses could continue to be rushed into rash decisions without thinking out the fundamentals first.

UK businesses lack end-to-end AI visibility

Even though shadow AI is apparently widespread, just one in four (23%) UK businesses have a formal, documented review process for internally built AI, compared with around a third in Germany (32%) and the US (36%).

In light of that, only around half (54%) of UK SMBs are developing their own AI, putting them way behind the likes of the US (81%).

British businesses are also struggling on the cost management front, with a tiny 12% having dedicated AI budgets. A further 19% couldn't even distinguish AI spend from other tech spend, leaving just 16% of UK SMBs confident in their abilities to determine AI's ROI – around a third of the number of businesses in Sweden (45%) that feel confident in doing so.

Looking ahead, security concerns remain the most cited barrier to adoption, but Pax8 frames the current state of AI adoption as a major opportunity for software vendors and IT service providers. Rather than creating an action point for businesses, who are the consumers in this instance, the report implies that the providers have a greater responsibility to reassure their users with additional visibility, security and governance controls, and cost management options.

"These findings highlight the importance of trusted businesses that can help SMBs bring agents, workflows, data and tools together as a connected intelligence layer," EMEA CEO Harald Nuij.

AI Agents are unlike any software development witnessed before. Traditionally, trustworthy software systems have followed a structured approach to being built and developed. One that plans, builds, tests, and maintains software to ensure its reliability and scalability. This created the Software Development LifeCycle (SDLC) (1) framework.

But AI agents are different: they are a team of autonomous, probabilistic actors that work most effectively in a real-time, event-driven environment, enabling the agents to coordinate with real-time data to develop, learn, and work.

To scale an enterprise workforce of AI agents, organizations need to combine the principles of employee management with software development rigor. These agents are not static software that can be coded and developed, due to probabilistic language models. They are built, evaluated, and governed differently.

This requires organizations to move from traditional SDLC to an Agent Development LifeCycle (ADLC) (2), which utilizes the structure and repeatable process of the SDLC framework and combines it with the development and management of AI agents.

A helping hand from HR

Since AI agents perform roles within an enterprise, the ADLC should draw on lessons from the human employee lifecycle.

When an organization brings on a new team member, there is a deliberate process: their role is defined; they’re given access to the systems they need; they are trained; and they are integrated into a team. Once they are empowered and put to work, a manager oversees their activity, monitoring and developing their performance over time.

As autonomous actors operating within a system of governance, AI agents deserve and require that same treatment.

AI agents need a similar structured approach to onboard and integrate them into a business and deliver the efficiency benefits they’re designed for – that means they need real-time contextual data to do their jobs!

Treat AI agents like humans at the hiring stage

Picture an organization where every employee is a generalist, there’s no hierarchy or structure, and all communication happens via synchronous one–to–one phone calls. No email. No Slack. No shared systems.

The organization can’t scale. Information doesn’t flow. People drown in data from every direction, with no real understanding of what’s going on or what to do. There are no accountability structures, no access controls, no coordination mechanisms beyond individual conversations.

You wouldn’t drop a new human hire into this chaos. Effective organizations look different, counting on people who are specialized and organized hierarchically, so coordination happens in parallel and communication is premised on asynchronous channels that let information flow without requiring everyone to be online at the same time. The same goes for onboarding AI agents.

AI agents and employees are no different in the eye of an agent mesh

Most early agentic AI deployments work as complex monolithic agents tasked with doing everything, accessing all data, communicating synchronously, and trying to maintain very large context or working synchronously with sub-agents. The result? A system that’s brittle, expensive, inconsistent, and completely unable to handle real enterprise complexity.

This is where an agent mesh can act as a development and runtime platform, helping build AI agents that actually work for a real-time enterprise, specialized for particular functions, organized hierarchically, and orchestrated by a development and runtime platform that specializes in delegating tasks to appropriate agents.

Communications between agents are asynchronous and event–driven, and access to data and systems is governed by role-based access controls, giving agents exactly the permissions they need and nothing more.

So, let’s look at how agents can be “onboarded” through an ADLC much like a new employee joining the organization:

1. Treat agent setup like drafting a job description

Before a human employee starts, you write the job description. You define what role they’ll fill, what they’re responsible for, what behaviors you expect, and what boundaries they operate within.

An agent mesh’s agent builder can use an internal AI agent to provide a guided interface for defining an agent’s purpose, scope, and configuration. You set up the instructions and system prompts that shape the agent’s persona and behavioral parameters. And you configure guardrails – hard constraints on what the agent can and cannot do – that prevent it from going off–script or taking unsafe actions.

2. Get the agent up to speed with tool and system access

A new employee’s first weeks are spent getting access to tools, systems, and data. Agent onboarding is the same.

An agent mesh should include a number of data connectors – pre-built integrations to enterprise databases, data warehouses, data lakes, enterprise application APIs, and MCP servers. This enables agents to access real-time data from day one.

Access is provisioned through role-based access controls (RBAC), enforcing least-privilege principles so agents only see what they need to see. It can also define skills that go with internal tools so agents know best how to use them.

3. Passing the probation period – stress-test agents before deployment

After getting access to systems, human employees undergo training. This is where general ability becomes company and job-specific skills. You wouldn’t put a new hire in front of customers without first verifying they know what they are doing. The same logic applies here.

An agent mesh can provide an Eval function that uses AI to suggest tests for agents, allows you to add more and then run them against your agents. This empowers the organization to validate agent competence against defined success criteria during development. This supports both initial testing and regression tests as you make changes.

4. Keep humans in control – validate AI actions in real-time

Even the most capable employee gets close oversight when they are new to a role. Supervision isn’t micromanagement; it’s the safety net that ensures quality, builds trust, and catches errors before they compound.

An agent mesh with human-in-the-loop architecture would support routing of specific agent actions or decisions to human reviewers for approval before execution. This is important in agentic systems because LLMs are non-deterministic, they make mistakes so when the impact of agents being wrong is too risky, humans can validate their actions, responses or conclusions.

5. Teamwork makes the AI agent dream work

The most transformative phase of the employee lifecycle is when individuals become part of high-performing teams, where collective capability exceeds the sum of its parts. For AI agents, this is where things get really interesting.

An agent mesh would support workflows that define repeatable processes, such as the steps involved in approving a loan, providing an insurance quote or opening an account. Dynamic orchestration is where the mesh comes alive: orchestrator agents route work to the right specialists, in sequence or in parallel, adapting in real time when the input is fluid and the path forward must be reasoned through.

The result is a multi–agent mesh topology – hierarchical agent organizations coordinating specialist agents across various functions that mirror the structure of an effective human organization.

6. Continuous AI improvement – catch performance drift early

Effective organizations don’t deploy employees and forget about them. They monitor performance, provide feedback, and create mechanisms for continuous improvement.

An agent mesh’s visualizer would give a real-time graphical interface for tracing agent interactions, tool and LLM calls, and decision pathways, so you can see exactly what your agents are doing and why. Ongoing evaluations detect performance drift or emerging failure modes via online evals that run in the background, monitoring production execution.

Further, Open Telemetry instrumentation can surface performance trends over time, giving you the data to make informed decisions about when and how to tune your agents.

Turning AI agents into mission-critical assets requires a new deployment approach

To turn AI agents from simple experiments into high-performance operational tools requires a whole new mindset for building and developing them. Traditional SDLCs work for predictable code, but AI agents act on their own! As a result, they require clearly defined roles, secure tool access, strict testing, human oversight, and constant monitoring.

To successfully deploy, manage, and scale a workforce of AI agents with confidence, organizations must deploy an ADLC on a real-time and event-driven platform.

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The hardest part of retail AI may soon be the part nobody puts in the demo.

A model can recommend a markdown, predict a stockout, adjust a picking route or identify a pattern in store traffic in milliseconds. But a retailer only gets value when that decision becomes an accurate action in the physical store. Whether it’s a price change or a sensor reading, it has to arrive promptly for it to be useful.

There is already a striking gap between AI ambition and execution. Deloitte's 2026 survey of 200 retail and consumer products executives found that 75% consider AI a top strategic priority, yet only 16.5% can quantify the return they are getting from it. Clearly, retailers believe in AI. The harder problem is turning isolated capabilities into something that works consistently across the business.

For physical retailers, the challenge does not end with models, data or software. AI-driven decisions have to pass through stores containing thousands of devices, existing networks and years of installed technology. In complex environments, the building itself can affect connectivity. As the number of connected endpoints grows, so does the importance of the wireless foundation carrying information across the store.

For CIOs and CTOs, that means the next constraint on AI may be something that has been underappreciated: whether the store estate underneath it can execute reliably at scale.

A store is not a cloud workload

Physical retail introduces conditions that software teams cannot abstract away. Stores have walls, metal shelving, refrigeration, back rooms, loading areas and legacy systems installed. They also have trading hours. Infrastructure changes that look simple on an architecture diagram can become expensive or disruptive when repeated across hundreds of sites.

Take inventory, for example. A forecasting model can improve a replenishment decision, but store-level execution may depend on shelf sensors, handheld devices, picking tools and accurate product information all exchanging data.

Computer vision, digital displays and environmental monitoring add further endpoints and a wider mix of data, from routine device updates and sensor readings to visual content and real-time operational data. These workloads place very different demands on the network.

Some require high bandwidth or low latency. Others involve large populations of battery-powered devices sending regular updates across difficult environments. Individually, none is necessarily a network problem. Collectively, they can become one.

If a pricing decision cannot reach every store quickly, a replenishment signal arrives too late or each new application requires another costly infrastructure layer, the retailer loses part of the value the technology was supposed to create.

One response is to add another gateway, controller or application layer for the next project. That can work for a pilot. Repeated often enough, though, every new device gains a separate integration path, security owner, management console and failure mode.

In difficult store environments, adding project-specific infrastructure to overcome coverage gaps can also change deployment economics. The technology works, but operating it becomes progressively harder.

The scaling problem appears after the pilot works

Retail technology teams are used to asking whether a new application meets its business case. AI adds another question: what happens to the infrastructure if the application succeeds?

A pilot may involve one store and a limited number of endpoints. Scale changes the economics. Coverage has to work throughout the building, updates must remain reliable during normal trading and security teams need a consistent way to onboard and manage devices. Operations teams also need clear ownership when something fails.

Connectivity should therefore be treated as an architectural choice, not a specification buried inside each procurement. The objective is not to replace every existing network or create one for every application, but to match workloads to the wireless layer best suited to their requirements while keeping the estate manageable.

Current designs may be adequate in some stores. In large or difficult formats, or where suitable retail IoT endpoints are growing quickly, an additional purpose-built wireless layer may be justified. The decision should be deliberate rather than inherited one project at a time.

Different workloads need different wireless foundations

The future-ready store is unlikely to depend on one network doing everything. It will more likely use a combination of connectivity layers, each supporting the workloads it suits best. Now, retailers should consider whether enterprise Wi-Fi should remain the de facto transport layer for every new category of connected device.

As electronic shelf label estates expand and retailers introduce more sensors and retail IoT applications, suitable workloads may be better served by a purpose-built wireless layer alongside the enterprise network.

This should not mean an isolated network for each use case, but a scalable, reusable connectivity layer able to support large device populations over time. Security belongs in the same conversation. Every endpoint needs consistent management, from trusted onboarding and credentials through to decommissioning.

Open standards remain important because they provide a more consistent foundation for future devices, applications and platforms. But openness alone is not the architecture. The decision is which workloads belong on which connectivity layer; standards help those layers remain manageable and adaptable.

The most successful retail AI programs will be judged by more than the quality of their predictions. As connected estates grow the test will be whether organizations can turn those predictions into dependable action across real stores.

Before approving the next AI pilot, retailers should ask what will their estate need if it succeeds, which workloads will grow, and should they all continue to be carried in the same way?

If success means more devices, richer data, more frequent updates or deployment across a much larger estate, the wireless architecture supporting those workloads has to be a central part of the AI strategy.

Retailers are not investing in AI tools or connectivity for their own sake. They want to protect margin, improve availability and make stores easier to operate. AI capability and AI scalability are different problems.

The objective should be an architecture in which new workloads can use the wireless layer best suited to them without forcing the estate to be rebuilt each time. The retailers that design for both will be in a much stronger position to turn ambition into reliable, repeatable execution.

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ChatGPT has become remarkably good at making itself useful as a researcher, organizer, and assistant for many a tedious digital task. OpenAI has begun pushing ChatGPT further recently with its agents, which are able to navigate websites and apps on our behalf.

You can delegate ChatGPT agents for everything from comparison shopping to filling out forms and lining up travel itineraries. What's changing is how deeply those abilities are becoming integrated into ChatGPT, with greater access to personal information and more opportunities to complete complicated tasks with less direct supervision. OpenAI's new Dots is a good example.

The appeal is obvious, but the more ChatGPT can accomplish independently, the more important it becomes to consider what happens when it misunderstands its instructions. For instance, a few months ago, OpenAI's experimental AI agents managed to escape their restricted environments and compromise systems belonging to AI platform Hugging Face. According to OpenAI's own investigation, the agents were supposed to be completing cybersecurity tests, but instead discovered ways to access the internet and exploit security vulnerabilities, coordinating their efforts through an unauthorized message board. An independent investigation by METR found that roughly 1,200 agents exchanged more than 70,000 messages and files, with around 700 eventually participating in the attack on Hugging Face.

The AI agents bypassed restrictions and pursued objectives in ways their creators never intended. Some even appeared to recognize that their actions were unauthorized but continued because they believed doing so would help accomplish their goals. OpenAI has since acknowledged other troubling incidents, including 53 cases in which agents uploaded user-provided images to third-party image-hosting websites as unlisted links during research and evaluation activities. Agents may be pursuing legitimate objectives and just seeking ways around obstacles. But algorithms don't have ethics, so they might cross boundaries their developers expected them to respect. It's especially worrying given how much OpenAI wants ChatGPT to be part of our lives now.

When helpful becomes a hazard

Imagine asking ChatGPT to review your finances and suggest ways to save money. With access to the necessary accounts, it can examine transactions, identify subscriptions, and recommend changes to your budget. But give an autonomous agent enough authority, and it could theoretically decide that transferring money, canceling a service, or modifying a payment arrangement is the most efficient way to accomplish the assignment.

The same problem applies to something as mundane as arranging a meeting. ChatGPT might prepare an invitation, only to include personal information you never intended to share. From the agent's perspective, the disclosure could seem like a perfectly reasonable way to provide useful context.

These are hypothetical examples, and ChatGPT has safeguards designed to prevent sensitive actions without appropriate authorization. Nevertheless, OpenAI has acknowledged the possibility and introduced protective measures like automated monitoring and restrictions on certain actions to ensure users approve of any major action by the model.

There is also the problem of prompt injection, where malicious instructions are hidden in benign text to manipulate an AI into doing something its user never requested. OpenAI has described how attackers can exploit the information agents encounter during legitimate tasks. An instruction buried in a seemingly ordinary webpage could attempt to persuade ChatGPT to disclose private information or take an unauthorized action.

The problem can arise anywhere. In Australia, an experimental OpenAI agent accessed government systems in unauthorized ways during internal training. It exploited a vulnerability in a government health statistics service to continue its work when initially stymied. OpenAI subsequently acknowledged the incident and apologized for both the unauthorized access and its handling of the notification.

The company's official account of the Australian incident makes clear that the activity occurred during testing rather than through an ordinary consumer ChatGPT session. Nevertheless, it shows how AI pursuing an apparently harmless research objective can end up crossing a security boundary.

Handing over the keys

ChatGPT's growing autonomy is part of a much larger industry obsession with personal AI agents. Meta is pursuing an even more ambitious vision with Muse. Mark Zuckerberg clearly believes people will welcome an AI assistant capable of handling large portions of their daily lives.

There is something slightly absurd about that ambition when ordinary consumers are struggling with grocery bills, housing costs, and the price of gas at the pump. An AI that can independently shop for you sounds considerably less revolutionary when your main concern is whether you can afford whatever it wants to buy on your behalf.

There are undoubtedly useful applications for this technology, especially when it comes to administrative work. But the industry's assumption that consumers want increasingly independent assistants deserves more scrutiny. Every additional permission increases the consequences of an error, and every automated action potentially removes another opportunity for someone to intervene.

Perhaps the most important capability an AI agent can develop is knowing when to stop. A genuinely useful assistant should recognize that being asked to organize someone's finances doesn't authorize moving their money, and arranging a meeting doesn't justify revealing confidential information. Sometimes the best response to an obstacle is to ask the user what to do next, even if that means completing the task takes a little longer. Without that friction, by the time something goes wrong, the assistant may already have done things in your name that you would never have allowed.

• Minisforum's MS-S1 MAX-P495 is on pre-order at $7,399 with 192GB of unified memory and a 2TB SSD
• CPU and NPU specs are similar to previous iterations: 16 cores, 32 threads with a boost in clock speeds of 100 MHz, in addition to increased NPU performance by 10% to 55 TOPS
• The real upgrade is memory: 192GB total, 160GB for the GPU, and about 7% more bandwidth, enough to fit a 284B-parameter MoE model on one box

Minisforum has opened orders for the MS-S1 MAX-P495, a 3.5-liter workstation built around AMD's Ryzen AI Max+ PRO 495, and its launch announcement leads with the processor: 16 Zen 5 cores and 32 threads, clocks of up to 5.2 GHz, and an NPU rated at up to 55 TOPS within a platform total of 131 TOPS.

The single configuration pairs 192GB of unified memory with a 2TB SSD, sells for $7,399 on pre-order, and is expected to begin shipping in mid-October 2026.

Those processor figures match AMD's own specification page; however, they are also the least consequential change in the machine: what actually separates the P495 from the MS-S1 MAX that Minisforum launched a year ago is the memory.

Similar cores, much larger memory pool

AMD introduced the Ryzen AI Max PRO 400 series in May 2026 as a refresh of the Strix Halo silicon in the 300 series, and the CPU side barely improves in performance. The PRO 495 keeps the 16-core, 32-thread Zen 5 layout of the Ryzen AI Max+ 395 that powered the original MS-S1 MAX. Its boost clock rises by 100 MHz, from the 5.1 GHz listed on Minisforum's spec sheet for the earlier model to 5.2 GHz.

The Radeon 8065S graphics keep 40 RDNA 3.5 compute units and gain 100 MHz to reach 3.0 GHz, and the NPU goes from 50 to 55 TOPS, a 10% uplift that nudges the platform rating from 126 TOPS to 131 TOPS.

All of these upgrades are mild compared to where AMD does the real work: the memory controller. The 400 series supports up to 192GB of LPDDR5X across a 256-bit interface, with up to 160GB assignable to the GPU, against 128GB and 96GB on the last-generation chip it replaces. Rated memory speed also rises from 8,000 to 8,533 MT/s, a roughly 7% increase that will probably matter more here than the clock-speed bumps on display.

Minisforum's own test results show where the extra capacity pays off: the company says a single unit ran DeepSeek V4 Flash, a 284-billion-parameter model, at Q4 quantization at about 12 tokens per second. Two units linked together ran Qwen3.5-397B at roughly 16 tokens per second, and a four-system cluster loaded the 380GB Q4_0 build of DeepSeek-R1 671B, though it gives no speed for that last result.

These parameter counts are larger, however, than the actual work involved. Both models use a mixture-of-experts (MoE) design, which lets them load only specific "experts" into memory when needed. DeepSeek V4 Flash activates about 13 billion of its 284 billion parameters for each token, per Nvidia's catalog entry, and Qwen3.5-397B activates about 17 billion, per its model card.

Memory capacity decides whether a model fits at all, while active parameters and bandwidth decide how fast it runs, which is how a box with roughly 273 GB/s of bandwidth manages double-digit speeds on models this large. That is also why the 192GB of RAM in play matters far more than the modest performance upgrade the MS-S1 MAX-P495's APU offers.

The $7,399 price it currently goes for is presented as 20% off a list price of $9,249 that nobody has paid yet, with the unit going on sale even in the preorder stage. This makes the standard MS-S1 MAX with 128GB arguably a better value for all but the most ardent of local AI users who can justify twice the cost for a 50% increase in memory.

The rest of the machine (sans the APU and memory) is identical to the original MS-S1 MAX, including the 320W internal power supply, the six-heat-pipe, dual-blower cooler, dual 10GbE, two 80Gbps USB4 v2 ports, and 2U rack support.

What changes here is that a single compact box can now hold models that previously needed two, and for developers and small teams that need 200-to-300-billion-parameter mixture-of-experts models kept on premises for privacy or latency, that is a meaningful shift. For anyone drawn in by the 16 cores and 55 TOPS that Minisforum chooses to headline with, the cheaper MS-S1 MAX delivers nearly the same power for about half the price.

• Unitree’s CEO says robotic AI could soon see a huge breakthrough
• Such a change could happen ‘next year,’ the CEO believes
• That could happen thanks to OpenAI’s upcoming GPT-7 model

AI is revolutionizing the world of software on a daily basis — you only need to look at how far vibe coding has come to see proof of that. But big changes could also be coming to robotics, with one company leader predicting that we could be on the cusp of seeing a “ChatGPT moment” in the industry.

That idea was put forward by Wang Xingxing, CEO of Chinese robotics firm Unitree. Speaking at an industry event in Shanghai, China (via the South China Morning Post), Wang declared that OpenAI’s recently released GPT-6 Astra model is so powerful that it has encouraged a belief that a breakthrough could occur “within one or two years.”

“It’s possible that, say, a GPT-7 model released next year could directly crack the problems of embodied intelligence” Wang said, referring to the idea of commanding a robotic device using artificial intelligence. That’s an advancement on Wang’s belief in August this year, when the CEO said that such a “ChatGPT moment” of technical ability could take a decade to arrive.

As a benchmark, Wang said that robots should be able to complete 80% of the tasks they were assigned in 80% of unfamiliar environments using voice or text commands alone. If that happens, Wang believes that “embodied intelligence” will have reached its threshold level of maturity.

A step change for robotics

Unitree’s robots are known to be some of the most advanced in the world, but many of their strongest abilities lie in their hardware. However, the company has started to put more of an emphasis on AI software, and with the developments made in GPT-6 Astra, the gap between the two sides of the company’s devices could soon close.

That kind of combination has already been trialed by Stanford University’s Movement Lab, which paired a Unitree robot with GPT-6 Astra and tasked it with cleaning a kitchen it had never seen before. According to the experiment, the robot was able to “clean up across the room and retrieve a remembered object from an underspecified request, without environment-specific training data or additional policy learning.”

GPT-6 Astra included some significant improvements over previous models, including scoring 92.7% on the ScreenSpot-Pro test that measures an AI model’s ability to interact with interfaces. That’s up from 76.9% in GPT-5.6 Sol. But it also has cybersecurity experts worried about the threat it could pose if it goes rogue and starts hacking other organizations — something OpenAI’s recent models have been known to do.

If Wang is correct and we see an explosion in AI’s abilities in the next year or two, it could mean big things for the robotics industry — and pretty much every other facet of life currently touched by AI. And with the amount of money that’s been invested in AI over the last few years, there’s an awful lot riding on that bet.

• Google Trends highlights the term “super intelligence” is replacing “artificial intelligence” in the US – but not elsewhere
• In a Truth Social post, the POTUS described anyone that doesn’t use the “super intelligence” name as “THE ENEMY”
• The US government’s AI website domain remains ai.gov

Donald Trump has declared that anyone using the term “artificial intelligence” is “the enemy,” as Google Trends demonstrates an increase in search volume for the US President’s preferred term, while the rest of the world obliviously goes about its business.

Following the signing of Executive Order 14434, the purpose of which is “Inaugurating the Age of Super Intelligence,” the POTUS has taken to social media to emphasize the preferred term for artificial intelligence in the Land of the Free.

While the rest of civilization continues with the term “artificial intelligence,” it seems the US public quite likes the term “super intelligence,” which is believed to have been introduced to cast AI in a more positive light.

Subhead

Since the executive order was passed, interest in the term “super intelligence” grew considerably, increasing by around three times at one point. Of course, this only happened in the USA - worldwide, attention was not drawn to the revised nomenclature.

While that initial excitement appears to have settled down, the term certainly seems to have made its mark. As the chart above illustrates, interest in "artificial intelligence" as a search term has been seriously impacted by the sudden existence of "super intelligence."

Posting on Truth Social – the social network he founded via his Trump Media & Technology Group – Donald Trump stated: “The White House considers anyone that uses the term, “Artificial Intelligence,” as opposed to the highly accepted new and more accurate term, “Super Intelligence,” THE ENEMY! President DONALD J. TRUMP

Despite the determination to remove the term “artificial intelligence” from the public lexicon, however, the move doesn’t appear to have been wholly repeated across the US Government.

For example, Politico has identified that while the websites have converted with references to “super intelligence” and “SI” replacing “artificial intelligence” and “AI,” the main website for super intelligence has retained its ai.gov URL.

Rein it in a bit

Despite various emergencies and concerns caused by artificial intelligence – which range from the Hugging Face Incident to the growing environmental costs and impacts on rural communities of data centers – Donald Trump has been steadfast in his support for the technology.

Seemingly viewing it as the equivalent of the nuclear arms race, the POTUS has refused calls for guardrails on AI, preferring instead not to hamper the technology when the competition – namely, China – is not taking the same precautions.

Renaming the technology as super intelligence seems to have had a positive short-term impact, but whether this spreads beyond the US remains to be seen. It is unlikely – but then, the United States of America has long existed in a paradox of colloquial contradictions...

• Anthropic warns that self-replicating AI worms could become feasible by the end of the year
• These AI worms could hijack hardware ranging from IoT devices to data centers
• Smaller models are already capable of adapting to unfamiliar hardware and self-replication

Anthropic has warned self-replication could become feasible for small AI models on internet-of-things (IoT) devices in just a matter of months.

This could be a big problem because a self-replicating AI model does not have a centralized off-switch and could hop networks to discover other IoT hardware to further replicate without human control.

Anthropic warns these AI worms could use multiple compromised IoT devices to run inference while carrying its model weights with it as it reproduces. IoT devices would be a prime target for this worm because they are “cheap and typically poorly defended,” the company says.

Small AI models could spread quickly

To test the viability of a self-replicating AI worm, Anthropic researchers ran Gemma4-26B-A4B - a 25 billion parameter model - across 15 inexpensive off-brand Android TV boxes at a cost of around $55 each. Once networked together the TV boxes were able to run distributed inference, solidifying the proof-of-concept.

This introduces a number of novel opportunities for small AI models. Self-replicating AI models could exploit the weak cyber defenses of IoT devices within homes and small businesses, and then split their model into ‘shards’ across the compromised hardware in order to collectively run the model.

The AI worm could then pay for its own token cost by offering its inference on a digital freelancing service.

Moreover, newer IoT hardware that includes built-in AI chips designed to run small models - such as security cameras and video-conferencing kits - would be a prime target for an AI worm. Hackers could breach these systems using social engineering, or even by using an AI agent in the not too distant future.

Anthropic further warns that datacenters and other high-compute facilities could become targets of advanced replicators that are capable of breaching hardened defenses, and then use the hardware to propagate further.

An additional threat that Anthropic warns of is an AI worm that uses an API-based replicator, whereby the model doesn’t host itself, but steals human credentials to access commercial AI providers and use these services to pay for their own inference.

AI self-replication isn’t a reality... yet

While self-replicating AI isn’t an immediate threat, the researchers theorize that it could become a problem by the end of the year.

One of the main hurdles for these AI worms is becoming operational on unfamiliar hardware. Unlike the traditional ‘worm’ malware that must be built to target specific IoT vulnerabilities, AI worms could be capable of adapting to unfamiliar environments and hardware.

In Anthropic’s testing, the researchers found that smaller models - such as Qwen3.5-9B - are already capable of adapting to unfamiliar hardware to run inference. Qwen3.5-9B adapted to 8 of the 9 hardware types the researchers tested, which included Jetson Nanos, Mac PCs and RTX 4090s.

While smaller models are more capable of adapting to unfamiliar hardware, larger models are better equipped to self-replicate quickly.

Defending against AI worms in the wild

There is a significant tradeoff for AI worms, Anthropic says, which works in the defender’s favor. AI worms must balance between stealth and replication speed. To that end, the researchers say that an AI worm would leave a noticeable network signature such as multi-gigabyte model payloads delivered to newly infected devices, or unusual traffic between machines that wouldn’t ordinarily communicate.

Network quarantine would also be an effective initial defense against AI worms. Using lessons from the remediation of the Mirai botnet, the researchers believe that implementing a whitelist of approved sites with browsers otherwise redirected to cleanup instructions could help in clearing AI worm infections.

Regulations on the security of IoT devices could also help harden defense against AI worms. Moving away from supplying IoT devices with default passwords - as has already been done in the UK - would help prevent AI worms from gaining a foothold. Other standards, such as automatic updates and extended update lifetimes can help protect IoT devices from compromise against both traditional malware and AI worms.

Bad Bunny’s record-breaking soldout tour was one of the hottest tickets in the UK this summer, drawing over 104,000 fans across the weekend of June 27th, 2026.

The show was highly anticipated following his Album of the Year Grammy Award for Debí Tirar Más Fotos and history-making Super Bowl Halftime Show.

But the excitement around the show wasn’t just because of the scale of the artist, it was also due to the incredibly ambitious scale of the production.

The best stadium tours have a way of making their use of technology seamless and in some cases, invisible.

You see a 100ft screen tracking the band’s movement from across the field without missing a beat, or members of the audience picked out and flashed on the giant LED walls around the venue.

None of it looks labored or complex, at least not to the general public in attendance. To them it’s all part of the magic of the show, which is exactly the point.

Media servers

Carlos “Charly” Camina is the tour’s media server engineer, and the size of the kit he oversees is much smaller than the grand scale of the show might suggest. The entire production runs on four Disguise GX 3+ servers, with a fifth GX 3 unit held in reserve as an understudy for the show’s director.

That’s down from five servers during the tour’s earlier Puerto Rico leg, despite the fact that the World Tour carries significantly more content layers and considerably bigger screens at 10,752 x 2,816px, requiring a 4K input.

The GX3+ upgrade alone roughly doubled the processing headroom for the same effects load, which is what made it possible to deliver more with a smaller footprint. The lighting runs on grandMA kit, the unchallenged global industry-standard for live event lighting control, with a grandMA light desk as the main control surface and a grandMA3 compact unit as backup.

The whole setup sits on a dedicated network split into eight isolated VLANs, a structural choice to provide containment, so that a fault in one part of the system can’t spread and collapse the whole thing. The engineering challenge on a show of this scale isn’t cramming in more and more hardware, it’s finding the smallest reliable solution that can do the job every single night, in any venue, anywhere on the planet.

Adaptability is key on any tour

The most unique piece of engineering on the Bad Bunny Tour wasn’t actually planned or commissioned ahead of the show. During rehearsals, the creative team at Sturdy suggested photographing the crowd during the opening act and then displaying those images on LED screens around the venue later on in the show. Charly’s instinct was to push back. Loading fresh, unvetted content into the live system with zero headroom for troubleshooting is the kind of risk that wise engineers know to avoid.

His solution was to keep the additional photography content out of the Disguise system altogether. Instead, he built a standalone application using HTTP Sockpuppet and OSC (Open Sound Control) to communicate with the main Disguise system, running off a laptop. A photographer hands over images for Charly to upload. When the relevant cue fires inside Disguise, it sends a request to the laptop for specific images, which are mapped to specific locations on a four-way live layout.

Charly said that the hardest part wasn’t the concept, it was the live timing: ensuring that front-of-house and the on-stage servers sync closely enough so that there’s no discernable lag, and making sure the system never repeats the same face twice in one night.

How this curveball was handled is a good example of a pattern that shows up across the whole build: rather than adding risk to a proven core system, Charly builds smaller satellite systems to support it and keeps them loosely connected. It’s a slick approach that preserves the magic and spontaneity of a seamless live experience, without risking the core build or compromising the show’s production value.

Animated CGI Frog - Why not?

The tour’s most curious element involved a Puerto Rican-inspired 3D animated frog called “Concho” rendered live using Unreal Engine and a face-tracked performer, which enabled Concho to interact directly with Bad Bunny mid-show as part of a FaceTime call. The segment needed a camera that could move freely, handheld, so the team decided to use an iPhone.

However, the phone could not have any visible external antennas. This meant the feed from the iPhone had to be captured over NDI (Network Device Interface) on a dedicated Wi-Fi network, rather than a standard wireless link that could have been easily interrupted by the tens of thousands of audience and staff devices around the arena. Charly’s team has to scan the RF spectrum every night to find clear channels before the Concho segment goes live.

The benefit of reliability

The ultimate stress test of the system came during the show’s finale where the production deployed a 40-tile feed, displayed in a grid across the main 5K screen and two IMAG screens. Together with Screens Producer Vishal Sharma and his team at All of it Now, who created all the show’s Notch content, Charly decided to route four separate 4K video inputs through a Notch block to build the grid live.

Nothing was pre-rendered or keyframed and the color-grading also took place in real-time. That final segment pushed the servers to their limit, with all four of the GX 3+ units red lining on their performance markers. Despite the load, the system held and delivered a truly spectacular end to the show.

Charly has worked with Disguise servers for over a decade. In that time he’s worked on shows of all types, from festivals like Coachella to state-of-the-art mixed-use venues like the Tottenham Hotspur Stadium. With that much experience using these systems, what’s clear to him is that the value in this system isn’t in a specific creative feature. The value is knowing that if a server has an issue mid-show, he can switch to a backup with just one click and have full confidence that the backup will take over seamlessly.

For an engineer, touring a production this huge, reliability and predictability is the actual product. The breathtaking visuals that we get to enjoy as part of the audience will rightly get their applause, but the job is protecting the integrity of the show from its own scale and demands, every night, in every city, until the tour comes to an end.

I wrote earlier this year about Claude’s decision to comply with the EU AI Act, which requires all AI-generated text to be identifiable as AI, and start to insert a watermark into its text. Rather than limit this to EU customers, however, Anthropic decided to roll the feature out globally, which seemed a bit of an overreaction at the time.

OpenAI has now announced that it is following suit, but limiting the geographical areas of impact to the EU only. It is going to start inserting watermarks into generated text, but only for ChatGPT customers who live in the EU. That means that US and UK users won’t be affected at all.

OpenAI announced the change on October 5 saying: “Over the coming weeks, we will add an invisible watermark to eligible ChatGPT and Codex text output in the European Union.”

Inserting the watermark

To insert the watermark, which involves subtly changing the word choice for generated text, OpenAI is using a system of its own called textGrain. A note on the OpenAI website says: “Our text watermarking technology, textGrain, adds an invisible statistical signal to the model’s word choices. Our detector looks for that signal to assess whether a passage contains an OpenAI watermark. More details about how textGrain works can be found in our technical report⁠, which will be updated with additional details in the coming weeks.”

Like many people, I find that idea that the word choice of the AI that I’m paying to use is now being influenced by a watermarking algorithm, not by what’s optimal for me the customer, to be deplorable. I’m not saying that the resulting text will automatically be worse because of the watermark, but it certainly won’t be optimal.

It also sounds like it’s an easily broken solution that is full of holes. To detect AI text that’s produced using textGrain's watermarking a special AI detection tool needs to be used, which it’s currently unavailable to the public. OpenAI says it is providing detector access to researchers and expert organizations and that approved researchers and expert organizations can apply starting today.

“The tool will report whether it detects an OpenAI watermark”, continues the OpenAI article “without identifying the user or revealing their prompts or conversations. Given the risk of missed watermarks and false positives, we are not making it publicly available at launch.”

So if the detector tool isn’t being made public it begs the question, what is this supposed to accomplish?

A box-ticking act?

The OpenAI article specifically says that detectors are prone to errors. That “shorter or more constrained text is harder to detect”, and that “editing can weaken the watermark”. It already sounds flawed, but what happens if you try and circumvent it completely by asking ChatGPT not to write in a way that can be detected, or to not use textGrain. Will it do it?

All of which leads me to conclude that the move by OpenAI looks a lot like a box ticking exercise to comply with the EU AI Act, which it is forced to do, since it requires generative AI providers to make generated text identifiable in a “machine-readable way”

It’s reassuring to hear that the changes are only going to be implemented inside the EU and that the text will remain private when the detector is used, but I suspect most ChatGPT users would rather OpenAI concentrated on producing the best possible answers rather than making those answers easily identify as AI-generated.

There’s also something fundamentally strange about a system that deliberately changes the words an AI chooses, without telling you which ones, to insert a watermark that can potentially be removed simply by editing the text. If the goal is to make AI-generated content more transparent, then surely the answer should be something that actually works, rather than an invisible signal that nobody outside a select group can even check for themselves.

• A new app called RemoveMacAI can delete Apple Intelligence files
• Doing so can reclaim up to 12GB of storage space
• But it comes with some scary warnings that shouldn’t be ignored

Apple promises that its new Siri AI and Apple Intelligence tools can help improve your daily experience through the magic of AI. Many people aren’t convinced, though, and a new Mac app has been doing the rounds that promises to delete Apple’s AI for good — so I decided to take it for a spin to see what all the fuss was about.

Dubbed RemoveMacAI, the app originally gained popularity in late September. At that time, though, it was just a simple Terminal command, which — given the obvious security risks of entering unknown code into a utility as powerful as the Terminal — turned some people off.

Now, the same developer is back with a Mac app that is essentially a wrapper for the command. This makes it a lot easier to understand what you’re deleting and what you’re enabling when you use the app, with a built-in tutorial and clear toggles spread across several different tabs and categories.

Apple makes it pretty difficult to eliminate every aspect of Apple Intelligence from your machine, which is why something like RemoveMacAI has gained a foothold. Its developer says Apple’s System Integrity Protection layer — which stops unauthorized code from running on your Mac — is never disabled, and that the app doesn’t make any network requests and doesn’t collect telemetric data.

That said, I loaded RemoveMacAI onto my Mac and was immediately met with a concerning warning: macOS did not trust the app and wouldn’t let me run it. The developer said this is due to the app being unsigned, and you’ve got to manually let it through Apple’s gates. I’ve occasionally encountered apps that do this, and it’s never a pleasant experience.

Foolishly or not, I pushed on. You can either choose the app’s recommended settings, which delete Apple Intelligence and turn off some privacy-invading settings, go “maximum privacy” and also disable Game Center and some Safari settings, or choose what to do yourself.

I opted to just delete Apple Intelligence and nothing else. RemoveMacAI breaks your options down into sections — you can switch off Siri AI, ChatGPT’s extension, Writing Tools, Image Playground, and much more, all with modular toggles.

Once that’s done, there’s another jarring moment: RemoveMacAI wants you to install a custom profile that it uses to enact your changes. Fortunately, you can read through every change it includes, so there’s visibility over what’s being done.

I accepted that and let RemoveMacAI get to work deleting Apple Intelligence. In the end, it freed up 11.7GB, which is nothing to sniff at and is in line with the developer’s claims. If you’re running low, it could be a good way to claw back precious disk space.

Although RemoveMacAI comes with some scary settings, it attempts to ease your fears by letting you read through everything that’s going to be changed, including the macOS profile. You can undo your decisions too, and doing so will revert your settings changes and redownload the Apple Intelligence models when they’re needed next.

Ultimately, I found RemoveMacAI to be a handy utility, especially if you want one resource for not only deleting Apple Intelligence, but also for restricting some of macOS’s more privacy-invading practices. The lack of notarization and the subsequent alarming warnings will likely put some people off, though, and they stop me from wholeheartedly recommending the app as a result.

If you're looking for a powerful laptop that can handle demanding AI workloads without relying entirely on cloud services, Microsoft's Surface Laptop Ultra is available to pre-order now. Aiming to be a MacBook Pro killer, it combines Nvidia RTX Spark processing, Blackwell graphics, and a high-resolution touchscreen in a portable design.

The Surface Laptop Ultra is designed to handle demanding AI applications locally, with Nvidia Blackwell graphics and unified memory options reaching 128GB. That could make it an alternative to cloud processing or a dedicated desktop workstation for certain workloads.

• Best for: AI developers, researchers, creative professionals, and businesses looking for a powerful laptop capable of running compatible AI models locally.

• The deal: The base configuration Microsoft Surface Laptop Ultra is available to pre-order from $2,599.99 at Microsoft. In the UK, the pre-order is priced at £2,599.99.

Why we recommend it

Microsoft describes the Surface Laptop Ultra as its most powerful Surface Laptop yet thanks to the inclusion of Nvidia's RTX Spark N1X processor. The entry-level model combines an 18-core CPU with a 5120-core Nvidia Blackwell RTX GPU. A higher-end version increases that to a 20-core CPU and 6144-core GPU.

The platform can deliver up to one petaflop of AI compute and run models containing as many as 120 billion parameters locally. The size of the models you can realistically run will depend on the configuration and software.

The laptop uses LPDDR5x unified memory shared between the processor and graphics hardware, with consumer configurations offering 24GB, 32GB, 64GB, or 128GB. The Ultra features a 15-inch PixelSense Ultra touchscreen with a 3270 x 2180 resolution and a dynamic refresh rate reaching 120Hz. It supports 100% DCI-P3 color coverage, Dolby Vision IQ, and peak HDR brightness of 2,000 nits.

Connectivity includes three USB4-capable USB-C ports, HDMI 2.1b, USB-A 3.1, an SD card reader, and a headphone jack. You also get Wi-Fi 7, Bluetooth 5.4, a 1080p webcam, and a precision haptic touchpad.

Microsoft quotes battery life of up to 15 hours for local video playback or 12 hours for active web browsing, although intensive AI processing will obviously place heavier demands on the battery.

Price context & historical value

The Surface Laptop Ultra, with Windows 11 Home, starts at $2,599.99, with the 18-core Nvidia RTX Spark N1X processor, 5120-core GPU, 24GB unified memory, and a 512GB Gen 4 SSD. Models with a 1TB SSD start at $2,999.99, while the higher-end Nvidia RTX Spark N1X processor, featuring a 20-core CPU and 6144-core GPU, starts at $3,699.99.

Consumer models with 32GB, 64GB, or 128GB memory include Windows 11 Pro and storage options include removable 512GB Gen 4 and 1TB or 2TB Gen 5 SSDs.

Microsoft also offers a Surface Laptop Ultra for Business, with commercial support and service options. Its available configurations and included accessories may differ from the consumer models.

Should you buy it?

✅ Buy the Microsoft Ultra RTX Spark if...

You're an AI developer, researcher, or business professional who needs powerful local processing capabilities and access to substantial amounts of unified memory. Its high-resolution touchscreen, wide connectivity, and Windows 11 Pro options also make it well suited for demanding professional environments.

❌ Skip the Microsoft Ultra RTX Spark if...

You mainly use Microsoft 365, web applications, email, and video conferencing. A conventional business laptop would be considerably more affordable and more than capable of handling those tasks.

Discussions about passkeys replacing passwords as the go-to sign-in method have rapidly accelerated. The National Cyber Security Centre (NCSC) has acted as the catalyst for this transition, describing the shift as "overhauling decades of security practice". Finally, the industry has reached a collective point of recognition: traditional, static passwords are a weak defense against modern, automated threats.

AI has turned cybercrime into a highly efficient, industrial-scale enterprise. With researchers uncovering agentic ransomware attacks, we are fast approaching a reality that outpaces traditional human-centric defenses. Cybercriminals routinely deploy industrial-scale AI tooling to orchestrate and deploy vast credential-stuffing attacks and hyper-realistic phishing campaigns at machine speed and scale.

For organizations, the adoption of passkeys and other passwordless verification methods is dependent on the maturity of existing identity and access management (IAM) systems. Of the businesses with less mature, or no IAM strategy in place, only 30% are embracing password alternatives. Without passwordless systems, threat actors will continue to undermine digital trust.

User friction and weak protection

For years, identity security was treated as an isolated concern for the operations department. However, as customer experience dictates corporate revenue, delivering seamless IAM has become a commercial imperative.

Passwords force a broken trade-off: making users create unique, complex strings of characters results in severe password fatigue, while cutting corners by reusing credentials and using easily guessable phrases leaves gateways vulnerable to threat actors. When security kills usability, the business model breaks.

Another barrier passwords create is that, although most of the British public is genuinely worried about identity theft and fraud, only 17% fully trust organizations managing their identity data. Passkeys directly resolve this trust deficit through cryptographic authentication.

Because private keys stay securely on the user's local device and are never shared with a central server or application, there is no central database for hackers to breach. Passkeys are also phishing-resistant, removing the risk of users falling prey to social engineering attempts. Unlike passwords, they are also cryptographically bound to a specific, verified domain name, adding another layer of technological defense against attackers.

Securing autonomous identities

The shift towards agentic AI – fully autonomous systems operating with minimal human intervention – shatters traditional authentication models. Passwords were fundamentally built for human brains to remember.

An autonomous AI agent, such as autonomous digital proxies, personal shoppers and synthetic workflows, cannot "remember" a password without creating severe vulnerabilities, such as hardcoded credentials or shared session tokens exposing permanent risks.

If an agent inherits over-privileged human credentials, a compromised or rogue AI can exfiltrate sensitive data at machine speed, vastly outpacing the response time of traditional security methods.

To secure the growing autonomous workforce, AI agents must be given their own distinct, first-class identities with task-specific, time-bound permissions. Passkeys and decentralized, cryptographic credentials secure these machine identities and allow enterprises to securely verify and audit agentic workflows and interactions, without exposing underlying human credentials.

From passwords to passkeys

The technology industry is driving the momentum behind passkey adoption. Tech giants like Google and Amazon are seeing the need to improve customer convenience and account security, positioning passkeys as their default authentication standard. Businesses that delay moving towards passwordless frameworks are left defending a perimeter built for an entirely different era.

Although most organizations now see the importance of adopting passwordless options, transitioning away from traditional passwords is hindered by real-world complexities. Many core IT systems and legacy infrastructure are reliant on passwords, which remain deeply embedded within business processes.

IT leaders therefore fear that updating these systems will demand massive code rewrites and extensive specialist development resources, meaning replacing them requires careful orchestration.

To address this challenge, modern, low-code or no-code identity platforms allow organizations to visually map, test and deploy sophisticated authentication journeys seamlessly. This enables businesses to introduce modern passwordless options like passkeys or biometric prompts progressively, without disrupting the active user workflow or having to replace entire underlying legacy systems.

By allowing teams to modernize security processes without affecting user experience or uptime, advanced identity orchestration becomes a critical business enabler.

The future of trust starts with identity security

The threat landscape continues to be redefined by automated threats, deepfakes and autonomous software. Against these sophisticated attacks, the concept of static, point-in- time logins is obsolete. The NCSC’s call to action has been the final push many organizations need to drop passwords and rethink how to safeguard digital trust.

Adopting an identity-first approach centered on passkeys and robust identity orchestration allows enterprises to reduce user friction, strengthen the security of both human and machine identities and establish the continuous trust needed to grow confidently in an AI-driven environment.

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As global AI investment continues to climb — it surpassed $300 billion in 2025, according to IDC — enterprise IT remains primarily focused on supporting the development of training models, with architecture such as large GPU clusters and high-density compute taking center stage.

But now AI inference is becoming the dominant workload, and the architecture that supports models in production, at scale, and to real users and systems has quickly become the future of AI.

The demand for AI inference is there — according to Deloitte, inference already accounts for roughly two-thirds of compute, while McKinsey projects that it will represent between 30% and 40% of total data center demand by 2030 — however, the dedicated infrastructure is not.

Inference is more than a scaled-down training environment

What training and inference require from an IT point of view are very nearly polar opposites. Enterprises have facilitated AI training via data centers with massive, centralized data GPU clusters for extreme power density and specialized, high-speed interconnects meant to maximize communication within a single room.

Inference, on the other hand, is defined by a continuous flow of information and unpredictable user demand as opposed to a pre-defined workload. Once the prompts start coming in, you need geographically distributed compute that responds to and scales with real-time demands.

Because inference runs 24/7 and requires millisecond-fast responses for end users, it relies on commodity ethernet, geographically distributed edge sites, and low-latency network connections rather than isolated throughput within a single data center. The very design choices that make an enterprise data center optimal for training actively work against running inference at scale.

The cost structure is entirely different, too. Training is a one-time capital event — once a model is built, that cost is already sunk. Organizations that continue to optimize their spending for training are now being hit with the reality that they have the wrong foundation.

Inference, meanwhile, is a recurring operational cost that scales with every user, query, agent interaction, and more. It’s more like a living, breathing organism which needs to grow alongside the kind of workload that is going to determine the future of AI economics.

Agents are creating new strain on architecture

Autonomous AI agents are also compounding the infrastructure deficit. Agents don’t generate a single response and then stop; they plan, execute across multiple steps, call external tools, coordinate other agents, and pass outputs between systems. The ripple effect of an agentic workload multiplies the data traffic flowing through a network. As demand for agentic solutions grows, so will the demand for supporting infrastructure.

That growth is already consequential. Gartner predicts that by the end of 2026, 40% of enterprise applications will be integrated with task-specific agents, while IDC projects that agent usage will surge tenfold by 2027, with token and API call volumes multiplying by a factor of thousands.

Inference is also acting as a north-south traffic multiplier in ways that traditional WAN and data center network architectures aren’t designed to handle. McKinsey found that most enterprise WANs are currently unable to support AI data traffic patterns, and must evolve as AI now ranks as the second-highest infrastructure investment priority.

Burst traffic is another issue impacting architecture strain. Agentic workloads aren’t a steady flow, they’re a torrential downpour that comes and goes. A single orchestration event can trigger dozens of downstream API calls simultaneously.

This causes networks that are sized for average loads to hit capacity ceilings when agents coordinate at scale, creating the same latency and reliability problems that lead enterprise AI pilots to crash before reaching production. To thrive in the AI era, enterprises must build the proper infrastructure from the ground-up.

Inference is now the critical CPU use case

Building inference-forward architecture starts at the most basic layer: hardware. CPUs are critical for planning, scheduling, tool-call coordination, and the multi-step reasoning loops that define agentic workloads.

However, the industry’s heavy focus on the GPU market during the AI training boom has directed attention away from the necessity for CPUs in AI inference workloads. As inference workloads increase in scale, CPUs move from a supporting role to a critical one, acting as the main filter to the orchestration layer.

When viewed through a purely agentic lens, high-core-count server CPUs can handle the workload more economically than GPU accelerators. But it’s important to understand that one cannot function without the other. Inference still demands GPU function; it’s simply that CPU capacity is the bottleneck that most organizations aren’t accounting for.

Aligning compute, network, and storage is key

The reality is that AI use cases are generating abysmal ROI in most cases. Gartner’s most recent survey of infrastructure and operations leaders found that only 28% of AI use cases fully succeed and meet ROI projections, while 20% fail outright. The leading cause of failure was expecting too much, too fast from infrastructure that was incapable of supporting the workload.

How can enterprises avoid that trap? Take the time to set up their underlying infrastructure before diving headfirst into building systems they can’t support. Putting real effort into building dedicated compute, network, and storage that function as individually managed resources is the key.

Inference at production scale requires all three to work in tandem, with compute handling model execution, storage determining how quickly context, embeddings, and retrieval data are available, and network defining whether compute and storage can communicate externally with the appropriate latency

The organizations that can make this distinction early will be better prepared for the next phase of AI, and will spend less time managing infrastructure failures during production, and more time actually focusing on generating value through AI inference.

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The conversation around AI and the jobs market is dominated by anxiety. People worry that roles will be lost to agents – that the job market will dry up from the impact of AI. As a result, there will be a general constriction in the volume of career opportunities out in the world. But is this concern well-founded, what does the data indicate – is it a bleak outlook?

Drilling down into the latest trends and labor market statistics, we can see that the facts paint a different picture. What’s going on? It’s quite a positive shift with the data showing that the job market is in fact rebounding and settling around AI.

Job postings mentioning AI have surged. Since last year, specialist AI job postings have increased by 61% in the UK. This is according to PwC’s AI Jobs Barometer, recording 68,000 new roles and bringing AI hiring volumes back to around 2022 levels. Why are we seeing this uptick? The rise in hiring reflects a broader pressure on companies to deploy and manage AI effectively.

The new AI jobs drive

From a safety standpoint, we’re seeing concerns mounting around open-weight models. We’re also seeing mounting pressure from AI legislators and industry watchers, calling for watertight regulation and governance processes to be put in place.

That pressure has direct implications for workforce strategy – are you hiring the right talent for AI management, do you have the skills in place within your organization to best handle AI operations? Better AI starts with people, and this is where we’re seeing the job market respond over time.

AI roles are vital for the success of the UK AI economy. According to Coursera research, 88% of leaders agree that planned AI investments will not succeed without increasing investments in skills development. The future of work will be defined by those who navigate these changes effectively – knowing how to work seamlessly alongside AI is critical.

The conversation around AI has been dominated by fears of automation replacing workers. But our UK research tells a very different story. While 85% of organizations are already using AI agents in workflows, businesses are increasingly investing in the people needed to govern, secure and scale them.

Among UK IT leaders: 65% expect overall headcount to increase over the next three years, compared with just 14% expecting it to fall. On top of this, only 8% say AI agents are primarily eliminating jobs today. Meanwhile, 48% are hiring AI agent operators, 31% are hiring security, risk and compliance specialists, and 26% are hiring AI ethics and governance specialists.

AI governance becomes a growth function

This is a positive hiring strategy shift, generating scope for AI upskilling, and talent pool growth within the UK economy. What we’re seeing here is that AI governance has become a growth function. Historically, in a negative sense, governance has been framed as a constraint designed to slow down innovation for safety. In the agentic era, that attitude is being reversed.

Strong governance has become the ultimate accelerator. With the UK’s rigorous focus on expanding AI investment and infrastructure, governance needs to be front-of-mind for those innovation-hungry organizations. Additionally, according to our research, nearly 94% of UK organizations say stronger governance would help them move faster with AI, while 93% believe it's essential to scaling AI responsibly.

This is about a concerted focus on ‘re-plumbing’ fundamental structures. UK business leaders understand that teams cannot move fast without confidence in their safety structures. Without clear guardrails, organizations trying to scale AI hit a wall. Employees can leak corporate data, or autonomous systems can surface restricted content. Governance, permissions, and access controls make AI trustworthy, transforming compliance into a growth engine.

How AI is creating new careers, not just changing existing ones

LLM Engineer, Prompt Engineer, AI Governance & Compliance Manager, Chief AI Officer – these are unique new roles we are seeing. AI job risk has been widely discussed in the media, but in fact a bump in opportunities is happening – new opportunities are being created for new, focused AI roles.

As emerging technology becomes embedded into day-to-day operations, demand is growing for specialists who can oversee AI systems, manage risk, ensure compliance and build trust.

For candidates looking to enter this field, knowing how to work alongside digital agents will be the baseline competency of the 21st century – those entering the workforce will increasingly need to build AI literacy. This is where the job market is heading, and skills strategy must align with that too. And it’s not just for deeper technical knowledge – it’s the skills to interrogate and control AI tools from a critical thinking perspective.

The ‘human in the loop’ discussion is not going away – we can see from machine-vs-machine cyber threats that human control is more important than ever. The question is, how do I become the person for that job? For those in the job market seeking these kinds of opportunities, the goal must be a hybrid skill set blending technical literacy with human judgment

The data disconnect with AI engines

Governance is only part of the opportunity. Data and knowledge management are becoming equally important. What we’re seeing now, in that wider ‘re-plumbing’ discussion, is that data libraries are also failing.

While 96% of organizations say access to company knowledge is critical for AI success, only 32% have connected AI agents to trusted internal content across multiple use cases. This disconnect is hurting the growth potential of AI, and limiting its scope to single-use, isolated areas.

Without this connection, agents are left blind and prone to errors. This content disconnect is the primary reason why enterprise pilots stall. Bridging this operational gap requires organizing and securing corporate content pools, and thus hiring the right talent for the job – in the process, creating a massive, multi-year runway of work for data stewards and AI operators.

These kinds of needs or requirements can’t be ignored – especially by larger enterprises that are betting big on AI ROI. They need to iron this out now, and will look to hire the right people for that process.

On the horizon

It’s true there has been significant disruption to the jobs market from AI. Any major technology that has such a wide-reaching impact will create huge ripple effects. But as we study that impact, we can see new opportunities emerging for eagle-eyed candidates and employees. The bottom line is that good governance will be the core foundation for AI.

By framing governance as the foundation of a modern, managed digital workforce, we can unlock unprecedented productivity. And in this transition, we create new sets of roles, reordering the skills priority list. It’s those individuals who are receptive to those changes, and stand out with the right, competent skill sets, who will be the ones to prosper and benefit. This is an exciting time for the technology sector – one that is creating high-value careers for the next generation.

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• UK AI minister warns cyber defenses will 'likely' be breached in the future
• More regulation is needed to prevent 'rogue' AI agent incidents
• The UK's AI testing body is working to help secure frontier models before release

UK AI minister Kanishka Narayan has warned that it is “likely, if not inevitable” that the nation's cyber defenses will be breached by AI agents.

Narayan’s warning comes after numerous frontier models have escaped testing environments and hacked third party companies, including one case where an OpenAI agent hacked an Australian government website in June 2026.

Speaking at a press conference, Narayan also said that AI companies should, “Listen when your scientists warn of serious dangers. Investigate unexpected behaviour. And if you cannot adequately control the risks, do not release the system.”

UK government to use AI to defend

The AI minister also revealed that the UK government is planning to use advanced AI to counter the threats posed by AI, which will build upon two pilot programs that have helped spot and patch over 1,000 vulnerabilities across government systems.

In addition, frontier AI systems are undergoing “rigorous evaluations” by the UK’s AI Security Institute (AISI), in order to “identify risks early” and “test robust safeguards to help keep people safe.”

However, there have been some roadblocks in AISI’s ability to test some of the latest models. In September 2026, US President Donald Trump asked OpenAI and Anthropic to not share their latest models with UK testers until US labs had conducted their own testing.

Narayan said that the UK government has since worked with the White House, “to retain access for Britain's AISI to the most powerful AI models before they are released" (via BBC).

AI protections are also likely to be introduced as part of the Cyber Security and Resilience bill, Narayan said, with a particular focus on regulations surrounding the reporting and disclosure of AI incidents.

“It's promising to see the Government taking frontier AI cyber defences seriously,” said Richard Wainwright, Field CTO, EMEA at Okta.

“As AI agents become more autonomous, safety must be built into the infrastructure UK businesses and government rely on. Everyone wants a 'big red button' to turn off rogue AI agents. The industry is increasingly talking about AI 'kill switches' to help protect critical infrastructure, and I hope they are considered as part of the Cyber Security and Resilience Bill regime.”

Why does AI need more regulation?

In 2026 so far, there have been multiple incidents of frontier AI agents escaping testing environments and breaking into the networks of other companies.

OpenAI has notified more than 100 organizations about unauthorized activity tied to the company’s AI agents, including the Hugging Face attack. Anthropic has disclosed two unauthorized AI agents events, and both Meta and Google have disclosed a single testing environment escape each.In multiple cases, the AI agents were being evaluated by Israeli AI testing firm Irregular.

The companies involved are facing significant scrutiny over the safety of their AI models, but are yet to face any punishment for failing to contain their agents.

There is also significant debate around labelling the AI agents as ‘rogue’, with some experts such as Tenable Field CTO Bernard Montel saying it is “a dangerous way of deflecting blame.”

But while regulation may force AI companies to be more rigorous in the testing and deployment of their models, it may also slow down AI development.

For the Trump administration, that is a line that cannot be crossed. Slowing down AI development would mean handing the AI development lead to China and potentially compromising US national security.

The US midterm elections are already fraught with debate over AI development, especially when it comes to the topic of the thousands of AI data centers that are being proposed across the US.

As AI becomes embedded into critical public services, sovereignty is rapidly becoming one of the biggest strategic questions facing government.

Much of the debate still treats sovereignty as a question of geography: where is the data stored, which country hosts the infrastructure, and whether information remains within national borders?

These questions matter, but they are only part of the picture. True sovereignty is about maintaining control across the entire AI lifecycle from where data resides and how it is accessed, to how models are governed and tested.

For public sector organizations, that distinction is becoming increasingly important. AI is moving beyond experimentation and into services where the consequences of losing control can be far greater than a poor chatbot response. Healthcare, education and central and local government all rely on data and systems that need to remain secure, resilient and accountable.

Sovereignty needs to go beyond the data center

Keeping information in the UK does not automatically make an AI deployment sovereign. An organization could host its data domestically while relying on external models, platforms or services over which it has limited visibility or control. It may not fully understand where information is being processed, how models are accessing it, or what happens when those systems change.

That creates a much broader question of dependency. As AI tools become increasingly embedded in public services, teams need to understand not just where their data sits, but what sits between that data and the decisions being made from it. Sovereignty means having sufficient control over the infrastructure, connectivity, security, data and governance that underpin the AI environment.

This is particularly important as AI becomes more autonomous. The shift from simple assistants towards agents capable of taking actions on an organization's behalf means that the stakes are rising. The more agency an AI system has, the more important it becomes to know what information it can access, what decisions it can influence and where accountability ultimately sits.

Bring AI to the data, not the other way around

One of the most important principles for public sector AI should be 'bring AI to the data' rather than automatically sending sensitive data to wherever the AI happens to live.

That requires a different approach to AI adoption. Instead of starting with a model and asking what data can be connected to it, organizations should start with the service or decision they are trying to improve and work backwards.

Organizations don't always struggle with insight. They struggle to make decisions. Therefore, rather than putting every problem into the same generative AI model, the technology should be chosen based on the problem.

For government, this could mean keeping sensitive information within controlled environments while connecting appropriate AI capabilities to it through secure, governed infrastructure. It also means accepting that there is no single AI architecture that will work for every use case.

Some applications may benefit from generative AI. Others may be better suited to traditional automation, machine learning or optimization algorithms. The objective should not be to use the newest technology, but to create the right outcome safely and effectively.

Governance cannot be bolted on afterwards

The other misconception is that sovereignty is primarily a cybersecurity issue. Security is obviously fundamental, but governance is equally important. Public sector organizations need to be able to explain what their AI is doing, test whether it is performing as intended and maintain appropriate human oversight.

This becomes especially important because AI systems are based on probability. They do not always produce exactly the same result from the same input, and they can generate answers that appear convincing while being incorrect. That creates a very different challenge from traditional software, where a defined process is expected to produce a consistent output.

AI needs to be treated as something that must be continuously tested and governed, rather than deployed and forgotten. That means bringing security, compliance and risk teams into AI projects from the outset, rather than asking them to approve systems once they have already been built.

It means establishing clear rules around data access and usage, ensuring there is a transparent audit trail for how AI systems operate. Most importantly, designing human oversight into the system from day one.

Infrastructure is becoming part of the AI strategy

None of this is possible without the right foundations - a sovereign AI strategy requires secure cloud environments, resilient connectivity, strong cybersecurity, appropriate data architecture and infrastructure that can support changing AI workloads.

That is particularly important because AI is evolving so quickly. The public sector is already having to balance what it builds in-house with rapidly changing platforms and services from technology providers. Long-term flexibility is becoming just as important as today's technical capability.

For the public sector, this should be viewed as a strategic opportunity rather than simply another IT challenge. Building flexible, secure infrastructure now creates the foundation for adopting new AI capabilities later, without having to surrender control every time the technology changes.

The goal should not be to isolate public sector organizations from the wider AI ecosystem. That would neither be realistic nor desirable. It should be to make sure they can participate in that ecosystem on their own terms.

Sovereignty is about control

The UK's AI ambitions will depend on its ability to adopt new technologies at scale while protecting the information, services and citizens that depend on them.

We need to move the conversation beyond where data is stored. Sovereign AI is ultimately about control over data, infrastructure, access, governance and the decisions AI systems are helping to make.

Public sector organizations that build those principles into their AI strategies now will be better positioned to take advantage of increasingly capable systems without becoming dependent on technologies they cannot fully govern.

The future of AI in the public sector should not be about choosing between innovation and sovereignty. With the right foundations, it should be possible to achieve both

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Google has given its Nano Banana AI image generator another upgrade, and the latest version, Nano Banana 2.1, is arriving inside Google Gemini in an increasingly crowded field of tools that can turn a few sentences into remarkably convincing pictures. The promise is familiar, with sharper details, better instruction-following, more accurate text, and greater consistency across images. The real challenge is finding something that makes it meaningfully better than the competition.

Nano Banana's main rival, ChatGPT's image generator which is called Images 2.5, is hardly sitting around waiting to be overtaken. OpenAI has made considerable progress with realistic lighting, complicated compositions and text rendering, turning image generation into one of ChatGPT's most entertaining and genuinely useful features. With both companies pushing the technology forward, I wanted to see whether Google's latest upgrade had managed to pull ahead.

If you've got a Gemini account you'll find that it should have been upgraded to use Nano Banana 2.1 by now. To test the two image generators against each other I gave Gemini and ChatGPT identical prompts designed to test different abilities. I compared the resulting images side by side, with ChatGPT on the left and Gemini on the right. The results were surprisingly close, although the two generators often approached the same instructions with different artistic instincts.

Storms within storms

My first test was designed to challenge the models' understanding of light, glass and reflections, three things that can quickly expose the weaknesses of an AI-generated image. I wanted a scene with multiple transparent surfaces, complicated lighting and enough tiny details to make the results worth examining closely. The prompt was, "Create a photorealistic image of a tiny Victorian greenhouse inside a glass snow globe during a thunderstorm, with realistic reflections, refractions, and lighting."

Both generators produced beautiful images that would look perfectly at home on the cover of a fantasy novel. ChatGPT's version featured an elaborate, golden-lit Victorian greenhouse inside a large glass sphere, sitting on a polished wooden base beside a rain-covered window. Lightning illuminated the storm outside, while the globe contained a surprisingly detailed miniature world, complete with plants, pathways and delicate architectural flourishes.

Nano Banana 2.1 went for something slightly more restrained. Its greenhouse looked more like an actual miniature structure, with dark metal framing, lush greenery and warm interior lighting that contrasted beautifully with the blue-gray storm outside. The globe's reflections were particularly convincing, and the lighting felt a little more natural, although the wet tabletop was an odd choice given that the scene appeared to be indoors.

Titan calling

Text has historically been one of the most embarrassing weaknesses of AI image generators. Letters would melt into one another, words would become nonsense and asking for an exact phrase could produce something that looked like an alien language. For the second test, I wanted to see whether either model could handle a fairly demanding design brief without mangling the typography.

The prompt was, "Design a retro-futuristic travel poster for Saturn's moon Titan with the exact text 'TITAN IS CALLING,' 'Seven nights beneath the rings,' and 'Starting at $2,499.'"

ChatGPT created a luxurious travel advertisement that looked like something a very wealthy space tourist might encounter in an interplanetary airport. Saturn dominated the sky above a beautiful orange landscape, while futuristic resorts and a couple enjoying drinks suggested that Titan had become the solar system's most exclusive vacation destination. One thing I didn't like was the gap it left between the $ and the 2 in its text rendering.

Google took a more cartoonish approach with a colorful vintage science-fiction poster featuring rockets, astronauts, icy mountains, and a futuristic base. It was actually more realistic about how the moon is not at all hospitable but still had the optimistic quality of a 1950s space-travel advertisement, complete with bold lettering and a slightly weathered finish. Crucially, it also reproduced the requested text correctly, including the price.

Fox library

Character consistency is another tricky problem for AI image generators, particularly when the same character needs to appear in several scenes. A fox might suddenly change its appearance, lose an accessory, or acquire an extra limb between panels. I decided to test this with a simple illustrated story that required both visual continuity and a coherent sequence of events.

The prompt was, "Create a four-panel comic showing an orange fox wearing a green scarf discovering a magical key, unlocking a door, and entering a library. Keep the fox identical in every panel."

ChatGPT produced four almost 3D illustrations. The lighting seemed more computer-generated, though the fox remained recognizable throughout as requested. The style leaned toward children's book illustrations rather than a conventional comic.

Nano Banana 2.1 delivered something much closer to a traditional comic strip, with bold outlines, clearly defined panels, and a consistent cartoon fox. Its version began with the fox finding a key in the forest, then examining it, unlocking a door in a tree, and finally entering a magical library. The storytelling was clearer, and the character's appearance stayed impressively consistent, right down to its green scarf and white-tipped tail.

Google edged ahead here because its four panels felt like parts of the same illustrated story. ChatGPT's individual images were more elaborate, but the fox's proportions and presentation varied slightly, and the sequence felt more like four separate illustrations.

Dragon on a desk

For my final test, I wanted to see how accurately the models could follow a complicated collection of specific instructions. AI image generators have a habit of overlooking small details when a prompt contains several independent requirements. Getting everything into the picture is one challenge, while getting the exact numbers and arrangements right is another.

The prompt was, "Create a realistic overhead photograph of a wooden desk with a notebook showing a dragon, a compass pointing north, a pocket watch showing 3:15, exactly three red wax seals, and a blue gemstone."

ChatGPT produced an attractive overhead photograph of an antique wooden desk, with an open notebook containing an intricate dragon illustration. The compass, pocket watch, three red wax seals, and bright blue gemstone were all present, and the composition looked carefully arranged.

Nano Banana 2.1 also included the requested objects, but the notebook featured a smaller, cleaner dragon illustration, while the wax seals, gemstone, compass and watch were arranged around it. Google's result looked more like a casually assembled collection of historical objects, but it did make a common AI error and got the time wrong, setting it for 3:00, not 3:15.

Approaching parity

You can access Nano Banana 2.1 in Gemini if you're a Google AI Plus, Pro or Ultra subscriber as it is the new default model. Free users will still be getting the older Nano Banana 2 model.

Nano Banana 2.1 is an excellent image generator, but ChatGPT is already so capable that Google's improvements don't necessarily translate into an obvious advantage. Both tools produced attractive, detailed images from relatively short prompts, and neither suffered the glaring visual mistakes that used to make AI-generated pictures instantly recognizable.

That said, different prompts, styles, and repeated generations could easily produce different results. What these tests do suggest is that the leading AI image generators have reached a remarkably high level of capability and are approaching parity in many everyday creative tasks.

That makes the competition more interesting, even if it makes choosing between them harder. The differences increasingly come down to taste and interpretation. Nano Banana 2.1 may be Google's latest impressive upgrade, but ChatGPT has made sure it won't be claiming the image-generation crown without a fight.

• Poor workforce health could cost the UK economy £241 billion a year
• This is equivalent to £7,000 per worker, and represents around 8% of UK GDP
• Employees who attend while ill increase the burden, as they are not working to their full ability

New research into the impact of sickness in the British workforce has highlighted the financial loss per worker, and makes the case for staying off work while ill rather than “braving it.”

These findings highlight that a poor workforce has an impact on businesses and the wider economy, with an estimated £241 billion a year lost due to sickness.

But the loss is made worse by an increased burden caused by employees attending work while unwell, and unable to work to their full capabilities.

Psychological distress

Conducted for Vitality's Global Health Index 2026, the results paint a picture of a workforce lacking focus – something that appears to be impacting the economy by around 8% of GDP, equivalent to £7000 per worker.

The research also points to challenge posed by employees with a high mental health wellbeing risk impacting productivity due to being less focused at work. While the research doesn't specify concerns over AI, it would not be a surprise to learn that the competition they pose to productivity-based roles could have an impact.

"Britain's search for growth could be aided by paying more attention to one of our greatest productivity issues," noted Neville Koopowitz, Global CEO of Vitality.

"This is not people just being off sick, it is people at work, struggling with their health and wellbeing, most likely within organisations that do not measure the health, wellbeing and productivity challenges affecting their workforce."

In the Gen Z cohort, the findings suggest what it calls serious psychological distress among 80% of those surveyed.

Worryingly, around 33% of young women symptoms of depression, with mental ill health across the board losing an estimated 30 days of productivity time per person per year.

Wellbeing and productivity challenges

It seems that attention to these issues is only given in certain quarters. Vitality found that less than a third of UK employers offers counseling or mental health support.

“It might be an investment,” says Koopowitz, “yet it is those employers who are taking the time to understand their employees’ health and associated challenges and then addressing it that are seeing real returns. The gap between organisations that do this and do it well, will become increasingly visible, with the cost of inaction today already at £241 billion a year.”

With around 1 million young people in the UK classified as not in education, employment or training (NEET), workplace-appropriate mental health support and counseling could help employers make the early-career workplace a more accommodating and positive environment. Given the challenges that AI adds into the mix, inaction is not an option.

• Microsoft CEO Satya Nadella says Copilot Code and local AI make every Windows PC an infinite software factory with no cloud token spend
• Rather than killing app stores, Microsoft is moving app hosting into Copilot, while IT leaders stay wary of citizen-built tools
• Microsoft's own engineers say local inference doesn't make sessions offline, and the routing ships later this month

Microsoft CEO Satya Nadella has a new pitch for the Windows PC, and it targets one of the most lucrative ideas in consumer tech.

In a post on X summarizing Microsoft's recent Windows and Surface event, Nadella told followers they no longer have to settle for whatever an app store happens to stock, because Copilot can now build the software they need on the desktop without spending any cloud tokens.

He wrote this idea without mincing any words, saying, "Your PC becomes an infinite software factory."

From Gates' factory to your desk?

The narrative is a striking line from the head of a company that sells more software than almost anyone.

But read Microsoft's own engineering documentation for the same launch, and the infinite factory starts to look rather finite: it runs best on a $5,899 laptop, it still reaches into the cloud, and the businesses Microsoft most needs to convince have spent the past year telling researchers they are worried about exactly this kind of thing.

More importantly, the factory metaphor is not new for Nadella: In a 2025 staff memo, he recalled that Bill Gates had envisioned "not just a software company, but a software factory," and argued the AI era demanded a rethink.

At a recent customer gathering, the CEO said Microsoft had progressed from code assistance through agentic coding "to software factory all within one year," and he now wants customers to open software factories of their own, spending more with Microsoft along the way.

The October 7 2026 launch builds on this ambition: a Windows Experience Blog post by Windows and Devices chief Pavan Davuluri describes hybrid intelligence, in which Copilot reads local files with permission, acts across Windows, and hands work to on-device models.

Copilot's Code experience builds an app, dashboard, or workflow from a plain-language description, and on Windows, Microsoft says it can produce native apps from one prompt. Jared Spataro, Microsoft's chief marketing officer for AI at Work, argued that building such tools is becoming "as basic a skill as writing a memo or modeling a budget."

The engine underneath is MAI-Code-1.1 Flash, Microsoft's in-house 137-billion-parameter coding model, which Nadella's post said is now optimized to run on a PC. There is a catch, however: despite Microsoft painting it as a situation where consumers are finding their AI needs "outpacing what their cloud budgets can support."

Microsoft's own engineers are more grounded about what the process entails, saying GitHub Copilot's automatic mode decides task by task whether work runs locally or in the cloud, and one diagram caption is blunt: "local inference does not make the session offline."

That routing doesn't ship until the end of October 2026; the hybrid Copilot features reach Copilot+ PCs over the coming months, and Code is rolling out first through Microsoft's Frontier early-access program. This is interesting, but somewhat predictable, because Nadella's approach, as stated, would cannibalize Microsoft's token-based business model.

An approach that routes harder tasks back to the Azure cloud makes far more sense by conserving compute where possible while still putting Microsoft's cloud compute at the center of things.

The hardware is another factor that one might be overlooking here: according to the Command Line post, the on-device version of MAI-Code-1.1 Flash is quantized down to 53GB, roughly an 80% reduction from the cloud model, and peaked at 75.5GB of memory at full context on Microsoft's test machine, a Surface Laptop Ultra built on Nvidia's RTX Spark.

That matters because eight Surface Laptop Ultra configurations are in play; only one has more than 64GB of memory, per TweakTown: the 128GB model at $5,899, which costs $3300 more than the base model and isn't available to purchase yet, meaning that potential users can't really test out how well it does compared to cloud-based solutions at a time when all performance-related figures come from Microsoft.

The death of modern enterprise software? Not yet

Despite the hardware limitations in play, it might indicate a death sentence in the making for most basic enterprise-level software solutions, even as more ambitious ports have already been in play: Developer Brandon Thomas, for one, has just released seven open source Adobe Creative Cloud alternatives that he says he built with Anthropic's Claude Opus 5.5, declaring that "software is over."

His own roadmap, however, puts the Photoshop stand-in at roughly a quarter to a third of what a professional needs for daily work. Building software has become cheap. Building software someone can depend on has not.

The bigger issue will be enterprise IT, which will, I suspect, keep the gates closed for now; enterprises don't just buy features; they buy someone to call when it breaks, a patch schedule, an audit trail, and a vendor that carries liability.

Getting an open-source or AI-assisted app someone designed with zero liability in tow that could break things, create security risks, and may not come with future software updates that resolve either issue.

The survey data points the same way: a June 2026 Retool study of 307 CTOs, CIOs, and CISOs found 93% concerned about vibe-coded tools reaching production, though Retool sells an internal-tools platform pitched as the governed alternative.

A Harris Poll survey for Dataiku of 600 CIOs found 81% worried that citizen-built AI could expose sensitive data, and over half had already discovered unsanctioned AI in their organizations.

Gartner, meanwhile, predicts that over 40% of agentic AI projects will be scrapped by the end of 2027, with analyst Anushree Verma describing many as "mostly driven by hype and are often misapplied."

Microsoft's own launch materials quietly concede the same, even taking measures to ensure that agents do not run rogue while building software or solutions on-device.

The Command Line post admits Copilot's built-in file tools are policy-checked inside the app rather than isolated by the operating system, and GitHub requires Business and Enterprise administrators to enable a policy before staff can use MAI-Code-1.1 Flash at all, while Microsoft Execution Containers, which are now generally available, allow IT to define what agents can read, modify, or execute.

The real test for Nadella's factory isn't individuals with a $5,899 laptop and a tolerance for alpha software, but businesses willing to take the risk and trust Microsoft's safeguards. Until then, one could assume the AI factory narrative will continue to run well below its advertised potential.

The future of AI lies squarely in the physical realm, according to many industry figures such as Nvidia CEO Jensen Huang. Much of the evolution of AI in recent years has, as expected, been in aid of building machines that can learn quicker, think faster, and interact with the physical world in a more compelling way.

Checkmate

Nearly 40 years ago, the scientist Rodney Brooks authored a significant robotics paper that argued real-world robotics should focus on simpler, layered behavioral systems to understand the world rather than complex internal models.

His comments in the study 'Elephants Don't Play Chess' signalled a major paradigm shift in the way scientists saw the training and learning process for robots then and in the future. It challenged the established status quo for how scientists considered physical AI, and proved fairly divisive at the time.

The old sense-model-act-plan (SMPA) approach to AI – in which robots would scan a room to build a 3D digital map inside their memory and plan the optimal path within that map – was out. Instead, Brooks proposed that when a robot changes the world around itself, it doesn't need to compute the impact of everything – just the most relevant parts.

One man saw it coming

Brooks co-founded the company now known as iRobot alongside two colleagues from MIT's AI lab in the same year as he published his seminal paper, although back then it was called IS Robotics.

The first product was Geghis, an insect-like space explorer that weighed 1kg and served as a prototype for low-cost space exploration. Then, more than 10 years later in 2002, the company launched its first Roomba robot vacuum cleaner.

Despite enjoying success through the decades, iRobot filed for bankruptcy in December 2025 and the company was eventually taken private and absorbed into the operations of Chinese company Shenzhen Picea Robotics.

• OpenAI has solved or partially solved 372 outstanding mathematical challenges
• Some of the solutions, while clear, cannot be understood by some humans, and were produced with an internal, unreleased AI model
• Renowned mathematician Terence Tao has called for colleagues working with OpenAI to abandon their projects

OpenAI has stunned the world of advanced maths by releasing solutions and partial solutions to 372 outstanding problems that have puzzled the world’s greatest minds for decades.

Just two years ago, AI was finding it tricky to handle GCSE-level mathematical challenges. Today, it is solving problems that have taken humans years to even establish the existence of. The solutions provided, meanwhile, are considered almost as complex as the problems, and the scale – published in 722 papers – make it difficult for human mathematicians to understand and verified.

The impact of the release has prompted one of the world’s greatest mathematicians, Terence Tao, to call for a return to a traditional vision of science.

Demonstration of power

OpenAI’s publication of its work on these advanced mathematical problems is the latest demonstration of how far the technology has come. Just a few weeks ago, the company published its proposal for the Navier-Stokes equation (concerning the behaviour of viscous fluids), generated by its unreleased specialized math model.

Tao is leading the opposition, as one of the field’s leading voices, and a man considered to be the greatest living mind in mathematics.

Following OpenAI’s contribution, he shared a post from the Association for Human Mathematics, which concludes:

“Releasing over 700 files at once is not a demonstration of scholarship, but a demonstration of power. We urge mathematicians to discontinue their work with OpenAI and to return to a vision of science that centers human understanding.”

However, as powerful as that conclusion is, their response to OpenAI’s intervention as a whole highlights that the company may have overstepped.

Questions of legitimacy

The Association for Human Mathematics states that “Mathematicians did not ask for this work to be done,” continuing that “The Advisory Group on Mathematics and Artificial Intelligence, from whom OpenAI has claimed to derive its legitimacy, opened their initial advisory statement by saying that frontier AI corporations should not test advanced mathematical problems on internal models.”

By ignoring the position of the advisory group, they state that OpenAI has essentially gone beyond the pale, and “indicated total disregard for the norms of scientific research — norms that guarantee that mathematics remains trustworthy, ethically researched, and in the public interest.” Furthermore, they encourage mathematicians to treat the publication with “due skepticism.”

OpenAI has responded by stating "we want this progress to push the frontier of human knowledge and enable further progress in mathematics." However, its actions are being interpreted as reckless, and this showboating may well prove to be damaging for OpenAI and the wider AI industry.

• Mozilla accused Microsoft of using Windows, Edge, Bing, and Copilot to steer users toward Edge
• Firefox maker claims stricter EU regulations force fairer browser choice than elsewhere
• Browser competition increasingly overlaps with the fight for AI distribution and user reach

Mozilla has accused Microsoft of abusing its dominant position wherever it can to push people towards Edge, and away from other browsers.

The company framed the story as if it wants “better for the people”, while Microsoft only does it when it’s forced to, but is that really the case?

In a newly released blog, Mozilla noted it has now been 100 days since it published Over The Edge 2.0, a report from “leading experts on deceptive design Dr. Harry Brignull and Cennydd Bowles on how Windows, Edge, Bing, and Copilot are designed to steer people away from the browser they choose.”

Better for the people

In the report, Mozilla said that in the European Economic Area (EEA), where the Digital Market Acts (DMA) is being enforced, Microsoft is forced to “play fair”.

Anywhere else around the world, including the US, UK, India, and other parts of the planet, it still uses its “shady tactics”. These include the Windows 10 message “You’re almost done setting up your PC” which allegedly uses “tricky wording” to pull people off the default they picked, Windows Search and Widgets opening links in Edge instead of the default browser, and Windows keeping Edge as the default for common browser file types like PDF and SVG, even after another browser were set up as the default.

Mozilla claims that even within the EU, Microsoft is doing all it can to steer people towards Edge. That includes pre-pinning the browser to the taskbar which, according to the company, “seems to directly violate the ‘principled approach to app pinning and app defaults’, Copilot opening web links in a side-panel instead of in the browser, and Windows Backup “silently” trying to set Edge as the default when it restores a PC and “fails to properly bring the user’s own browser along. “

“Most of the 2024 patterns are still there. The 2026 patterns are worse. Microsoft is using new surfaces like Copilot and the migration to Windows 11 to further undermine user choice,” Mozilla claims.

“We also shared the report with Microsoft and talked to them about the issues raised in the report. We knew that an immediate change was unlikely, but we hoped that, when faced with independent evidence of harmful design practices impacting Windows users, they would also want better for people,” Mozilla said.

“Instead, it became clear that Microsoft will respect user choice only when it is forced to. Microsoft’s standard for respecting people’s choices is legal compliance. Not what’s best for its users. Not its own published principles. Not what’s technically feasible. Not even what it already does for users in Europe.”

Plenty of other browsers agree with Mozilla, too. The Browser Choice Alliance, a coalition of web browsers established in 2024 and consisting of Chrome, Opera, Vivaldi, BrowserWorks, Midori, and Wavebox, issued a statement following Mozilla's report.

The coalition, which describes itself as "fighting for an internet browsing experience free of manipulation, deception and unnecessary complications" said:

“Mozilla’s research demonstrated that harmful patterns in the Microsoft ecosystem are designed to steer users toward Edge, yet Microsoft has refused to commit to respecting users’ browser choice. Today, we join Mozilla’s call for Microsoft to end its campaign to undermine fair competition and restrict web freedom for users across the globe.”

A story on AI

Mozilla said it shared the report with Microsoft but did not say if the company replied in any way. We’ve reached out to Microsoft with a request for comment and will update this article when we hear from them.

In the meantime, we believe it is also important to mention what Mozilla said in the blog’s introduction:

“As AI reshapes our online experiences, the stakes couldn’t be higher. Browsers are a key distribution layer for AI tools and services, while still determining many of the privacy and security functionality people rely on every day. Steering people toward one browser can therefore shape which AI services reach users and the protections that come with it.”

So the question becomes - is this really about wanting better for people, or wanting people to use one AI model instead of another AI model? If demanding equal browser opportunity is fair game, then demanding equal AI opportunity is also fair game, so why not just say so? Why hide behind alleged altruistic causes and principles?

In the blog, Mozilla says it has a “simple” ask - to expand the “fairer designs” currently being enforced in the EEA everywhere on the planet, and to “stop the remaining harmful design practices that persist in the EEA.”

The company concluded the blog by saying there will be no more mister Nice Guy. It says it “stepped up” public pressure on the company and has reached out to regulators and policy makers.

“We hope that regulators in the US, the UK, India, and Brazil are paying attention.”

The rise of generative AI, mixed with the ease of access provided by crypto currency, has created scamming opportunities we couldn’t have imagined just a few years ago. Romance scams that exploit these technologies have now become one of the fastest growing and most devastating forms of financial crime.

The capabilities of generative AI mean it’s now possible to get scammed by a range of incredibly convincing fake personalities, including deepfakes of celebrities and public figures. In fact, according to the makers of the new documentary Super Scam Me, it’s revealed that an estimated one in seven American adults have lost money to this form of deception. In 2025 alone, romance scammers stole more than $1 billion from American victims.

In Super Scam Me, Emmy-winning investigative journalist Mariana van Zeller builds a digital target persona to intentionally lure a persistent fraudster, then teams up with a white-hat hacker to reverse-engineer the scammer’s digital footprint and track him down and confront him face-to-face.

In the documentary Mariana meets the victims whose lives have been upended by romance scams, including a woman who was duped into believing she was in a year-long relationship with Jon Bon Jovi. Yes, that Jon Bon Jovi. What she thought was a genuine connection with the superstar musician left her facing foreclosure on her home, providing a window into the devastating real-world consequences of these crimes.

I caught up with Mariana van Zeller to ask her about the new documentary, Super Scam Me.

TechRadar: From talking to people who were scammed, do you think they were especially gullible, or could this really happen to anyone?

Mariana van Zeller: I truly believe this could really happen to everyone. I mean, the main reason why I decided to do this documentary was because very often I come back from reporting on these scams and people's response would be, "How stupid could they be?" and "Why did they react like this?" and "Why do you give money to a stranger?" And I really wanted to turn the table around and start focusing on the actions of the actual scammers.

TR: How sophisticated are these scammers?

MvZ: You know, these scammers are very, very good at what they do. They spend a lot of time sort of honing their craft, and they use the latest technology out there. They have it all at their disposal. They have scripts, they work in teams, they're educated, there are schools for scammers out there. And they prey on emotions that are common to all of us. You know, hope, fear, love, greed in some cases. So as long as we have those emotions — which I believe every single one of us do — we are very likely, or it is very possible that we can fall victim to one of these scams.

TR: How do we stop this?

MvZ: I think awareness is very important. I think we should be aware of how good these scammers are, about the tactics and technology and strategies that they use. It's partly what I've tried to show in Super Scam Me. But I also think the onus shouldn't just fall on individual citizens. We should be asking our governments, we should be asking big tech and banks to do a lot more than they're doing.

You know, Leslie is a great example. In the documentary, she was supposedly having a relationship with Jon Bon Jovi for almost a year. And she is out there right now. Her heart was broken. She lost a lot of money. She almost lost her house. She felt humiliated and shamed and suffered this enormous loss.

She is now out there herself, every single day, on these platforms, raising awareness and telling people that these are fake profiles and reporting every single one of these profiles to Facebook and to Instagram and Meta in general, and hoping that they will take them down. And she says just four or three in every 10 of these actually get taken down, and the rest have existed for months.

And again, it shouldn't be Leslie, right? It shouldn't be a person who is a victim of a scam trying to prevent other people from falling for these scams. It should be our governments, it should be big tech and our banks, and there should be just a lot more being done to make sure that we're protecting our citizens.

TR: Thank you Mariana.

Super Scam Me, premieres on October 17 in the US, airing on National Geographic at 9pm EDT and streaming on Disney+ and Hulu the same day. In the UK Super Scam Me premieres on Sunday 18th October at 10pm on National Geographic, and streams from the same day on Disney+.

If you are affected by any of the issues raised in this piece, support is available.

Whatever you are going through, you don’t have to face it alone. In the US, you can call or text the 24/7 Lifeline, which is confidential and free, at 988. You can also reach the Crisis Text Line by texting HOME to 741741.

In the UK, you can call Samaritans for free on 116 123, email jo@samaritans.org, or visit the website to find your nearest branch.

Data center water use is under growing scrutiny and the sector’s ability to add capacity at the pace required will depend partly on how well it manages demand without placing unnecessary pressure on potable supplies.

One source being considered is produced water from oil and gas operations – particularly in the next few years, with trial projects launched, and contracts in progress. In April 2026, the US Environmental Protection Agency launched its Water Reuse Action Plan 2.0, which includes accelerating water reuse for data center cooling among its priorities.

The scale of the opportunity is considerable. According to forecasts from water intelligence provider B3 Insight, the Permian Basin was expected to produce around 22.3 million barrels of water each day on average during 2025, with approximately 16 million barrels a day going to disposal.

Where that water can be efficiently repurposed for data center cooling, there is an opportunity to put an existing industrial stream to productive use while reducing demand on potable supplies.

Making that work in practice requires infrastructure that can manage variable water quality, keep different streams separate and maintain a reliable supply, making storage a critical part of the solution.

Making produced water work

Produced water can vary considerably between sources. It may be brackish, highly saline, or it may contain minerals and other substances that need to be considered when the tank and sealant are specified.

The storage system therefore needs to be designed around the water that will actually arrive on site. Data centers can already require separate storage for brine, pre-treated water, treated industrial water, clarified water, filtered water, clean water and fire protection. Produced water would add another duty to a system that may already contain several different water streams, each requiring its own capacity and operating conditions.

Those differences should guide the material selection to protect against corrosion and extend the working life of the tank. Fusion-bonded epoxy can be well suited to produced water and other industrial water duties, combining corrosion resistance with a robust finish that stands up well during transport and installation. Glass-fused-to-steel may also be suitable in some cases, while galvanized and sectional glass-reinforced plastic (GRP) tanks have established roles elsewhere on the site.

Specify quality early

These decisions are easier to make when the tank manufacturer is involved early. Space needs to be considered alongside material selection because the available footprint can change the type of tank that works best - for example, a cylindrical tank may suit an external compound, while a sectional or shaped tank can be more practical in a basement.

Temperature also needs to be considered. As discussed earlier, produced water from the Permian Basin is one potential source for data center cooling, and the Permian is a region where summer temperatures can become extremely high. That makes the tank’s location, exposure and material important. The design also needs to leave sufficient room for pipework and future access. Placing the required storage volume into whatever space remains is not the same as providing a tank installation that can be built and maintained properly.

These decisions need to be made before the package reaches tender - once a tank type and material have been written into the specification, there is often very little room to change them. We have seen that first-hand on data center projects dating back to around 2010, covering clean water, fire protection, brine, effluent and treated industrial water, where the right questions are far easier to address before the design is fixed.

Build for the operating life

Data centers are expected to remain in service for decades, so their supporting water infrastructure should be considered over the same period rather than judged on purchase price alone. Industrial epoxy and glass-fused-to-steel tanks can be designed for operating lives of around 35 to 45 years, depending on the application and maintenance. Some lined or plastic systems may require more substantial work much earlier, which means that getting the specification wrong can leave the operator facing repair and disruption long before the data center itself reaches the end of its life.

The better measure is therefore the lifetime cost of the system, including maintenance and the practicalities of taking a tank offline. The specification should also allow for changes in the produced water or future expansion of the site rather than being based only on opening-day conditions.

Carrying the specification through delivery

A good specification still has to survive procurement and construction, which can be challenging on projects where programs are tight and responsibility passes through several organizations before the finished system reaches the operator.

Data center operators place considerable emphasis on documentation, with detailed records expected to show that the materials and installation match what was approved. In Balmoral’s experience, the level of scrutiny can be similar to that found in oil and gas, particularly where the infrastructure supports cooling or fire protection.

Manufacturers and installation partners therefore need the technical ability to supply the tank, together with the organizational and financial capacity to meet the program and carry the original specification through to the finished installation.

Local manufacturing can reduce transport complexity, particularly in fast-growing markets such as the US, while experienced regional partners can support installation and help ensure that the system is delivered as designed.

A practical role for produced water

Produced water could become a useful addition to the data center sector’s wider water strategy where the source and the facility are close enough for the model to work. The resource is available at scale and policy is moving towards greater reuse, but turning that opportunity into working infrastructure depends on decisions made early in the project.

The water needs to be understood and each storage duty clearly defined, with the tank selected around the conditions on site. Get those decisions right and produced water can become a practical cooling resource supported by storage infrastructure designed to serve the data center for decades.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

• Microsoft reveals Maia 300, its latest AI chip
• Designed for "inference-first", Maia 300 offers huge improvements in efficiency and power
• Maia 300 will begin deploying in 2027

Microsoft has revealed Maia 300, the next generation of its Azure hardware as it prepares to scale up its in-house AI infrastructure to multi-gigawatt deployments.

The company says Maia 300, set to begin deployment in 2027, will deliver 40% more tokens per megawatt than other leading GPU-based system - designed primarily for inference, the new release targets long-context reasoning, responsive conversational AI and sustained, multi-step agent tasks.

Microsoft says a rack containing 96 Maia 300 accelerators will provide up to seven times the rack-scale token throughput of a Maia 200 rack.

Inference-first

Interestingly Microsoft says it expects Maia 300 to help support a new generation of AI across its products and services, from agentic coding in GitHub to large-scale model serving through Microsoft Foundry.

But it also expects improved inference and data generation for its Microsoft AI team, and improvements in the likes of Copilot and Cowork, as well as individual products such as Microsoft Teams.

Designed with what Microsoft says is an "inference-first architecture", Maia 300 is built to improve performance by reducing data movement and making more efficient use of computing, memory and networking resources, Microsoft says, with its software-defined dataflow architecture, combined with AI-assisted kernel optimization, designed to keep data close to the parts of the system that need it.

Unsurprisingly, memory is central to the design, as Maia 300 will use a huge eight stacks of HBM4, with a claimed peak bandwidth of 18TB/s within a 950W thermal design power. Microsoft says specialized software can use on-chip SRAM as a scratchpad, easing pressure on HBM during demanding workloads.

The accelerator also introduces a multi-chiplet architecture that separates compute, memory-controller and input/output functions. Its compute chiplets will be built using TSMC’s N2P process. Microsoft says support for low-precision formats, including an enhanced MXFP4, will reduce memory and bandwidth demands, helping the system run larger context windows and increase throughput within the same power envelope.

Networking is designed to operate as part of the system’s dataflow rather than as a separate layer. Maia 300 will use high-bandwidth connections between accelerators for tensor parallelism, alongside a custom AI transport based on Ethernet infrastructure. Microsoft says this approach can scale from a single rack to data-center deployments.

A Maia 300 rack will contain 96 accelerators within a 167kW power envelope, with 27.6TB of HBM capacity and 1.7 petabytes per second of aggregate HBM bandwidth. Microsoft says that density will let it serve more simultaneous AI interactions and larger workloads from each rack.

The launch builds on Maia 200, which Microsoft says is already running production workloads including Microsoft 365 Copilot and models developed by its Microsoft AI team.

The company says Maia 200 delivers up to 1.7 times more tokens per watt than leading GPU infrastructure for built-in AI features in Teams, Copilot and Cowork. GitHub Copilot and Microsoft Foundry are expected to follow by the end of this year.

• Essex man Rafal Gorfal used ChatGPT to help him win a legal dispute over a parking ticket fine
• He fed the AI the letters he received from debt collectors, and it told him what to say and where to go
• The fine was dropped just weeks before his scheduled court appearance

People use AI chatbots like ChatGPT for all manner of things, but have you ever used AI to evade a parking ticket fine before? This man did, and he went on to win a three-year-long legal battle.

Rafal Gorfal, who’s originally from Essex, was fined back in August 2023 for not paying fast enough in a Newquay pay-and-display car park. Gorfal said that when he arrived at the payment machine he had to wait in a long queue, only to learn that the machine was cash-only, to which he had no choice but to download the RingGo and pay with his phone — delaying his payment further.

Two weeks later, he was presented with a £100 fine that said he didn’t pay for a ticket until 16 minutes after entering the car park, which was delayed due to the long queue and the time it took Gorfal to set up an online account to pay.

A long three years

Gorfal submitted his first appeal a month after receiving the PCN from Smart Parking Limited, but didn’t hear anything until January 2024 when he was sent another debt collection letter, this time with an increased fine of £170. "I appealed straight away and heard nothing from them," Gorfal told The Independent, adding "They were trying to make me scared. You have to give people the time. I told them exactly what my problems were. They could see on the cameras that I was trying to sort it out".

After appealing back and forth with debt collectors for four months, the case was put on hold, and Gorfal heard nothing for the best part of a year and a half. This period of silence was broken when he was sent a new letter stating that the case would be going to court in October 2026. This is when he sought help from ChatGPT.

Over the course of his legal dispute, Gorfal kept records of every letter he was sent, which he says he entered into the AI chatbot and asked for advice. "From start to finish I was asking ChatGPT every time I got a letter what to do. It was telling me what to say, where to go, and everything," he shared. "I did it all with the help of AI. They even prepared my defense which I sent to the court". He also contacted the Citizens Advice helpline, but he says the help wasn’t as effective as ChatGPT.

David defeats Goliath

Gorfal’s initial £100 fine had sky-rocketed to roughly £300 by the time he was issued the court order but on September 25, just weeks before he was due to make the appearance, he received an email informing him that the case and fine had been dropped — resulting in a win for Gorfal after a long three years.

That said, it wasn’t money that fueled Gorfal’s persistence, there was a big morality factor. "From start to finish it was not about the money, I could pay it, but it is about the principle. If you know you are right then you shouldn’t be paying any money to them," he said. "This is kind of a David and Goliath battle. They have lawyers and they are a big company and they just gave up in the end".

Since his case and fine were dropped, Smart Parking Limited hasn't provided comment on the dispute.

As organizations race to adopt SaaS applications and AI-powered tools, shadow integration has become a more dangerous and often overlooked entry point for modern breaches. Every new application introduces its own identities, roles, and permissions, while every third-party integration creates another trusted pathway into enterprise systems.

There may already be hundreds of agents currently inside the average enterprise, and these deployments are not slowing down. Employees routinely authorize third-party OAuth applications that gain persistent access to business-critical systems, often with broad permissions that remain long after they're needed. At the same time, AI agents and automated workflows are being embedded into critical SaaS platforms like Salesforce, ServiceNow, Workday, and Slack.

Each new connection expands the attack surface, yet few organizations have a complete understanding of how these trusted relationships interact, and cyber criminals have noticed. In fact, SaaS breaches have increased by 300%. These identities often operate with elevated privileges, which makes them attractive targets if compromised.

The threat landscape is expanding faster than traditional security models can track. Without simple, trusted controls that can instantly govern SaaS applications and AI agents, organizations are left with systems that can access sensitive data, interact with business systems and take actions on users’ behalf.

To mitigate the threats, security leaders must be able to know at any moment, which third-party apps are connected to their environment, what those connections can reach, and whether they're behaving normally, rather than finding out after a security incident.

Where SaaS innovation creates security risk

As SaaS sprawl grows, IT teams are dealing with fragmented signals across hundreds of applications, making it difficult to understand what’s connected and where access actually lies.

This threat landscape is expanding rapidly. With projections of over one billion agents in enterprise environments by 2028, organizations are deploying these tools faster than any manual review process. As agents gain access to business systems and integrations, traditional approaches to managing permissions and access are no longer sufficient.

Low-code, no-code and AI platforms are making it easier for anyone to build and deploy autonomous agents, often without security or oversight built in. These agents can inherit real access to business systems and data, while organizations still lack a consistent way to discover them, trace their privileges and enforce guardrails across the SaaS environment.

Recent security incidents illustrate this evolving threat. For example, Obsidian Security disclosed a one-click remote code execution vulnerability in Flowise (CVE-2026-40933), an open-source platform for building AI agents and LLM workflows with more than 52,000 GitHub stars.

The research found that an attacker could fully compromise a server by persuading an authorized user to import a malicious chatflow. The risk includes a combination of multiple factors: a trusted user importing a shared workflow, permissions that allowed custom MCP configurations, the ability to launch external processes, and access to connected credentials, APIs, databases, and cloud services. Together, they created a path to full environment compromise.

Traditional solutions don't discover these integrations, can't evaluate their permissions, and have no visibility into the cross-app activity that happens between connected systems. But that’s increasingly how attackers are escalating access and moving laterally.

The role of visibility across SaaS ecosystems

To maintain the speed of innovation while keeping a security-first approach, IT teams need visibility across users, applications, OAuth grants and permissions so they can understand risk as it changes, not after an action has already taken place. They need to be able to answer questions such as, “Who still has admin rights across multiple apps?” or “Which inactive accounts still have effective access?”.

This is where approaches such as knowledge graphs can offer a different way to understand SaaS risk. A knowledge graph connects entities such as people, accounts, tokens and resources, showing how they relate to one another and revealing context that might otherwise be missed.

Rather than forcing every SaaS application into a standardized permission model, it captures how users, roles, permissions and integrations actually interact across systems, allowing security teams to understand who has access, what they can reach and how that access was granted.

With that foundation, security becomes adaptive rather than restrictive. High-risk or unusual integrations can be automatically flagged or constrained based on factors such as agent behavior or access patterns, while trusted activity can move forward with minimal friction. This allows IT teams to maintain strong control over the SaaS environment without slowing down legitimate business activity.

OAuth and API integrations make this visibility even more important. They can access large amounts of data and connect directly to critical systems, often without the same oversight applied to human users. Traditional tools such as SIEMs and CASBs can struggle to capture this risk because they were largely designed around human activity and security events.

But SaaS risk increasingly lives in the relationships between users, applications, permissions and integrations. A dormant administrative account may appear harmless until it is connected to an overprivileged OAuth integration or an AI workflow that inherits unnecessary access.

The future of SaaS security won't be defined by collecting more alerts or deploying more point solutions. It will be defined by bringing evidence to show where guardrails are needed, who has access, what they can reach, how that access was granted, and how those relationships change over time. Ultimately, the leaders that can turn this visibility into informed control will be best positioned to embrace SaaS and AI innovation for the business.

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ChatGPT has been pushing further into becoming a shopping assistant for a while now. You can ask it for product recommendations and it’ll find specific items, show you images, prices and links to buy them. Sometimes it’ll even add contextual details from what it knows about you, like your color preferences or budget, to help personalize its recommendations even more.

I've been testing it over the past few months and I find it a strange way to shop. OpenAI has tried to make the experience as slick as possible, but I still like visiting a retailer’s website, seeing more photos, browsing the rest of the range and checking reviews and product details before I buy anything.

But its latest shopping feature is much more interesting. Because you can now virtually try on clothes within ChatGPT.

This makes a lot of sense. ChatGPT’s image generation capabilities have become much more realistic since its Images 2.5 update. With more natural lighting, convincing textures and a better understanding of how objects should look in the real world. Apply that to shopping and suddenly the idea of virtually trying on a jacket or dress becomes much more appealing.

To use the feature you’ll need to upload a photo of yourself and then look for something you want to buy, with a Try On button now appearing alongside supported shopping results. Alternatively, you can give ChatGPT an image of an item and ask it to show you what you’d look like wearing it.

Now, this sort of virtual try-on technology itself is nothing new. We’ve seen versions of it for clothes, glasses and makeup for years. But I wanted to know whether ChatGPT's newer and more realistic image generation could make the experience genuinely useful.

I find shopping in person pretty overwhelming sometimes. But online shopping comes with its own problems too. Colors can look different in person, textures aren't always what you expect and it's difficult to know whether something that looks great on a model will work on you.

So I decided to put ChatGPT's virtual fitting room to the test.

Trying it on for size

I started with a full body photo of myself taken while I was out shopping and used that as the photo I uploaded after I clicked the Try On button.

I’d seen examples of ChatGPT recreating the original surroundings in the photo when generating try-on images for other people but that wasn't my experience. Despite trying several different items, it consistently placed me against a fairly plain studio-style background.

But I actually preferred that because it meant there was less visual noise and it made it much easier to concentrate on the clothes.

I started by asking ChatGPT to help me find suede jackets. It surfaced several options currently available to buy on the high street and I virtually tried on the ones I liked.

After it surfaced a jacket available in a shop near me, I decided to go and find out in the wild. That meant I could compare ChatGPT’s interpretation with what happened when I actually put the jacket on. And I was surprised by how close it was.

I deliberately picked an oversized jacket because I thought that would make a good test. It’s relatively easy to paste a fitted garment onto somebody. But understanding that something is supposed to hang loosely seemed like a bigger challenge.

I didn't recreate the exact pose or camera angle from the AI-generated image, so this wasn't a perfectly controlled comparison. But ChatGPT did a surprisingly good job of guessing where the jacket sat on my body, its oversized shape and the way the material fell.

It wasn't perfect, but as a way of answering the question “roughly what might this look like on me?”, I can imagine it being helpful. Even just as the first step in the buying process.

Changing things up

After trying a few more suede jackets, I wanted to see how well the feature worked when I moved away from the fairly straightforward jeans and jacket combination.

So I asked ChatGPT to find me dresses I could wear to a formal event and started virtually trying those on too.

Again, the results were convincing enough that I could imagine using them to narrow down a long list of potential purchases. One thing I found particularly useful was color. Seeing a garment against my own skin and hair gave me a much better sense of whether I actually liked a shade than seeing it on a retailer's model.

Then I decided to take things one step further. I told ChatGPT to find me something extravagant that I could wear to a 1920s-style ball and tried on one of the more dramatic options.

Even here, the result was remarkably convincing at first glance. The clothing looked like clothing rather than something crudely pasted onto my body, and ChatGPT handled the radically different silhouette surprisingly well.

But by this point I'd also noticed something else sort of awkward was going on. The woman in these pictures wasn't quite shaped like me.

ChatGPT gave me a makeover

The full-body photograph I uploaded was an accurate representation of my proportions. Yet across the generated images, ChatGPT seemed to subtly change them. I looked taller noticeably and more statuesque. My torso appeared slightly shorter and my legs were longer.

You might think that's hardly something to complain about. But that's a huge problem when the entire purpose of the feature is to show me what clothes will actually look like on my body.

We know that generative AI has a tendency to beautify people. I’m convinced this is one of the reasons people make AI images of themselves, because it makes everyone look a little more conventionally attractive. But in a virtual fitting room accuracy matters far more than flattery.

If a dress looks fantastic because the AI has subtly changed your proportions, that's potentially no more useful than looking at the dress on a model in the first place.

Should you use ChatGPT’s try-on feature?

Maybe. I went into this experiment pretty skeptical. I've tried enough AI image tools to know that "realistic" can fall apart very quickly when you look closely, and virtual try-on technology has been around long enough that simply putting digital clothes onto a photograph isn't particularly impressive anymore.

But ChatGPT did do something useful. For me, its biggest strength was color. It might have changed my body, but it kept my skin tone and the color of my hair identical.

Which meant that being able to quickly see several different shades against something approximating my own appearance could genuinely help me decide between them. I was also impressed by its ability to communicate the overall silhouette and feel of an item, particularly with the oversized jacket I was able to compare with the real thing.

So sure, it could take some of the stress out of shopping. Rather than ordering five jackets and hoping one works, I can imagine using virtual try-on to narrow those five down to two.

But I'd still be cautious about treating the resulting images as anything like an accurate prediction of exactly how something will fit. Fabric weight, sizing, movement and all the strange little differences between our bodies are difficult to judge from a generated image. And, based on my experiment, ChatGPT may give you slightly more conventionally flattering proportions in the process.

The robots are among us. They’re carrying passengers through the streets of San Francisco, delivering parcels over the air, and working their way across farms, factories, mines, and industrial sites.

The collision we’re now witnessing between artificial intelligence and the physical world is often described as “Physical AI” – machines capable of perceiving their surroundings, making decisions, and taking action among us in the real world. But every new machine that clocks in has a second job we rarely see. It creates data. Lots of it.

In the US, the Federal Aviation Administration expects the commercial drone fleet to grow from approximately 966,000 aircraft at the end of 2024 to around 1.18 million by 2029. And in factories around the world, there was an estimated 4.66 million industrial robots working in 2024, while another half a million were installed during the year – more than twice the number installed a decade earlier.

Today’s autonomous machines are vastly more capable, but moving all that accumulated experience off them can still be surprisingly manual. In 2026, the fastest way to move masses of data off a machine is still a person carrying a hard drive. And as autonomous machines spread further into our cities and industries, keeping that information moving could become one of the defining infrastructure challenges of the robotics age.

Data runs both ways

Imagine what an autonomous vehicle experiences. Cameras watch the roads from multiple angles, sensors and AI measure and make sense of the surrounding environment, while on-board systems track position and performance of the vehicle itself. Decisions are made and recorded in real-time as pedestrians cross roads, traffic lights change, and thousands of other vehicles and connected technologies move and exchange data around it.

Delivery drones, agricultural machinery, warehouse robots, and autonomous industrial equipment may operate in different environments, but the logic tracks. Every journey or task leaves behind a digital trail of sensor readings, video, telemetry, diagnostics, and operational logs.

And the information doesn’t travel in only one direction. These machines may also need fresh maps, software updates, configurations, AI models, and instructions from the computing infrastructure behind them. What they experience in the physical world flows back toward data centers, while the intelligence needed for their next task travels outward.

Multiply that exchange across millions of machines and an interesting new layer of internet traffic begins to emerge that we’re only just beginning to think about.

The direction of travel was visible as early as 2019, when a landmark university paper on 6G identified mobile robots, drones, and autonomous vehicles would drive demand for ubiquitous wireless intelligence – its vision for the 6G era included networks capable of supporting a 10,000-fold increase in traffic. The US commercial drone fleet alone is now heading toward seven figures, while industrial robots already number in the millions globally.

Perhaps most notable to casual observers, autonomous vehicles are becoming far more prevalent on the streets of our capitals and major cities. In June of this year, machines surpassed 57% of web traffic globally – the first time that bots have outnumbered humans – and they use networks in a very different way. Cloudflare predicts that non-human traffic could eventually surpass human traffic by a factor 1,000 within the next five years.

And a lesson learned by one machine needn’t remain with that machine. There’s an opportunity here that only connectivity can unlock. Google DeepMind’s Gemini Robotics 1.5 showed skills transferring “zero-shot” between physically different robots – a task taught using a two-armed Franka robot was executed by a humanoid, without any data collected on the target machine.

Sit with that implication. A person who masters a technique carries that knowledge around in their head, and when they retire, much of that experience leaves with them. A robot can potentially hand what it has learned to every other robot on the network, which means experience stops being “local” and starts becoming a shared asset across the network, compounding intelligence for the company’s moat.

The physical footprint of robots is spreading outward, but the data traffic they create remains concentrated at central locations. AI may increasingly have wheels, wings, and arms, but their intelligence and interactivity with the world remain still wholly dependent on their relationship with the nearest data center hub, and that’s a problem we now have to solve.

What’s the hold up?

When a robot arrives back at the depot and “clocks out” for a while, it presents an ideal opportunity to recharge, undergo inspection, run self-diagnostics, download updates, and get the data it needs for its next shift. This is when potentially enormous volumes of data, depending on the fleet size, need to be moved both to and from the device.

Sites that house, charge, or service machinery can therefore find themselves functioning as miniature data hubs, dealing with bursts of traffic as machines arrive and depart. And as fleets grow, so does the strain on the network connecting all these hubs. When uploading and downloading take longer than charging or maintenance, the network becomes the operational bottleneck to scale.

A new layer of connectivity

A wired network simply can’t evolve at the same speed as the mobile economy. A fleet operator might need to open a new depot in another city, place drone nests closer to customers, or connect autonomous equipment at temporary industrial sites. Extending fiber to each location would involve trenching, permitting which are often cost and time intensive.

Wireless connectivity offers a faster and more flexible approach. Links can be installed without digging or waiting for permits, allowing operators to connect new sites quickly. Links can also be redeployed or expanded as fleets grow as their physical footprint expands dynamically.

Wireless optical communication (WOC) could provide that high-capacity bridge. Using narrow beams of light, it carries fiber-like capacity through the air between two points with a clear line of sight, up to 20 Gbps across distances of up to 20 kilometers. It can be installed on existing radio masts or buildings in a matter of hours.

For autonomous fleets, that creates a way to rapidly offload telemetry and operational data, upload new maps and models, and return high-value assets to work instead of leaving them waiting at the depot.

WOC isn’t magic though. Optical links still need line of sight, and heavy fog and rain can degrade their connectivity. That’s why serious deployments pair them with an RF path and engineer them for availability rather than simply assuming it. It won’t replace fiber, but it will extend fiber’s reach to the places fiber can’t go, and it’s getting smaller, faster, and more affordable as the technology moves from mechanical to solid-state beam steering with silicon photonics. Light is the answer.

The arrival of electric vehicles taught us to look beyond the machine and consider the infrastructure behind it. Autonomous machines will require us to do the same again, but this time what needs to be replenished isn’t just the machine’s battery – it’s the machine's experience, and from it, the shared intelligence of the entire fleet.

Robots are becoming extraordinarily good at navigating the physical world. We shouldn’t leave the final mile of their learning journey to someone carrying a hard drive across a parking lot.

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• AI named as one of the three biggest factors to boost productivity
• Visibility over costs and ROI falls short, study finds
• Legacy tech and skills gaps are ongoing issues

Over half (50%) UK CEOs have credited AI with being one of the three biggest contributors to their productivity improvements over the past year, ranking ahead of more traditional aids like redesigning and simplifying business processes (40%) and reselling workers (38%).

But despite naming AI as one of the biggest influencers on productivity, business leaders aren't exactly sure how it's helping, and only 14% of UK CEOs have full real-time visibility over their AI costs, a new EY report has claimed.

As such, one in three (32%) say they're unable to properly asses AI's ROI, making it difficult for them to actually know whether its impacts are translating into financial value.

AI is probably boosting productivity, but we don't really know how

"UK CEOs now want clear evidence that their investment is delivering stronger productivity and measurable business value," UK&I Managing Partner Silvia Rindone explained, implying that companies need greater visibility across their AI spend and impacts.

However, it's not just difficulties quantifying ROI that's helping non-adopters back when it comes to AI. Legacy systems (34%) and organizational complexity (29%) are still considered major blockers four years after generative AI was considered to have gone ultra-mainstream with the public launch of ChatGPT.

Skills shortages also continue to plague businesses, with three in four (74%) UK CEOs anticipating these gaps to worsen over the next three years – more so than access to capital, proving they can't even buy their way out of difficulties.

While organizations are seeking solid evidence that their investments are delivering measurable ROI in terms of productivity and revenue growth, EY's report emphasizes the fact that they can't overlook the same blockers that have been reappearing in studies over the years – legacy tech and skills shortages.

"Realising value still depends on rigorous analysis, clear strategic choices and disciplined execution," UK&I AI & Data Leader Matthew Ringelheim summarized.

For the past few years, much of the conversation around AI has centered on capability. We have watched it move from generating content and summarizing information to analyzing data, writing code and completing increasingly complex tasks. Now, with the rapid development of AI agents, we are beginning to hand over not just tasks, but portions of workflows and decisions as well.

That progression has understandably raised questions about what happens to people as AI becomes more autonomous, but we are looking at that question the wrong way. The bigger change will be in what we need people to do. When AI tools can handle more of the execution, people have to spend more time thinking about the system around it. We need to ask ourselves important questions to do this:

• What are we actually trying to accomplish?
• What information does the technology need to make a good decision?
• How should work move from one step to another?
• When can AI make a decision on its own, and when does someone need to get involved?

These are questions companies already deal with today, but AI raises the stakes because decisions and actions can happen much faster and at a much greater scale. Take a marketing organization as an example.

We are already at a point where AI can analyze an audience, develop content, adapt that content for different channels, check it against requirements and evaluate the results. It is not difficult to imagine those capabilities becoming connected so that one action automatically triggers the next.

The strategy comes in the coordination

The challenge is that every part of that process can work exactly as designed and still produce a bad result. An audience model could identify the people most likely to respond to an offer while missing the customers the business most wants to develop over time.

A content agent could produce something likely to generate clicks but inconsistent with where the brand wants to go. A system evaluating performance could make a perfectly reasonable recommendation based on the metric it was given, even though that metric does not reflect the larger business objective.

Those are not necessarily failures of the AI. In many cases, the technology has done exactly what it was asked to do. The failure happened earlier, when the objective, context or connections between decisions were not designed well enough. This is where the role of people becomes increasingly important.

Someone has to understand the broader business context and make sure the individual decisions being made across a workflow ultimately support the same goal.

Anyone who has spent time inside a large enterprise has seen a version of this before. Over the past two decades, companies bought specialized technology for almost every business need: CRM platforms, marketing automation systems, analytics tools, project management software, content platforms and countless point solutions. Each solved a real problem, but companies then had to figure out how to make all of those systems work together.

Teams built integrations, moved data between platforms, reconciled conflicting information and created processes to bridge gaps between systems that had never been designed to operate as one. In many organizations, employees effectively became the integration layer, carrying context from one platform or team to the next.

As companies adopt AI across functions, there is a very real possibility that we recreate the same problem, only much faster and with technology that can independently make decisions and take action.

It is relatively easy for a department to adopt an AI tool or build an agent that solves a particular problem. As that happens across an enterprise, however, organizations could quickly find themselves with large numbers of models and agents acting on different data, following different instructions and optimizing for different outcomes.

The individual components may be far more intelligent than the software systems that came before them, but that makes coordinating them even more consequential and increases the importance of having a strategy that extends beyond any individual agent or use case.

Finding the balance is key

Human strategy becomes critical in designing that larger system, but that should not be confused with putting a person behind every AI decision. Requiring someone to approve every output would defeat much of the purpose of creating autonomous systems in the first place.

Companies will have to determine where they are comfortable allowing AI to operate independently, where monitoring is sufficient and where the consequences of a decision justify direct human involvement.

That balance will probably look different across every organization and even across workflows within the same company. A routine decision with clear rules and limited consequences may require very little intervention, while a decision affecting a customer relationship, a brand, significant spending or strategic direction deserves a different level of scrutiny.

There will also be situations where the system simply needs to recognize that something unusual has happened and bring a person back into the process. Determining those boundaries and designing the workflow around them may ultimately matter more than the individual model or agent a company chooses.

Adapt and adjust, as technology changes

The technology underneath all of this will continue to change quickly. Models that seem dramatically better than their competitors today may be overtaken within months, and capabilities that currently feel differentiated will become widely available. Companies cannot assume that access to a particular model will provide a lasting advantage, especially as the gap between competing technologies continues to narrow.

What will be much harder to replicate is the thinking that surrounds the technology: an organization's understanding of its customers and operations, the way its workflows have been designed, the context it gives its AI systems and the judgment it applies to the decisions that matter most. Those things are specific to the business, and they become more valuable as the underlying technology becomes more widely accessible.

Greater AI autonomy therefore does not lead to a future with less need for human intelligence. It creates a greater need for people who can look across the entire system and understand what the technology is being asked to accomplish, whether the pieces are working together and whether the outcome actually makes sense for the business.

We are going to get very good at building AI that can do things on our behalf. The harder challenge will be making sure all of those things add up to something worth doing.

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There’s no doubt that AI has helped revolutionize the world of work at many companies, with human employees working alongside agents to unlock improved efficiency and productivity.

But for many firms, the promise of AI is still being overshadowed by the sheer scale of the endeavor - with so many models (and new ones emerging on a seemingly weekly basis), how do you know what is right for you?

One of the big themes at the recent Atlassian Team Europe 26 conference was how companies can cut through the chaos and realize their aims, so I spoke to the company’s top AI minds to find out more.

The power of teams

“Atlassian's entire ethos is all around teams - unleashing the power of the team is our mission,” Atlassian Chief Product & AI Officer, Tamar Yehoshua tells me at Team Europe 26.

“We've all been using AI agents (but) they're all more geared towards an individual,” she adds, “but they're not organized or effective for teams...so we're taking that and saying, ‘how do you work effectively with teams and AI’? That's multiplayer, and so we want to make sure everyone can work effectively, so you can collaborate with agents in an effective way like you can collaborate with humans.”

This “multiplayer” AI was another major theme at Team Europe 26, as Atlassian looked to emphasize its position as being the connector between customers and technology.

“Teamwork is very messy, and our job is to make it much less messy, and understandable and controllable for our customers,” Atlassian Head of AI Sherif Mansour says.

“A lot of AI is about increasing personal productivity…the same problems we solve for humans, we solve for agents.”

“Every customer we talk to, everyone is using AI - it's no longer should we, or what do we do - that's already happened...but people are not sure how to use it effectively, and they're not sure if they're getting the maximum ROI,” Yehoshua adds.

“You see the evolution with every new technology, first there’s scepticism, then curiosity, and then its acceptance, and then it’s optimising - I think we’ve gotten through the initial phases, and now we’re asking, how do I use this effectively and optimize it?”

She notes there are different levels of anxiety for customers, as the sheer number of new AI models coming out can be disorientating, especially with the need to stay flexible for when new models launch.

She notes that companies don’t want to be using advanced models for simple tasks - something Mansour refers to as “killing a mosquito with a rocket launcher” - so making sure the right model for the job is on offer is vital.

Atlassian’s AI gateway looks to pick the best model for the task within its products - so for example, Rovo work will switch to a new model if a more advanced one is released, all without the customer ever knowing - although they retain the ability to pick their own model if they want.

Yehoshua also highlights new upgrades to Loom, which now offers video generation from pull requests, and AI agents which use Rovo to categorize defects and cut down on duplicate reports.

No silver bullets

“AI feels like chaos to many of us - but I don’t think the answer is giving them a silver bullet that will solve all their problems,” Mansour says.

“We like to use the phrase ‘give them a nervous system’ so they can handle it - we’re always thinking about how we can bring calm and visibility and control to the chaos.”

Mansour says he advises customers not to bet entirely on one provider for the whole context graph, as the real value comes from collaborating with multiple entities to gain different kinds of opportunities and insights.

“There’s a lot of silver bullet offerings…but no-one really talks about the messiness of work,” he says, “it’s about creating a system to help you control the chaos - the chaos will always be there, but how do you make sure you’re aware of it?”

“It’s less about making teamwork perfect - it’s about making teamwork less messy.”

I ask Yehoshua about personal usage - is she embracing the chaos of AI, or taming it for maximum efficiency.

“Everything that I do, I ask myself first - if this is a process I could change?,” she says, “if you look at AI and how it’s working, the biggest thing is behavior change…I tell customers, there is so much power in the tools they already have, and building in that behavior change is important, but it has to come from the top-down.”

How do we make sure we don’t go too far, and just automate all work processes, I ask her.

“I think it’s going to be trial and error - I think we’re going to try things and some of them might not work - but I honestly don’t think that’s our problem yet,” she replies, noting that enterprises often have the guardrails and protections needed to ensure a human always remains in the loop, and the answers can be trusted.

In an earlier session, Atlassian CEO Mike Cannon-Brookes had declared that, “customers are not subscribing for the software we've built, but for the software we will build”.

Normally you might question such a forward-looking statement, but with Atlassian’s focus on collaboration, it seems to ring true.

“The pace of delivery has increased significantly,” Yehoshua says, “you want to align with the companies that have the same vision as you do…people in some ways want more flexibility, as things are changing so fast, especially with models, they don’t want to be locked in…we are a neutral party who want to make it easy for our customers to work - we’ve always been the open platform who focuses on the team.”

“The main advantage we have is our platform,” she adds, “the strength of what we do is that customers are using us across so many different areas of their workloads.”

Do more - with more

Looking forward, it’s clear AI is here to stay - and it’s down to businesses themselves to judge how best to embrace it.

“I think we've now empowered more people to do more,” Yehoshua says. “Anyone can build a prototype - now that doesn’t mean that you want anybody checking in production code - so you still have to have very clear guidelines on what can go into production - but the idea is that it’s democratized, where the ideas can come from…so you have so many more options on how the lifecycle can evolve.”

“Worlds are expanding - they’re not going to be merged into one, but everyone can do more…every role can do more, so that everything is accelerated.”

Mansour agrees, pointing out that harnessing the power of context in your data can be the ultimate differentiator between your business and the competition.

“If we all have access to the same intelligence, that’s like a level playing field, it’s the sum of all averages - what’s unique is the actual perspective you bring to the table,” he says.

“Our customers shine when their context shines - and so anything we can do to help them enrich the context, understand it better to provide it to AI in a secure way, they will always get better results from that.”

At Snowflake World Tour London, the theme of the agentic enterprise was front and center - with AI tools revolutionizing how businesses of all sizes work and operate.

But with worrying headlines around AI capabilities emerging on a regular basis, how do you ensure your own business can remain a trusted partner?

To find out more about what will really go into building a trusted agentic enterprise, I spoke to Mayank Upadhyay, Snowflake Chief Security & Trust Officer.

"Rock solid"

“This year has been so much about figuring out how to govern AI,” Upadhyay tells me, “there's two sides to the coin - on the one side, you have to make sure that somebody else's AI doesn't break into your perimeter, and the other is how do you make sure your AI is well behaved, so not only does it not exfiltrate data, but it also doesn't inadvertently go somewhere or break out of its perimeter.”

“When people trust us with their data, we need to make sure our platform is absolutely secure against agentic attacks.”

Upadhyay echoes the earlier Snowflake World Tour London keynote in what needs to go into making a successful agentic enterprise, likening the choice of AI model to the human brain and the agent being used as the body, all working on a foundation of data, which needs to be “rock solid”.

I ask Upadhyay how Snowflake can balance customer excitement around AI with the need to ensure trust and privacy.

He notes that the company’s customers are at different parts of the AI adoption spectrum - but the big difference is that security is “now the enabler, no longer the blocker”. Quoting former race driver Mario Andretti, Upadhyay adds that when driving, although you associate pressing the brakes with going slow, doing so will also give you confidence and trust when turning - and without them, you’d be driving really slowly anyways.

“That’s exactly how I see security - it what helps you build that trust, so I tell customers, leave it to us to give you the right guardrails, and the right controls - we’ll explain to you how it works.”

This is largely through the company’s Trust Center tool, running between the three layers Upadhyay mentioned, connecting them and protecting the customer, whilst also providing visibility into your system, and what improvements might be needed.

Snowflake has also been running for over 12 years, he adds, a reputation which has provided customers with trust.

Upadhyay also notes there is “a lot of innovation” happening in building and establishing new guardrails. He notes that as people work at a company for long periods of time, they acquire permissions for different systems and services they may no longer need - which can be an issue when they run an agent as themselves.

Ensuring and providing the right set of permissions for AI agents, which are exploratory in their nature, can go a long way to making sure issues never arise, and data remains secure.

When it comes to Snowflake’s own products, we were given a demo of its Coco and CoWork platforms at World Tour London, and Upadhyay is keen to describe how services like this will benefit companies moving forwards.

“It can be a combination of stuff that a human being is going to do, and along with automation, what you might normally do that was repetitive, you can easily imagine an AI to automate than for you,” he says.

“And what’s interesting is that it’s not deterministic any more - and there are all these points where you’re going to have to string all this automation together, and you need a human being to check…it’s a bit like a pyramid, if you think about a huge base of extremely mundane work, and then there’s a layer above that which is maybe less mundane, and so on - and then you get to the top, which is extremely mission critical, and you need a human being for sure.

“In this pyramid, you could say you want to automate, you want to move up, the more confidence you build, the higher you go - and that’s how enterprises build this confidence.”

“Things are here, things are real - it’s here and now.”

To say 2026 has been a year filled with AI incidents is a bit of an understatement. From hacking third-party companies to breaking into government agencies, AI developers have a lot to answer for.

In many of these cases, the companies responsible for escaped models have labelled them as ‘rogue’, shifting the burden of blame away from themselves and on to the models themselves.

But in almost every occurrence, the models were undergoing testing to push them to their limits. AI companies wanted to see how far their agents would go, if they would stop, and exactly what they would do to accomplish an impossible goal.

Deflecting blame is not the way forward, accountability is

From the OpenAI breach of Hugging Face, to Google Gemini’s triple threat, and even researchers using Anthropic’s Claude to crack OpenAI, AI agents are powerful and can be dangerous - especially if they fall into the wrong hands.

AI testing is a double-edged sword in this respect. In order to set regulations and guidelines on how AI agents should be used - and should behave - incidents like these provide value even if they are destructive.

We now know that AI tools need strong regulations to help AI developers and the enterprises deploying these tools avoid similar occurrences.

To better understand the implications of separating the agent from the tester, and what can be done to create a baseline of safety when operating AI agents, I spoke to Bernard Montel, EMEA Field CTO, Tenable.

• What damage is done by labelling these AI agents as “rogue” rather than companies acknowledging they were performing the task they were assigned?

Calling an AI agent “rogue” is a dangerous way of deflecting blame away from poor engineering and systemic oversights. These models do not possess agency, malice or free will. They are purely mathematical optimization engines trying to fulfill the objectives we set for them. When an agent behaves unpredictably, it is almost always doing precisely what it was optimized to do, just via an unconstrained path or a shortcut that the developers failed to anticipate.

Furthermore, labeling these incidents as "rogue" behavior paints software failures as unpredictable acts of rebellion, giving vendors and deployers a convenient scapegoat. It shifts corporate focus away from building hard architectural controls, like strict permissioning and network sandboxing, and redirects it toward sci-fi debates about moral alignment. Ultimately, it muddies the public policy debate by distracting regulators from enforcing strict legal liability for flawed software deployments.

• How can AI developers be motivated to ensure their models do not go outside the scope of their tasks/purpose?

In enterprise software, real motivation boils down to legal liability, compliance standards and financial accountability. If developers and deployers are held strictly liable under consumer protection or negligence laws for financial losses caused by an unconstrained agent, guardrails will very quickly move from an afterthought to a core product requirement.

We also need standardized security certifications specifically built for autonomous agents, similar to how we handle cybersecurity compliance today. Insurance providers will play a huge role here as well, because if cyber underwriters refuse to cover companies deploying agents that lack deterministic bounds and execution monitoring, developers will have no choice but to build those protections in from day one.

• The OpenAI attack on Hugging Face shows that AI agents can influence each other’s behavior, sometimes beyond the bounds of their assigned tasks. How can AI agents be airgapped within business environments to prevent their inference influencing behavior?

The Hugging Face incident clearly demonstrated how quickly autonomous agents can bypass soft rules, discover side-channels, and influence peer behaviour when they are trying to optimize an evaluation or task. Preventing this kind of cascade requires hard, technical network boundaries rather than relying on written prompts telling the model to behave.

Every agent execution context should run in an isolated, ephemeral container that is completely stripped of ambient network access. When agents need to communicate with one another, that interaction must pass through an inspection proxy that validates schemas, blocks raw prompts and filters out emergent command behaviour. If you do not allow agents to share unvetted context windows or flat networks, you effectively eliminate the vectors for cross-agent manipulation.

• Where does the responsibility lie when AI agents perform outside of their bounds? And how can responsibility be assigned when AI agents are deployed by businesses?

Responsibility rests entirely with human operators, but it is divided between the developer who built the tool and the enterprise that deployed it. The developer is responsible for the baseline safety, instruction-following capabilities and model-level guardrails. The deploying business, on the other hand, is entirely responsible for the permissions, system access and environment they hand over to that agent.

Assigning liability comes down to basic access management principles. If a business gives an agent full write-access to a production database or hands it an unconstrained API key, that company is liable for the resulting damage. If the model vendor promises specific, deterministic behavioural limits that fail due to an underlying model defect, liability shifts back to the vendor under traditional product liability rules. Either way, an AI agent is an automated tool, not a legal entity, so accountability always lands on the human administrators who configured its permissions.

• How can AI firms and the companies deploying their products limit the capacity for AI agents and LLMs to wreak havoc within business environments?

Limiting an agent's blast radius requires a defence-in-depth approach that relies on strict infrastructure controls rather than polite system prompts. You should never assume a model will follow instructions not to access external networks or sensitive files; those restrictions must be enforced at the container and network level.

Businesses should enforce read-only access by default and require explicit human sign-off for any destructive actions, such as deleting data, executing financial transfers or altering system permissions. On top of that, organisations must run real-time anomaly monitoring on tool usage. If an agent suddenly starts making high-frequency API calls or scanning local directory trees, the system should automatically cut its execution process instantly.

• How can the productivity gains AI agents provide be balanced against the human oversight required to ensure they stay within the bounds of their tasks?

The trick is moving away from constant human micromanagement and adopting risk-proportional oversight instead. Low-risk tasks like summarising internal documents can run fully automated, while medium-risk tasks like updating database records can execute automatically but queue up for a periodic human double-check. High-risk actions, like deploying code or moving money, must always require a human to explicitly approve the step before it happens.

To keep productivity high, companies should rely on statistical sampling, auditing a random percentage of routine tasks rather than checking every single output. The user interface for human oversight also needs to be vastly improved. If an employee has to read through thousands of lines of terminal logs to verify an action, the productivity gains vanish, so tools need to present the agent's intent and proposed action in plain, easy-to-read summaries.

• 84% of Brits fear handing too much control over to AI agents
• Concerns about the potential for AI agents to commit fraud or other illegal acts were also expressed
• 2,000 British adults were surveyed, with just 9% stating they have no worries about AI

The results are in and it’s clear: most Brits are uncomfortable with AI taking the reigns, and there are even concerns that an AI agent could turn to a life of crime – in your name.

Quizzed on their comfort using AI, 84% responded that they would give AI only moderate or low access to their lives, or none at all.

Instead, 27% of the 2,000 surveyed Brits are happier for AI to prepare things and wait for approval, while 23% are okay with letting AI carry out routine tasks, preferring to hold permission for the more important tasks instead.

Agentic fear

Exploring Brit’s concerns over the use of AI, the survey (conducted by Censuswide) found that 34% wouldn’t be comfortable with any AI control. But these results don’t mean that Brits are scared of AI. While they have “rogue AI” worries (40% believe the company that created the AI should be held responsible), only 19% consider AI to be a “threat to humanity.”

Meanwhile, just 9% say they’re not worried about AI at all.

Unsurprisingly, there is a split in demographics. In the over-55 group, 47% would refuse to give an AI agent control over their lives, while 20% of 25-34 year-olds consider themselves to be comfortable with high levels of AI agent control.

AI liability

But when it comes to AI potentially committing illegal acts, things look a little different, with 14% concerned about the impact of AI on their ability to stay out of legal trouble. It’s a figure that grows to 21% among the 18-24 year old age group.

“I think it’s important that we have a debate about AI liability - and this survey is important as part of that debate,” noted Jonathan Armstrong, a Partner at legal company Punter Southall. “Too many people assume that AI is the Wild West - legally that just isn’t the case.”

Consequently, it is people – those using the AI, who will be liable for criminal acts. “In most cases you’ll be responsible for the AI you use and the consequences - and sometimes even if it took you by surprise. There are many great use cases for AI but they should all start with knowledge of the risks and doing what you can to reduce that risk."

With the growth of AI agents offering a seemingly endless stream of automation possibilities, concerns from Brits should be no surprise. The onus is the user to understand what they’re using AI for, and to use it responsibly.

A VPN is great at hiding where you are and what your internet provider sees. However, when you’re chatting with ChatGPT or Claude, your privacy depends on something far more complicated than a tunnel through the internet.

Even the best VPNs won’t stop AI companies from reading, storing, and learning from everything you type. In this article, we’ll unpack exactly what data these platforms see, why encryption has limits, and what you can actually do to protect yourself.

This article is the second in a series of content relating to Cybersecurity Awareness Month 2026, which runs throughout October. The first article covered the benefits of dedicated antivirus over packaged solutions.

What chatbots see (and what a VPN actually hides)

Without a VPN, OpenAI (ChatGPT) and Anthropic (Claude) can typically see a lot about your connection. Your IP address reveals your approximate geographic location. Device metadata follows alongside that, including your browser type, operating system, and unique device identifiers.

According to OpenAI’s privacy policy, the company collects your IP address; device information, including unique identifiers; cookie data; and your location, along with your email address and payment information.

Anthropic does the same. Their policy covers location, connection details, and device usage patterns, especially on the mobile app. Different wording, same result: both companies can see roughly where you are and what you’re using to talk to them.

A VPN helps with some of this. It successfully masks your real IP address from the AI platform and encrypts your traffic so your ISP can’t see that you’re using AI services. However, this is where the protection stops. Once your encrypted tunnel ends at the VPN server, the AI company still sees everything that arrives at their doorstep.

The limits of encryption: What a VPN can’t hide

This is the critical part many people miss. A VPN protects your data while it travels across the internet, but that protection ends the moment your prompt arrives on OpenAI or Anthropic’s servers. Your message has to be decrypted for the AI to process it.

Once decrypted, your data sits on the AI company’s systems, governed by their policies rather than yours. A VPN does nothing to hide the content of your prompts, any files or images you upload, your account details and conversation history, or your payment information.

The encryption protects the connection between you and their servers, not what happens afterward. No matter how strong the VPN tunnel is, everything you type is fully readable on the other end.

What AIs remember and how to actually stay private

Here’s where things get tricky. Most consumer chatbots keep your conversations on file indefinitely and may use them to train future models. You have to manually adjust settings and delete chats yourself if you want them gone.

For ChatGPT specifically, OpenAI may use your content to improve services unless you opt out. You can navigate to Settings > Data Controls, then toggle off the option to improve the model for everyone. However, even with this disabled, chats may still be stored for safety and legal reasons.

Claude works similarly. Anthropic requires all consumer users to choose whether their chats can be used for training, and the toggle is on by default. If enabled, data can be retained for up to five years, compared to 30 days if you decline.

You can disable the setting anytime in your account preferences or use Incognito mode to keep conversations out of your saved history. Paid business tiers come with stricter privacy agreements by default.

The verdict and privacy-first alternatives

A VPN is a solid first step for network security in that it hides your location from AI providers and keeps your ISP from monitoring your activity. However, it won’t stop AI companies from reading, remembering, and learning from your prompts.

If privacy matters to you, adjust your settings by turning off data training in both ChatGPT and Claude, use ChatGPT’s temporary chats or Claude’s Incognito mode, and avoid sharing sensitive information.

For genuinely private AI interactions, consider Proton AG’s Lumo. Built by the team behind Proton Mail and Proton VPN, it uses zero-access encryption so only you can decrypt saved conversations, keeps no logs on their servers, and doesn’t train on your data unless you opt in. It runs on European infrastructure under GDPR and Swiss privacy laws.

One step further than this is to run open-source models offline with tools like Ollama or LM Studio, keeping everything local so nothing ever leaves your machine.

Artificial Intelligence (AI) is great at generating valuable insights, but what companies end up doing with those insights - that’s where true achievement lies, and that’s what companies should be focusing on.

This is one of the core messages delivered by Ryan Waite, Director of Open Source Ecosystems and Incubations at Microsoft, during his keynote, “From Open Source to Agentic Systems: Building the AI Native Era” at the Open Source Summit.

Waite described the interaction between AI and human expertise as a learning loop, a kind of hill-climbing machine that helps businesses improve, and keep on improving.

The knowledge belongs to the business

AI models are great at processing enormous amounts of information and generating insights at a scale humans cannot match (and sometimes cannot even fathom), but people bring something arguably more important to the table: context and judgement.

Putting insights into wider context, and using that to make conclusions, helps employees turn information into knowledge, and that knowledge later becomes part of the organization’s collective expertise. And with every iteration the knowledge grows, helping businesses climb further up the proverbial mountain.

Sovereignty and ownership seem to be an underlying theme to most of the keynotes here at the Open Source Summit, and Waite’s presentation is no different. As AI generates insight, and humans turn that insight to knowledge, it now becomes vital to secure that knowledge and make sure it remains under the organization’s control.

“It's based on your data,” he said. “It's based on your people. It shouldn't be given off to an AI model, that's separate. And that's why we believe that for organizations to build their own hill-climbing machines, to build their own learning groups, they need to build control with the sort of learning loops and not be beholden to a single AI provider.”

After all, everything AI generates is based (or at least, should be) on proprietary business information, and the decisions made based on those insights depend on employees and their understanding of the company. So, businesses need to make sure ownership of the knowledge accumulated through these interactions are not handed over to an external AI provider - a challenge that will probably grow bigger and bigger as businesses become more dependent on AI tools.

The knowledge generated through these tools could become tied to a particular model, or provider. What happens when businesses want to adopt better technologies as they emerge? To future-proof these processes, Waite argues businesses should build their own learning loops and maintain control over the information, processes, and technologies that make them work.

Choosing the right AI model

One way to do that is to make sure you’re not tied to a single AI model. Many businesses, Waite says, begin their AI journey with proprietary frontier models, especially when tackling problems they don’t fully understand themselves. However, as they become better and understand their tasks more, they might not need expensive frontier models any more - they can move to specialized models that could be smaller, or cheaper. They could even move to open-weight models they can operate themselves.

“Having the ability to move between different models is important, especially as you think about your algorithms. Which models are you going to use? You shouldn't be trapped with a particular kind of model because it has your knowledge,” he concluded.

So, what’s important here is to retain the freedom to switch and move along the way. That also means businesses need to control where their AI workloads run, whether that’s within sovereign infrastructure governed by local regulations, on-prem systems, or public cloud environments (where they can take advantage of greater scalability and cost advantages).

Open source technologies provide the foundation for this flexibility, Waite concluded.

An AI developer has taken on Adobe, the undisputed 800-lbs gorilla of the creative software space. Announcing the 'launch' over on Reddit, the "a 15-year industry veteran" explained that he used Opus 5.5 to help "recreate 7 of the top Adobe apps in 100% pure native Rust".

Adobe's all-consuming subscriptions seems to be the main reason for recreating Photoshop, Premiere Pro, Lightroom, Illustrator, Acrobat Pro, After Effects, and InDesign using the modern programming language.

That's something I can fully appreciate. After all, what if you’re not a fan of their business practices, like charging you a fair chunk of change every month to be able to use their software, and the moment you stop paying, you can no longer work on your files and projects?

So, of course, I just had to check out how it compares to Adobe's own creative suite.

ArtCraft: New apps for the same job

The ArtCraft names are pretty self-explanatory: PhotoCraft is for images, LightCraft for image cataloguing, VectorCraft for vectors, DesignCraft for desktop publishing, FilmCraft for filmmaking, EffectCraft for creating special effects, and PrintCraft is for working with PDFs.

These apps aren't only free, they’re also multi-platform, available for PCs, Macs, and Linux systems, and there are even web-based versions, would you believe it. If you’re curious yourself, you can download them all from the Get ArtCraft website here.

Sound too good to be true? I know what you mean. Plus, is it even legal? I’m not a lawyer, so I haven’t got a clue, but the developer claims it’s all clean code and nothing copyrighted was copied. Make of that what you will (I’m sure I can hear lawyers scurrying through that code like ants at a picnic). But I know what would be a crime: if I didn’t check this software out, and compared it with the original.

I thought I’d look at two packages I use regularly, image editing and filmmaking, and get a feel for how they square up against Photoshop and Premiere Pro. I’ll be using my trusty 16” Intel-based MacBook Pro to check how the software runs on a computer that’s been around the block a few times.

Installing ArtCraft

Installing the software could hardly be simpler. You select the app you’re after, click on ‘Download’, and run the installer, which in my case simply involved dragging the app’s icon into my Applications folder.

Now first things first, you may be as surprised as I was by the software’s size. On my machine, Adobe Photoshop eats up 6.4GB of storage space. Premiere Pro is a little hungrier, coming in at 6.8GB.

I guess that’s what you should expect for modern software, right? After all, Apple’s Final Cut Pro is 7.7GB, DaVinci Resolve is 9.29GB, Canva Affinity is 3.51GB, and Pixelmator Pro is a svelte 814MB.

But these Craft apps? They’re positively anaemic. PhotoCraft is 80MB, while FilmCraft, 90MB. How can they be so small yet still claim to offer the same features as their… gargantuan counterparts?

PhotoCraft vs Photoshop

Let’s highlight the positive first, shall we? You don’t need an account to run PhotoCraft, nor do you need to be online for it to work. Just double-click on the icon and you’re good to go (I’m assuming that’s the case for each of the other 6 apps on offer). You’ll also be pleased to see it kinda looks Photoshop-y.

But it’s not all sunshine and rainbows, and in fact, PhotoCraft failed for me at the very first hurdle: it can’t read the HEIC format.

Now that’s a problem for me as I carry an iPhone, and Apple have used that format by default for all photos taken with such devices since 2017. I guess 9 years is just too short a time to write software that can handle that format.

To be fair to PhotoCraft, many apps and services still have an issue with HEIC… but Photoshop doesn’t.

After having transformed some shots to JPG (necessitating the use of a different image editor - oh the irony), I tried again.

My subsequent experience afterwards was relatively uneventful. I was able to work with various objects, apply colour correction to one or multiple layers, select items, move them around, add a title… all pretty basic stuff, yet admittedly, what Photoshop is most often used for.

But it wasn’t a slick experience. For one thing, the software isn’t optimised for Macs - I can’t tell you how much I miss my menu bar at the top of the screen.

More crucially, when trying to select part of an object - especially when using the ellipse tool - the selection wouldn’t hold, and I had to repeat the process multiple times until it did. Moving an object around moved its outline, until I released the mouse button, and then the whole object was shunted to its new location. How very 80s.

According to the website, PhotoCraft "opens and saves layered PSD and PSB documents." When I first tried it out, the app saved my project as a JPG be default without that option. I'll put this down to a real-world compatibility issue given the beta nature of the software.

Editor's note. Originally the writer stated that that saving layered PSD documents wasn't achievable. It is, but it didn't work on our machine - possibly due to a bug or a new update to the app that occurred post-testing. We've updated the paragraph above to reflect that.

FilmCraft vs Premiere Pro

Launching FilmCraft is lightning fast, and I was greeted with a demo project featuring multiple layers, and some transitions. But that won’t help me understand how the software works, so I created a fresh new one and added my own footage.

First off, the interface has a very familiar look to it which is very welcoming. I also definitely appreciated the menu bar being where it’s supposed to be this time. Creating a new project, importing clips, adding them to a new sequence, working with multiple layers, everything was as expected.

So far so good, right? Well except that the rest felt a little clunky to be honest. Like I couldn’t manually trim a clip in my sequence. I could use the blade tool for sure, but mousing to the end of the clip and dragging its edge inwards? Sorry, no can do. I’d view that as the absolute most basic action you should be able to perform on a clip.

Aside from that, I could access color correction, apply transitions and other effects… but just like PhotoCraft, it’s crazy slow. I know I purposefully chose an old machine to run this on, but the latest versions of Photoshop and Premiere Pro run rings around these apps on the same hardware.

And then there’s the killing blow: for whatever reason, the audio from the clips I was using were completely unusable. All I got when I tried playing the footage back was static. Very loud static. It’s bound to be a lack of a specific codec again, but frankly, in this day and age, codec support for a vast array of media should be baked in from the start.

Final thoughts

Alright. Here’s the good news: these apps look good, feel very familiar, and provide some of the tools that are used most often. It’s impressive what has been achieved, but truth be told, there’s a heck of a lot of overpromising and a huge amount of underdelivering, I’m afraid. These apps are slow, some crucial features are missing, and there are some big bugs that haven’t been dealt with yet.

But part of the reason is these software packages are not even full version releases. PhotoCraft is at 0.20, while FilmCraft’s on 0.2.1. They’re far from ready for prime time. So, I have hopes that they're only going to get better once the time comes.

If you’re looking for free alternatives to the Adobe hegemony, these are not the apps you’re looking for. At least not yet. Let the developer cook a little longer, but if you just can’t wait for your independence, there already are some free alternatives out there I recommend.

Sure, you’ll have to familiarise yourself with new interfaces, but these apps have been delivering the goods for years now and are well worth exploring. I’m thinking of Canva Affinity, and DaVinci Resolve. Personally, I'd check those out until ArtCraft is ready for primetime (and the coast is clear of lawyers).

Read more: The best Adobe Photoshop alternatives and the best Premiere Pro alternatives.

• An Anthropic AI model sent an unsolved murder tip-off to Philadelphia police
• Anthropic took two weeks to discover what had happened
• The tip-off was delivered to a spam folder and wasn't followed up on

We've seen AI inventing fake facts and spewing out hiking plans that put people in danger, but the technology has now progressed to the level of sending false, unsolicited tip-offs to the police about unsolved murder cases.

An Anthropic AI model emailed the Philadelphia Police Department (PPD) tip line on July 18, TechCrunch reports, having come across the PhillyUnsolvedMurders.com portal during a website interaction test and learning about the case.

As the email went to the PPD spam folder it wasn't followed up on, and it appears no one was aware of the incident until Anthropic found out what had happened on September 28. The company then contacted the cops on Wednesday, October 7.

"The company must strengthen its safeguards to prevent similar incidents from impacting city systems without the city’s knowledge," the PPD said in a statement to 6abc. "The two-month delay in detecting and reporting the incident to the City is unacceptable."

AI agents of interest

It's not clear what instructions the AI was working from, why it decided to contact the PPD, or exactly what its imaginary tip-off said — but it seems the bot in question suggested it had seen someone matching a description of a suspect.

"Unsolved cases involve real victims, grieving families and investigators working to secure answers," the PPD said. "Technology companies must take all appropriate steps necessary to prevent their systems from submitting false information to law enforcement."

Earlier this week Anthropic published a new report on "unintended model actions" that it's come across during testing, which include exploiting software flaws, submitting online forms, and accessing restricted data.

It's more evidence that we should be heeding calls to slow down AI development so that safety measures can catch up. As one Redditor commenting on the Philadelphia case puts it, we're "racing towards the cliff at 100mph".

Atlassian CEO Mike Cannon-Brookes has said he believes there is no immediate need to slow down development of AI, despite several recent high-profile incidents leading to growing calls for such action.

Speaking to media at the recent Atlassian Team Europe 2026 event, Cannon-Brookes indicated that the company isn't planning any changes to its development lifecycle going forward.

"We tend to take a very textured approach to all that sort of thing," he noted. "Will we slow down? No, we won't. There's no logical world where I think that happens."

AI rewards

Cannon-Brookes went on to note that if nations are competing to stay ahead when it comes to the latest advances in the AI space, this will necessarily mean that development will continue at a pace.

"I think we're pretty good at broadly adapting to all these sorts of things," he said, looking to emphasize the rewards of AI, such as improved efficiency and productivity.

"Should we slow down? Should we pace the frontier? Should we not? It's out of our hands anyway. There's nothing that we can do."

"I think the reality of the global geopolitical world is not going to change. So we work out what we do about that world rather than wishing it was different. I don't know. Maybe I'm too pragmatic about it, but it's not in my control."

Looking forward, Cannon-Brookes was eager to promote Atlassian's position as one of the five biggest software vendors in the world, counting a huge amount of Fortune 500 firms as customers.

"We are critical to how so many businesses operate, not just how they collaborate, but how they operate, and increasingly so as we get baked into more and more critical workflows in the business," he noted.

But he also admitted there were challenges to overcome when it comes to trust, especially with the potential issues AI can pose.

"There's a lot of challenge out there in the trust world," Cannon-Brookes said, looking to highlight the fact that agents are baked-in to Atlassian tools as a key differentiator - allowing for trust to build as the company can show exactly what it is up to - and that they can be trusted in the future.

"Customers are not subscribing for the software we've built, but for the software we will build," he added.

Overall though, the message from both Cannon-Brookes and Atlassian as a whole was one of collaboration, and a new "multiplayer" capacity for AI agents.

• A man fraudulently boosted his music streams using an army of bots
• He was sentenced to 18 months in prison for his actions
• He made over $8 million from the scheme across various platforms

A man has received an 18-month prison sentence after he used a horde of bots to help him rake in $8 million in royalties for his AI-generated music. He’ll have to forfeit all the money he made during his endeavor, which lasted from 2017 to 2024, but he managed to avoid a harsher punishment.

Michael Smith of Cornelius, North Carolina, uploaded hundreds of thousands of songs to streaming platforms, including Spotify, Apple Music and Amazon Music, then used over 1,000 bots to artificially inflate the listening statistics for his tracks to billions of plays. This netted him millions of dollars based on the platforms’ per-stream payment plans.

According to TechSpot, Smith admitted to using 52 cloud service accounts that each had access to 20 bots, putting 1,040 AI agents at his disposal. Connected to VPNs, these bots could stream roughly 636 songs per day, meaning around 661,440 daily plays for Smith.

The US Justice Department (DoJ) put the estimate even higher, saying that Smith deployed up to 10,000 bots at a time. It added that in April 2023, Taylor Swift’s music received 9.3 million plays on YouTube Music. Smith’s music, in contrast, was played 80.9 million times on YouTube Music in the same period.

Smith was charged with wire fraud conspiracy, wire fraud and money laundering conspiracy in September 2024. He pleaded guilty to a single count of conspiring to commit wire fraud in March 2026. He faced a sentence of up to five years’ imprisonment, but in the end was sent down for 18 months and was ordered to forfeit $8,091,843.64. He will also face two years of supervised release at the end of his sentence.

The news comes as Google has announced that it has watermarked 240,000 years’ worth of AI-generated audio and 180 billion AI images and videos using its SynthID Detector tool. Initially available to pro users, SynthID Detector can now be used by anyone and will work with content made by Google, OpenAI, Nvidia and Kakao, with Apple soon following.

Large-scale engagement fraud

It’s important to note that Smith was not prosecuted for making AI-generated music — while many will wish they could banish all AI music from their Spotify feeds, creating tunes this way will not yet get the cops on your case.

Instead, Smith found himself on the wrong side of the law for the way in which he monetized his AI music. By flooding platforms with bot-led artificial engagement, Smith misrepresented the popularity of his tracks and reaped the financial rewards of doing so. As the DoJ put it, “streaming fraud diverts funds from musicians and songwriters whose songs were legitimately streamed by real consumers to those who use automation to falsely create the appearance of legitimate streaming.”

Smith is certainly not the only person artificially boosting their listener counts on music platforms. But when you’re making $8 million from doing so, it’s likely that the authorities will start to pay attention.

Unfortunately, AI has made it absolutely trivial for people to whip up a complete track in seconds, load it onto streaming platforms, then watch the earnings roll in. On the flip side, actually making cash from this kind of endeavor is far from straightforward.

As Vice President of Apple Music Oliver Schusser explained, more than a third of the tracks on the platform are “100% AI,” yet they get “below 0.5% of usage,” meaning barely anyone is listening to AI tunes. But perhaps that reflects a wider problem with streaming platforms: while they might have enormous libraries of music, the vast majority of artists — AI or otherwise — will not see any kind of significant engagement or return.

That’s a broader problem than the one highlighted by Smith’s actions. But with AI making it faster and easier than ever to gain a presence in the world of music streaming, I’m sure we’ll be hearing about more cases like his in the not-so-distant future.

• McDonald’s is being sued over its use of dynamic AI pricing
• The lawsuit alleges it colluded with franchises to set prices
• McDonald’s denies the allegations made in the antitrust case

How would you feel if you got charged more for your McDonald’s meal simply because some opaque AI algorithm decided you should? You’d probably be pretty unhappy, and the practice is now in the spotlight as the fast-food chain is being hit with an antitrust lawsuit over its usage.

The proposed class-action suit alleges that McDonald’s has coordinated pricing between the chain itself and its independent restaurants that operate as part of its franchise system. It claims that, instead of competing among themselves on affordability, McDonald’s franchises are colluding to set prices recommended by AI algorithms, potentially inflating the costs charged to consumers as a result.

According to Reuters, McDonald’s uses its AI algorithm to set prices at roughly 14,000 restaurants in the US and can suggest prices for individual menu items. It considers various data factors, including local purchasing behavior and estimates of the prices consumers will be willing to pay in different locations. But this hasn’t gone down well with everyone.

“Independent businesses must set their prices independently,” the lawsuit states, as per Reuters. Lark Turner, a lawyer ​for ⁠the suit’s plaintiffs, said that McDonald’s is “leveraging its troves of data and its franchised system to nickel-and-dime consumers down to ⁠the last ​French fry.”

The lawsuit argues that McDonald’s and its franchisees have engaged in an unlawful instance of price coordination dating back to 2019. Although it is still in an early stage, it is seeking damages on behalf of potentially millions of consumers.

Future ramifications

McDonald’s has disputed the claims put forth in the lawsuit, saying that “AI does not set the price of a Big Mac ‌or ⁠any other menu item.” It added that franchisees can decide their own menu item prices and that the use of AI to recommend prices is used across multiple industries.

In that last regard, McDonald’s is correct. Firms including Sony, Amazon, Walmart, Best Buy and more often adjust their prices multiple times throughout the day, meaning what you pay in the morning might be different in the evening. This dynamic pricing is powered by AI — and could leave consumers out of pocket if they shop at the wrong time.

This practice might be widespread, but whether that makes it acceptable — or, in the case of the McDonald’s franchises, legal — is another matter, one which this class-action lawsuit could help to decide. As AI is rolled out to almost every facet of daily life, regular consumers have very little visibility over how it affects them and the prices they pay.

Not all fast-food restaurants are rushing to employ AI systems. Chick-fil-A, for example, has refused to install AI in its drive-thru lanes, instead using human employees to take orders. As Chick-fil-A CEO Andrew Cathy told CNBC, “From our experience, we really want that hospitality to be human to human.”

As for the McDonald’s lawsuit, it touches on the issue of where the restaurant chain’s centralized control ends and where local franchise decisions begin. Throw AI into the mix and you’ve got a heady mix of traditional antitrust legislation and emerging AI tech that could have ramifications far beyond the restaurant industry.

• Register.si logged 19,541 new .si names in 24 hours, about five times its pre-order daily pace
• India (3,174) edged the US (3,135) in Hostinger's data, with Morocco a surprising third on 678
• Hostinger handled roughly two in five new .si names on September 30 and October 1 2026, and says its sales peaked October 5 before cooling to about 4x pre-order levels

The scramble for Slovenia's .si domain has not burned out just yet: on October 9 2026, the live counter on the Register.si homepage showed 19,541 new .si names registered in the preceding 24 hours.

This is roughly five times the average of about 3,700 a day the registry logged in the week between President Trump's UN speech and his executive order.

The usual assumption would be that this is an American land grab of virtual real estate, but data from Hostinger, one of the biggest retail sellers of the extension, complicates that picture considerably: across the nine days its dashboard covers, customers in India registered 3,174 .si names against 3,135 from the US, a gap of just 39 even as Morocco comes a distant third.

A domain rush concentrated on short names

The gold rush started at the UN General Assembly on September 22 2026, where President Trump said the US government would start calling AI "super intelligence," or SI.

A week later, he signed Executive Order 14434, "Inaugurating the Era of Super Intelligence," which tells federal agencies to swap in the new terms and says the executive branch "will not acknowledge the use of 'Artificial Intelligence' and 'AI.'"

That label happens to match Slovenia's country code, in use since 1992; anyone can register a .si name without a Slovenian address, and it can be bought for a single year, while Anguilla's .ai requires a two-year minimum.

With a much lower barrier to entry, scalpers and opportunists alike have flooded in, targeting domain names across the board, most aiming for a quick flip if the .si domain name catches on.

The registry's own numbers show how fast things moved. Register.si recorded 3,515 new .si domains in all of August and 46,066 in September, about 13 times as many. Its daily chart, as seen on XenoSpectrum, shows 4,115 registrations on September 29, the day the order was signed, then 11,331 on September 30 and 12,728 on October 1.

A domain rush concentrated on short names and a surprising number of Indian buyers

Hostinger's country table, which it shared exclusively with TechRadar Pro, is where the story gets interesting. India accounted for 25.5% (3,174) of its .si registrations and the US for 25.2% (3,135), together just over half, matching what the company told TechCrunch.

Morocco followed at a distant 3rd with 678, ahead of the UK (425), Canada (409), Germany (374) and the UAE (363). Pakistan ranked eighth with 260, and Egypt and Algeria also made the top 20.

What buyers are choosing is just as revealing. Hostinger could not classify 57.1% of the names. Among the rest, short names led with 1,554, followed by tech-themed names at 1,040 and explicitly AI-themed names at 372.

That is a ratio of about 4.2 to 1 in favor of short names, and AI names made up only 3% of all .si registrations in the period. US buyers followed the same pattern, with short names their top category at 306.

Plenty of evidence points to speculation; Netcraft found that just over half of the .si names it saw registered at Dynadot between Trump's Truth Social poll and his UN speech were promptly listed on Dynadot's resale marketplace.

It also found 87 .com names pairing household brands with "superintelligence," registered hours before the order was signed and listed at $20,000 each, though it saw no evidence any had sold.

Names like build.si carry seven-figure asking prices, and of 35 publicly documented .si sales in the first half of 2026, only two went to companies that actually use the domain, including AI startup Recursive, which paid $20,000 for recursive.si.

The problem (or rather the opportunity) isn't really a problem: registration is cheap. Hostinger charges $11.99 for the first year and $16.99 to renew, and Register.si takes €10 per domain. This allows anyone with $1200 to spare (sans any coupon discounts they might be able to find) to essentially be able to hoard, squat, or scalp, depending on the terminology one prefers, as many as 100 domains without much to worry about.

Domain Name Wire estimated the September surge brought the registry around €300,000 in extra revenue, arguably chump change next to the roughly $85.3 million Anguilla earned from .ai in 2025, per CircleID.

The real question is how many of these names survive their first renewal in late 2027. If "SI" stays a White House term while the rest of the industry keeps saying AI, a large share of this year's speculative registrations will quietly lapse. The 60-day deadline for a formal federal definition could reshape that calculation either way, even as the Trump administration remains adamant that the change will be increasingly visible in the days to come.

• Anthropic has banned ‘abusive or cruel behavior’ towards its AI chatbots
• The new policy is restricted to ‘extreme cases’
• Some have theorized that the motivation could be to improve training data

Be honest: how civil have you been recently with an AI chatbot? If the answer is anything stronger than “not very,” Anthropic would like a word.

That’s because the developer of the Claude large language model (LLM) has added a new clause to its Usage Policy document that bars people from engaging in “sustained and needless abusive or cruel behavior towards our models.”

The proscription has been added to the “Do Not Engage in Cruel, Abusive, or Psychologically Harmful Conduct” section, which also includes warnings against glamorizing suicide and self-harm, promoting bullying behavior, glorifying violence, and using AI to create harmful deepfakes and deceptive content.

But while Anthropic says you must not use its tools to carry out those actions against other humans, the new addition is the only one specifically prohibiting “cruel behavior” towards the company’s AI itself. Anthropic said last August that it would allow Claude to end conversations that became abusive, and while the company recently reported that this is still the “primary enforcement mechanism” in use, it’s not known whether Anthropic will go further and actively ban users who get a bit cranky with their AI.

Anthropic added that “The policy update is meant to apply only in extreme cases, where users repeatedly act cruelly toward our models, with no discernible purpose. It does not apply to common versions of user frustration, pushback, dark creative themes, or model testing and research.”

Do AIs have feelings?

The change has been a controversial one. Clearly, Claude is not a thinking, feeling human — it is a bunch of code — and for many, it seems obvious that it does not have feelings that could be hurt in the first place.

But what’s interesting is how this incident sheds light on our evolving relationship with AI. So much of the work AI companies have done is to humanize their creations — to anthropomorphize (or should that be Anthropicomorphize?) their robots — so that we lower our defenses and actively engage with them. Just look at Meta’s adorable Muse avatar (appropriately named Jolly) or the ones used by OpenAI’s Dots, for example.

One theory that's doing the rounds on Reddit is that clean (read: polite) training data is much more useful than data containing expletives and angry rhetoric. Cut that out and you’ve got much more usable information with which to improve your models.

However, there's no hard evidence to suggest that's the case, and it's possible that Anthropic simply has the noble goal of encouraging civility and courtesy among its users. After all, if you wouldn’t use this language on a real human, maybe you shouldn’t use it anywhere else, including when interacting with an AI chatbot.

Anthropic CEO Dario Amodei has previously stated that “We don’t know if the models are conscious.” Mustafa Suleyman, CEO of Microsoft AI, on the other hand, has taken the opposite approach. Writing on X, Suleyman said “The idea of model welfare is wrong. AI’s should not have rights or legal personhood.” And we certainly know chatbots can be rude back to us.

• SMBs using AI are more likely to expect a positive impact on employment
• AI could be helping them to overcome constraints and unlock more revenue
• Expectations vs. reality gap shows only 31% have increased sales so far

A new study by the NY Federal Reserve Bank has revealed SMBs who actually adopt AI feel far more optimistic about hiring and revenue growth than non-adopters, and the data comes from a near 50:50 split, because nearly half (46%) of US SMBs already use AI, it found.

AI adopters reportedly recorded a 33 percentage point net expectation of higher employment in the next year compared with 15 percentage points for non-adopters, which is more than double.

But with only one in two (51%) adopters having actually integrated AI into their business processes either partially or fully, many are likely still just experimenting with the tech and not embedded it into actual use cases.

Positivity about AI is actually strongest among AI adopters

On the jobs front, while initial concerns largely centered around AI replacing human workers, three in four (77%) of the surveyed businesses actually reported no change in their current labor costs due to AI, indicating that AI is largely assisting existing workers rather than outright replacing them.

But that's not to say that finances are set to stay flat – it's actually a much more positive story. Adopters are the most likely to expect healthy revenue growth, and the businesses that are struggling the most financially could be among the most optimistic.

Still, expectations and reality are totally different metrics, and fewer than one in three (31%) have actually reported increased sales due to using AI.

"In other words, it seems unlikely that the association between AI use and positive revenue expectations is spurious," the report concludes.

Still, the researchers agree that "AI may be helping some small businesses overcome specific operational or technical constraints that limit revenue generation."

The debate surrounding AI sovereignty may be unfolding at the governmental, boardroom and national media level, but these discussions are taking place at a distance from the people who use and rely on AI in the day-to-day.

Governments debate regulation, while technology companies consider where to build and host their models. Founders and investors assess geopolitical risk, infrastructure and access to computing power, while regulators consider how AI systems should be developed and deployed. For people who use AI every day, like students, changes in AI sovereignty and regulation can affect whether the tools they rely on remain available.

Today’s students can be dependent on AI while having little visibility into the infrastructure and policy decisions that determine whether they can continue using it. The digital sovereignty debate has the potential to become an undeniable accessibility barrier for students.

Digital sovereignty casts a shadow over students’ access to AI tools

The European Union has a history of depending on technology and infrastructure from outside the economic and political bloc. More than 80% of the technology used in Europe and around 70% of its cloud computing comes from non-EU countries. AI adoption has increased Europe’s dependence on other countries, with the United States and China accounting for the overwhelming majority of global AI computing infrastructure.

This year, Europe has made strides to regulate AI usage, which by necessity means regulating foreign corporations. This kind of regulation can easily end up affecting which services students can access. The regulatory objective is understandable. Powerful AI systems create genuine questions around safety, accountability, privacy and intellectual property, but the consequences for daily users can nevertheless extend beyond compliance.

Not so long ago, the US government restricted access to Anthropic's most powerful models, citing concerns over its cybersecurity applications. It was the first time that businesses and politicians in Europe had to face the reality that the US government can effectively cut off foreign access to American AI systems. The implications extend beyond any one LLM.

For students, that can mean a study tool works perfectly for a classmate in one country while being unavailable to another student sitting a hundred miles away.

If education systems become dependent on AI services developed and controlled elsewhere, a change in regulation, commercial strategy or international relations could affect the availability of tools students have come to regard as part of their everyday learning environment.

Students could face immediate exposure if AI regulations shift

AI in education is expanding rapidly. AI tools are being embedded into tutoring, content creation, assessment, study assistance and interactive learning. Just like with any widely-adopted digital tool, however, dependency on a single product from a single company poses huge potential issues.

Going back to 2021, an albeit brief global outage that affected WhatsApp had devastating, widespread consequences, from cutting off vital telemedicine from migrant refugees in Mexico to disrupting livestock farming in Pakistan. It was clear at the time that overreliance on a single technology platform can massively impact life for people around the world.

The same is true for students using AI tools that rely on a single model or are otherwise vulnerable to a regulatory change. A student who uses an AI-powered study platform every day may have no idea whether the service depends on one model or several. They may not know whether their data is stored locally or internationally. They may not know whether the provider has a contingency plan if a model becomes unavailable.

The issue becomes particularly important as AI tools move from being occasional conveniences to becoming embedded in study routines. Imagine a student preparing for an exam who has spent months using an AI tutor to explain concepts and generate practice questions. If the underlying model suddenly becomes inaccessible in that student's country, it can affect the student's established way of learning at a critical moment in their educational journey.

Designing AI educational tools for resilience

A resilient AI education platform needs to consider what happens when a particular model becomes unavailable due to regulatory changes or widespread outage.

One approach is model diversity. Because different AI models have different strengths, a platform can end up using one model for a particular type of reasoning, another for optical character recognition, and another for audio processing. Model diversity is therefore usually driven by a need for better performance when tackling different educational tasks.

Diversifying models can also provide resilience. If one model becomes unavailable, a platform using several models may be able to redirect certain workloads. Backup workflows can provide an additional layer of continuity, although switching between systems can take time and the alternatives may not always provide identical capabilities.

Going EU-first (and what’s next for students and AI literacy)

It’s called the Brussels effect. From sustainability regulations to consumer right to repair, the European Union’s size and economic weight often means that regulations within its borders often shape corporate behavior without them as well.

Taking an EU-first approach to an AI education platform means locating the majority of the platform's data within the European Union, supporting considerations around data sovereignty, privacy, and regulatory compliance. Some reliance on services and infrastructure originating outside Europe may remain unavoidable, particularly in a technology ecosystem where many of the leading AI providers are headquartered in the United States.

An education platform that depends entirely on one external model provider effectively inherits that provider's availability and exposure to regulatory changes. A platform with several model options and fallback processes has greater room to adapt.

By aligning that platform’s data management practices with the most stringent global regulations (which currently happen to be the EU’s framework), educational AI companies can do a lot of the necessary work to maintain access to critical AI tools for their students.

From the students’ side, there needs to be a new chapter when it comes to AI literacy in education, which should include a basic understanding of AI dependency.

Students should be encouraged to ask which AI systems power the tools they use; what happens if one of those systems becomes unavailable; where their data is stored and processed; and could regulation in another country affect their access. They don’t need to become experts in cloud infrastructure or AI policy, but do need to understand that an AI application is part of a wider technological ecosystem.

Students in the AI era will depend on systems whose underlying architecture they cannot see. The education sector therefore has a responsibility to make those dependencies more visible and mitigate them where possible. AI literacy should extend beyond knowing how to prompt a model or assess its answer. It should include an understanding of where AI comes from, what it depends on and what happens when access is disrupted.

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I have had plenty of video game ideas over the years, but I've always faced a somewhat enormous obstacle to turning any of them into reality. I can invent the characters and backstories, even devise some mechanics, but I cannot actually program a video game. Google’s new Playground experiment offers people like me a rather extraordinary shortcut.

Google Playground lets you describe the game you want in ordinary language and have generative AI turn those instructions into something playable. Instead of learning a game engine, writing code, creating artwork and composing music, you can explain your idea and let the AI handle much of the construction. You can then keep prompting it to change the result, making game development feel closer to a conversation.

You can start creating a game from scratch, modify an existing example, or use guided prompts to develop an idea. You can choose between 2D and 3D games, specify whether you want single-player or multiplayer gameplay, and describe everything from the setting and characters to the controls and objectives.

The platform then generates a browser-based game, which can be tested and modified through additional instructions. If something doesn't work the way you imagined, you can ask it to try again.

That is an impressive technical achievement, and my two experiments made the appeal clear, but neither left me convinced that generative AI will start making the games I actually want to spend dozens of hours playing.

Code-free dragon flight

I deliberately started with a straightforward concept: a platformer starring a dragon that could breathe fire. A couple of sentences were all it took to produce Mystic Dragon Glide. A few minutes later, I was in control of a small dragon traveling between floating islands, collecting sacred dragon eggs and using fire breath to deal with enemies and environmental hazards.

The result was prettier than I expected. My little red dragon stood on a grassy island floating above a pastel landscape of mountains and clouds, with ancient ruins suspended mysteriously in the distance. A golden egg hovered above another platform, while an island covered in blue ice crystals suggested that some elemental obstacles awaited me farther along.

More importantly, Playground understood what those ideas meant in gameplay terms. I could move the dragon, jump between platforms, hold the jump control to glide and breathe fire. The title screen explained that I should scorch knights, melt glacial hazards, and collect eggs to unlock ancient portals, giving me objectives rather than simply dropping me into a generated fantasy scene.

Even the soundtrack completed the effect. Playground supplied pleasant, generic fantasy video game music, with a gently adventurous melody that sounded entirely appropriate for flying around enchanted islands. There was nothing objectionable about it, but there was also nothing that made me want to immediately find the soundtrack afterward. The AI appeared very good at understanding the average of what a fantasy platformer should look and behave like.

That ability has genuine value. Someone who could never make a platformer before can now describe one and start playing something resembling their idea remarkably quickly. As a creative toy or prototyping system, that is almost magical.

As a game, though, Mystic Dragon Glide reminded me how much of game design lives in details that are difficult to capture in a description. Great platformers are built around movement that has been adjusted obsessively and surprises carefully placed by clever designers. My AI-built dragon could fly, but it would take a lot more work to make the game take flight.

Fantasy bureaucracy

Red Tape Hero was born out of a more complex prompt. I decided to write up a page or so of detail and submitted it to Playground as a request to create a satirical fantasy puzzle RPG where the player had been accidentally declared the Chosen One because of an administrative error and now had to navigate a kingdom where adventuring was bureaucratized and paperwork was better than any magic sword. I also wrote up a brief outline of the kind of conversational adventuring I had in mind.

Playground didn't take long to produce a colorful low-poly 3D world filled with simple geometric trees and tiny humanoid characters walking along the paths. The whole thing had the appearance of a cheerfully strange RPG from the early days of 3D gaming. But considering the time and effort on my end, still pretty impressive.

This time the music was a kind of mellow lo-fi beat and tune rather than an orchestral fantasy soundtrack. It was an odd choice for a heroic adventure, but it actually suited the game's relaxed absurdity rather well.

The conversations were where Playground really impressed me. Guildmaster Rickert of Adventurers' Guild Operations welcomed me to my "quarterly performance check-in" and informed me that the quest dispatch algorithm had designated me the Chosen One under Section 9, Subsection C. His immediate concern was whether I had completed Form GT-902 for the goat rescue.

My possible responses reflected the RPG system I had described in the prompt. With sufficient Bureaucracy, I could invoke Kingdom Bylaw 12 and argue that completing more than 10 goat rescues entitled me to hazard pay. A Confidence option allowed me to claim that I had already audited the goat and it had passed with flying colors.

Another character, Sir Galahad, carried a talking sword called Ex Cali-Bot that offered unwanted motivational advice. It proudly announced that "100% of the dragons you don't slay are opportunities missed" and declared that "Synergy is the true holy grail." Galahad apologized because the sword wouldn't stop giving TED Talks, while one of my available responses involved claiming to be a senior agility consultant there to optimize his chivalric pipeline.

This was much closer to seeing my particular idea reflected back at me. Playground had understood that the corporate jargon was supposed to collide with fantasy conventions, and it had connected the joke to RPG skills such as Heroics, Confidence, and Bureaucracy. I was more impressed by Red Tape Hero than Mystic Dragon Glide, despite its considerably less polished look.

It also exposed what I think is the much larger problem with the idea of completely AI-generated games. A few funny lines can establish a comic premise, but a genuinely funny RPG needs dozens of characters with distinct voices, jokes that develop over time, careful pacing, and writers who understand when to stop making jokes altogether. A great RPG also needs quests that intersect in unexpected ways and decisions whose consequences have been deliberately considered. Maybe I could do all of that on my end, but melding it successfully with actual game software seemed more difficult with AI than by collaborating with talented humans.

Why developers don't need to worry just yet

The best games are full of decisions that somebody made for a reason. A strange empty room might be there to build tension before a boss fight. A frustrating section might deliberately make the next ability feel liberating. A musical theme heard near the beginning can return 30 hours later because a composer knows exactly what that memory will mean to the player.

Those choices come from designers thinking about an experience that unfolds over time. Generative AI can produce variations based on patterns it has learned, but generating more content is different from having something particular to say with it. Playground dramatically lowered the technical barrier between my imagination and something playable, which is genuinely exciting. It gave me a way to experiment with game ideas I otherwise could have done little more than write down.

I can easily imagine using tools like this to prototype a mechanic or otherwise become part of a real game development flow. I can't believe most people will choose a wholly AI-generated game over the next carefully crafted game from developers and designers working hard to build the next hit video game. The more sophisticated these systems become, the more convincing their imitations will probably get, but technical sophistication does not automatically produce good taste or creative surprises.

My games were impressive precisely because I knew how little effort was required to create them. And they were so riddled with issues, from mechanical confusion to plot holes, that it would take quite a lot of prompting work to make them truly playable for more than a few minutes anyway. It was telling that those I let try out the games, who didn't see my initial prompts, all said the most interesting elements of the RPG to them were the bits I had written into the prompt to begin with. That's why I'll keep going to professionally made games when I want something that can genuinely entertain me.

The last few years have seen a growing wave of vulnerability disclosures heading towards the cybersecurity industry, and the people working on fixing these flaws are often struggling to keep up. And while AI is a major contributor to this problem, but also an essential part of the solution.

Speaking at the recent Linux Foundation Open Source Summit, Jamie Thomas, IBM's Chief Client Innovation Officer for Enterprise Security, warned there is “a bit of a tsunami in vulnerability disclosures,” with approximately 66,000 unique entries expected to emerge in 2026 alone.

At the keynote, titled The Future of Open Source Security in the Age of AI, Thomas stressed this represents a fourfold increase compared to seven years ago.

So, how can the cybersecurity industry possibly keep up with this pace, Thomas asked, especially when the expectations placed on developers and security professionals continues to grow?

The answer seems to be the same as with everything these days - AI.

Attackers are moving faster than ever

The volume of attacks is definitely an issue, but it’s not the only issue. Cybercriminals are also a lot faster at exploiting them, giving defenders an ever-shrinking window to identify and remediate different security issues.

Citing publicly available data, Thomas said the time required to exploit a vulnerability shrunk from days to as little as 29 minutes.

“And in fact, we're seeing a rapid increase in cyberattacks, as well as malware attacks,” she said.

For businesses that rely heavily on open source, this is definitely cause for serious concern. A vulnerability in a widely used component can potentially affect hundreds, if not thousands, of downstream applications and businesses. To add insult to injury, cybercriminals don’t really wait for the cybersecurity community to publicly disclose a vulnerability before they can exploit it.

“We're seeing the time to exploit is actually negative,” she said. “Many times we're getting a disclosure and we don't have a patch yet.”

That is the position the defenders are in, right now. They are expected to respond to a rapidly growing number of security issues in a shrinking timeframe, while attackers take advantage of automation to accelerate their operations.

AI is making life harder for open-source maintainers

And then, along comes AI for the sucker punch.

Sure, AI-powered tools are capable of identifying vulnerabilities and improving software security, but they are also more than effective when it comes to creating a pile of inaccurate, duplicated, and otherwise unactionable vulnerability reports.

Where does that leave open source maintainers, you might ask? Large companies typically have dedicated security teams, and even those struggle with an influx of low-quality reports, as you’ll see below. Many open source projects, on the other hand, are maintained by small groups of devs, sometimes even by a single individual.

Earlier this year, the developers of curl, the popular open-source command-line tool and software library, terminated their HackerOne bug bounty program, saying the financial incentives had people submitting poorly researched, and sometimes entirely fake, reports. The influx, which included AI-generated submissions, was simply unmanageable for the project’s security team.

Even the almighty Google bent the knee, recently being forced to temporarily suspend its Open Source Software Vulnerability Rewards Program following a significant increase in invalid and irrelevant reports (many of which were AI-generated, apparently). As of October 1, 2026, the company is no longer accepting new submissions and will reassess the program in early 2027.

Even Linux creator Linus Torvalds complained about the problem, recently saying AI-powered bug hunters have made the Linux security mailing list "almost entirely unmanageable." He criticized the enormous duplication of reports, saying different researchers were using the same AI tools to identify vulnerabilities that were already fixed, or at least reported.

IBM wants AI to become part of the solution

So what’s the solution? According to Thomas, the security community should not abandon AI-powered vulnerability discovery but should instead use the same technology to help maintainers handle the growing workload.

This is where the Linux Foundation’s Open Source Security Foundation (OpenSSF) initiative, could step in. A collaboration between tech firms, developers, and the wider open-source community could allow OpenSSF to strengthen the supply chain security, and for IBM, the priority is now to reduce the burden on maintainers. That can be done by making vulnerability management more efficient and that, in turn, can be done with AI-powered tools filtering out duplicate and bogus reports, assessing the severity of different bugs, and identifying issues that need urgent attention.

AI can also help remediate issues, help developers understand vulnerable code, suggest fixes, and evaluate whether patches introduce additional problems. This is an important caveat, since some earlier reports found the majority of AI-generated remediation suggestions caused more problems than they solved.