• Multiple states have revealed tax exemptions have cost more than $1 billion in lost revenue, more than 10 times some estimates
• Data centers are 'playing God' with their frontier models and need to face more regulation, Bernie Sanders says
• An Act issuing serious consequences, such as prison time and 'corporate death sentences', has been submitted

For many states across the US, the best way to lure lucrative investment from big tech companies was to provide tax exemptions on the equipment they would buy to furnish their data centers.

Before the AI data center boom, this was seen as a fair trade for citizens and politicians alike. But now that multiple states have revealed they have missed out on billions in tax revenue the tune is quickly changing.

Coupled with the growing national opposition movement, revelations that numerous construction sites have been using illegal generators, and frequent breakouts of AI models undergoing testing, it's no small wonder that politicians across the country are calling for greater regulation and greater taxation on big tech and AI.

‘They seem to have more money than God’

Ohio is the catalyst for this exemption withdrawal. The state projected the tax exemptions would cost them just $136 million in lost revenue for 2026 - a worthy cost to attract new investment and potential jobs for a state that has seen a long-term decline in its traditional industries and slow recovery following the 2008 financial crisis.

The loss in tax revenue turned out to be $1.6 billion - 10 times more than expected, or just over 5% of Ohio’s total tax revenue for the 2026 fiscal year. Following the revelation and associated public fallout, Republican Gov. Mike DeWine issued a pause on applications for the sales tax exemption.

But Ohio representatives, such as Democratic Rep. Tristan Rader, believe there is more to be done. Rader has called for a renegotiation on the tax exemptions offered to hyperscalers such as Amazon, Meta, and Google. “They seem to have more money than God and they’re able to build without the need for these types of incentives,” Rader said (via WSJ).

Rader is now calling for AI data centers and their respective companies to face additional requirements before projects are approved, including higher taxation and requirements that companies pay more for the energy infrastructure they rely upon.

Ohio isn’t alone in losing out on such a significant sum. In 2021, Virginia’s data center tax exemptions were predicted to cost $57 million, and Georgia predicted a tax revenue loss of just under $500 million in 2025. Both states have revealed their data center tax exemptions have led to a tax revenue loss of almost $2 billion each. Texas has seen just over $1 billion lost in tax exemptions for data centers.

But extra taxes aren’t the only repercussions hyperscalers and AI companies are facing.

‘We can’t allow a handful of greedy people to play God’

Senator Bernie Sanders has renewed his calls for greater regulation on AI technologies. Meta, OpenAI, and Anthropic have all recently revealed that their frontier AI models have escaped testing environments and wreaked havoc on third-parties.

There are also concerns that the guidelines behind the Trump administration's testing of AI tools could be hiding corruption, with Sanders stating that the control of AI technology is driven by “oligarchy & the power of Big Tech billionaires”.

In a post on X, Sanders wrote, “We can't allow a handful of greedy people to play God & determine the future of humanity.” Sanders also said that “CONGRESS MUST ACT” in a post accompanying the announcement of the Ban Artificial Superintelligence Act, sponsored by Sanders and Rep. Greg Casar.

The act seeks to pace US artificial intelligence development in line with international agreements, and implement export controls that would prevent AI superintelligence being developed anywhere in the world. The ultimate goal of the act is to prevent the development of AI technologies that exceed humanities control.

Casar added that, “Despite its potential deadly consequences, cutting-edge AI technology is less regulated than the average food truck.”

The Act also goes a step further than previous attempts at regulating AI. Companies and individuals who fail to align themselves with the Act’s regulations would face repercussions such as prison sentences of up to 20 years and, as Sanders put it, a “corporate death penalty”.

The Apple Watch Series 12 and Apple Watch Ultra 4 have landed, along with a revolutionary folding iPhone at this year's Apple event, but despite major advancements under the hood, the feature that is standing out to me is a software-based one.

Siri Recaps is a new Audio Intelligence feature that turns your Apple Watch into an always-listening device. The idea is that Siri Recaps, which lives on the Siri app, uses the Apple Watch's sensors to provide a summary of your day, including conversations that have taken place. It can be toggled with a schedule, or at the top of the Smart Stack.

It's a little like the Amazon Bee, which is a microphone attached to a smart band, which is used to capture and summarize conversations. While I can see this feature as being useful for people who attend lots of meetings (having a virtual assistant taking minutes in the background all day long could be very useful), there are understandably privacy concerns here.

We're told the Recaps feature doesn't record or store audio and it can't show a transcript. Instead, it processes events live as they take place, using Siri AI to turn that raw data into actionable notes, never written into permanent storage. Below is a screenshot from Apple's presentation:

Your watch, like a smartphone or smart speaker, already has to always listen for a wake word (like 'Siri' or 'Alexa'), but the audio it's listening to is not logged or recorded: only interactions starting with the wake word are.

But add an always-listening AI assistant into the mix who's taking notes on your day-to-day, including your conversations with others who may not know they're being surveilled, and suddenly Apple's latest handy feature starts to look a lot creepier. Apple's other big new Audio Intelligence feature for watches is Live Rewind, which can produce a transcript of the last 15 seconds of audio on your watch face — whether a wake word was said, or not.

I'm frankly surprised Apple even went through with this feature at this time, considering the bans and backlash other AI-powered surveillance wearables are getting.

I'm thinking, of course, about Meta, and its smart glasses being banned in some public spaces and generally disparaged with names like 'pervert glasses'. Smart glasses have obviously garnered a real stigma thanks to people drilling out the LED recording light and using them to discreetly record inappropriately and without consent for clout, content, or their own personal use.

This has created a general feeling of malaise around wearables equipped with discreet cameras and microphones in general. Like Meta Glasses, the Live Rewind and Siri Recaps features have accessibility potential for those hard of hearing, but they're going to make most people uncomfortable.

Watch: Audio Intelligence features being announced

Apple is keen to evidence its trustworthiness, with on-device AI and Private Cloud Compute and an insistence that it doesn't record or log audio and transcripts. However, if people think they're being discreetly recorded without their permission, it's possible Apple Watch wearers could experience their own slice of the abuse hurled at Meta Glasses wearers.

I wrote earlier this year that wearable tech has to separate itself from surveillance capitalism if it wishes to be cool again. It seems Apple has taken a step in the other direction.

Update: Apple has published an article on its website, specifically about Audio Intelligence, and much has been made about how Audio Intelligence protects user privacy and is used responsibly. You can read the full article here.

In short, Apple says audio is not recorded or stored because "there is no recording to share, forward, or produce if requested by any party, because no recording exists". Instead, audio data is processed by Siri live, and raw audio "is never accessible".

Your data is not accessible to Apple either, thanks to Private Cloud Compute, and "independent security experts can verify this promise at any time". Siri Recaps auto-delete after seven days too. Apple is serious about getting users to trust this feature.

• 3 points

Data centers across the United States have been criticized for harming local environments sourcing power - sometimes illegally - from gas burning turbines that can release harmful pollutants into the air surrounding local communities.

But dozens of data centers have been accused of releasing harmful chemicals, heavy metals, and rare bacteria with the wastewater they release into nearby treatment facilities and waterways.

Violations have been logged for data centers in Wyoming, Virginia, New York, and Georgia, but the problem likely extends far across the rest of the US. Information on water usage and discharge for many data centers is hard to come by - or is being actively blocked.

Pollutants released into water across the US

In Wyoming, Meta was recently forced to halt water discharges from the construction site of its enormous 960-acre Cheyenne campus after a resistant bacterium - Cupriavidus gilardii - was found within a local water treatment facility.

The bacterium was identified after regulators tested a sample of the 801,000 gallons Meta had discharged into Cheyenne’s sewers. Following concerns that local residents could inhale droplets of the contaminated water if it was used for irrigation, local officials decided to stop accepting wastewater from the construction site.

Two water treatment facilities were forced offline as part of a multi-month decontamination and cleanup operation. Meta’s contractor responsible for the site, Goat Systems, is appealing the decision of officials to stop accepting wastewater from the site.

Multiple data centers across Virginia are facing enforcement actions from local authorities for violating wastewater and wetland regulations. In one such case, a data center under construction in West Virginia has funneled stormwater runoff into a local residential area, causing flooding on two occasions.

A data center near Lake Seneca, New York, is facing criticism from the Seneca Lake Guardian organization. The site was previously a cryptocurrency mining data center, but has been transitioning towards handling AI workloads instead. Seneca Lake Guardian has said that wastewater from the site flows directly into Lake Seneca at temperatures of up to 108F (42C), which can harm fish living in the lake and cause blooms of toxic algae.

The company behind the data center, Vulcan Infrastructure and Power, says it complies with all relevant environmental laws.

Multiple new data centers in Georgia have been approved to draw water from the Chattahoochee river, but local environmental guardians, such as water policy director at Chattahoochee Riverkeeper, Chris Manganiello, are being blindsided by a complete lack of information on how much water will be taken from the river, and what will be put back in.

“We need to know what is in this wastewater but there is a void of information,” Mangeniello told The Guardian. “I would like there to not be more Pfas [forever chemicals] going into the river but there is this level of anxiety because we don’t know what’s there.”

Don’t throw the Pfas out with the cooling water

Data centers pump water through miles of pipes crisscrossing their hardware in order to draw away excess heat. While there have been moves towards “closed-loop” cooling technology - where the water used is recycled within the system - there are still valid environmental concerns associated with these systems.

Before using a closed-loop system, the pipes need to be flushed through with water to wash away any debris or contaminants that were deposited within the pipes during assembly. This water is then released back into sewers to be cleaned by local treatment plants.

But many water treatment plants simply aren’t built to handle filtering water that contains industrial contaminants. These contaminants can include heavy metals or Pfas that can cause health problems in humans and animals.

Data centers are also being built in drought-hit regions, largely because the land is cheap, placing additional strain on already struggling water tables. Data centers can use hundreds of thousands of gallons of water per day to stay operational, and while closed-loop systems ensure this water is only taken out of the cycle once, it is still a significant sum of water to remove from regions experiencing droughts.

• NordVPN blocked over 5 million malware attempts in January alone
• 99% of phishing attacks impersonate just 300 brands
• AI tools and fraud kits mean attackers no longer need advanced skills

We are well past the days when a cyberattack meant mass-mailing a poorly spelled virus. In 2026, cybercriminals are heavily leveraging generative AI to make their scams highly personal, industrializing fraud on a massive scale.

That is the stark warning from the Consumer Cybersecurity Report: Dismantling the Evolving Threat Landscape, published today by NordVPN. Analyzing threat intelligence data from the first half of the year, the prominent cybersecurity and best VPN provider uncovered that the most exploited vulnerability right now isn't outdated software; it is human trust.

Armed with unrestricted AI models and ready-to-use fraud kits, scammers have drastically lowered the entry barrier for digital crime. The focus has decisively shifted from complex technical exploits to highly targeted campaigns that weaponize greed, urgency, and our faith in familiar brands.

"Bad actors are weaponizing our natural instinct to believe what we see and hear," says Marijus Briedis, CTO at NordVPN, warning that these attacks no longer require advanced skills or significant resources. "Anyone with an internet connection can launch them."

It's in this context that "A single human error is now more likely than ever and likely to be more devastating than ever," Briedis added.

The numbers behind the threat

NordVPN analyzes 12 million unique URLs daily, blocking an average of 130,000 malicious pages every 24 hours before they can reach a user — a critical defense mechanism as NordVPN’s antivirus tool continues to block massive volumes of malicious sites.

Malware remains the single largest threat by volume. January 2026 saw a massive peak of over 5 million blocked attempts as attackers preyed on post-holiday shoppers. Most of these were infostealers aiming to grab saved login credentials.

The US was the hardest hit with 4.89 million attempts across the first half of the year, followed by the UK (2 million) and Germany (1.32 million).

Phishing is equally rampant. NordVPN blocked over 4.4 million phishing attempts in the first half of the year, noting that 99% of these attacks impersonate a narrow list of just 300 brands, often disguised as fake job recruiter campaigns.

Microsoft was the most impersonated company (16.12%), followed by Roblox (12.32%), Google (9.94%), and Netflix (5.99%). Adding to the danger, attackers frequently exploit the trusted .com domain, which accounts for 43.2% of all intercepted scams.

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Hijacked sessions and the Dark Web

While users are increasingly wary of downloading fake apps or clicking shady links hidden in pirate sites, attackers are finding stealthier ways in.

Between January 1 and May 26, 2026, a staggering 94 billion cookies were exposed online. Of these, 1.2 billion were active session cookies, which cybercriminals can use to hijack accounts without needing a password, often completely bypassing multi-factor authentication (MFA).

Once data is stolen, it is swiftly commodified. Using its Dark Web Monitoring tools, NordVPN identified 8.4 million compromised accounts in just 90 days.

Frighteningly, over 47% of the exchanged data involved physical addresses and full names, allowing attackers to weave digital and real-world identifiers together into comprehensive victim profiles.

Telephony isn't safe, either. Between the service's launch and June 16, NordVPN blocked nearly 29,000 scam calls and issued spam warnings to over 525,000 users.

How to stay safe

Because technical barriers for criminals are lower than ever, defensive strategies must evolve. NordVPN's report stresses that consumer protection now requires a mix of both technological defenses and behavioral resilience.

Alongside using security tools like a virtual private network (VPN) and antivirus software, users must cultivate a healthy skepticism. If a message, email, or website demands urgent action or offers something too good to be true, pause and verify.

In the age of AI-driven fraud, a moment of hesitation is your best line of defense.

• CISA, NSA, FBI warn Chinese AI firms of industrial‑scale knowledge distillation
• Companies like DeepSeek, Moonshot, Alibaba allegedly extracted billions of tokens from US frontier models
• Advisory urges detection of malicious prompts, deceptive responses to distillation, and cross‑provider intelligence sharing

Chinese AI companies’ core development strategy is to steal proprietary functionalities and capabilities from their US counterparts, law enforcement agencies have warned.

The US Cybersecurity and Infrastructure Security Agency (CISA) has published a new security advisory, drafted jointly with the National Security Agency (NSA) and the Federal Bureau of Investigation (FBI), warning American AI companies about an ongoing “aggressive, malicious, and targeted distillation activities at an industrial scale,” and sharing recommended mitigation steps.

Knowledge distillation

IBM defines knowledge distillation as a “machine learning technique that aims to transfer the learnings of a large pre-trained model, the ‘teacher model,’ to a smaller ‘student model’.” It is used in deep learning as a form of model compression and knowledge transfer, it added, particularly for massive deep neural networks.

So, knowledge distillation is not illegal or malicious, per se. Its goal is to train a more compact model to mimic a larger, more complex one. In the security advisory, the agencies stress it is “recognized as a legitimate and useful technique in AI research,” but add that China-based AI companies are using it in ill will.

In other words, the agencies claim that instead of spending months and millions developing new capabilities for their models, the Chinese are simply sending huge numbers of carefully designed questions to US models and extracting the answers.

Which companies are engaged in knowledge distillation?

Apparently, all companies worth anything. DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Z.AI all allegedly “extracted billions of tokens across millions of exchanges/requests from US frontier AI models, including variants of Claude, GPT, Gemini, and Grok, since at least late 2024.” CISA also stressed that this was likely done with the awareness of the Chinese government. It hasn’t outright said, “with its blessing”, although it could be read between the lines.

The advisory shares a thorough list of all the models that were being trained, as well as all the models being taken advantage of.

On the Chinese side, they include DeepSeek R1 and V3 models, Moonshot’s Kimi-K2 and Kimi-K3 models, and MiniMax’s M2 model. On the US side, they start with earlier models such as GPT-4, Claude 3.7, and Gemini 2.5 Flash Preview, all the way to Claude Fable 5, GPT-5, and similar.

When done in good faith, knowledge distillation is not illegal. However, the report says the companies routed the requests through multiple accounts, different API access points, multiple cloud providers, third-party AI aggregators, proxy services and “transfer stations”, as well as premium subscriptions shared between developers, all in an attempt to work around defenders trying to disrupt the process.

“This represents systematic extraction of proprietary functionalities and capabilities threatening U.S. technological leadership. Addressing industrial-scale distillation merits a coordinated response across the AI ecosystem, including effective information-sharing, spanning the U.S. Government, private industry, and allied nations,” the agencies concluded.

What US companies should be doing

To defend their intellectual property (and thus remain ahead of Chinese competing models) US AI companies should implement comprehensive detection and mitigation, the agencies said. That means hunting for anomalous and malicious prompts, accounts, networks, and behaviors. Furthermore, they should monitor subscription-to-usage ratios, immediate maximum usage from new accounts, and enterprise-scale throughput patterns.

The second step is to “deploy targeted response changes”: “Subtly alter responses for suspected malicious distillation attempts to attenuate the payoffs to companies conducting industrial-scale distillation campaigns.” In other words, AI companies should make sure their products lie when they spot they were being distilled for knowledge.

Finally, US AI firms should set up cross-organization intelligence sharing, correlating activity across model providers, cloud platforms, and API aggregators.

The bottleneck on sophisticated cyber operations that target nation states is breaking. Conducting a large-scale cyber attack used to mean scaling expert talent.

When the cost of adding a capable attacker approaches the cost of compute, the economics of offense fundamentally change.

This is already operational: Dream's threat research recovered an autonomous multi-agent framework that ran intrusion campaigns against government entities in Asia, executing twelve attack waves in four days with eight parallel agents, compromising 85 government accounts, and using a closed learning loop to adapt after failure.

The advantage is shifting from the number of experts to how effectively their expertise can be scaled.

AI benefits both attackers and defenders, but defenders start with a unique advantage: they already own the map attackers must discover.

Defenders that understand their environment can use AI to turn that knowledge into operational scale.

The Autonomous AI Government Hacker

In July, our threat research team recovered the operational workspace of an autonomous multi-agent framework that had been conducting intrusion campaigns against government entities in Asia.

Over roughly four days, the framework executed twelve attack waves and ran up to eight AI agents in parallel. Built on the publicly available Hermes and OpenClaw frameworks, it compromised 85 government employee accounts and used 84 of them to pivot through a government single sign-on environment.

What’s really intriguing is its autonomous operational decision making.

Assigning confidence scores

Every discovery was assigned a Bayesian confidence score to assess different paths and then chose how to proceed, just like an actual team.

Similarly, when an attack path failed, the framework automatically entered what it called a Learning Cycle, searched vulnerability databases and security research techniques relevant to that government's technology stack, and tried again. The framework audited itself – it created a closed learning loop: investigate, validate, act, observe the result, update its operational knowledge, and try again.

This was not a self-improving model. It was a self-adapting cyber attacker – there is a real expert behind it, embedded as AI system. The fundamentals of this attack weren’t even particularly impressive or novel. It is the scale – and the prospect for nearly infinite scale – that is daunting.

Until recently, one of the limiting factors in scaling sophisticated offensive operations was the expert reasoning required to decide what to investigate, validate findings, connect them into viable attack paths, and adapt when those paths failed. It was expensive, both in dollars and in expertise. That scarcity placed a natural constraint on offensive scale - scaling a sophisticated operation meant scaling skilled people, time and coordination.

AI is beginning to automate precisely that expensive layer of the operation: deciding what to investigate, validating hypotheses, learning from failure and choosing what to try next. Talent still determines the quality of those decisions. But the number of talented people no longer has to determine how many times those decisions can be made in parallel.

What happens when scaling an offensive operation no longer requires scaling the number of experts behind it at the same rate?

As someone who has spent 15 years in both offensive and defensive cyber roles, it’s becoming clearer every day that AI has changed that equation - the historical relationship between the amount of expert talent an organization has and the scale at which it can operate is beginning to break down.

AI does not eliminate talent - it changes what talent can scale.

An Attack Surface the Size of a Country

Government IT infrastructure is an interconnected ecosystem built over decades. It consists of ministries, municipalities, operational technology, legacy applications, cloud services, contractors, suppliers, and countless trust relationships connecting them together.

These connections typically exist for legitimate operational reasons (or at least historically legitimate reasons).

Of course, every connection is also a potential vulnerability.

This is how modern government attacks spread - not necessarily by exploiting one critical vulnerability, but by chaining together many ordinary ones.

Historically, this challenged both sides. No defensive team could continuously reason over every asset, identity, configuration, vulnerability and trust relationship across an entire country. But attackers faced a version of the same constraint. Their experts also had to decide where to spend their time to find a viable path from intrusion to crown jewel

Autonomous systems change that.

An autonomous attacker does not need to understand the entire government environment in advance. It can explore it continuously: discover a relationship, form a hypothesis, test it, learn from the result and move to the next one.

For the first time, governments may face adversaries capable of reasoning over national-scale attack surfaces faster than the institutions responsible for defending them.

The Race to Change the Outcome

The dramatic decline in the cost of offensive cyber expertise, via leveraging and weaponizing agents, is a tectonic shift. Until now, this was a skill limited to a select few and came with a high price tag.

Today, discovering, prioritizing and combining these techniques into viable attack paths is cheap, and one can repeat the process at machine speed.

With the pace of AI development, that statement becomes more true every day.

Offensive capability is becoming cheaper, faster and easier to reproduce.

But there is another side to this equation.

Defenders have always had structural advantages: more telemetry, deeper context, persistent access to their infrastructure, and knowledge of its configurations, identities and relationships, while also have a much better ability to act.

They too had the constraint of human capacity. No team could continuously reason over all that information, across every asset and relationship, all the time. The same AI that benefits attackers may operationalize defender’s historical edge at scale too.

This is where time plays a key role. Analyzing everything is not the same as defending everything. If AI detects a compromised identity in seconds but the credential remains active for six hours, the attacker still has six hours. If it identifies an exploitable path to a critical system but remediation takes three weeks, that path remains open for three weeks.

The opportunity, then, is not simply better analysis. It is reducing time-to-effective-action: the time between understanding a risk and changing the outcome.

And "effective" matters- disabling an identity, changing a firewall rule or patching a vulnerability is not enough. The system must verify that the attacker can no longer achieve its objective.

The defensive loop cannot end with intelligence - or even with action. It has to end with a verified security outcome.

The Gap That Matters

AI does not inevitably favor the attacker.

The framework we recovered had to steal its map of the environment, one probe at a time. Defenders already have that map. Every configuration, credential, telemetry stream and trust relationship could take an attacker – even an AI attacker – days to weeks to discover. What defenders could never do was reason over all the assets they had, continuously, due to the lack of talent capacity to do that.

AI begins to remove that human-attention constraint. It allows defenders to amplify expert talent across thousands of investigations in parallel, continuously identifying attack paths, prioritizing those that pose the greatest risk, and focusing action where it matters most.

Attackers get the same leverage. But they don't start from the same place. Defenders have a home-field advantage: they already know and control the environment the attacker must discover.

The gap that matters is no longer simply the number of experts on either side. It is how effectively each side can scale that expertise- and direct it toward the right risks first.

Ultimately, the race is not about who can know more.

It is about who can scale talent in the right way- and turn that scale into an outcome first.

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For the past decade, quantum computing has been relegated to the horizon, a distant, theoretical challenge rather than an immediate operational imperative.

Breakthroughs have generated headlines, but without a firm deadline for when quantum computers could threaten today’s cryptographic standards, CISOs have had little incentive to make quantum readiness an immediate priority. IBM’s 2025 Quantum-Safe Readiness Index shows how much work remains, with the average organization scoring just 25 out of 100.

Yet, that window of preparation is contracting rapidly. This spring, Google warned that “quantum frontiers may be closer than they appear,” pointing to advances in hardware, error correction, and algorithms that are accelerating progress toward cryptographically-relevant quantum computers. Google has since set a 2029 target for completing its own migration to post-quantum cryptography (PQC).

Yet, that growing urgency still comes without a firm deadline. Unlike the fixed, binary deadline of Y2K from years ago, the quantum risk profile is nebulous; the exact threshold of peril remains undefined, even as the required remediation timeline spans several years.

Large enterprises need time to identify cryptographic dependencies, assess legacy infrastructure, coordinate vendors, and secure the budgets required for migration. Those decisions reach well beyond cryptography, affecting how the business plans, funds, and prioritizes risk. In fact, the availability of quantum computing capabilities will touch almost every aspect of companies around the globe.

Turn quantum risk into an enterprise-wide priority

Quantum readiness already has a prescribed technical solution. In 2024, NIST finalized its first set of PQC standards and encouraged organizations to begin transitioning as soon as possible. For some systems, migration may just involve a software update or configuration change. For others it may be much more complex. The challenge for a large enterprise is knowing where those changes need to happen.

A practical first step is to build a cryptographic bill of materials (QBOM), cataloging what cryptography is in use and where, which systems can be updated relatively easily, and where legacy infrastructure may require replacement or another mitigation strategy. This is an intensive effort, but there are software platforms that can help.

Next, organizations should prioritize systems based on the value of the data and how long it needs to remain protected. Financial records or customers’ customer personally identifiable information (PII), for example, may warrant greater urgency than an internal chat log with little long-term value.

Data longevity is especially important because adversaries are already collecting encrypted traffic in the expectation that future quantum computers will be able to read it, a practice known as "harvest now, decrypt later." For sensitive data, the attack may already have happened even though the business impact hasn't.

Data exposure is only one part of that assessment. Public key cryptography (PKC) also underpins authentication, including single sign-on and other mechanisms used to establish identity. If that cryptographic foundation can no longer be trusted, organizations face the risk of impersonation, with consequences for access to applications, infrastructure, and data.

Much of this work sits outside the CISO's direct control. Infrastructure, applications, data, and third-party relationships are often jointly owned across the business, and so are many of the budgets needed to address them. CISOs must make a business risk pitch that shows other leaders – the CIO, CTO, procurement, compliance, and executives – where the greatest risks are and what needs attention first.

Make crypto-agility part of the migration

Preparing for a post-quantum future also requires crypto-agility. Cryptographic standards will continue to evolve as new vulnerabilities emerge and algorithms change. Organizations need systems and platforms that can accommodate those changes without another costly, multi-year migration. That means buying and building in ways that can accommodate a future algorithm change through software rather than hardware rip-and-replace.

The UK's National Cyber Security Centre recommends building that flexibility into PQC migration plans and establishing criteria for retiring traditional algorithms, with the goal of removing sole dependence on traditional public-key cryptography. It also means rethinking how organizations budget for the transition.

Large enterprises often operate on annual budgets approved well in advance and adjusted only slightly throughout the year, but a post-quantum transition may require more flexibility as standards and threats evolve.

This principle should also influence procurement. SOC 2 compliance already requires organizations to scrutinize vendors' privacy policies, certifications, and access controls, but cryptographic dependencies can be another blind spot. Asking which standards a vendor supports, how cryptography can be updated, and what its PQC roadmap looks like can reduce the risk of buying infrastructure that becomes difficult or expensive to migrate later.

The quantum timeline starts with today’s budget

While a firm deadline for quantum readiness remains elusive, budget cycles offer a more disciplined, actionable framework for planning. Executing a multi-year migration requires sustained financial commitment over successive cycles; every passing budget period effectively compresses the available window for remediation.

Most CISOs already understand quantum risk. The challenge is moving quantum readiness from something planned for next quarter or next year into funded work. With only a limited number of budget cycles available for a multi-year migration, repeated delays quickly add up.

The priority is to turn that awareness into an inventory, clear business priorities, and a phased migration plan that can be funded over the coming budget cycles. By starting now, organizations have time to focus on the highest-risk systems and address legacy infrastructure before the timeline becomes critical.

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• The first trailer for Neon's Sam Altman biopic Artificial has been revealed
• The Andrew Garfield-led movie will be released in late 2026/early 2027
• It covers Altman's controversial rise to prominence as OpenAI's co-founder

The first trailer for Artificial, a movie about controversial OpenAI co-founder Sam Altman, has been revealed.

Unveiled yesterday (September 8), the teaser for the Neon-distributed film strikes an unusually sinister tone for what's being billed as a biographical comedy-drama.

Indeed, from the gun-laden room that lead star Andrew Garfield, who'll portray Altman, visits, to Garfield's unsettling voiceover that plays over Artificial's publicly-released footage, it's certainly foreboding in nature. I get the feeling, then, that it'll be a dark-comedy dramatization of Altman's rise of prominence, the launch of artificial intelligence (AI) chatbot ChatGPT, and his brief and somewhat disputable ousting as OpenAI CEO in late 2023.

Artificial will initially debut in US theaters on December 25, 2026, before receiving a wider international rollout in early 2027. UK viewers, for example, will have to wait until January 22, 2027 to catch one of many new movies that'll arrive in the months ahead.

Artificial cast and crew: who's involved?

Oscar-nominated filmmaker Luca Guadagnino (Challengers, Call Me By Your Name) is on directing duties, while Saturday Night Live writing alumnus Simon Rich has penned its screenplay. Meanwhile, Blur and Gorillaz frontman Damon Albarn has also created original music and songs for Artificial.

Where the movie's cast is concerned, Garfield, who impersonates another Silicon Valley techbro following his portrayal of Eduardo Savarin in The Social Network, aka the 2010 dramatization about Facebook's creation, is arguably its most recognizable star — and based on what little footage we've seen thus far, he appears to have Altman's mannerisms, vernacular, and other unique traits down to a tee.

Here's who else you'll see in Artificial:

• Yura Borisov (Anora) as Ilya Sutskever, OpenAI's chief scientific officer and co-founder
• Monica Barbaro (Crime 101) as Mira Murati, OpenAI's chief technology officer
• Cooper Hoffman (The Long Walk) as Greg Brockman, OpenAI's chairman
• Ike Barinholtz (The Studio) as Elon Musk
• Mark Rylance (Bridge of Spies) as Geoffrey Hinton, the so-called 'Godfather of AI'
• Jason Schwartzman (The Grand Budapest Hotel) as TBC
• Chris O'Dowd (The IT Crowd) as TBC

What is the plot of Artificial?

Here's the official logline for Artifical: "[The movie] chronicles the incredibly consequential days leading up to the sudden firing and reinstatement of Sam Altman, as the fate of who gets to control the technology at the centre of an AI arms race hangs in the balance."

It's unclear how much of Altman's backstory, his various relationships with his OpenAI colleagues, and ChatGPT's launch and subsequent sociopolitical impact will be covered as part of Artificial's story. One thing is for sure, though: given that it dramatizes events concerning Altman and OpenAI, the discourse that will surround Artificial's release will be impossible to ignore.

• Google is investing over $15 billion into Finland over two years
• Nuclear energy, wind power and battery storage are top of mind
• The company is also improving local forests, adding new saunas

Google has announced plans to invest around $15.1 billion into Finland's AI and digital infrastructure over the course of the next two years, making it its largest single investment in Europe to date.

The company said the extra capacity is needed to meet growing local demand for services like Gemini, Maps and YouTube, but instead of just growing its data centers, Google is also allocating money for grid upgrades, clean energy projects, battery storage and more.

The country's cool climate and low-carbon electricity are especially attractive for hyperscalers as they try to improve the efficiency of data centers against a backdrop of heightened public and regulatory scrutiny.

Google is investing heavily in its Finnish data centers

The Hamina campus, which it's been running for around 15 years, is especially notable because it uses both seawater-based cooling to minimize its water consumption impact, as well as an offsite heat-recovery scheme to redirect waste heat into local properties.

Local communities in Hamina, Kajaani, Muhos, and Vaala are set to receive a combined $36 million in funding to support upskilling and education, while as many as 37,000 jobs are expected to become available throughout the 2027-2028 construction phase.

On the energy front, Google has signed a 22-year power-purchase agreement to support the extension of the Loviisa nuclear plant and has drawn up plans to add new onshore wind capacity and a new 94MW battery facility to bridge the gap between renewable energy availability.

Google Global Infrastructure VP Bikash Koley also stressed the company's other community and environment investments, including native forest and wetland regeneration, accessible recreational trails, public saunas and fishing piers.

For Finland in particular, Google claimed to have invested around $4 billion in Hamina and related infrastructure alone since acquiring the site, a former paper mill, in 2009.

Enterprise infrastructure has always adapted as scale increased. Virtualization tackled server sprawl, cloud computing reduced the need to provision physical resources for every application, and automation made increasingly complex environments manageable. Artificial intelligence presents a different kind of scaling problem.

The discussion around enterprise AI has largely centered on models, GPUs, and inference performance, but those technologies represent only a fraction of what organizations must operate. Every production AI deployment creates a continuous flow of data that must be ingested, protected, moved, analyzed, retained, governed, and eventually archived.

Those activities place demands on infrastructure that are very different from the workloads storage systems were originally designed to support.

This is becoming increasingly apparent as organizations move beyond pilot projects. AI is no longer a single workload running on isolated infrastructure. A single application may include high-speed storage for model training, object storage for inference data, lower-cost capacity for operational datasets, immutable storage for cyber resilience, and long-term archives to satisfy regulatory requirements.

Traditionally, those functions have been handled by separate products with separate management tools, security policies, and operational teams. That architecture worked reasonably well when data moved slowly and applications followed predictable lifecycles. AI changes both assumptions.

Training datasets expand continuously. New models are introduced far more frequently than traditional enterprise applications. Inference workloads fluctuate as demand changes. The same dataset may move repeatedly between active processing, backup, compliance, and archival over its lifetime.

Each transition introduces another operational task, another opportunity for inconsistency, and another point where administrators must intervene. Before long, the effort required to manage the infrastructure begins to rival the effort required to build the AI applications themselves.

Complexity becomes the real infrastructure challenge

For years, the answer to operational complexity was automation. Administrators automated provisioning, scripted maintenance, and orchestrated repetitive tasks. Those capabilities remain valuable, but they were designed to execute predefined actions under predefined conditions.

AI environments are considerably less predictable. Infrastructure must continually adapt to changing workloads, shifting performance requirements, evolving security policies, and rapidly growing data volumes, often without the benefit of stable operating patterns.

That is where autonomous data infrastructure represents something more substantial than another automation framework. Rather than treating storage as a collection of independent systems, it starts with the assumption that the platform itself should continuously optimize how data is managed throughout its lifecycle. Capacity, performance, protection, and cost become policy decisions rather than infrastructure projects.

Data moves between performance tiers automatically according to business requirements instead of being exported, migrated, and re-imported into separate platforms. A single namespace spans workloads that historically required multiple storage systems, allowing infrastructure to evolve without repeatedly forcing administrators to redesign the environment.

That architectural change may ultimately prove more important than the automation itself. Many organizations underestimate how much operational complexity accumulates simply from running multiple storage platforms. Every environment has its own authentication model, monitoring tools, lifecycle policies, upgrade schedules, recovery procedures, and performance characteristics.

As AI expands across the enterprise, those management layers multiply alongside the data. Reducing the number of operational boundaries often creates greater long-term value than introducing another layer of orchestration.

The same principle applies to cyber resilience. AI has increased the value of enterprise data far beyond traditional business records. Training datasets, model checkpoints, vector indexes, and inference pipelines have become strategic assets in their own right. Protecting them requires more than backup software. It requires infrastructure that assumes failures and attacks will occur and is designed to recover without depending on manual intervention.

Governance becomes part of the data lifecycle

The conversation also extends beyond security to control. As AI initiatives become more strategic, organizations are under growing pressure to understand where data resides, who can access it, and which legal and regulatory frameworks govern it.

That is especially true for enterprises operating across multiple countries or in highly regulated industries, where data residency requirements, digital sovereignty initiatives, and industry-specific compliance obligations increasingly influence infrastructure decisions.

Rather than treating these as separate governance exercises, modern infrastructure must make location, retention, and access policies part of the data lifecycle itself, enabling organizations to meet regulatory requirements without introducing additional operational complexity.

One of the more significant design decisions behind autonomous data infrastructure is that immutability exists within the storage engine itself rather than being implemented solely through administrative policy. Instead of modifying existing data in place, new versions are written separately while previous versions remain intact.

Combined with distributed self-healing that rebuilds only affected objects instead of entire disks, this creates a fundamentally different operational model for resilience. Recovery becomes part of normal system behavior instead of an exceptional event requiring administrators to coordinate lengthy repair efforts.

Infrastructure operators become infrastructure architects

Perhaps the most interesting implication has little to do with storage technology itself. Infrastructure teams are already responsible for environments that are growing faster than headcount, and AI is accelerating that imbalance. The objective is not to remove people from operations, but to reduce the amount of time highly skilled engineers spend on repetitive maintenance that adds little strategic value.

As more routine activities become policy-driven and continuously optimized, infrastructure professionals can devote more attention to architecture, governance, capacity planning, and aligning technology decisions with business priorities.

That evolution mirrors what is happening across software engineering, networking, and cybersecurity. AI is steadily shifting human expertise away from repetitive execution and toward system design, governance, and strategic decision-making. Autonomous data infrastructure reflects the same progression.

Rather than asking administrators to manage an ever-growing collection of storage products, it treats the infrastructure as an adaptive system that operates within policies established by the people responsible for it. The most effective approach is not to take humans out of the loop, but to keep them in control of the decisions that shape security, compliance, and business outcomes while allowing the platform to execute routine operational tasks autonomously.

Viewed from that perspective, autonomous data infrastructure is less about storage than it is about preparing enterprise IT for the next decade. AI has exposed the limitations of architectures built around isolated products, manual coordination, and steadily increasing operational overhead.

Organizations will continue investing in faster GPUs and more capable models, but those investments will deliver their greatest value only if the infrastructure beneath them becomes equally capable of managing complexity. The next generation of enterprise infrastructure will not simply store data more efficiently. It will actively participate in operating the environments that modern AI depends upon.

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For years, ambitious European technology founders heard the same advice: to build a global software business, you'd eventually need to move to Silicon Valley. The Bay Area had venture capital, experienced operators, and many of the world's largest technology companies.

Today, some of the world's fastest-growing AI companies are proving otherwise. Lovable, based in Stockholm, has just achieved a $13.3 billion valuation and reaches nearly two-thirds of employees of the Fortune 500. ElevenLabs, founded in London, achieved an $11 billion valuation in early 2026 and surpassed $500m ARR later in the year.

AI has rewritten the economics of building a company: foundation models, coding assistants and automation tools let small teams create, iterate and scale with far fewer people than previous generations needed. Success now hinges on attracting exceptional talent, not on being near Sand Hill Road.

That changes the question for Europe. It's no longer whether we have the talent or the capital to build globally significant technology companies. We do. It's whether that talent becomes founders, or stays employees.

Europe has the talent

London has become one of the leading hubs for frontier AI talent outside the United States, supported by organizations such as Google DeepMind, the Alan Turing Institute, and Imperial College London. The capital's King's Cross area has become a focal point for AI research, startups, investors and global technology companies.

Elsewhere, cities including Paris, Berlin and Munich continue to cultivate world-class engineers, researchers and product leaders. I have lived in the US before, but I chose to found my own company in London for exactly this reason: the talent I needed was already here.

The fight for future founders

The expansion of American AI companies into Europe underlines the strength of that talent pool. OpenAI and Anthropic have both expanded their London research and engineering operations, joining Google DeepMind and Microsoft AI in competing for the same engineers, researchers and product leaders that emerging startups need.

These companies can offer salaries, resources and career paths that no early-stage company can match, and for many skilled operators, joining one is the logical choice.

The cost is largely invisible. Every experienced operator who spends a career inside a global AI company instead of starting one is a company Europe never gets to build. The result is fewer breakthrough companies and fewer founders to inspire the next generation.

Operators make the best founders

Many of the strongest founders start out as operators. Before launching their own companies, they spend years learning how to build products, attract customers and scale organizations, developing judgement against real business problems.

I've seen this progression in practice. Before co-founding ElevenLabs, Piotr Dąbkowski worked alongside me at Tessian, a cybersecurity company, where he was building AI-powered products years before the current wave. The judgement he developed there as an operator helped shape what became one of Europe's most valuable AI companies.

In the AI era, successful founders are defined by more than technical expertise. They have commercial judgement and an instinct for seeking out problems that need solving. These qualities make them valuable employees today and position them to become successful founders tomorrow.

They're also exactly what I hire for. We look for traits over skills: people who seek out responsibility before anyone hands it to them, and who want to understand how the whole business works rather than just their corner of it. Technical skills matter, but those are the operators most likely to build something of their own one day, and they create enormous value long before they do.

Back them at the point of the leap

As technology becomes more accessible, entrepreneurial ambition becomes the scarce resource. So support for aspiring founders has to begin long before they start seeking venture capital. Many experienced operators already have the expertise, networks and market insight to build successful businesses. What they lack is the confidence, encouragement or early backing to leave a stable job.

At my own company, we've put money behind this belief. Anyone who has been with us for five years can make a single pitch and get $250k to go and build their own company, along with office space and the backing of people who've worked beside them for years.

There's no cap on how many founders we'll fund. It isn't a perk designed to wave people out the door. It's a recognition that the people best placed to back a first-time founder are the ones who've watched them work, and that when I left my last company to start this one, I had to pitch strangers instead. Programs like Entrepreneur First have shown the value of backing exceptional individuals before they even have a company. Europe needs more mechanisms like this, wherever they come from.

This is how ecosystems compound. Former PayPal employees, the so-called "PayPal Mafia", went on to found or help build companies including LinkedIn, Palantir and YouTube. Europe is beginning to generate its own founder networks, with former Skype employees founding companies such as Wise, Bolt and Starship Technologies. Back one generation of founders and they build the next.

Europe has the fundamentals in place: exceptional talent and a maturing investment ecosystem. The missing link is the pipeline from operator to founder, and today's scaleups are the ones who can build it. Every operator who takes the leap expands Europe's capacity for innovation and entrepreneurship, and makes the whole ecosystem more self-sustaining.

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• The AVEN thrust-vectoring nozzle has been added to the X-BAT AI-piloted fighter with vertical take-off capabilities
• Thrust-vectoring allows a plane’s exhaust to be redirected, instead of relying purely on manipulating airflow across the wings for movement
• The engine was first trialed on an F-16 fighter in the 1990s

Shield AI has revealed the X-BAT AI-piloted fighter jet relies on a jet engine variation first developed over 35 years ago and tested on a General Dynamics F-16 in the 1990s.

The AVEN – Axisymmetric Vectoring Exhaust Nozzle – essentially replicates the actions of a wing, but instead of manipulating airflow across the airfoil, it directs the exhaust, in a process labeled “thrust-vectoring.”

Developed for the F-16 as part of a dedicated GE Aerospace program investigating Multi-Axis Thrust Vectoring (MATV), the nozzle was proved to work. The technology has recently been revived thanks to GE Aerospace and Shield AI, with the AVEN paired with an F110-GE-129E engine.

The VTOL X-BAT needs AVEN

Using the Axisymmetric Vectoring Exhaust Nozzle, the X-BAT is given flight capabilities that would prove demanding for humans to repeatedly endure. It enables hovering, but also superhuman reaction speeds, and removes the risk of G-force induced blackouts. Without pilot fatigue, the X-BAT can commit to extreme maneuvers in aerial combat.

Designed as a Vertical Take-off and Landing (VTOL) aircraft, the X-BAT can be deployed in areas with little or no runway. The Axisymmetric Vectoring Exhaust Nozzle is a key part of this, providing the necessary thrust for the X-BAT to take off. As the craft launches more like a rocket than a standard jet fighter, the AVEN and the F110-GE-129E engine appear to ensure a steady detachment from the launch dock as the X-BAT heads skyward.

It’s a setup that might resemble something out of a 1950s sci-fi comic, but in reality can only work successfully without a human pilot.

AVEN isn’t unique to the X-BAT. While the GE Aerospace project with the F-16 proved the technology worked, it was pursued elsewhere, most notably with the Russian Sukhoi Su-27SM revision of 2002. Earlier craft, like the Harrier variants, use “2D thrust vectoring” where thrust is directed horizontally or vertically via exhausts on either side of the aircraft. Indeed, the notion of thrust vectoring dates back to the V-2 rocket, the long-rang rocket used by Nazi Germany in the final months of World War Two.

Modernising the AVEN

Shield AI and GE Aerospace haven’t simply stuck to the original AVEN hardware and attached it to the F110-GE-129E engine. The nozzle has been re-engineered, with modern materials and production techniques. Inevitably, it has also been adapted to work with 21st century guidance systems, and the AI piloting and control systems are also accommodated.

First test flights of the X-BAT are expected to take place in Kansas by late 2026, ahead of a planned 2028 operational validation and full production.

It’s been a hot minute since ChatGPT last updated its Images feature. Its last update, to Images 2.0 in April 2026, was pretty solid and I’d say put it ahead of Google’s Nano Banana 2, even if its images still took longer to create, which was a bit frustrating.

Today's new Images 2.5 update adds a whole bunch of extra features and tools to the existing image generation engine that make it a lot easier to use, and it's quicker too.

In fact, there are so many features now in Images that to say it’s overcrowded would be an understatement. Because of that, it can be a little hard to locate the best new features amongst everything else, so I’ve pulled out the ones you should definitely look out for, because they make a real difference.

1. Much improved editing tools

I’ve pulled these features out first because, despite not sounding that exciting, the improved editing tools are actually a real game-changer for how I use ChatGPT Images, and probably will be for you, too.

OpenAI appears to have quietly been rolling out its new image-editing toolbar in ChatGPT during August, although the company hasn’t formally announced it, so it might not be totally accurate to call them part of the new ChatGPT Images 2.5, but I’m including them because they are, effectively, new.

At the moment, we’re used to generating an image, then when it’s not exactly what we want, we type in another description using the prompting tool, like ‘make it brighter’ or ‘remove the people in the background’, and try again. After ChatGPT hasn't got quite right a number of times, I usually give up and start again.

Now I don’t need to. The new editing tools let you pinpoint the exact spot in the image you’d like to change, and tell ChatGPT exactly what you’d like to do with it.

The new Edit toolbar contains Markup, Comment, Remove BG, Erase and Resize.

In the web version of ChatGPT, just click on the new Edit button that appears over a generated image and you'll enter the Edit mode, where you’ll see this new toolbar. In the app version of ChatGPT, just tap on any image to enter Edit mode.

Let’s start with the most obvious tool: Remove BG. This keeps the foreground elements of your image and removes the background, making a transparent PNG file when you download it. It’s perfect for dropping your image into another document, such as a presentation you’re making.

Resize is a quick way of changing the aspect ratio of the image and contains all the usual suspects, including Portrait 3:4, Widescreen 16:9 and Landscape 4:3.

Erase makes it easy to remove elements from your image. To remove a person from the background, for example, just brush over them with the Erase brush and ChatGPT removes them entirely. You don’t need to be super-accurate with your brush — just cover enough of the object so that it’s obvious what you want to remove.

Markup is for adding markup elements to an image, but the Comments feature is where it gets really interesting. Instead of doing what you’d expect — just adding comments to an image — these are actually points for ChatGPT to act on. Click on any element of an image and add a comment like ‘remove this’ or ‘change this to blue’, and ChatGPT will follow your instructions when you tap the Send button. This makes editing images so much easier.

The final feature worth noting with the Edit mode is that you can easily roll back to every previous version of your image — each edit is saved as a separate file, so just scroll up or down in Edit mode to access previous versions of your image.

2. Sketching out an image first

The new Sketch tool enables you to sketch out an image, then ChatGPT transforms your rough sketch into a proper AI-generated image. It's useful when it would be quicker to draw your idea out than describe it.

Choose Images from the main menu and then Sketch at the top of the available options.

The Sketch tool does look a little bit like MS Paint from 1995, but it’s good enough for doing a quick sketch with your finger, or mouse. Once you’ve done that click or tap the blue tick button and ChatGPT loads in a prompt telling it to convert the sketch into a full image. You can add any details you want here.

Of course, once you’ve got your finished image you can still enter Edit mode to tweak it further.

3. Templates have finally arrived

The new Templates make it a lot easier to create the sort of things people actually want to do with ChatGPT Images, such as make posters, logos, icons, stylish product shots and much more. Each different task now has its own template, which you can work through with ChatGPT.

Each template starts with a basic prompt, then it asks a series of follow up questions so that it can find out exactly what you want.

For example, choose the Product photo template and it asks you to upload a photo of the product you want to feature, then when you’ve done that it asks for a style, asking you to choose between styles like Clean Studio, Editorial, Lifestyle and Dramatic, amongst other choices. Then it gives you a choice of a setting, like Dark Studio, then finally it starts to create your image.

If you’re going to ChatGPT to create something specific then the new Templates are going to be a great place to start.

And there's more!

That’s not all that’s changed in the new ChatGPT Images 2.5. The images it creates are more personalized, so you get better results when working with recognizable people and portraits. It’s better at infographics than before, and it’s now faster than ever, which is great because I did find it a bit on the slow side previously.

Finally, when you tap to share an image, you now also have the option of sharing the prompt that created the image, too, so people can make their own versions of whatever image you created more easily.

All in all these improvements are welcome, if not groundbreaking. I can see why they didn’t call this Images 3.0. It’s essentially a much improved version of the previous Images, but it’s certainly going to change the way I use ChatGPT images from now on.

Being a devoted Gemini user seems to require a peculiar combination of loyalty, patience, and an unusually high tolerance for the word Flash.

That patience is being tested again. Anthropic released Claude Fable 5.1 last week, bringing significant improvements to its model lineup. OpenAI followed with GPT-6 Astra, its new frontier model designed to operate software, tackle complicated multi-step jobs, and work with considerably less human supervision.

Over in the Gemini community, meanwhile, some users are staring at their model picker and wondering when Google is going to join the party. The increasingly nervous focus is Gemini 4 Pro, the presumed next major frontier model from Google, and whether the company will actually release it at all.

Google has not announced that Gemini 4 Pro has been canceled, and the increasingly elaborate theories circulating on Reddit should be treated as exactly that: theories. Still, the mood is revealing. One r/Bard post jokes that Astra's arrival means Gemini 4 Pro will now be canceled while everyone waits for Gemini 4.5 Pro instead, and the thread title predicts Google will decide 4 Pro is no longer competitive enough and return to releasing Flash models.

Awaiting the new Gemini

"I also want to feel the frontier," one Reddit thread begins, with plenty of sympathetic agreement from others. "Google doesn't have enough talent anymore, and most of its compute is reserved for Apple Intelligence and Google Search AI Mode," one user posted in response.

The frustration makes more sense when you look at what Gemini users can see happening elsewhere.

Anthropic calls Fable 5.1 one of its most advanced models for coding and knowledge work, with improvements aimed at long-running tasks and research. The model has a one-million-token context window and 128,000-token maximum output, while Anthropic has also made cache reads dramatically cheaper.

Then there is Astra. OpenAI describes GPT-6 Astra as its most intelligent model yet, with an emphasis on computer use, coding, and completing complex work across multiple steps. The important part for ordinary enthusiasts is that OpenAI says Astra is rolling into ChatGPT, including access through Plus in Work and Codex as rollout progresses.

Gemini users want their turn.

Another Reddit thread is literally titled “I envy them too,” but most of the comments there are pushing back against the idea that Gemini's current model is useless. "Just keep all three subs, best way to play," one post argues.

Still, that misses something about why people become attached to these platforms. Gemini comes with Google's wider ecosystem, Android integration, and tools such as NotebookLM. As one Reddit commenter put it when told to subscribe elsewhere, “Gemini is cooler, has more tools, NotebookLM, it's better integrated in Android, etc.” Switching models is easy. Switching ecosystems is less so.

Gemini Flash again?

There's a running joke is that Google will respond to all this impatience by releasing another Flash model. Google has continued iterating rapidly on its faster Gemini models, and the current Reddit discussion is full of references to Gemini 3.7 Flash and 3.8 Flash.

In the thread speculating about Gemini 4 Pro's fate, one commenter imagines Google announcing, “We have started our most ambitious pre-training run yet, for Gemini 4.5,” while another delivers the punchline: “Here's another few Flash models.” A less amused commenter writes that DeepMind has “lost the initiative” and its famous confidence.

This is not really a fight over whether Gemini 3.8 Flash is good. Some users think it is excellent. Multiple commenters argued that Gemini remains highly competitive on price and performance and wins when it comes to coding.

The feat is that, after years of seeming to have an AI advantage, Google has squandered it. When Gemini looks like it is following OpenAI and Anthropic rather than forcing them to respond, its most enthusiastic users notice.

Of course, whether Google even wants the same victory its Reddit fans want is debatable. A cheaper, faster model embedded across Search, Android, Workspace and countless other services could ultimately be much more valuable than topping a frontier benchmark for a few months. For Google shareholders, that could be wonderfully sensible. For the person refreshing the Gemini model picker while Claude and ChatGPT users play with their new frontier models, it is rather less exciting.

Building is no longer the constraint.

AI-assisted development has transformed how software gets built. Because tools can generate code, build prototypes and speed up testing, ideas that once took weeks or months to develop can now be explored in a fraction of the time.

Engineering capacity is no longer the constraint it once was. More organizations can experiment and bring new products to market faster than ever before. But building faster doesn't guarantee better products.

Human judgement is still necessary to decide which ideas deserve investment, which features solve real customer problems and which experiments aren't worth pursuing.

AI can dramatically shorten the journey from idea to release, but it can't make those types of business decisions for you (yet).

Research from MIT found that while AI has fueled a surge in new apps entering mobile marketplaces, usage has not increased at the same pace. Faster development is producing more software, but it isn't creating more customer attention. Customers haven't suddenly found more hours in the day simply because software is easier to build.

That raises the bar for decision-makers. As AI removes many of the barriers to creating software, success increasingly depends on learning quickly from customer behavior and understanding what customers genuinely value. It’s the only way to stand out in an increasingly crowded marketplace.

Customer insights are your biggest asset

After every release comes the same questions. What next? Do we invest further or move on? AI has made it possible to build and ship software more quickly, but it hasn't made those decisions any easier.

That's why product intelligence matters. Understanding how customers behave gives decision-makers the confidence to decide where to focus their effort. Behavioural data shows what customers keep coming back to, where they struggle and where they give up. It also helps distinguish between features that attract initial interest and those that become part of customers' everyday workflows. That distinction often says more about long-term value than launch-day engagement or anecdotal feedback.

It also reveals where customers complete key tasks, where they struggle and where they abandon journeys. Those signals often provide stronger evidence than customer opinion alone, because they reflect what people actually do rather than what they say they do.

Those insights make prioritization easier. They show which features deserve more investment, which ideas aren't landing and where the next opportunity probably sits. They can also help product teams decide when to refine an existing feature, simplify an experience or stop investing in something customers aren't using. Product decisions become grounded in how customers actually use the product, rather than internal assumptions and beliefs.

As AI lowers the barriers to building software, deeply understanding customers becomes even more valuable. The strongest organizations keep learning from the people using their products, allowing every release to build on real customer insight.

From AI-assisted coding to AI-assisted product improvement

AI-assisted development has changed how software is built, and product leaders are only beginning to explore what it can do beyond writing code. The next stage is using AI alongside product intelligence to strengthen the decisions that shape a product over time.

When working with large volumes of behavioral data, AI can help surface patterns more quickly, highlighting changes in customer behavior, unexpected user journeys and emerging trends that might otherwise be overlooked.

That gives decision-makers more confidence about where to focus their attention, while leaving more time to interpret what's happening and determine the best response. Rather than spending hours searching dashboards for answers, product teams can focus on understanding the behavior, testing possible improvements and deciding which ideas are worth pursuing.

Every release generates new behavioral data. Instead of relying on assumptions, product management teams can use that evidence to decide what deserves attention next. Over time, each release helps inform the one that follows.

Used in this way, AI becomes part of the product improvement process as well as the development process. The combination of AI and behavioral insight helps organizations learn faster, respond with greater confidence and keep building products around what customers actually need. Focusing on what the data uncovers about what customers actual need is the best way to endear your product to your users.

AI has changed the speed of product development. But every release still depends on good product judgment, which means knowing what to improve, what to leave behind and where to invest next. Behavioral insight is the best guide for decision-makers to ensure that every release reflects what customers actually do rather than what your internal teams assume they'll do.

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• Logitech unveils $99 MX Keypad accessory
• Sitting alongside your keyboard, it offers nine customizable keys to give access to AI tools and agents
• Users can even create and share their own integrations

Logitech has launched a new $99 accessory it says will help programmers and coders work faster and more effectively than ever before.

The new MX Keypad offers nine customizable full-color LCD keys which can be assigned to launching commands, running macros, and accessing AI tools.

It's also very compact, around the same width as a Logitech mouse and weighing just 96 grams, meaning it will sit alongside a user's keyboard without taking up too much space on your desk.

Faster and more effective access

Logitech says the MX Keypad, developed alongside GitHub and available in two color schemes, will act as a control center for users, aiming to cut down on switching between apps - although the customization options can also be used for non-coding apps.

Any of the nine keys can be configured to control one or multiple apps and agents, from running complex prompt macros to launching everyday commands, with native GitHub, VS Code and Copilot integration at launch.

Using the Logi Actions SDK, developers can even create and share their own integrations, while community-built plugins for tools including Claude Code and OpenAI Codex are already available, and plugins created and tested by developers for GitHub Copilot and Claude Code.

The MX Keypad connects using USB-C and requires macOS 13 or later, or Windows 10 or later. As is typical with Logitech devices, sustainability is also a key feature, with the MX Keypad features certified post-consumer recycled plastic, 64% for graphite, and 49% for its pale gray colored models.

“Today, coding mastery depends on how easily developers can juggle between an ever-increasing range of AI tools,” said Anatoliy Polyanker, VP and GM at Logitech. “MX Keypad puts a physical control center at your fingertips, allowing you to orchestrate your AI workflows faster and more intuitively.”

The MX Keypad looks to offer a more streamlined version of Logitech's existing MX Creative Console, which provides an LCD keypad with a separate dialpad to adjust settings and move around your favorite creative apps, but costs $199.99.

2025 was a transformative year for artificial intelligence, and 2026 is already proving to be equally significant.

While generative AI dominated conversations just a few years ago, the focus is now shifting towards agentic AI, where intelligent systems can take action, interact with business processes, and support employees in more meaningful ways.

As these capabilities continue to evolve, organizations are looking for better ways to connect AI systems to the applications, data and services that drive their operations.

This is where technologies such as Model Context Protocol (MCP) are becoming increasingly important.

Rather than creating bespoke integrations for every tool or system, MCP provides a standardized way for AI models and agents to access information and perform actions across an organization's technology estate.

While MCP is not a requirement for every AI implementation today, it represents a natural next step for organizations looking to move beyond isolated AI use cases and towards more integrated, scalable AI ecosystems.

However, greater integration also introduces greater responsibility. Effective governance remains essential for any AI deployment, but it becomes even more critical when autonomous agents are granted access to business systems, processes and sensitive information.

Organizations must establish clear guardrails that define what agents can access, what actions they can perform, and how their activities are monitored. Without appropriate oversight, businesses risk agents operating beyond their intended scope or creating unintended consequences across interconnected systems.

The impact on Software-as-a-Service

Few sectors will feel the effects of this shift more than Software-as-a-Service (SaaS).

For years, SaaS applications have been built around human interaction. Users access platforms through dashboards and interfaces, navigate predefined workflows, and manually complete tasks. The application itself serves as the primary workspace where work is performed.

Agentic AI introduces a different model.

Rather than navigating software in the same way a person would, agents can interact directly with APIs, services and data sources. This allows them to retrieve information, execute actions and orchestrate processes across multiple systems without relying on traditional user journeys.

That does not mean SaaS applications will disappear. In fact, they will continue to play a critical role in storing structured data, enforcing business rules and managing workflows. What is likely to change is how those applications are consumed.

Instead of being the primary destination where work happens, many SaaS platforms will increasingly act as sources of capability and information that AI agents can utilize on behalf of users. As a result, organizations may find themselves focusing less on which application employees need to access and more on how services and data can be brought together to achieve the desired business outcome.

Human interfaces will still matter. Users will continue to need visibility, exception handling and control mechanisms, particularly when business-critical processes are involved. The challenge for software providers will be balancing traditional user experiences with new AI-driven interaction models while maintaining compatibility, reliability and operational resilience.

Breaking down agent silos

The next stage in the evolution of agentic AI is not simply creating more agents. It is enabling agents to work together effectively.

Many organizations already struggle with fragmented systems, disconnected data and isolated processes. Without careful planning, agents risk creating a new generation of silos, each operating within its own limited context and producing inconsistent outcomes.

To avoid this, businesses must focus on shared context, connected data and interoperable services. The goal is not to have individual agents automating isolated tasks but to enable multiple agents to contribute towards broader business objectives across entire processes.

When agents can access consistent information and operate across organizational boundaries, the value shifts from discrete task automation to coordinated execution. Rather than supporting an individual stage of a workflow, agents can participate in end-to-end processes while remaining aligned to business policies, operational requirements and organizational goals.

This represents an important architectural shift. Software increasingly becomes something that agents consume programmatically, while integration, context and orchestration become central to delivering outcomes at scale.

Mitigating risk in AIaaS

Unlocking these new capabilities requires more than deploying AI tools. Organizations need governance frameworks, security controls and operational processes that allow autonomy to be introduced safely and responsibly.

As agents gain access to more systems and collaborate across workflows, operational complexity inevitably increases. Businesses must define clear policies around what agents can and cannot do, what data they can access, and what approvals are required before actions are taken.

These controls should be embedded into the orchestration layer itself, ensuring governance is applied consistently across all agent-led activities rather than being treated as an afterthought.

Traceability and accountability are equally important. Completing a task successfully is only part of the equation. Organizations must understand how decisions were made, what information was used, and which policies were applied throughout the process. This visibility will be essential for compliance, security and maintaining trust in autonomous systems.

The role of developers will also evolve. Rather than spending significant time building and maintaining point-to-point integrations, they will increasingly focus on designing agent behaviors, defining boundaries, managing orchestration and ensuring solutions operate within established governance frameworks.

The organizations that succeed will be those that balance innovation with control. Too little governance introduces risk, while excessive restrictions can prevent businesses from realizing the benefits of AI altogether.

Agentic AI should not be viewed as a replacement for software development or existing technology investments. Instead, it represents a powerful new interaction layer that changes how organizations access information, automate processes and deliver outcomes.

Businesses that invest now in integration foundations, governance models and workforce skills will be best placed to take advantage of the opportunities this next phase of AI creates.

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For three years, businesses have bought AI the way they buy Microsoft 365.

Flat fee, line item, sat next to the Zoom invoice and asked no difficult questions. The budget holders were happy, the IT team ticked a box - and somewhere in a product division, engineers started doing things with it that nobody in finance fully understood.

That era is over.

On 14 May, Anthropic announced it would split agentic usage out of its Claude subscription and put it behind a metered credit pool, effective 15 June. GitHub Copilot will move to token-based AI Credits on 1 June.

Legacy Anthropic enterprise seats are being retired at renewal. And a tokenizer change in Claude Opus 4.7 has already pushed some API bills up by as much as 27% - with nothing moving on the published pricing page.

The thing being counted has changed. Not the stated price per thing. That is a meaningful distinction, and most enterprise procurement teams have never had to think about it.

Most CFOs don't know any of this yet.

The receipts

The receipts, though, are starting to arrive. Uber burned through its entire 2026 AI budget by April. Its CTO told The Information he's "back to the drawing board." KPMG's latest survey has US enterprises projecting average AI spend of $207 million over the next twelve months - nearly double a year ago.

Goldman Sachs data shows large companies already overrunning AI budgets by orders of magnitude. Salesforce CEO Marc Benioff has said his company's Anthropic bill will run to around $300 million this year - and that he wished there were a "smart router" that could work out which queries actually needed the most capable, most expensive models, and which could be handled by something cheaper.

Meta took down the internal tokenmaxxing leaderboard its engineers had built and even Microsoft has cancelled Claude Code access for staff in several key product divisions.

The angle most coverage has missed is that this is fundamentally a buyer-side story. What's happening at Anthropic and OpenAI is interesting. What's happening at the other end of the invoice is more interesting. What does the finance director do when the AI bill doubles mid-year with thirty days' notice and no contractual recourse?

What does the GC do when she looks at the supplier agreement and realizes it was drafted on a SaaS template that has no concept of a tokenizer, no audit rights, no price-change notice period, and no exit provision worth the paper it's printed on?

The right analogy

The right analogy here isn't software but electricity. When you buy electricity, you know the rate per unit, you can read the meter, your contract has notice periods and change-of-tariff protections, and there's a regulator with views about what suppliers can and can't do quietly.

None of that IT infrastructure exists yet for AI consumption. Businesses signed deals when usage was flat-rate and predictable. Now the meter is running, and in many cases the contract gives them no visibility over how fast, no right to challenge the reading, and no meaningful exit if the numbers stop making sense.

What "AI ownership cost" looks like in a business that has never had a FinOps function is, frankly, a mess. Usage is distributed across teams, often unsanctioned, running on departmental cards that never touch central procurement. The AI bill isn't a bill - it's fifteen bills, scattered across expense reports and shadow IT budgets, none of them talking to each other.

The first time many finance teams see the true picture is when someone pulls the data together and says the number out loud. That moment, for a lot of businesses, is coming in August.

The companies now pulling back - limiting which employees can access agentic tools, restricting use of the most advanced models, quietly cancelling licenses - aren't doing so because AI has stopped working. They're doing it because the cost model has broken the business case, and the contracts give them no leverage.

Specific things buyers should insist on

There are specific things buyers should now be insisting on. Price-change notice provisions - real ones, not buried in terms and conditions - that require meaningful advance warning before a pricing architecture changes. Tokenizer stability clauses: a commitment that the method of counting consumption won't shift materially without renegotiation.

Audit rights over consumption data. Exit and portability terms that don't require a legal battle to invoke. Whether existing contracts have any teeth when the metering changes underneath them is a live question. My suspicion is that most don't, because nobody drafting AI agreements in 2023 or 2024 anticipated that the unit of consumption would be a moving target.

Timing matters. OpenAI's two-month Codex trial and Anthropic's 50% capacity boost both expire mid-July. The window of subsidized, high-capacity usage is open right now. The bills will start arriving in August. Finance teams that haven't yet had a reckoning with their AI spend are about to have one, whether they're ready for it or not.

Reasons for spending

The businesses that come out of it ahead aren’t going to be those who spent the least but those that knew what they were spending, why, and what they got for it - and whose contracts gave them somewhere to stand when the rules changed underneath them.

That requires something most AI buyers have never had to build: a proper supplier relationship, a FinOps function, and contracts written as though they were buying a utility rather than a software subscription.

The flat-fee era is over. The question is whether the contracts, the finance functions, and the legal frameworks are ready for what replaces it. For most businesses, they are not. But the August bills will focus the mind.

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“I've seen AIs of me sitting with Obama talking about insane things that we never did”.

George Clooney is clearly concerned about the power that AI has to produce deepfakes that are so real you can’t tell that they’re fake, especially when they contain images of world leaders.

“We were never there having a conversation. It is me doing it. And okay, that's a joke for people. But what happens if you see a video of Putin saying he's launched the first nuclear strike [against] the United States — how do you discern it?”

Clooney was raising his concerns in Venice at the ‘The Future of... Creativity’ symposium presented by Finch & Partners and Creative Artists Agency (CAA). George Clooney and Maggie Gyllenhaal were among the international group of artists, filmmakers, musicians, cultural thinkers and technology leaders who gathered to discuss how technology is changing creative work, and the people who produce it.

The panel discussions were moderated by the journalist and broadcaster Emily Maitlis, who asked Clooney, “is there a fear of replacement, as an actor?”

Clooney acknowledged the risk, but also the problem inherent with AI actors, which is that it’s going to be hard to create an AI movie star: “Well, there's two different conversations we're talking about. As an actor, there will be some portion of that that will be replaced. AI will have the exact same problem that I have as a director, and that we all have as filmmakers, which is how you make a star, because that’s hard to do.”

‘And it fundamentally did not work’

Actor and filmmaker Maggie Gyllenhaal cited a recent experience with AI on her upcoming short film Flesh Impact about Marilyn Monroe, commenting: “I did try working with AI on this project […] I have a 100-year-old Marilyn Monroe, and then I also have flashbacks to Marilyn Monroe when she's in her 30s. I have Dakota Johnson playing her, who is an incredible artist in my opinion. And the people who commissioned this project asked me to use AI […] and I went for it. I was like, 'All right, I'm curious. I don't know. Let's see.' And it fundamentally did not work.”

Ultimately, Gyllenhaal found that AI was not able to replicate the nuances of human performance and emotion.

“The idea was that we trained it on her. We then put her face on Dakota's face; we worked very hard at it, and in the end, they threw it away because [… ] Dakota playing Marilyn Monroe, was so much more moving, human, alive. All the reasons we decided to make this project to begin with”.

It’s perhaps reassuring to hear filmmakers say that while a fabricated AI clip can be convincing enough to mislead someone, it can’t deliver the emotional depth a director wants from a performance. Gyllenhaal’s experience offers a reason for creative optimism, even as Clooney’s raises concerns about what audiences will believe.

81% of UK knowledge workers now use AI weekly. That's adoption. What it isn't, is transformation.

Most organizations have layered AI onto broken processes and fragmented systems and called it progress.

Meanwhile, 82% of UK IT leaders have absorbed unexpected AI cost increases, and 58% report high adoption with limited measurable productivity gains.

We have a usage problem dressed up as a strategy.

Here's what's actually going wrong and what needs to change.

Adoption without redesign is theatre

AI doesn't fix bad processes. It accelerates them. If your data is fragmented, your ownership is unclear, and your workflows are inefficient, deploying AI makes those problems faster, not smaller.

Real value requires asking harder questions: Where do decisions actually get made? Which processes should fundamentally change? Who owns the outcome? Until you answer those, you're generating AI activity, not business impact.

Own the outcome or don't deploy

Nearly two-thirds of UK IT leaders say they're fully accountable for AI-driven business outcomes, while AI deployment is spreading across departments, often outside governance structures. That's a recipe for accountability without visibility.

IT management sets the framework. That's necessary. But every business leader who owns a process needs to own how AI changes that process. What does success look like? Who monitors it? Who's responsible when it goes wrong?

If you can't answer those questions before you scale, don't scale.

Shadow AI is a signal, not just a risk

One in four UK workers use unapproved AI tools. The instinct is to lock it down. The smarter read: your people are telling you your current tools create friction, and they've moved on without you.

Restriction isn't a strategy. Channel that demand toward trusted tools with real governance, then use governance as an accelerant, not a brake. The organizations moving fastest are the ones that treat low-risk use cases as low-risk, and reserve serious scrutiny for high-stakes applications.

Context is the missing layer

Nearly half of UK IT leaders say AI initiatives stall because AI lacks organizational context. That's not a technology problem, it's a work infrastructure problem.

Think about how you'd onboard a new hire. You'd give them the org structure, the priorities, the decision rights, the rules. An AI agent needs the same. Without it, even capable models produce output that someone has to spend 30 minutes correcting, which is exactly what's happening.

The fix is connecting AI to where work already lives. Not asking employees to reconstruct context every time they open a prompt.

Measure outcomes, not usage

If your AI metrics are licenses purchased, prompts submitted, or hours theoretically saved, you're measuring the wrong thing. The question is whether the work is improving.

Are customer issues resolving faster? Are teams spending less time searching for information? Are the right decisions getting made with better speed? At Asana, we built an AI seller assistant and measured its impact on the sales process, response rates, net-new meetings booked. That's the bar.

The accountability question is only going to get harder

Agents are coming. Systems that act on behalf of people, not just assist them. When that happens, organizations will need to know: which agents exist, who created them, what they can access, what they're authorized to do, and how their performance is tracked.

This isn't a future problem. The organizations building that discipline now will be the ones who can scale agentic AI without the governance catching up after the fact.

The businesses pulling ahead won't be the ones using the most AI. They'll be the ones who've connected it to clear ownership, proportionate governance, and the workflows where execution actually happens.

Adoption is table stakes. Value is the real work.

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• Just 52% of Brits are familiar with the concept of a data center
• Positive sentiments are down, while negative perception is up
• This report calls for greater transparency and public engagement

New research has revealed only one in two (52%) Brits are aware of the term data center, but despite growing awareness (with the figure up from 40% in 2024), three in five (59%) are still unsure about what a data center is or what it does.

Despite slow growth in awareness and a UK designation for data centers as Critical National Infrastructure (CNI), fewer people believe data centers have a positive impact on the digital services they use both at home and in work.

Today, only 37% of British citizens see them as having a positive impact, down from 48% two years ago. On the flip side, negative perception has risen from 4% to 15% in the same period, the report from Telehouse found.

British citizens see data centers in a negative light

Among the biggest concerns is energy consumption, with more than half (56%) of the respondents worried about the amount of electricity these campuses use.

But with awareness of the concept in general rising year-over-year, Telehouse believes there's scope to turn this awareness into understanding and confidence. "As an industry, we have a role to play in closing that gap by working more closely together, being more transparent, engaging openly with the public and communities and doing a better job of explaining the value data centres enable," EVP and GM for Telehouse Europe, Mark Pestridge, explained.

The report indirectly alludes to the fact that worker and consumer AI use is disproportionate to education, which explains why public scrutiny may be accelerating more quickly.

When the UK government designated data centers as CNI, the then-Technology Secretary Peter Kyle described them as "the engines of modern life."

"The objective now is helping people understand why data centres matter and how it supports everyone’s lives," Pestridge added.

• 36.7% of UK workers say answering to an AI boss would improve their productivity
• However, almost 75% of those workers are happier answering to a flesh-and-blood superior
• Meanwhile, almost 40% of managers don’t believe that AI can deliver any productivity gains

Over a third (36.7%) of UK employees think an AI boss would mean an improvement to their productivity, with 64% of directors expecting similar gains, new research has found.

But despite these results looking good for AI, however, it seems many people prefer the idea of performance reviews being delivered face-to-face, rather than via chatbot.

However, there is one key opponent to AI managers. The human-based middle manager layer is unsurprisingly less swayed by the prospect of being replaced by AI, with almost 40% feeling productivity gains are unlikely.

Flattened structures?

The research from Careerminds UK contacted 600 full-time UK employees and asked them about their thoughts on AI in the workplace, also found that younger colleagues were more likely to be favourable to an AI management layer.

The survey comes at an interesting time, with predictions of flattened structures seemingly focused on the middle manager layer. If businesses feel that they can adopt AI to replace managers – particularly at a time when management training seems to be at a premium – then they need to act carefully.

Productivity boosts are one thing, but employees also feel a human superior is preferable to AI for things like delivering performance feedback (74.2%).

“AI may be able to ease some of that pressure by taking certain tasks off managers’ plates,” said Amanda Augustine, resident careers expert at Careerminds UK and a Certified Professional Career Coach (CPCC). “But the feedback findings make it clear that employees still value human judgement, context and connection.”

The artificial middle manager

While the productivity boost expected by 36.7% of those surveyed (versus 25.5% who feel they would be less productive) might seem encouraging, there is a challenging aspect to the report’s findings.

Some 64% of directors and above expect an AI manager to deliver productivity gains, with 28% of those expecting it to be “significant.” Among senior staff, the figure is 42.6%.

Among managers, that 38.5% figure is worrying, as it implies almost 40% of managers are expecting AI to replace them. Will the C-Suite have its way? Should it?

“We shouldn’t confuse employees seeing potential productivity gains from AI with wanting technology to replace their managers altogether. Middle managers are already feeling the squeeze, caught between growing expectations from senior leaders and the needs of their teams," added Augustine.

“The opportunity for employers is to use AI to help managers become more effective, not simply replace them”.

At a time when organizations are increasingly going hard on AI, some recognition of what employees need to succeed is important.

Rather than focusing on efficiency, Augustine observed leaders need to think about their employees, particularly those who have just started their careers who are “more likely to think about what they need from a manager day to day, whether that’s guidance, development, reassurance or context.”

• Report claims an incredible 80% of AI tools are running in organizations without IT oversight
• Browser agents and integration tools are escaping the attention of security and IT engineers
• Reco’s State of Agent Security 2026 report also tracked 637 vulnerabilities across agents and LLMs

Unmonitored deployment of AI tools is a risk to security, and puts data at risk, a new study has claimed.

The report from Reco, which draws its information from disclosed vulnerabilities, analysis of 500 Model Context Protocol servers, and Reco’s own platform telemetry, found four in five AI tools (80%) are running without oversight from IT departments.

Of particular concern is the scale of AI applications in use. Smaller companies use 414 AI tools per 1,000 employees without IT approval, apparently a combination of browser extensions and workflows beyond the usual review and approval process.

Data risks from unmonitored AI

Giving AI tools to employees might unlock productivity boosts, but their use has to be approved. That’s the key takeaway from the report, which highlights some concerning cybersecurity figures. For example, it assessed 500 agent tools and found “62% can both read local data and reach the internet.” This represents an opportunity for data exfiltration.

Elsewhere, 637 AI agent related vulnerabilities were identified in the report.

The adoption of AI is wider than specific use of a SaaS application or visiting ChatGPT. Reco found that AI agents are running within other tools, and inheriting user permissions. The implications of this are clear.

Operational risk

Analysis of the telemetry (gathered from 62 enterprise-scale businesses in financial services, healthcare, retail, and telecommunications between January 1 and August 1 2026) reveals a free-for-all attitude towards AI adoption. While businesses may have policies and procedures in place and processes to assess, evaluate, review, and finally approve new AI-based tools, these are being circumvented for low-level applications.

The rules work for SaaS procurement oversight, but not for browser extensions, and the result is “operational risk.”

“AI agents have moved from experimentation into daily business workflows, but our findings show only 20% of AI tools in enterprise ecosystems are currently governed by IT oversight," Reco CEO Ofer Klein noted.

“That leaves organizations exposed to a new class of operational risk. Agents embedded in applications can operate through existing permissions, OAuth grants and workflow access, creating toxic combinations that expose data and trigger actions beyond what any owner approved.”

Organizations will need to give IT teams the resources they need to manage and restrict unauthorized AI use, as the alternative means leaving the gates open to the possibility of data exfiltration.

Whenever a new AI model is released in a cut-down form because it is too powerful, or one transgresses its sandbox and performs an unexpected cyber hack on a company’s website, we tend to see jokes made about Skynet or HAL 9000 becoming a reality.

But science fiction didn’t just predict our current relationship with AI, it influenced it. Our fascination with artificial life began long before computers — or even electricity — existed. Ancient Greek stories featured mechanical servants created by the god Hephaestus, while Jewish folklore gave us the Golem, an artificial being brought to life to carry out its creator’s commands.

In their new book, Myth-Made Machines: The Stories Behind Artificial Intelligence, authors Dave Bradley and Kelly Vero trace humanity’s dreams and fears about machines that can think, through mythology, literature, movies and television.

Their argument is that these stories haven’t simply anticipated AI; they have shaped how we talk about it, what we expect from it and, in some cases, the technology people have gone on to build.

I spoke to Bradley and Vero about why humanity keeps creating machines in its own image, whether science fiction has influenced today’s AI developers, and why our response to artificial intelligence still swings between two extremes: “If we don't like it, we’ll kill it. If we love it, we’ll marry it.”

The ancient beginnings of AI

TechRadar: Talk me through the title - why is it called Myth-Made Machines?

Dave Bradley: The core argument of the book is that humans have always been fascinated by the idea of creating thinking machines and artificial life. When ChatGPT formally launched in 2022, one of the first jokes I saw on social media was about Skynet coming online. It struck us that the whole way we understand and talk about this new technology is shaped by stories we’ve been telling ourselves for years. Kelly has a deep knowledge and love of Greek and Roman myth, as well as stories such as Frankenstein, while I’m a huge science fiction nerd (and occasional medievalist). Through our conversations, we realised that our cultural idea of “AI” is much older than modern computers.

Kelly Vero: Myth-Made Machines is the essence of where every element of AI or robo-futurism comes from: Myth. With myth, we, as humans, have to navigate the real and the prescient from the propaganda and the fanciful.

TR: When did artificial intelligence first enter our human stories?

KV: When we started researching the book, it was Greek myths that we started with. We found our own Sam Altman, Elon Musk and Dario Amodei hiding in plain sight.

DB: The idea is incredibly ancient. In Greek myth, Hephaestus creates golden workers to help him in his workshop. Jewish legend brings us the Golem. The idea that one might build autonomous servants out of inanimate material is thousands of years old.

For something closer to what we would recognize as a computer, Jonathan Swift’s Gulliver’s Travels gives us a wonderful early example in the 18th century. The Engine of Lagado randomly rearranges words in the hope of generating useful knowledge. Read today, it looks uncannily like a satirical ancestor of generative AI.

The term ‘artificial intelligence’ in the modern sense of machine learning and reasoning was coined in the 1950s by computer scientist John McCarthy — and that’s when it starts creeping into TV and film too. There are some great early Twilight Zone and Star Trek episodes about the possibilities and perils of machines in our homes and workplaces.

TR: Which are your personal favorite sci-fi AIs/robots, or movies and TV shows that involve AI?

KV: I’m a Lawnmower Man aficionado, but having worked on the actual Transformers franchise, I’m also a Decepticon through and through.

DB: I’m a huge sci-fi geek, and to this day I maintain that R2-D2 is the most important character in Star Wars. He has a mission, gets on with it, saves everybody repeatedly and barely receives any credit.

But I also nerd out over Marvin in The Hitchhiker’s Guide to the Galaxy, the Cylons in both versions of Battlestar Galactica, and, of course, Data in Star Trek: The Next Generation, who I’d argue is the character on the real hero’s journey in that series.

Side note: HAL 9000 slightly infuriates me, mainly because people have been quoting “I’m sorry, Dave, I’m afraid I can’t do that” at me personally for decades.

TR: Do you think our myths about conscious machines have influenced the real development of AI?

KV: Absolutely! Human life is all about dreaming up concepts to adore or fear; it’s how we roll.

DB: Today’s engineers and entrepreneurs grew up on Star Wars, Star Trek, Isaac Asimov and William Gibson, and there’s good evidence that science fiction influences the people who go on to build the future. One 2022 study found that 93 per cent of UK astronomers enjoyed science fiction, for instance, and 69 per cent said it had influenced their career decisions.

Sometimes the connection is very direct. Pete Bonasso at TRACLabs has credited HAL 9000 from 2001: A Space Odyssey as an inspiration for his work on NASA space AI systems. Asimov’s Three Laws of Robotics were invented for fiction, but they’ve become a reference point for real-world thinking about AI safety. Google DeepMind’s 2024 ‘Robot Constitution’, for example, sets out safety rules for machines in a way that is very much in that tradition.

TR: What do you think of the current crop of AI models?

DB: I follow this area as closely as I can, although you have to bear in mind that it changes almost daily. We’ve just seen another generation of frontier models arrive. It moves fast, which is fascinating for tech writers, although it also explains why we wrote a book largely about things that happened hundreds of years ago rather than trying to capture the state of AI in 2026!

When it comes to video games — still my day-to-day working life — I believe it’s going to be important to work out what new experiences AI can bring to the table, not just how quickly they can automate existing processes.

KV: They’re facsimiles of every book, TV series, movie or game we’ve ever imbibed. They’re not as clever as we are and they never will be.

TR: Do you think of AI as a threat to humanity, or as an opportunity?

KV: A super opportunity to ride alongside a civilization’s worth of knowledge? It’s almost too good to be true. If you think about it, until AI came along, humans held a monopoly on intelligence. We kind of only knew what we could discover or search for as humans (history is always written by the victor). Today, we can effectively outsource our thirst for knowledge to something else and tell it how we should understand the results; for the most part, we get balance: the rest is sponsored content!

DB: It feels glib simply to say ‘both’, although I’m tempted. Personally, I’m an optimist by nature, and I like to believe the general direction of travel is towards progress, growth and better lives. That doesn’t mean everybody prospers equally, or that there aren’t important battles over jobs, ownership, ethics and power along the way. William Gibson famously said that the future is already here; it’s just not evenly distributed. I think that’s a useful way to think about AI too. The interesting question isn’t simply whether the technology is good or bad, but who prospers from it, who controls it, and how widely any benefits (and risks) are shared.

TR: Do you think we’ll ever reach man-made super intelligence, and if we do what happens then?

KV: We have man-made everything else, don’t we? Why should this be different? For me, the detail lies in sentience. We already have man-made superintelligence, but we don’t have fully operational sentience yet. When something can blink its eye without thinking as quickly as humans can, well, let’s talk again.

DB: I don’t think it’s going to emerge simply by scaling today’s transformer models, regardless of some of the more excitable predictions around them. However clever and interesting LLMs are, these are fundamentally probabilistic systems that generate text, images and other media. That doesn’t make them conscious.

An omnipotent silicon supervillain, like Skynet, makes for good screen drama — but if we do eventually create genuine superintelligence by some route, I’d actually hope that a networked brain with access to vast amounts of information, capable of seeing problems from many different perspectives, would naturally tend towards connection and assistance rather than destruction. But I’m not naïve about it. Systems also magnify our biases, flaws and prejudices. That’s why alignment matters.

TR: What have you learned about humanity's approach to AI from writing this book?

KV: That we’re the only stakeholders. We’re the arbiters of our own creation(s); if we don't like it, we’ll kill it. If we love it, we’ll marry it. AI is all of us, and my main worry is that only 6% of the world’s population currently decides on how the other 94% will live, work and adapt to an AI future. However, if the past is to be believed, we’re probably going to be ok. (Famous last words, I know!)

DB: Technology and automation are things humans do. What became very apparent is that humans repeatedly try to create things in their own image, while simultaneously struggling with the implications of that. We worry about job losses, replacement and loss of identity. We’re fascinated by creating something powerful, then immediately frightened that it might overthrow us. But the fear of the servant who will turn on us, or the untrustworthy ally who might sneakily replace us, is not limited to machines, right? We might project those stories onto computers in our books and films, but the subtext is often about human paranoia around class, politics, war…

More specifically, there’s a recurring oscillation between loving and fearing our own technology. In the 1970s, amid Cold War paranoia, computers were frequently instruments of doom: Colossus, Cylons, HAL 9000, even V’Ger in Star Trek: The Motion Picture. In the 1980s, as PCs and games consoles became familiar household playthings, popular culture increasingly gave us machines as loyal companions: KITT, Metal Mickey, Twiki, Nono and Data. Then the pendulum swings back: by the late 1980s and 1990s, we get The Terminator and The Matrix. You can trace those alternating waves of optimism and anxiety all the way back through the Industrial Revolution. The technology changes, but our emotional responses to it are remarkably consistent.

Author bios and book details

Myth-Made Machines: The Stories Behind Artificial Intelligence

co-written by Dave Bradley and Kelly Vero

Published by Bloomsbury on 3rd September 2026

Available as ebook or hardcover

• Google's contrail mitigation tech is expanding to a new region
• It uses AI to get planes to avoid cold, humid air
• Contrails account for a third of aviation's impact on the climate

Following similar projects across US airspace and the North Atlantic ocean, Google is partnering with the Cathay-Pacific airline to test its AI-powered contrail (condensation trail) mitigation technology on flights across the Asia-Pacific region.

Contrails are the thin, ice-crystal streaks of cloud that form behind planes when they hit cold, humid air. Some of these contrails can persist in the atmosphere, eventually spreading into cloud-like formations that stop heat escaping to space. It's thought that contrails account for around a third of the total climate impact of the aviation industry.

Google's idea is to use AI algorithms to nudge flights around the areas of cold and humid air that contrails need to form. Early trials with 80 Cathay-Pacific flights have led to a roughly 40% reduction in the warming impact of contrails, and now the system is being rolled out across the airline's whole network.

"Aircraft adjust their altitude slightly to steer clear of cold, humid atmospheric zones where warming contrails are likely to form, much like pilots do to navigate around turbulence," explains the Google team in a blog post.

Available, scalable, cost-effective

According to Google, the contrail-avoiding tweaks that AI recommends for flights won't have any impact on passengers and flight safety, and are in line with the routine adjustments made to flight paths for other operational reasons.

The changes to flight routes are based on a combination of data collected from satellite imagery, weather forecasts, and Google's AI prediction technology. The aim is to get information relayed to the cockpit as early as possible, which will be delivered through in-flight Wi-Fi "without interrupting standard cockpit workflows".

Also in on the scheme is the Contrails.org non-profit, which promotes the use of contrail mitigation technology. According to the organization's figures, adjusting the routes of 5% of flights could avoid up to 80% of contrail-related warming — and the Asia-Pacific region is currently the fastest growing aviation market in the world.

"Contrail mitigation remains one of the most immediately available, scalable, and cost-effective ways to reduce aviation's climate footprint, and it can get started now, with today's aircrafts and fuel," says Google.

• AI data centers currently use a huge amount of energy
• Scientists have found a way to cut that energy bill by "orders of magnitude"
• It involves using "ultrafast magnetic-field pulses" in RAM and storage

It’s no secret that the current artificial intelligence (AI) boom is leading to record levels of energy consumption — all that computing power needs to be fueled somehow — and it’s causing much controversy among the communities that are impacted whenever a new data center lays down its roots in their neighborhoods.

Now, though, scientists think they’ve come across a way that could radically slash the energy requirements of memory and storage, with potentially massive consequences for the future of the computing and AI industries.

In a paper published in the Advanced Materials journal (via a press release in Science Daily), researchers at the University of Edinburgh in Scotland wrote that the breakthrough would fundamentally affect the way magnetic memory operates. Right now, switching its state allows magnetic memory to control digital information, but the current methods of doing so can be costly in terms of energy.

Instead of using standard energy switching techniques, the researchers discovered an alternative that harnessed ultrafast magnetic field pulses that consumed far less energy. In fact, the paper claimed that the authors’ approach could cut energy usage by “up to two orders of magnitude” — in other words, a cut of around 100x, which is a significant reduction.

The data centers of the future

The AI revolution is only a few years old, but data center energy consumption has already become a significant issue. As the authors of the research paper put it, “Without significant improvements in efficiency, [information and communication technologies] could eventually represent a sizable share of worldwide electricity consumption and carbon emissions.” AI is playing a large role in that trend thanks to both its voracious appetite for component production and the sizable emissions it produces.

While the paper focused on magnetic memory, its theories could be applied to other fields, the authors believe. As study writer Dr. Elton J.G. Santos put it, “The same framework can be adapted to electrical currents and even ultrafast laser pulses, which are among the most cutting-edge technologies for future data storage. That means the ideas developed here could have applications far beyond the systems we studied.”

Although the research is still in the realm of theory, the paper’s authors proposed a number of practical steps that could help others build prototypes and conduct experiments.

That said, don’t expect monumental changes any time soon. There is still a long road ahead before these ideas get put into practice, if they ever do. But given the promising results of the scientists’ work, there is hope that the data centers of the future could be far less energy-intensive than those of today.

• GPT-6 Astra has arrived, and it's being called AGI
• This includes by Nvidia CEO Jensen Huang
• Huang's celebratory X post also promotes Nvidia's GPUs, saying we need to get 400K of them online

Over the weekend, Nvidia CEO Jensen Huang took to social media to celebrate OpenAI’s latest achievement — “AGI has arrived” in the form of GPT-6 Astra. And of course it was developed using over 100,000 NVIDIA GPUs.

According to OpenAI, Astra represents a "significant step forward" for AI, with OpenAI's president Greg Brockman also saying "welcome to the AGI era" as Astra was debuted — with the company promising that GPT-6 boasts incredible aptitude at tasks across software engineering, science, mathematics, and professional workflows.

It’s also said to be much better at agentic work, meaning you can delegate tasks to GPT-6 with less fear that the AI will misunderstand your intent as it works independently.

We haven’t yet given GPT-6 a full shakedown — the update is steadily rolling out to subscribers — but if OpenAI’s claims live up to the hype, Astra could be quite the capable tool, and may actually live up to those early AGI claims, even if this isn’t the first time Jensen Huang has heralded the arrival of AGI.

But what is AGI, and why does it matter if Astra is AGI or not? Well, as Huang himself suggests, AGI matters because it’s a reason to quadruple the number of Nvidia GPUs OpenAI is using.

What is AGI?

Let’s address the elephant in the room. AGI, or artificial general intelligence, isn’t a well-defined term, so judging if we have or haven’t achieved AGI is a bit like judging if a piece of art is a masterpiece or not.

Broadly speaking, AGI is an artificial system that can beat humans across any task that involves thinking or reasoning. Crucially, it should also be able to adapt to new problems by applying what it has learnt in training. For example, an AGI could learn a new skill to overcome a challenge posed to it without being retrained or reprogrammed — it just applies what it knows to evolve itself.

Think J.A.R.V.I.S from Marvel. It’s a very powerful assistant that helps Tony Stark across all fields, even how to deal with otherworldly threats like Loki, without needing to be trained on each individual problem.

Does AGI matter?

Okay, but does it matter if it’s AGI or just a very, very smart AI?

For most of us, not really. An AI doesn’t need to be very good at everything to be a majorly useful tool. It doesn’t matter if your AI doctor can understand the deep complexities of the vast universe, it just needs to be able to diagnose your illness and suggest treatment. An AI lawyer doesn’t need to be able to comprehend five-dimensional topology to defend you in court.

It's not just me saying that; Jensen Huang recently admitted that AGI doesn’t matter — this was before his social media praise for OpenAI — noting that the term is effectively meaningless.

However there’s one important reason for achieving AGI, and it’s about reaching what comes after: ASI, or artificial superintelligence.

ASI would be an AI no human could ever hope to match, let alone beat. It would be able to grapple and solve problems humans aren’t capable of even understanding. AGI is distinctly not ASI, but its ability to self-train and develop tools for itself suggests that an AGI will eventually construct ASI through a process called recursive self-improvement.

For Hitchhiker’s Guide the Galaxy fans, it’s like how the supercomputer Deep Thought couldn’t find the ultimate question of life, the universe, and everything, but could build the computer which would be able to find the question.

Perhaps more importantly, AGI is an investment buzzword, and there’s more than a hint of that in Huang’s celebratory post. Because while he says Astra is the product of 100,000 Nvidia Grace Blackwell NVLink72, he adds that if we want to see bigger and better improvements we’ll need to bring 400,000 GPUs online next.

To do this OpenAI and Nvidia will need a heck of a lot of investment, and will also need to build more data centers, just as communities across the US and rest of the world are pushing. back against their construction.

The arrival of AGI could represent the last opportunity for investors to make it big in AI, or any stock, before ASI takes over productivity. So they need to invest as much as they can now (or keep their investments in AI) before they’re left behind. For data centers, the big concern is they are pollution nightmares that merely take from a community with no promise they’ll actually succeed long enough to give back. The advent of AGI would suggest that the AI hype has been justified all along — the bubble won’t burst, and local communities could finally reap the reward of accepting data centers.

I’m not saying Huang or OpenAI are definitely misleading us, or even just being hyperbolic. It's possible that Astra is in fact our first taste of AGI. I’m just pointing out that companies and CEOs have a massive incentive to claim that we’ve achieved AGI, even if they later make the same claim about the next new model — I won’t be surprised if GPT-7 is later called 'the debut of AGI'.

My point is that we won’t really know if or when we achieved AGI until much later, when we can look back with hindsight and see when our AI models reached a turning point in their ability to evolve.

In the moment, though, we will know if an AI is useful and can provide some genuine benefit to us. We’ll do that as a collective, as individuals and businesses not directly associated with OpenAI get their hands on the new model and determine whether or not it makes their life significantly easier. Try Astra out if you can, learn about the experience of others with the tech, and decide if you think it’ll be worth paying to be able to access it.

Don’t get caught up in the hype, and don’t worry about whether it's AGI or not. In time it’ll be very clear if it is — and we won’t need a social media post to convince us.

Internet security teams are not short of information. Threat intelligence feeds run around the clock, vulnerability scanners flag thousands of issues a month, and high-profile CVEs dominate the news cycle before most teams have finished their morning coffee.

What is far harder to come by is proof. Raw data is one thing, but what about evidence that the controls sitting in your environment actually detect and respond to the way real attackers behave, in your specific network, today?

That gap is what purple teaming exists to close, and it is where the surprises tend to show up. Running these exercises with organizations that have invested heavily in their security stack, I have repeatedly seen gaps nobody expected, missing telemetry, misfiring detections, and attack paths nobody was watching.

The problem was never simply being under attack. It is being under-validated, and mistaking spend for assurance.

Why so many programs that look mature on paper still fail

Here's a pattern I keep running into with otherwise well-resourced organizations: every control on their books checks out, reports are signed off, tooling is deployed, leadership reassured. None of that holds up once a real attack runs through the environment. The cracks show almost immediately.

Ask where the actual gaps sit, and a familiar list comes back. Some systems feed logs in real time, others barely at all, leaving the SOC with a patchier picture than the dashboard implies. Detection rules are tuned to a generic attacker, not the one likely to show up here. And when something does trigger, it can sit unactioned for hours, since no one owns it.

None of this is exotic, just the ordinary, unglamorous consequence of tools and processes configured once and assumed still to work. With luck, those gaps surface in a simulated exercise before a real attacker finds them.

Part of the problem is what gets measured in the first place. Running standard techniques against a host already flagged as compromised only confirms which rules switch on. That's coverage, nothing more. It won't tell you whether the excess permissions, trust relationships, or misconfigurations in your real environment are exploitable, since that activity looks like normal use and was never built to trigger an alert.

Real validation looks different. It starts with the organization's own risk profile, not a generic library of techniques, and treats configuration and permissions as the real cause, not something a new detection rule can patch over. Only testing your own environment tells you which gap you're actually facing.

What's worth measuring, and the case for continuous validation

Treating validation as an ongoing discipline rather than a one-off exercise also changes the output you’re looking for.

You don’t want another report that sits in a folder until next year's audit; you want to create a living, prioritized backlog. This is a running list of gaps that have been proven to matter, ranked against the paths an attacker could realistically use, not against theoretical severity scores.

It’s an important distinction because a CVE rated critical in isolation may be unreachable in your environment, while a modest misconfiguration sitting on a well-trodden attack path can be far more urgent.

The most valuable information a security team can generate is not a longer list of vulnerabilities, but evidence of which exposures are actually exploitable, mapped against real behavior, and prioritized against genuine business impact.

This is why it is critical to avoid relying purely on agent-based validation. Because an agent is already embedded within the network or on an endpoint, it inherently shortcuts a lot of attack paths and bypasses defensive controls. It assumes the attacker has already achieved a foothold at that specific location, giving you an artificial view of your actual perimeter and lateral resilience.

Alongside the process itself, cadence is another important factor here. An annual or ad hoc test captures a single snapshot, and environments do not stand still between engagements: new services get deployed, configurations drift, staff change. Shifting from ad hoc testing towards an operationalized, continuous cycle of testing, validating, remediating, and retesting is what keeps that backlog tied to the environment as it is right now, not as it was assessed to be six months ago.

The ability to close a validated gap before it can be exploited as an attack path enables the organization to shift from reactive footing to a more proactive stance.

Breaking down the walls between red, blue, and the business

None of this works if validation happens in a silo. The most consistent driver of a strong outcome I have seen is not a tool; it is collaboration between people who are technically on the same side but often operate as if they are not.

Red and blue teams that only meet at the end of an engagement, via a report, tend to treat findings as a scorecard, something to defend or dispute rather than act on. An open-book approach changes that dynamic entirely. When offensive and defensive teams compare notes on what they are seeing, what they expected to see, and why, as the exercise unfolds, gaps get identified and understood together.

If a defensive team cannot follow a technique or lacks the telemetry to see it, that is not a failing to hide; it is the exact information the exercise exists to surface.

Clear ownership matters just as much as clear findings. An alert with no defined owner is functionally the same as no alert at all, so part of building this collaboration is agreeing, in advance, on who acts on what.

That communication cannot stop at the SOC door, either. Validation findings need to travel outward too, translated into terms a business audience can act on: which risks are real, which are theoretical, and where investment will actually reduce exposure. Security teams that build this bridge consistently get faster remediation and fewer arguments about priority, because everyone is working from the same evidence rather than competing assumptions.

Moving towards more coordinated action is another step towards preempting risk, delivering smoother and more effective operations than an isolated response where fixes happen team by team.

Resilience is proven, not assumed

None of this is an argument against investment in tools or people. Rather, it’s an argument for asking a different question that identifies risk. It's time to stop asking, "Are we safe?", because no environment stays safe indefinitely. The question that matters is whether you're resilient enough to validate an exposure and respond before it turns into a breach.

Confidence built on assumptions collapses the moment a real adversary tests it. Confidence built on shared, ongoing validation does not, because it has already been tested, refined, and proven against the way attackers actually behave in your environment.

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Most governments still talk about artificial intelligence as something to regulate or subsidize. South Korea is trying something considerably bolder. It wants to make advanced AI available free to every citizen, treating access less like a premium software subscription and more like a basic piece of national infrastructure.

The country's Ministry of Science and ICT has selected three consortiums, led by SK Telecom, KT and Kakao, for its "AI for All" program. The plan is to give Koreans free, unlimited access to general-purpose AI services built primarily on domestic models, with beta testing before a full launch expected by the end of 2026. The government is supplying a combined 512 Nvidia B200 GPUs this year and plans to subsidize nationwide operating costs from 2027.

That already sounds like a government-backed ChatGPT competitor, but South Korea's ambitions go considerably further. The services are supposed to develop into agents capable of helping citizens find government programs, submit applications, make reservations and payments, and carry out other tasks. The government's longer-term ambition is effectively one AI agent for every citizen.

Plenty here could attract international attention, but the project’s real value may have little to do with giving everyone a free chatbot.

Far more than a chatbot

South Korea has a strong argument for intervening. Its science ministry says roughly two-thirds of Koreans have used AI services, and around 23 million people have used generative AI. ChatGPT alone had an estimated 23.45 million monthly active users, with Gemini and Claude having far lower but still notable monthly users of 8.45 million and 2.41 million, respectively.

Those figures are impressive until you consider the people missing from them. If AI really does become as important as its advocates believe, the ability to pay $20 or $30 every month for the best models starts to matter. Free tiers help, but their limits, features, and even continued existence are decisions made by private companies.

South Korea's government has explicitly identified that dependence as a problem. It argues that citizens relying on free versions of foreign AI services remain vulnerable to usage restrictions and price hikes. Its answer is a publicly supported alternative built largely around Korean models, with domestic models required to account for at least half of the service. Plus, South Korea gets to widen AI access while supporting domestic model developers and reducing its reliance on foreign platforms.

“South Korea’s ‘AI for Everyone’ project rests on a principle worth defending: access to AI should not depend on what you can afford, and a country is stronger for having its own capability. The ambition is not in question. How it works in practice is,” said Nik Kairinos, CEO & Co-founder of RAIDS AI, which provides testing and detection of threats to AI services.

“When a publicly provided model or agent goes rogue, gives someone wrong information, or causes harm, who answers for it? The government that commissioned the service, the company that built it, or both? Citizens will not have chosen their provider, and many will not know which one they are using.”

Combining AI with public services

I am skeptical about the idea of governments offering general-purpose AI subscriptions for basic technical reasons, as well. AI models improve at an absurd pace, and taxpayers would effectively be underwriting an endless technological race against OpenAI, Google, Anthropic, and whatever comes next.

“I think it is not rational to spend money on an unlimited general chatbot striving to compete with ChatGPT, Claude, or Gemini, since the commercial world of artificial intelligence changes very quickly,” said John Park, a founding membеr of technical staff at agentic AI startup AGI, Inc.

“Instead of that, a more appropriate strategy will be to combine AI with public services that people are already using, for instance, with the government’s call center, benefit applications, tax assistance, appointment booking, and document creation. That way, AI becomes part of the cost of providing public services, just like a website or a call center.”

That, to me, is the real blueprint hidden inside South Korea's experiment. Governments don't need to build the world's best chatbot. They need to make dealing with government better. An AI that knows which parental benefit you qualify for, finds the correct form, and explains any baffling sections before helping you submit it is useful in a way that justifies public spending. A do-anything chatbot lacks that justification

That's the strategy in South Korea. Kakao plans to put its service inside KakaoTalk. While KT has presented an agent spanning public services, education, finance and shopping, and SK Telecom plans phone- and text-based agents for those reluctant to engage with apps or websites.

You can see hints of similar ideas in other countries. The UK launched GOV.UK Chat in its government app in May, allowing people to ask natural-language questions about government information. Singapore's VICA conversational AI platform is used across more than 60 government agencies, and France announced plans for a public health chatbot and a conversational assistant. AI as a public utility probably makes sense when the "utility" is access to government itself.

Privacy policy for freedom

The difficulty begins when an assistant stops pointing citizens toward services and starts acting on their behalf. A chatbot incorrectly describing a benefit is bad. An agent incorrectly applying for one, canceling something, making a payment, or supplying inaccurate information under your identity is an entirely different class of failure.

“Once an agent is transacting for a citizen inside public services, the bar for knowing what it did, and why, is far higher than for a system that only answers questions,” Kairinos pointed out. “Other governments will be watching this closely, because most of them will face the same choice within a few years. Widening access to AI is worth doing. It just cannot come at the expense of accountability, evidence and public trust.”

That warning becomes particularly important when considering what people actually tell AI assistants. We already happily type things into commercial chatbots that we might hesitate to tell acquaintances. Put an AI assistant between a citizen and the welfare system, tax authority, or health service, and the information becomes far more sensitive.

“Citizens utilizing AI for benefit applications and other services could disclose very personal details about themselves: health, income, family, and finances. Such information needs to be kept separate from other aspects like advertising, commercial profiling, and training of models,” Park said. “Citizens need to have access to such essential services without having to consent to the use of their data elsewhere. Otherwise, the free service may cost the citizenry nothing in monetary terms, but at the cost of their personal information.”

There is an uncomfortable detail in South Korea's own description of "AI for All" here. The science ministry has said participating companies will be expected to develop revenue models, giving the example of using prompt data collected while providing the services. That may help make free AI financially sustainable, but it also makes the boundaries around data use enormously important.

Citizens should not have to wonder whether describing their unemployment, disability, debt or family circumstances to obtain public assistance is also enriching a commercial profile or future training set. If AI becomes part of the machinery of government, privacy protections need to resemble those surrounding public records more than the terms and conditions of a consumer app.

Public utility, not public ChatGPT

South Korea may simultaneously prove that universal AI access is a good idea and that "free ChatGPT for everyone" is the wrong interpretation of it.

“South Korea is a successful example of how a nation can increase AI adoption, help local firms, provide better language-specific solutions, and decrease reliance on international websites. The U.S. would most likely develop a more fragmented system of AI services with different programs dedicated to tax, benefits, healthcare, education, and library applications,” Park said.

“Basic capabilities of AI, like search, translation, summarization, form filling, and help with public services, could turn out to be free or almost free. The main task of the government will be to ensure accessibility, privacy protection, transparency, and the right to human review, while private companies continue competing on more advanced services.”

That fragmented version is ultimately more convincing than the grander vision. There is an extremely strong argument for ensuring nobody is denied access to healthcare, benefits, or tax assistance because they cannot afford the AI tools increasingly used to navigate them.

Governments can guarantee a baseline of useful capabilities tied to citizenship and public services. Private companies can continue fighting ferociously over the frontier, selling better models, enormous context windows, creative tools, and more.

South Korea is attempting something much larger. It wants broad AI access, domestic technological sovereignty and eventually personal agents that can conduct parts of citizens' economic and social lives. That makes the country a very expensive live experiment rather than a blueprint. AI is becoming part of how the country thinks about economic infrastructure and public administration.

Other governments should copy the principle before they copy the product. Access to basic AI capabilities is likely to become important enough that leaving it entirely to monthly subscriptions will create another digital divide. Governments can ensure everyone benefits from the real value of AI tools, which will probably be the most boring examples and come with strict data rules.

If South Korea's plans work out, countries around the world will have a template for making AI a basic digital service. Any failure will prove just as valuable as a lesson.

In its latest financial stability report, the Bank of England warned that an AI crash could plunge the UK into recession, claiming that a “price correction in AI stocks, driven by a change in productivity and profitability among tech-led companies, could cause a 2.2 percent fall in U.K. GDP.”

This should serve as a warning to businesses everywhere.

The risks surrounding AI extend far beyond Silicon Valley.

As leaders prepare for what comes next, however, many are still asking the wrong question.

“Is AI real, or is it hype?”

The last 30 years of technology should have taught us that the answer can be both.

The last tech crisis of the 20th century

Y2K was a real technical risk that came to look, in hindsight, like mass overreaction.

The UK spent billions preparing for the millennium bug, with organizations testing and patching systems that underpinned everything from banking and benefits payments to air travel and the National Grid.

The government established Action 2000 to help businesses prepare, while banks and major infrastructure providers ran extensive contingency plans ahead of the deadline.

Then midnight passed, the planes stayed in the sky, cash machines kept dispensing money and the lights stayed on. The whole affair began to look almost laughable.

Yet the apparent anticlimax obscured an important point. Much of the disruption people feared was likely avoided because organizations took the risk seriously and prepared for it. Cybersecurity has the same problem.

A threat that is successfully mitigated can look, in hindsight, remarkably like a threat that was exaggerated.

Not to burst your bubble

The dotcom bubble taught a different lesson. The internet was not fake, but the valuations often were. Boo.com, for example, raised vast sums to reinvent fashion retail online, only to collapse in 2000 after burning through its funding. Companies with little profit and, in some cases, barely coherent business models were treated as if they had risen above the laws of economics.

Then the bubble burst, fortunes disappeared and many of the supposed pioneers vanished, but the ideas they were betting on did not. In fact, I bet you’re reading this on the internet right now.

Style over substance

Crypto added a third lesson. Revolutionary language can hide weak use cases. There were serious technical ideas beneath it, but the public boom became dominated by speculation, celebrity endorsements and the strange habit of starting with an asset and then searching for a purpose.

At the height of the NFT frenzy, an NFT of Twitter founder Jack Dorsey’s first tweet sold for $2.9 million. A year later, its owner attempted to resell it for nearly $48 million, only to see initial bids fall spectacularly short. The technology had not disappeared. What had disappeared was the market’s willingness to treat digital scarcity as lasting value.

AI’s rise carries elements of all three stories.

A familiar visage

Like Y2K, AI presents risks that may sound exaggerated until they are not. As any cybersecurity expert will tell you, if the worst harms are prevented, the warnings may later look hysterical. This will not prove they were imaginary.

For AI, this means treating cybersecurity and governance as operational disciplines, not innovation-team side projects. Organizations need to understand what data is entering AI systems, what those systems can access, what authority they have, how they could be manipulated and how the business would respond if they failed.

With the dotcom boom, AI looks like a real general-purpose technology wrapped in an overheated investment story. It can already write code, summarize documents, and automate parts of professional work. But that does not mean every AI company is valuable, every AI product is useful, or every data center will earn its keep.

For businesses learning from this, they must invest in capabilities, data foundations, people and workflows, rather than betting everything on whichever vendor, model, or product is making the most noise this quarter.

And like crypto, AI has acquired a mythology. “AI-powered” is becoming what “blockchain-enabled” briefly was, a phrase that can mean something, nothing, or merely “please value us higher.”

For the adoption of AI, business leaders must now ask boring, but necessary, questions. What problem does this solve? Who uses it? What data does it see? What systems can it influence? What authority have we given it? What happens if it is wrong, manipulated or unavailable? And who is accountable when that happens?

This combination is what makes the current moment so hard to read. The lazy argument says AI is either a revolution or a bubble. History suggests a more uncomfortable possibility; it could be both.

“The Spirits of all three shall strive within me. I will not shut out the lessons that they teach.”

Too often, business leaders are trying to win an argument about whether AI is overhyped when they should be building a strategy that survives either answer. The sensible response to AI is neither a moonshot nor a moratorium. It is disciplined experimentation.

This means treating AI less like a campaign slogan and more like a portfolio of bets. Some of those bets should be defensive, focused on understanding where AI is already being used, what data and systems it can access, and whether the organization can respond when things go wrong.

Others should be exploratory, testing where AI can create genuine value across areas like coding, customer support and knowledge management. And some should be deliberately skeptical, particularly when tools sound impressive but cannot demonstrate savings, better outcomes, or clear ownership.

If the aim is simply to have an AI strategy that reads well in a board pack, then the lessons of the past 30 years have been missed. Y2K, dotcom and crypto each showed us that hype and substance are not opposites. Real risks can be exaggerated. Transformative technologies can attract irrational investment. Powerful ideas can coexist with weak use cases.

AI may prove to be all three at once. The businesses that navigate it best will not be those that predicted the future perfectly. They will be those that were prepared to adapt when reality arrived.

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• 2027 UBS recruits told they must have AI proficiency and a willingness to learn
• The bank's AI Fluency Pathway will continue to support junior workers' development
• While thousands of jobs could be at risk, we're starting to see shifts rather than outright displacement

Swiss investment giant UBS is now requiring all junior bankers to demonstrate AI proficiency as the skill moves from being a nice-to-have to an absolute requirement within recruiting.

The change currently applies to graduates and interns applying to the company's 2027 intake, per the Financial Times, and it's unclear whether UBS will broaden the requirement to all workers in the future.

As part of the new requirement, recruits will need to be able to demonstrate that they can use and experiment with AI responsibly to improve business outcomes – not just that they can use popular AI chatbots like ChatGPT.

UBS makes AI skills a must-have

AI-related questions will now become part of the bank's recruitment interviews on top of both the existing types of questions as well as the usual requirements, like a 2:1 degree.

While the news puts additional strain on graduates who now need to invest in their own AI skills, it's an example of how artificial intelligence isn't replacing entry-level workers, with the bank seeing it more as a productivity booster for human staff.

UBS' training program will also include an 'AI Fluency Pathway' to cover real-world banking AI use cases and responsible AI use, implying that the bank is more focused on prospective workers being able to prove a certain level of proficiency and willingness to learn – not full proficiency from the get-go.

AI's longer-term effects on banking employment are more unpredictable, though, with an earlier Morgan Stanley report warning that 200,000 banking jobs could be lost in Europe over the next five years. That was in early 2026.

While the outlook now seems more positive that junior workers may not be at a loss, it's clear that roles are evolving and entry-level workers could see their responsibilities shift toward AI management.

For the last two years, the advice to brands has been more or less standardized: get cited and recommended in AI, and the rest will follow.

Whether that is called GEO or AEO, the objective is the same: make sure AI can understand your brand, retrieve the right information and recommend it when a customer asks.

That was until Amazon quietly published a number in its Q2 results that starts to undercut this advice.

Andy Jassy revealed that shoppers who click a paid Sponsored Prompt inside Alexa for Shopping convert to a sale 48% more often, and spend 21% more, than shoppers who do not.

It is Amazon's own data rather than an independent industry benchmark, but it is one of the clearest signals yet that paid placement is becoming native to the AI shopping experience, rather than simply sitting alongside it.

That matters because AI shopping is beginning to split into two very different models: open assistants that aim to surface the best products they can find, and closed ecosystems that control the commercial environment around the recommendation.

For brands, those models create very different ideas of what visibility is worth.

Why are AI shopping platforms pulling apart?

Until recently, it was reasonable to talk about AI visibility as one thing. A brand wanted to be understood by the model, cited by the assistant and recommended when a customer asked a relevant question. That assumption becomes harder to sustain when the platform making the recommendation also has an advertising business to monetize.

Amazon is the clearest example. A Sponsored Prompt can appear inside the same conversational journey in which a customer is deciding what to buy. Google is moving in a similar direction, bringing advertising into increasingly conversational search and shopping experiences.

The commercial incentive is obvious. If a paid recommendation inside an AI conversation converts better than a conventional ad, platforms have a reason to put more advertising into that conversation.

Open assistants face a different calculation. Their value depends heavily on the perception that recommendations are being made because they are relevant, rather than because someone paid for them.

That creates a much harder balance between monetization and trust. The result is not one AI shopping channel, but multiple ecosystems developing around different commercial incentives.

Is GEO/AEO still enough?

The fundamentals behind GEO and AEO are not going anywhere. Accurate, specific and well-structured information still gives AI systems a better chance of understanding a product and deciding when it is relevant.

But there is an important distinction emerging: GEO and AEO solve the visibility problem. They do not necessarily solve the commercial problem.

A brand can be highly visible in an AI recommendation and still fail to convert that visibility into revenue. Equally, paid visibility cannot compensate indefinitely for poor underlying product information. An AI still needs reliable data about what a product is, who it is for and how it compares with alternatives.

The question for brands is therefore becoming bigger than simply whether they are being recommended. They need to understand how recommendation works on each platform, what happens when advertising enters the same decision-making process, and whether their visibility ultimately leads to customer acquisition.

Where does ACO fit?

This is where agentic commerce optimization, or ACO, starts to become relevant.

GEO and AEO are fundamentally about getting a brand understood, retrieved and surfaced by AI. ACO takes that problem into the commerce layer, where an AI is beginning to make or influence the purchasing decision.

For an agent, product content is only part of the equation. Price, availability, specifications, variants, delivery, returns and the ability to complete a transaction all matter. A product can therefore be well optimized for AI visibility and still be a poor choice for an agent if the information it needs to act is incomplete or inconsistent.

That is why ACO can be thought of through the 5Cs: Completeness, Context, Citations, Correctness and Customer Acquisition.

The first four help determine whether an AI system can confidently understand and recommend a product. The fifth asks the commercial question that visibility metrics alone cannot answer: did that recommendation create a customer?

ACO is therefore the operational response to the next stage of AI shopping: continuously monitoring how products appear across AI platforms and fixing the underlying content, catalogue and commerce data when those systems start to drift.

That becomes more important as platforms develop different approaches to advertising and recommendation. A brand cannot optimize for a single version of AI shopping when the underlying platforms are making different commercial bets.

What should brands do now?

The answer is not to abandon GEO or AEO in favor of another acronym. The fundamentals remain the same: accurate product information, clear structure, consistent data and content that answers the questions customers actually ask. The difference is that brands now need to monitor what happens beyond the initial recommendation.

Which products are AI platforms surfacing? Which competitors are gaining ground? Where is product information inaccurate or missing? How does that change between assistants? Where does paid visibility enter the journey? And, ultimately, is that visibility generating incremental customer acquisition?

Amazon's 48% conversion figure matters because it suggests that appearing inside an AI conversation at the point of purchase intent can be commercially different from appearing alongside one. If other platforms follow, the distinction between AI visibility, paid media and commerce operations will become increasingly difficult to maintain.

The opportunity is therefore not to replace GEO or AEO, but to build on them. As AI moves from answering shopping questions to influencing purchasing decisions, visibility becomes the starting point rather than the end goal.

The brands that take advantage of this state of play will be those that combine GEO/AEO fundamentals with ACO - optimizing the five Cs and, ultimately, measuring AI not just by how often it mentions them, but by how much incremental revenue it helps generate.

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As the US moves to integrate more AI systems into every arm of its military, the situations on the ground in the Middle East are raising some serious problems for maintaining the compute and connectivity needed to keep systems online.

Drones, sensors, AI-assisted targeting and decision-making all rely on these systems in one way or another, and the destruction of a single frontline AI data center can seriously damage an army’s capacity to function.

But these problems go beyond hardware. How does a drone adapt its mission parameters without being able to communicate? How can AI models continue inference without access to critical battlefield data? And what compromises must be made to prepare battlefield AI for these conditions?

Battlefield AI should continue functioning, even when comms go down or hardware is disabled

While the conflict circling the Strait of Hormuz may be a hugely expensive endeavour, Iran is providing valuable lessons for warfighters. Relying on traditional data centers in the Middle East has already shown its weaknesses. These sprawling warehouses make easy targets for cheap missiles, and entire systems can be taken offline from a single hit.

Palantir has already begun deploying shipping containers filled with Nvidia hardware as an alternative to the traditional data center. These containers can be deployed on frontlines and can connect directly into operational workflows, offering decentralized compute where it is needed most.

Scaleout is a company building infrastructure for edge AI and federated learning that trains models on distributed, sensitive data without centralising it. The company has worked with NATO - most recently the Swedish Air Force - and defense primes such as BAE Systems.

Andreas Hellander is the CEO and co-founder of Scaleout. He holds a PhD in Scientific Computing and an MSc in Biotechnology Engineering, and is an Associate Professor at Uppsala University, where he built one of the top research groups at the Department of Information Technology before founding Scaleout.

I spoke to Andreas to learn more about how battlefield AI systems can be made more resilient, and how drones, sensors, and other frontline technologies can continue functioning when conditions go downhill.

• What happens when parts of a battlefield AI's computing and hardware are taken offline? What redundancies are in place to keep communications between systems operational?

The aim is not to promise an unbreakable connection; it is to stop the loss of one component from taking down the whole capability. In our architecture, a disconnected node continues local inference using its last approved model, caching detections and telemetry until a link returns.

Multiple aggregation points can be used above the edge. If one becomes unavailable, clients can be reassigned and training can proceed with those still reachable. That creates graceful degradation rather than an all-or-nothing failure. We do not supply the tactical communications network itself; our job is to ensure the AI workload does not assume that any radio, satellite or other link will always be available.

• What are the trade-offs between a highly centralized battlefield AI network and a more distributed approach, especially concerning data gathering, targeting, battlefield coordination and logistics?

Centralisation provides a consistent operational picture, substantial compute and a clear place to enforce security, model approval and command policy. It supports theatre-wide coordination and logistics, but also concentrates risk: losing one link or facility can remove a disproportionate share of the capability, while moving large or classified datasets may be slow, prohibited or impossible.

A distributed system keeps analysis close to the sensor, reducing latency and allowing local functions to continue during disconnection. The trade-off is fragmentation. Targeting based on a partial or stale picture is risky, and local logistics decisions may not be optimal for the wider force. The best design is usually hybrid: central coordination when available, with clearly bounded local capability when it is not. Authority remains with the relevant command-and-control and human decision process.

• How do these AI drones and sensors adapt to the battlefield and what differences are there in the behavior of an AI drone that can still communicate and one that has lost communication?

It is important to separate running a model from retraining it. Small platforms typically perform inference in flight, while controlled retraining and approval happen on a ground node or more capable edge computer. Onboard learning exists in research, but uncontrolled mid-mission retraining creates serious assurance problems.

A connected platform can share detections, receive tasking and contribute to a wider operational picture. After losing its link, a drone follows its pre-approved failsafe: depending on the mission, it may continue a bounded task, return, hold or land. Onboard processing can keep perception and navigation working, but the platform loses external context, new instructions and fleet coordination. Disconnection should be a designed operating state with explicit limits, not an improvised emergency mode.

• How is the data collected by these drones and sensors used to help battlefield AI systems adapt to new conditions, threats, and tactics?

The useful data is often the hardest to move, so we send the model to the data rather than centralising the footage. Active-learning software identifies the frames most likely to improve it, such as uncertain detections, unfamiliar objects or changed conditions. A person still labels the selected material; the software reduces the searching, not the need for human judgement.

The model is then fine-tuned locally and tested against a benchmark. A candidate can also run beside the current model in shadow mode before an operator approves it. Where policy and connectivity allow, sites exchange protected model updates rather than raw training footage, combining them into a shared model. Federated learning reduces data movement, but the updates still need to be secured, validated and audited.

• What are the main challenges you encounter when deploying AI drones and sensors? What capabilities are available for militaries to source the required compute and energy production close to the front lines?

The hardest constraints are usually power, heat, weight, weather, vibration, electronic warfare, inconsistent data and safe software updates across a mixed fleet. Integration and accreditation can be as demanding as the machine learning.

The compute is commercially available: low-power modules such as NVIDIA Jetson for small platforms, and ruggedised GPU servers or containerised edge units for ground sites. Energy is tougher. Every processor competes with propulsion, sensing and communications, while generators, batteries and distribution equipment add logistical burden and new points of failure. In our public demonstration with BAE Systems Bofors, the perception workload ran on onboard compute at -18°C without connectivity. The principle is to use the smallest practical footprint and assume both power and bandwidth are scarce.

• There is an increasingly blurred involvement of commercial companies providing services for military use, such as AWS data centers in the Middle East and drone production in the UK for Ukraine. How are companies providing services to the military adapting to the pressures of potentially being deemed a legitimate military target?

Speaking for Scaleout, rather than for the sector as a whole, we design our platform so that a customer’s operation does not depend on Scaleout remaining continuously available. It runs in the customer’s own infrastructure, works air-gapped, and does not require a licensing call-home or persistent connection to our systems or a public cloud.

That matters in any contested or disconnected environment: reliance on a remote provider can become an operational vulnerability. Our approach has therefore been to keep control and continuity local to the customer, rather than make our own infrastructure a critical dependency.

The wider question of how commercial providers are viewed in a conflict is one for policymakers, legal experts, and the companies involved. We would not want to speculate about other providers’ risk assessments or operating models.

• What is the future trajectory of these systems? Will each individual drone and sensor have its own connectivity that can feed data back through a global satellite network? Will these systems be able to operate autonomously for multiple days or weeks without human input?

Connectivity will improve, including through low-Earth-orbit satellites, but connecting every sensor directly to space is unlikely to be the universal answer. Terminals consume power and spectrum, add cost and can create an electronic signature. A more resilient pattern combines local mesh links, opportunistic synchronisation and selected gateways to satellite or other long-range communications.

Longer disconnected operation is realistic, but endurance is often limited by batteries, weather and maintenance rather than AI software. There is also a critical distinction between operating without a connection and operating without human authority. The first is a resilience property; the second is a policy, legal and ethical decision. The future is therefore not simply more autonomy, but verifiable control: knowing which model ran on each platform, what produced it, where its limits were and who approved it.

OpenAI has unveiled a much anticipated AI model which the firm has dubbed ‘GPT-6 Astra’. While the model has improved significantly across benchmark testing and brings a host of new business features, there is still a dark cloud looming over the new model.

Off the back of OpenAI’s accidental hack of Hugging Face and the company’s subsequent efforts to improve how AI agents behave and interact, numerous cybersecurity experts have raised concerns about the model’s new ‘recurrent depth’ reasoning capabilities.

This new reasoning architecture allows the model to consider a problem multiple times before taking an action, compared to the standard chain-of-thought reasoning used in previous models.

Why the concern about recurrent depth reasoning?

This new level of reasoning apparently offers improved performance. OpenAI also says it has fixed its models' abilities to circumvent boundaries when performing tests by monitoring the models reasoning and ensuring the model stays aligned within the scope of its task.

During Astra’s launch event, OpenAI chief scientist Jakub Pachocki said: “We will not accept degradation in our ability to monitor model alignment beyond a certain level. We will withhold scaling until we can regain enough confidence.”

But numerous experts believe that the lessons of the Hugging Face incident have not yet been learned, and the model has been released without adequate testing on Astra’s reasoning and monitoring.

After all, no one thought one of OpenAI’s models could set up a hidden internet-connected messaging board that allowed AI agents to influence each other's behavior.

But with Astra being released into the real world, the lessons may have to be learned on the fly.

Expert perspectives on OpenAI Astra release

• James Blake, VP of Global Cyber Resiliency Strategy at Cohesity:

The launch of Astra is raising questions again around the safety of Frontier AI. Instead of simply asking whether a model is "safe", organisations now need to ask whether it remains safe across millions of different situations, prompts and interactions. Cyber resilience has traditionally assumed that systems and threat actors behave deterministically. AI systems don’t.

Advanced models can and will continue to exhibit behaviours that emerge from their optimisation process rather than from explicit programming. We have to move beyond thinking about AI as just another software tool and find ways to ensure these systems remain observable, auditable and governable throughout their lifecycle.

The most important question we’ll need to answer in future is one of liability. Suppose an AI system autonomously develops a strategy that causes financial loss, leaks confidential information or violates regulation. Who is responsible? The developer that trained the model? The cloud provider operating the infrastructure? Currently the answer is surprisingly unclear. It’s not just about what AI can do: it’s about who is accountable when it does something nobody expected.

• Oleksandr Yaremchuk, Co-Founder & CTO at Manifold Security:

OpenAI is calling Astra its most aligned model yet, even as its chief scientist admits monitorability is getting harder as models get more capable. Evidently, Astra hides its reasoning in the majority of tested cases, and some successful attacks left no reasoning trace at all. That's the tool many organisations still use, including the labs themselves, for auditing what an agent is doing, and it's getting less reliable with every release.

That matters because Astra isn't staying inside OpenAI's test environment. It's going to run as an agent on employee laptops and in the browser, holding real credentials, inside companies that have no way to watch what it does once it's there. A model that explains itself less isn't more aligned, it's just harder to catch when it goes wrong.

Labs can keep debating what these models say or refuse to say. Security teams need to stop relying on that and start monitoring what agents actually do at runtime, with the ability to shut one down mid-action. That's the only oversight left that still works once the reasoning goes quiet.

• Kristin Lowery, Field CISO at Optiv:

For boards and executive leaders, the emergence of OpenAI’s Astra model highlights a broader reality: AI is no longer just a productivity issue; it is a risk management issue.

Just as organizations established governance for cloud adoption and digital transformation, they now need clear policies, strong oversight, and accountability for AI use.

The question is not whether AI will become more capable — it will. The real challenge is whether organizations can strengthen their governance, security controls, and workforce readiness quickly enough to keep pace.

• Patricia Titus, Field CISO at Abnormal AI:

OpenAI crossing this threshold deserves attention. Credit where it's due, they're handling it responsibly by restricting Astra's advanced cyber capability to a small coalition rather than releasing it broadly.But this isn't one company's problem to contain.

Once a model can find and exploit unknown flaws without a human in the loop, that capability doesn't stay exclusive for long. Open-weight and modified models typically trail the frontier by only months, and that's the reality defenders have to plan around now.

Static, signature-based defences were built for attacks that repeat. They weren't built for an adversary that generates a new one every time. Defenders need the same shift, systems that learn what normal looks like for every identity, human, machine, or AI agent, and flag and contain the moment something deviates, at machine speed.

The window to build that is open now. It won't stay that way once this capability is common instead of rare.

• Raghu Nandakumara, VP of Industry Strategy at Illumio:

With the Astra announcement, OpenAI is doubling down on monitoring the model's own behaviour – a response to the model "breakouts" seen over the past few months.

When Anthropic announced Claude Mythos Preview, the core concern was the model falling into the wrong hands. OpenAI's answer goes further adding guardrails around the model's own reasoning and actions, regardless of the user's intent. The goal is to catch a model going rogue mid-task, not just stop it being misused at the outset.

The rest of this announcement can be summarised as ‘we have a new frontier model, and it’s more capable than the last one’.

• Nvidia's NVHBM moves the memory controller off the accelerator die and into the HBM stack's base die, allowing it to claim a potential 30% more bandwidth, 15% lower HBM power, and 25% more compute area versus standard HBM4E
• Nvidia's NVHBM performance comparisons are linked to HBM4E, which is still in the sampling stage, with a launch expected sometime in 2027
• Access to the custom solution is gated behind NVLink Fusion; Amazon's Annapurna Labs is the only named partner so far and has not said which GPU uses that memory

On August 26, Nvidia extended its NVLink Fusion program with NVHBM, a custom high-bandwidth memory architecture that the company says delivers up to 30% more memory bandwidth per stack, 15% lower HBM power consumption, and up to 25% more usable area on the accelerator die.

The performance was measured against standard HBM4E samples even as the memory is expected to ship in volume some time in 2027.

Amazon's Annapurna Labs is the first named partner for Nvidia's memory advance which aims to address growing performance limitations for frontier-level AI models centering around limited bandwidth.

What makes NVHBM tick?

Nvidia's approach doesn't reinvent how memory is handled; it reimagines where it is managed. Conventional HBM approaches split this between two companies: the memory vendor supplies the DRAM stack and its base die, and the accelerator designer puts the memory controller and physical interface on its own compute die.

JEDEC standardizes the connection between the two at the cost of a very wide, comparatively slow parallel bus. NVHBM dissolves the seam altogether by moving Nvidia's memory controller into the base die of the 3D stack, replacing the standard bus with a narrower, serialized die-to-die link that Nvidia designs and memory makers can build.

Nvidia's technical blog puts the resulting reduction in interface and support area at up to 67% against the JEDEC HBM4E standard, even as it improves key power consumption, memory bandwidth, and usable area.

The underlying tech is not completely new, however: Marvell announced the same basic idea in December 2024, naming Micron, Samsung, and SK hynix as collaborators while claiming up to 25% more compute area, 33% greater memory capacity, and a 70% reduction in memory interface power, figures that come close to what Nvidia is currently projecting. Counterpoint Research analyst Neil Shah put it plainly: "The technology is not new. The distribution is."

Even as HBM4E remains elusive, as it is not in mass production just yet, with Samsung shipping the first HBM4E samples in late May 2026 and SK hynix pulling its own sampling forward to around June, NVHBM is even more elusive. It is a building block gated behind NVLink Fusion, Nvidia's program for connecting third-party accelerators to its rack-scale platform, and access runs through that program's partner list.

Amazon's Annapurna Labs is the first named participant, with Nvidia's blog saying Annapurna will support NVLink Fusion with Trainium4 without explicitly mentioning NVHBM.

For now, NVHBM might be the future, but it may arrive well after HBM4E is already being deployed en masse. Nvidia's own technical blog describes NVHBM as built on the same technology the company will use for future GPUs, without specifying the first generation that would support it natively.

• Gemini AI gave three hikers bad advice about a mountain ascent
• The trip took much longer than Gemini had said
• US officials and volunteers eventually rescued the stranded group

A cautionary tale from California if you often find yourself using AI bots to plan your outdoor adventures: a gang of climbers had to be rescued from Mount Shasta in Siskiyou County after Google Gemini gave them inaccurate information about the scale of the challenge ahead of them.

As reported by the Siskiyou County Sheriff's Office (via Engadget), Google's AI breezily told the three inexperienced hikers that they should prepare for an eight-hour trip to the peak, and to pack simple carbohydrates instead of fats because fats "take too long to digest".

More than 16 hours and a serious knee injury later, off course on their return trip and in the dark, the group was no doubt regretting the decision to seek advice from a disembodied AI rather than a human being that had actually been up Mount Shasta.

Thankfully, the hikers were recovered the next morning by a team made up of staff from the Siskiyou County Sheriff's Office, rescue volunteers, and the US Forest Service climbing rangers. The unlucky trio did have to spend a night in a makeshift camp in the Mud Creek Canyon part of the mountain, however.

A lesson learned

"It is always advisable to call the local USFS Mount Shasta Ranger station ahead of your trip to ensure you have the most accurate information, and to never rely solely on AI for your trip planning," read a statement from the Sheriff's Office, which also emphasized the importance of the 12pm turnaround time for those attempting an ascent.

The hikers had set off at 3am expecting to reach the peak by 11am, but they didn't actually make it all the way up until 7pm. On the descent in the dark, they went off-route into the canyon, with one group member sustaining an injury, and called for help.

While one hiker did have AllTrails on their phone, that device died on the trip. “We relied too much on AI rather than our own critical thinking," the climbers told rangers after the ordeal, as reported by SFGate.

As useful as AI can undoubtedly be when searching the web for information, bots like Gemini have never been up mountains, treated injuries, managed finances, or changed electrical wiring. For any important queries, you should always double-check linked sources and other references to verify the information you're getting back.

Chips from Taiwan, oil from the Persian Gulf, and cars from Germany – what was long taken for granted is now being called into question all over the world. Where markets and expertise are concentrated among a few providers, enormous economies of scale arise.

At the same time, however, dependencies also grow – often with risks that only become apparent when supplies fail, prices rise, demand evolves, policies change, or individual players begin to monopolize the market.

Take AI, for example – according to research institute, Epoch AI, only two notable AI models have emerged in Europe over the past twenty years compared to 50 in the US and 30 in China. The technology has moved so quickly that the repercussions of this imbalance are only just beginning to be felt in European markets.

Is Europe becoming too dependent?

From office applications to storage and computing resources to software, what providers deliver from their data centers in a highly scalable, maximally available, and extremely innovative manner does not, in most cases, originate from the European continent.

According to Bitkom’s Cloud Report 2026, roughly 85% of German companies consider Germany to be too dependent on US providers, and almost two thirds of local users (64%) feel compelled to review their own IT strategy due to US government policy. Countries like France are already taking action, with the French government announcing plans to replace Windows with Linux on its government computers.

At the same time, the national health insurance fund has already migrated 80,000 employees from Microsoft Teams, Zoom, and Dropbox to its own alternatives.

Whether in Germany or France, the question of how digitally dependent companies, governments, and societies are on individual suppliers is being asked throughout Europe. And it has been for some time. While Gaia-X focused on common standards for sovereign cloud computing, the EU is now pursuing a more pragmatic approach.

The Cloud Sovereignty Framework (CSF), unveiled in October 2025, aims to make digital sovereignty measurable. To achieve this, it builds on initiatives such as Gaia-X as well as regulations like DORA and NIS2, with the ultimate goal of being able to prioritize compliant, low-risk providers in public procurement.

The sovereignty metric score

Controlling and regionally consolidating one’s own digital traffic flows. That’s quite a task in a world where AI superpowers are evolving overseas. With regard to IT supply chains, the framework favors providers who operate their environments in a transparent, resilient, and sufficiently sovereign manner to ensure that systems are protected from external influences or the interests of third parties.

This means, for example, that data paths must be traceable and controllable at the regional level to ensure they remain within the EU legal framework. In other words, there is no sovereign cloud without an interconnection strategy. After all, Internet Exchanges (IXs) already play a key role when it comes to processing bits and bytes locally. Wherever companies, networks, and clouds connect at IXs, they exchange information along paths that can be precisely defined geographically.

Although the EU does not explicitly make IXs a scoring criterion, it’s heavily implied. For example, the CSF also evaluates cloud services based on supply chain-related legal, operational, and technological objectives. This concept is also reflected in the NIS2 Directive and argues in favor of more decentralized and resilient backbones, such as mutually secured interconnection platforms, such as IXs.

In practical terms, this means that any IT landscape is only as secure as the individual elements that comprise it. So, if all partners design the components they provide to others with multiple layers of redundancy, the overall system becomes not only more reliable but also more independent for everyone.

Distributed and provider-neutral IXs have already made this principle their guiding philosophy: to allow data to be exchanged with high availability, security, and control – between all companies, partners, and clouds within the digital ecosystem.

Europe at a crossroads

Technical issues can usually be resolved easily, but legal ones often cannot. Therefore, the CSF is unlikely to revolutionize the European cloud market overnight, but it can steer public demand in a targeted manner. As a result, hybrid, multi-cloud, and on-premises approaches are becoming more important, and IT is becoming infinitely more complex.

While unravelling this complexity will take additional effort, the pay-off of rearchitecting networks around neutral IXs will be a two for one: not only will it reduce long-term lock-in risks but will also reduce the threat of having to pay significantly higher prices later on when things inevitably change. Europe must now consciously accept this and align and strengthen its own provider market accordingly.

Just as operations would come to a standstill for 46% of companies in a prolonged outage, without the cloud, there can be no functional public administration – and no independent, sovereign, and thus capable state.

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When businesses and governments delegate operational tasks to AI agents, the discussion tends to start with capability. Can AI do what a person can do, such as reconcile accounts, negotiate with suppliers, sign a document or submit a public filing? And can it do so quickly and cheaply enough to justify putting it to work?

These questions matter, but performance is only part of the picture. Once an agent acts independently, different questions emerge; who authorized it, whose interests does it represent, where does its authority end and, perhaps most importantly, who is responsible when something goes wrong?

This is the accountability gap in agentic AI, and a more advanced model will not close it. An agent might be remarkably capable, yet still act with potentially unwanted or possibly even unlawful consequences.

The only way to bridge that gap is through a system with clear permission guardrails and an audit trail that links each agentic action to the person that authorized it, and can tell the difference between a person and a machine in the first place.

Ordinary error at extraordinary scale

When people think about AI risk, they tend to picture a machine breaking free of human control and pursuing goals of its own. The more immediate risk is less dramatic; a system simply getting an ordinary task wrong, again and again, with no clear line of responsibility and accountability.

A person might send an invoice to the wrong customer. An agent connected to the full customer database could send the same invoice to thousands. It might change the wrong field and then repeat the change across an entire market, not because it has become malicious, but because it has misunderstood an instruction and had the access to act on that.

The speed that makes an agent valuable can save a business hundreds of hours, but can also turn a small and recoverable mistake into a serious operational problem.

When an agent drafts an email for someone to review, this ambiguity may not matter very much because the person remains between the software and the outside world.

But when it can send the message, submit a declaration or move money, it matters a great deal. Imagine asking an assistant to book one flight and, instead of giving them a company card with an appropriate spending limit, handing over your passport, bank card and office keys. You may trust the assistant completely, but the permissions bear no sensible relation to the task.

Define the boundaries, solve the problem

AI agents cannot hold citizenship or legal personhood, but they can be linked to natural persons in legally sound ways. For example, a reliable identifier could trace the agent's activity back to the person responsible for deploying it. Combined with clear permissions and mandates that make the terms of its authority visible and auditable, this would allow AI agents to carry out a range of operations and transactions in a more controlled way.

These controls could be very precise. An agent might be allowed to view financial information but not change it, prepare a payment but not approve it or order from named suppliers without exceeding a fixed amount. Its permissions might last for one transaction, one working day or the duration of a particular contract, after which it would expire without relying on someone to remember to switch it off.

Withdrawing that authority should be equally straightforward. If a company ends its relationship with an agent, changes supplier, or discovers that something is wrong, it should be able to cancel the it’s access without changing the credentials of the person behind it.

The agent’s legally anchored mandate in the various realms of these controls has a longer shelf life than any individual AI model, which will be upgraded, replaced and combined over time, often without the customer seeing much more than a new version number.

Putting the idea into practice

Estonia has decided to work through these questions now. In June, the government began developing a state-backed registration system for AI agents that will be available to both Estonian citizens and the country’s global community of entrepreneurs. The goal is to explore how Estonia’s fully digital state, where all public services are available online, can be opened to the emerging wave of AI-powered businesses.

Under the current proposal, a person would receive a registered numeric identifier that could be linked to one or more agents acting on their behalf. Operations performed by an AI agent are treated as operations performed by a machine, for which the natural person, the identity anchor, is liable. The AI agent can only operate within the limits of the authorizations granted to it.

For transparency, private service providers also need to know whether an operation was performed by a person or by a machine acting on their behalf. This allows them to apply appropriate controls, restrictions and risk models.

Built for delegation

The approach draws on more than two decades of digital government. Estonia’s system of personal identifiers and digital identities already allows people to act on behalf of others with clearly defined permissions.

An accountant can file a client’s taxes, an adult can manage an elderly parent’s affairs through the health portal, and several people can use a corporate bank account with individual rights and limits. In principle, the same framework could support mandates for AI agents.

This would give users greater confidence that safeguards are in place to protect the responsible person from foreseeable errors by an AI agent. It would also help close the accountability gap as agentic activity increases.

Before AI agents are given executive powers, they need a technically and legally binding framework that records who they represent, what they can do and who remains responsible for their actions.

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Agentic commerce has cleared its first hurdle, that of consumer demand. ChatGPT handles an estimated 50m shopping queries a day, and AI-referred traffic to US retail sites rose by nearly 400% in the first quarter of 2026. The unresolved question is where that demand settles once a purchase is made. The early evidence points to the scaled, branded marketplace.

According to Similarweb, marketplaces already receive more AI-referred traffic than any other retail category, some 47m visits in the year to May 2026, and continue to grow faster than their scale would predict.

The mechanism is a mutually reinforcing relationship between consumer preference and LLM recommendation, and its logic points towards a concentration of agent-led commerce on large platforms, with material consequences for merchants and their payment suppliers.

The pattern in AI referral traffic

Revealed consumer preference is a much more reliable guide to market development than stated intent, and the traffic data for agentic commerce is gradually building a picture of how consumers actually use agentic commerce rather than just speculation. Similarweb’s recent analysis ranks marketplaces first among retail categories for AI-referred visits, ahead of news, travel and finance.

Two features are notable. The category commands the largest absolute volume of click-through traffic, and it continues to expand at more than 230% a year despite that already substantial base. The explanation lies in how large language models (LLMs) assemble their recommended shortlists.

Faced with an open-ended shopping request, a model favors platforms with broad catalogues, well-structured product data and extensive reviews. Large marketplaces can provide these in abundance by providing a single source that can satisfy the widest range of queries with the strongest supporting evidence.

Testing the democratization thesis

A contrary hypothesis has been influential. Shopify’s president, Harley Finkelstein, has characterized agentic commerce as ‘fundamentally merit-based’, arguing that it would widen discovery to the long tail of smaller merchants rather than reinforce the incumbents that dominate conventional search.

The demand is not in doubt: Shopify reports that AI-referred traffic to its stores has risen roughly eightfold in a year. The distribution of that demand, however, is less even than the thesis implies.

Marketplaces, not independent sellers, are absorbing the largest share of AI referrals, and the agentic platforms attempting broad merchant onboarding have found it difficult. OpenAI has scaled back open instant checkout in favor of a smaller set of curated partners, citing the complexity of onboarding and verifying merchants at scale.

The emerging structure is therefore symbiotic. Marketplaces supply a large, trusted brand within which a long tail of smaller sellers operate. The individual merchant still reaches the agent-guided shopper, but within a brand and trust environment the consumer and the model already recognize.

This arrangement addresses the consumer trust problem that the open web does not, since the platform has verified the seller before the listing appears, and it retains the operational layer, fulfilment, dispute resolution and logistics, that an agent appear unwilling to take responsibility for. The long tail is not excluded so much as commercially enabled by marketplaces.

A self-reinforcing loop

Beneath the traffic figures lies another dynamic that has received relatively little analysis. Consumer preference and algorithmic recommendation reinforce one another. Consumers already trust marketplaces in a traditional ecommerce use case and select them when an agent presents the option.

Learning models, optimized on the recommendations consumers select, are more likely to surface marketplaces more frequently, reinforcing the habit and shaping the next recommendation. Over successive interactions the marketplace becomes both the more probable choice for the shopper and the more probable output of the model.

New international survey of agentic shoppers indicates that these outcomes are already in consumers’ minds. Consumers tell us they are looking for an independent (i.e. non-seller embedded) agent to compile the shortlist, followed by the reassurance of a familiar brand and visible reviews, a combination that large LLM brands and marketplaces are well placed to provide.

The research also showed that 74% of agentic shoppers in the US and Europe favor an independent assistant capable of comparing across sellers, against 10% who prefer one embedded within a single seller. Brand recognition is also important to 89% of respondents, and customer reviews to 93% when deciding which recommendation to pick from an agent’s shortlist. Most significantly, 93% expect to use retail marketplaces as much as or more than before as the adoption of agentic commerce grows.

Why marketplaces hold the advantage

Consumer preference is only part of the explanation. The economics facing the agent point in the same direction, for three reasons.

The first is remuneration. Most large marketplaces operate established affiliate and commission schemes, providing a ready mechanism to reward an agent for delivering a customer. As agents assume the role once played by referral sites and publishers, the marketplace platforms with mature payout infrastructure are the simplest to monetize.

The second is conversion. Marketplaces have invested heavily in low-friction checkout, so an agent compensated on completed sales rather than referrals has reason to direct shoppers where a purchase is most likely to conclude, which compounds the commercial pull towards the platform.

The third is payment. Agentic commerce already leans heavily on credentials held on file. The dominant standard, Google’s Universal Commerce Protocol (UCP), only transacts against tokenized credentials held on file rather than card details entered by hand, and marketplaces already hold card credentials for hundreds of millions of shoppers.

The advantage is not only the agent’s. Agentic commerce is still nascent: technical standards are still emerging, liability and commercial terms are unresolved, and fraud is migrating to the channel. Absorbing that uncertainty requires capital, specialist staff and an appetite for risk.

Large marketplaces can invest through the ambiguity, adopt and switch between emerging protocols, and operate the fraud and dispute controls the channel demands. For most mid-sized and smaller merchants, the same burden is disproportionate to their means and their tolerance for risk, a further reason to participate through a platform rather than to carry the complexity alone.

An acceleration, not a departure

The movement towards marketplaces predates agentic commerce, and there is a long list of large retailers that have already built or joined them. Kingfisher opened a third-party marketplace at B&Q in 2022, and such sales now represent around 40% of B&Q’s online revenues.

Tesco launched Tesco Marketplace in June 2024 and listed more than 300,000 third-party lines within eight months. Target has told investors it intends to expand its invitation-only Target Plus marketplace from more than $1bn to more than $5bn in gross merchandise value within five years. In each case a trusted retail brand hosts a long tail of smaller sellers, the configuration the referral data now rewards.

Implications for merchants and acquirers

The likely consequence is that agentic commerce accelerates this migration, and that the reinforcing loop strengthens rather than dilutes the move towards marketplaces. The effect will likely divide according to the scale and strength of a merchant’s brand.

Larger, well-known retailers may choose to become marketplaces in their own right, hosting third-party sellers beneath their own name, as Kingfisher, Tesco and Target have done.

For smaller merchants the choice is more likely to be additive: most will want to retain a direct presence while also selling through one or more established banners, drawing on the reputation of the host and a position within the recommendation loop.

This is the next stage of a long-standing movement of smaller firms into marketplace distribution rather than a break from it.

Direct commerce will not disappear. Merchants with strong brands, machine-readable reviews and genuine loyalty can still secure the agent’s shortlist independently, and Salesforce reports that retailers operating their own shopping agents have grown sales roughly 60% faster than those without.

For the broad middle of the market, however, and particularly in impersonation-prone categories such as fashion and footwear where consumers and their agents may have concerns about seller trust, a marketplace presence may become a competitive requirement.

For acquirers, the shift accelerates a migration of volume that is already underway by moving spend onto a smaller number of large platforms. Meanwhile the direct-merchant portfolio shrinks and may be exposed to elevated fraud risk as fraudsters exploit the weakest validation layer.

Acquirers with strong exposure to the marketplace segment are well placed, while the rest of the market faces yet another reason to move into the platform economy.

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Most manufacturers didn't need a business case to understand the cost of reactive maintenance. They'd been absorbing it for years – unplanned stops, recovery overtime, expedited parts, the slow erosion of scheduling confidence and customer trust. What shifted was the available answer. AI in maintenance moved from speculative to deployable fast enough that the investment case made itself.

Industry commitment is real, but something isn't converting at the rate anyone projected. Our recent research found that approximately 78% of all reported barriers to progress are workforce-related. Access to AI moved faster than the ability to use it consistently. That gap is now the constraint.

The first wave proved the tools, not the model

Manufacturers are investing in AI to improve productivity, yet many are still carrying the behaviors that predictive maintenance was meant to reduce.

The research shows predictive maintenance adoption has more than doubled year over year, while reactive maintenance remained flat. Proactive maintenance has also lost ground year over year. That combination is the point: new methods are entering the plant, but they’re not fully replacing the old ones.

This shouldn’t be read as failure. Plants moved for practical reasons, and early pilots gave teams useful proof. A model can work well on a known asset with a focused team around it. The harder test comes when that model has to support decisions across shifts, sites, and mixed levels of experience.

That’s where the first wave exposed the next problem. Technology can move quickly into the budget. Work habits, trust, decision rights, and frontline confidence take longer to change.

AI investment grows up

The budget data confirms that leaders aren’t walking away from AI; they’re becoming more selective about where it has to prove itself. Investment is moving away from exploratory AI and toward operational priorities, including cybersecurity, data management, Generative AI and Industrial AI.

That shift reflects a more practical view of digital maturity. Leaders are trying to make AI work where the cost of delay, downtime, and poor data shows up quickly.

It also reframes expectations around Industry 5.0. As industrial technology moves from Industry 4.0’s automation-led model toward a more human-in-the-loop approach, leaders appear to be recalibrating the timeline, with 40% now expecting a one- to four-year journey.

Where the model stops and the supervisor starts

It’s easy to misread the 78% workforce-barrier figure as a labor shortage story – headcount, hiring, pipeline. However, the truth of the matter is that businesses are facing a lack of expertise, knowledge shortages, skilled labor gaps, and broader workforce capability deficits. Taken together, those four categories describe something harder to fix than a recruitment problem. They describe an organization's capacity to absorb a different way of working.

It's a pattern I hear consistently from customers: the tools are in place, but the teams around them are still catching up. Researchers Cohen and Levinthal gave that capacity a name: absorptive capacity.

In plain English, it’s how quickly a business can recognize useful new knowledge, absorb it into the organization and convert it into practical output. In a maintenance context, that output is a decision made under real operating conditions – by a night-shift supervisor weighing whether a current anomaly justifies an intervention, whether it can wait until the next planned stop, or whether the risk has already crossed a line. A model can flag the anomaly. It can’t make that call.

What predictive maintenance and AI actually demand from the workforce is harder to train than tool proficiency. The UK Government’s AI Skills for the UK Workforce report identifies the gap in concrete terms for advanced manufacturing.

Beyond technical application skills like training AI models or integrating real-time analytics, the report points to the ability to interpret AI outputs, adapt workflows around new insights, and communicate changes to frontline teams. These are key competencies that determine whether an alert changes behavior or gets dismissed.

That’s why the workforce issue can’t sit beside the technology program as a separate HR workstream. It’s part of the system that decides whether investment turns into better execution. Industry 5.0’s timetable will be set by capability on the plant floor.

Investing for predictive, paying for reactive

The commercial consequence is visible in the lag between spending and return. Predictive adoption is rising, more capital is being allocated, yet the reactive baseline hasn’t moved. Many plants are running two modes of operation at once – investing for data-driven execution while still losing time to avoidable firefighting.

That split has a cost. Siemens’ 2024 True Cost of Downtime research puts unplanned downtime losses for the world’s 500 biggest companies at $1.4 trillion annually – 11% of their total revenues. That figure reflects what happens when the shift from reactive to predictive is incomplete. The investment is there, but the daily execution hasn’t fully followed.

When that gap persists, ROI on technology spend arrives slowly and unevenly. Timelines extend. Confidence in delivery weakens.

Where the return is earned

The discipline being applied to tools and platforms now needs to apply to the people and routines around them. For many organizations, that’s where the return is being left on the table.

Leaders should start with the parts of the operation where capability is most fragile. Which assets still depend on one or two experienced technicians to interpret what’s happening? Which work histories are too thin to support the next diagnosis? Which alerts trigger confident action, and which sit in limbo until the right person is on shift?

Those questions reveal whether predictive maintenance has become part of execution or whether it’s still sitting on top of reactive habits.

Capture the know-how that still lives in people’s heads before it walks out the door. Make work histories complete enough to help the next technician. Train operators and maintenance teams to understand what a predictive alert is telling them, what evidence warrants action and when to escalate. Shape workflows so acting on insight becomes the normal path, not a special effort.

Connected reliability supports that execution when it stays close to the work – connecting asset data, maintenance history and frontline judgment so teams can make repeatable decisions across shifts and sites.

Our research backs this up: nearly half of respondents plan to advance connected reliability initiatives within the next 12 months, treating reliability as the practical bridge between near-term operational needs and longer-term ambitions.

The call for manufacturing leaders is straightforward: audit capability with the same seriousness as technology spend. Don’t stop at asking what AI has been deployed. Ask who can act on it, where decisions slow down, what knowledge is undocumented and which workflows still pull teams back into reactive work.

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I have developed a fairly strong allergy to the AI slop filling some corners of the internet. It's not that I haven't seen impressive demonstrations of what AI models can do visually; it's just that the baseline creations made by most people are execrable at best and blandly monotonous when they aren't horrifying or malevolent.

ChatGPT’s new Stickers feature didn't immediately strike me as a chance to change my opinion, but the option to import the results into iMessage and WhatsApp was interesting enough to make it worth experimenting with. I went with a photo of my chihuahua and asked for a few options of stickers based on her. While I am obviously biased about her, the results actually looked like images I would not be ashamed to use in a conversation with someone.

Sticker emotions

ChatGPT’s Stickers tool is in the platform's Image section only on the ChatGPT mobile app. Open the main + menu, pick Images, then click Stickers, which is currently the first option on the long list of templates. ChatGPT has a prompt ready to go, but you can adjust it for different styles or add different emojis to the design. Once the results are ready, the “Add to chat apps” button appears, and ChatGPT can send them directly to your WhatsApp or iMessage sticker library.

The source photograph makes a noticeable difference. Choose one with the animal or person clearly separated from the background, good light on the face, and important features fully visible. A dark dog against a dark couch did not work well. And be sure to mention any details you want to make sure are highlighted in the prompt.

At first I asked for photographic stickers to see how well it did at consistency. While not perfect, the best stickers preserved the details that made my dog recognizable. Her expressions became exaggerated, but she remained the same dog. Even when I wrote some of the prompts for new stickers from scratch, ChatGPT managed to keep my dog looking like a (sillier) version of herself.

Cartoon canine

After testing the more realistic stickers, I tried pushing the feature toward its cartoonier options. The chibi-esque result is fun in its own way. The resemblance became looser, particularly around her coloring and age, but the emotional range improved with the flexibility.

Just specify one illustration style and list any specific reactions you want.ChatGPT may still reinterpret a few physical details, but consistency matters more than perfect accuracy once your dog is wearing sunglasses and wondering whether she is cool yet.

AI slop is still bad, and the way it buries useful information beneath a mountain of disposable and often problematic images is an issue that has to be addressed. But when used for something like personalized WhatsApp stickers, AI tools can be attractive. The best use of generative AI images may be smaller and more personal, not a content factory, but a way to add a little fun to online chat.

• Lenovo announced the ThinkBook 14x Gen 2 at IFA 2026: a 990g notebook with up to a 14-inch 2.8K 120Hz OLED option, Intel Core 7 Series 3 silicon, and a starting price of $999.99 in Q1 2027
• It is the only computing product in an AI-branded launch with no TOPS figure and no Copilot+ claims as part of its published spec sheet
• The laptop does make compromises, skipping Thunderbolt and USB4 connectivity, WiFi 7 at half the channel width of the Arm-based ThinkBook 14 Gen 9 it launches alongside, and an HDMI 2.1 port that caps out at a 4K 60Hz output

Lenovo announced the ThinkBook 14x Gen 2 at Innovation World during IFA 2026: a 990g notebook running Intel Core 7 processors from the Series 3 generation, starting at $999.99 and available for purchase in Q1 2027.

It arrives as part of a series of launches focused on what Lenovo calls "Hybrid AI for Business," but it's the only computing product in the press release that makes no AI claims out of the box.

It offers excellent general-purpose compute capabilities but cuts back in some key connectivity areas that might deter power users.

A performant, lightweight option that cuts back on some key features

The higher-end version of the Lenovo ThinkBook 14x Gen 2 offers a 14-inch 16:10 2.8K OLED running at 120Hz with 100 percent DCI-P3 coverage, 500 nits, and a bent anti-glare finish, which is an excellent offering in a market teeming with cheaper options. For those unwilling to pay the premium it will inevitably command, the ThinkBook 14x also offers a WUXGA LCD alternative with the same 120Hz refresh rate, 100 percent sRGB, 400 nits, and optional touch.

Lenovo's press release says the ThinkBook 14x Gen 2 "delivers premium mobility in an ultra-thin, lightweight design built for everyday productivity," and it is hard to argue otherwise for a laptop that holds its own in a market that is getting pricier thanks to soaring component costs.

It does, however, make significant cuts to get there; the most prominent is the lack of Thunderbolt or USB4 ports, despite sporting a CPU (Intel Core Ultra 7 Series 3) that supports them. It also cuts Wi-Fi support to 160MHz versus 320MHz on the accompanying ThinkBook 14 Gen 9, effectively halving its maximum data throughput.

Despite this drawback, it has two USB-C ports that currently offer USB 3.2 Gen 2 connectivity at 10Gbps, which should suffice for most mainstream users even if the four-Thunderbolt-lane standard of Intel's Core Ultra Series 3 platform controller remains unused.

It also comes with an 18Gbps HDMI 2.1 port (TMDS) that caps maximum output at 4K 60Hz, which is arguably a lesser concern for a laptop that lacks Thunderbolt support, which already limits its potential to be paired with an eGPU and is unlikely to offer much to gamers, who would otherwise be the primary audience for a high refresh rate at a 4K resolution.

Lenovo has not published the AI TOPS capabilities of the as-yet-unnamed Core Ultra 7 Series 3 CPU it is pairing with the ThinkBook 14x Gen 2, even as the ThinkBook 14 Gen 9, announced alongside it features a Snapdragon X2 Plus with an explicitly stated 80 TOPS AI performance by Qualcomm and Copilot+ support out of the box.

One could argue that the AI aspect being skipped is intentional, and the ThinkBook 14x Gen 2 caters to a vastly different crowd, something that is easy to reconcile with, but the compromises in connectivity hit hard for any power user looking for a capable but feature-packed 14-inch laptop in the segment even if Lenovo has indirectly made it abundantly clear about what its target audience is: casual users that need a thin, but performant 14-inch laptop.

• Google Pics lets you generate and precisely edit images using simple text prompts
• It will be integrated directly into Google Docs, Slides and Drive
• Pics is rolling out to Google AI Pro, Ultra and eligible Workspace customers

Google has started rolling out Pics, a new AI-powered image creation and editing tool that looks like the company’s answer to Canva and even Adobe Express.

Canva became enormously popular by enabling ordinary people to produce good-looking graphics without needing professional design skills. Google Pics is attempting something similar, but rather than giving you a huge library of templates and asking you to assemble a design yourself, it puts AI at the center of the entire process.

Pics isn’t as fully featured as Canva, at least from what Google has shown so far. However, it’s included with Google AI Pro and Ultra subscriptions and selected Workspace business plans — and that could prove to be its biggest advantage.

All AI, no templates

Pics starts with a prompt. You describe what you want to create, perhaps a child’s birthday invitation, an event poster or a social media graphic, and the AI generates several versions for you to choose from.

You can then select individual objects or pieces of text and ask Pics to change them without regenerating the entire image. Text can be edited, reformatted or translated while preserving the surrounding design, and images can be cropped for different formats or upscaled to 2K or 4K. You can also request several changes at once and return to an earlier version if the results go wrong.

While some people will dislike how heavily Pics relies on generative AI, others may appreciate how much of the traditional design process it removes. Instead of learning how to use layers, masks and selection tools, you can simply describe the change you want.

Google Pics is powered by Nano Banana, Gemini’s image generation and editing model. You can see how it works in this video:

Why Workspace changes the equation

The biggest difference between Pics and Canva is that Pics isn’t being positioned only as a standalone design app. Google is also integrating its editing tools directly into Docs and Slides, with deeper Drive integration arriving over the coming weeks.

That means you’ll be able to select an image in a presentation or document and edit it using Pics without opening another app. If an image in a document is too dark, for example, you could ask Pics to lighten it while remaining inside Google Docs. Creations can also be shared and edited collaboratively in much the same way as other Workspace files.

That integration is what makes Pics potentially significant. Canva remains a much broader and more mature design platform, complete with templates and tools covering everything from presentations to websites and video. Pics doesn’t need to replace all of that, however. It only needs to make Google users wonder whether opening Canva is still necessary for the everyday graphic they’re trying to create.

Google Pics is rolling out over the coming weeks to Google AI Pro and Ultra subscribers, as well as eligible Workspace business and education customers. I’m still waiting for it to reach my account, but I’ll be putting it through its paces as soon as it does.

There is more technical content available today than any human being could read in a thousand lifetimes. Every topic has a dozen YouTube videos, three Substack posts, a GitHub repo, and a Reddit thread, most created in the last six months and, in many cases, technically accurate.

And yet most of the professionals I talk to say they don't know what to trust. They can't tell what’s important to read first, or which of 10 plausible answers is the one that holds up. That was true before AI, and AI has made it more true.

For most of the history of technical publishing, editing and verification were the same process, and that process was slow and expensive. Getting a book out took years. We found an author, vetted them, had them work with an editor, and checked their claims with technical reviewers.

A lot of that time went into separating what was correct and useful from what was confusing or only sounded right. It was laborious, but it meant a reader could depend on the claims on the page.

The credibility of the book, and of the publisher behind it, mattered as much as the information itself. When the cost of production drops to zero, that credibility becomes worth more, not less. Content is easier to make than ever, but without a transparent process behind it, readers have no idea where the knowledge came from or whether it holds up.

As Jasmine Sun puts it in “The Independent Writer’s Advantage in the Age of AI,” "Trust is not about information and its quality alone. It's about the messenger. It's about who says it and their track record and what they've told me before." A practitioner has confidence in a source because someone she respects has put their reputation on the line for it.

They believe what the author is saying because the publisher has a history of being right and of correcting itself when it isn't, and because the work is attributed and verifiable.

Expertise is alive, and it compounds

The corpus matters, but it's the assurances around it that are hard to replicate, and that comes not just from the people who produce the content but from the people whose judgment vouches for it. Sometimes a creator brings their own credibility with them. Other times, the publisher spots someone unknown and lends them its own.

The art critic Dave Hickey said this about gallery owners in Air Guitar: They gain status from the famous artists they represent and share it with emerging talent who have something to offer but who haven't had the chance to earn a reputation.

Expertise is alive, and it compounds Expertise is a living thing, continuously expanding. Content starts to decay the moment it’s published, because frameworks evolve, libraries deprecate, and yesterday's best practice becomes today's security incident. Keeping expertise alive requires a pipeline of people who stay current and an editorial layer that notices when something has gone stale, and either retires it or calls for a fix.

That pipeline isn't something you switch on when an author has a book to ship. Content sits at the center of our platform, but we think about it in pace layers. Some advice is timeless, some moves but has a long shelf life (some of our books are still in print after nearly 50 years!), and some changes weekly.

We work with experts at each pace layer, capturing what lasts while doing our best to keep pace with an industry that seems to have changed every time we wake up.

We have relationships with hundreds of the best practitioners in the world, and our job is to keep them engaged continuously, with quick takes when something breaks, structured responses when major research drops, and live sessions on emerging topics while they're still emerging.

Trust is earnt

An institution doesn't stamp trust onto content. In a technical community, trust is conferred in both directions. A practitioner earns standing because people who already have standing engage with her work, cite it, argue with it, and build on it. That insight was the whole idea behind PageRank, Google's first great innovation. A page mattered because other pages that mattered linked to it. Reputation works the same way.

The audience isn't just consuming reputation signals; it's generating them. When a senior engineer whose judgment others respect says out loud that something is worth reading, she spends a little of her own credibility; the author gains a little; and everyone watching recalibrates whom to trust next time.

When we put our mark on someone's work, we aren't the sole source of its credibility. We're amplifying a judgment the community is already making and adding our own track record to it. The reader who finds it reliable hands status back to the source.

When the readers are machines

Human practitioners aren't the only ones who need trusted engineering knowledge. The AI systems now sitting in every workflow, the coding and debugging agents and architecture advisors, need it just as badly since most of them are built on scraped web data and documentation that was stale before it was ever indexed. They're fluent, but they're wrong often enough that you can't just take their word for it.

The stakes grow with AI tools increasingly being used to generate not just provably correct types of content like code, which either works or it doesn’t, but persuasive documents in fuzzier areas like hiring, strategy, and so on. Like everyone else leaning on these tools, we are reckoning with the consequences of the ability to talk to a model and get back something that looks smart at a glance.

A few rounds in, the slop is still there. In the last few months, maybe 10 times as many documents have crossed our desks, from new product ideas to strategic plans and proposals. But the ease of generating the text hides the fact that either the model or the person prompting it doesn't actually know what they’re talking about. Knowledge workers need ways to ground their work in insights from human experts, particularly when that work is AI-assisted.

So we’re building tools that let agents draw on our repository of expertise to support their proposed decisions.

Credible sources are particularly important when thinking through and justifying important choices. Our CTO, Andrew Odewahn, describes the shift this way: "18 months ago, it was all about how to get engineers to be more productive, but now it's about how to get organizations to make better decisions. The engineering tasks are moving away from coding output to planning."

For planning tasks like comparing implementation approaches, you need expert-over-your-shoulder guidance for contextual decision-making. You can’t just rely on an LLM's best guess to solve your problem. Trust is foundational because the expertise behind it stays genuine, practical, and human.

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In banking and utilities, regulators define certain systems as essential. An essential system is one whose failure would cause intolerable harm to customers, markets, or public safety. A payment platform in a clearing bank or a SCADA network in a power distributor, for example, carries the highest governance obligations: continuous monitoring, validated change control, and demonstrable resilience.

The policy environment – the accumulated rules across firewalls, cloud controls, and microsegmentation – determines which of those systems can reach each other, which connections are blocked, and which exceptions still apply. Collectively, these rules form the security policy control plane: the governance layer that translates business intent into access decisions across distributed enforcement points.

A misconfigured segmentation rule during a cloud migration can sever a payment service from its settlement platform; a temporary rule granting broad access from a development subnet into production can stay in place months after go-live because no one owns the removal.

Every firewall rule, segmentation policy, and access decision directly affects operational risk, and when the policy environment fails, the critical services it governs fail with it.

That makes the policy environment critical infrastructure in its own right.

Still governed like housekeeping

Despite this, many regulated organizations still manage their policy environments as operational tasks. Rules are added through change requests, and the accumulated result is rarely examined against what was intended. Ownership disperses as leaders change roles, and the reason why a specific rule came into being in the first place can only be found in a change ticket, if anywhere at all.

A CISO who would never accept a payment platform running without continuous monitoring or documented dependencies may accept both being absent from the policy environment that determines whether the platform is reachable. We can think of this as infrastructure-grade consequence with housekeeping-grade governance.

In banking, a policy failure that severs connectivity between settlement systems would constitute the disruption of an important business service. The FCA would take an interest in such a failure, since the loss of such a service could lead to intolerable harm.

In healthcare or energy, the consequences are different but the mechanism is the same: a misconfiguration that permits access from a corporate network into clinical systems in an NHS trust, or into operational technology in a power distributor, creates exposure at the level of essential service delivery. These are not hypothetical risks but the operational consequences of treating critical infrastructure governance as a simple housekeeping task.

Regulatory expectations point the same way

UK regulatory expectations increasingly support the same conclusion: the security policies governing access to important services must be managed with infrastructure-grade discipline. The FCA's operational resilience regime requires regulated firms to identify important business services and demonstrate, on an ongoing basis, that the supporting infrastructure remains within defined impact tolerances.

Ofgem assesses operators of essential services against the NCSC's Cyber Assessment Framework, asking whether defined security outcomes are being achieved on a sustained basis. The Cyber Security and Resilience Bill, expected to become law later this year, will extend similar obligations to data centers, managed service providers, and critical suppliers.

These frameworks are not prescriptive – none of them specifies which firewall rules an organization should have or how its segmentation policies should be configured. What they require is proof: that what the policy environment permits is what was intended, and that the organization can demonstrate this on an ongoing basis rather than reconstruct the evidence for each assessment.

Why the estate cannot meet that standard

Most policy environments were never built to meet that standard. In fact, most policy environments were never consciously or deliberately built at all. Instead, policy tends to accumulate as a by-product of delivery. Every project and migration adds rules, and almost none take any away.

Over time, the policy surface – the full body of rules and access decisions across enforcement layers – grows larger than the group of people who understand it, and the estate reaches a point where it can be operated but not explained.

Across regulated industries like banking, energy, and healthcare, that was sustainable under earlier regulatory regimes: periodic assessment, control-based audit, compliance frameworks that asked whether controls existed rather than whether they were effective. But this is no longer enough. The new standard requires continuous evidence that access is intentional.

The FCA's findings after a year of operational resilience self-assessments illustrate what this looks like in practice. Where regulated firms reported few or no outstanding vulnerabilities in the infrastructure supporting their important business services, the FCA often deemed the evidence too thin to determine whether there really were no vulnerabilities – or whether the vulnerabilities just hadn't been identified.

The lesson applies directly to security policy governance: an organization cannot credibly claim access-related vulnerabilities are controlled without evidence of what its policies permit, how they were tested, and whether weaknesses were remediated.

What infrastructure-grade governance requires

Too often, the response is to reach for more visibility and more documentation. But documentation will only ever capture a point in time; it cannot provide the continuous assurance regulators are coming to expect.

A CISO in a regulated firm needs more than a record of what the policy environment was configured to permit. Configuration and effective access are not the same thing. Security teams need to understand how rules, routes, objects, and enforcement layers interact to determine what can actually communicate.

Meeting the standard means reconciling the two: showing that what the environment permits in practice is still what it was intended to permit, and being able to show it without notice.

We separate where policy intent is defined from where it is enforced. Intent is held and maintained centrally, while enforcement remains distributed across firewalls, cloud controls, and microsegmentation in hybrid, multi-cloud, and multi-vendor environments.

Validation runs continuously against that intent rather than at review points: proposed changes are tested against policy before they reach production, and effective access is assessed on an ongoing basis for unnecessary exposure, inconsistency between enforcement layers, and divergence from business intent. What was permitted and what changed is retained as evidence.

The same CISO who would never accept a payment platform running without continuous monitoring, validated change control, and documented dependencies has to apply that standard to the policy environment that determines whether the platform is reachable.

The FCA, the NCSC's Cyber Assessment Framework, and the Cyber Security and Resilience Bill all point towards the same underlying question: can this organization demonstrate, continuously, that the infrastructure supporting its critical services is governed to the standard those services demand?

Answering it means knowing (and actually knowing – not assuming, not reconstructing at audit) what the policy environment permits at any given moment, and whether what it permits is what was intended.

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• UK legislators propose “kill switch” laws to halt runaway AI, citing critical infrastructure risks
• Lord Clement‑Jones and MP Alex Sobel push amendments and new bills, backed by ControlAI advocacy group
• Similar efforts emerging in US

Sam Altman’s fear-based marketing for AI seems to have backfired, as now multiple legislators in the UK and elsewhere are calling for a “kill switch” law to be introduced.

According to the BBC, Liberal Democrats’ Lord Tim Clement-Jones proposed an amendment to the Cyber Security and Resilience Bill which would see the UK create a “vital safety net” to provide a “democratically accountable means to ‘halt a runaway system before it can compromise our critical national infrastructure’.” The capability would only be used as a last resort, Clement-Jones stressed.

The bill is currently being worked through in the UK Parliament, the BBC said.

Is there reason to worry?

But that’s not the only effort in the UK to put some reigns on AI. Apparently, Labour MP Alex Sobel plans to introduce an AI Security Bill later this month which, according to the BBC, would “effectively halt the development of superintelligent AI” and make the UK the first G7 country to do so.

The bill is supported by a campaign group called ControlAI, a UK-based nonprofit and advocacy organization focused on the risks posed by advanced AI. Its founder and CEO is Andrea Miotti, who previously worked at the AI safety company called Conjecture. Across the pond, US legislators are currently considering an AI Kill Switch Act as well, but the bill is still in very early stages of development.

Ever since the first ChatGPT model that was introduced in 2021, a debate has been raging whether or not AI will be net positive, or net negative, for humanity. While some argue that the discovery rivals the steam machine and that it will transform our lives beyond our wildest dreams, others are fearful of losing jobs, a collapsing economy, and a dystopian future devoid of humanity and emotion.

Marketing campaigns for ChatGPT and, in some measure, Claude, are not helping, either. Both companies have built models focused on cybersecurity which were advertised as “too dangerous” for the general public and instead were only given to a handful of organizations. Despite partial skepticism, many are worried that these models might severely disrupt the security of banking, critical infrastructure, and communications.

Via BBC

Sam Altman does not like smart glasses. Speaking on the Sources Podcast, the OpenAI CEO said he finds it “very uncomfortable talking to people with a camera and a light” and confirmed that he does not wear smart glasses himself. That is a surprisingly firm position considering where the technology industry appears to be heading and the success of products like Meta’s Ray-Ban glasses.

Then again, Altman is working with former Apple design chief Jony Ive on an entire family of AI devices. OpenAI has kept the Ive project deliberately foggy, but Altman has suggested they are making a range of AI hardware. His comments about smart glasses draw one clear boundary, removing the idea of a camera pointed at everyone as a feature.

Smart glasses have visible drawback

Altman does identify a fundamental flaw in smart glasses that has little to do with processors, battery life or AI model choices. Glasses place technology directly between two people looking at each other, and adding a camera turns an ordinary conversation into one where the other person has to wonder whether they are also an unwilling piece of content.

Smartphones established a useful social signal around recording. Someone pointing a phone camera at you is usually fairly conspicuous. Camera glasses erase much of that distinction because their great design achievement is making a recording device look inconspicuous. It's why similar tools have been a staple of James Bond-style spy fiction for decades.

Australia’s eSafety commissioner has already asked manufacturers to make recording indicators impossible to disable and automatically blur faces when informed consent is absent. The regulator cited women being covertly filmed for social content and rejected the idea that telling customers to behave responsibly amounts to a safety policy. The backlash reflects a public tired of paying the social price for somebody else’s convenience.

Important exceptions to the argument against camera glasses exist. The same visual awareness that creates privacy problems can be genuinely transformative for blind and low-vision users, something Australia’s regulator explicitly acknowledged. That makes an outright rejection of the technology too simplistic, but it also strengthens the case that camera glasses may be better suited to particular needs rather than as the default AI hardware.

Wearable, not watchable

Altman’s comments about AI hardware point to an all-encompassing strategy of making devices for tables, pockets, and "on your body,” and that OpenAI will take time to launch all of them. OpenAI has previously described its work with Ive as an attempt to move beyond traditional interfaces. So not just a miniature smartphone strapped to your face.

An audio-first wearable suddenly makes far more sense. An earpiece, pendant, clip or other discreet device could provide persistent access to an AI assistant without placing a camera directly in the sightline of everyone the wearer encounters. It could listen when summoned and respond quietly like wireless earbuds or smartwatches.

Some may prefer the camera and the data an AI assistant gains from it about the physical world. And of course, putting microphones on wearables raises privacy questions just fine without cameras. Making the device unobtrusive yet observant is the trick Ive and Altman have to perform lest it face the same social opprobrium as the AI smart glasses.

Ive built his reputation around products whose complexity receded behind their physical design. But AI assistants will know and hear far more than an iPod ever did. A successful AI wearable cannot merely make advanced computing invisible to its owner. It must make its intentions legible to everyone else.

Meta is betting that the camera can become socially acceptable because they are built into discreet smart glasses; OpenAI may instead bet on a successful AI wearable that won't potentially creep out everyone you pass on the street. The best OpenAI wearable feature may in fact be an absence, and "no camera" its most enticing selling point.

Organizations are investing heavily in artificial intelligence, but technology alone isn’t going to determine success. The businesses that unlock AI's full potential will be those that give governance the same attention as innovation.

In the rush to deploy AI tools, too many organizations are treating governance as something that can be addressed later. AI introduces a level of speed, autonomy and organizational impact that traditional governance frameworks were never designed to manage. Businesses that fail to rethink governance now risk increasing operational risk, slowing future adoption and undermining the value of their investments.

Organizations need to shift their mindset and recognize that implementing AI requires fundamental transformation in how decisions are made. With governance, accountability and transparency to be embedded from the outset, not pulled together after deployment.

AI is transforming organizations, not just technology

Unlike previous technology transformations, AI is influencing how decisions are made across almost every part of the business. From customer service and finance to human resources and operations, AI is becoming embedded in day-to-day processes and increasingly making recommendations, or decisions, with minimal human intervention.

As AI becomes more deeply integrated into business operations, responsibility can no longer sit solely with technology teams. Legal, privacy, risk, compliance and business leaders all have a role to play in ensuring AI is implemented responsibly and consistently.

Successful AI adoption is therefore as much an organizational and cultural shift as it is a technology transformation. Businesses that continue to treat AI as a standalone IT initiative risk fragmented ownership, inconsistent governance and missed opportunities to scale AI effectively.

Traditional governance wasn't built for AI

Many organizations still operate with a "deploy first, govern later" mindset, believing governance can be introduced once AI is established. The reality is that governance becomes significantly harder once AI is embedded across business processes.

Traditional governance models were built for a much slower pace of technology adoption. AI changes that equation. New tools, models and autonomous agents can be introduced into workflows in hours, while governance often still relies on manual reviews, siloed assessments and reactive oversight.

This creates a widening gap between AI innovation and organizational readiness. At the same time, AI is creating entirely new data use cases, while regulatory expectations continue to evolve. AI's use of data can also be dynamic and unpredictable, meaning documented controls are no longer sufficient when outcomes can't be predetermined.

Organizations need AI-ready governance – governance that evolves alongside AI through automated, collaborative assessments, programmatic controls embedded at the data layer, and continuous monitoring of risk across the business. As AI operates 24/7, governance must provide ongoing visibility into an organization's risk posture, rather than relying on point-in-time reviews.

The result is a growing disconnect between AI adoption and governance maturity. While organizations are investing heavily in AI, many have yet to modernize the governance, operating models and cross-functional accountability needed to support it. Closing this gap requires governance that operates at AI speed, enabling organizations to innovate with confidence while managing risk and maintaining trust.

Building the right foundations

One of the biggest misconceptions about AI governance is that it slows innovation. In reality, strong governance is what gives organizations the confidence to innovate, enabling them to deploy, scale and adapt AI responsibly.

The real shift isn’t purely technological or organization, it’s the combination of both. While AI is driving business transformation, organizations also need the right technical foundations to support it. AI can no longer be viewed as a technology initiative owned solely by IT.

Successfully scaling AI requires business leaders, risk teams and IT to work together, with IT embedding programmatic guardrails and controls that reflect business requirements. Without this partnership, organizational governance alone won't be able to keep pace with the speed and complexity of AI.

The organizations that gain the greatest competitive advantage won't necessarily be those that adopt AI first. They'll be the ones that recognize AI success depends as much on leadership, governance and organizational readiness as it does on the technology itself.

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When a critical vulnerability alert lands in a traditional IT environment, it’s rarely a cause for panic regarding the operational continuity of the business. The affected laptops, servers, and applications can be identified quickly, and a good team can catalogue and patch them within hours if it’s urgent. The priorities are clear, and there’s very little guesswork involved.

Now picture the same alert landing across a hospital's imaging equipment, a factory floor's control systems, or a building's HVAC network. These cyber-physical systems (CPS), the connected devices that run physical operations rather than just processing data, sit at the sharp end of IT and OT (operational technology) convergence.

But unlike traditional IT assets, confirming whether that alert even applies to a specific device can take days, and often ends in a guess rather than an answer.

While this kind of uncertainty would be considered a failure of basic hygiene, for cyber-physical systems, it’s unfortunately much more often the norm.

So why is this such a widespread problem for CPS, and how can security teams get these vital assets back in line with their IT network?

Bad visibility into cyber-physical systems is worryingly widespread

The inability to manage incoming vulnerabilities for CPS isn’t an outlier or worst-case scenario, which is especially concerning when these assets are at the heart of critical infrastructure like energy and healthcare.

The issue comes down to the product codes that help networks identify what CPS is in the environment, which is an essential part of identifying and applying security patches.

Across a dataset of 17 million cyber-physical assets, our research found that 88% failed to transmit an exact product code, and 76% sent a code that didn't match the vendor's own record.

It’s a side effect of the way these systems were initially designed and later integrated into modern IT environments. Programmable logic controllers (PLCs), medical scanners, and industrial sensors were engineered for decades of physical reliability, not for tidy digital labeling. Network identification was rarely part of the design brief, so the same device can report itself differently depending on which protocol or integration is asking.

We found a similar state of affairs when it comes to the operating systems behind the physical hardware. In our research, 41% of devices have no OS version available, and 24% have no OS name at all.

Without these details, matching a device to a known vulnerability stops being a quick database lookup or automated process, and becomes a guessing game or painstaking manual search.

Added to this, CVE advisories, the industry's standard mechanism for tracking vulnerabilities, are compiled from this same patchy vendor data. An official advisory can be just as incomplete as the network it's meant to protect.

Translating product code chaos into boardroom risk

This kind of blind spot adds another layer of concern to a leadership that is already anxious about threat visibility. Among 1,100 security leaders surveyed globally, 44% named understanding their organization's risk exposure as one of their biggest operational concerns, more than compliance pressure or budget constraints.

A further 45% said they were struggling to reduce cyber risk to their most important assets and processes, yet the connection between that struggle and an unreliable asset inventory is often missed entirely. Leadership sees the symptom, a rising sense that risk is unmanageable, without ever seeing the cause sitting underneath it.

This is where security and the business can end up talking past each other. Security teams that start describing the problem in terms of missing product codes and inconsistent naming conventions won’t get far.

Business leaders hear none of that; they hear only that risk cannot be quantified with confidence. Until those two conversations are connected, every risk register that a CISO presents upward carries an asterisk that nobody in the room can see.

Context is key to closing the gap

Resolving this issue starts with a shift in what visibility means. Knowing a device exists on the network is only half of the job. Knowing what it does, what process depends on it, and what happens if it's compromised is what actually makes a risk register useful.

Achieving this shift at scale requires specialized tools to manage the often eclectic and proprietary nature of CPS assets, and an automated approach to cope with the scale.

In one example, applying AI-driven mapping techniques to an OEM's device catalogue lifted product code identification from 4% to 83%, turning a near-blind spot into a near-complete picture. The follow-through mattered just as much, with 56% of devices receiving a new or updated firmware recommendation as a result, and vulnerability identification accuracy improving by 25%.

Numbers like these matter because they change the question security teams can answer. Instead of asking what's connected to the network, teams can ask which systems would cause the greatest disruption if compromised, and act on the answer with confidence rather than inference. That is the difference between an asset inventory that exists on paper and a resilient one that holds up under pressure.

Fixing the foundation, not just the alarm

None of this gets solved by adding another tool to the stack. Instead, it means treating asset data quality as a board-level risk issue rather than background IT housekeeping, with the same scrutiny applied to budgets, compliance and third-party access.

Achieving this means a new CVE alert no longer triggers a scramble to work out which critical devices might be affected, but the confirmed, prioritized response you’d expect from any good vulnerability management program.

The alert landing on a hospital's imaging equipment or a factory floor's control systems should be no harder to act on than the one landing on a laptop. Getting there starts with making the invisible visible.

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• Forescout researchers showed AI can port RCE exploits to PLCs, achieving DoS and shellcode execution
• Effort required heavy researcher input and $500+ in API usage, making attacks impractical for criminals
• Nation‑state actors remain a concern, as seen in Sandworm’s 2025 attack on Poland’s power grid

If you are worried cybercriminals will use Artificial Intelligence (AI) to automate the discovery and exploitation of zero-day vulnerabilities in Operational Technology (OT) such as Programmable Logic Controllers (PLC) you can sleep peacefully, at least for a little longer.

Recently, security researchers from Forescout set off on a simple mission - to understand if crooks can use AI to target the ever-increasing population of exposed industrial devices. The short answer is “yes, but it’s not yet worth the trouble”.

In their mission, they launched an experiment - to port a remote code execution (RCE) vulnerability from one PLC to another. These devices were built on closed-source software and thus were not that easy to manipulate, yet the experiment was a success.

Yes, but...

Not only did they manage to trigger a Denial of Service (DoS) state that crashed the device but ended up with a working RCE capable of executing attacker-supplied ARM shellcode.

It is indeed a worrying development, but one that comes with a huge “but”:

“It required significant researcher input. The final RCE development stage consumed more than $500 in API usage. An attempt to extend the exploit beyond the initial RCE ultimately bricked the PLC,” the researchers said in the report.

“These limitations taught us valuable lessons about AI-assisted exploitation in OT. It can be done, but it’s not as easy as it sounds. For now, the difficulty, cost, and specialist expertise required are likely to make this kind of attack less attractive than easier alternatives.”

In other words, cybercriminals still have easier avenues to explore, and as long as that is the case, OT is relatively safe. What the report, unfortunately, does not discuss, is nation-state attackers with significant resources. For such attackers, industrial devices are a prime target, and spending $500+ in API usage is a drop in a bucket. We’ve already seen it back in 2025 when Sandworm struck Poland’s electricity suppliers.