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Since 1998, we've been enjoying the feature films and TV shows of one Guy Ritchie. A man who arguably defined the British gangster sub-genre, cult hits like Snatch and Lock, Stock and Two Smoking Barrels have stood the test of time.

Fast-forward to 2026, and streaming hits like Netflix's The Gentlemen and Paramount+'s MobLand are all anybody wants to talk about... and rightly so. And that's all without scratching the surface of the projects Ritchie has worked on in between.

But can you fill in the gaps? I've created the ultimate quiz for any and all Guy Ritchie fans, focusing on his feature-length movies and TV shows over the last three decades.

I didn't say it was going to be easy, though... so do your worst.

Of course, all of this is up to the time of writing — we already know that we've got plenty more Ritchie projects to look forward to.

Young Sherlock season 2, The Gentlemen season 3 and MobLand season 3 have all either been greenlit or are in development. There's a few more titles to add to this list... but that would be giving away the quiz questions.

Much like Ritchie-style gangster, I'm just not that nice.

• French streaming service Qobuz is introducing tags to denote AI-slop music
• The firm also shared stats on how many people engage with this kind of audio
• Meanwhile, Spotify wants you to use its new AI agent to listen to music

When AI-made music is covered in the media, it's often painted to be an insidious success story; reports on artists including Velvet Sundown, Breaking Rust and Enlly Blue invariably mention high stream numbers, along with huge estimated revenues. If that rightly frustrates you, know that the problem is occasionally with the reporting, and not with AI music being popular.

French streaming service Qobuz has never been backwards about coming forwards when tackling what Managing Director Dan Mackta refused to call music at all, when we spoke to him earlier in the year. And now, Qobuz has unveiled a feature which adds tags to music it detects was generated with AI. This goes further than Spotify's opt-in AI persona label, by highlighting real artists' slop tracks. So expect to see it on Dr. Dre's new music, for example.

This continues a run of anti-AI moves from streaming services such as Qobuz, Deezer and Tidal, which have all identified sentiment to slop at being overwhelmingly negative. Highlights include Tidal giving no royalties to AI music, and Deezer adding a filter to remove AI tracks.

The major outlier is Spotify, which has made small steps to combat AI music, but some steps backwards too. Recently, it teamed up with Meta's AI agent Muse (no, not the Plug In Baby band, the chatbot that Meta kicked the band off its social handles for). This partnership lets you... queue up music, give voice commands to control your playlists, create playlists, and set up playing schedules. How did we cope before AI?

But perhaps the war against AI is all for naught? Because when unveiling its new AI tag system, Qobuz also shared some statistics on this kind of 'music' (word used lightly), which recontextualize the whole thing.

Case in point: apparently only 0.38% of streams on Qobuz are from AI-generated tracks. Yes, less than half a percent. For every 263 streams, only one comes from an AI track. If all the streams were represented by the population of the US, team AI would just about fill up Dallas.

There's more: over a third of all AI albums receive not a single listen. Not even from their creator. That's about four million songs, apparently, all of which are created just to take up space (and have been removed from Qobuz's library, so if you missed them, it's too late).

And if that wasn't enough, apparently 60% of all AI-music is demonetized by Qobuz's anti-fraud systems. This is based on the brand's AI Charter, unveiled in February, which detects AI music created solely for financial gain.

Qobuz is the original hi-res music service and as the connoisseurs' choice (devoid of many extra AI add-ons), one could argue that very few users are hitting up Qobuz for AI tracks, but still, the figures are telling. According to these Qobuz stats, you're largely all avoiding AI slop — well done you, take a pat on the back — and sticking to real music made by real people. Well, mostly. Looking at you, 0.38%...

You will almost certainly have heard many business and technology leaders extolling the virtue of AI. They will enthusiastically tell you that the technology will have a transformative impact on businesses across the globe.

I think they are right. However, with two important caveats: AI must be given the right jobs, and the people using it must trust what it does.

Too often, AI is deployed across a business without much thought, as overconfident bosses assign agents tasks it isn't designed for. The problem this creates is that many employees using AI tools every day are not yet prepared for their new role in managing these agents. Trust only comes from experience and that builds over time.

When an AI system provides you with results that make sense and actually helps with your work, people start to feel more confident in using it. But if it makes a mistake, that confidence can be lost in an instant. If things go wrong, employees start finding ways around AI or stop using it altogether, and when leaders ignore employee concerns about making AI work better, its benefits are undermined.

The best way to ensure that AI delivers the efficiencies it promises is to ensure that it is doing the right job, and this isn't as straightforward as it sounds.

AI works on probabilities, producing answers that are likely to be right. The key word here is ‘likely', which is why it occasionally hallucinates.

Being probabilistic makes AI very effective when dealing with unclear or complicated information. But it's not the best fit for tasks where a wrong answer could have serious consequences.

There are three core areas where AI really can make a difference. Firstly, when processing documents; secondly, decision support; and finally, personalization.

AI is especially effective at tasks involving many documents. It can extract, classify and summarize information that would normally take humans many hours to review. For example, an AI system could compare contracts against a standard set of clauses, or organize a large collection of customer emails by the issues they concern. This way, a person only has to look at the important findings instead of reviewing entire documents.

AI can also play a key role in supporting company executives to make decisions, as it's very good at taking lots of different pieces of information, finding patterns and delivering options as to what to do next.

It is, however, important to remember that AI is not always the right technology to make that decision. For example, imagine you're buying something and have to choose among many different suppliers.

AI can look at all the information, like how well they've done in the past, what they can deliver, how quickly it is likely to arrive and whether the supplier offers discounts for repeat purchases. It can even articulate why one option might be better than another. In most scenarios, though, it is the human who makes the final choice.

For some businesses, personalization is a promising tool, as AI can be used to make products more relevant to the individual using them. This is an area where working with probabilities can become a key driver for AI. The content only has to be good enough to make that connection so the recipient feels they are being addressed in a bespoke way.

One of the key concerns companies should have about managing AI is that while AI-generated answers can sound assured, there is still a possibility that the technology has got something wrong. If that answer is allowed to trigger action without validation, a small error can travel rapidly through a workflow.

Think about what happens when someone makes a small mistake with a rule or detail in a contract. By the time someone catches the error, it may have already been added to records, sent to people outside the company, or even used to create new software.

To remind myself of AI’s limitations, I find it useful to think of the technology as the equivalent of a super-intelligent, hard-working intern. Interns need guidance and oversight, which should be provided by experienced, knowledgeable colleagues. Crucially, their access to sensitive information needs to be limited and granted only as they demonstrate they can make sound decisions.

AI needs to be managed in the same way. It should only be given greater levels of control when it has proved itself, and then its work must still be monitored by humans.

A good example of the importance of managing AI is its role in software development. AI can deliver a lot of code quickly, but its fast pace means the quality isn't always the same as that of human developers.

In fact, managing large amounts of AI-generated code can be a real headache for IT professionals. For example, surging token consumption and a shift to consumption-based pricing is ballooning AI coding costs, forcing developers to be selective on when and where they apply AI models, as reported by Gartner.

The most important design decision is where AI is allowed to reason. The riskiest place is deep in the implementation layer, where a small mistake can cause big problems that are hard to fix.

I believe a sound approach is to use AI to work with business ideas and rules. This way, it's easier for people to check and understand what the AI is suggesting.

Domain-specific languages and other abstractions used in model-driven development can provide that separation. AI helps create or refine a high-level application model, while a developer then validates the model before a deterministic platform transforms it into executable software.

AI can suggest how something should be done, but it doesn't have to be responsible for executing every step.

Abstraction helps developers, and in agent-driven systems it becomes a control tool. It simplifies complex systems and prevents small errors from becoming big problems, making AI output easier to test and fix.

As AI becomes more prevalent, developers' roles will evolve. They will still write code, but a larger part of their role will be overseeing systems, checking outputs and catching mistakes, a change that brings with it the opportunity to learn new skills.

Developers will increasingly work at the level of systems and architecture, understanding the business rationale, recognizing patterns and judging whether AI-generated code fits the wider application and holds together.

One company that has innovated in this area and is already reaping the rewards for their experience is Ford. They recently hired 350 experienced engineers to help train younger colleagues and improve their AI and automation tools.

The shift was driven by the fact that the AI tools weren't producing the results they wanted without the expertise brought by seasoned professionals. Now, these experienced engineers act as internal auditors, reviewing designs and finding potential problems before they become major issues on the factory floor.

When you combine AI's ability to recognize patterns and process information with human knowledge and experience, it becomes a much more powerful tool. This combination is what makes it truly effective, not just relying on one or the other.

Enterprises should also resist the pressure to deploy agents everywhere simply because competitors appear to be doing so. When it comes to using AI, leaders need to think carefully about each situation.

They should ask themselves three important questions. First, are they asking AI to only suggest what to do or to actually do it? Next, can someone check and fix what the AI says before an error spreads? Finally, what would happen if the system is certain about something but is actually wrong?

The answers play a big role in deciding how much freedom to give the technology. For example, a tool that summarizes documents might only need a quick check, but a system that suggests business decisions needs someone's logged approval.

When companies start using AI for specific, practical tasks, it shows employees that it can really make a difference in their work. This approach gives the company a chance to get its data, rules and processes in order. If people can see that the system is working well, and they understand what it can and can't do, they start to trust it more. Over time, this trust grows.

The key is to create a work setup where humans and machines do what they're good at. AI is great at processing large volumes of information and surfacing patterns. People are good at understanding the bigger picture, pointing out mistakes, and taking responsibility for their actions.

The wrong job to give to AI is any task where a mistake carries real consequences, no one checks the work before it causes harm, or the system sounds certain while being wrong. Companies that respect this division will build the confidence to give agentic AI greater autonomy over time. Those who ignore it may discover that a single poorly chosen task can undermine their entire AI strategy.

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For decades, banking technology has been built around a simple sequence: a person makes a financial decision and the bank’s technology processes it. A customer decides to open an account, move money into savings or apply for a loan, and the bank’s systems execute that instruction.

Artificial intelligence can potentially reverse that sequence. Instead of waiting for a human to specify an action, AI can interpret the person’s goals, understand the financial context around that goal, and determine what happens next. It could recognize that a customer is likely to face a cash shortfall, identify the available ways to address it, and potentially execute the appropriate action.

McKinsey estimates that generative AI could create $200 billion to $340 billion in annual value for the banking industry. Yet much of the industry is adding AI to systems designed for the old model, not rebuilding the model around AI.

The problem is that AI inherits the same product silos and process boundaries. Banks gain another layer of technology, but not the cross-system decision-making needed to realize AI’s full potential.

The most visible applications of AI in banking are often the easiest ones to deploy. Banks are using AI tools to improve customer service, automate fraud detection, personalize recommendations, summarize documents and accelerate credit decisions. Lloyds Banking Group, for example, says more than 50 AI use cases were rolled out across the group in 2025, generating around £50 million in value, with more than £100 million in additional value expected in 2026.

McKinsey has made a similar observation based on the industry's experience with generative AI. Simply adding AI on top of existing processes will not produce transformational change and can instead create another layer of technical debt.

The difference between an AI-native architecture and a chatbot attached to an existing system can be tested with three questions.

• Is the AI following a fixed sequence of instructions, or can it choose and coordinate actions within a defined system of guardrails, trusted data sources and approved tools?
• Is the process designed for the agent to act, with human review and every decision recorded for analysis?
• And are permissions, monitoring and regulatory controls built into the workflow, particularly for critical functions such as compliance and fraud prevention?

If the answer is no, the company has added an AI interface without redesigning the underlying process.

The more significant transformation begins when the bank starts with the customer’s objective, not with the banking product.

Consider a customer who wants to maintain a certain level of liquidity while earning as much as possible on excess cash. An intelligent banking system could continuously monitor their balance, upcoming payments, income, available credit and other relevant information, then determine whether money should remain liquid, be invested elsewhere or be used to reduce borrowing.

While the system can continue making decisions as the customer’s circumstances change, that does not require customers to surrender control immediately. Adoption can begin with low-risk actions, such as moving excess cash into savings or setting aside VAT for future tax payments, before expanding into more consequential decisions.

This is already beginning to appear in financial institutions, although mostly in bounded applications. Deutsche Bank, for example, has deployed an agentic AI system for third-party risk management in which several AI agents retrieve relevant controls, analyze supporting documentation, and propose assessment outcomes. Human assessors remain responsible for reviewing or overriding those recommendations.

Like a new employee, an AI agent should receive defined permissions. Transparent activity logs, alerts, approval thresholds and the ability to override decisions would make that principle visible in the product and enforceable by regulators.

Under this premise, the bank becomes an intelligent execution layer that continuously manages financial activity to accomplish a defined objective.

This shifting structure is also visible outside traditional banking. Visa and Mastercard are both building infrastructure for AI-initiated payments, allowing agents to act on behalf of consumers and businesses. Visa Intelligent Commerce is designed to let AI agents find and purchase products on a user’s behalf, with tokenized credentials, authentication and spending controls built into the payment flow.

As agents move from recommending actions to executing them, banks will need to ensure transactions remain within the customer’s intent and risk tolerance. The institution remains responsible for keeping the agent within those limits.

Deloitte found that integration with existing systems and tools is the top modernization challenge for 77 percent of banking executives deploying AI, ahead of security, compliance and cloud interoperability concerns.

Traditional banking systems separate payments, lending, accounts and compliance across different applications. AI agents need to work across those boundaries, combining data and actions from several systems to make a single decision.

Banks therefore need an orchestration layer that allows AI to access those systems without requiring another custom integration for every use case. The core platforms can remain systems of record, while more of the decision-making happens above them.

This gives AI-forward fintechs such as Revolut or Ramp, as well as new entrants designing their infrastructure from scratch, an advantage over institutions that must retrofit deeply embedded systems. If regulation remains broadly unchanged, the first major financial institution built around continuous decision-making could emerge within five years.

It may not be a bank in the strict regulatory sense, but it could perform an increasing share of a bank’s functions, allowing customers to manage their finances.

To thrive, I believe banks need to become institutions organized to make continuous decisions for the customer’s benefit, not simply to follow instructions. And when it comes to AI, they must stop treating it as a fancy tool to add and start treating it as something to build around.

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• Amazon is re-hiring former workers back into AI and cloud roles
• The company is investigating the impacts of its office-working mandates
• Despite 30,000 layoffs in the past year, Amazon continues to hire for certain roles

Amazon is reportedly trying to re-hire old workers, including many who were affected by previous rounds of layoffs.

Recruiter emails cited by Business Insider reveal that rather than filling roles that have emerged following layoffs, the company is actually targeting specific ex-workers it seems, with the company's cloud computing business predominantly seeing these hiring efforts.

AWS, cloud computing, AI, ML and agentic AI are apparently the focus of these efforts, with one recruiter reportedly referring to the scheme as 'Swami's Boomerang Reengagement Initiative' – Swami Sivasubramanian is a VP for AWS Agentic AI.

The company is also said to be making it easier for former employees to return, offering shortened interview processes that may skip over some of the screening steps that aren't all that necessary.

Besides trying to attract former talent, Business Insider also reports that Amazon is digging deeper into why they left, and the potential impacts of its return-to-office (RTO) mandates. Specifically, the tech giant wants to know whether the RTO changes were behind some ex-workers' departures.

While company spokesperson Haley Silva told the publication that Amazon isn't specifically targeting people who left because of the RTO and that its in-office expectations haven't changed, it's an interesting perspective from a company that went pretty much all-in on the RTO years ago in a bid to drive more in-person collaboration and to boost productivity.

All of this comes in the wake of major ongoing layoffs – the company fired around 30,000 workers just in the past year. But it's apparently all part of a broader shift in focus, because instead of cutting headcount as aggressively as that 30k figure suggests, Amazon's more likely looking to lose workers in certain areas of the business and gain workers in more important areas.

• A bug in Windows 11's September update broke File History
• Microsoft admitted the backup feature was scuppered and has apparently now fixed it
• However, the cure is in an optional update, so it might be best to wait for the full release of the upgrade next month

Microsoft has admitted that Windows 11 has a bug that messes up a backup feature in the OS, although a new optional update fixes it (but not quite fully).

Windows Latest reports what's been a slightly odd tale in that the bug in File History has been kicking about since the September update first emerged over two weeks ago, but Microsoft didn't acknowledge it — even though it was a widely reported issue.

File History is a feature that enables you to keep files backed up to an external drive, and while it doesn't see a whole lot of use these days, it's obviously important for those who do still avail themselves of the functionality.

Microsoft informed Windows Latest that: "After installing the September 2026 Windows security update KB5124008, some customers using File History, might be unable to create or update backups. File History is used to back up files to an external drive or network location."

The good news here is that the latest September update, which is optional — it's a preview of the October update in fact — does fix this bug, as confirmed by Windows Latest, but it seemingly doesn't fully iron out every issue with File History.

Windows Latest noticed a post on Microsoft's Learn portal from a user who confirms that the new September preview update does mean that File History is working again and making backups, but there still appears to be an issue with the 'Run now' ability and problems with synchronization of save times therein.

However, this isn't a bug caused by Microsoft's recent work on Windows 11, and in fact this glitch dates back to a Windows 11 update in July 2026.

At least File History appears to be cured of this latest feature-breaking bug, and hopefully the other problem highlighted on Reddit will be fixed before too long.

You don't necessarily want to download an optional update, of course, as it may bring in other bugs — after all, it's still a beta. The safest bet is to wait for the October update to arrive in two and a half weeks (on October 13) and then install that as the remedy for your File History blues (while temporarily keeping backups a different way, manually or on OneDrive, in the meantime).

Windows 11 bugs have remained a problem throughout 2026, and every year before for that matter (particularly with the 24H2 release). Microsoft still needs to do better in this regard considering this is one of the thornier issues that has continually dogged its OS, which it's busy fixing this year.

The trouble with the long-running overly buggy nature of Windows 11 is that it's making some users hold off updating the OS, and I don't mean for just a week or two. (I'd advise a week's breathing room in general myself, just to see if there are any major bugs or nasty showstoppers with any given update).

For example, this Redditor notes: "I am once again advising everyone to disable Windows updates. Let people test the bugs for a few months, then update. Your PC isn't gonna implode because you are 3 months behind on Windows 11 versions."

This was in reply to a comment which simply asked: "Is there a Windows 11 core feature that hasn't been broken at least twice since release?"

Cynicism prevails around Windows 11 updates in many corners of the web these days, and I think this is something Microsoft needs to act on. Yes, Windows is a very complex operating system that needs to cope with umpteen PC and device configurations, and a labyrinth of legacy features that must be maintained for a niche set of customers, but still — that's ultimately not an excuse.

Something needs to be done to improve QA and testing, otherwise it's going to be difficult to declare that 'Windows 11 is fixed' next year, which is Microsoft's goal (presumably).

• Diablo 4 game director Brent Gibson says, "it's about damn time" the game comes to Nintendo Switch 2
• He says there were moments when the team thought it "impossible," but they managed to overcome the issues
• Gibson admits there are some "concessions," but they're barely noticeable is very happy with the result

Diablo 4 is finally available on the Nintendo Switch 2, and while porting the game to the handheld console came with some hurdles, game director Brent Gibson is quite pleased with it.

"First off, it's about damn time," Gibson said during a press Q&A at BlizzCon that TechRadar Gaming attended. "We've wanted it on the Switch for a very long time. It's a big game. I think we've had moments where [we thought] it's impossible. There's no way we could do it. But the team that took that on? Boy, did they do an amazing job."

Gibson went on to explain how the team optimized the game for handheld, admitting that there were some issues it had to work around, but overall the team delivered, and it was worth the wait.

"The concessions we had to make are barely noticeable. I'm so impressed," he said. "There are some things that we have to do for performance, obviously, but I think the expectation once you're going to handheld, we nailed it; it's locked.

"It's fluid. It's easily readable. I love the Switch's screen, and so it pops very nicely on that screen. It was challenging. But ultimately, I think it was worth the wait for how much work we had to put into getting it in there."

Blizzard just kicked off Diablo 4 - Season of Hell's Legacy, the game's 15th season, which arrived on September 15. Fans can also look forward to Diablo 5, which is slated to launch in 2029.

However, Gibson has put concerns to rest and stated outright that Diablo 4 players don't need to worry about the game's future because of Diablo 5's announcement.

"If you're wearing the sign in the middle of Times Square that says 'The End is Nigh', the end is not nigh," he said. "We have a lot coming."

• Gambit researchers uncovered ongoing AI‑driven skimming campaign stealing 600,000+ payment records since July 2026
• Attackers used three autonomous harnesses (Strix, Cairn, Hermes) to compromise dozens of retail sites cheaply
• Victims include major US firms; campaign shows AI enables faster, persistent, low‑cost cyberattacks at scale

In July 2026, a hacker tasked autonomous AI agents to attack retail organizations around the world, deploy credit card skimmers, and steal payment data.

Since then, the bots launched hundreds of attack projects, compromised dozens of organizations, and stole at least 600,000 payment records - and to make matters worse, the campaign is still live, attacking and breaking into websites as we speak.

All of this was reported by security researchers Gambit, who said they managed to recover the operator’s staging server and through it - reconstruct the ongoing campaign. They also saw the skimmers live on victim websites, and sifted through logs and AI claims found on the attacker’s server. In just five days, between September 10 and 15, the agents made 105 attack waves and compromised 27 organizations “to varying degrees.”

Among the victims are a Fortune 500 hospitality company, a “major” US airline, a large private US industrial supplies distributor, and a US online fashion retailer. One of the AI tools would use a website ranking service to produce a list of potential targets, focusing primarily on those running custom-built software.

But the victims are not the “interesting” part of this story - the attackers are. Gambit believes they are financially motivated Chinese threat actors. They are using three AI “harnesses” (frameworks, essentially), which can run almost the entire attack chain autonomously, striking around 10 companies a day, for a handful of dollars per company.

In four weeks, the attackers spent around $7,000, meaning that their entire cost for the operation so far was no more than $18,000. Breaking it down, it means that the attacker spent around $25 per target.

“Spread over the companies attacked, this is a marginal cost of a few US dollars to a few tens of US dollars for each targeted company,” Gambit’s researchers said. “The operator’s own cost review gives a similar figure, a mean of $25.46 over 101 completed scans, from $3.13 for the cheapest target to $79.31 for the most expensive.”

“Where access was achieved, it usually took less than a day, and in many cases just a few hours. We also detected instructions in the attacker’s playbook that could disrupt the operations of a company as a result of data deletion or cleanup procedures run by the agent - and this has indeed happened in some of the breaches,” Gambit said.

The three harnesses are called Strix, Cairn, and Hermes.

Gambit describes Hermes as an open source autonomous AI agent with a persistent memory, skills that the agent wrote and edited itself, a searchable archive of past sessions, scheduled jobs, and a web console. On the staging server the researchers analyzed, it loaded a Chinese system persona called “SOUL - Red Team Operator”, which contained 121 skills (78 attack skills).

“Hermes is the operator’s console for orchestrating the activity and for direct hacking activities,” Gambit explained. “It used Anthropic’s opus-4.6 (after newer models refused its requests), with 1,951 prompts typed by the human across 260 sessions - only a few prompts per target. The human prompts are short instructions in Chinese, usually launching an attack, tasking the agent with a general next step, or what to do next after achieving access.”

Strix is an open-source AI pentest tool, while Cairn is an autonomous pentest engine. It receives target domains and an objective, such as to get a shell or admin access, then runs for hours until it achieves the objective, times out, or is stopped. Cairn used DeepSeek v4.1 Flash, it was said.

Gambit’s researchers seem to be rather impressed with the campaign. They described it as very low cost, with a level of patience, persistence, and creativity that most human attackers would be “unlikely to sustain”, managing to achieve “far greater results, far faster.”

They have also called to arms, urging organizations to “adapt to a reality where attacks are significantly faster and more comprehensive.” To do that, they must adopt a resilience-first mentality and deploy a security stack that can match the AI on speed.

Many of the affected organizations were notified, and the skimmers were removed, they said.