OpenAI has closed its Sora video generation app just months after launch, highlighting a gap between technical capability and sustained user demand.

What Happened?

OpenAI has confirmed it is shutting down both the Sora consumer app and its associated web platform, bringing an end to its short-lived push into AI generated video as a social experience.

In a message shared on Twitter, the Sora team said: “We’re saying goodbye to Sora. To everyone who created with Sora, shared it, and built community around it: thank you.” The company added that “what you made with Sora mattered, and we know this news is disappointing,” signalling an orderly wind-down rather than a sudden withdrawal.

The decision also includes the end of OpenAI’s partnership with The Walt Disney Company, which had aimed to bring licensed characters into AI generated video.

A Strong Launch That Quickly Faded

Sora launched to significant attention, driven by its ability to generate realistic video and audio from simple text prompts. Early demonstrations suggested it could produce content that appeared close to professionally created footage.

Initial adoption reflected this interest. The app reached one million downloads faster than ChatGPT and climbed to the top of app store rankings within weeks of release.

However, that momentum didn’t last. Downloads declined sharply in the months following launch, with reports indicating a drop of more than 40 per cent by early 2026. User spending and engagement followed a similar pattern.

Despite millions of installs, the app generated relatively limited revenue, highlighting a disconnect between curiosity and long-term use.

Why Users Left

The central issue appears to have been retention rather than capability.

Sora offered impressive outputs, but struggled to establish itself as a daily habit. Its social feed, designed to showcase AI generated clips in a format similar to short-form video platforms, didn’t develop into a sustained engagement channel.

Concerns around misuse also played a role. For example, the platform faced criticism over deepfakes, non-consensual imagery and the use of copyrighted characters. These issues required tighter controls, which in turn reduced the flexibility that had initially driven interest.

At the same time, questions remained about the value of AI generated content without a clear human origin. Even where the visuals were convincing, it often lacked the context or meaning that drives engagement, and in some cases contributed to a wider sense of low-value, mass-produced content.

A Strategic Shift Away From Creative Tools

OpenAI has said the decision to close Sora will now allow it to focus on other areas, particularly robotics and more practical AI applications.

The company is increasingly directing resources towards systems that can perform real-world tasks, as well as agent-based tools capable of acting with a degree of autonomy.

This reflects a broader recalibration, and it seems that while AI generated media attracted significant attention, it has proven harder to turn into a reliable product category with strong user retention and monetisation.

The closure also suggests that OpenAI is prioritising areas where AI can deliver measurable utility, rather than relying on novelty or entertainment value alone.

The Wider AI Market

Sora’s lifecycle offers a useful case study in how AI products are evaluated in practice. While the technology itself was widely seen as impressive, that alone wasn’t enough to sustain the platform. Adoption actually depends on whether users find ongoing value, not just initial interest, and products that fail to become part of regular workflows or habits are, therefore, unlikely to justify continued investment at scale.

The decision also highlights the growing importance of trust, safety and intellectual property in AI driven platforms. These factors can directly affect both user behaviour and commercial viability.

At the same time, competition in the AI video space continues to increase, with other platforms exploring similar capabilities. This suggests the technology itself will persist, even if specific products do not.

What Does This Mean For Your Business?

For UK businesses, this development underlines the importance of focusing on practical outcomes when evaluating AI tools.

Impressive demonstrations can generate interest, but long-term value depends on whether a solution improves productivity, reduces cost or enhances customer experience in a measurable way.

It also reinforces the need to consider governance and risk. Issues such as content ownership, misuse and regulatory compliance are likely to shape how AI tools can be deployed in real-world settings.

The fate of Sora is also a reminder that not every high-profile AI launch will translate into a successful product. Organisations that assess new technologies based on sustained usefulness, rather than initial hype, are more likely to make sound investment decisions as the AI landscape continues to evolve.

Google has introduced a Gemini-powered dark web intelligence service designed to help organisations identify real cyber threats faster by filtering vast volumes of online criminal activity into relevant, actionable insights.

What’s Been The Problem With Dark Web Monitoring?

Security teams have long relied on dark web monitoring tools to detect leaked data, stolen credentials and early signs of attack activity. These tools typically scan forums and marketplaces using keywords linked to a company’s name, domains or assets.

The problem is not a lack of data, but the opposite. Most tools generate large volumes of alerts, many of which are irrelevant or duplicated, creating a high level of noise that slows down response times.

Google has highlighted this issue directly, noting that “most threat intelligence teams have plenty of data, as they’re inundated with thousands of false positives that can all too easily obscure the threats that matter most.”

How Gemini Changes The Approach

The new capability, delivered through Google Threat Intelligence, Google’s enterprise platform for tracking and analysing cyber threats, uses Gemini to analyse millions of dark web events each day and identify those that are relevant to a specific organisation.

Instead of relying on static keywords, the system builds a dynamic profile of a business, including its operations, structure and digital footprint. This allows it to detect threats even when attackers avoid naming a target directly.

Google explained that the system “uses Gemini to autonomously build an organisational profile that is specific to your business operations and mission,” enabling it to adapt as the organisation changes over time.

From Alerts To Context And Explanation

A key difference in this approach is the shift from raw alerts to what Google describes as “reasoned answers.”

For example, rather than simply flagging suspicious activity, the system explains why a particular event matters and how it connects to the organisation. This is designed to help security teams make faster, more informed decisions without needing to manually investigate every signal.

Internal testing suggests the platform can analyse millions of external events daily with up to 98 per cent accuracy, significantly reducing false positives compared to traditional tools.

Responding To An AI Driven Threat Landscape

The launch reflects a broader change in cybersecurity. Attackers are increasingly using AI tools to research targets, identify vulnerabilities and craft more convincing phishing campaigns.

This creates a situation where defensive tools must operate at similar speed and scale. Google has positioned its new service as a way to give security teams an advantage in what it describes as an increasingly automated threat environment.

The company said the goal is to “translate vast dark web data into precise, relevant insights delivered at the speed of AI,” helping organisations act earlier in the attack lifecycle.

A Push Towards Automated Security

The dark web monitoring service is one element of a wider strategy focused on what Google calls agent-driven security operations.

Alongside this launch, the company is introducing AI agents that can investigate alerts, gather evidence and provide verdicts within security workflows. This reflects a move away from manual analysis towards more automated, intelligence-led defence.

At the same time, Google has stepped back from consumer-focused dark web tools, instead prioritising enterprise systems that provide clearer and more actionable outputs.

What Does This Mean For Your Business?

For UK businesses, this signals a change in how cyber threats are detected and prioritised.

Traditional monitoring approaches that rely on keywords and manual analysis are likely to become less effective as attackers adapt and avoid obvious identifiers. Systems that can understand context and connect indirect signals will become increasingly important.

There is also a clear operational benefit. Reducing false positives and focusing on relevant threats can help security teams respond faster and use resources more efficiently, particularly for organisations without large in-house teams.

However, reliance on AI-driven intelligence also introduces new considerations around trust, oversight and data handling. Businesses will need to ensure they understand how these systems make decisions and how sensitive information is used within them.

It seems that cybersecurity is increasingly moving towards automated, context-aware systems that operate at scale, and organisations that adopt these capabilities early will be better positioned to keep pace with increasingly sophisticated threats.

A technical issue on the UK’s company register may have exposed personal data linked to millions of businesses.

The problem affected Companies House, which holds records for over five million UK firms. A system fault reportedly allowed certain details, such as names and contact information, to be accessed or surfaced in unintended ways.

Companies House said it has fixed the issue and is investigating, though the full scale of exposure remains unclear. The incident adds to ongoing concerns about how publicly available company data can be misused, particularly when combined with other sources.

For businesses, the key step is to review what information is publicly listed, ensure it is accurate, and remain cautious of unsolicited contact referencing company data. Monitoring for unusual activity and strengthening verification processes can help reduce risk.

A London startup is using AI engineered enzymes to break down one of the world’s toughest plastics and turn it back into high quality raw materials, offering a potential route to large scale circular manufacturing.

Why Nylon 6,6 Has Been So Hard To Recycle

Nylon 6,6 is a high performance synthetic plastic made from petroleum based chemicals, engineered to be exceptionally strong, heat resistant and durable. It is widely used in products that need to withstand stress and high temperatures, including sportswear, carpets, car airbags and industrial components.

However, those same properties have also made it extremely difficult to recycle. Traditional mechanical recycling degrades the material, while chemical recycling often requires clean, single source inputs and high energy processes. As a result, less than one per cent of nylon 6,6 is typically recycled at end of life.

This has left industries reliant on virgin petroleum feedstocks, locking in both cost volatility and significant carbon emissions.

How Epoch Biodesign’s Technology Works

Epoch Biodesign has developed a process that uses AI designed enzymes to break nylon 6,6 back down into its original building blocks, known as monomers.

Rather than using whole biological systems, the company deploys a cascade of highly specific enzymes, each targeting a particular chemical bond within the polymer. This allows the material to be deconstructed step by step into adipic acid and hexamethylenediamine, the same inputs used to produce new nylon.

More Than 90 Per Cent Of Original Material Recovered

The process recovers more than 90 per cent of the original material and produces output that meets virgin quality standards. As the company explains, “we produce textile grade recycled nylon 6,6, suitable for the most demanding fibre applications,” enabling direct reuse without changes to existing manufacturing processes.

From Waste To Feedstock At Industrial Scale

A key advantage of the approach is its ability to handle real world waste streams. For example, most discarded textiles are blends, often combining nylon with elastane, coatings or other fibres that make them unsuitable for conventional recycling.

Epoch’s system processes mixed inputs and separates the chemistry at a molecular level. According to the company, “we accept nylon 6,6 from a wide range of mixed waste streams, regardless of form, colour, or composition,” removing one of the biggest barriers to scaling textile recycling.

The process also operates at low temperatures and standard pressure, reducing energy use compared to traditional chemical methods. This creates a pathway to lower cost and lower emission recycling at scale.

Why Investors And Industry Are Paying Attention

The company has raised more than $50m in total funding, including a recent $12m round backed by apparel brand lululemon and climate focused investors. It is also working with Invista, one of the world’s largest nylon producers, to develop recycled nylon at commercial scale.

This level of backing indicates a clear commercial opportunity. Nylon feedstock prices have recently seen sharp increases, driven by volatility in petrochemical markets. By using waste as its input, Epoch’s model is less exposed to these fluctuations.

Founder Jacob Nathan has framed the shift in simple terms, describing waste textiles as a new resource rather than a problem, with the company’s process designed to “transform waste into recycled, drop in materials at low temperatures and low cost.”

A Growing Field Of Enzymatic Recycling

Epoch is part of a wider movement applying biology and AI to plastic recycling challenges.

Companies such as Carbios (in France), have developed enzyme based processes to break down PET plastics used in bottles and packaging, and are now scaling industrial facilities, while Samsara Eco, based in Australia, is also using engineered enzymes to recycle mixed plastics and textiles, including nylon blends.

What sets Epoch apart is its focus on nylon 6,6, which has historically been far more difficult to recycle than PET, and its ability to process mixed and contaminated inputs.

What This Means For Materials And Manufacturing

This development highlights a broader shift in how materials are produced and reused. Instead of relying on fossil resources, manufacturers could increasingly source feedstock from waste streams.

For sectors such as fashion, automotive and industrial manufacturing, this offers a way to reduce both emissions and supply chain risk without compromising material performance. The ability to produce “drop in” replacements is particularly important, as it avoids the need for costly redesign or requalification of products.

At the same time, it highlights the growing role of AI in industrial chemistry, where it is being used to solve problems that were previously too complex or slow to address through traditional research methods.

What Does This Mean For Your Organisation?

For UK businesses, this signals that circular materials are moving closer to commercial reality, particularly in sectors that rely on high performance plastics.

Companies involved in manufacturing, product design or supply chains should begin assessing how recycled inputs could be integrated into their operations, especially where sustainability targets or regulatory pressures are increasing. Technologies that deliver virgin quality materials from waste are likely to gain traction quickly once scaled.

There is also a strategic opportunity to reduce exposure to volatile raw material markets. Processes that decouple production from fossil fuel inputs offer greater pricing stability and long term resilience.

This story highlights how waste is now increasingly being treated as a resource, and businesses that adapt early to circular supply models should be better positioned as these technologies move from pilot to mainstream industrial use.

Microsoft’s new Copilot Excel Agent can generate fully structured spreadsheets for you based on a simple prompt, and this video shows how it can build tables, apply formulas and organise data in seconds instead of starting from scratch.

[Note – To Watch This Video without glitches/interruptions, It may be best to download it first]

Both Microsoft 365 and Google Workspace automatically save previous versions of files, so you can quickly restore an earlier version if something is overwritten, deleted or changed by mistake.

Why This Matters

It is easy to accidentally overwrite a document, delete key content or save unwanted changes, especially when multiple people are working on the same file.

In many cases, users assume the work is lost and start recreating it from scratch.

Version history allows you to go back to an earlier version of the file, often within seconds, without needing backups or IT support.

This feature is built into modern cloud platforms and works automatically in the background.

How To Use Version History In Microsoft 365

1. Open the file in Word, Excel or PowerPoint (desktop or web).
2. Click the file name at the top of the window.
3. Select Version history.

You will see a list of previous versions with timestamps.

4. Select a version to preview it.
5. Click Restore to revert to that version, or save a copy if needed.

How To Use Version History In Google Workspace

1. Open the file in Google Docs, Sheets or Slides.
2. Click File.
3. Select Version history, then See version history.

You will see a timeline of changes on the right-hand side.

4. Click on a version to preview it.
5. Select Restore this version if you want to revert.

What To Know

– Version history works automatically for files stored in OneDrive, SharePoint or Google Drive.
– Multiple versions are typically retained for a period of time, depending on settings.
– You can often see who made changes and when.

A Practical Approach

If a file is changed unexpectedly, check version history before trying to fix it manually.

It takes seconds to access and can save significant time by restoring a clean version of your work without starting again.