❌

Normal view

Canon cameras just got a free update that unleashes immersive 3D photo capabilities — here's how

16 September 2026 at 13:57
  • Canon announced its 3D image generation 'Dual Pixel 3D' tech
  • It uses depth data from a single shot via Dual Pixel CMOS AF, through a free Windows-only app coming in October
  • The EOS R10 and most models up to the EOS R5 series are compatible

Canon introduced a new Dual Pixel CMOS-type sensor and autofocus system in the EOS R5 back in 2020, and a string of cameras have since inherited this tech, most recently the EOS R8 Mark II announced today. And it's these cameras that just got a free upgrade that can add another dimension to photos: Dual Pixel 3D.

The new software leverages "depth and phase-detection data captured in a single shot via Dual Pixel CMOS AF", says Canon, using "an exclusive algorithm to convert your photos into highly realistic 3D imagery", plus 3D models in OBJ or GLB formats, or as an MP4 video, "complete with rich photographic textures."

This depth-adding tech will come in the (free) Dual Pixel 3D Converter software, which will be available for Windows only (Windows 11 64-bit 24H2 or later), from October 3.

So what exactly can users expect from the software, and which cameras are compatible? Let's take a look.

An extra dimension

Billed as 'simple 3D image generation technology', Dual Pixel 3D enables users to create immersive three-dimensional images and models from a single RAW photo, without needing specialized equipment or viewing devices. Canon also says the technology works with JPEGs captured by the new EOS R8 Mark II.

These files contain depth information from slightly different views, captured by each pixel's two photodiodes, which are then converted into 3D shape data.

Users will be able to fine-tune the degree of depth in the new software, for a more pronounced 3D effect or a subtler appearance. The depth information in Dual Pixel CMOS-captured photos also simplifies background removal, should users want to output a subject onto a plain background as a 3D model, or as a stereographic photo.

Can has shared tips for how to take photos designed to be converted for 3D on its YouTube channel (see below).

Photographers working in product and still life photography stand to benefit from the creative possibilities in particular. We'll be sure to try out the new software when it's released on October 3, and share our experience of making 3D imagery with it.

Compatible Canon cameras and lenses

Here's every Canon mirrorless camera that features Dual Pixel CMOS AF and can, therefore, enjoy the new Dual Pixel 3D feature, provided images are captured with a compatible lens:

And you'll need to be using one of these lenses;

RF24-70mm F2.8 L IS USM

RF24-50mm F4.5-6.3 IS STM

RF24-105mm F4 L IS USM

RF24-105mm F4-7.1 IS STM

RF35mm F1.8 MACRO IS STM

RF50mm F1.8 STM

RF70-200mm F2.8 L IS USM

RF70-200mm F4 L IS USM

RF85mm F2 MACRO IS STM

RF100mm F2.8 L MACRO IS USM

RF-S18-45mm F4.5-6.3 IS STM

RF-S18-150mm F3.5-6.3 IS STM

DDR5 RAM pricing appears to be spiralling out of control even more — it's 5.5x pricier than before the memory crisis, and 2027 could get worse

16 September 2026 at 13:54
  • DDR5 RAM has hit a new pricing high in the German market
  • It's now 544% up on the price it was before the RAM crisis began
  • This inflation backs up predictions of the memory shortage worsening, and Dell has just hiked the price of one of its popular laptops, too

If you wanted a telling example of how bad the cost of RAM is getting, the latest report on DDR5 pricing makes it crystal clear – and it reaches us as Dell moves to hike the price of its affordable XPS 13 laptop (in a roundabout way).

I'll come back to the ins-and-outs of what's happening with Dell, but first the DDR5 price jump, which has happened over in Germany.

3D Center keeps tabs on the monthly pricing of DDR5 memory in the German retail market – some 20 products are covered in this largest market in Europe – and as Wccftech flagged, the report for September shows this RAM is now 544% up on the price it was in July 2025 (the baseline level before the crisis began).

This is a big leap from 486% where the DDR5 price index sat in August, and since June, when it was at 419% having remained flat for much of the year. This obviously represents a depressingly hefty additional toll on the wallets of PC buyers or those upgrading their memory.

Meanwhile, as Mashable noticed, Dell has effectively upped the price of its XPS 13 laptop in the US, without actually increasing the baseline cost – instead of doing that, the laptop maker has introduced a new entry-level model with less storage.

So, previously the XPS 13 started from $699 in the US for the model with 8GB of RAM and 512GB of storage. What Dell has done is brought in a new model with 256GB of storage at that $699 level, so while technically the cheapest XPS 13 is still at that price, you're getting a lower spec for the money.

The old baseline model with 8GB RAM and a 512GB SSD is now $799, so it's been hiked by $100 – a pretty nasty bump in cost that moves this notebook into a distinctly less affordable price bracket.

If you want the XPS 13 with 16GB of system memory (and 512GB of storage), that's also gone up $100 so it's now at $999. Do note that these are student-targeted laptops, and students get a $100 discount on the XPS 13, so you will pay less in that case. However, while $599 (with $100 off) remains a tempting price in this climate, 256GB storage is cutting it thin.

Analysis: as predicted, 2026 is turning much nastier with RAM

RAM sticks stacked on top of each other

(Image credit: Shutterstock)

The DDR5 price spike is truly worrying in my opinion, simply because it's an acceleration of an already worsening picture. Yes, it's only one retail market and a snapshot of the overall landscape as such, but the same pattern is seen elsewhere. Just look at Tom's Hardware's price tracker that shows 32GB of DDR5-5600 RAM in the US is now $409 (at the time of writing) compared to $72, which was the lowest price seen before the memory crisis.

That's over a 5.5x price increase in the US market (mirroring the German level of RAM inflation in this particular case). The end result is that what used to be a no-brainer of future-proofing any desktop PC – particularly a gaming rig – with 32GB of RAM is now a difficult choice to make, as sticking with 16GB is saving you $200 (or the regional equivalent). Bearing in mind that you're battling all sorts of other component inflation if you're building a new PC, too (most notably with the likes of SSDs and increasingly GPUs).

We were told that the second half of 2026 would see more RAM price misery, and these German market figures illustrate exactly that. Since June, buyers in the country have witnessed an effective 30% increase in the cost of DDR5 (when it was already hugely overinflated). It's worse than I imagined it could get, frankly, and I fear that the most pessimistic predictions – which also forecast a very rough 2027 – are very much on the money.

All these RAM price hikes are eventually reflected in the cost of laptops, of course, and the Dell XPS 13 provides an apt illustration here. Interestingly enough, I recently wrote about Dell's revelation of its new 14S laptop – which was shown off at IFA, but without any price tag being attached – saying that this made me nervous about how much the notebook might cost.

Given this XPS 13 hike, it's easy to see why Dell was hesitant, and to be fair to the company, it isn't the only manufacturer that's reticent to pin launch prices on incoming devices. This is becoming a trend because the memory shortage, and its knock-on effects, are making life difficult for all device makers.

Meta leak suggests camera-free smart glasses are on the way — as thousands download the 'Zuckoff' anti-smart glasses app

16 September 2026 at 13:46
  • New camera-free smart glasses from Meta are rumored
  • The wearable could be unveiled as early as next week
  • A new glasses-detecting app is proving popular with users

It's fair to say that the various iterations of Meta's smart glasses had been quite well received by users — as you'll see from our own reviews — but over time, the general public has become less and less comfortable with the idea of being secretly spied on through the cameras embedded in these wearables.

That might explain Meta's plans to release some spectacles with cameras on board, as reported by The Information. According to the outlet's sources, the glasses have the codename Luna, and will be available in two styles called Clubmaster and Burbank.

With no camera, the focus will be on the audio capabilities of the glasses, which will let you have conversations with Meta's own AI chatbot. The report says we could get a grand unveiling at the Meta Connect event next week, on September 23 and 24.

While Meta's smart specs have always been fitted with LED lights that show when they're recording, modders were finding ways around this, enabling more covert video and photo capture. While those mods have since been disabled and prevented by software updates, it's still left people with a general sense of unease.

The backlash continues

ZuckOff

The ZuckOff app on iOS (Image credit: ZuckOff)

As Meta apparently preps its next pair of smart glasses, Wired reports on the ZuckOff app that takes a swing at the Meta CEO in its name, and alerts users when they might be in the presence of someone wearing Meta glasses. The app uses Bluetooth to scan the surrounding area for these wearables.

With several thousand downloads on both Android and iOS, the app is clearly proving popular. It's free to download and use in its basic form, with a pro version available for $9.99 / £9.99 / AU$14.99 a year that scans continuously and gives you scan histories.

It's not your only option either: if you're on Android, Nearby Glasses is open source and free to use, and does a similar sort of job as ZuckOff without the provocative name. That app has now racked up more than 100,000 downloads.

There's definitely been a shift in perception when it comes to glasses that can record, warning light or no warning light — even though these specs have been around for years. There's even talk of bans and government regulation coming in at some point, which Meta might be trying to get ahead of with its next pair of glasses.

Planning permission for new Scottish AI datacentres suspended for up to a year

16 September 2026 at 19:28

MSPs vote for strict environmental impact assessments and will delay decisions until national strategy is developed

Holyrood has voted to pause planning applications for all new AI datacentres for up to a year until the Scottish government has developed a national strategy.

On Wednesday, MSPs backed a Scottish Labour motion to halt new schemes for up to 12 months while rules on hyperscale datacentres are developed in what could be a blow to the British government’s AI strategy.

Continue reading...

© Photograph: Murdo MacLeod/The Guardian

© Photograph: Murdo MacLeod/The Guardian

© Photograph: Murdo MacLeod/The Guardian

Third-party WooCommerce plugin hits WordPress sites with PHP backdoor abusing recently patched vulnerability

16 September 2026 at 12:50
  • Defiant warned of active exploitation of WooCommerce Wholesale Lead Capture Plugin flaw (CVE‑2026‑27540)
  • Critical unauthenticated file‑upload bug lets attackers deploy PHP webshells for site takeover
  • Patch released in Feb 2026 (v2.0.3.2); Wordfence blocked 100,000+ attacks, users urged to update and check uploads

A critical vulnerability in a popular WooCommerce plugin is being actively exploited to upload malware and possibly take over entire websites, security experts have warned.

The plugin in question is called Wholesale Lead Capture Plugin for WooCommerce. It adds a dedicated registration and onboarding system for wholesale and B2B customers, letting businesses collect company and other custom information, review applications, assign wholesale user roles, and automate registration and onboarding emails.

It is a premium plugin that costs between $99 and $600 and which, according to the Wordpress store page, has more than 20,000 active installations.

A thousand victims

The plugin was vulnerable to an unauthenticated arbitrary file-upload flaw which, as the name suggests, allows unauthenticated parties to upload arbitrary files, including PHP webshells and executable code that can result in full site takeover. It is tracked as CVE-2026-27540 and carries a severity score of 9.0/10 (critical).

Versions 2.0.3.1 and older were said to be affected. Version 2.0.3.2, released on February 20, was said to have addressed the bug, meaning the patch has been available for almost half a year. However, WordPress security outfit Defiant said its Wordfence web application firewall blocked more than 100,000 attacks, and Wordfence added that it observed two attack spikes - one between June 4 and 17, and another one between July 1 and August 30.

In these incidents, the attackers were mostly uploading reconnaissance webshells, possibly mapping out the landscape before deploying more serious malware.

“The uploaded shell.php is a PHP webshell that reports host details and provides a browser-based upload form for writing additional malicious files to the site,” the researchers said.

If you are running the plugin, it is advised you update it to the newest version as soon as possible, and check upload directories for unexpected, or recently created, PHP files.

Via BleepingComputer

‘If you’re building Frankenstein, stop’: JD Vance dismisses calls for AI regulation

US vice-president’s comments come as former Anthropic researcher revisits recent claim AI could destroy humanity

The US vice-president has dismissed calls for global regulation of AI safety risks, telling companies creating the most advanced models: “If you’re building Frankenstein, stop.”

In remarks addressed towards Dario Amodei, the co-founder of Anthropic who has called on Washington DC to coordinate control of AI systems, including with China, JD Vance said: “If you’re gonna create Frankenstein, don’t come to the government and say we need regulation.”

Continue reading...

© Photograph: Mark Schiefelbein/Reuters

© Photograph: Mark Schiefelbein/Reuters

© Photograph: Mark Schiefelbein/Reuters

Chrome and Edge browsers hijacked by KREMLIN malware for credential and token session theft

16 September 2026 at 11:05
  • Elastic Security Labs uncovered REF9334, a Brazilian banking malware campaign active since May 2025
  • Malware “Kremlin” deploys fake docs and malicious Chrome/Edge extensions to steal banking data
  • 1,515 infections found, 98% in Brazil

Security researchers from Elastic Security Labs have discovered a new Brazilian banking malware campaign that uses browser extensions to compromise users and steal sensitive information.

In an in-depth report published earlier this week, the researchers said the campaign has been active since at least May 2025. Dubbed REF9334, the campaign uses fake banking, invoice, and business documents, to trick victims into installing malware which, in turn, deploys a malicious extension in Chrome and Edge browsers.

The researchers named the malware “Kremlin”, and say it can steal browser credentials, cookies, session information, monitor browser activity, grab screenshots, and steal information from websites that the victims visit. But the goal of the campaign is primarily to target Brazilian bank users.

A thousand victims

The malware really makes an effort to hide and persist in the target environment. For example, it first checks to see if it’s in a sandbox and if so - it simply won’t run. If instead it determines that it’s running on a real user’s computer, it will deploy an extension with the name “AVSync System Inc.” in an attempt to trick the victim into thinking they have an antivirus addon running in the browser.

It also doesn’t use a fixed C2 server, but rather stores the information on the Ethereum blockchain, since it’s a lot harder to disrupt the communication between the operators and the infected machines that way.

During their investigation, Elastic researchers were able to take control of a domain that the malware used and discovered that it had infected 1,515 systems. Almost all of them (98%) were located in Brazil. They were also able to register the network canary domain and point it to their webhost, which resulted in the loader assuming it was in a sandbox. This also meant “the infections have not moved past the initial access”, Elastic explained.

The full list of indicators of compromise can be found on this link.

Via The Hacker News

New laptop ditches noisy fans and heat sinks for tiny 'cooling chips' — it's much thinner and lighter than you'd expect

16 September 2026 at 10:40

BERLIN — Lenovo has unveiled a concept laptop that uses high-speed air to silently cool its processor, instead of conventional fans or heat sinks. The new system is powered by a tiny computer chip that can fit between your fingers.

The new laptop, called AeroBlade, is fitted with four low-power cooling chips that are both "solid-state," meaning there are no moving parts, and "active" — they use a deliberate cooling mechanism — as opposed to relying on passive thermal energy transfer.

The cooling chips contain tiny membranes that vibrate at ultrasonic frequencies to create 2,000 pascals of back pressure, forcing ambient air through tiny internal jets onto a copper base layer at speeds of around 124 miles per hour (200 kilometers per hour). This air carries heat away from the laptop's central processing unit (CPU) and graphics processing units (GPU), before being violently expelled from tiny vents at its rear hinge.

Lenovo representatives unveiled their new AeroBlade machine at Lenovo Innovation World '26, which took place Sept. 3 at IFA 2026 in Berlin, which itself ran between Sept. 4 and 8.

Made by engineers at Frore Systems, the low-power AirJet Mini chip measures 1.63 x 1.06 x 0.1 inches (41.5 x 27 x 2.65 mm) and weighs just 0.02 pounds (9 grams). Because it contains tech designed to replace conventional cooling technologies, the AeroBlade, which is fitted with a 14-inch (355.6 mm) OLED display, is much thinner and lighter than comparable laptops

The AeroBlade measures less than 0.39 inches (10 mm) thick — where comparably sized machines measure 0.51 inches (12 mm) at their thinnest — and weighs just 1.8 pounds (830 g). Laptops as light as this exist, like the 1.76-pound (799 g) Acer Swift Blade 14, but they're rare. Counterparts with 14-inch screens are normally considered light if they fall between 2.2 to 2.86 pounds (1 to 1.3 kilograms).

A new approach to keeping cool

The Lenovo AeroBlade and the chip that fits inside

Four AirJet Mini chips are fitted inside the new Lenovo concept laptop. (Image credit: Lenovo and Frore Systems)

Both companies are framing the introduction of this technology as a way to cool down the next generation of high-end laptops that will wield on-device artificial intelligence (AI).

Most laptops marketed as "AI PCs" just feature a neural processing unit (NPU) that can run simple AI workloads, such as manipulating or enhancing a webcam's video feed in real time.

But some future laptops will aim to let users run powerful large language models or AI agents without needing to connect to the internet. An example is the forthcoming series of Nvidia RTX Spark laptops, which will feature a new chip designed specifically to orchestrate AI agents locally.

Running on-device AI models usually demands significant power funneled into professional-grade graphics processing units (GPUs). Fans are deployed in all kinds of laptops to keep their chips cool, but they can be noisy. Instead of or in addition to fans, passive heat sinks, found in machines like the MacBook Air or the Microsoft Surface Pro, use metal plates positioned close to heat sources to absorb thermal energy. But these laptops are more prone to throttling — when their software pulls back performance to reduce temperature.

The AirJet Mini is a different way of cooling, relying on its own high-velocity jet mechanism to flush hot air away. Frore representatives said in a statement that the device operates at just 21 decibels at peak power — equivalent to the sound of leaves rustling or a whisper — and is capable of removing 7.5 watts of heat. Four of these chips would remove 30W of heat — which is in line with the heat expulsion of conventional laptop fans, although there is variation between models.

The AeroBlade's 2,000 pascals of back pressure a measure of opposing force pushing against the direction of airflow) is roughly 10 times that found in regular laptop fans, which typically falls between 100 and 200 pascals.

Although the underlying cooling technology is a few years old, having first been announced in December 2022, AeroBlade represents the first time it's been used in a fully operational laptop. The chip was previously integrated into the ZOTAC ZBOX PI430AJ Pico mini PC in 2023, while there are no commercially available tablets or laptops available with this chip. The AeroBlade laptop remains a concept model, with no information yet as to when production-ready machines fitted with this new cooling technology will emerge.

Can you match these ancient devices to their pictures? Find out with our computing quiz!

Wednesday briefing: Why tech companies might be only too happy for us to believe AI will ‘kill us all’

16 September 2026 at 06:45

In today’s newsletter: It is hard to tell fact from fiction when it comes to AI. What is really going on – and what should the government do about it?

Good morning. As a general rule, it pays to be suspicious of any gigantic company that claims it’s developing a tool capable of destroying humanity. But in recent days, a number of warnings from the AI industry have suggested that even tech insiders are starting to worry about what they have unleashed.

In a lofty essay published on Saturday, Dario Amodei, the founder of Anthropic (the company behind Claude), argued that tech companies need to “slow” the pace at which they’re developing the newest and most sophisticated AI models.

Lucy Letby | Three babies might have survived if hospital had acted upon concerns over Lucy Letby, an inquiry has found. Lady Justice Thirlwall condemned the ‘complete failure’ to protect babies on neonatal unit at Countess of Chester hospital.

UK politics | The leader of Reform UK in Wales stood down after arrest on suspicion of assault. Dan Thomas, a former Tory councillor, was elected to Senedd as leader of the opposition in May.

AI | The progressive senator Bernie Sanders and rightwing strategist Steve Bannon have called for restrictions on artificial intelligence (AI) but offered competing visions for what they termed a “cold war” with China.

UK news | Young people in Rotherham face a local jobs market with the fewest suitable opportunities in Britain, according to a report that warns stark regional divisions are fuelling a crisis in youth work.

Defence | John Healey is in talks with the Canadian government about joining a new global defence bank intended to help allies rearm to counter mounting security threats, just weeks after Rachel Reeves rejected the move.

Continue reading...

© Photograph: Manuel Orbegozo/Reuters

© Photograph: Manuel Orbegozo/Reuters

© Photograph: Manuel Orbegozo/Reuters

Anthropic lands deal in $31bn datacentre in western Queensland, David Crisafulli says

16 September 2026 at 06:20

Premier describes deal as ‘major win’ that will deliver more jobs for the state and put more energy into its grid

Anthropic, the AI company behind the large language model Claude, has done a deal to lease the site of its first Australian datacentre in western Queensland.

The $31.9bn datacentre will be the largest of its kind in Australia, according to planning documents.

Continue reading...

© Photograph: Dado Ruvić/Reuters

© Photograph: Dado Ruvić/Reuters

© Photograph: Dado Ruvić/Reuters

Nvidia RTX 5090 GPU prices skyrocket to $9,000 — as fresh RTX 6000 rumor suggests a 2027 launch that fills me with dread

16 September 2026 at 01:00
  • Nvidia's RTX 5090 is now over $9,000 in the US
  • A new rumor suggests that RTX 6000 gaming GPUs are coming in 2027, maybe in the first half of the year
  • If true, the price tag that's attached to the RTX 6090 will likely be colossal, and lower-tier GPUs could be pricey too

Nvidia's RTX 5090 flagship has hit new heights in terms of ridiculous asking prices, just as a new rumor suggests that RTX 6000 (Rubin) GPUs are coming in 2027 — earlier than anticipated, and to say the price of those next-gen offerings is a worry given the current climate is a huge understatement.

A Redditor pointed out the current state of RTX 5090 prices, which are close to commanding $10,000 in the US for some models. Notably, the Asus ROG Astral RTX 5090 will set you back a princely $9,659 on Newegg US, although that's not sold directly by the retailer.

Indeed, none of the big retailers have any stock of the RTX 5090 anymore, and the only graphics cards you can buy are from third-party marketplace sellers, often shipping from Asia (that ROG Astral GPU comes from Hong Kong).

You can find more expensive RTX 5090s than that if you want to get the gold-plated version (Dhahab Edition) of the ROG Astral, which is now $11,999. The very cheapest RTX 5090 that I could find at the time of writing is an MSI Ventus model that's priced at $6,549 on Newegg.

What about outside of the US? Price inflation is rampant in other countries too. If we take the UK as an example, you can pick up an RTX 5090 for around £4,500 on Overclockers UK, which is still an eye-watering asking price. (And those are the cheapest GPUs — they go up in price from there to £6,600 for the aforementioned Asus ROG Astral RTX 5090).

It's an alarming situation, and one that heaps worries on top of a new rumor about the RTX 6000 launch as aired by Moore's Law is Dead (MLID) on YouTube (via TweakTown).

Previous rumors have contended that Nvidia's looking at 2028 for the arrival of its next-gen GeForce graphics cards, but a source at the firm told MLID that this is incorrect, and Team Green is aiming for a 2027 launch. Moreover, the current plan is still to get the RTX 6000 ball rolling for PC gamers in the first half of next year, meaning within the next nine months.

There's an obvious sticking point here, though: if the RTX 6000 generation is this close, how come we haven't had much in the way of leaks yet, and nothing of any real substance? The source claims that this is due to Nvidia's management successfully clamping down on leaks with the next-gen products.

The first offering will presumably be the RTX 6090 (or maybe an RTX 6080 as well), and it's going to have some kind of interesting spin on the design — though we aren't told what. It could have beefed up hardware to better cope with running DLSS 5, perhaps, as that would make sense — something to help deal with the performance hit of neural rendering would clearly be very welcome.

Nvidia RTX 6000 leak slide

(Image credit: Moore's Law is Dead on YouTube)

Analysis: house deposit or GPU?

There are obvious problems with a possible RTX 6000 launch next year, if we take this rumor at face value. As noted, the RTX 5090 is now being sold for beyond ridiculous prices — and so how much is an RTX 6090 going to run? The fiscal equivalent of an ultramarathon, presumably, and with AI demand likely coming into play with this GPU, the next-gen flagship could be 'unobtainium,' as the disgruntled gamers out there are already predicting.

When you consider that the RAM crisis is going to get worse next year, going by the more recent forecasts from analysts and industry insiders, video RAM is going to be more problematic — and how much of the stuff will be on an RTX 6090? — it all adds up to a frightening prospect for an MSRP. And the likely reality is that the price to actually buy the next-gen Rubin gaming flagship will blow well past that towering recommended cost. (The RTX 6080 won't likely be much different, either.)

For all these reasons, I find it difficult to believe that Nvidia will go ahead and launch the RTX 6000 series next year. Surely it makes sense to wait longer, although what Team Green may know is that 2028 isn't going to be any better for RAM price inflation, and so perhaps that's why next year is greenlit for next-gen GeForce graphics cards to arrive. It's a sobering thought. As one Redditor observed: "Can't wait to decide if I want a down payment on a house or an Nvidia GPU."

NYT Strands hints and answers for Wednesday, September 16 (game #927)

16 September 2026 at 00:00
Looking for a different day?

A new NYT Strands puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Tuesday's puzzle instead then click here: NYT Strands hints and answers for Tuesday, September 15 (game #926).

Strands is the NYT's latest word game after the likes of Wordle, Spelling Bee and Connections – and it's great fun. It can be difficult, though, so read on for my Strands hints.

Want more word-based fun? Then check out my NYT Connections today and Quordle today pages for hints and answers for those games, and Marc's Wordle today page for the original viral word game.

SPOILER WARNING: Information about NYT Strands today is below, so don't read on if you don't want to know the answers.

NYT Strands today (game #927) - hint #1 - today's theme

What is the theme of today's NYT Strands?

• Today's NYT Strands theme is… Hard not to notice

NYT Strands today (game #927) - hint #2 - clue words

Play any of these words to unlock the in-game hints system.

  • BRING
  • GIFT
  • HALF
  • STING
  • BIRTH
  • STIR

NYT Strands today (game #927) - hint #3 - spangram letters

How many letters are in today's spangram?

• Spangram has 11 letters

NYT Strands today (game #927) - hint #4 - spangram position

What are two sides of the board that today's spangram touches?

• First side: top, 1st column

• Last side: bottom, 4th column

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

NYT Strands today (game #927) - the answers

NYT Strands answers for game 927 on a blue background

(Image credit: New York Times)

The answers to today's Strands, game #927, are…

  • JAZZY
  • VIVID
  • BRIGHT
  • FLASHY
  • STRIKING
  • VIBRANT
  • SPANGRAM: EYECATCHING
  • My rating: Hard
  • My score: 1 hint

My first thoughts about today’s theme were obvious ones like conspicuous, but surely this clue couldn’t be so blatant — especially as there was no letter P on the entire board.

There were, though, two letter Zs, so I managed to get JAZZY immediately, followed by VIVID and BRIGHT — the theme nailed down to describing the loud and garish. I was able to make some progress, but still needed a hint to get FLASHY.

I kicked myself that I’d missed the spangram EYECATCHING, as EYE was staring at me in my favorite starting position in the top left-hand corner.

I ended happy to have completed a tricky game, but annoyed that I’d missed a golden, flickering neon sign of an opportunity for a spangram-first. Congratulations if you spotted it.

Yesterday's NYT Strands answers (Tuesday, September 15, game #926)

  • JOIST
  • BOLT
  • HINGE
  • NAIL
  • KNOB
  • BRACKET
  • ANCHOR
  • SCREW
  • SPANGRAM: HARDWARE

What is NYT Strands?

Strands is the NYT's not-so-new-any-more word game, following Wordle and Connections. It's now a fully fledged member of the NYT's games stable that has been running for a year and which can be played on the NYT Games site on desktop or mobile.

I've got a full guide to how to play NYT Strands, complete with tips for solving it, so check that out if you're struggling to beat it each day.

Quordle hints and answers for Wednesday, September 16 (game #1696)

16 September 2026 at 00:00
Looking for a different day?

A new Quordle puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Tuesday's puzzle instead then click here: Quordle hints and answers for Tuesday, September 15 (game #1695).

Quordle was one of the original Wordle alternatives and is still going strong now more than 1,500 games later. It offers a genuine challenge, though, so read on if you need some Quordle hints today — or scroll down further for the answers.

Enjoy playing word games? You can also check out my NYT Connections today and NYT Strands today pages for hints and answers for those puzzles, while Marc's Wordle today column covers the original viral word game.

SPOILER WARNING: Information about Quordle today is below, so don't read on if you don't want to know the answers.

Quordle today (game #1696) — hint #1 — Vowels

How many different vowels are in Quordle today?

• The number of different vowels in Quordle today is 4*.

* Note that by vowel we mean the five standard vowels (A, E, I, O, U), not Y (which is sometimes counted as a vowel too).

Quordle today (game #1696) — hint #2 — repeated letters

Do any of today's Quordle answers contain repeated letters?

• The number of Quordle answers containing a repeated letter today is 0.

Quordle today (game #1696) — hint #3 — uncommon letters

Do the letters Q, Z, X or J appear in Quordle today?

• No. None of Q, Z, X or J appear among today's Quordle answers.

Quordle today (game #1696) — hint #4 — starting letters (1)

Do any of today's Quordle puzzles start with the same letter?

• The number of today's Quordle answers starting with the same letter is 0.

If you just want to know the answers at this stage, simply scroll down. If you're not ready yet then here's one more clue to make things a lot easier:

Quordle today (game #1696) — hint #5 — starting letters (2)

What letters do today's Quordle answers start with?

• S

• P

• B

• F

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

Quordle today (game #1696) — the answers

Quordle answers for game 1696 on a yellow background

(Image credit: Merriam-Webster)

The answers to today's Quordle, game #1696, are…

  • SPEAR
  • PRICE
  • BLUNT
  • FISHY

My mistake today was guessing the words in the wrong order.

Following a hunch, I incorrectly guessed “saver” when there were far more gettable quadrants that would not have left me in a situation where I had just one guess to get the final word.

Fortunately I scraped through.

Daily Sequence today (game #1696) — the answers

Quordle Daily Sequence answers for game 1696 on a yellow background

(Image credit: Merriam-Webster)

The answers to today's Quordle Daily Sequence, game #1696, are…

  • CRANK
  • GENIE
  • TORCH
  • REMIT

Quordle answers: The past 20

  • Quordle #1695, Tuesday, 15 September: SHUSH, LITHE, DELTA, BUGLE
  • Quordle #1694, Monday, 14 September: ZEBRA, LEGAL, RATIO, TROLL
  • Quordle #1693, Sunday, 13 September: LUCID, SPRAY, MANGE, EMAIL
  • Quordle #1692, Saturday, 12 September: SUPER, BRUSH, RESET, SOWER
  • Quordle #1691, Friday, 11 September: PLUSH, PAINT, RIVAL, AFOUL
  • Quordle #1690, Thursday, 10 September: AGENT, EXILE, TITAN, PUREE
  • Quordle #1689, Wednesday, 9 September: HAPPY, GONER, SMACK, MOTIF
  • Quordle #1688, Tuesday, 8 September: FLUNG, DIARY, HALVE, BERTH
  • Quordle #1687, Monday, 7 September: VISIT, CLANK, FEVER, OCEAN
  • Quordle #1686, Sunday, 6 September: PILOT, HONOR, BOWEL, TAUNT
  • Quordle #1685, Saturday, 5 September: GNASH, GEEKY, WHELP, PUTTY
  • Quordle #1684, Friday, 4 September: QUICK, BEEFY, TRUCK, SNAIL
  • Quordle #1683, Thursday, 3 September: SIGMA, ESSAY, MERRY, LLAMA
  • Quordle #1682, Wednesday, 2 September: INCUR, FELLA, TITAN, FROZE
  • Quordle #1681, Tuesday, 1 September: NASAL, ROUTE, ANKLE, MIGHT
  • Quordle #1680, Monday, 31 August: ALIVE, LOUSE, ABOVE, MOSSY
  • Quordle #1679, Sunday, 30 August: USING, SORRY, FILET, PIPER
  • Quordle #1678, Saturday, 29 August: OVINE, ALLOY, VOICE, CARVE
  • Quordle #1677, Friday, 28 August: WAIST, EDICT, BONUS, DEUCE
  • Quordle #1676, Thursday, 27 August: SHEEN, TRICE, WAGON, BEVEL

The Physics That Curves a Ping-Pong Ball Just Showed Up in Quantum Light

15 September 2026 at 23:21
AI Generated Illustration of Quantum Spin EffectResearchers have directly observed the optical Magnus effect for the first time, revealing a tiny shift in laser interactions that could affect the precise control of qubits in quantum computers. Anyone who has watched a spinning soccer ball curve around a defensive wall has seen what happens when spin changes an object’s path through the [...]

Finally! How to get a truly random number — and prove it

15 September 2026 at 11:30

Every time someone attempts to create an unbreakable password or send a private message, they must trust a number. That number is supposed to be one that a computer has generated completely at random. Unfortunately, no computer has been able to actually guarantee that this number is truly random. Until now.

To get truly random numbers, physicists at ETH Zurich in Switzerland turned to the quantum world. They can prove that the numbers their technique spits out really are random (with only a vanishingly small chance that they are wrong).

A number that’s proven to be random can help protect private data through encryption. Here’s why. Encryption works by turning private information into a scrambled code using a secret “key.” The key is built from a random number. If that number is truly random, then no one can unscramble the information without the key. However, if it’s not truly random, a hacker could figure out the pattern behind it and reconstruct the key. And that would expose the formerly masked information.

“When randomness is weak, the keys built from it become guessable. An attacker can shrink work that should take billions of years into something practical,” explains David Holtzman. In other words, a guessing game that could take longer than the age of the universe becomes a task that a computer might pull off in months or years.

Holtzman did not take part in the new work. Still, he knows about such things. He’s worked as a cryptographer for the U.S. National Security Agency and now runs a cybersecurity firm called Naoris Quantum Protocol.

The new study cites real cases where weak randomness let attackers steal private data — and offers a new solution.

a partial image of a sheep, only showing the image on the left side, a diagonal line cuts through the image in the middle and shows how the sheep is partially vidisble with a weak randomness encryption, the right hand side is encrypted using the new certified randomness and appears as rainbow static
A photo of a sheep encrypted using weak randomness (center) and ETH Zurich’s new certified randomness (right). Only truly random numbers scramble the image beyond recognition. ETH Zurich

Solving the bias problem

Regular computers haven’t been able to make truly random numbers because those machines are designed to be predictable. In a normal computer, the same starting conditions should always produce the same outcome. To create a “random” number, these devices use a formula and a starting number, called a “seed.” And while the outcome may look random, it’s really only sort of random.

Scientists call this pseudo-randomness. Why? If someone guesses the seed, they can rebuild the whole sequence of steps a computer takes to make a “random” number. This will break the code meant to securely mask data.

But a natural solution exists in the quantum world. The word “quantum” describes the very smallest things in our universe, such as tiny particles. In this realm of microscopic particles, physical processes are actually random. In fact, researchers have already used quantum devices to generate randomness. But so far, no device used to capture that randomness has been perfect. Small hardware flaws have kept the results from being perfectly random.

This video explains the three types of randomly generated numbers and why only quantum random-number generation can lead to truly random numbers, which are essential for guaranteeing data security.

In the past, researchers have tried to build better, less-biased devices. The ETH Zurich team found a different way: a two-part system that works even if the devices are imperfect. The setup relies on running two experiments at once, in two different places, explains physicist Renato Renner. This kept the results of one from being able to directly affect the other.

Think of the split experiments like two rubber balls. The balls look the same. But each one has its own tiny surface imperfections. Those unique features give each ball its own slight bias, making it bounce one way more often than another. Because each ball was made separately, its imperfections have nothing to do with the other’s.

In the same way, ETH Zurich’s two devices each produced random numbers with its own bias. But because the devices were separated, the biases in their outputs were completely unrelated. Renner’s team collected results from its two experiments, then combined their findings. To do this, they used something called a randomness extractor.

That extractor doesn’t fix either experiment’s output to be perfectly random on its own. Instead, it looks at the two outputs together. Then it combines them using a mathematical formula. Because the flaws in each device’s random number are unrelated, combining them cancels those flaws out. The result is truly unpredictable.

For the ETH Zurich experiment, the team used two quantum devices 30 meters (about 100 feet) apart. This separation made sure no signal — even if it were traveling at the speed of light — could pass between them before each outcome was generated. It’s one guarantee that the output from one could not influence the other.

A long silver metal pipe and a stack of electronic equipment take up half of a long hallway
A 30-meter (100-foot) pipe separates ETH Zurich’s two quantum devices. That distance ensures that no signal, not even one moving at the speed of light, could pass between them during a measurement.Kilian Kessler/ETH Zurich

Each device measured the outcome of a quantum event, such as a photon passing through a light-splitting device. Running the results from both through the randomness extractor increased the randomness already present in each.

Renner’s group described how it did this on May 27 in Nature.

Passing the ‘Bell test’

Proving randomness is very hard, says Nicole Yunger Halpern, who did not take part in the new work. “We can certify a random-number generator only by proving that no possible attack can have corrupted it,” explains this quantum physicist. She works at the U.S. National Institute of Standards and Technology in Gaithersburg, Md.

To confirm that the output from its extractor was genuine, the ETH Zurich team used something known as a Bell test. This physics test proves a quantum result wasn’t faked or planned in advance.

Passing this test meant something important. The team didn’t have to trust that their devices were perfect. A device might have a hidden flaw or secretly been tampered with. Either way, it couldn’t fake the exact pattern found. This pattern could only be due to genuine, unpredictable quantum behavior.

Scientists call this type of proof “device-independent.” The certainty comes from the test itself. There is no need for blind faith in the equipment or the test setup.

In 2025, a team led by Minzhao Liu at JPMorgan Chase in New York City claimed it had generated numbers with “certified randomness.” However, its claim relied on one big premise. It assumed that no classical — that is, non-quantum — computer would ever be powerful enough to copy its results. (Otherwise, a hacker using an ordinary computer might eventually guess or predict that team’s random numbers.)

“Our result,” says Renner, “does not require any [limits] on the capabilities of someone trying to predict the randomness.” As such, he claims this is the first real-world showcase that natural quantum randomness can be turned into something perfectly random. Renner and a co-worker first proposed this idea in 2012. Now, he’s helped show it’s true.

Do you have a science question? We can help!

Submit your question here, and we might answer it an upcoming issue of Science News Explores

The complicated AI infrastructure market

15 September 2026 at 10:00

There is a quiet war happening underneath the AI boom, and most people are not paying attention to the right front. While headlines focus on which model is winning, which startup is raising money, or which chatbot is gaining users, a parallel race is playing out in data centers and corporate boardrooms across the world. The race is for AI infrastructure: the GPU clusters, power contracts, cooling systems, networking fabric, orchestration tools, and inference services that make modern AI actually work. The companies fighting in that race are called neoclouds.

Understanding the importance of neoclouds matters for the same reason understanding cloud computing mattered in 2008. The companies that first saw the shift to cloud and positioned themselves around it correctly ended up controlling a massive layer of the technology industry. The same dynamics are playing out in AI infrastructure right now. But the neocloud market is more complicated than it looks. Not all of these companies do the same thing. Grouping them together as if they are all just GPU rental businesses misses the point entirely. The more useful way to understand the space is to recognize that neoclouds are evolving into four distinct categories, each with a different focus, a different customer, and a different role in the AI stack.

What a neocloud actually is

Before getting into categories, it helps to establish what the term means. A neocloud is a cloud infrastructure provider designed specifically for AI workloads. That sounds simple, but it covers a lot of ground. General-purpose hyperscalers like AWS, Azure, and Google Cloud were engineered to support a wide variety of workloads across many industries. They built architecture around flexibility, breadth of services, and broad enterprise appeal. Neoclouds took a different approach. They built around one mandate: make AI compute as fast, dense, and scalable as possible.

That means designing facilities around high power density, integrating fast interconnects between GPUs, investing in liquid cooling and energy-aware cooling, and building orchestration around tools like Kubernetes and Slurm that are purpose-built for distributed AI training. It also means competing aggressively for access to the latest Nvidia accelerators, because in AI infrastructure, GPU access is often the single most important competitive advantage a company can have.

The result is a fragmented but deeply competitive market. Some neoclouds are focused on physical capacity. Some are focused on making GPU compute accessible to developers. Some are building enterprise-grade infrastructure that can compete directly with hyperscalers in specific AI domains. And some are focused on one of the most important long-term bets in AI: inference. Let’s take a closer look at the four categories of neoclouds.

Full-stack AI infrastructure

The first and most prominent category is full-stack AI infrastructure providers. These companies are trying to become the AWS of the AI era, yet they are built from the ground up around GPUs rather than general-purpose compute. Their goal is to own as much of the AI infrastructure experience as possible: raw GPU clusters, networking, storage, orchestration, managed Kubernetes, Slurm scheduling, private clusters, enterprise support, training workloads, and inference services.

The companies in this space include CoreWeave, Nebius, Lambda, Crusoe, Nscale, and Fluidstack. CoreWeave has emerged as the clearest example of this category. It built its identity around massive GPU clusters optimized for large-scale AI training, paired with Kubernetes-native orchestration and enterprise contracts worth billions. Nebius took a different path, combining deep engineering expertise with a sovereign and European-oriented strategy while expanding heavily into US data centers. Lambda started as a developer and researcher-friendly GPU cloud for machine learning but has moved deliberately upmarket, adding superclusters and private cluster services.

Developer and self-serve GPU clouds

The second category is developer and self-serve GPU clouds. These platforms are not competing for billion-dollar enterprise contracts. They are competing for developers, startups, researchers, and smaller teams who need GPU access quickly without negotiating long-term deals. The product experience here is self-serve: pick a GPU, spin up an instance, deploy a model, run a notebook, test a workload, or launch a small cluster.

The brands that fit here include Vultr, Runpod, Civo, Lambda, and Together AI. Vultr brings GPU compute into a broader cloud platform with a global footprint and self-service deployment. Runpod has built a loyal developer following by keeping the experience simple and fast. Civo targets developers who prefer a Kubernetes-first environment. Lambda still offers strong self-serve GPU instances and 1-Click Clusters alongside its enterprise offerings. Together AI goes beyond raw GPU rental to combine inference, fine-tuning, model hosting, and dedicated clusters in one developer-accessible platform.

This category matters because not every AI team needs thousands of GPUs and a dedicated account manager. A lot of important work happens at the research bench, the startup prototype stage, and the individual developer level. These clouds serve that market, and they often grow into larger accounts as teams scale.

Inference-first AI clouds

The third and increasingly important category is inference-first AI clouds. Training gets the dramatic headlines, but inference is where AI products actually live. Every chatbot response, coding assistant completion, voice agent interaction, and image generation request is inference. And unlike training, which happens in bursts, inference runs all day, every day, for every user.

The companies to watch include Together AI, Groq, CoreWeave Inference, Nebius Token Factory, Crusoe Managed Inference, and Nscale Serverless Inference. Together AI has built one of the clearest inference-first platforms in the market, especially for open source and open-weight models, while also supporting fine-tuning and dedicated GPU clusters. Groq stands apart because its identity is tied to ultra-fast inference running on its own language processing unit (LPU) architecture, which inverts the traditional GPU paradigm. CoreWeave Inference extends CoreWeave’s infrastructure story into serverless and dedicated model-serving. Nebius Token Factory and Crusoe Managed Inference represent how full-stack players are adding inference layers to monetize their GPU capacity differently. Nscale Serverless Inference is Nscale’s move into on-demand model serving without infrastructure management.

What makes this category so compelling is the economic direction. As AI moves from a handful of large frontier labs into millions of production applications, the demand for inference infrastructure will likely dwarf training demand. Companies that control inference capacity may end up controlling the more durable long-term revenue stream.

Data-center and capacity-heavy players

The fourth category is the most physically grounded. These are the companies that are building and controlling the real estate of AI. This goes beyond GPU clouds in the traditional sense. These companies are acquiring land, securing power contracts, designing data centers, developing campuses, deploying high-density racks, and racing to control capacity that genuinely cannot be replicated quickly.

The brands that fit here include Fluidstack, Applied Digital, Core Scientific, Voltage Park, Crusoe, and Nscale. Some of these companies also offer cloud services, but their strategic advantage is fundamentally about commanding physical infrastructure. Fluidstack specializes in delivering large-scale custom GPU cluster capacity and custom data center solutions for enterprises and AI labs. Applied Digital provides hosting infrastructure for AI and high-performance computing at scale. Core Scientific transitioned from crypto-mining data centers into AI infrastructure. Voltage Park is focused on large-scale GPU capacity for AI and research organizations.

Crusoe and Nscale appear in this category as well as in others, which gets to the heart of one of the most important points about the neocloud market overall. These categories are not clean boxes. Companies routinely sit across multiple categories, and competitive pressure is pushing most of them to expand their scope. Crusoe is both a cloud provider and a data-center developer. Nscale is both a cloud platform and a major infrastructure builder. The companies that ultimately win may be those that can span the four categories most effectively.

Why the category framing matters

Understanding these categories is not just an academic exercise. It has real consequences for how companies make buying decisions, how investors evaluate the space, and how technology leaders think about AI infrastructure strategy.

A company focused on physical capacity is playing a fundamentally different game than one selling managed inference APIs. They may appear in the same technology articles and compete for some of the same headlines, but their economics, customer relationships, and long-term differentiation look very different. From an enterprise buyer perspective, understanding which layer you are actually buying from matters before you sign a contract. Some neoclouds are building long-term platform relationships. Others are offering capacity to fill an immediate gap. The category determines which is which.

The neocloud market is young, fast-moving, and genuinely complicated. New entrants, mergers and acquisitions, and platform expansion are all reshaping the landscape in real time. But the underlying logic remains constant. AI infrastructure is becoming a critical layer of the technology stack, and whoever controls that layer will have enormous influence over how AI gets built, deployed, and monetized.

The companies in this market are not all the same. Paying attention to what they actually do, rather than just what they call themselves, is how you separate the ones that will shape AI’s future from the ones that were simply first to send a press release.

Why a decade of doomsday warnings failed to slow the AI race

15 September 2026 at 09:00

From Stephen Hawking to Jacob Coxon’s viral Anthropic resignation, fears that AI could threaten humanity have shaken the industry without stopping its pursuit

Before an Anthropic researcher resigned and declared human extinction imminent last week, tech leaders and scientists had sounded the alarm about a superintelligent AI ending humanity for over a decade.

The development of artificial intelligence “could spell the end of the human race”, warned professor and astrophysicist Stephen Hawking in 2014 – a little less than a decade before the public got its hands on the generative AI features of the original version of ChatGPT.

Continue reading...

© Photograph: Michael Dwyer/AP

© Photograph: Michael Dwyer/AP

© Photograph: Michael Dwyer/AP

Digital ID comes of age as pubs and bars get go-ahead for biometric scan tech

New laws coming into force this week aim to streamline age checks although old-style plastic IDs will still be allowed

Younger drinkers in England and Wales will be able to prove their age with a digital ID starting this week as new laws give pubs, off-licences, nightclubs and restaurants the right to use biometric scanning technology to check customers are over 18.

Customers will be able to download software that scans their faces into their own phone to generate a QR code proving their age based on a stored proof of age. Other tech under consideration as part of the government push to digitise age verification for alcohol sales include a device which scans the veins in a drinker’s finger to verify their age. That system has not yet been approved for use.

Continue reading...

© Photograph: Keith Morris/Alamy

© Photograph: Keith Morris/Alamy

© Photograph: Keith Morris/Alamy

A Quantum Computer Could Make Electron Microscopes Far More Powerful

15 September 2026 at 00:48
Quantum Electron Microscope Artistic VisualizationA new invention could significantly improve electron microscopy by integrating a small quantum computer directly into the microscope. For delicate samples such as individual proteins, electron microscopy faces a fundamental tradeoff. Producing a clearer image generally requires sending more electrons through the specimen, but increasing the dose also raises the risk of damaging the very [...]

How Microsoft connects your data across the enterprise

14 September 2026 at 19:55

For CIOs and CDOs heading into Microsoft Fabric and SQL Community Conference Europe, the main question is not which data technology sits at the center. It is whether the data platform improves revenue, productivity, and decision speed while creating a trusted foundation for AI at scale. That should drive the data strategy.

Enterprise AI is forcing companies to tackle a familiar problem, with data spread across ERP, CRM, operational systems, databases, applications, warehouses, and analytics platforms built over years. That was manageable for reporting and point-to-point integration. Copilots and agents raise the stakes because they need to reason across customers, orders, inventory, financial implications, operating constraints, permissions, and business rules at the same time.

That moves the discussion from architecture into CIO and CDO strategy. What outcomes are we trying to improve? What data and context are required? What needs to move versus connect? Where do Microsoft Fabric, Azure Databases, and other platforms fit? How will governance and authority follow the data? Where should the data be stored and what sovereignty or regulatory restrictions apply? And how will we measure results?

Microsoft is positioning its broader data platform around AI business value. That framing is useful when it translates into an operating strategy. As I wrote in Forbes about Microsoft Build in June, the next phase of AI depends less on the model and more on data readiness, governance, interoperability, and operational execution. The conference should give customers a closer view of how those pieces come together. The question for CIOs and CDOs is how that direction fits their existing data estate.

The AI problem is usually a data problem

Many AI initiatives stall on data readiness rather than the model itself. The issue is not one statistic. It is the combination of fragmented data, inconsistent definitions, and the effort needed to prepare and govern information before AI can use it reliably.

Copilots and agents need trusted context around customers, financials, inventory, operations, permissions, and business rules. AI exposes existing weaknesses. Duplicate customers, poor product data, and inaccurate inventory become more consequential when AI is recommending or taking action. AI does not create the data quality problem; it makes the operating cost harder to ignore.

The goal is to make more of the existing data estate useful for AI without turning modernization into a wholesale rebuild.

Microsoft Fabric and Azure Databases play different roles

For customers, the question is how Microsoft’s broad portfolio translates into architectural choices, including what should move, what should connect, and what can stay in place without limiting the result the business is trying to achieve.

Azure Databases support transactional and operational workloads, each with distinct performance and application requirements. Fabric provides a unified SaaS experience across data integration, engineering, analytics, real-time intelligence, data science, and business intelligence. Fabric IQ adds a semantic intelligence layer that provides shared business context for Copilot and agents. Some workloads may belong in Fabric, others in Azure Databases, and some on third-party platforms; moving them adds cost without creating enough business value.

The architecture should follow workload, data maturity, economics, and business value, not a single destination.

Connected data has to improve the decision

Microsoft’s broader data platform can connect systems of record and operational applications with analytics and AI. Azure Databases, Fabric, OneLake, Power BI, and governance can play different roles. What matters is whether those connections shorten the path from operational data to a decision, not how neatly the products line up.

A customer record may live in CRM, orders in ERP, inventory in operational databases, and demand models in an analytical environment. Fabric Real-Time Intelligence can add live events to that picture. A manufacturer can stream equipment events or production events, detect a developing issue, and alert teams while there is still time to change the outcome. The value comes from reasoning across that context so the business can understand what is happening, why it matters, and what action makes sense next.

Trusted context turns data into AI business value

I recently wrote in InfoWorld that trusted context is becoming the currency for enterprise AI. That is especially relevant here because once copilots and agents rely on enterprise data, then quality, permissions, lineage, and security become operating requirements, not background data issues.

Knowing inventory is 8,000 units is data. Knowing whether those units are available, committed elsewhere, in the wrong distribution center, or on quality hold is business context. As AI moves from answering questions to taking action, that context determines whether the result can be trusted.

Microsoft frames AI business value around growth, insight, and scale. Each needs an operating definition. Growth can mean new revenue, better customer responsiveness, or growing without adding cost at the same rate. Insight means combining context across Microsoft 365, Fabric, and operational systems to make better decisions faster. Scale means expanding AI without rebuilding governance for every use case.

A reusable data foundation can also reduce pipeline and management effort while improving decisions around pricing, inventory, customers, supply chain, and finance. Microsoft cites coverage of more than 100 standards. The practical question is whether AI can scale without risk and complexity growing at the same pace. For multinational or regulated organizations, that also includes where data is stored and processed and which sovereignty requirements apply.

Agentic workloads raise the bar. Organizations need to know what information informed a decision, who or what had authority to act, which policies applied, and how a wrong action can be reviewed or reversed.

That is where the data platform becomes part of what I describe as decision architecture. Context explains what is happening. Decision logic determines what should happen next. Authority defines what a person or agent can do. Orchestration coordinates systems and people. Execution completes the action and provides a path to review or reverse it. Trusted data feeds that architecture. Without trusted data, automation will be difficult to trust at scale.

Microsoft’s breadth is most useful when it reduces complexity

Microsoft’s advantage may be its enterprise footprint. Azure Databases, Fabric, OneLake, Power BI, Microsoft 365, business applications, developer tools, identity, security, and partners touch many of the places where information is created and work gets done.

Most customers will keep data across multiple systems. Microsoft’s opportunity is to use that breadth to create a simpler path from data to workflow to business outcome without requiring everything to live in one place.

Customer proof points matter because data strategies can get abstract. The KPMG Australia example is useful because KymChat moved beyond an internal productivity tool into a client-facing offering, creating a path from internal AI investment to new business. Microsoft also reports that improvements to the data foundation increased KymChat search quality from 50% to 91%, with results delivered in under a second.

BMW Group provides a more recognizable operating example. Microsoft says Azure helped BMW make vehicle data delivery and analysis 10 times faster, cutting the lead time for engineering insights from days to hours or minutes. Audi adds an AI deployment example. It used Azure AI Foundry, Azure App Service, and Azure Cosmos DB to launch a secure HR assistant in two weeks and is extending the same framework to eight additional agents across the enterprise. Levi Strauss & Co. offers another angle. It consolidated nine ERP systems on Azure, reported a twofold improvement in latency and a 60% improvement in IOPS and now uses Fabric IQ for companywide cost reporting. These are the kinds of proof points that connect platform architecture to time, cost, productivity, and decision quality.

Interoperability matters across the enterprise estate

Microsoft sits in a market with Snowflake, AWS, Google Cloud, Oracle, and others, each with different strengths across infrastructure, databases, analytics, openness, and AI. Databricks also plays an important role, but the connection is different because Azure Databricks is provided as a first-party service directly on Azure. Most large enterprises will remain heterogeneous, making interoperability, cross-platform governance, and incremental modernization central to strategy.

The opportunity is to make Microsoft’s data platform useful across the mix of systems that customers already run. Most enterprises are not starting from a blank sheet, so the value is in helping them connect, govern, and modernize over time without forcing everything into one place.

CIO and CDO strategy should start with the business outcome

This is where the conversation becomes most useful at the Microsoft Fabric and SQL Community Conference Europe. For CIOs and CDOs, an architecture diagram matters more when it is backed by a decision model. A useful data and AI strategy should work backward from the business outcome through five decisions.

  1. Start with the business outcome. Define what needs to improve, including revenue, margin, productivity, customer experience, decision speed, working capital, or time to value. Let the technology choice follow the objective.
  2. Map the data and context required. Identify the operational, transactional, and analytical information, semantics, and business rules that influence the outcome.
  3. Decide what moves and what connects. Determine where Fabric, Azure Databases, and non-Microsoft platforms fit. Let workload, economics, and value drive modernization rather than a single destination.
  4. Design governance, authority, and economics together. Define how security, permissions, lineage, and controls span operational, analytical, and AI workloads, and identify opportunities to simplify pipeline, integration, and management efforts.
  5. Measure the operating result. Set KPIs before scaling. Technology adoption is not the outcome. CIOs and CDOs should know which business measures should change, by how much, and over what period.

That is how the data platform becomes enterprise strategy, tying architecture, governance, AI, and economics to operating outcomes and bringing business leaders into the discussion before the architecture is set.

What customers will still want to understand

Microsoft has many of the pieces required to connect enterprise data, analytics, and AI. Customers will keep looking for guidance on the roles of Azure Databases, Fabric, and the broader data platform, along with cost, licensing, migration, and governance across heterogeneous environments. They will also want more proof points from KPMG, BMW Group, Audi, and Levi Strauss & Co. that start with a business result.

That is where Microsoft’s positioning around a data platform for AI business value comes together. The value is in helping customers connect platform choices to better decisions, faster action, and measurable operating results.

After three decades in enterprise technology, I keep coming back to the same point. The technology is only part of the answer. For CIOs and CDOs, the harder part is deciding how the data platform fits into the way the business operates, how decisions get made, and where AI can improve the outcome. That is really the question behind how Microsoft connects your data across the enterprise. The conference should give customers a useful view of how Microsoft is bringing those pieces together and where that can create better business outcomes.

❌