TECH

The iPhone 18 Pro’s variable aperture is unnecessary for most people

The new camera on the iPhone 18 Pro introduced a feature that garnered significantly more attention at launch than anything else: a variable aperture system for the main (48 MP) lens, capable of switching between f/1.48 and f/4.0. While the company’s marketing might make this sound revolutionary, the reality is that the mechanism will have an almost imperceptible impact on the daily lives of the vast majority of users.

For those who have mastered professional photography and use manual controls, a variable aperture offers precise adjustments over light intake and the natural optical blur (bokeh) of a scene.

However, for the average user who relies solely on automatic mode, the system will remain locked at an f/1.8 aperture almost all the time—behaving exactly as it did in previous iPhone generations.

Is there any real advantage for those who use automatic mode? Furthermore, this feature is far from new to smartphones—even setting aside the Samsung Galaxy S9, which featured a simpler system capable of switching between only two, much more limited, aperture settings.

The only significant practical benefit occurs in extremely low-light environments. In such situations, the lens opens to its widest setting of f/1.48, allowing for greater physical light capture and faster shutter speeds, which helps reduce motion blur. Yet, this same result could be achieved by maintaining a fixed, wider physical aperture and adjusting exposure through image processing—without the need for a moving mechanical component inside the camera module.

For years, brands like Xiaomi and Huawei have incorporated lenses with variable apertures—offering settings similar to those found on the iPhone 18 Pro—into their own high-end smartphones. The most striking example is the Huawei Pura 80 Ultra, which combines this technology with a 1-inch sensor and continues to lead the global DxOMark camera rankings, pushing Apple’s latest release into second place.

If you aren’t a professional content creator—or don’t plan on using camera apps with advanced manual controls—you’ll be paying a premium for moving parts within the sensor that are unlikely to make any real difference to your final results. Your photos will still be excellent, but not because of any lens variability when shooting in automatic mode.

If you have any other questions about the device, check out our full review of the iPhone 18 Pro. In the meantime, we’d love to know: do you typically adjust your camera settings manually before taking a photo, or do you prefer to rely entirely on your phone’s automatic mode?

Testing the iPhone 18 Pro’s most-hyped feature The iPhone 18 Pro has a variable aperture. According to Apple’s keynote, it’s the brand’s «biggest camera improvement in years». Supposedly, it provides «more control for light intake and depth of field,» enabling «new levels of creativity». Well, well. That does sound great.

But is there any truth to these salesy promises? In a bid to find out, I put the feature under the microscope – and got a mixed first impression. In most situations, the variable aperture makes zero difference to your photos. But that doesn’t mean it’s entirely pointless.

Very little control over depth of field...If you’re a photography buff, you’ll be familiar with the concept of a variable aperture from using traditional cameras. It’s a mechanism that uses multiple blades to increase or decrease the size of the lens opening. On larger cameras, variable apertures are indispensable because they serve two important functions:

Controlling light intake: If you’re taking pictures in blazing sunlight, a fully open lens lets in so much light that the camera’s shutter can’t expose the sensor for a short enough period of time. As a result, photos turn out overexposed. A variable aperture solves that problem.

Controlling depth of field: The larger the lens opening, the smaller the area in focus. Photographers often want this, as it means the subject in focus stands out from the background. It’s a technique used in portrait photography, for instance. In other scenarios, however, every area of the photo needs to be equally sharp. Landscape photography is a prime example of this. Variable apertures allow you to control that depth of field.

On a smartphone, these functions are mostly unnecessary. This is down to the device’s tiny image sensor (1/12 of the size of a full-frame sensor) and its electronic shutter. It already achieves high enough shutter speeds – even in direct sunlight with the lens wide open. As a result, you don’t need a variable aperture to get proper exposure.

The small sensor also means that the focal plane is super wide anyway – especially with a wide-angle lens like the one on the iPhone’s main camera (24 mm 35 mm equivalent). The maximum aperture of ƒ/1.48 produces roughly the same depth of field as ƒ/5.2 on a full-frame camera. Photos come out pretty sharp all over, unless you get extremely close to the subject. The new aperture can only make the focal plane larger, not smaller. That means pronounced blurring (known as «bokeh») still has to be created digitally using Portrait Mode.

Strictly speaking, the «more control over exposure and depth of field» promised in Apple’s promotional materials isn’t a lie. However, on a smartphone, the benefits are far less significant than the company suggests.

That being said, the feature does have two minor uses. Firstly, the variable aperture allows you to deliberately increase exposure time to create motion blur. Though, only within a narrow range of ambient light. The iPhone’s aperture can be adjusted from just ƒ/1.48 to ƒ/4. Outdoors, in daylight, the shutter speed is still much too fast, even at ƒ/4. And in very low light, it doesn’t make any difference, because you’ll be opening the aperture all the way anyway. At best, it’ll make a difference in a handful of scenarios – and only at dusk.

The second benefit of the variable aperture is similarly limited. Videos look more natural when the shutter speed of individual frames is about half the frame rate. For example, 1/60 second at a frame rate of 30 frames per second (FPS). This is because our eyes expect some motion blur in videos. Again, in specific lighting situations, the variable aperture can be the deciding factor in whether to set the shutter speed to 1/250 or 1/60 of a second. In most cases, though, you’ll still need an ND filter if you want to pull this off.

The variable aperture on the iPhone 18 Pro (ranging from f/1.48 to f/4.0) has drawn mixed reactions; it is generally considered of little use to the average user, serving more as a niche feature for content creators and photography enthusiasts than as a revolutionary advancement for daily use.

Why don’t most people need it?

• Small sensor: Due to the tiny size of a smartphone sensor, the depth of field is naturally quite deep. The optical background blur (bokeh) produced at f/1.48 is limited—very similar to the effect that software-based portrait modes have been achieving for years.

• Over-reliance on automatic exposure: For everyday photography—such as quick snapshots of family, pets, or food—the iPhone’s automatic mode intelligently adjusts the image, eliminating the need for the user to manually manipulate physical aperture blades.

• Limitations in daylight: In very bright or sunny environments, even when stopping down to f/4.0, the phone’s shutter speed remains extremely fast; this does not fully replace the need for professional-grade filters (such as ND filters).

Who actually benefits from this feature?

• Videographers and content creators: Those who utilize manual controls—such as the 180-degree shutter rule—to achieve more natural, cinematic motion blur in their videos.

• Photography enthusiasts: Those who prefer using "Pro" mode to lock white balance and manually adjust light intake and sharpness for specific conditions (such as during twilight).

 

by mundophone

 

TECH

How to mod NVIDIA DLSS 5 neural rendering into unsupported games for a huge FPS lift

A developer has vibe-coded a new ReShade add-on file that offloads the DLSS Neural Rendering workloads to a second GPU, almost negating the otherwise massive DLSS NR performance hit. Given the original DLSS 5 shots we saw when Jen-Hsun first showed off the feature around GDC and GTC in the Spring were running on two graphics cards, that's not wholly surprising, but this mod certainly does open the the potential for keeping your old GPU around for neural coprocessing in the future.

Like almost all of the current DLSS 5/Neural Rendering implementations we've seen where the user gets to pick the game, the model, and the weight of the effects, the Neural Coprocessor add-on file—mgpu_bridge—is designed for ReShade, an app used to inject different visual effects into PC games. The files exist on Github, can be downloaded and added into a game that's already been modded with DLSS 5 support, and could dramatically improve the performance.

This is, for me, the biggest issue with both the homebrew mods for DLSS Neural Rendering and the only release implementation we've seen from Nvidia and the developers themselves: the hefty performance penalty from turning it on.

Nvidia itself has touted a 50 - 60% frame rate hit as a blanket figure—regardless of game and how much AI filtering it's doing—as what it is expecting to see from enabling DLSS NR. Andy benchmarked NBA 2K27 ahead of launch and found that sometimes the frame rate impact can be even higher than that.

I mean, it's not as bad as when you try and run DLSS NR on AMD GPUs, but I've noted these kinds of performance hits when you run the new feature through ReShade, too. But Marcelo Guibout has coded a ReShade add-on file that allows a second GPU—here an RTX 5060 Ti 16 GB—to take the final finished frame and perform the neural rendering pass on that GPU and output on its own monitor. That means it doesn't have to hand the frame back, which adds its own performance hit and latency.

If you read that headline and you thought "wait, I thought DLSS 5 hurt your FPS", you're right. The idea here is that NBA 2K27 has been updated with the new DLSS 5 Neural Rendering that NVIDIA announced prior to the RTX Spark platform launch, and folks have been replacing the old library with the new one for a big performance lift with very minimal changes in image quality.

There are reports all over X about this; the most convincing is probably this one from Detro (@detro_official) where he compares Judy with the old DLSS 5, the new DLSS 5, and without DLSS 5 on an RTX 4080 SUPER. With the old version his upscale was taking 8.8ms; with the new version, just 5.4ms. His frame rate jumps from 108 FPS to 150 FPS, although we should note those numbers are with 4x multi-frame generation, so the actual frame rates are 27 FPS and 37.5 FPS. That's still a 39% uplift, which is massive for a DLL swap.

The two shots do look a bit different; that could be down to differences in the new model, but it could also potentially be down to a shift in the time of day. That's the reason given for the difference in looks between these two shots from Forza Horizon 6, by Gaming Optimized (@GamingOptimiz):

The performance is again, relatively low—jumping from 27 FPS to 37 FPS—but it's only a GeForce RTX 4060 card. In that context, it's actually quite impressive that the GPU manages to pull out 37 FPS average with DLSS 5 enabled in Forza Horizon 6! That same user posted a comparison in Resident Evil 9, as well:

There's definitely some image quality difference between the older and newer model, as Resident Evil 9 does not have a dynamic time of day system like the others. However, the difference is decidedly minor, and that's incredibly impressive considering how much faster the new model is. If NVIDIA can continue making gains like this, it's possible that Neural Rendering might become mainstream.

Of course, that depends entirely upon the game you're using it with, and whether its art style suits the photorealistic look of DLSS 5 NR. For example, I personally don't particularly care for the effects of DLSS 5 NR on Judy's face in Cyberpunk 2077 (top). However, for games that are less stylized (like NBA 2K27), it definitely gives a wild uplift in the visuals, and this performance gain is absolutely welcome.

If you want to try this out for yourself, it's not exactly a straightforward process. You're going to have to download ReShade (the Addons version, which makes it unsuitable for online games), and join the RenoDX Discord to grab the modified version that allows you to inject DLSS 5. You'll also need the new version of the DLL, currently hosted on X here, although who knows how long that will stay up. Once you have those, put everything into the same directory with the game executables, and then when in-game, press the "Home" button (by default) to customize the effect. There's a more detailed guide in the RenoDX Discord, so head over to their website if you want to join.

 

by mundophone

 

TECH

NVIDIA GeForce NOW unlocks 4X frame gen with full DLSS 4.5 controls

NVIDIA continues to tune its GeForce NOW cloud game streaming service with a steady rollout of upgrades and capabilities, the latest of which sees the service gain DLSS 4.5 knobs and dials for more granular DLSS control, complete with Ray Reconstruction and up to 4X Dynamic Multi-Frame Generation. The caveat is that the newest bells and whistles are exclusive to the top subscription tier.

Specifically, that means you'll need a GeForce NOW Ultimate membership to take full advantage. Priced at $19.99 per month or $199.99 per year (which equates to $16.67 per month), Ultimate subscribers streaming from NVIDIA's highest-performance cloud rigs get access to pods equipped with GeForce RTX 5080-class hardware. Other perks include support for up to a 5K resolution, gaming at up to 360 frames per second, NVIDIA Reflex support, Cloud G-Sync, and up to 8-hour gaming sessions, on top of everything included in the Performance tier (like HDR10 and SDR10 support, ultrawide monitor support, and persistent in-game settings).

The addition of DLSS 4.5 controls with 4X Frame Gen serves as another selling point to splurge on the top subscription tier.  DLSS 4.5 override controls are available on multiple platforms too, including Windows, macOS, Chrome OS, Linux, and SteamOS. Same goes for browser apps, including Chrome (Windows, macOS, and Chromebook), Edge (Windows only), Firefox (Windows only), Opera GX (Windows or macOS), and Safari (macOS).

In a support document, NVIDIA notes that "compatibility and official DLSS support can vary by game and override type," and so it recommends that most users select the "Recommended" option before playing around with manual overrides.

Outside of the new controls, NVIDIA added 10 more games to its streaming library:

Deep Dish Dungeon (New release on Steam, Oct. 13)

Planet Zoo 2 (New release on Steam, Oct. 13)

Total War: SHOGUN 2 – Complete Edition (New release on Steam, Oct. 13)

Valor Mortis (New release on Steam, Oct. 13)

Among the Trolls (New release on Steam, Oct. 14)

over the hill (New release on Steam, Oct. 14)

Warhammer 40,000: Boltgun 2 (New release on Steam, Oct. 14)

Beyond These Stars (New release on Steam and Xbox, Oct. 15), available on Game Pass

Tenebris Somnia (New release on Steam, Oct. 16)

The Witcher 3: Wild Hunt – Remastered (Xbox), available on Microsoft Store

Users who want to try out the new GeForce NOW controls without committing to a month or year of service can purchase a day pass, which is priced at $7.99 for Ultimate.

mundophone

TECH

RTX Spark vs M5 Pro

A direct comparison has been published between NVIDIA's RTX Spark and Apple's M5 Pro, evaluating which chip delivers the better laptop experience for users who need both productivity performance and GPU-accelerated workloads. The comparison comes at a pivotal moment, as RTX Spark represents NVIDIA's first entry into the PC CPU market with an Arm-based system-on-chip that integrates a desktop-class Blackwell GPU directly alongside ARM CPU cores, while Apple's M5 Pro is the latest iteration of the company's custom silicon that has defined premium laptop performance since the M1 debut in 2020.

On the CPU front, Trusted Reviews notes that Apple's M5 Pro holds advantages in single-threaded performance thanks to years of hardware-software co-design and TSMC's latest manufacturing process. However, RTX Spark's 20-core Arm CPU closes the gap significantly in multi-threaded workloads, particularly in scenarios that benefit from the chip's unified memory architecture and NVLink-C2C interconnect providing 600 GB/s of bandwidth between CPU and GPU. The comparison highlights that while Apple's lead in raw CPU benchmarks persists, the performance gap has narrowed to levels where real-world differences in productivity applications are minimal.

Where the comparison tilts decisively in RTX Spark's favor is GPU-accelerated workloads. The integrated Blackwell GPU with 6,144 CUDA cores — matching the desktop RTX 5070 — supports DLSS 4.5 with Multi-Frame Generation, full DirectX 12 Ultimate ray tracing, and can run AI models with up to 120 billion parameters locally. Trusted Reviews notes that for gaming, 3D rendering, and on-device AI inference, RTX Spark laptops offer capabilities that Apple's M5 Pro cannot match, making them a compelling choice for users who need workstation-class GPU performance in a portable form factor. The article concludes that the choice between the two platforms will largely depend on whether users prioritize Apple's ecosystem integration and proven battery life, or RTX Spark's superior GPU performance and AI capabilities when devices ship later this year.

Nvidia’s RTX Spark “Superchip” grabbed all the attention when it was first announced in June. At the time, it was revealed that RTX Spark-powered laptops will be made by Asus, Dell, HP, Lenovo, MSI, and Microsoft. Earlier this month, Microsoft unveiled the Surface Laptop Ultra with a 20-core Nvidia Grace CPU and up to 128 GB of LPDDR5X unified memory. Now, this machine has been tested against the much cheaper M5 Pro-powered MacBook Pro with some interesting results.

Starting with CPU benchmarks, the Surface Laptop Ultra (20-core CPU, 128 GB RAM) was consistently slower than the MacBook Pro (M5 Pro, 64 GB RAM). The testing included Cinebench 2026, Cinebench 2024, and Blender. In some cases, it was even slower than the Legion Pro 5i (Intel Core Ultra 9 275HX, 130W) and the HP ZBook Ultra (Ryzen AI Max+ Pro 395, 70W). We saw similar results with the Asus ProArt P16, which is even more expensive (as tested) than the Surface Laptop Ultra.

Creative workflows...It may come as no surprise to some, but the M5 Pro-powered MacBook Pro (14-inch) consistently beat the Surface Laptop Ultra with its 20-core N1X chip. Hardware Canucks posted their results showing comparisons in several creativity tools such as Adobe Photoshop, Lightroom, DaVinci Resolve Studio, as well as some AI workflows. Keep in mind that Nvidia has designed and marketed the RTX Spark platform to be an AI workhorse.

In every test, the Surface Laptop Ultra lagged behind the MacBook Pro. With the Adobe suite of applications, both Photoshop and Lightroom had the Surface Laptop Ultra (80W) trailing far behind. Photoshop, in particular, had it at the bottom, performing worse than the Intel Core Ultra 9 275HX (130W) and the Ryzen AI Max+ Pro 395 (70W).

On the GPU side, Premiere Pro and DaVinci Resolve performance was compared, and the Surface Laptop Ultra’s 6,144 CUDA-core GPU was a bit slower than the M5 Pro’s 20-core GPU (15W). Notably, Premiere Pro isn’t yet optimized for the RTX Spark platform, and DaVinci Resolve is heavily optimized for Apple’s M-series chips.

AI workflows...Coming to running LLMs, specifically the GGUF (GPT-Generated Unified Format) ones that support both Mac and Windows, only one test had the N1X (128 GB) beat the M5 Pro (64 GB), and that was Qwen 3.8 Q4. On the heavier Q8 model, the M5 Pro pulls ahead once again, and the gap remains as the loads increase.

The comparison with the M5 Pro MacBook Pro is quite interesting because of the price points. The Surface Laptop Ultra (as tested) is priced at $5,900, while the MacBook Pro (as tested) comes in at $3,700. That’s $2,200 more for worse performance. It should be noted that apps will become more optimized for RTX Spark, and the performance gap will reduce over time. Furthermore, gaming-focused scenarios still favor the RTX Spark, though our testing shows it performs close to the RTX 5060 or RTX 5070 laptop GPUs.

The NVIDIA RTX Spark (featuring the N1X chip) and the Apple M5 Pro represent a major showdown between ARM-based architectures and unified memory systems, both designed for content creators and local artificial intelligence (AI) workloads on laptops.

Key Specifications:

• NVIDIA RTX Spark (N1X):

• CPU: Up to 20 ARM-based cores (available in 18- or 20-core variants, balancing performance and efficiency). 

• GPU: Blackwell architecture with up to 6,144 CUDA cores. 

• Unified Memory / Bandwidth: Utilizes LPDDR5X with a bandwidth of approximately 273 GB/s to 300 GB/s (depending on the model/configuration). 

• Operating System: Runs Windows 11 (utilizing x86 emulation via Prism).

• Apple M5 Pro:

• CPU: Apple’s proprietary ARM-based architecture (available in 14-, 15-, or 18-core configurations). 

• GPU: Apple’s high-efficiency integrated GPU. 

• Unified Memory / Bandwidth: Approximately 307 GB/s of memory bandwidth. 

• Operating System: macOS (with native optimization via MLX and creative software suites).

Performance and Comparison:

• Artificial Intelligence (AI) and Local Models:

• The RTX Spark excels in generative AI workflows (such as image generation, Stable Diffusion, or Flux) thanks to NVIDIA’s CUDA software ecosystem and dedicated accelerators. 

• Conversely, for the inference of smaller language models using optimized frameworks (like MLX on Mac), the M5 Pro holds an advantage in efficiency and raw speed in certain scenarios—though the M5 Max outperforms both, benefiting from significantly higher memory bandwidth. • Content Creation and Video Editing:

• The MacBook Pro with the M5 Pro chip is consistently faster and more stable in applications such as Adobe Photoshop, Lightroom, Premiere Pro, and DaVinci Resolve Studio (all of which feature mature optimization for Apple Silicon chips). 

• The RTX Spark (found in models like the Asus ProArt or Surface Laptop Ultra) still suffers from a lack of optimization in certain editing software, leading to higher latency or dropped frames when handling complex timelines, especially when compared directly to Apple’s hardware.

• Bandwidth:

• The M5 Pro offers approximately 307 GB/s of bandwidth, surpassing the 273 GB/s – 300 GB/s range of the RTX Spark in terms of token decoding speed and memory bottleneck mitigation.

The NVIDIA RTX Spark (an SoC featuring a 20-core ARM CPU and an integrated Blackwell-based GPU equivalent to an RTX 5070) and the Apple M5 Pro (an Apple Silicon architecture with up to 18 cores) represent distinct visions for high-performance portable computing.

Key differences:

• Graphics Performance and Gaming: The RTX Spark holds a significant advantage in dedicated graphics, full ray tracing (DirectX 12 Ultimate), support for DLSS 5 with neural rendering, and compatibility with hundreds of games at launch (running either natively or via emulation within the Windows/Microsoft ecosystem). While the M5 Pro excels in efficiency, it is limited when it comes to demanding AAA games and the DirectX graphics ecosystem.

• Local Artificial Intelligence (AI): The RTX Spark integrates cores with direct support for the CUDA ecosystem and is capable of running large-scale local AI models, whereas the M5 Pro relies on the closed-source optimization of macOS and Apple’s unified architecture.

• CPU and Efficiency: The M5 Pro shines in single-thread performance and seamless hardware-software integration (macOS), consuming less power during everyday productivity tasks. The RTX Spark (utilizing licensed ARM cores, such as the Cortex-X925) focuses on raw GPU power and leverages partnerships with Microsoft to power laptops like the ASUS ProArt and the Microsoft Surface Laptop Ultra.

• Price and Ecosystem: Laptops equipped with the RTX Spark are positioned as high-end, premium devices—often comparable in price to MacBook Pro models—yet they require emulation to run traditional x86 Windows software. In contrast, the M5 Pro operates entirely natively within the macOS environment.

 

mundophone

 

TECH

Human learning is an underestimated factor in the success of human–AI teams

Humans collaborating with AI hold the promise of achieving superior outcomes compared to either acting alone (i.e., human-AI synergy). However, the conditions that facilitate such synergy when humans are advised by AI are not well understood. A recent meta-analysis showed that, on average, human-AI combinations do not outperform the better individual agent. We argue that this pessimistic conclusion arises from insufficient attention to human learning in experimental designs. To substantiate this claim, we reanalyzed all 74 studies included in the original meta-analysis and found that most previous research overlooked design features that foster human learning (e.g., outcome feedback to participants). Our reanalysis further revealed that studies providing outcome feedback show tentatively higher synergy than those without outcome feedback. 

Crucially, feedback paired with AI explanations was associated with positive synergy, while explanations without feedback were associated with negative synergy-suggesting that explanations improve synergy mainly when humans can learn to verify the AI's reliability through feedback. Our reanalysis suggests that the current literature underestimates the potential of human-AI collaboration because it predominantly relies on paradigms that do not facilitate human learning, thus hindering humans from effectively adapting their collaboration strategies. However, experiments directly varying learning opportunities are needed for stronger, causal conclusions. We advocate for a paradigm shift in human-AI interaction research that explicitly addresses human learning and thus enhances our understanding of and support for successful human-AI collaboration.

New study suggests that human–machine interaction is better than its reputation. Human–AI collaboration is widely seen as the work model of the future: The expectation is that together, humans and AI will be more productive than either working alone. Yet the research to date seemed to tell a different story: On average, human–AI teams did less well than the better of the two alone. A reanalysis led by researchers at the Max Planck Institute for Human Development now suggests this verdict was overly pessimistic. 

“The potential of human–AI collaboration is underestimated.” That is the central conclusion of the new study published in the Proceedings of the National Academy of Sciences, and conducted by an international team led by Julian Berger and Stefan Herzog from the Center for Adaptive Rationality at the Max Planck Institute for Human Development. The research team took a closer look at a review study that made waves in 2024. The review analyzed 74 studies on human–AI interaction, spanning a wide range of tasks from classifying images and predicting house prices to writing code. 

Its findings were sobering: On average, the synergy effect was negative. In other words, the human–AI team performed worse together than the better of the two working alone. This fueled doubts about whether human–AI collaboration was worth pursuing at all. 

Learning effects underestimated...Is the situation really that bleak? That is the question the researchers set out to answer in their new review. They revisited the 74 studies, examining the nature of the human–AI interaction in more detail. They wanted to know whether the picture would look any different if AIs explained their results or if humans were given feedback on what the right answer would have been after each decision, giving them the chance to learn and adapt to the algorithm. 

The first surprising result was that most studies did not offer learning opportunities. Only 10 of the 74 gave participants any feedback at all, and none varied it experimentally. “Learning effects in human–machine interaction remain virtually unexplored,” says Julian Berger, who explored this topic as part of his doctoral dissertation at the Max Planck Institute for Human Development. 

 To tease out these effects, the researchers dug deeper. Using a statistical method known as meta-regression, they compared studies with and without feedback, factoring in whether the AI explained its recommendations. And here came the second surprising result: When participants had access to both feedback and explanations, the human–AI tandem tended to outperform the better partner working alone. On the other hand, explanations alone—without feedback—were associated with a distinctly negative synergy. 

What appears to be the key is a combination of explanation and feedback. Feedback alone—without explanations—made no clearly discernible difference. This result suggests that “explanations improve synergy mainly when humans can learn to verify the AI’s reliability through feedback,” the researchers write. 

Open questions: causality and generative AI...Because none of the 74 studies varied the learning opportunities provided within an experiment (e.g., feedback vs. no feedback), the studies were compared based on their design features. The researchers stress that experiments directly manipulating learning opportunities are needed for stronger, causal conclusions to be drawn. Another open question is how the findings translate to generative AI and large language models (LLMs). “Verbal interaction with chatbots could make it easier to provide people with personalized feedback and explanations, thereby stimulating learning. But for that to happen, people also need to find out what is actually correct; a chatbot’s answers can be wrong and do not constitute independent feedback,” says Stefan Herzog, Senior Research Scientist in the Center for Adaptive Rationality at the Max Planck Institute for Human Development. “At the same time, new risks arise from cognitive ‘offloading’—that is, delegating mental work to AI. In the medium and long term, this could jeopardize learning success or even lead to loss of skills.” 

Factoring human learning into AI implementation...To get a clearer picture, the researchers call not only for further experiments but, above all, for “a paradigm shift in human–AI interaction research that explicitly addresses human learning.” They emphasize that “supporting human learning—particularly by providing outcome feedback and AI explanations—is an underappreciated and understudied but promising lever for increasing human–AI synergy. Our findings paint a more optimistic picture and highlight the need to explicitly and experimentally study when and how fostering human learning boosts human–AI synergy.” 

The research already supports a clear recommendation: AI is a tool, and like any tool it must be designed with humans in mind from day one. “When designing AI systems and interactions with them, we need to consider humans as learning beings from the outset,” Herzog stresses. Only then can the full potential of the interaction be realized. 

Earlier analyses of human–machine interaction mostly showed negative synergy effects. A review of 74 studies suggested that human–AI teams perform less well than the better of the two working alone. 

These 74 studies have been reanalyzed by researchers from the Center for Adaptive Rationality at the Max Planck Institute for Human Development. Their results paint a much more positive picture. 

The reason for the difference: The studies systematically underestimated human learning ability; most did not examine it a all. Yet when participants were able to learn from feedback and explanations, human–AI teams tended to outperform the better partner alone. 

Need for a paradigm shift: The researchers call for future work on human–AI interaction to explicitly factor human learning ability into studies and experiments. 

Human learning is undoubtedly one of the most underestimated yet critical factors for the success of teams comprising both humans and Artificial Intelligence (AI).

Organizations often focus almost entirely on training and "teaching" the AI ​​model (Machine Learning), overlooking the fact that the true effectiveness of this partnership relies on a two-way street. For collaboration to be productive, humans must also undergo a profound process of adaptation and skill development.

Human learning is an underestimated factor in human–AI teams because prior research largely ignored whether human participants could adapt, learn, and verify AI reliability through feedback.

Recent reanalysis by researchers at the Max Planck Institute for Human Development of 74 previous studies revealed key insights:

• Lack of Learning Opportunities: Out of 74 analyzed studies, only 10 provided outcome feedback to human participants.

• Overlooking Synergy Potential: Because humans could not adapt their collaboration strategies or learn how to use the tool, human–AI teams often performed worse than the best individual working alone (negative synergy).

• Misleading Explanations: Explanations from an AI without outcome feedback were actually linked to negative synergy, as users could not tell if the rationale was trustworthy.

Why human learning changes the outcome:

• Feedback + Explanations: When participants received both clear outcome feedback and AI explanations, human–AI teams regularly outperformed the better partner working alone.

• Verifying Reliability: Feedback allows humans to test and verify whether the AI's advice is accurate, building an effective working strategy over time.

• Designing for Learning: Treating humans as active learners—rather than passive recipients of AI output—unlocks true positive synergy in technology design.

🌐 Why is Human Learning Neglected?

• The "Plug-and-Play Tool Myth": There is a misconception that generative or predictive AI functions like traditional software. In reality, it behaves more like a dynamic—and sometimes unpredictable—colleague.

• Focus on the Algorithm: Millions are invested in calibrating data and training neural networks, yet almost nothing is spent on training the professionals who will interpret and act upon the technology's outputs.

📊 What Do Humans Need to Learn?

For this symbiosis to occur, the human learning curve must address three key pillars:

AI Mental Models Understanding what AI can and cannot do, as well as its biases and limitations. Avoiding blind trust in or complete rejection of the technology.

Prompt Engineering & Dialoguen...Learning to formulate questions, provide context for problems, and refine responses. Extracting maximum value and accuracy from the tool.

Critical Interpretation Developing the ability to audit AI responses (curation). Mitigating the risks of "hallucinations" and technical or ethical errors. 🚀 The Impact of Learning on Team Dynamics

When the human element is integrated into the learning process, team dynamics shift dramatically:

1. Reduced Technology Abandonment: Professionals who understand how AI works are less likely to give up on it after the first error.

2. Co-evolution: As humans learn to guide the AI, interactions generate better data, which in turn improves the algorithm's long-term performance.

3. From Operator to Supervisor: The human role shifts from "executing tasks" to "managing intelligence," requiring greater critical thinking and decision-making skills.

Max Planck Institute for Human Development 

TECH

AI could reduce workers' bargaining power, says ECB

Artificial intelligence could diminish the bargaining power of workers in the Eurozone, even before causing significant shifts in employment. This warning comes from Philip Lane, Chief Economist at the European Central Bank (ECB), who suggests that the potential for certain roles to be replaced could weaken workers' ability to negotiate wage increases.

This assessment arrives at a time when inflation remains under pressure from energy costs, and companies are maintaining modest hiring plans. According to Lane, the threat of automation could introduce a new factor that restrains wage growth—even if artificial intelligence has not yet triggered substantial changes in overall employment levels.

How AI might affect workers' bargaining power... In a speech delivered on October 13 in Dublin, during the International AI Summit, Philip Lane explained that artificial intelligence has not yet significantly altered general employment trends in the Eurozone. Nevertheless, hiring plans remain cautious, which particularly affects individuals seeking their first job.

The economist argues that the possibility of replacing certain functions with AI systems could limit workers' ability to demand better pay. This pressure could temper wage growth and reduce the share of economic income that goes to labor.

This mechanism does not necessarily require layoffs. If workers believe their roles could be automated, they may have less leverage to negotiate salary increases or seek better conditions elsewhere.

However, the analysis does not demonstrate that this dynamic is already producing measurable effects. Lane presents it as a possibility that helps in understanding the evolution of the labor market. The official had already raised this possibility on October 5, linking the potential replacement of workers by AI to a cooling in labor demand and the difficulty in securing wage increases that could offset rising energy prices.

Half of the surveyed workers already use AI...Concerns regarding the effects of automation arise at a time when artificial intelligence is being rapidly adopted by European companies.

According to a study published by the ECB in August 2026, the share of surveyed workers who use AI in their professional activities doubled over two years—from 26% in 2024 to 52% in 2026.

These findings are based on the ECB’s Consumer Expectations Survey, which collects monthly responses from approximately 20,000 individuals across 11 eurozone countries.

Participants who use these tools reported time savings; however, these gains are concentrated in specific sectors and specialized tasks, such as programming. Despite this rapid adoption, the ECB believes it is still too early to discern any significant impact on the economy's aggregate productivity.

Furthermore, this transformation does not affect all professions in the same way. Some roles may see increased demand for skills that complement artificial intelligence, while others face a higher risk of being replaced.

This disparity underscores the importance of professional adaptation. As discussed in our article on the use of AI in the workplace, the automation of tasks does not necessarily mean the elimination of entire professions; rather, it may shift the skills required and change how companies organize work.

Investment in AI also exerts pressure on interest rates. The economic consequences of artificial intelligence extend beyond employment and wages. Philip Lane considers it plausible that the global growth in AI investment, coupled with expectations of future productivity gains, is contributing to the rise in long-term interest rates.

Developing models, building data centers, and expanding computational infrastructure require substantial investment. Financing these activities has contributed to an increase in debt within the technology sector, particularly in international markets.

Although the scale of investment in Europe is smaller than that seen in the United States and other regions, the rise in international interest rates is also spilling over into the euro area.

For the ECB, this development represents a tightening of financial conditions. Higher long-term interest rates can reduce corporate investment, increase financing costs, and dampen economic activity.

These effects are relevant to monetary policy decisions, as tighter financial conditions can help reduce inflationary pressures over the medium term.

Lane also warned of the risks associated with the high valuations of AI companies and the increased issuance of debt to finance the sector. A revision of growth expectations could affect financial markets and call into question some of the investments already made.

The ECB acknowledges the potential of AI for the European economy...Despite the identified risks, Philip Lane believes that artificial intelligence could boost productivity and help offset the economic effects of Europe’s aging population.

The technology could enable companies to produce more with the resources at their disposal, but the outcomes will depend on how AI is integrated into work processes and on the ability of organizations and workers to adapt.

The main uncertainty lies in the distribution of these gains. An improvement in productivity does not, in itself, guarantee proportional wage increases—especially if automation reduces the demand for certain skills.

For now, the ECB does not see a widespread transformation of employment driven by artificial intelligence. The possibility that the technology may be limiting workers' bargaining power remains an economic hypothesis that will need to be tested against trends in wages, hiring, and productivity.

Artificial intelligence can reduce workers' bargaining power by automating routine tasks, shifting the balance from labor toward capital, and muting hiring plans.

Key factors in AI's impact on bargaining power:

• Muted Hiring and Leverage: Slow or cautious hiring limits employees' ability to switch jobs for higher pay or use competing offers as leverage.

• Risk of Replacement: The threat of AI replacing specific worker roles lowers the pressure on companies to raise wages during high inflation.

• Shift to Capital: As firms invest more in automation and software tools, their direct dependence on individual workers decreases.

• Increased Productivity Demands: Many organizations use AI to extract more output from existing employees rather than increasing headcount or raising compensation.

• Algorithmic Management: Data-driven oversight and monitoring can intensify workloads, shrink worker autonomy, and complicate collective organizing.

Artificial intelligence reduces workers' bargaining power by shifting economic leverage toward employers through several core mechanisms:

• Threat of substitution: The potential for AI and automation to replace routine, medium-skill, or specialized human tasks makes firms less dependent on individual workers or labor supply, lowering employees' confidence and leverage to demand higher pay.

• Muted hiring and mobility: When companies scale back or freeze general hiring—relying instead on AI to extract more output from existing staff—workers have fewer alternative job openings or competing offers to use as leverage in wage negotiations.

• Algorithmic management and surveillance: Employers increasingly use data-driven systems to track productivity, set workloads unilaterally, and anticipate or disrupt union-organizing efforts, weakening collective voice.

• Shifting labor share of income: As capital is funneled into intelligent infrastructure and automation platforms rather than human wages, the overall labor share of total income shrinks, dampening wage growth even during periods of high inflation.

mundophone

 

DIGITAL LIFE

Smartphone chatting and payment habits help ai identify daily stress, small study finds

Your smartphone may be able to tell you when you are having a stressful day based on how you use it, a KAIST research team has found.

The AI-assisted technique, developed by a team at the KAIST School of Computing, assesses users' daily stress based on the everyday tasks they repeatedly perform on their phones, the university said Thursday.

The best-performing model identified stressful days with an average accuracy of 75.9 percent among participants tracked for about a month, and 73.8 percent among those tracked for about 105 days.

The research ties mental health monitoring to the smartphone, a device most people now carry everywhere. Because phone usage records capture changes in everyday behavior over time, they are increasingly used as a digital indicator of mental health conditions such as stress and depression.

Earlier studies mostly measured screen time or which apps people used, but those numbers can change whenever someone switches apps. The researchers gave the example of someone who moves from KakaoTalk to WhatsApp or Line. The app changes, but the task stays the same: chatting on a messenger.

The KAIST team instead focused on what users do on their phones rather than which apps they use, so the data stays consistent even when people switch apps.

The team analyzed data from 26 university students and office workers in their 20s and 30s, divided into 16 short-term participants, who participated for 30.5 days on average, and 10 long-term participants, who took part in the study for 105.1 days on average. The participants provided both phone data and daily self-assessments on stress, which were used to create a personal model.

The best-performing model distinguished stressful days from stress-free ones with an average accuracy of 75.9 percent for the short-term group and 73.8 percent for the long-term group.

The study showed a link between phone habit and stress, not that any habit causes it. The research also did not record what users typed or what their messages said, to protect privacy. Instead, it logged how often a person typed and how they moved between screens.

"We looked at the smartphone not simply as a device that measures usage time, but as a living space where a person's communication, information searches and financial activities repeat," said Prof. Lee Ui-chan of KAIST's School of Computing.

“By turning complex smartphone records into units of behavior that people can understand, this study lays the groundwork for analyzing long-term changes in mental health more reliably,” Lee said.

Three types of smartphone users...Participants were divided into three groups according to their habits. “Pure social” users mainly used their phones to communicate, maintain relationships, and form new social connections.

“Light process” users spent less than four to five hours a day checking news, viewing entertainment, using social platforms, and passively scrolling. About one third were classified as “heavy process” users who spent four to five hours or more on their phones each day, primarily consuming content rather than talking with others.

“Our main finding is that personality alone—such as a high level of neuroticism—does not make someone addicted to their phone. People who are more prone to negative emotions like anxiety, depression, or stress do not automatically become addicted. What really matters is how well they can control their behavior, emotions, and impulses and how strongly they fear missing out,” said Dr. Johanna Takács, senior research fellow at the Faculty of Health Sciences at Semmelweis University and lead researcher of the study.

Passive scrolling strains attention...The findings point to passive content consumption as a particularly concerning behavior. Social feeds and short videos deliver a rapid succession of novel stimuli, continually pulling attention from one item to the next. Prolonged exposure to that pattern may make sustained concentration more difficult and contribute to mental fatigue.

On cognitive tests, “heavy process” users performed worse than expected for their age on measures of attention and memory. They also slept more poorly, sought new stimulation more intensely, and found it harder to resist the urge to check their phones or satisfy an impulse immediately.

Time still mattered, but primarily for physical health. Longer use was associated with posture-related effects that may extend beyond neck discomfort to balance, reaction time, and everyday movement.

Because the research identified associations rather than assigning participants to different smartphone habits, it cannot prove that phone use directly caused the observed mental or physical differences. The results instead highlight patterns that may help identify which behaviors deserve closer attention.

How text neck affects the body...“When people hold their phones at waist or chest level and look down at them, the head moves forward, creating a forward head posture, also known as ‘text neck.’ This puts extra strain on the neck and can cause pain or early wear and tear of the neck joints. The neck contains receptors that tell the brain how the head is positioned. If a forward head posture is maintained for long periods, these signals can change and lead to balance problems. These effects are not limited to phone use but can also influence everyday movements,” said Beáta Seregély, assistant lecturer and expert responsible for the physical measurements in the study.

The head becomes harder for the neck to support as it shifts forward, increasing the load on muscles and joints. At the same time, prolonged changes in head position may interfere with sensory information that helps the brain coordinate posture and balance.

Building healthier phone habits...The researchers do not recommend banning smartphones or blaming young people for using them. Their findings instead support helping users become more deliberate about when, why, and how they reach for their devices.

Practical steps include silencing unnecessary notifications, especially alerts from social media and messaging apps; setting limits on social media use; avoiding phones before bedtime; exercising regularly; and keeping the screen closer to eye level to reduce strain.

Mental health strategies may be just as important. Behavioral training and psychological support can help young people strengthen self-control and become less anxious about missing social events. As those pressures ease, people who are sensitive, anxious, or prone to stress may be far less likely to develop problematic smartphone habits.

A new study by researchers at KAIST (Korea Advanced Institute of Science and Technology) shows that artificial intelligence can identify daily stress with up to 75.9% accuracy by analyzing routine smartphone habits rather than just total screen time:

• Focus on Intent, Not Apps: Instead of tracking specific app names (which change or get swapped out by users, like moving from KakaoTalk to WhatsApp), the AI focuses on what a user is trying to accomplish—such as messenger chatting, web browsing, financial payments, or reading and writing emails.

• SHIRBT-MMF Technology: The research team developed a system called "Smartphone Human Interaction Routine Behavior Tasks" (SHIRBTs) using a technique named Multi-Level Sequential Pattern Mining (ML-SPM) combined with a large language model (LLM) to categorize everyday screen transitions.

• Accuracy Rates:

• Reached 75.9% average accuracy for short-term participants (tracked for an average of 30.5 days).

• Reached 73.8% average accuracy for long-term participants (tracked for an average of 105.1 days).

• This performance was about 3.5 to 5.5 percentage points higher than traditional app-usage tracking methods.

• Participant Group: The study analyzed real-world data from 26 university students and office workers in their 20s and 30s using Android devices.

• Privacy Protection: The system did not store actual message text or contents; it only logged behavioral patterns (like frequency and screen transitions), and contact identifiers were processed using hashing (converting data into anonymous values).

• Important Caveat: The researchers note that the study shows an association between behavioral patterns and stress, not a direct causal relationship

A research team at KAIST (Korea Advanced Institute of Science and Technology) developed an AI-assisted technique that identifies daily stress by tracking routine smartphone usage, achieving an accuracy of up to 75.9%.

Key findings of the study:

• Accuracy: The top-performing personal AI model distinguished stressful days from stress-free days with an average accuracy of 75.9% for short-term participants (tracked for roughly 30 days) and 73.8% for long-term participants (tracked for about 105 days).

• Behaviors Tracked: The system analyzes everyday actions such as typing frequency, screen transitions, communication patterns, information searches, and financial/payment activities.

• Privacy-First Design: To protect user privacy, the AI did not read the content of text messages or record what users typed, focusing instead on behavioral metadata like movement between screens and interaction rhythms.

• Participants: The study tracked Android smartphone users—consisting of 26 university students and office workers in their 20s and 30s—who logged their phone habits alongside daily self-assessments of stress.

• Correlation, Not Causation: The researchers noted that the findings show a strong association between phone habits and stress, rather than proving a direct causal relationship where a specific habit causes stress.

by mundophone