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ASUS ZenBook Duo 2025 (UX8406): Great Screens, Shame about the GPU

Phil
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ASUS ZenBook Duo 2025 (UX8406): Great Screens, Shame about the GPU

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ASUS Zenbook Duo (UX 8406) 2025 Review: Key Specifications

Key specs Value
CPU Intel Core Ultra 9 285H
NPU Intel AI Boost
Graphics Intel Arc Graphics (integrated)
Memory 32GB LPDDR5X
Storage 1TB SSD
Screen size 2x 14in
Screen type OLED
Resolution 2880 x 1800 x 2
Max refresh rate 120Hz
Colour gamut (measured) 98% P3
Brightness (measured) 366 nits
Ports 1x USB 3.2 Gen 1 Type-A, 2x Thunderbolt 4, 1x HDMI 2.1, 1x 3.5mm audio
Wireless connectivity Wi-Fi 7, Bluetooth 5.4
Dimensions 31.35 x 21.8 x 2 cm
Weight 1.65 kg

Design and Build

Very little has changed on the outside of the ASUS Zenbook Duo. It still has the chunky plastic and aluminium build quality, the same pattern of lines on the lid, and is slightly thicker and heavier than you’d expect from a Core Ultra laptop – the extra screen hidden under the keyboard accounts for that, and we’re thankful that OLEDs are thinner than IPS panels for this reason (though the screens here are still a little reflective). It’s the same charcoal grey, and has the same flap on the back that keeps the screens upright when you’ve detached the keyboard.

Performance

None of this is to say that you can’t run creative apps on the Zenbook Duo. You absolutely can throw InDesign layouts around, build up intricately layered creations in Photoshop, edit video in DaVinci Resolve (though our testing with that app failed while rendering a 100Mbps, 4K, HEVC video file, which may have been too much for it – the laptop was still receiving firmware and driver updates during our testing, so it’s possible a future download could improve stability here) and lots more besides. It will just take a little longer than something with an Nvidia chip in it.

Battery Life

The ZenBook Duo 2025’s battery life comes out at 10h 16m in our looping video test – that’s two hours less than the previous generation’s score, but it will still keep you going all day, especially if you use power-saving features or let it go to sleep at lunchtime. This test was only using one screen, so you may see the endurance dip if using both.

Price

This is an expensive laptop, coming in at $1,799.99/£2,099 at the time of writing. You could pick up two of the base-price MacBook Airs for the same cost, or many other entries on our big list of the best laptops for graphic design. Perhaps a massive gaming desktop would feel like a better use of the cash (or more)? When you’ve got it set up and are working away, enjoying the extra screen space from a portable machine, it feels like good value in a way that few other machines do. You’re getting something unusual, something that stands out, something that works well, and which is well designed and built. And that’s priceless.

Who is it for?

If you like to show off in a coffee shop then this is the perfect machine. It will undoubtedly draw admiring glances and penetrating questions from fellow laptop connoisseurs. Mostly, it’s for those who feel constricted by the current batch of laptops with screens that, despite being bright, colourful and high-res, are held back by their singularity. If your work needs regularly spill over onto a second monitor, then having an extra one you can take with you makes a whole lot of sense. You can always use it in standard laptop mode if you’re feeling shy.

Should I buy it?

Buy it if:

  • You need the screen space
  • You want to be the centre of attention
  • You want something that feels different

Don’t buy it if:

  • You need more GPU power
  • Or want a Copilot+ machine
  • Or a Mac

Also consider

  • Other laptops with similar specifications
  • Other laptops for graphic design

FAQs

Q: Is the ASUS Zenbook Duo a good laptop for graphic design?
A: Yes, it is suitable for graphic design, but it may not be the best option due to its limited GPU power.

Q: Is the ASUS Zenbook Duo expensive?
A: Yes, it is a high-end laptop with a price tag of $1,799.99/£2,099.

Q: Is the ASUS Zenbook Duo worth it?
A: Yes, it is a unique and well-designed laptop that offers excellent screen space and performance, making it worth the investment for those who need it.

How LetzAI Empowered Creativity with Scalable, High-Performance AI Infrastructure

Avery Winslow
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How LetzAI Empowered Creativity with Scalable, High-Performance AI Infrastructure

LetzAI: Empowering Creativity with Scalable, High-Performance AI Infrastructure

The Problem

In 2023, Neon Internet CEO and co-founder Misch Strotz was struck by a clever idea: give Luxembourg residents the power to easily generate local images using AI. Within a month, Luxembourg-focused LetzAI V1 went live. Encouraged by strong local demand, Strotz and his team began working on a global version of the platform.

The Solution

LetzAI opted for Gcore’s state-of-the-art NVIDIA H100 GPUs in Luxembourg. This was the perfect option, allowing the company to keep its model training and development local. With Gcore, LetzAI can rent GPUs rather than entire servers, making it a far more cost-effective solution by avoiding unnecessary costs like excess storage and idle server capacity.

The Result

LetzAI was able to adapt its app to run in containers, configure model training tasks to run on GPU Cloud, and use Everywhere Inference for image generation and upscaling. In just two months, LetzAI V2 launched to serve users around the world.

Empowering Creativity with Scalable, High-Performance AI Infrastructure

With Gcore’s continued support, LetzAI quickly deployed V3. The Gcore team was incredibly responsive to the company’s needs, guiding them to the best solution for their evolving requirements. This has given LetzAI a powerful and efficient infrastructure that can flex according to demand.

Platform Evolution and AI Innovation without Limits

As its models exceed user expectations worldwide, LetzAI can rely on Gcore to handle a high volume of requests. Confident about generating a limitless number of high-quality images on the fly, LetzAI can continue to scale rapidly to become a sustainable, innovation-driven business.

Conclusion

LetzAI’s partnership with Gcore has enabled the company to democratize and personalize AI-powered image generation, empowering creators to unlock endless possibilities. As LetzAI continues to evolve, its partnership with Gcore will be central to its continued success.

FAQs

Q: What is LetzAI?
A: LetzAI is a platform for generating high-quality AI-generated images.

Q: What is the mission of LetzAI?
A: LetzAI’s mission is to democratize and personalize AI-powered image generation.

Q: How did LetzAI overcome the challenge of training and launching V2?
A: LetzAI partnered with Gcore to access their state-of-the-art NVIDIA H100 GPUs in Luxembourg.

Q: What is Everywhere Inference, and how does it benefit LetzAI?
A: Everywhere Inference is a technology that reduces the latency of output and enhances the performance of AI-enabled apps, allowing LetzAI to optimize its workflows for more accurate, real-time results.

Apple’s AI Watch concept is already being called a mistake

Phil
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Apple’s AI Watch concept is already being called a mistake

The Rise and Fall of AI Gadgets and Apple’s Latest Ambitions

The Rollercoaster of AI Development

In the not-so-distant future, the tech world’s response to the rise of AI over the last few years will be studied. It’s been a rollercoaster of hasty pivots, rushed launches, and occasionally, impressive breakthroughs. However, while generative AI models have taken the internet by storm, several attempts to create ‘AI gadgets’ have failed.

The Flop of AI Gadgets

From the Rabbit R1 to the Humane Pin, various AI gadgets fell on their faces last year. But perhaps the most surprising flop of all has come courtesy of Apple, whose lofty claims about Apple Intelligence and Siri’s transformative potential for the iPhone are yet to materialise.

Apple’s Latest Ambitions

According to new reports, Apple is working on turning Apple Watches into "AI devices with cameras" by 2027. Mark Guran, an Apple leaker, claims that the company is considering adding cameras to both its standard Series watches and Ultra models, which would allow the Watch to use AI features such as Visual Intelligence. Currently limited to the iPhone 16 lineup, Visual Intelligence lets you quickly learn more about the places and objects around you using the iPhone’s camera.

A Misguided Focus on AI?

However, it’s clear from an increasing chorus online that Apple’s focus on AI is starting to feel misguided. "Instead of trying to justify their spend on AI by adding useless tech to other projects, they should just transfer those teams to other more promising programs and admit they made a misstep," one Redditor comments in response to the report. Another adds, "Apple, I will buy a toothbrush from you if you would please stop with your AI obsession". And there are plenty of comments lamenting the state of Siri itself: "I just want to be able to ask Siri to open my garage without her going ‘okay’ and then turning on every light in my house."

Conclusion

With Apple’s AI-improved Siri missing in action (which is allegedly causing serious tension in the company) and dedicated AI gadgets flopping left and centre, it’s hard to get excited about the idea of an ‘Apple Intelligence Watch’.

FAQs

Q: What are the latest developments in Apple’s AI efforts?
A: Apple is reportedly working on turning Apple Watches into "AI devices with cameras" by 2027.

Q: Why is there a lack of excitement about the Apple Intelligence Watch?
A: The lack of excitement stems from Apple’s AI-improved Siri being missing in action and dedicated AI gadgets flopping left and centre.

Q: What are some of the criticisms of Apple’s focus on AI?
A: Some critics argue that Apple is devoting too many resources to AI, rather than focusing on more promising areas. Others are frustrated with the lack of tangible benefits from Apple’s AI efforts, such as Siri’s limitations.

Petabyte-Scale Video Processing with NVIDIA NeMo Curator on NVIDIA DGX Cloud

Jordan Waverly
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Petabyte-Scale Video Processing with NVIDIA NeMo Curator on NVIDIA DGX Cloud

Load Balancing with Auto-Scaling

Traditional data processing often relies on batch processing, where large volumes of data are accumulated to process at one time, progressing through one stage at a time. This introduces two primary problems:

* Efficiency: It’s difficult to efficiently use heterogeneous resources. When the batch is working on a CPU-heavy stage, the GPU resources will be underused, and the reverse is also true.
* Latency: The need to store and load intermediate data products to and from the cluster storage between processing stages introduces significant latency.

In contrast, streaming processing directly pipes intermediate data products between stages, and begins next-stage processing on individual data as soon as the previous stages are complete.

GPU Workers During a Run of NeMo Curator

The efficient use of GPU resources can be further demonstrated by tracking the worker utilization of GPU stages of the NeMo Curator pipeline during execution.

This experiment was done with 32 GPUs on four nodes. If you run with fewer GPUs, the GPU worker usage will be lower because the auto-scaler has a coarser granularity for GPU allocation. If you run with more GPUs, the GPU worker usage will be even closer to 100%.

Request Early Access

We have been actively working with a variety of early access account partners to enable best-in-class TCO for video data curation, unlocking the next generation of multi-modal models for physical AI and beyond:

  • Blackforest Labs
  • Canva
  • Deluxe Media
  • Getty Images
  • Linker Vision
  • Milestone Systems
  • Nexar
  • Twelvelabs
  • Uber

To get started without needing your own compute infrastructure, NeMo Video Curator is available through early access on NVIDIA DGX Cloud. Submit your interest in video data curation and model fine-tuning to the NVIDIA NeMo Curator Early Access Program and select “managed services” when submitting your interest. NeMo Video Curator is also available through early access as a downloadable SDK, suitable for running on your own infrastructure, also through the Early Access Program.

Conclusion

In conclusion, the NeMo Curator pipeline has demonstrated significant improvements in efficiency and throughput, while also providing a scalable and flexible solution for video data curation and model fine-tuning. With its ability to efficiently utilize GPU resources and its support for auto-scaling, the NeMo Curator pipeline is well-positioned to meet the demands of the growing physical AI market.

FAQs

Q: What is the NeMo Curator pipeline?

A: The NeMo Curator pipeline is a flexible, GPU-accelerated streaming pipeline for large-scale video data curation and model fine-tuning.

Q: What are the benefits of using the NeMo Curator pipeline?

A: The NeMo Curator pipeline provides a scalable and flexible solution for video data curation and model fine-tuning, offering significant improvements in efficiency and throughput while providing a lower total cost of ownership (TCO).

Q: How does the NeMo Curator pipeline use GPU resources?

A: The NeMo Curator pipeline uses GPU resources efficiently, with the auto-scaler rearranging resources to keep GPU stage workers busy over 99.5% of the time.

Q: How can I get started with the NeMo Curator pipeline?

A: You can get started with the NeMo Curator pipeline by submitting your interest in video data curation and model fine-tuning to the NVIDIA NeMo Curator Early Access Program and selecting “managed services” or “downloadable SDK” when submitting your interest.

OpenAI Says Its AI Voice Assistant is Now Better to Chat With

Avery Winslow
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OpenAI Says Its AI Voice Assistant is Now Better to Chat With

OpenAI Updates Advanced Voice Mode to Enhance User Experience

Reducing Interruptions and Improving Personality

OpenAI released updates on Monday for Advanced Voice Mode, its AI voice feature that enables real-time conversations in ChatGPT, to make the AI assistant more personable and interrupt users less frequently.

Addressing a Common Issue

OpenAI’s latest update aims to address a frequent problem with AI voice assistants, which often interrupt users when they pause to think or take a deep breath. This can be frustrating and disrupt the flow of conversation. The new update ensures that free users of ChatGPT now have access to a new version of Advanced Voice Mode that lets users pause without being interrupted.

Improved Experience for Paid Users

Paying users of ChatGPT, including subscribers to OpenAI’s Plus, Teams, Edu, Business, and Pro tiers, will also now get less frequent interruptions when using Advanced Voice Mode, as well as an improved personality for the voice assistant. An OpenAI spokesperson describes the new AI voice assistant as "more direct, engaging, concise, specific, and creative in its answers."

Competition in the AI Voice Assistant Space

The improvements to Advanced Voice Mode come amid intense pressure from competitors in the AI voice assistant space. Sesame, an Andreessen Horowitz-backed startup created by Oculus cofounder Brendan Iribe, recently went viral for its natural-sounding AI voice assistants, Maya and Miles. Larger players are also stepping more aggressively into the AI voice assistant space, such as Amazon, which is readying the release of its LLM-powered version of Alexa.

Conclusion

The updates to Advanced Voice Mode demonstrate OpenAI’s commitment to enhancing the user experience and addressing common pain points. By reducing interruptions and improving the personality of its AI assistant, OpenAI is poised to stay competitive in the rapidly evolving AI voice assistant space.

Frequently Asked Questions

Q: What is Advanced Voice Mode?
A: Advanced Voice Mode is a feature in ChatGPT that enables real-time conversations with AI assistants.

Q: What is the purpose of the latest update to Advanced Voice Mode?
A: The update reduces interruptions and improves the personality of the AI assistant to provide a more personable and engaging experience.

Q: Who can access the improved Advanced Voice Mode?
A: Free users of ChatGPT and paying users of OpenAI’s Plus, Teams, Edu, Business, and Pro tiers can access the improved Advanced Voice Mode.

Q: What are the benefits of the improved Advanced Voice Mode?
A: The improved Advanced Voice Mode reduces interruptions, provides a more personable and engaging experience, and offers more direct, engaging, concise, specific, and creative answers from the AI assistant.

FBI Launched Task Force to Investigate Tesla Attacks

Avery Winslow
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FBI Launched Task Force to Investigate Tesla Attacks

FBI Forms 10-Person Task Force to Combat Vandalism and Violence Targeting Tesla

New York Post Report Reveals Existence of Task Force

A report in the New York Post on Monday revealed the existence of a new 10-person FBI task force focused on vandalism and other activity aimed toward Tesla in response to the actions of Elon Musk.

FBI Director’s Statement

FBI Director Kash Patel shared the story on X, stating, "The FBI has been investigating the increase in violent activity toward Tesla, and over the last few days, we have taken additional steps to crack down and coordinate our response." He also referred to the acts as "domestic terrorism."

Lack of Formal Designation for Domestic Terrorist Groups

Unlike international groups branded as terrorism that the US government can bar support for, the US does not have a similar formal designation for domestic terrorist groups. A recent report by Wired cites civil liberties experts who said possible effects of the designation could give law enforcement more surveillance authority over Musk protesters and possibly the ability to share information from investigations with Musk and Tesla.

Incendiary Devices Found at Tesla Showroom in Austin, Texas

According to CNBC, earlier on Monday, police said they had found multiple "incendiary devices" at a Tesla showroom in Austin, Texas.

Task Force Tracks Protests and Investigates Dox Site

The Post article also stated that the task force is tracking the "Tesla Takedown" mass protests scheduled for March 29th and looking into a "Dogeque.st" site that claimed to dox some Tesla owners and locations, which it said appears to be run out of Sao Tome, the second-smallest country in Africa. 404 Media reports that after going offline the same day it appeared, a version of the site has reappeared on the dark web.

Conclusion

The formation of a 10-person FBI task force to combat vandalism and violence targeting Tesla is a significant development in the ongoing saga. The lack of a formal designation for domestic terrorist groups is a concern, as it may limit the government’s ability to effectively crack down on such activity. The discovery of incendiary devices at a Tesla showroom and the investigation into a dox site highlight the severity of the situation.

FAQs

Q: What is the purpose of the 10-person FBI task force?
A: The task force is focused on vandalism and other activity aimed toward Tesla in response to the actions of Elon Musk.

Q: What is the current status of the task force?
A: The task force has been formed and is actively investigating and tracking various incidents.

Q: Are there any specific incidents being investigated by the task force?
A: Yes, the task force is investigating multiple incidents, including the discovery of incendiary devices at a Tesla showroom in Austin, Texas, and a dox site claiming to target Tesla owners and locations.

Q: What is the current stance on domestic terrorism?
A: The US does not have a formal designation for domestic terrorist groups, which may limit the government’s ability to effectively combat such activity.

Software Engineers Use AI

Avery Winslow
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Software Engineers Use AI

Will AI Fully Eat Programming Jobs One Day?

The Coders Have Spoken

Almost every coder we surveyed had strong opinions on the matter. Here’s a summary of the responses, with boldface emphasis preserved:

The Doom Prophets

A small but vocal group insists AI will devour programming jobs in time. They warn that corporate bosses will slash payrolls the moment AI looks capable, leaving human engineers debugging their own obsolescence.

The Skeptics

The skeptics scoff, arguing AI is more like a hyperefficient intern—useful, but clueless—that can’t handle context, edge cases, or real problem-solving.

The Realists

The realists see AI as a force multiplier, not a job killer—automating repetitive coding but leaving the creativity, architecture, and debugging to humans. "If AI does eat programming," one put it, "I’ll just switch to debugging AI."

The Verdict

The real verdict? AI isn’t coming for your job—but it is changing it. Adapt or get left behind.

The ChatGPT Controversy

However, our survey results were not without controversy. ChatGPT made several mistakes throughout the process. It fabricated quotes, misread results, generated incomprehensible graphs, and, at multiple points, stopped counting "freelancers" as a category entirely. Rude.

Final Tips

When asked for final tips, ChatGPT suggested "a touch of snark, because AI discourse is full of strong opinions."

Conclusion

In conclusion, AI is not a job killer, but it is a game-changer. It’s up to us to adapt and evolve with the technology. The future of programming is not about replacing human jobs, but about augmenting our capabilities and working together with AI to create something new and innovative.

FAQs

Q: Will AI fully eat programming jobs one day?
A: No, AI is not a job killer, but it is changing the way we work.

Q: What are the potential benefits of AI in programming?
A: AI can automate repetitive tasks, freeing up time for more creative and high-value work.

Q: What are the potential drawbacks of AI in programming?
A: AI can replace some jobs, and it requires significant training and expertise to use effectively.

Q: How can I adapt to the changing landscape of programming?
A: Stay up-to-date with the latest developments in AI and programming, and be willing to learn new skills and adapt to new technologies.

You Can Now Download the Source Code That Sparked the AI Boom

Emerson Blake
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You Can Now Download the Source Code That Sparked the AI Boom

Teaching Computers to See: The Rise of AlexNet

A Watershed Moment in AI History

On Thursday, Google and the Computer History Museum (CHM) jointly released the source code for AlexNet, the convolutional neural network (CNN) that many credit with transforming the AI field in 2012 by proving that "deep learning" could achieve things conventional AI techniques could not.

A Departure from Traditional AI Approaches

Deep learning, which uses multi-layered neural networks that can learn from data without explicit programming, represented a significant departure from traditional AI approaches that relied on hand-crafted rules and features.

Accurate Image Recognition

The Python code, now available on CHM’s GitHub page as open source software, offers AI enthusiasts and researchers a glimpse into a key moment of computing history. AlexNet served as a watershed moment in AI because it could accurately identify objects in photographs with unprecedented accuracy—correctly classifying images into one of 1,000 categories like "strawberry," "school bus," or "golden retriever" with significantly fewer errors than previous systems.

Implications and Concerns

Like viewing original ENIAC circuitry or plans for Babbage’s Difference Engine, examining the AlexNet code may provide future historians insight into how a relatively simple implementation sparked a technology that has reshaped our world. While deep learning has enabled advances in health care, scientific research, and accessibility tools, it has also facilitated concerning developments like deepfakes, automated surveillance, and the potential for widespread job displacement.

The Birth of AlexNet

As the CHM explains in its detailed blog post, AlexNet originated from the work of University of Toronto graduate students Alex Krizhevsky and Ilya Sutskever, along with their advisor Geoffrey Hinton. The project proved that deep learning could outperform traditional computer vision methods.

A Turning Point in Computer Vision

The neural network won the 2012 ImageNet competition by recognizing objects in photos far better than any previous method. Computer vision veteran Yann LeCun, who attended the presentation in Florence, Italy, immediately recognized its importance for the field, reportedly standing up after the presentation and calling AlexNet "an unequivocal turning point in the history of computer vision."

Conclusion

The release of AlexNet’s source code provides a unique opportunity for researchers and enthusiasts to examine the technology that has had a profound impact on the field of AI. As we continue to explore the potential and limitations of deep learning, it is essential to acknowledge the historical significance of AlexNet and its role in shaping our understanding of computer vision.

FAQs

Q: What is AlexNet?
A: AlexNet is a convolutional neural network (CNN) that won the 2012 ImageNet competition by recognizing objects in photos far better than any previous method.

Q: Who developed AlexNet?
A: AlexNet was developed by University of Toronto graduate students Alex Krizhevsky and Ilya Sutskever, along with their advisor Geoffrey Hinton.

Q: What is the significance of AlexNet?
A: AlexNet is considered a watershed moment in AI history, as it proved that deep learning could achieve things conventional AI techniques could not, and it has had a profound impact on the field of computer vision.

Code Mind

Parker Lennox
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Code Mind

I. Syntax and Emptiness

When we first learn to program, everything is an obstacle. Syntax errors are indecipherable, types are Byzantine, indentation and symbols are in a foreign tongue. Our minds are full of questions and doubt. And yet, in that state of confusion, there is a sense of clarity and balance if we look for it. We are present. We are paying attention.

A more experienced developer may work in smoother fashion. They no longer think about where semicolons go, or how to declare a variable. But this fluency can be an impediment as well. They may assume understanding where there is none. They stop asking why.

II. The Simplicity of Understanding

The beginner’s focus is on making things work. This is everyone’s focus of course (we spend 99.9% of our time in a broken state after all), but for the beginner the search lacks focus. We pile on logic, conditions, flags. Our code is often verbose. This is not wrong. It is part of the path. But over time, we develop simplicity—not the kind that comes from cleverness, but the kind that comes from understanding.

III. Version Control and Non-Attachment

Git is a wonderful tool. It can be maddening, but when used artfully and mindfully it gives us an opportunity to practice non-attachment. We write, we commit, we rewrite. We branch and merge. We make mistakes and revert. We learn to let go of what we wrote yesterday.

IV. Bugs as Teachers

Bugs are not a sign of imbalance. A computer can never be out of balance actually. If a cosmic ray flips a bit, that cascade of failures is the new balance. If you unplug it, its empty, and balanced. When things appear chaotic, this is an opportunity to learn. Every bug is a mirror. It shows us how we misunderstood the system, how we made assumptions, how we moved quickly.

V. Architecture and the Space Between

A good system has room to breathe. It has space for change, for growth. It does not predict every requirement. It is not overbuilt. Like a mind with open doors, it invites what is needed, and lets go of what is not.

VI. Collaboration and the Ego

When we code alone, we are gods in our own tiny worlds. But when we code in teams, we are students again.

VII. Refactoring and Renewal

We don’t make mistakes. We make one continuous mistake, and then we refactor to create a better mistake. Refactoring deepens our understanding. The first version of any system is like a sketch—useful, but incomplete. As we work with it, we see new shapes. We learn its rhythms.

VIII. Always Returning

Every system will break. Every technique will grow old. Every library will be deprecated. In this way, software humbles us. It reminds us that we are not building forever. We are building for now. And we are part of that forever.

Conclusion

In conclusion, the mind is a powerful tool for software development. By cultivating a calm, open, beginner’s mind, we can write better code, and create better systems. We must let go of ego, attachment, and the illusion of mastery. We must be willing to learn, to refactor, and to always return to the beginner’s mind.

FAQs

Q: What is the beginner’s mind?
A: The beginner’s mind is a state of curiosity, openness, and non-attachment. It is the ability to approach a problem with a blank slate, without assumptions or preconceptions.

Q: How do I cultivate a beginner’s mind?
A: Cultivate a beginner’s mind by being open to new ideas, willing to learn, and non-attached to your own understanding. Practice mindfulness, meditation, and self-reflection to quiet your ego and clear your mind.

Q: Is it possible to achieve mastery in software development?
A: Mastery is a myth. The moment you think you have mastered software development, you stop growing. The best developers are always learning, always improving, and always returning to the beginner’s mind.

Q: How do I balance ego and humility in software development?
A: Balance your ego by recognizing that you don’t know everything, and that there is always more to learn. Practice humility by being open to feedback, and willing to admit when you are wrong.

Staying Ahead of AI-Powered Deception

Parker Lennox
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Staying Ahead of AI-Powered Deception

Deepfakes: The Growing Threat to Authentication and Security

The Rise of Deepfakes

Thanks to AI’s nonstop improvement, it’s becoming difficult for humans to spot deepfakes in a reliable manner. This poses a serious problem for any form of authentication that relies on images of the trusted individual.

Early Beginnings of Deepfakes

The first deepfake can be traced back to 1997, when a project called Video Rewrite demonstrated that it was possible to reanimate video of someone’s face to insert words that they did not say. Early deepfakes required considerable technological sophistication on the part of the user, but that’s no longer true in 2025. Thanks to generative AI technologies and techniques, like diffusion models that create images and generative adversarial networks (GANs) that make them look more believable, it’s now possible for anyone to create a deepfake using open source tools.

The Impact on Society and Security

The ready availability of sophisticated deepfake tools poses serious repercussions for privacy and security. Society suffers when deepfake tech is used to create things like fake news, hoaxes, child sexual abuse material, and revenge porn. Several bills have been proposed in the U.S. Congress and several state legislatures that would criminalize the use of technology in this manner.

The Financial Impact and Fraud

The impact on the financial world is also quite significant, in large part because of how much we rely on authentication for critical services, like opening a bank account or withdrawing money. While using biometric authentication mechanisms, such as facial recognition, can provide greater assurance than passwords or multi-factor authentication (MFA) approaches, the reality is that any authentication mechanism that relies on images or video in part to prove the identity of an individual is vulnerable to being spoofed with a deepfake.

Fraudsters’ Paradise

Fraudsters, ever the opportunists, have readily picked up deepfake tools. A recent study by Signicat found that deepfakes were used in 6.5% of fraud attempts in 2024, up from less than 1% attempts in 2021, representing more than a 2,100% increase in nominal terms. Over the same period, fraud in general was up 80%, while identity fraud was up 74%, it found.

The Threat is Real and Growing

The threat posed by deepfakes is not theoretical, and fraudsters currently are going after large financial institutions. Numerous scams were cataloged in the Financial Services Information Sharing and Analysis Center’s 185-page report. For instance, a fake video of an explosion at the Pentagon in May 2023 caused the Dow Jones to fall 85 points in four minutes. There is also the fascinating case of the North Korean who created fake identification documents and fooled KnowBe4 – the security awareness firm co-founded by the hacker Kevin Mitnick (who died in 2023) – into hiring him or her in July 2024. "If it can happen to us, it can happen to almost anyone," KnowBe4 wrote in its blog post. "Don’t let it happen to you."

Counter-Measures

However, some approaches to countering the deepfake threat show promise. iProov, a biometric authentication software company, developed patented flashmark technology to detect deepfakes. They use a proprietary flashmark technology during sign-in. By flashing different colored lights from the user’s device onto his or her face, iProov can determine the "liveness" of the individual, thereby detecting whether the face is real or a deepfake or a face-swap.

Conclusion

The AI technology that enables deepfake attacks is liable to improve in the future. That is putting pressure on companies to take steps to fortify their authentication process now or risk letting the wrong people into their operation.

FAQs

Q: What is a deepfake?
A: A deepfake is a photograph, video, or audio that’s been edited in a deceptive manner using artificial intelligence.

Q: How do deepfakes work?
A: Deepfakes use generative AI technologies and techniques, like diffusion models that create images and generative adversarial networks (GANs) that make them look more believable.

Q: What are the consequences of deepfakes?
A: Deepfakes can be used to create fake news, hoaxes, child sexual abuse material, and revenge porn, among other malicious activities.

Q: How can companies protect themselves from deepfakes?
A: Companies can use biometric authentication mechanisms, such as facial recognition, and implement additional security measures, like multi-factor authentication, to prevent deepfake attacks.

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