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Yesterday, Target Corporation announced news that no one wants to hearespecially just before the holidays. The Minneapolis-based retail giant informed employees that it is gearing up to eliminate 1,800 corporate roles at the company. Heres when the layoffs will happen and what it means for the company and its employees. Target to cull its corporate workforce by 8% On Thursday, Targets chief operating officer, Michael Fiddelke, who is set to become the companys new CEO in February, reportedly sent a memo to employees at the 440,000-strong company. According to numerous media reports, Fiddelkes memo didnt beat around the bush: the company has decided to eliminate 1,800 positions. Yet the layoffs will not hit the majority of the companys workforce. Much of its 440,000 employees work as retail members across its nearly 2,000 stores in the United States. The layoffs are not expected to impact these retail employees, who the company will rely heavily on during the upcoming holiday period. Instead, the job cuts will hit Targets corporate workforce. And as CNBC notes, citing the memo, 1,800 positions will be eliminated. The eliminations include 1,000 direct employee layoffs and another 800 roles that will no longer be filled. In his memo sent to Target staff, Fiddelke said the elimination of 1,800 corporate roles represents a reduction of about 8% of our global HQ team. Fiddelkes memo included the usual platitudes that company leadership makes when laying off employees, noting the real impact that the layoffs will have on Targets team and that the company never makes such moves lightly. However, he also argued the layoffs are a necessary step in building the future of Target and enabling the progress and growth we all want to see. Of course, the future of Target will be of little consequence to those losing their jobs ahead of the holidays. Fast Company has reached out to Target for comment and will update this story if we hear back. A spokesperson told CNBC that, in addition to severance packages, those laid off will receive benefits and pay until January 3. Competitors Walmart and Amazon are thriving The layoffs may not be much of a surprise to people who have been paying attention to Target’s recent struggles. While the companys competitors, such as Walmart and Amazon, have seen their businessesand stock pricesthrive, its been the opposite story for Target. As my colleague Elizabeth Segran explored in May, Target had a “terrible, horrible, no good, very bad year.” In January, in the lead-up to Donald Trumps inauguration, Target announced it was reversing course on its celebrated diversity, equity, and inclusion (DEI) commitments. Many loyal Target customers saw that as capitulation, and the backlash, in the form of boycotts, was swift, with foot traffic to many of its stores falling by up to 7.7%. And its not like President Trump has done Target many favors, as his tariffs have had a measurable impact on Target’s costs. The company acquires most of its items from overseas, including China. It must now pay more for those products and thus take a hit on its bottom line, or pass the cost of those price increases onto customers, which could lead to them buying less. And those customers are already under pressure from inflation, which has caused them to pull back on their discretionary spending. Thats a big problem for Target, as the majority of the goods it sells are discretionary items. All of these issues are something that incoming CEO Fiddelke is going to have to fix, and, with yesterdays announcement, it appears that he thinks layoffs are part of that fix. TGT investors dont seem to care about the layoffs So far at least, Target’s investors don’t seem to think these layoffs will meaningfully impact the company in the short term. Often when a corporate giant announces mass layoffs, the companys stock price spikes. Thats because layoffs are seen as the fastest way for a company to reduce its costs, which can help increase profits. But looking at Target’s stock price (NYSE: TGT) this morning, it appears as if investors are shrugging off the news. As of this writing, TGT stock is up just half a percent to $94.75 in premarket trading. Yesterday, TGT shares closed up just a quarter of a percent to $94.25. This suggests that investors are going to need to see a lot more change at the companyand with its financesto get them excited about the stock again. And TGT stock has had a bad run as of late. As of yesterdays close, TGT shares were down more than 30% since the year began. Over the past 12 months, TGT shares have fallen more than 36%. And over the past five years, TGT shares have dropped a staggering 41%. During that same five-year timeframe, shares in competitor Walmart (NYSE: WMT) are up 122% and shares in Amazon.com (Nasdaq: AMZN) are up almost 38%.
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E-Commerce
One of my Bentley University students put it plainly the other day: AI taking entry-level jobs is a when, not an if. But in venture capital, 70% of the decision is reading the founder and teamand thats something AI cant do. That simple breakdown , 70% people, 30% productflips the usual narrative about finance. For decades, finance was defined by numbers. Analysts lived and died by the spreadsheets. Today, AI can run discounted cash flows, parse a term sheet, and size a market faster than any junior associate. But if you talk to people in venture capital, theyll tell you the math has never been the most important part. The numbers matter, of course, but the difference between betting on a future unicorn and losing it all is whether you can read the humans across the table. AIs takeover of the crunching jobs Students see whats coming. The entry-level finance roles that once trained armies of analysts are increasingly exposed to automation. AI models can scan thousands of comparable companies in seconds, build slide decks, even flag anomalies that a first-year hire would have spent a weekend catching. In fact, McKinsey estimates that nearly half of finance tasks could already be automated by existing AI tools. What was once seen as a rite of passagethe long hours bent over Excelmay soon look as outdated as typewriters in an accounting office. Thats why many students I teach dont just worry about if AI will change their jobs. They assume it already has. The conversation now is how to build careers in finance when machines are better, faster, and cheaper at the very tasks that used to get you in the door. The venture capital exception Venture capital, especially at the earliest stages, offers a counterintuitive lesson. The math can only take you so far. Market sizing, revenue projections, even technical due diligenceall of it is valuable, but none of it predicts success the way the founder does. Investors Ive spoken with put it bluntly: youre betting on people, not just products. Thats the essence of the 70/30 rule my student repeated: 70% of the investment decision is about the team. Only 30% is about the idea. Surveys of venture investors back this up. First Round Capital, for example, has consistently found that founder quality outranks product or market sizing in early-stage decisions. You want founders with resilience, persuasion, and the grit to pivot when the idea inevitably changes. AI can tell you the addressable market. It cannot tell you if this particular founder has the charisma to convince skeptical investors, the judgment to hire the right people, or the sheer stubbornness to keep going after three failed prototypes. As one early-stage investor told me recently, AI can show me a founders track record in five seconds, but it cant tell me whether they can read a room or take a punch. Thats still my job. Why Gen Z gets it What strikes me is how quickly students are internalizing this. Rather than seeing their careers in finance as doomed, they are recalibrating. They want to be the people who can build relationships, persuade others, and trust their instincts. Theyre preparing not to be number crunchers, but decision-makers. In class discussions, they talk about internships where AI already plays a role in screening deals. Their takeaway isnt despairits clarity. They see that the irreplaceable part of the job is not working the models but reading the room. They know AI may one day suggest which startup should succeed, but it wont sit across the table from a founder at 11 p.m., hear the quiver in their voice, and know whether thats nerves or conviction. Gen Z, often criticized for being too soft, may be better positioned than we think. Theyve grown up digital. They understand the strengths of machines, but they also see the limits. Theyre comfortable letting AI do the heavy lifting if it means their human skills rise in value. Lessons for the future of work This shift should force us to rethink not only finance, but the future of work itself. If venture capital is a case study, the lesson is that industries once defined by quantitative rigor may end up placing even greater value on qualitative judgment. Harvard Business Review has made a similar point, noting that as AI scales technical analysis, soft skills are fast becoming the hardest skills to replace. The irony is rich: the most number-driven fields may be the ones where numbers matter least. And if thats true, Gen Z might just be the generation that restores humanity to the financial worldnot by rejecting technology, but by mastering what machines cant. Back to the 70/30 rule That brings me back to my student. Their comment wasnt just about venture capital. It was about what comes next in finance, and perhaps beyond. If AI eats the numbers, the real work will be reading people. AI owns the math. But the gutthe judgment, empathy, and intuition that turn data into decisionsstill belongs to us. In the end, the algorithms will get faster, but the best investors will always pause, look across the table, and trust what no machine can calculate: the human pulse of possibility.
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E-Commerce
Hello again, and thank you, as always, for spending time with Fast Companys Plugged In. Apple is legendary for figuring out what people want before they realize they want it. But since 2021, its MacBook Pro hasn’t been like that at all. Instead, this venerable laptop’s recent design has reflected Apple’s willingness to trust its customers’ judgmenteven when it’s been at odds with the company’s own instincts. In part, that’s because of a 2016 reimagining of the MacBook Pro that didn’t stick. Atypically, Apple then went on to reverse many of the changes it had made. The fancy function-key replacement known as the Touch Bar went bye-bye. And several mundane-but-useful features Apple had axed came back, including the MagSafe power connector, HDMI port, and SD Card slot. The result was a computer that was noticeably chunkier than the MacBook Air. But it was also particularly well tailored to the needs of people who prize sheer usefulness above all else. It was a workhorseyou know, professional. The newest 14-inch MacBook Pro, which I’ve been using for a little less than a week (the company provided a unit for review), retains that vision. Actually, it retains everything about its immediate predecessor except the chip. A classic example of a “speed bump” upgrade, the machine is now powered by Apple’s next-generation M5 processor. So are updated versions of the iPad Pro and Vision Proan unusual example of disparate Apple products shipping with the same new chip all at once. In another break from recent years, Apple isn’t immediately rolling out the new MacBook Pro in multiple variants: fast, faster, and fastest. Only the entry-level 14-inch model is getting a new chip. Higher-end Pros (including the 16-inch version) are still equipped with last year’s M4 Pro and M4 Max chips, leaving the MacBook Pro lineup in transition. For now, the M5 MacBook Pro, with a starting price of $1,599, occupies a middle ground among Apple laptops. Many people will be delighted with the cheaper, lighter, thinner MacBook Air, an exemplary laptop in its own right. Others whose jobs involve particularly computationally intensive work, such as heavy-duty video editing, might opt for the M4 Pro or M4 Max versions of the MacBook Pro, or wait for M5 Pro and M5 Max ones. Apple itself described the M5 chip as suitable for “college students, business users, and aspiring creators,” a pretty concise way of encompassing the M5 MacBook Pro’s sweet spot. Still, even though I’m a happy owner of a 15-inch MacBook Air, spending time with the new MacBook Pro made clear to me why some people would gladly pay a premium for it. For starters, there are several fundamental ways in which the MacBook Pro is enough better than the MacBook Air to matter. That starts with the screen, which has brighter Mini-LED lighting and, thanks to ProMotion technology, smoother scrolling. The $150 Nano-texture display option, which Apple included on my review unit, does the job when it comes to the promised glare reduction: I felt more like I was reading off paper than a shiny LCD. The Pro’s six-speaker audio system, with a subwoofer, is a significant upgrade over the Air’s. Its claimed battery life is longerup to 24 hours of video streaming versus 18 for the Air. Yes, that’s an upgrade from impressively long to remarkably long, but anything that reduces a portable computer’s reliance on chargers and AC outlets is a blessing. Buy a MacBook Pro, and you may also be able to avoid dealing with the dreaded, easy-to-misplace adapters known as dongles. It has three USB-C ports versus the Air’s two, and they’re divvied between the left and right sides, letting you plug in cables any which way without having to snake them around. As for the built-in HDMI and SD Card slot, their survival in 2025 is miraculous given that Apple tried to eliminate them almost a decade agobut I, for one, would use the SD slot all the time to transfer photos and videos from my Fujifilm camera. Okay, how about that new M5 chip? Apple emphasizes its improved performance in AI-intensive tasks such as applying filters to video and running local LLMs. Jason Snell of Six Colors benchmarked the new MacBook Pro and found it substantially quicker than the MacBook Air. His charts also show MacBook Pro models with M4 Pro and M4 Max chips remaining faster still, because they have more CPU and GPU cores than the M5. In my informal experiments with tasks such as editing images in Photoshop and outputting video projects from DaVinci Resolve, the M5 MacBook Pro was a rocket. But with the exception of epoch-shifting moments such as Apple’s 2020 transition from Intel chips to ones it designed itself, the launch of a new processor is rarely a reason to rush out and buy a new computer. Instead, most people should hold off on springing for a new machine until their current one is showing its agea buying strategy Apple tacitly acknowledges in its marketing for the M5 MacBook Pro by comparing its performance to its M1 ancestor, which dates to 2020. The best reason to get a new computer is as an insurance plan against future obsolescence. The M5 MacBook Pro is just a faster version of a familiar laptop. But it’s well positioned to stay useful even as AI becomes a more pervasive element of MacOS and Mac apps. Apple certainly won’t leave the MacBook Pro feeling so familiar forever. Last week, Bloombergs Mark Gurman reported that the company is currently working on a thinner, lighter version with an M6 chipand the Mac line’s first-ever touchscreen. Here’s hoping that no matter how much the MacBook Pro evolves, it retains the quiet emphasis on straightforward, well-appointed productivity that defines it. Youve been reading Plugged In, Fast Companys weekly tech newsletter from me, global technology editor Harry McCracken. If a friend or colleague forwarded this edition to youor if you’re reading it on FastCompany.comyou can check out previous issues and sign up to get it yourself every Friday morning. I love hearing from you: Ping me at
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It used to be that artificial intelligence would leave behind helpful clues that an image it produced was not, in fact, real. Previous generations of the technology might give a person an extra finger or even an additional limb. Teeth could look odd and out of place, and skin could render overly blushed, like something out of Pixar. Multiple dimensions could befuddle our models, which struggled to represent the physical world in a sensical way: Ask for an image of salmon swimming in a river, and AI might show you a medium-rare salmon steak floating along a rapturous current. Sure, we were in the uncanny valley. But at least we knew we were there. Thats no longer the case. While there are still some analog ways to detect that the content we see was created with the help of AI, the implicit visual tip-offs are, increasingly, disappearing. The limited release of Sora 2, OpenAIs latest video-generation model, has only hastened this development, experts at multiple AI detection companies tell Fast Companymeaning we may soon come to be entirely dependent on digital and other technical tools to wade through AI slop. That has ramifications not only for everyday internet users but also for any institution with an interest in protecting its likeness or identity from theft and misappropriation. Even [for] analysts like me who saw the evolution of this industry, it’s really hard, especially on images, Francesco Cavalli, cofounder of one of those firms, Sensity AI, tells Fast Company. The shapes, the colors, and the humans are perfect. So without the help of a tool now, it’s almost impossible for the average internet user to understand whether an image or a video or a piece of audio is AI-generated or not. Visual clues are fading The good news is that at least for now there are still some telltale visual signs that content was generated via artificial intelligence. Researchers are also hunting for more. While extra fingers appear less common, AI image generation models can still struggle to produce sensible text, explains Sofia Rubinson, a senior editor at Reality Check, a publication run by the information reliability company NewsGuard. Remember that surveillance video of bunnies jumping on a trampoline that turned out to be AI-produced? You might just have to consider whether rabbits actually do that, Rubinson says. We really want to encourage people to think a little bit more critically about what they’re seeing online as these visuals are going away, she adds. Rubinson says its possible to search for whether a portion of a video has been blurred out, which might suggest that a Sora 2 watermark used to be there. We can also check who shared it. Toggling to an accounts page sometimes reveals a trove of similar videosan almost-certain giveaway that youre being served AI slop. On the flip side, usernames wont necessarily help us discern who really produced content: As Fast Company previously reported, its somewhat easy, though not always possible, to grab a Sora 2 username associated with a famous person, despite OpenAIs rules on using other peoples likenesses. Ultimately, we may need to become fluent in a models individual style and tendencies, argues Siwei Lyu, a professor at the State University of New York at Buffalo who studies deepfakes. For instance, Sora 2-generated speech can appear a little too fast. (Some have dubbed this an AI accent.) Still, Lyu warns that these indications are subtle and can often be missed when viewing casually. And the technology will improve, which means its unlikely such hints will be around forever. Indeed, researchers say the visible residue that AI was involved in creating a piece of content already seems to be fading. The tips that we used to give in terms of visual inconsistencies are disappearing, model after model, says Emmanuelle Saliba, a former journalist who now leads investigations at GetReal Security, a cybersecurity firm working on detecting and studying AI-generated and manipulated content. While incoherent physical movement used to indicate AIs use in the creation of an image, Sora 2 has improved significantly on mimicking the real world, she says. At Reality Defender, also a deepfake detection firm, every one of the companys researchershalf of whom have doctorateshave now been fooled by content produced by newer generations of AI. Since the launch of Sora, every single one of them has mislabeled a deepfake as real or vice versa, Ben Colman, cofounder and CEO of Reality Defender, tells Fast Company. If people who’ve been working on this for 5 to 25 years cannot differentiate real from fake, how can average users or those using manual detection? Labels wont save us, either. While companies have touted watermarking as a way to identify AI-generated content, simple workarounds appear to foil these tools. For instance, videos from OpenAIs Sora come with a visual watermarkbut online tools can remove them. OpenAI, like other companies, has committed to the C2PA standard created by the Coalition for Content Provenance and Authenticity. That specification is supposed to encode the provenance, or source, of a piece of content into its metadata. Yet the watermark can be removed by screenshotting an image created by OpenAI technology. Even dragging and dropping that image, in some cases, can remove the watermark, Fast Companys tests with the tool show. OpenAI concedes this flaw, but a spokesperson said they werent able to reproduce the drag-and-drop issue. When Fast Company posed questions about this vulnerability to Adobe, which operates the C2PA verification tool, the company said the issue was on OpenAIs end. Updating methodologies Of course, the companies Fast Company spoke to are interested in selling various products designed to save us from the deepfake deluge. Some envision that AI content detection might go the way of virus scanning and become integrated into myriad online and workplace tools. Others suggest that their platforms will be necessary because the rise of tools like Sora 2 will make video call-based verification obsolete. Some executives believe their products will play a role in protecting brands from embarrassing AI-generated content. In response to the release of the Sora app, a few of these firms do say theyre seeing growing interest. Still, like humans, even these companies need to update their methodologies when new models are released. Even if the human cannot spot anything from the tech point of view, there’s always something to investigate, Sensitys Cavalli says. This often requires a mixed-methods approach, one that takes into account a range of factors, including studying a files metadata and discrepancies in background noise. Sensitys detection models are also retrained and refined when new models come online, Cavalli adds. ut even this isnt always perfect. Lyu from SUNY Buffalo says that while the detection systems his team has developed still work on videos produced with Sora 2, they have lower accuracy compared to their performance on generative AI models. And thats after some fine-tuning. Hany Farid, a UC Berkeley professor who cofounded GetReal Security and serves as its chief science officer, says the companys forensic and data techniques have seen better but not perfect generalization in the latest models. In the case of Sora 2, some of the companys video techniques have remained effective, while others have required fine-tuning, he says, adding that the audio detection models still work robustly. Thats a change from earlier eras of generative AI, when forensic techniques had to be continuously updated to apply to the latest models. For our digital-forensic techniques, this required understanding specific artifacts introduced by the AI models and then building techniques to detect these artifacts. For our more data-based techniques, this required generating content from the latest model and retraining our models. Whether these deepfake detection methods will continue to hold up is unclear. In the meantime, it seems that were increasingly heading toward a world flooded by AI but still building its seawalls. The name of GetReal Security has been updated.
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It used to be that artificial intelligence would leave behind helpful clues that an image it produced was not, in fact, real. Previous generations of the technology might give a person an extra finger or even an additional limb. Teeth could look odd and out of place, and skin could render overly blushed, like something out of Pixar. Multiple dimensions could befuddle our models, which struggled to represent the physical world in a sensical way: Ask for an image of salmon swimming in a river, and AI might show you a medium-rare salmon steak floating along a rapturous current. Sure, we were in the uncanny valley. But at least we knew we were there. Thats no longer the case. While there are still some analog ways to detect that the content we see was created with the help of AI, the implicit visual tip-offs are, increasingly, disappearing. The limited release of Sora 2, OpenAIs latest video-generation model, has only hastened this development, experts at multiple AI detection companies tell Fast Companymeaning we may soon come to be entirely dependent on digital and other technical tools to wade through AI slop. That has ramifications not only for everyday internet users but also for any institution with an interest in protecting its likeness or identity from theft and misappropriation. Even [for] analysts like me who saw the evolution of this industry, it’s really hard, especially on images, Francesco Cavalli, cofounder of one of those firms, Sensity AI, tells Fast Company. The shapes, the colors, and the humans are perfect. So without the help of a tool now, it’s almost impossible for the average internet user to understand whether an image or a video or a piece of audio is AI-generated or not. Visual clues are fading The good news is that at least for now there are still some telltale visual signs that content was generated via artificial intelligence. Researchers are also hunting for more. While extra fingers appear less common, AI image generation models can still struggle to produce sensible text, explains Sofia Rubinson, a senior editor at Reality Check, a publication run by the information reliability company NewsGuard. Remember that surveillance video of bunnies jumping on a trampoline that turned out to be AI-produced? You might just have to consider whether rabbits actually do that, Rubinson says. We really want to encourage people to think a little bit more critically about what they’re seeing online as these visuals are going away, she adds. Rubinson says its possible to search for whether a portion of a video has been blurred out, which might suggest that a Sora 2 watermark used to be there. We can also check who shared it. Toggling to an accounts page sometimes reveals a trove of similar videosan almost-certain giveaway that youre being served AI slop. On the flip side, usernames wont necessarily help us discern who really produced content: As Fast Company previously reported, its somewhat easy, though not always possible, to grab a Sora 2 username associated with a famous person, despite OpenAIs rules on using other peoples likenesses. Ultimately, we may need to become fluent in a models individual style and tendencies, argues Siwei Lyu, a professor at the State University of New York at Buffalo who studies deepfakes. For instance, Sora 2-generated speech can appear a little too fast. (Some have dubbed this an AI accent.) Still, Lyu warns that these indications are subtle and can often be missed when viewing casually. And the technology will improve, which means its unlikely such hints will be around forever. Indeed, researchers say the visible residue that AI was involved in creating a piece of content already seems to be fading. The tips that we used to give in terms of visual inconsistencies are disappearing, model after model, says Emmanuelle Saliba, a former journalist who now leads investigations at GetReal Labs, a cybersecurity firm working on detecting and studying AI-generated and manipulated content. While incoherent physical movement used to indicate AIs use in the creation of an image, Sora 2 has improved significantly on mimicking the real world, she says. At Reality Defender, also a deepfake detection firm, every one of the companys researchershalf of whom have doctorateshave now been fooled by content produced by newer generations of AI. Since the launch of Sora, every single one of them has mislabeled a deepfake as real or vice versa, Ben Colman, cofounder and CEO of Reality Defender, tells Fast Company. If people who’ve been working on this for 5 to 25 years cannot differentiate real from fake, how can average users or those using manual detection? Labels wont save us, either. While companies have touted watermarking as a way to identify AI-generated content, simple workarounds appear to foil these tools. For instance, videos from OpenAIs Sora come with a visual watermarkbut online tools can remove them. OpenAI, like other companies, has committed to the C2PA standard created by the Coalition for Content Provenance and Authenticity. That specification is supposed to encode the provenance, or source, of a piece of content into its metadata. Yet the watermark can be removed by screenshotting an image created by OpenAI technology. Even dragging and dropping that image, in some cases, can remove the watermark, Fast Companys tests with the tool show. OpenAI concedes this flaw, but a spokesperson said they werent able to reproduce the drag-and-drop issue. When Fast Company posed questions about this vulnerability to Adobe, which operates the C2PA verification tool, the company said the issue was on OpenAIs end. Updating methodologies Of course, the companies Fast Company spoke to are interested in selling various products designed to save us from the deepfake deluge. Some envision that AI content detection might go the way of virus scanning and become integrated into myriad online and workplace tools. Others suggest that their platforms will be necessary because the rise of tools like Sora 2 will make video call-based verification obsolete. Some executives believe their products will play a role in protecting brands from embarrassing AI-generated content. In response to the release of the Sora app, a few of these firms do say theyre seeing growing interest. Still, like humans, even these companies need to update their methodologies when new models are released. Even if the human cannot spot anything from the tech point of view, there’s always something to investigate, Sensitys Cavalli says. This often requires a mixed-methods approach, one that takes into account a range of factors, including studying a files metadata and discrepancies in background noise. Sensitys detection models are also retrained and refined when new models come online, Cavalli adds. But even this isnt always perfect. Lyu from SUNYBuffalo says that while the detection systems his team has developed still work on videos produced with Sora 2, they have lower accuracy compared to their performance on generative AI models. And thats after some fine-tuning. Hany Farid, a UC Berkeley professor who cofounded Reality Defender and serves as its chief science officer, says the companys forensic and data techniques have seen better but not perfect generalization in the latest models. In the case of Sora 2, some of the companys video techniques have remained effective, while others have required fine-tuning, he says, adding that the audio detection models still work robustly. Thats a change from earlier eras of generative AI, when forensic techniques had to be continuously updated to apply to the latest models. For our digital-forensic techniques, this required understanding specific artifacts introduced by the AI models and then building techniques to detect these artifacts. For our more data-based techniques, this required generating content from the latest model and retraining our models. Whether these deepfake detection methods will continue to hold up is unclear. In the meantime, it seems that were increasingly heading toward a world flooded by AI but still building its seawalls.
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E-Commerce
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